WorldWideScience

Sample records for spectrum sensing algorithms

  1. Cognitive Radio Transceivers: RF, Spectrum Sensing, and Learning Algorithms Review

    Directory of Open Access Journals (Sweden)

    Lise Safatly

    2014-01-01

    reconfigurable radio frequency (RF parts, enhanced spectrum sensing algorithms, and sophisticated machine learning techniques. In this paper, we present a review of the recent advances in CR transceivers hardware design and algorithms. For the RF part, three types of antennas are presented: UWB antennas, frequency-reconfigurable/tunable antennas, and UWB antennas with reconfigurable band notches. The main challenges faced by the design of the other RF blocks are also discussed. Sophisticated spectrum sensing algorithms that overcome main sensing challenges such as model uncertainty, hardware impairments, and wideband sensing are highlighted. The cognitive engine features are discussed. Moreover, we study unsupervised classification algorithms and a reinforcement learning (RL algorithm that has been proposed to perform decision-making in CR networks.

  2. Novel Spectrum Sensing Algorithms for OFDM Cognitive Radio Networks.

    Science.gov (United States)

    Shi, Zhenguo; Wu, Zhilu; Yin, Zhendong; Cheng, Qingqing

    2015-06-15

    Spectrum sensing technology plays an increasingly important role in cognitive radio networks. Consequently, several spectrum sensing algorithms have been proposed in the literature. In this paper, we present a new spectrum sensing algorithm "Differential Characteristics-Based OFDM (DC-OFDM)" for detecting OFDM signal on account of differential characteristics. We put the primary value on channel gain θ around zero to detect the presence of primary user. Furthermore, utilizing the same method of differential operation, we improve two traditional OFDM sensing algorithms (cyclic prefix and pilot tones detecting algorithms), and propose a "Differential Characteristics-Based Cyclic Prefix (DC-CP)" detector and a "Differential Characteristics-Based Pilot Tones (DC-PT)" detector, respectively. DC-CP detector is based on auto-correlation vector to sense the spectrum, while the DC-PT detector takes the frequency-domain cross-correlation of PT as the test statistic to detect the primary user. Moreover, the distributions of the test statistics of the three proposed methods have been derived. Simulation results illustrate that all of the three proposed methods can achieve good performance under low signal to noise ratio (SNR) with the presence of timing delay. Specifically, the DC-OFDM detector gets the best performance among the presented detectors. Moreover, both of the DC-CP and DC-PT detector achieve significant improvements compared with their corresponding original detectors.

  3. Malicious Cognitive User Identification Algorithm in Centralized Spectrum Sensing System

    Directory of Open Access Journals (Sweden)

    Jingbo Zhang

    2017-11-01

    Full Text Available Collaborative spectral sensing can fuse the perceived results of multiple cognitive users, and thus will improve the accuracy of perceived results. However, the multi-source features of the perceived results result in security problems in the system. When there is a high probability of a malicious user attack, the traditional algorithm can correctly identify the malicious users. However, when the probability of attack by malicious users is reduced, it is almost impossible to use the traditional algorithm to correctly distinguish between honest users and malicious users, which greatly reduces the perceived performance. To address the problem above, based on the β function and the feedback iteration mathematical method, this paper proposes a malicious user identification algorithm under multi-channel cooperative conditions (β-MIAMC, which involves comprehensively assessing the cognitive user’s performance on multiple sub-channels to identify the malicious user. Simulation results show under the same attack probability, compared with the traditional algorithm, the β-MIAMC algorithm can more accurately identify the malicious users, reducing the false alarm probability of malicious users by more than 20%. When the attack probability is greater than 7%, the proposed algorithm can identify the malicious users with 100% certainty.

  4. Spectrum sensing algorithm based on autocorrelation energy in cognitive radio networks

    Science.gov (United States)

    Ren, Shengwei; Zhang, Li; Zhang, Shibing

    2016-10-01

    Cognitive radio networks have wide applications in the smart home, personal communications and other wireless communication. Spectrum sensing is the main challenge in cognitive radios. This paper proposes a new spectrum sensing algorithm which is based on the autocorrelation energy of signal received. By taking the autocorrelation energy of the received signal as the statistics of spectrum sensing, the effect of the channel noise on the detection performance is reduced. Simulation results show that the algorithm is effective and performs well in low signal-to-noise ratio. Compared with the maximum generalized eigenvalue detection (MGED) algorithm, function of covariance matrix based detection (FMD) algorithm and autocorrelation-based detection (AD) algorithm, the proposed algorithm has 2 11 dB advantage.

  5. Application of a Channel Estimation Algorithm to Spectrum Sensing in a Cognitive Radio Context

    Directory of Open Access Journals (Sweden)

    Vincent Savaux

    2014-01-01

    Full Text Available This paper deals with spectrum sensing in an orthogonal frequency division multiplexing (OFDM context, allowing an opportunistic user to detect a vacant spectrum resource in a licensed band. The proposed method is based on an iterative algorithm used for the joint estimation of noise variance and frequency selective channel. It can be seen as a second-order detector, since it is performed by means of the minimum mean square error criterion. The main advantage of the proposed algorithm is its capability to perform spectrum sensing, noise variance estimation, and channel estimation in the presence of a signal. Furthermore, the sensing duration is limited to only one OFDM symbol. We theoretically show the convergence of the algorithm, and we derive its analytical detection and false alarm probabilities. Furthermore, we show that the detector is very efficient, even for low SNR values, and is robust against a channel uncertainty.

  6. Analyzing Chaos Systems and Fine Spectrum Sensing Using Detrended Fluctuation Analysis Algorithm

    Directory of Open Access Journals (Sweden)

    Javier S. González-Salas

    2016-01-01

    Full Text Available A numerical study that uses detrended fluctuation analysis (DFA algorithm of time series obtained from linear and nonlinear dynamical systems is presented. The DFA algorithm behavior toward periodic and chaotic signals is investigated and the effect of the time scale under analysis is discussed. The displayed results prove that the DFA algorithm response is invariant (stable performance to initial condition and chaotic system parameters. An initial idea of DFA algorithm implementation for fine spectrum sensing (SS is proposed under two-stage spectrum sensor approach with test statistics based on the scaling exponent value. The outcomes demonstrate a promising new SS technique that can alleviate several imperfections such as noise power uncertainty and spatial correlation between the adjacent antenna array elements.

  7. A Spectrum Sensing Method Based on Signal Feature and Clustering Algorithm in Cognitive Wireless Multimedia Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yongwei Zhang

    2017-01-01

    Full Text Available In order to solve the problem of difficulty in determining the threshold in spectrum sensing technologies based on the random matrix theory, a spectrum sensing method based on clustering algorithm and signal feature is proposed for Cognitive Wireless Multimedia Sensor Networks. Firstly, the wireless communication signal features are obtained according to the sampling signal covariance matrix. Then, the clustering algorithm is used to classify and test the signal features. Different signal features and clustering algorithms are compared in this paper. The experimental results show that the proposed method has better sensing performance.

  8. Compressive sensing based algorithms for electronic defence

    CERN Document Server

    Mishra, Amit Kumar

    2017-01-01

    This book details some of the major developments in the implementation of compressive sensing in radio applications for electronic defense and warfare communication use. It provides a comprehensive background to the subject and at the same time describes some novel algorithms. It also investigates application value and performance-related parameters of compressive sensing in scenarios such as direction finding, spectrum monitoring, detection, and classification.

  9. Generalized eigenvalue based spectrum sensing

    KAUST Repository

    Shakir, Muhammad

    2012-01-01

    Spectrum sensing is one of the fundamental components in cognitive radio networks. In this chapter, a generalized spectrum sensing framework which is referred to as Generalized Mean Detector (GMD) has been introduced. In this context, we generalize the detectors based on the eigenvalues of the received signal covariance matrix and transform the eigenvalue based spectrum sensing detectors namely: (i) the Eigenvalue Ratio Detector (ERD) and two newly proposed detectors which are referred to as (ii) the GEometric Mean Detector (GEMD) and (iii) the ARithmetic Mean Detector (ARMD) into an unified framework of generalize spectrum sensing. The foundation of the proposed framework is based on the calculation of exact analytical moments of the random variables of the decision threshold of the respective detectors. The decision threshold has been calculated in a closed form which is based on the approximation of Cumulative Distribution Functions (CDFs) of the respective test statistics. In this context, we exchange the analytical moments of the two random variables of the respective test statistics with the moments of the Gaussian (or Gamma) distribution function. The performance of the eigenvalue based detectors is compared with the several traditional detectors including the energy detector (ED) to validate the importance of the eigenvalue based detectors and the performance of the GEMD and the ARMD particularly in realistic wireless cognitive radio network. Analytical and simulation results show that the newly proposed detectors yields considerable performance advantage in realistic spectrum sensing scenarios. Moreover, the presented results based on proposed approximation approaches are in perfect agreement with the empirical results. © 2012 Springer Science+Business Media Dordrecht.

  10. Coalition Formation and Spectrum Sharing of Cooperative Spectrum Sensing Participants.

    Science.gov (United States)

    Zhensheng Jiang; Wei Yuan; Leung, Henry; Xinge You; Qi Zheng

    2017-05-01

    In cognitive radio networks, self-interested secondary users (SUs) desire to maximize their own throughput. They compete with each other for transmit time once the absence of primary users (PUs) is detected. To satisfy the requirement of PU protection, on the other hand, they have to form some coalitions and cooperate to conduct spectrum sensing. Such dilemma of SUs between competition and cooperation motivates us to study two interesting issues: 1) how to appropriately form some coalitions for cooperative spectrum sensing (CSS) and 2) how to share transmit time among SUs. We jointly consider these two issues, and propose a noncooperative game model with 2-D strategies. The first dimension determines coalition formation, and the second indicates transmit time allocation. Considering the complexity of solving this game, we decompose the game into two more tractable ones: one deals with the formation of CSS coalitions, and the other focuses on the allocation of transmit time. We characterize the Nash equilibria (NEs) of both games, and show that the combination of these two NEs corresponds to the NE of the original game. We also develop a distributed algorithm to achieve a desirable NE of the original game. When this NE is achieved, the SUs obtain a Dhp-stable coalition structure and a fair transmit time allocation. Numerical results verify our analyses, and demonstrate the effectiveness of our algorithm.

  11. User Classification in Crowdsourcing-Based Cooperative Spectrum Sensing

    Directory of Open Access Journals (Sweden)

    Linbo Zhai

    2017-07-01

    Full Text Available This paper studies cooperative spectrum sensing based on crowdsourcing in cognitive radio networks. Since intelligent mobile users such as smartphones and tablets can sense the wireless spectrum, channel sensing tasks can be assigned to these mobile users. This is referred to as the crowdsourcing method. However, there may be some malicious mobile users that send false sensing reports deliberately, for their own purposes. False sensing reports will influence decisions about channel state. Therefore, it is necessary to classify mobile users in order to distinguish malicious users. According to the sensing reports, mobile users should not just be divided into two classes (honest and malicious. There are two reasons for this: on the one hand, honest users in different positions may have different sensing outcomes, as shadowing, multi-path fading, and other issues may influence the sensing results; on the other hand, there may be more than one type of malicious users, acting differently in the network. Therefore, it is necessary to classify mobile users into more than two classes. Due to the lack of prior information of the number of user classes, this paper casts the problem of mobile user classification as a dynamic clustering problem that is NP-hard. The paper uses the interdistance-to-intradistance ratio of clusters as the fitness function, and aims to maximize the fitness function. To cast this optimization problem, this paper proposes a distributed algorithm for user classification in order to obtain bounded close-to-optimal solutions, and analyzes the approximation ratio of the proposed algorithm. Simulations show the distributed algorithm achieves higher performance than other algorithms.

  12. Remote sensing with laser spectrum radar

    Science.gov (United States)

    Wang, Tianhe; Zhou, Tao; Jia, Xiaodong

    2016-10-01

    The unmanned airborne (UAV) laser spectrum radar has played a leading role in remote sensing because the transmitter and the receiver are together at laser spectrum radar. The advantages of the integrated transceiver laser spectrum radar is that it can be used in the oil and gas pipeline leak detection patrol line which needs the non-contact reflective detection. The UAV laser spectrum radar can patrol the line and specially detect the swept the area are now in no man's land because most of the oil and gas pipelines are in no man's land. It can save labor costs compared to the manned aircraft and ensure the safety of the pilots. The UAV laser spectrum radar can be also applied in the post disaster relief which detects the gas composition before the firefighters entering the scene of the rescue.

  13. A robust power spectrum split cancellation-based spectrum sensing method for cognitive radio systems

    International Nuclear Information System (INIS)

    Qi Pei-Han; Li Zan; Si Jiang-Bo; Gao Rui

    2014-01-01

    Spectrum sensing is an essential component to realize the cognitive radio, and the requirement for real-time spectrum sensing in the case of lacking prior information, fading channel, and noise uncertainty, indeed poses a major challenge to the classical spectrum sensing algorithms. Based on the stochastic properties of scalar transformation of power spectral density (PSD), a novel spectrum sensing algorithm, referred to as the power spectral density split cancellation method (PSC), is proposed in this paper. The PSC makes use of a scalar value as a test statistic, which is the ratio of each subband power to the full band power. Besides, by exploiting the asymptotic normality and independence of Fourier transform, the distribution of the ratio and the mathematical expressions for the probabilities of false alarm and detection in different channel models are derived. Further, the exact closed-form expression of decision threshold is calculated in accordance with Neyman—Pearson criterion. Analytical and simulation results show that the PSC is invulnerable to noise uncertainty, and can achive excellent detection performance without prior knowledge in additive white Gaussian noise and flat slow fading channels. In addition, the PSC benefits from a low computational cost, which can be completed in microseconds. (interdisciplinary physics and related areas of science and technology)

  14. A robust power spectrum split cancellation-based spectrum sensing method for cognitive radio systems

    Science.gov (United States)

    Qi, Pei-Han; Li, Zan; Si, Jiang-Bo; Gao, Rui

    2014-12-01

    Spectrum sensing is an essential component to realize the cognitive radio, and the requirement for real-time spectrum sensing in the case of lacking prior information, fading channel, and noise uncertainty, indeed poses a major challenge to the classical spectrum sensing algorithms. Based on the stochastic properties of scalar transformation of power spectral density (PSD), a novel spectrum sensing algorithm, referred to as the power spectral density split cancellation method (PSC), is proposed in this paper. The PSC makes use of a scalar value as a test statistic, which is the ratio of each subband power to the full band power. Besides, by exploiting the asymptotic normality and independence of Fourier transform, the distribution of the ratio and the mathematical expressions for the probabilities of false alarm and detection in different channel models are derived. Further, the exact closed-form expression of decision threshold is calculated in accordance with Neyman—Pearson criterion. Analytical and simulation results show that the PSC is invulnerable to noise uncertainty, and can achive excellent detection performance without prior knowledge in additive white Gaussian noise and flat slow fading channels. In addition, the PSC benefits from a low computational cost, which can be completed in microseconds.

  15. Efficient Error Detection in Soft Data Fusion for Cooperative Spectrum Sensing

    KAUST Repository

    Saqib Bhatti, Dost Muhammad

    2018-03-18

    The primary objective of cooperative spectrum sensing (CSS) is to determine whether a particular spectrum is occupied by a licensed user or not, so that unlicensed users called secondary users (SUs) can utilize that spectrum, if it is not occupied. For CSS, all SUs report their sensing information through reporting channel to the central base station called fusion center (FC). During transmission, some of the SUs are subjected to fading and shadowing, due to which the overall performance of CSS is degraded. We have proposed an algorithm which uses error detection technique on sensing measurement of all SUs. Each SU is required to re-transmit the sensing data to the FC, if error is detected on it. Our proposed algorithm combines the sensing measurement of limited number of SUs. Using Proposed algorithm, we have achieved the improved probability of detection (PD) and throughput. The simulation results compare the proposed algorithm with conventional scheme.

  16. On Spectrum Sensing for TV White Space in China

    Directory of Open Access Journals (Sweden)

    Christian Kocks

    2012-01-01

    Full Text Available In the field of wireless communications the idea of cognitive radio is of utmost interest. Due to its advantageous propagation properties, the TV white space can be considered to become the first commercial application of cognitive radio. It allows the usage of secondary communication systems at non-occupied frequency bands. Within this paper, spectrum sensing algorithms are introduced for the three predominant Chinese TV standards DTMB, CMMB, and PAL-D/K. A prototype platform is presented and its underlying architecture based on a combination of DSP and FPGA is illustrated including the setup of the cognitive radio application. Furthermore, the performance of the sensing algorithms implemented on the prototype platform is shown in comparison to simulation results.

  17. Linear Algorithms for Radioelectric Spectrum Forecast

    Directory of Open Access Journals (Sweden)

    Luis F. Pedraza

    2016-12-01

    Full Text Available This paper presents the development and evaluation of two linear algorithms for forecasting reception power for different channels at an assigned spectrum band of global systems for mobile communications (GSM, in order to analyze the spatial opportunity for reuse of frequencies by secondary users (SUs in a cognitive radio (CR network. The algorithms employed correspond to seasonal autoregressive integrated moving average (SARIMA and generalized autoregressive conditional heteroskedasticity (GARCH, which allow for a forecast of channel occupancy status. Results are evaluated using the following criteria: availability and occupancy time for channels, different types of mean absolute error, and observation time. The contributions of this work include a more integral forecast as the algorithm not only forecasts reception power but also the occupancy and availability time of a channel to determine its precision percentage during the use by primary users (PUs and SUs within a CR system. Algorithm analyses demonstrate a better performance for SARIMA over GARCH algorithm in most of the evaluated variables.

  18. Digital FMCW for ultrawideband spectrum sensing

    Science.gov (United States)

    Cheema, A. A.; Salous, S.

    2016-08-01

    An ultrawideband digital frequency-modulated continuous wave sensing engine is proposed as an alternative technique for cognitive radio applications. A dual-band demonstrator capable of sensing 750 MHz bandwidth in 204.8 µs is presented. Its performance is illustrated from both bench tests and from real-time measurements of the GSM 900 band and the 2.4 GHz wireless local area network (WLAN) band. The measured sensitivity and noise figure values are -90 dBm for a signal-to-noise ratio margin of at least 10 dB and ~13-14 dB, respectively. Data were collected over 24 h and were analyzed by using the energy detection method. The obtained results show the time variability of occupancy, and considerable sections of the spectrum are unoccupied. In addition, unlike the cyclic temporal variations of spectrum occupancy in the GSM 900 band, the detected variations in the 2.4 GHz WLAN band have an impulsive nature.

  19. Generalized mean detector for collaborative spectrum sensing

    KAUST Repository

    Shakir, Muhammad Zeeshan

    2013-04-01

    In this paper, a unified generalized eigenvalue based spectrum sensing framework referred to as Generalized mean detector (GMD) has been introduced. The generalization of the detectors namely (i) the eigenvalue ratio detector (ERD) involving the ratio of the largest and the smallest eigenvalues; (ii) the Geometric mean detector (GEMD) involving the ratio of the largest eigenvalue and the geometric mean of the eigenvalues and (iii) the Arithmetic mean detector (ARMD) involving the ratio of the largest and the arithmetic mean of the eigenvalues is explored. The foundation of the proposed unified framework is based on the calculation of exact analytical moments of the random variables of test statistics of the respective detectors. In this context, we approximate the probability density function (PDF) of the test statistics of the respective detectors by Gaussian/Gamma PDF using the moment matching method. Finally, we derive closed-form expressions to calculate the decision threshold of the eigenvalue based detectors by exchanging the derived exact moments of the random variables of test statistics with the moments of the Gaussian/Gamma distribution function. The performance of the eigenvalue based detectors is compared with the traditional detectors such as energy detector (ED) and cyclostationary detector (CSD) and validate the importance of the eigenvalue based detectors particularly over realistic wireless cognitive environments. Analytical and simulation results show that the GEMD and the ARMD yields considerable performance advantage in realistic spectrum sensing scenarios. Moreover, our results based on proposed simple and tractable approximation approaches are in perfect agreement with the empirical results. © 1972-2012 IEEE.

  20. Cooperative Spectrum Sensing over Non-Identical Nakagami Fading Channels

    KAUST Repository

    Rao, Anlei

    2012-09-08

    Previous works in cooperative spectrum sensing assumed that the channels for sensing and reporting are independent identical distributed (i.i.d). A more practical and appropriate assumption, however, should be that the sensing channels and reporting channels are independent but not necessarily identically distributed (i.n.i.d). In this paper, we derive the false-alarm probability and the detection probability of cooperative spectrum sensing with energy fusion over i.n.i.d Nakagami fading channels. Selected numerical results show that cooperative spectrum sensing still gives considerably better performance results even over i.n.i.d fading channels.

  1. Efficient Wideband Spectrum Sensing with Maximal Spectral Efficiency for LEO Mobile Satellite Systems

    Directory of Open Access Journals (Sweden)

    Feilong Li

    2017-01-01

    Full Text Available The usable satellite spectrum is becoming scarce due to static spectrum allocation policies. Cognitive radio approaches have already demonstrated their potential towards spectral efficiency for providing more spectrum access opportunities to secondary user (SU with sufficient protection to licensed primary user (PU. Hence, recent scientific literature has been focused on the tradeoff between spectrum reuse and PU protection within narrowband spectrum sensing (SS in terrestrial wireless sensing networks. However, those narrowband SS techniques investigated in the context of terrestrial CR may not be applicable for detecting wideband satellite signals. In this paper, we mainly investigate the problem of joint designing sensing time and hard fusion scheme to maximize SU spectral efficiency in the scenario of low earth orbit (LEO mobile satellite services based on wideband spectrum sensing. Compressed detection model is established to prove that there indeed exists one optimal sensing time achieving maximal spectral efficiency. Moreover, we propose novel wideband cooperative spectrum sensing (CSS framework where each SU reporting duration can be utilized for its following SU sensing. The sensing performance benefits from the novel CSS framework because the equivalent sensing time is extended by making full use of reporting slot. Furthermore, in respect of time-varying channel, the spatiotemporal CSS (ST-CSS is presented to attain space and time diversity gain simultaneously under hard decision fusion rule. Computer simulations show that the optimal sensing settings algorithm of joint optimization of sensing time, hard fusion rule and scheduling strategy achieves significant improvement in spectral efficiency. Additionally, the novel ST-CSS scheme performs much higher spectral efficiency than that of general CSS framework.

  2. Multislot Simultaneous Spectrum Sensing and Energy Harvesting in Cognitive Radio

    Directory of Open Access Journals (Sweden)

    Xin Liu

    2016-07-01

    Full Text Available In cognitive radio (CR, the spectrum sensing of the primary user (PU may consume some electrical power from the battery capacity of the secondary user (SU, resulting in a decrease in the transmission power of the SU. In this paper, a multislot simultaneous spectrum sensing and energy harvesting model is proposed, which uses the harvested radio frequency (RF energy of the PU signal to supply the spectrum sensing. In the proposed model, the sensing duration is divided into multiple sensing slots consisting of one local-sensing subslot and one energy-harvesting subslot. If the PU is detected to be present in the local-sensing subslot, the SU will harvest RF energy of the PU signal in the energy-harvesting slot, otherwise, the SU will continue spectrum sensing. The global decision on the presence of the PU is obtained through combining local sensing results from all the sensing slots by adopting “Or-logic Rule”. A joint optimization problem of sensing time and time splitter factor is proposed to maximize the throughput of the SU under the constraints of probabilities of false alarm and detection and energy harvesting. The simulation results have shown that the proposed model can clearly improve the maximal throughput of the SU compared to the traditional sensing-throughput tradeoff model.

  3. SpecNet: Spectrum Sensing Sans Frontieres

    OpenAIRE

    Iyer, Anand Padmanabha; Chintalapudi, Krishna; Navda, Vishnu; Ramjee, Ramachandran; Padmanabhan, Venkata N; Murthy, Chandra R

    2011-01-01

    While the under-utilization of licensed spectrum based on measurement studies conducted in a few developed countries has spurred lots of interest in opportunistic spectrum access, there exists no infrastructure today for measuring real-time spectrum occupancy across vast geographical regions. In this paper, we present the design and implementation of SpecNet, a first-of-its-kind platform that allows spectrum analyzers around the world to be networked and efficiently used in a coordinated manner...

  4. Experimental scheme and restoration algorithm of block compression sensing

    Science.gov (United States)

    Zhang, Linxia; Zhou, Qun; Ke, Jun

    2018-01-01

    Compressed Sensing (CS) can use the sparseness of a target to obtain its image with much less data than that defined by the Nyquist sampling theorem. In this paper, we study the hardware implementation of a block compression sensing system and its reconstruction algorithms. Different block sizes are used. Two algorithms, the orthogonal matching algorithm (OMP) and the full variation minimum algorithm (TV) are used to obtain good reconstructions. The influence of block size on reconstruction is also discussed.

  5. Spectrum sensing using single-radio switched-beam antenna systems

    DEFF Research Database (Denmark)

    Tsakalaki, Elpiniki; Wilcox, David; De Carvalho, Elisabeth

    2012-01-01

    of the reactive loads rotate the narrowband beampattern to different angular positions dividing the whole space around the cognitive receiver into several angular subspaces. The beampattern directionality leverages the performance of spectrum sensing algorithms like the energy detection by enhancing the receive......The paper describes spectrum sensing using single-radio switched-beam arrays with reactance-loaded parasitic elements. At a given frequency, the antenna's loading conditions (reactive loads) are optimized for maximum average beamforming gain in the beampattern look direction. Circular permutations...

  6. Robust Spectrum Sensing Demonstration Using a Low-Cost Front-End Receiver

    Directory of Open Access Journals (Sweden)

    Daniele Borio

    2015-01-01

    Full Text Available Spectrum Sensing (SS is an important function in Cognitive Radio (CR to detect primary users. The design of SS algorithms is one of the most challenging tasks in CR and requires innovative hardware and software solutions to enhance detection probability and minimize low false alarm probability. Although several SS algorithms have been developed in the specialized literature, limited work has been done to practically demonstrate the feasibility of this function on platforms with significant computational and hardware constraints. In this paper, SS is demonstrated using a low cost TV tuner as agile front-end for sensing a large portion of the Ultra-High Frequency (UHF spectrum. The problems encountered and the limitations imposed by the front-end are analysed along with the solutions adopted. Finally, the spectrum sensor developed is implemented on an Android device and SS implementation is demonstrated using a smartphone.

  7. Implementation of a Wavefront-Sensing Algorithm

    Science.gov (United States)

    Smith, Jeffrey S.; Dean, Bruce; Aronstein, David

    2013-01-01

    A computer program has been written as a unique implementation of an image-based wavefront-sensing algorithm reported in "Iterative-Transform Phase Retrieval Using Adaptive Diversity" (GSC-14879-1), NASA Tech Briefs, Vol. 31, No. 4 (April 2007), page 32. This software was originally intended for application to the James Webb Space Telescope, but is also applicable to other segmented-mirror telescopes. The software is capable of determining optical-wavefront information using, as input, a variable number of irradiance measurements collected in defocus planes about the best focal position. The software also uses input of the geometrical definition of the telescope exit pupil (otherwise denoted the pupil mask) to identify the locations of the segments of the primary telescope mirror. From the irradiance data and mask information, the software calculates an estimate of the optical wavefront (a measure of performance) of the telescope generally and across each primary mirror segment specifically. The software is capable of generating irradiance data, wavefront estimates, and basis functions for the full telescope and for each primary-mirror segment. Optionally, each of these pieces of information can be measured or computed outside of the software and incorporated during execution of the software.

  8. Energy Efficient Cooperative Spectrum Sensing in Cognitive Radio Networks Using Distributed Dynamic Load Balanced Clustering Scheme

    Directory of Open Access Journals (Sweden)

    Muthukkumar R.

    2017-04-01

    Full Text Available Cognitive Radio (CR is a promising and potential technique to enable secondary users (SUs or unlicenced users to exploit the unused spectrum resources effectively possessed by primary users (PUs or licenced users. The proven clustering approach is used to organize nodes in the network into the logical groups to attain energy efficiency, network scalability, and stability for improving the sensing accuracy in CR through cooperative spectrum sensing (CSS. In this paper, a distributed dynamic load balanced clustering (DDLBC algorithm is proposed. In this algorithm, each member in the cluster is to calculate the cooperative gain, residual energy, distance, and sensing cost from the neighboring clusters to perform the optimal decision. Each member in a cluster participates in selecting a cluster head (CH through cooperative gain, and residual energy that minimises network energy consumption and enhances the channel sensing. First, we form the number of clusters using the Markov decision process (MDP model to reduce the energy consumption in a network. In this algorithm, CR users effectively utilize the PUs reporting time slots of unavailability. The simulation results reveal that the clusters convergence, energy efficiency, and accuracy of channel sensing increased considerably by using the proposed algorithm.

  9. Energy Efficient Cooperative Spectrum Sensing in Cognitive Radio Networks Using Distributed Dynamic Load Balanced Clustering Scheme

    Directory of Open Access Journals (Sweden)

    Muthukkumar R.

    2016-07-01

    Full Text Available Cognitive Radio (CR is a promising and potential technique to enable secondary users (SUs or unlicenced users to exploit the unused spectrum resources effectively possessed by primary users (PUs or licenced users. The proven clustering approach is used to organize nodes in the network into the logical groups to attain energy efficiency, network scalability, and stability for improving the sensing accuracy in CR through cooperative spectrum sensing (CSS. In this paper, a distributed dynamic load balanced clustering (DDLBC algorithm is proposed. In this algorithm, each member in the cluster is to calculate the cooperative gain, residual energy, distance, and sensing cost from the neighboring clusters to perform the optimal decision. Each member in a cluster participates in selecting a cluster head (CH through cooperative gain, and residual energy that minimises network energy consumption and enhances the channel sensing. First, we form the number of clusters using the Markov decision process (MDP model to reduce the energy consumption in a network. In this algorithm, CR users effectively utilize the PUs reporting time slots of unavailability. The simulation results reveal that the clusters convergence, energy efficiency, and accuracy of channel sensing increased considerably by using the proposed algorithm.

  10. Achieving Efficient Spectrum Usage in Passive and Active Sensing

    Science.gov (United States)

    Wang, Huaiyi

    Increasing demand for supporting more wireless services with higher performance and reliability within the frequency bands that are most conducive to operating cost-effective cellular and mobile broadband is aggravating current electromagnetic spectrum congestion. This situation motivates technology and management innovation to increase the efficiency of spectral use. If primary-secondary spectrum sharing can be shown possible without compromising (or while even improving) performance in an existing application, opportunities for efficiency may be realizable by making the freed spectrum available for commercial use. While both active and passive sensing systems are vitally important for many public good applications, opportunities for increasing the efficiency of spectrum use can be shown to exist for both systems. This dissertation explores methods and technologies for remote sensing systems that enhance spectral efficiency and enable dynamic spectrum access both within and outside traditionally allocated bands.

  11. TESTBED IMPLEMENTATION OF MULTI DIMENSIONAL SPECTRUM SENSING SCHEMES FOR COGNITIVE RADIO

    Directory of Open Access Journals (Sweden)

    Deepa N Reddy

    2016-06-01

    Full Text Available Cognitive Radio (CR is a promising technology to exploit the underutilized spectrum. Spectrum sensing is one of the most important components for the establishment of cognitive radio system. Spectrum sensing allows the secondary users (SUs to detect the presence of the primary users (PUs. The aim of this work is to create a CR environment to study the spectrum sensing methods using Universal software radio Peripheral (USRP boards. In this paper a novel method of estimation of spectrum opportunities in multiple dimensions especially the space and the angle dimensions are carried out on USRP boards. This paper typically provides the experimental results carried out in an indoor wireless environment. To enhance the sensing performance the space dimension is firstly studied using spatial diversity of the cooperative SUs. Secondly the receiver diversity is analyzed using multiple antennas to enhance the error performance of the wireless system. The spectrum usage is also determined in the angle dimension by investigating the direction of the dominant signals using MUSIC algorithm.

  12. PERFORMANCE OPTIMIZATION OF COGNITIVE RADIO WITH WIDEBAND SPECTRUM SENSING

    Directory of Open Access Journals (Sweden)

    E. Saraniya

    2014-09-01

    Full Text Available Cognitive radio (CR technology allows the unlicensed user to access the licensed spectrum bands. Spectrum sensing is an essential function in cognitive radio to detect the spectrum holes and opportunistically use the underutilized frequency bands without causing interference to primary user (PU. In this paper we are maximizing the throughput capacity of cognitive radio user and hence the performance of spectrum sensing and protection to licensed user improves over a wideband spectrum sensing band. The simulation of cognitive radio is done by analyzing the performance of energy detector spectrum sensing technique to detect primary user and to formulate the optimization using multiband joint detection method (MJD to achieve suitable trade- off between secondary user access and primary user network. The main aim of this paper is to maximize the probability of detection and to decrease the probabilities of miss detection and false alarm. To maximize the throughput it requires minimizing the throughput loss caused by miss detection and the significant reduction in probability of false alarm helps in achieving the spectral efficiency from the secondary user’s perspective. The simulation results show that the performance increases with the MJD method.

  13. Upper bounds for Neyman-Pearson cooperative spectrum sensing

    KAUST Repository

    Zahabi, Sayed Jalal

    2011-06-01

    We consider a cooperative spectrum sensing scenario where the local sensors at the secondary users are viewed as one-level quantizers, and the quantized data are to be fused under Neyman-Pearson (N-P) criterion. We demonstrate how the N-P fusion results in a randomized test, which represents the total performance of our spectrum sensing scheme. We further introduce an upper performance bound for the overall primary user signal detection. An analytical procedure towards the upper bound and its relevant quantization setup at the local sensors are proposed and examined through simulations. © 2011 IEEE.

  14. Upper bounds for Neyman-Pearson cooperative spectrum sensing

    KAUST Repository

    Zahabi, Sayed Jalal; Tadaion, Ali Akbar; Aissa, Sonia

    2011-01-01

    We consider a cooperative spectrum sensing scenario where the local sensors at the secondary users are viewed as one-level quantizers, and the quantized data are to be fused under Neyman-Pearson (N-P) criterion. We demonstrate how the N-P fusion results in a randomized test, which represents the total performance of our spectrum sensing scheme. We further introduce an upper performance bound for the overall primary user signal detection. An analytical procedure towards the upper bound and its relevant quantization setup at the local sensors are proposed and examined through simulations. © 2011 IEEE.

  15. Compressive Sensing for Spread Spectrum Receivers

    DEFF Research Database (Denmark)

    Fyhn, Karsten; Jensen, Tobias Lindstrøm; Larsen, Torben

    2013-01-01

    With the advent of ubiquitous computing there are two design parameters of wireless communication devices that become very important: power efficiency and production cost. Compressive sensing enables the receiver in such devices to sample below the Shannon-Nyquist sampling rate, which may lead...... the bit error rate performance is degraded by the subsampling in the CS-enabled receivers, this may be remedied by including quantization in the receiver model.We also study the computational complexity of the proposed receiver design under different sparsity and measurement ratios. Our work shows...

  16. A localized cooperative wideband spectrum sensing for dynamic access of TV bands using RF sensor networks

    KAUST Repository

    Mirza, Mohammed

    2011-07-01

    In this paper we address and simulate a Radio Frequency (RF) sensor network for a cooperative spectrum sensing and localization scheme. The proposed method integrates a Wavelet based Multi-Resolution Spectrum Sensing (MRSS), an N-bit hard combination technique for cooperative decision making and a Received Signal Strength (RSS) based localization algorithm to detect the availability of frequency bands and the location of the usable base station. We develop an N-bit hard combination technique and compare its performance to a traditionally used 2-bit hard combination for cooperative sensing. The key idea is to design a novel RF sensor network based cooperative wideband spectrum sensing and localization scheme by using a wavelet based Multi-Resolution Spectrum Sensing (MRSS) and Received Signal Strength (RSS) Localization techniques which were originally proposed for cognitive radio applications. The performance evaluations are also done to show the different detection accuracies for varying parameters such as number of sensor nodes, Signal to Noise Ratios (SNR) and number of averaged Power Spectral Densities (PSD). The proposed scheme improves the problems of shadowing, fading and noise. In addition, the RSS based localization technique was shown to be an acceptable means of estimating the position of the available transmitter. © 2011 IEEE.

  17. A Novel Spectrum Scheduling Scheme with Ant Colony Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Liping Liu

    2018-01-01

    Full Text Available Cognitive radio is a promising technology for improving spectrum utilization, which allows cognitive users access to the licensed spectrum while primary users are absent. In this paper, we design a resource allocation framework based on graph theory for spectrum assignment in cognitive radio networks. The framework takes into account the constraints that interference for primary users and possible collision among cognitive users. Based on the proposed model, we formulate a system utility function to maximize the system benefit. Based on the proposed model and objective problem, we design an improved ant colony optimization algorithm (IACO from two aspects: first, we introduce differential evolution (DE process to accelerate convergence speed by monitoring mechanism; then we design a variable neighborhood search (VNS process to avoid the algorithm falling into the local optimal. Simulation results demonstrate that the improved algorithm achieves better performance.

  18. Blind spectrum reconstruction algorithm with L0-sparse representation

    International Nuclear Information System (INIS)

    Liu, Hai; Zhang, Zhaoli; Liu, Sanyan; Shu, Jiangbo; Liu, Tingting; Zhang, Tianxu

    2015-01-01

    Raman spectrum often suffers from band overlap and Poisson noise. This paper presents a new blind Poissonian Raman spectrum reconstruction method, which incorporates the L 0 -sparse prior together with the total variation constraint into the maximum a posteriori framework. Furthermore, the greedy analysis pursuit algorithm is adopted to solve the L 0 -based minimization problem. Simulated and real spectrum experimental results show that the proposed method can effectively preserve spectral structure and suppress noise. The reconstructed Raman spectra are easily used for interpreting unknown chemical mixtures. (paper)

  19. A combined spectrum sensing and OFDM demodulation scheme

    NARCIS (Netherlands)

    Heskamp, M.; Slump, Cornelis H.

    2009-01-01

    In this paper we propose a combined signaling and spectrum sensing scheme for cognitive radio that can detect in-band primary users while the networks own signal is active. The signaling scheme uses OFDM with phase shift keying modulated sub-carriers, and the detection scheme measures the deviation

  20. A data compression algorithm for nuclear spectrum files

    International Nuclear Information System (INIS)

    Mika, J.F.; Martin, L.J.; Johnston, P.N.

    1990-01-01

    The total space occupied by computer files of spectra generated in nuclear spectroscopy systems can lead to problems of storage, and transmission time. An algorithm is presented which significantly reduces the space required to store nuclear spectra, without loss of any information content. Testing indicates that spectrum files can be routinely compressed by a factor of 5. (orig.)

  1. A Decentralized Eigenvalue Computation Method for Spectrum Sensing Based on Average Consensus

    Science.gov (United States)

    Mohammadi, Jafar; Limmer, Steffen; Stańczak, Sławomir

    2016-07-01

    This paper considers eigenvalue estimation for the decentralized inference problem for spectrum sensing. We propose a decentralized eigenvalue computation algorithm based on the power method, which is referred to as generalized power method GPM; it is capable of estimating the eigenvalues of a given covariance matrix under certain conditions. Furthermore, we have developed a decentralized implementation of GPM by splitting the iterative operations into local and global computation tasks. The global tasks require data exchange to be performed among the nodes. For this task, we apply an average consensus algorithm to efficiently perform the global computations. As a special case, we consider a structured graph that is a tree with clusters of nodes at its leaves. For an accelerated distributed implementation, we propose to use computation over multiple access channel (CoMAC) as a building block of the algorithm. Numerical simulations are provided to illustrate the performance of the two algorithms.

  2. Concept for a hyperspectral remote sensing algorithm for floating marine macro plastics.

    Science.gov (United States)

    Goddijn-Murphy, Lonneke; Peters, Steef; van Sebille, Erik; James, Neil A; Gibb, Stuart

    2018-01-01

    There is growing global concern over the chemical, biological and ecological impact of plastics in the ocean. Remote sensing has the potential to provide long-term, global monitoring but for marine plastics it is still in its early stages. Some progress has been made in hyperspectral remote sensing of marine macroplastics in the visible (VIS) to short wave infrared (SWIR) spectrum. We present a reflectance model of sunlight interacting with a sea surface littered with macro plastics, based on geometrical optics and the spectral signatures of plastic and seawater. This is a first step towards the development of a remote sensing algorithm for marine plastic using light reflectance measurements in air. Our model takes the colour, transparency, reflectivity and shape of plastic litter into account. This concept model can aid the design of laboratory, field and Earth observation measurements in the VIS-SWIR spectrum and explain the results. Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. An efficient and fast detection algorithm for multimode FBG sensing

    DEFF Research Database (Denmark)

    Ganziy, Denis; Jespersen, O.; Rose, B.

    2015-01-01

    We propose a novel dynamic gate algorithm (DGA) for fast and accurate peak detection. The algorithm uses threshold determined detection window and Center of gravity algorithm with bias compensation. We analyze the wavelength fit resolution of the DGA for different values of signal to noise ratio...... and different typical peak shapes. Our simulations and experiments demonstrate that the DGA method is fast and robust with higher stability and accuracy compared to conventional algorithms. This makes it very attractive for future implementation in sensing systems especially based on multimode fiber Bragg...

  4. A localized cooperative wideband spectrum sensing for dynamic access of TV bands using RF sensor networks

    KAUST Repository

    Mirza, Mohammed; Alouini, Mohamed-Slim

    2011-01-01

    In this paper we address and simulate a Radio Frequency (RF) sensor network for a cooperative spectrum sensing and localization scheme. The proposed method integrates a Wavelet based Multi-Resolution Spectrum Sensing (MRSS), an N-bit hard

  5. Energy detection for spectrum sensing in cognitive radio

    CERN Document Server

    Atapattu, Saman; Jiang, Hai

    2014-01-01

    This Springer Brief focuses on the current state-of-the-art research on spectrum sensing by using energy detection, a low-complexity and low-cost technique. It includes a comprehensive summary of recent research, fundamental theories, possible architectures, useful performance measurements of energy detection and applications of energy detection. Concise, practical chapters explore conventional energy detectors, alternative forms of energy detectors, performance measurements, diversity techniques and cooperative networks. The careful analysis enables reader to identify the most efficient techn

  6. Parallel-Computing Architecture for JWST Wavefront-Sensing Algorithms

    Science.gov (United States)

    2011-09-01

    results due to the increasing cost and complexity of each test. 2. ALGORITHM OVERVIEW Phase retrieval is an image-based wavefront-sensing...broadband illumination problems we have found that hand-tuning the right matrix sizes can account for a speedup of 86x faster. This comes from hand-picking...Wavefront Sensing and Control”. Proceedings of SPIE (2007) vol. 6687 (08). [5] Greenhouse, M. A., Drury , M. P., Dunn, J. L., Glazer, S. D., Greville, E

  7. An adaptive tensor voting algorithm combined with texture spectrum

    Science.gov (United States)

    Wang, Gang; Su, Qing-tang; Lü, Gao-huan; Zhang, Xiao-feng; Liu, Yu-huan; He, An-zhi

    2015-01-01

    An adaptive tensor voting algorithm combined with texture spectrum is proposed. The image texture spectrum is used to get the adaptive scale parameter of voting field. Then the texture information modifies both the attenuation coefficient and the attenuation field so that we can use this algorithm to create more significant and correct structures in the original image according to the human visual perception. At the same time, the proposed method can improve the edge extraction quality, which includes decreasing the flocculent region efficiently and making image clear. In the experiment for extracting pavement cracks, the original pavement image is processed by the proposed method which is combined with the significant curve feature threshold procedure, and the resulted image displays the faint crack signals submerged in the complicated background efficiently and clearly.

  8. Quasi Gradient Projection Algorithm for Sparse Reconstruction in Compressed Sensing

    Directory of Open Access Journals (Sweden)

    Xin Meng

    2014-02-01

    Full Text Available Compressed sensing is a novel signal sampling theory under the condition that the signal is sparse or compressible. The existing recovery algorithms based on the gradient projection can either need prior knowledge or recovery the signal poorly. In this paper, a new algorithm based on gradient projection is proposed, which is referred as Quasi Gradient Projection. The algorithm presented quasi gradient direction and two step sizes schemes along this direction. The algorithm doesn’t need any prior knowledge of the original signal. Simulation results demonstrate that the presented algorithm cans recovery the signal more correctly than GPSR which also don’t need prior knowledge. Meanwhile, the algorithm has a lower computation complexity.

  9. Zombie algorithms: a timesaving remote sensing systems engineering tool

    Science.gov (United States)

    Ardanuy, Philip E.; Powell, Dylan C.; Marley, Stephen

    2008-08-01

    In modern horror fiction, zombies are generally undead corpses brought back from the dead by supernatural or scientific means, and are rarely under anyone's direct control. They typically have very limited intelligence, and hunger for the flesh of the living [1]. Typical spectroradiometric or hyperspectral instruments providess calibrated radiances for a number of remote sensing algorithms. The algorithms typically must meet specified latency and availability requirements while yielding products at the required quality. These systems, whether research, operational, or a hybrid, are typically cost constrained. Complexity of the algorithms can be high, and may evolve and mature over time as sensor characterization changes, product validation occurs, and areas of scientific basis improvement are identified and completed. This suggests the need for a systems engineering process for algorithm maintenance that is agile, cost efficient, repeatable, and predictable. Experience on remote sensing science data systems suggests the benefits of "plug-n-play" concepts of operation. The concept, while intuitively simple, can be challenging to implement in practice. The use of zombie algorithms-empty shells that outwardly resemble the form, fit, and function of a "complete" algorithm without the implemented theoretical basis-provides the ground systems advantages equivalent to those obtained by integrating sensor engineering models onto the spacecraft bus. Combined with a mature, repeatable process for incorporating the theoretical basis, or scientific core, into the "head" of the zombie algorithm, along with associated scripting and registration, provides an easy "on ramp" for the rapid and low-risk integration of scientific applications into operational systems.

  10. Optimality of Multichannel Myopic Sensing in the Presence of Sensing Error for Opportunistic Spectrum Access

    Directory of Open Access Journals (Sweden)

    Xiaofeng Jiang

    2013-01-01

    Full Text Available The optimization problem for the performance of opportunistic spectrum access is considered in this study. A user, with the limited sensing capacity, has opportunistic access to a communication system with multiple channels. The user can only choose several channels to sense and decides whether to access these channels based on the sensing information in each time slot. Meanwhile, the presence of sensing error is considered. A reward is obtained when the user accesses a channel. The objective is to maximize the expected (discounted or average reward accrued over an infinite horizon. This problem can be formulated as a partially observable Markov decision process. This study shows the optimality of the simple and robust myopic policy which focuses on maximizing the immediate reward. The results show that the myopic policy is optimal in the case of practical interest.

  11. Integrating sensing across a broader spectrum to support homeland security

    Science.gov (United States)

    O'Brien, Thomas W.; Finkelstein, Marc

    2003-08-01

    All objects and activities give off energy in some part of the spectrum, may leave tell-tail signs from their previous activities (e.g., earth scaring or vapor trails), or leave information about relationships that they may have with other entities and activities (e.g., networks). Many of these phenomenologies are either not picked up by current stovepiped sensors, or the data supplied by those sensors are not fully exploited to properly observe them. In either case, new sensor data as well as the better exploitation of existing data could be used to provide, or at least cross cue or correlate with other sensor data to detect, identify, geolocate or track different kind of problems. Current sensors are often designed for specific purposes and are capable of sensing only limited parts of the spectrum. Significantly broadening the sensing spectrum will be an essential element of solving the emerging class of new "hard problems". There are many other observables available that could be exploited to assist in that process. Thus one could broaden the sensing to observe those phenomenologies associated with combustion effluents; thermal radiation; magnetic anomalies; seismic movement; acoustics; unintended electromagnetic emissions, changing weather conditions, logistics support indicators, debris trails; impressed observables (such as tagging); and others. What's needed is a disciplined, analytical process that can map observables to sensors, and ultimately to mission utility. The process, described in this SPIE presentation will address a specific example on the flow from the establishment of requirements to prosecutable observables, to objectives, to identification of sensors and assets, to the allocation of sensors and assets to observables, all based on optimizing mission utility.

  12. Novel Cooperative Spectrum Sensing Methods And Their Limitations

    DEFF Research Database (Denmark)

    Kiilerich Pratas, Nuno

    2012-01-01

    $-calculus, denoted as Bounded Broadcast Calculus. This analysis is done over centralized, decentralized and relay aided topologies. The outcome of this analysis is a theorem where it is stated, which properties a protocol should have so that it can be deemed correct, i.e. that it performs as intended, over each...... source. The node selection scheme is proposed in a centralized and in a decentralized version. These versions can complement each other and therefore lead to a more robust cooperative spectrum sensing mechanism....

  13. WSNs Microseismic Signal Subsection Compression Algorithm Based on Compressed Sensing

    Directory of Open Access Journals (Sweden)

    Zhouzhou Liu

    2015-01-01

    Full Text Available For wireless network microseismic monitoring and the problems of low compression ratio and high energy consumption of communication, this paper proposes a segmentation compression algorithm according to the characteristics of the microseismic signals and the compression perception theory (CS used in the transmission process. The algorithm will be collected as a number of nonzero elements of data segmented basis, by reducing the number of combinations of nonzero elements within the segment to improve the accuracy of signal reconstruction, while taking advantage of the characteristics of compressive sensing theory to achieve a high compression ratio of the signal. Experimental results show that, in the quantum chaos immune clone refactoring (Q-CSDR algorithm for reconstruction algorithm, under the condition of signal sparse degree higher than 40, to be more than 0.4 of the compression ratio to compress the signal, the mean square error is less than 0.01, prolonging the network life by 2 times.

  14. Local Competition-Based Superpixel Segmentation Algorithm in Remote Sensing

    Directory of Open Access Journals (Sweden)

    Jiayin Liu

    2017-06-01

    Full Text Available Remote sensing technologies have been widely applied in urban environments’ monitoring, synthesis and modeling. Incorporating spatial information in perceptually coherent regions, superpixel-based approaches can effectively eliminate the “salt and pepper” phenomenon which is common in pixel-wise approaches. Compared with fixed-size windows, superpixels have adaptive sizes and shapes for different spatial structures. Moreover, superpixel-based algorithms can significantly improve computational efficiency owing to the greatly reduced number of image primitives. Hence, the superpixel algorithm, as a preprocessing technique, is more and more popularly used in remote sensing and many other fields. In this paper, we propose a superpixel segmentation algorithm called Superpixel Segmentation with Local Competition (SSLC, which utilizes a local competition mechanism to construct energy terms and label pixels. The local competition mechanism leads to energy terms locality and relativity, and thus, the proposed algorithm is less sensitive to the diversity of image content and scene layout. Consequently, SSLC could achieve consistent performance in different image regions. In addition, the Probability Density Function (PDF, which is estimated by Kernel Density Estimation (KDE with the Gaussian kernel, is introduced to describe the color distribution of superpixels as a more sophisticated and accurate measure. To reduce computational complexity, a boundary optimization framework is introduced to only handle boundary pixels instead of the whole image. We conduct experiments to benchmark the proposed algorithm with the other state-of-the-art ones on the Berkeley Segmentation Dataset (BSD and remote sensing images. Results demonstrate that the SSLC algorithm yields the best overall performance, while the computation time-efficiency is still competitive.

  15. Local Competition-Based Superpixel Segmentation Algorithm in Remote Sensing.

    Science.gov (United States)

    Liu, Jiayin; Tang, Zhenmin; Cui, Ying; Wu, Guoxing

    2017-06-12

    Remote sensing technologies have been widely applied in urban environments' monitoring, synthesis and modeling. Incorporating spatial information in perceptually coherent regions, superpixel-based approaches can effectively eliminate the "salt and pepper" phenomenon which is common in pixel-wise approaches. Compared with fixed-size windows, superpixels have adaptive sizes and shapes for different spatial structures. Moreover, superpixel-based algorithms can significantly improve computational efficiency owing to the greatly reduced number of image primitives. Hence, the superpixel algorithm, as a preprocessing technique, is more and more popularly used in remote sensing and many other fields. In this paper, we propose a superpixel segmentation algorithm called Superpixel Segmentation with Local Competition (SSLC), which utilizes a local competition mechanism to construct energy terms and label pixels. The local competition mechanism leads to energy terms locality and relativity, and thus, the proposed algorithm is less sensitive to the diversity of image content and scene layout. Consequently, SSLC could achieve consistent performance in different image regions. In addition, the Probability Density Function (PDF), which is estimated by Kernel Density Estimation (KDE) with the Gaussian kernel, is introduced to describe the color distribution of superpixels as a more sophisticated and accurate measure. To reduce computational complexity, a boundary optimization framework is introduced to only handle boundary pixels instead of the whole image. We conduct experiments to benchmark the proposed algorithm with the other state-of-the-art ones on the Berkeley Segmentation Dataset (BSD) and remote sensing images. Results demonstrate that the SSLC algorithm yields the best overall performance, while the computation time-efficiency is still competitive.

  16. A Review on Spectrum Sensing for Cognitive Radio: Challenges and Solutions

    Directory of Open Access Journals (Sweden)

    Yonghong Zeng

    2010-01-01

    Full Text Available Cognitive radio is widely expected to be the next Big Bang in wireless communications. Spectrum sensing, that is, detecting the presence of the primary users in a licensed spectrum, is a fundamental problem for cognitive radio. As a result, spectrum sensing has reborn as a very active research area in recent years despite its long history. In this paper, spectrum sensing techniques from the optimal likelihood ratio test to energy detection, matched filtering detection, cyclostationary detection, eigenvalue-based sensing, joint space-time sensing, and robust sensing methods are reviewed. Cooperative spectrum sensing with multiple receivers is also discussed. Special attention is paid to sensing methods that need little prior information on the source signal and the propagation channel. Practical challenges such as noise power uncertainty are discussed and possible solutions are provided. Theoretical analysis on the test statistic distribution and threshold setting is also investigated.

  17. A Novel Wireless Power Transfer-Based Weighed Clustering Cooperative Spectrum Sensing Method for Cognitive Sensor Networks.

    Science.gov (United States)

    Liu, Xin

    2015-10-30

    In a cognitive sensor network (CSN), the wastage of sensing time and energy is a challenge to cooperative spectrum sensing, when the number of cooperative cognitive nodes (CNs) becomes very large. In this paper, a novel wireless power transfer (WPT)-based weighed clustering cooperative spectrum sensing model is proposed, which divides all the CNs into several clusters, and then selects the most favorable CNs as the cluster heads and allows the common CNs to transfer the received radio frequency (RF) energy of the primary node (PN) to the cluster heads, in order to supply the electrical energy needed for sensing and cooperation. A joint resource optimization is formulated to maximize the spectrum access probability of the CSN, through jointly allocating sensing time and clustering number. According to the resource optimization results, a clustering algorithm is proposed. The simulation results have shown that compared to the traditional model, the cluster heads of the proposed model can achieve more transmission power and there exists optimal sensing time and clustering number to maximize the spectrum access probability.

  18. A Novel Wireless Power Transfer-Based Weighed Clustering Cooperative Spectrum Sensing Method for Cognitive Sensor Networks

    Directory of Open Access Journals (Sweden)

    Xin Liu

    2015-10-01

    Full Text Available In a cognitive sensor network (CSN, the wastage of sensing time and energy is a challenge to cooperative spectrum sensing, when the number of cooperative cognitive nodes (CNs becomes very large. In this paper, a novel wireless power transfer (WPT-based weighed clustering cooperative spectrum sensing model is proposed, which divides all the CNs into several clusters, and then selects the most favorable CNs as the cluster heads and allows the common CNs to transfer the received radio frequency (RF energy of the primary node (PN to the cluster heads, in order to supply the electrical energy needed for sensing and cooperation. A joint resource optimization is formulated to maximize the spectrum access probability of the CSN, through jointly allocating sensing time and clustering number. According to the resource optimization results, a clustering algorithm is proposed. The simulation results have shown that compared to the traditional model, the cluster heads of the proposed model can achieve more transmission power and there exists optimal sensing time and clustering number to maximize the spectrum access probability.

  19. Multicarrier Spread Spectrum Modulation Schemes and Efficient FFT Algorithms for Cognitive Radio Systems

    Directory of Open Access Journals (Sweden)

    Mohandass Sundararajan

    2014-07-01

    Full Text Available Spread spectrum (SS and multicarrier modulation (MCM techniques are recognized as potential candidates for the design of underlay and interweave cognitive radio (CR systems, respectively. Direct Sequence Code Division Multiple Access (DS-CDMA is a spread spectrum technique generally used in underlay CR systems. Orthogonal Frequency Division Multiplexing (OFDM is the basic MCM technique, primarily used in interweave CR systems. There are other MCM schemes derived from OFDM technique, like Non-Contiguous OFDM, Spread OFDM, and OFDM-OQAM, which are more suitable for CR systems. Multicarrier Spread Spectrum Modulation (MCSSM schemes like MC-CDMA, MC-DS-CDMA and SS-MC-CDMA, combine DS-CDMA and OFDM techniques in order to improve the CR system performance and adaptability. This article gives a detailed survey of the various spread spectrum and multicarrier modulation schemes proposed in the literature. Fast Fourier Transform (FFT plays a vital role in all the multicarrier modulation techniques. The FFT part of the modem can be used for spectrum sensing. The performance of the FFT operator plays a crucial role in the overall performance of the system. Since the cognitive radio is an adaptive system, the FFT operator must also be adaptive for various input/output values, in order to save energy and time taken for execution. This article also includes the various efficient FFT algorithms proposed in the literature, which are suitable for CR systems.

  20. A Robust Parallel Algorithm for Combinatorial Compressed Sensing

    Science.gov (United States)

    Mendoza-Smith, Rodrigo; Tanner, Jared W.; Wechsung, Florian

    2018-04-01

    In previous work two of the authors have shown that a vector $x \\in \\mathbb{R}^n$ with at most $k Parallel-$\\ell_0$ decoding algorithm, where $\\mathrm{nnz}(A)$ denotes the number of nonzero entries in $A \\in \\mathbb{R}^{m \\times n}$. In this paper we present the Robust-$\\ell_0$ decoding algorithm, which robustifies Parallel-$\\ell_0$ when the sketch $Ax$ is corrupted by additive noise. This robustness is achieved by approximating the asymptotic posterior distribution of values in the sketch given its corrupted measurements. We provide analytic expressions that approximate these posteriors under the assumptions that the nonzero entries in the signal and the noise are drawn from continuous distributions. Numerical experiments presented show that Robust-$\\ell_0$ is superior to existing greedy and combinatorial compressed sensing algorithms in the presence of small to moderate signal-to-noise ratios in the setting of Gaussian signals and Gaussian additive noise.

  1. Sum Utilization of Spectrum with Spectrum Handoff and Imperfect Sensing in Interweave Multi-Channel Cognitive Radio Networks

    Directory of Open Access Journals (Sweden)

    Waqas Khalid

    2018-05-01

    Full Text Available Fifth-generation (5G heterogeneous network deployment poses new challenges for 5G-based cognitive radio networks (5G-CRNs as the primary user (PU is required to be more active because of the small cells, random user arrival, and spectrum handoff. Interweave CRNs (I-CRNs improve spectrum utilization by allowing opportunistic spectrum access (OSA for secondary users (SUs. The sum utilization of spectrum, i.e., joint utilization of spectrum by the SU and PU, depends on the spatial and temporal variations of PU activities, sensing outcomes, transmitting conditions, and spectrum handoff. In this study, we formulate and analyze the sum utilization of spectrum with different sets of channels under different PU and SU co-existing network topologies. We consider realistic multi-channel scenarios for the SU, with each channel licensed to a PU. The SU, aided by spectrum handoff, is authorized to utilize the channels on the basis of sensing outcomes and PU interruptions. The numerical evaluation of the proposed work is presented under different network and sensing parameters. Moreover, the sum utilization gain is investigated to analyze the sensitivities of different sensing parameters. It is demonstrated that different sets of channels, PU activities, and sensing outcomes have a significant impact on the sum utilization of spectrum associated with a specific network topology.

  2. An Energy-Efficient Spectrum-Aware Reinforcement Learning-Based Clustering Algorithm for Cognitive Radio Sensor Networks.

    Science.gov (United States)

    Mustapha, Ibrahim; Mohd Ali, Borhanuddin; Rasid, Mohd Fadlee A; Sali, Aduwati; Mohamad, Hafizal

    2015-08-13

    It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach.

  3. Soft cooperative spectrum sensing performance under imperfect and non identical reporting channels

    KAUST Repository

    Ben Ghorbel, Mahdi; Nam, Haewoon; Alouini, Mohamed-Slim

    2015-01-01

    in cooperative spectrum sensing techniques assume perfect channels between the cooperating users, this paper studies the effect of imperfect channels when local users report their sensed information to the fusion center. Cooperative detection and false

  4. Higher-Order Cyclostationarity Detection for Spectrum Sensing

    Directory of Open Access Journals (Sweden)

    Julien Renard

    2010-01-01

    Full Text Available Recent years have shown a growing interest in the concept of Cognitive Radios (CRs, able to access portions of the electromagnetic spectrum in an opportunistic operating way. Such systems require efficient detectors able to work in low Signal-to-Noise Ratio (SNR environments, with little or no information about the signals they are trying to detect. Energy detectors are widely used to perform such blind detection tasks, but quickly reach the so-called SNR wall below which detection becomes impossible Tandra (2005. Cyclostationarity detectors are an interesting alternative to energy detectors, as they exploit hidden periodicities present in man-made signals, but absent in noise. Such detectors use quadratic transformations of the signals to extract the hidden sine-waves. While most of the literature focuses on the second-order transformations of the signals, we investigate the potential of higher-order transformations of the signals. Using the theory of Higher-Order Cyclostationarity (HOCS, we derive a fourth-order detector that performs similarly to the second-order ones to detect linearly modulated signals, at SNR around 0 dB, which may be used if the signals of interest do not exhibit second-order cyclostationarity. More generally this paper reviews the relevant aspects of the cyclostationary and HOCS theory, and shows their potential for spectrum sensing.

  5. Sensing Technologies for Autism Spectrum Disorder Screening and Intervention

    Directory of Open Access Journals (Sweden)

    John-John Cabibihan

    2016-12-01

    Full Text Available This paper reviews the state-of-the-art in sensing technologies that are relevant for Autism Spectrum Disorder (ASD screening and therapy. This disorder is characterized by difficulties in social communication, social interactions, and repetitive behaviors. It is diagnosed during the first three years of life. Early and intensive interventions have been shown to improve the developmental trajectory of the affected children. The earlier the diagnosis, the sooner the intervention therapy can begin, thus, making early diagnosis an important research goal. Technological innovations have tremendous potential to assist with early diagnosis and improve intervention programs. The need for careful and methodological evaluation of such emerging technologies becomes important in order to assist not only the therapists and clinicians in their selection of suitable tools, but to also guide the developers of the technologies in improving hardware and software. In this paper, we survey the literatures on sensing technologies for ASD and we categorize them into eye trackers, movement trackers, electrodermal activity monitors, tactile sensors, vocal prosody and speech detectors, and sleep quality assessment devices. We assess their effectiveness and study their limitations. We also examine the challenges faced by this growing field that need to be addressed before these technologies can perform up to their theoretical potential.

  6. Geometry correction Algorithm for UAV Remote Sensing Image Based on Improved Neural Network

    Science.gov (United States)

    Liu, Ruian; Liu, Nan; Zeng, Beibei; Chen, Tingting; Yin, Ninghao

    2018-03-01

    Aiming at the disadvantage of current geometry correction algorithm for UAV remote sensing image, a new algorithm is proposed. Adaptive genetic algorithm (AGA) and RBF neural network are introduced into this algorithm. And combined with the geometry correction principle for UAV remote sensing image, the algorithm and solving steps of AGA-RBF are presented in order to realize geometry correction for UAV remote sensing. The correction accuracy and operational efficiency is improved through optimizing the structure and connection weight of RBF neural network separately with AGA and LMS algorithm. Finally, experiments show that AGA-RBF algorithm has the advantages of high correction accuracy, high running rate and strong generalization ability.

  7. Performance of target detection algorithm in compressive sensing miniature ultraspectral imaging compressed sensing system

    Science.gov (United States)

    Gedalin, Daniel; Oiknine, Yaniv; August, Isaac; Blumberg, Dan G.; Rotman, Stanley R.; Stern, Adrian

    2017-04-01

    Compressive sensing theory was proposed to deal with the high quantity of measurements demanded by traditional hyperspectral systems. Recently, a compressive spectral imaging technique dubbed compressive sensing miniature ultraspectral imaging (CS-MUSI) was presented. This system uses a voltage controlled liquid crystal device to create multiplexed hyperspectral cubes. We evaluate the utility of the data captured using the CS-MUSI system for the task of target detection. Specifically, we compare the performance of the matched filter target detection algorithm in traditional hyperspectral systems and in CS-MUSI multiplexed hyperspectral cubes. We found that the target detection algorithm performs similarly in both cases, despite the fact that the CS-MUSI data is up to an order of magnitude less than that in conventional hyperspectral cubes. Moreover, the target detection is approximately an order of magnitude faster in CS-MUSI data.

  8. A genetic algorithm based method for neutron spectrum unfolding

    International Nuclear Information System (INIS)

    Suman, Vitisha; Sarkar, P.K.

    2013-03-01

    An approach to neutron spectrum unfolding based on a stochastic evolutionary search mechanism - Genetic Algorithm (GA) is presented. It is tested to unfold a set of simulated spectra, the unfolded spectra is compared to the output of a standard code FERDOR. The method was then applied to a set of measured pulse height spectrum of neutrons from the AmBe source as well as of emitted neutrons from Li(p,n) and Ag(C,n) nuclear reactions carried out in the accelerator environment. The unfolded spectra compared to the output of FERDOR show good agreement in the case of AmBe spectra and Li(p,n) spectra. In the case of Ag(C,n) spectra GA method results in some fluctuations. Necessity of carrying out smoothening of the obtained solution is also studied, which leads to approximation of the solution yielding an appropriate solution finally. Few smoothing techniques like second difference smoothing, Monte Carlo averaging, combination of both and gaussian based smoothing methods are also studied. Unfolded results obtained after inclusion of the smoothening criteria are in close agreement with the output obtained from the FERDOR code. The present method is also tested on a set of underdetermined problems, the outputs of which is compared to the unfolded spectra obtained from the FERDOR applied to a completely determined problem, shows a good match. The distribution of the unfolded spectra is also studied. Uncertainty propagation in the unfolded spectra due to the errors present in the measurement as well as the response function is also carried out. The method appears to be promising for unfolding the completely determined as well as underdetermined problems. It also has provisions to carry out the uncertainty analysis. (author)

  9. Centralized cooperative spectrum sensing for ad-hoc disaster relief network clusters

    DEFF Research Database (Denmark)

    Pratas, Nuno; Marchetti, Nicola; Prasad, Neeli R.

    2010-01-01

    Disaster relief networks have to be highly adaptable and resilient. Cognitive radio enhanced ad-hoc architecture have been put forward as a candidate to enable such networks. Spectrum sensing is the cornerstone of the cognitive radio paradigm, and it has been the target of intensive research....... The main common conclusion was that the achievable spectrum sensing accuracy can be greatly enhanced through the use of cooperative sensing schemes. When considering applying Cognitive Radio to ad-hoc disaster relief networks, spectrum sensing cooperative schemes are paramount. A centralized cluster...

  10. GMG: A Guaranteed, Efficient Global Optimization Algorithm for Remote Sensing.

    Energy Technology Data Exchange (ETDEWEB)

    D' Helon, CD

    2004-08-18

    The monocular passive ranging (MPR) problem in remote sensing consists of identifying the precise range of an airborne target (missile, plane, etc.) from its observed radiance. This inverse problem may be set as a global optimization problem (GOP) whereby the difference between the observed and model predicted radiances is minimized over the possible ranges and atmospheric conditions. Using additional information about the error function between the predicted and observed radiances of the target, we developed GMG, a new algorithm to find the Global Minimum with a Guarantee. The new algorithm transforms the original continuous GOP into a discrete search problem, thereby guaranteeing to find the position of the global minimum in a reasonably short time. The algorithm is first applied to the golf course problem, which serves as a litmus test for its performance in the presence of both complete and degraded additional information. GMG is further assessed on a set of standard benchmark functions and then applied to various realizations of the MPR problem.

  11. Outage Analysis of Spectrum-Sharing over M-Block Fading with Sensing Information

    KAUST Repository

    Alabbasi, Abdulrahman

    2016-07-13

    Future wireless technologies, such as, 5G, are expected to support real-time applications with high data throughput, e.g., holographic meetings. From a bandwidth perspective, cognitive radio is a promising technology to enhance the system’s throughput via sharing the licensed spectrum. From a delay perspective, it is well known that increasing the number of decoding blocks will improve the system robustness against errors, while increasing the delay. Therefore, optimally allocating the resources to determine the tradeoff of tuning the length of decoding blocks while sharing the spectrum is a critical challenge for future wireless systems. In this work, we minimize the targeted outage probability over the block-fading channels while utilizing the spectrum-sharing concept. The secondary user’s outage region and the corresponding optimal power are derived, over twoblocks and M-blocks fading channels. We propose two suboptimal power strategies and derive the associated asymptotic lower and upper bounds on the outage probability with tractable expressions. These bounds allow us to derive the exact diversity order of the secondary user’s outage probability. To further enhance the system’s performance, we also investigate the impact of including the sensing information on the outage problem. The outage problem is then solved via proposing an alternating optimization algorithm, which utilizes the verified strict quasiconvex structure of the problem. Selected numerical results are presented to characterize the system’s behavior and show the improvements of several sharing concepts.

  12. Spectrum Sensing and Primary User Localization in Cognitive Radio Networks via Sparsity

    Directory of Open Access Journals (Sweden)

    Lanchao Liu

    2016-01-01

    Full Text Available The theory of compressive sensing (CS has been employed to detect available spectrum resource in cognitive radio (CR networks recently. Capitalizing on the spectrum resource underutilization and spatial sparsity of primary user (PU locations, CS enables the identification of the unused spectrum bands and PU locations at a low sampling rate. Although CS has been studied in the cooperative spectrum sensing mechanism in which CR nodes work collaboratively to accomplish the spectrum sensing and PU localization task, many important issues remain unsettled. Does the designed compressive spectrum sensing mechanism satisfy the Restricted Isometry Property, which guarantees a successful recovery of the original sparse signal? Can the spectrum sensing results help the localization of PUs? What are the characteristics of localization errors? To answer those questions, we try to justify the applicability of the CS theory to the compressive spectrum sensing framework in this paper, and propose a design of PU localization utilizing the spectrum usage information. The localization error is analyzed by the Cramér-Rao lower bound, which can be exploited to improve the localization performance. Detail analysis and simulations are presented to support the claims and demonstrate the efficacy and efficiency of the proposed mechanism.

  13. An Improved Biclustering Algorithm and Its Application to Gene Expression Spectrum Analysis

    OpenAIRE

    Qu, Hua; Wang, Liu-Pu; Liang, Yan-Chun; Wu, Chun-Guo

    2016-01-01

    Cheng and Church algorithm is an important approach in biclustering algorithms. In this paper, the process of the extended space in the second stage of Cheng and Church algorithm is improved and the selections of two important parameters are discussed. The results of the improved algorithm used in the gene expression spectrum analysis show that, compared with Cheng and Church algorithm, the quality of clustering results is enhanced obviously, the mining expression models are better, and the d...

  14. Learning Frameworks for Cooperative Spectrum Sensing and Energy-Efficient Data Protection in Cognitive Radio Networks

    Directory of Open Access Journals (Sweden)

    Vinh Quang Do

    2018-05-01

    Full Text Available This paper studies learning frameworks for energy-efficient data communications in an energy-harvesting cognitive radio network in which secondary users (SUs harvest energy from solar power while opportunistically accessing a licensed channel for data transmission. The SUs perform spectrum sensing individually, and send local decisions about the presence of the primary user (PU on the channel to a fusion center (FC. We first design a new cooperative spectrum-sensing technique based on a convolutional neural network in which the FC uses historical sensing data to train the network for classification problem. The system is assumed to operate in a time-slotted manner. At the beginning of each time slot, the FC uses the current local decisions as input for the trained network to decide whether the PU is active or not in that time slot. In addition, legitimate transmissions can be vulnerable to a hidden eavesdropper, which always passively listens to the communication. Therefore, we further propose a transfer learning actor–critic algorithm for an SU to decide its operation mode to increase the security level under the constraint of limited energy. In this approach, the SU directly interacts with the environment to learn its dynamics (i.e., an arrival of harvested energy; then, the SU can either stay idle to save energy or transmit to the FC secured data that are encrypted using a suitable private-key encryption method to maximize the long-term effective security level of the network. We finally present numerical simulation results under various configurations to evaluate our proposed schemes.

  15. Application of an ADS-B Sense and Avoid Algorithm

    Science.gov (United States)

    Arteaga, Ricardo; Kotcher, Robert; Cavalin, Moshe; Dandachy, Mohammed

    2016-01-01

    The National Aeronautics and Space Administration Armstrong Flight Research Center in Edwards, California is leading a program aimed towards integrating unmanned aircraft system into the national airspace system (UAS in the NAS). The overarching goal of the program is to reduce technical barriers associated with related safety issues as well as addressing challenges that will allow UAS routine access to the national airspace. This research paper focuses on three novel ideas: (1) A design of an integrated UAS equipped with Automatic Dependent Surveillance-Broadcast that constructs a more accurate state-based airspace model; (2) The use of Stratway Algorithm in a real-time environment; and (3) The verification and validation of sense and avoid performance and usability test results which provide a pilot's perspective on how our system will benefit the UAS in the NAS program for both piloted and unmanned aircraft.

  16. Spectrum Sensing Experimentation for LTE and WiFi Unlicensed Band Operation

    Directory of Open Access Journals (Sweden)

    N. Milošević

    2016-11-01

    Full Text Available If several different systems operate in the same frequency band, a coordination between them is needed for effective use of the available spectrum. The coordination is especially important if the systems are not designed to operate in such an environment. The very important initial phase of the coordination process is acquiring of the spectrum usage map or spectrum sensing. The paper describes the spectrum sensing experimentation in the unlicensed 5 GHz band during the WiFi or LTE transmission. It describes the experiment workflow and depicts the obtained results. The experiments were performed at NITOS testbed at the University of Thessaly, Greece, and show that it is possible to determine whether WiFi or LTE transmission is sensed. Therefore, based on spectrum sensing it will be possible to coordinate a shared access of WiFi and LTE users in the unlicensed 5 GHz band.

  17. The spectral positioning algorithm of new spectrum vehicle based on convex programming in wireless sensor network

    Science.gov (United States)

    Zhang, Yongjun; Lu, Zhixin

    2017-10-01

    Spectrum resources are very precious, so it is increasingly important to locate interference signals rapidly. Convex programming algorithms in wireless sensor networks are often used as localization algorithms. But in view of the traditional convex programming algorithm is too much overlap of wireless sensor nodes that bring low positioning accuracy, the paper proposed a new algorithm. Which is mainly based on the traditional convex programming algorithm, the spectrum car sends unmanned aerial vehicles (uses) that can be used to record data periodically along different trajectories. According to the probability density distribution, the positioning area is segmented to further reduce the location area. Because the algorithm only increases the communication process of the power value of the unknown node and the sensor node, the advantages of the convex programming algorithm are basically preserved to realize the simple and real-time performance. The experimental results show that the improved algorithm has a better positioning accuracy than the original convex programming algorithm.

  18. The MUSIC algorithm for sparse objects: a compressed sensing analysis

    International Nuclear Information System (INIS)

    Fannjiang, Albert C

    2011-01-01

    The multiple signal classification (MUSIC) algorithm, and its extension for imaging sparse extended objects, with noisy data is analyzed by compressed sensing (CS) techniques. A thresholding rule is developed to augment the standard MUSIC algorithm. The notion of restricted isometry property (RIP) and an upper bound on the restricted isometry constant (RIC) are employed to establish sufficient conditions for the exact localization by MUSIC with or without noise. In the noiseless case, the sufficient condition gives an upper bound on the numbers of random sampling and incident directions necessary for exact localization. In the noisy case, the sufficient condition assumes additionally an upper bound for the noise-to-object ratio in terms of the RIC and the dynamic range of objects. This bound points to the super-resolution capability of the MUSIC algorithm. Rigorous comparison of performance between MUSIC and the CS minimization principle, basis pursuit denoising (BPDN), is given. In general, the MUSIC algorithm guarantees to recover, with high probability, s scatterers with n=O(s 2 ) random sampling and incident directions and sufficiently high frequency. For the favorable imaging geometry where the scatterers are distributed on a transverse plane MUSIC guarantees to recover, with high probability, s scatterers with a median frequency and n=O(s) random sampling/incident directions. Moreover, for the problems of spectral estimation and source localizations both BPDN and MUSIC guarantee, with high probability, to identify exactly the frequencies of random signals with the number n=O(s) of sampling times. However, in the absence of abundant realizations of signals, BPDN is the preferred method for spectral estimation. Indeed, BPDN can identify the frequencies approximately with just one realization of signals with the recovery error at worst linearly proportional to the noise level. Numerical results confirm that BPDN outperforms MUSIC in the well-resolved case while

  19. Analysis of Practical Implementation for Secure Spectrum Sensing in Cognitive Radio Networks

    DEFF Research Database (Denmark)

    Ivanov, Antoni; Mihovska, Albena Dimitrova; Tonchev, Krasimir

    2017-01-01

    Spectrum sensing is vitally important functionality for the cognitive radio (CR) device because it allows for assessing, which part of the spectrum is unoccupied and suitable for temporal use. Most of the proposed research efforts until now have been based on theoretical findings about the perfor......Spectrum sensing is vitally important functionality for the cognitive radio (CR) device because it allows for assessing, which part of the spectrum is unoccupied and suitable for temporal use. Most of the proposed research efforts until now have been based on theoretical findings about...

  20. Performance of Cooperative Spectrum Sensing over Non-Identical Fading Environments

    KAUST Repository

    Rao, Anlei; Alouini, Mohamed-Slim

    2012-01-01

    Different from previous works in cooperative spec- trum sensing that assumed the sensing channels independent identically distributed (i.i.d.), we investigate in this paper the independent but not identically distributed (i.n.i.d.) situations. In particular, we derive the false-alarm probability and the detection probability of cooperative spectrum sensing with the scheme of energy fusion over i.n.i.d. Rayleigh, Nakagami, and Rician fading channels. From the selected numerical results, we can see that cooperative spectrum sensing still gives considerably better performance even over i.n.i.d. fading environments.

  1. Capacity limits introduced by data fusion on cooperative spectrum sensing under correlated environments

    DEFF Research Database (Denmark)

    Pratas, Nuno; Marchetti, Nicola; Rodrigues, Antonio

    2010-01-01

    spectrum sensing scheme, by measuring the perceived capacity limits introduced by the use of data fusion on cooperative sensing schemes. The analysis is supported by evaluation metrics which account for the perceived capacity limits. The analysis is performed along the data fusion chain, comparing several...... scenarios encompassing different degree of environment correlation between the cluster nodes, number of cluster nodes and sensed channel occupation statistics. Through this study we motivate that to maximize the perceived capacity by the cooperative spectrum sensing, the use of data fusion needs...

  2. Performance of Cooperative Spectrum Sensing over Non-Identical Fading Environments

    KAUST Repository

    Rao, Anlei

    2012-09-08

    Different from previous works in cooperative spec- trum sensing that assumed the sensing channels independent identically distributed (i.i.d.), we investigate in this paper the independent but not identically distributed (i.n.i.d.) situations. In particular, we derive the false-alarm probability and the detection probability of cooperative spectrum sensing with the scheme of energy fusion over i.n.i.d. Rayleigh, Nakagami, and Rician fading channels. From the selected numerical results, we can see that cooperative spectrum sensing still gives considerably better performance even over i.n.i.d. fading environments.

  3. Hard Fusion Based Spectrum Sensing over Mobile Fading Channels in Cognitive Vehicular Networks.

    Science.gov (United States)

    Qian, Xiaomin; Hao, Li; Ni, Dadong; Tran, Quang Thanh

    2018-02-06

    An explosive growth in vehicular wireless applications gives rise to spectrum resource starvation. Cognitive radio has been used in vehicular networks to mitigate the impending spectrum starvation problem by allowing vehicles to fully exploit spectrum opportunities unoccupied by licensed users. Efficient and effective detection of licensed user is a critical issue to realize cognitive radio applications. However, spectrum sensing in vehicular environments is a very challenging task due to vehicle mobility. For instance, vehicle mobility has a large effect on the wireless channel, thereby impacting the detection performance of spectrum sensing. Thus, gargantuan efforts have been made in order to analyze the fading properties of mobile radio channel in vehicular environments. Indeed, numerous studies have demonstrated that the wireless channel in vehicular environments can be characterized by a temporally correlated Rayleigh fading. In this paper, we focus on energy detection for spectrum sensing and a counting rule for cooperative sensing based on Neyman-Pearson criteria. Further, we go into the effect of the sensing and reporting channel conditions on the sensing performance under the temporally correlated Rayleigh channel. For local and cooperative sensing, we derive some alternative expressions for the average probability of misdetection. The pertinent numerical and simulating results are provided to further validate our theoretical analyses under a variety of scenarios.

  4. Verification-Based Interval-Passing Algorithm for Compressed Sensing

    OpenAIRE

    Wu, Xiaofu; Yang, Zhen

    2013-01-01

    We propose a verification-based Interval-Passing (IP) algorithm for iteratively reconstruction of nonnegative sparse signals using parity check matrices of low-density parity check (LDPC) codes as measurement matrices. The proposed algorithm can be considered as an improved IP algorithm by further incorporation of the mechanism of verification algorithm. It is proved that the proposed algorithm performs always better than either the IP algorithm or the verification algorithm. Simulation resul...

  5. 47 CFR 15.717 - TVBDs that rely on spectrum sensing.

    Science.gov (United States)

    2010-10-01

    ... Television Band Devices § 15.717 TVBDs that rely on spectrum sensing. (a) Parties may submit applications for... that are identical in electrical characteristics and antenna systems may be certified under the...

  6. Cluster-based spectrum sensing for cognitive radios with imperfect channel to cluster-head

    KAUST Repository

    Ben Ghorbel, Mahdi

    2012-04-01

    Spectrum sensing is considered as the first and main step for cognitive radio systems to achieve an efficient use of spectrum. Cooperation and clustering among cognitive radio users are two techniques that can be employed with spectrum sensing in order to improve the sensing performance by reducing miss-detection and false alarm. In this paper, within the framework of a clustering-based cooperative spectrum sensing scheme, we study the effect of errors in transmitting the local decisions from the secondary users to the cluster heads (or the fusion center), while considering non-identical channel conditions between the secondary users. Closed-form expressions for the global probabilities of detection and false alarm at the cluster head are derived. © 2012 IEEE.

  7. Performance analysis of spectrum sensing with multiple status changes in primary user traffic

    KAUST Repository

    Tang, Liang; Chen, Yunfei; Hines, Evor L.; Alouini, Mohamed-Slim

    2012-01-01

    In this letter, the impact of primary user traffic with multiple status changes on the spectrum sensing performance is analyzed. Closed-form expressions for the probabilities of false alarm and detection are derived. Numerical results show

  8. Cluster-based spectrum sensing for cognitive radios with imperfect channel to cluster-head

    KAUST Repository

    Ben Ghorbel, Mahdi; Nam, Haewoon; Alouini, Mohamed-Slim

    2012-01-01

    Spectrum sensing is considered as the first and main step for cognitive radio systems to achieve an efficient use of spectrum. Cooperation and clustering among cognitive radio users are two techniques that can be employed with spectrum sensing in order to improve the sensing performance by reducing miss-detection and false alarm. In this paper, within the framework of a clustering-based cooperative spectrum sensing scheme, we study the effect of errors in transmitting the local decisions from the secondary users to the cluster heads (or the fusion center), while considering non-identical channel conditions between the secondary users. Closed-form expressions for the global probabilities of detection and false alarm at the cluster head are derived. © 2012 IEEE.

  9. Soft cooperative spectrum sensing performance under imperfect and non identical reporting channels

    KAUST Repository

    Ben Ghorbel, Mahdi

    2015-02-01

    Cooperation among cognitive radio users improves the spectrum sensing performance by combining local decisions measured over independent sensing channels, allowing reduction of miss-detection and false alarm probabilities. While most of the works in cooperative spectrum sensing techniques assume perfect channels between the cooperating users, this paper studies the effect of imperfect channels when local users report their sensed information to the fusion center. Cooperative detection and false-alarm probabilities are derived for a general scheme of imperfect reporting channels under non necessarily identical sensing and reporting channels. Numerical simulations show that imperfect reporting channels should be considered to optimize the cooperative sensing in terms of consumed energy and probability of error.

  10. Programmable genetic algorithm IP core for sensing and surveillance applications

    Science.gov (United States)

    Katkoori, Srinivas; Fernando, Pradeep; Sankaran, Hariharan; Stoica, Adrian; Keymeulen, Didier; Zebulum, Ricardo

    2009-05-01

    Real-time evolvable systems are possible with a hardware implementation of Genetic Algorithms (GA). We report the design of an IP core that implements a general purpose GA engine which has been successfully synthesized and verified on a Xilinx Virtex II Pro FPGA Device (XC2VP30). The placed and routed IP core has an area utilization of only 13% and clock speed of 50MHz. The GA core can be customized in terms of the population size, number of generations, cross-over and mutation rates, and the random number generator seed. The GA engine can be tailored to a given application by interfacing with the application specific fitness evaluation module as well as the required storage memory (to store the current and new populations). The core is soft in nature i.e., a gate-level netlist is provided which can be readily integrated with the user's system. The GA IP core can be readily used in FPGA based platforms for space and military applications (for e.g., surveillance, target tracking). The main advantages of the IP core are its programmability, small footprint, and low power consumption. Examples of concept systems in sensing and surveillance domains will be presented.

  11. Multi-Objective Clustering Optimization for Multi-Channel Cooperative Spectrum Sensing in Heterogeneous Green CRNs

    KAUST Repository

    Celik, Abdulkadir

    2016-06-27

    In this paper, we address energy efficient (EE) cooperative spectrum sensing policies for large scale heterogeneous cognitive radio networks (CRNs) which consist of multiple primary channels and large number of secondary users (SUs) with heterogeneous sensing and reporting channel qualities. We approach this issue from macro and micro perspectives. Macro perspective groups SUs into clusters with the objectives: 1) total energy consumption minimization; 2) total throughput maximization; and 3) inter-cluster energy and throughput fairness. We adopt and demonstrate how to solve these using the nondominated sorting genetic algorithm-II. The micro perspective, on the other hand, operates as a sub-procedure on cluster formations decided by the macro perspective. For the micro perspectives, we first propose a procedure to select the cluster head (CH) which yields: 1) the best CH which gives the minimum total multi-hop error rate and 2) the optimal routing paths from SUs to the CH. Exploiting Poisson-Binomial distribution, a novel and generalized K-out-of-N voting rule is developed for heterogeneous CRNs to allow SUs to have different local detection performances. Then, a convex optimization framework is established to minimize the intra-cluster energy cost by jointly obtaining the optimal sensing durations and thresholds of feature detectors for the proposed voting rule. Likewise, instead of a common fixed sample size test, we developed a weighted sample size test for quantized soft decision fusion to obtain a more EE regime under heterogeneity. We have shown that the combination of proposed CH selection and cooperation schemes gives a superior performance in terms of energy efficiency and robustness against reporting error wall.

  12. Decentralized cooperative spectrum sensing for ad-hoc disaster relief network clusters

    DEFF Research Database (Denmark)

    Pratas, Nuno; Marchetti, Nicola; Prasad, Neeli R.

    2010-01-01

    cooperative schemes becomes essential. A cluster based decentralized orchestration cooperative sensing scheme is proposed, where each node in the cluster decides which spectrum it should monitor, according to the past sensing decisions of all the cluster nodes. The proposed scheme performance is evaluated...... through a framework, which allows gauging the accuracy of multi narrow-band spectrum sensing cooperative schemes as well as to gauge the error in the estimation of each of the channels un-occupancy. Through that evaluation it is shown that the proposed decentralized scheme performance reaches...... the performance of the correspondent centralized scheme while outperforming the Round Robin scheme....

  13. A Robust FLOM Based Spectrum Sensing Scheme under Middleton Class A Noise in IoT

    Directory of Open Access Journals (Sweden)

    Enwei Xu

    2017-01-01

    Full Text Available Accessibility to remote users in dynamic environment, high spectrum utilization, and no spectrum purchase make Cognitive Radio (CR a feasible solution of wireless communications in the Internet of Things (IoT. Reliable spectrum sensing becomes the prerequisite for the establishment of communication between IoT-capable objects. Considering the application environment, spectrum sensing not only has to cope with man-made impulsive noises but also needs to overcome noise fluctuations. In this paper, we study the Fractional Lower Order Moments (FLOM based spectrum sensing method under Middleton Class A noise and incorporate a Noise Power Estimation (NPE module into the sensing system to deal with the issue of noise uncertainty. Moreover, the NPE process does not need noise-only samples. The analytical expressions of the probabilities of detection and the probability of false alarm are derived. The impact on sensing performance of the parameters of the NPE module is also analyzed. The theoretical analysis and simulation results show that our proposed sensing method achieves a satisfactory performance at low SNR.

  14. Self-organized spectrum chunk selection algorithm for Local Area LTE-Advanced

    DEFF Research Database (Denmark)

    Kumar, Sanjay; Wang, Yuanye; Marchetti, Nicola

    2010-01-01

    This paper presents a self organized spectrum chunk selection algorithm in order to minimize the mutual intercell interference among Home Node Bs (HeNBs), aiming to improve the system throughput performance compared to the existing frequency reuse one scheme. The proposed algorithm is useful...

  15. A Collaborative Approach for Monitoring Nodes Behavior during Spectrum Sensing to Mitigate Multiple Attacks in Cognitive Radio Networks

    Directory of Open Access Journals (Sweden)

    Mahmoud Khasawneh

    2017-01-01

    Full Text Available Spectrum sensing is the first step to overcome the spectrum scarcity problem in Cognitive Radio Networks (CRNs wherein all unutilized subbands in the radio environment are explored for better spectrum utilization. Adversary nodes can threaten these spectrum sensing results by launching passive and active attacks that prevent legitimate nodes from using the spectrum efficiently. Securing the spectrum sensing process has become an important issue in CRNs in order to ensure reliable and secure spectrum sensing and fair management of resources. In this paper, a novel collaborative approach during spectrum sensing process is proposed. It monitors the behavior of sensing nodes and identifies the malicious and misbehaving sensing nodes. The proposed approach measures the node’s sensing reliability using a value called belief level. All the sensing nodes are grouped into a specific number of clusters. In each cluster, a sensing node is selected as a cluster head that is responsible for collecting sensing-reputation reports from different cognitive nodes about each node in the same cluster. The cluster head analyzes information to monitor and judge the nodes’ behavior. By simulating the proposed approach, we showed its importance and its efficiency for achieving better spectrum security by mitigating multiple passive and active attacks.

  16. Microwave Remote Sensing Modeling of Ocean Surface Salinity and Winds Using an Empirical Sea Surface Spectrum

    Science.gov (United States)

    Yueh, Simon H.

    2004-01-01

    Active and passive microwave remote sensing techniques have been investigated for the remote sensing of ocean surface wind and salinity. We revised an ocean surface spectrum using the CMOD-5 geophysical model function (GMF) for the European Remote Sensing (ERS) C-band scatterometer and the Ku-band GMF for the NASA SeaWinds scatterometer. The predictions of microwave brightness temperatures from this model agree well with satellite, aircraft and tower-based microwave radiometer data. This suggests that the impact of surface roughness on microwave brightness temperatures and radar scattering coefficients of sea surfaces can be consistently characterized by a roughness spectrum, providing physical basis for using combined active and passive remote sensing techniques for ocean surface wind and salinity remote sensing.

  17. Throughput Maximization Using an SVM for Multi-Class Hypothesis-Based Spectrum Sensing in Cognitive Radio

    Directory of Open Access Journals (Sweden)

    Sana Ullah Jan

    2018-03-01

    Full Text Available A framework of spectrum sensing with a multi-class hypothesis is proposed to maximize the achievable throughput in cognitive radio networks. The energy range of a sensing signal under the hypothesis that the primary user is absent (in a conventional two-class hypothesis is further divided into quantized regions, whereas the hypothesis that the primary user is present is conserved. The non-radio frequency energy harvesting-equiped secondary user transmits, when the primary user is absent, with transmission power based on the hypothesis result (the energy level of the sensed signal and the residual energy in the battery: the lower the energy of the received signal, the higher the transmission power, and vice versa. Conversely, the lower is the residual energy in the node, the lower is the transmission power. This technique increases the throughput of a secondary link by providing a higher number of transmission events, compared to the conventional two-class hypothesis. Furthermore, transmission with low power for higher energy levels in the sensed signal reduces the probability of interference with primary users if, for instance, detection was missed. The familiar machine learning algorithm known as a support vector machine (SVM is used in a one-versus-rest approach to classify the input signal into predefined classes. The input signal to the SVM is composed of three statistical features extracted from the sensed signal and a number ranging from 0 to 100 representing the percentage of residual energy in the node’s battery. To increase the generalization of the classifier, k-fold cross-validation is utilized in the training phase. The experimental results show that an SVM with the given features performs satisfactorily for all kernels, but an SVM with a polynomial kernel outperforms linear and radial-basis function kernels in terms of accuracy. Furthermore, the proposed multi-class hypothesis achieves higher throughput compared to the

  18. Securing Collaborative Spectrum Sensing against Untrustworthy Secondary Users in Cognitive Radio Networks

    Science.gov (United States)

    Wang, Wenkai; Li, Husheng; Sun, Yan(Lindsay); Han, Zhu

    2009-12-01

    Cognitive radio is a revolutionary paradigm to migrate the spectrum scarcity problem in wireless networks. In cognitive radio networks, collaborative spectrum sensing is considered as an effective method to improve the performance of primary user detection. For current collaborative spectrum sensing schemes, secondary users are usually assumed to report their sensing information honestly. However, compromised nodes can send false sensing information to mislead the system. In this paper, we study the detection of untrustworthy secondary users in cognitive radio networks. We first analyze the case when there is only one compromised node in collaborative spectrum sensing schemes. Then we investigate the scenario that there are multiple compromised nodes. Defense schemes are proposed to detect malicious nodes according to their reporting histories. We calculate the suspicious level of all nodes based on their reports. The reports from nodes with high suspicious levels will be excluded in decision-making. Compared with existing defense methods, the proposed scheme can effectively differentiate malicious nodes and honest nodes. As a result, it can significantly improve the performance of collaborative sensing. For example, when there are 10 secondary users, with the primary user detection rate being equal to 0.99, one malicious user can make the false alarm rate [InlineEquation not available: see fulltext.] increase to 72%. The proposed scheme can reduce it to 5%. Two malicious users can make [InlineEquation not available: see fulltext.] increase to 85% and the proposed scheme reduces it to 8%.

  19. Wideband spectrum sensing order for cognitive radios with sensing errors and channel SNR probing uncertainty

    KAUST Repository

    Hamza, Doha R.

    2013-04-01

    A secondary user (SU) seeks to transmit by sequentially sensing statistically independent primary user (PU) channels. If a channel is sensed free, it is probed to estimate the signal-to-noise ratio between the SU transmitter-receiver pair over the channel. We jointly optimize the channel sensing time, the sensing decision threshold, the channel probing time, together with the channel sensing order under imperfect synchronization between the PU and the SU. The sensing and probing times and the decision threshold are assumed to be the same for all channels. We maximize a utility function related to the SU throughput under the constraint that the collision probability with the PU is kept below a certain value and taking sensing errors into account. We illustrate the optimal policy and the variation of SU throughput with various system parameters. © 2012 IEEE.

  20. Wideband spectrum sensing order for cognitive radios with sensing errors and channel SNR probing uncertainty

    KAUST Repository

    Hamza, Doha R.; Aï ssa, Sonia

    2013-01-01

    A secondary user (SU) seeks to transmit by sequentially sensing statistically independent primary user (PU) channels. If a channel is sensed free, it is probed to estimate the signal-to-noise ratio between the SU transmitter-receiver pair over the channel. We jointly optimize the channel sensing time, the sensing decision threshold, the channel probing time, together with the channel sensing order under imperfect synchronization between the PU and the SU. The sensing and probing times and the decision threshold are assumed to be the same for all channels. We maximize a utility function related to the SU throughput under the constraint that the collision probability with the PU is kept below a certain value and taking sensing errors into account. We illustrate the optimal policy and the variation of SU throughput with various system parameters. © 2012 IEEE.

  1. Experimental Validation of Advanced Dispersed Fringe Sensing (ADFS) Algorithm Using Advanced Wavefront Sensing and Correction Testbed (AWCT)

    Science.gov (United States)

    Wang, Xu; Shi, Fang; Sigrist, Norbert; Seo, Byoung-Joon; Tang, Hong; Bikkannavar, Siddarayappa; Basinger, Scott; Lay, Oliver

    2012-01-01

    Large aperture telescope commonly features segment mirrors and a coarse phasing step is needed to bring these individual segments into the fine phasing capture range. Dispersed Fringe Sensing (DFS) is a powerful coarse phasing technique and its alteration is currently being used for JWST.An Advanced Dispersed Fringe Sensing (ADFS) algorithm is recently developed to improve the performance and robustness of previous DFS algorithms with better accuracy and unique solution. The first part of the paper introduces the basic ideas and the essential features of the ADFS algorithm and presents the some algorithm sensitivity study results. The second part of the paper describes the full details of algorithm validation process through the advanced wavefront sensing and correction testbed (AWCT): first, the optimization of the DFS hardware of AWCT to ensure the data accuracy and reliability is illustrated. Then, a few carefully designed algorithm validation experiments are implemented, and the corresponding data analysis results are shown. Finally the fiducial calibration using Range-Gate-Metrology technique is carried out and a <10nm or <1% algorithm accuracy is demonstrated.

  2. Green Cooperative Spectrum Sensing and Scheduling in Heterogeneous Cognitive Radio Networks

    KAUST Repository

    Celik, Abdulkadir

    2016-09-12

    In this paper, we consider heterogeneous cognitive radio networks (CRNs) comprising primary channels (PCs) with heterogeneous characteristics and secondary users (SUs) with various sensing and reporting qualities for different PCs. We first define the opportunity as the achievable total data rate and its cost as the energy consumption caused from sensing, reporting, and channel switching operations and formulate a joint spectrum discovery and energy efficiency objective to minimize the energy spent per unit of data rate. Then, a mixed integer nonlinear programming problem is formulated to determine: 1) the optimal subset of PCs to be scheduled for sensing; 2) the SU assignment set for each scheduled PC; and 3) sensing durations and detection thresholds of each SU on PCs it is assigned to sense. Thereafter, an equivalent convex framework is developed for specific instances of the above combinatorial problem. For comparison, optimal detection and sensing thresholds are also derived analytically under the homogeneity assumption. Based on these, a prioritized ordering heuristic is developed to order channels under the spectrum, energy, and spectrum-energy limited regimes. After that, a scheduling and assignment heuristic is proposed and is shown to perform very close to the exhaustive optimal solution. Finally, the behavior of the CRN is numerically analyzed under these regimes with respect to different numbers of SUs, PCs, and sensing qualities.

  3. Multiobjective Optimization of Linear Cooperative Spectrum Sensing: Pareto Solutions and Refinement.

    Science.gov (United States)

    Yuan, Wei; You, Xinge; Xu, Jing; Leung, Henry; Zhang, Tianhang; Chen, Chun Lung Philip

    2016-01-01

    In linear cooperative spectrum sensing, the weights of secondary users and detection threshold should be optimally chosen to minimize missed detection probability and to maximize secondary network throughput. Since these two objectives are not completely compatible, we study this problem from the viewpoint of multiple-objective optimization. We aim to obtain a set of evenly distributed Pareto solutions. To this end, here, we introduce the normal constraint (NC) method to transform the problem into a set of single-objective optimization (SOO) problems. Each SOO problem usually results in a Pareto solution. However, NC does not provide any solution method to these SOO problems, nor any indication on the optimal number of Pareto solutions. Furthermore, NC has no preference over all Pareto solutions, while a designer may be only interested in some of them. In this paper, we employ a stochastic global optimization algorithm to solve the SOO problems, and then propose a simple method to determine the optimal number of Pareto solutions under a computational complexity constraint. In addition, we extend NC to refine the Pareto solutions and select the ones of interest. Finally, we verify the effectiveness and efficiency of the proposed methods through computer simulations.

  4. Hard Decision Fusion based Cooperative Spectrum Sensing in Cognitive Radio System

    Directory of Open Access Journals (Sweden)

    N. Armi N.M. Saad

    2013-09-01

    Full Text Available Cooperative spectrum sensing was proposed to combat fading, noise uncertainty, shadowing, and even hidden node problem due to primary users (PUs activity that is not spatially localized. It improves the probability of detection by collaborating to detect PUs signal in cognitive radio (CR system as well. This paper studies cooperative spectrum sensing and signal detection in CR system by implementing hard decision combining in data fusion centre. Through computer simulation, we evaluate the performances of cooperative spectrum sensing and signal detection by employing OR and AND rules as decision combining. Energy detector is used to observe the presence of primary user (PU signal. Those results are compared to non-cooperative signal detection for evaluation. They show that cooperative technique has better performance than non-cooperative. Moreover, signal to noise ratio (SNR with greater than or equal 10 dB and 15 collaborated users in CR system has optimal value for probability of detection.

  5. On Transform Domain Communication Systems under Spectrum Sensing Mismatch: A Deterministic Analysis.

    Science.gov (United States)

    Jin, Chuanxue; Hu, Su; Huang, Yixuan; Luo, Qu; Huang, Dan; Li, Yi; Gao, Yuan; Cheng, Shaochi

    2017-07-08

    Towards the era of mobile Internet and the Internet of Things (IoT), numerous sensors and devices are being introduced and interconnected. To support such an amount of data traffic, traditional wireless communication technologies are facing challenges both in terms of the increasing shortage of spectrum resources and massive multiple access. The transform-domain communication system (TDCS) is considered as an alternative multiple access system, where 5G and mobile IoT are mainly focused. However, previous studies about TDCS are under the assumption that the transceiver has the global spectrum information, without the consideration of spectrum sensing mismatch (SSM). In this paper, we present the deterministic analysis of TDCS systems under arbitrary given spectrum sensing scenarios, especially the influence of the SSM pattern to the signal to noise ratio (SNR) performance. Simulation results show that arbitrary SSM pattern can lead to inferior bit error rate (BER) performance.

  6. System Capacity Limits Introduced by Data Fusion on Cooperative Spectrum Sensing under Correlated Environments

    DEFF Research Database (Denmark)

    Pratas, Nuno; Marchetti, Nicola; Prasad, Neeli R.

    2010-01-01

    on cooperative sensing schemes. The analysis is supported by evaluation metrics which accounts for the perceived capacity limits. The analysis is performed along the data fusion chain, comparing several scenarios encompassing different degrees of environment correlation between the cluster nodes, number......Spectrum sensing, the cornerstone of the Cognitive Radio paradigm, has been the focus of intensive research, from which the main conclusion was that its performance can be greatly enhanced through the use of cooperative sensing schemes. Nevertheless, if a proper design of the cooperative scheme...... is not followed, then the use of cooperative schemes will introduce some limitations in the network perceived capacity. In this paper, we analyze the performance of a cooperative spectrum sensing scheme based on Data Fusion, by measuring the perceived capacity limits introduced by the use of Data Fusion...

  7. PERFORMANCE OF OPPORTUNISTIC SPECTRUM ACCESS WITH SENSING ERROR IN COGNITIVE RADIO AD HOC NETWORKS

    Directory of Open Access Journals (Sweden)

    N. ARMI

    2012-04-01

    Full Text Available Sensing in opportunistic spectrum access (OSA has a responsibility to detect the available channel by performing binary hypothesis as busy or idle states. If channel is busy, secondary user (SU cannot access and refrain from data transmission. SU is allowed to access when primary user (PU does not use it (idle states. However, channel is sensed on imperfect communication link. Fading, noise and any obstacles existed can cause sensing errors in PU signal detection. False alarm detects idle states as a busy channel while miss-identification detects busy states as an idle channel. False detection makes SU refrain from transmission and reduces number of bits transmitted. On the other hand, miss-identification causes SU collide to PU transmission. This paper study the performance of OSA based on the greedy approach with sensing errors by the restriction of maximum collision probability allowed (collision threshold by PU network. The throughput of SU and spectrum capacity metric is used to evaluate OSA performance and make comparisons to those ones without sensing error as function of number of slot based on the greedy approach. The relations between throughput and signal to noise ratio (SNR with different collision probability as well as false detection with different SNR are presented. According to the obtained results show that CR users can gain the reward from the previous slot for both of with and without sensing errors. It is indicated by the throughput improvement as slot number increases. However, sensing on imperfect channel with sensing errors can degrade the throughput performance. Subsequently, the throughput of SU and spectrum capacity improves by increasing maximum collision probability allowed by PU network as well. Due to frequent collision with PU, the throughput of SU and spectrum capacity decreases at certain value of collision threshold. Computer simulation is used to evaluate and validate these works.

  8. Secure Cooperative Spectrum Sensing for the Cognitive Radio Network Using Nonuniform Reliability

    Directory of Open Access Journals (Sweden)

    Muhammad Usman

    2014-01-01

    Full Text Available Both reliable detection of the primary signal in a noisy and fading environment and nullifying the effect of unauthorized users are important tasks in cognitive radio networks. To address these issues, we consider a cooperative spectrum sensing approach where each user is assigned nonuniform reliability based on the sensing performance. Users with poor channel or faulty sensor are assigned low reliability. The nonuniform reliabilities serve as identification tags and are used to isolate users with malicious behavior. We consider a link layer attack similar to the Byzantine attack, which falsifies the spectrum sensing data. Three different strategies are presented in this paper to ignore unreliable and malicious users in the network. Considering only reliable users for global decision improves sensing time and decreases collisions in the control channel. The fusion center uses the degree of reliability as a weighting factor to determine the global decision in scheme I. Schemes II and III consider the unreliability of users, which makes the computations even simpler. The proposed schemes reduce the number of sensing reports and increase the inference accuracy. The advantages of our proposed schemes over conventional cooperative spectrum sensing and the Chair-Varshney optimum rule are demonstrated through simulations.

  9. A CR Spectrum Allocation Algorithm in Smart Grid Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Wei He

    2014-10-01

    Full Text Available Cognitive radio (CR method was introduced in smart grid communication systems to resolve potential maladies such as the coexistence of heterogeneous networks, overloaded data flow, diversity in data structures, and unstable quality of service (QOS. In this paper, a cognitive spectrum allocation algorithm based on non-cooperative game theory is proposed. The CR spectrum allocation model was developed by modifying the traditional game model via the insertion of a time variable and a critical function. The computing simulation result shows that the improved spectrum allocation algorithm can achieve stable spectrum allocation strategies and avoid the appearance of multi-Nash equilibrium at the expense of certain sacrifices in the system utility. It is suitable for application in distributed cognitive networks in power grids, thus contributing to the improvement of the isomerism and data capacity of power communication systems.

  10. Distributed Algorithm for Voronoi Partition of Wireless Sensor Networks with a Limited Sensing Range.

    Science.gov (United States)

    He, Chenlong; Feng, Zuren; Ren, Zhigang

    2018-02-03

    For Wireless Sensor Networks (WSNs), the Voronoi partition of a region is a challenging problem owing to the limited sensing ability of each sensor and the distributed organization of the network. In this paper, an algorithm is proposed for each sensor having a limited sensing range to compute its limited Voronoi cell autonomously, so that the limited Voronoi partition of the entire WSN is generated in a distributed manner. Inspired by Graham's Scan (GS) algorithm used to compute the convex hull of a point set, the limited Voronoi cell of each sensor is obtained by sequentially scanning two consecutive bisectors between the sensor and its neighbors. The proposed algorithm called the Boundary Scan (BS) algorithm has a lower computational complexity than the existing Range-Constrained Voronoi Cell (RCVC) algorithm and reaches the lower bound of the computational complexity of the algorithms used to solve the problem of this kind. Moreover, it also improves the time efficiency of a key step in the Adjust-Sensing-Radius (ASR) algorithm used to compute the exact Voronoi cell. Extensive numerical simulations are performed to demonstrate the correctness and effectiveness of the BS algorithm. The distributed realization of the BS combined with a localization algorithm in WSNs is used to justify the WSN nature of the proposed algorithm.

  11. The Physics of Compressive Sensing and the Gradient-Based Recovery Algorithms

    OpenAIRE

    Dai, Qi; Sha, Wei

    2009-01-01

    The physics of compressive sensing (CS) and the gradient-based recovery algorithms are presented. First, the different forms for CS are summarized. Second, the physical meanings of coherence and measurement are given. Third, the gradient-based recovery algorithms and their geometry explanations are provided. Finally, we conclude the report and give some suggestion for future work.

  12. A real-time MTFC algorithm of space remote-sensing camera based on FPGA

    Science.gov (United States)

    Zhao, Liting; Huang, Gang; Lin, Zhe

    2018-01-01

    A real-time MTFC algorithm of space remote-sensing camera based on FPGA was designed. The algorithm can provide real-time image processing to enhance image clarity when the remote-sensing camera running on-orbit. The image restoration algorithm adopted modular design. The MTF measurement calculation module on-orbit had the function of calculating the edge extension function, line extension function, ESF difference operation, normalization MTF and MTFC parameters. The MTFC image filtering and noise suppression had the function of filtering algorithm and effectively suppressing the noise. The algorithm used System Generator to design the image processing algorithms to simplify the design structure of system and the process redesign. The image gray gradient dot sharpness edge contrast and median-high frequency were enhanced. The image SNR after recovery reduced less than 1 dB compared to the original image. The image restoration system can be widely used in various fields.

  13. Model-based remote sensing algorithms for particulate organic carbon

    Indian Academy of Sciences (India)

    negligible loss of spectral information from additional modes. The use of POC algorithms ... and mesoscale circulation system (Vastano et al. 1995; Walker 1996 .... fiber filters were combusted in a thermolyne type. 1300 furnace along with ...

  14. Historical Feature Pattern Extraction Based Network Attack Situation Sensing Algorithm

    OpenAIRE

    Zeng, Yong; Liu, Dacheng; Lei, Zhou

    2014-01-01

    The situation sequence contains a series of complicated and multivariate random trends, which are very sudden, uncertain, and difficult to recognize and describe its principle by traditional algorithms. To solve the above questions, estimating parameters of super long situation sequence is essential, but very difficult, so this paper proposes a situation prediction method based on historical feature pattern extraction (HFPE). First, HFPE algorithm seeks similar indications from the history si...

  15. The Normalized-Rate Iterative Algorithm: A Practical Dynamic Spectrum Management Method for DSL

    Directory of Open Access Journals (Sweden)

    Statovci Driton

    2006-01-01

    Full Text Available We present a practical solution for dynamic spectrum management (DSM in digital subscriber line systems: the normalized-rate iterative algorithm (NRIA. Supported by a novel optimization problem formulation, the NRIA is the only DSM algorithm that jointly addresses spectrum balancing for frequency division duplexing systems and power allocation for the users sharing a common cable bundle. With a focus on being implementable rather than obtaining the highest possible theoretical performance, the NRIA is designed to efficiently solve the DSM optimization problem with the operators' business models in mind. This is achieved with the help of two types of parameters: the desired network asymmetry and the desired user priorities. The NRIA is a centralized DSM algorithm based on the iterative water-filling algorithm (IWFA for finding efficient power allocations, but extends the IWFA by finding the achievable bitrates and by optimizing the bandplan. It is compared with three other DSM proposals: the IWFA, the optimal spectrum balancing algorithm (OSBA, and the bidirectional IWFA (bi-IWFA. We show that the NRIA achieves better bitrate performance than the IWFA and the bi-IWFA. It can even achieve performance almost as good as the OSBA, but with dramatically lower requirements on complexity. Additionally, the NRIA can achieve bitrate combinations that cannot be supported by any other DSM algorithm.

  16. The Normalized-Rate Iterative Algorithm: A Practical Dynamic Spectrum Management Method for DSL

    Science.gov (United States)

    Statovci, Driton; Nordström, Tomas; Nilsson, Rickard

    2006-12-01

    We present a practical solution for dynamic spectrum management (DSM) in digital subscriber line systems: the normalized-rate iterative algorithm (NRIA). Supported by a novel optimization problem formulation, the NRIA is the only DSM algorithm that jointly addresses spectrum balancing for frequency division duplexing systems and power allocation for the users sharing a common cable bundle. With a focus on being implementable rather than obtaining the highest possible theoretical performance, the NRIA is designed to efficiently solve the DSM optimization problem with the operators' business models in mind. This is achieved with the help of two types of parameters: the desired network asymmetry and the desired user priorities. The NRIA is a centralized DSM algorithm based on the iterative water-filling algorithm (IWFA) for finding efficient power allocations, but extends the IWFA by finding the achievable bitrates and by optimizing the bandplan. It is compared with three other DSM proposals: the IWFA, the optimal spectrum balancing algorithm (OSBA), and the bidirectional IWFA (bi-IWFA). We show that the NRIA achieves better bitrate performance than the IWFA and the bi-IWFA. It can even achieve performance almost as good as the OSBA, but with dramatically lower requirements on complexity. Additionally, the NRIA can achieve bitrate combinations that cannot be supported by any other DSM algorithm.

  17. A joint image encryption and watermarking algorithm based on compressive sensing and chaotic map

    International Nuclear Information System (INIS)

    Xiao Di; Cai Hong-Kun; Zheng Hong-Ying

    2015-01-01

    In this paper, a compressive sensing (CS) and chaotic map-based joint image encryption and watermarking algorithm is proposed. The transform domain coefficients of the original image are scrambled by Arnold map firstly. Then the watermark is adhered to the scrambled data. By compressive sensing, a set of watermarked measurements is obtained as the watermarked cipher image. In this algorithm, watermark embedding and data compression can be performed without knowing the original image; similarly, watermark extraction will not interfere with decryption. Due to the characteristics of CS, this algorithm features compressible cipher image size, flexible watermark capacity, and lossless watermark extraction from the compressed cipher image as well as robustness against packet loss. Simulation results and analyses show that the algorithm achieves good performance in the sense of security, watermark capacity, extraction accuracy, reconstruction, robustness, etc. (paper)

  18. Research on compressive sensing reconstruction algorithm based on total variation model

    Science.gov (United States)

    Gao, Yu-xuan; Sun, Huayan; Zhang, Tinghua; Du, Lin

    2017-12-01

    Compressed sensing for breakthrough Nyquist sampling theorem provides a strong theoretical , making compressive sampling for image signals be carried out simultaneously. In traditional imaging procedures using compressed sensing theory, not only can it reduces the storage space, but also can reduce the demand for detector resolution greatly. Using the sparsity of image signal, by solving the mathematical model of inverse reconfiguration, realize the super-resolution imaging. Reconstruction algorithm is the most critical part of compression perception, to a large extent determine the accuracy of the reconstruction of the image.The reconstruction algorithm based on the total variation (TV) model is more suitable for the compression reconstruction of the two-dimensional image, and the better edge information can be obtained. In order to verify the performance of the algorithm, Simulation Analysis the reconstruction result in different coding mode of the reconstruction algorithm based on the TV reconstruction algorithm. The reconstruction effect of the reconfigurable algorithm based on TV based on the different coding methods is analyzed to verify the stability of the algorithm. This paper compares and analyzes the typical reconstruction algorithm in the same coding mode. On the basis of the minimum total variation algorithm, the Augmented Lagrangian function term is added and the optimal value is solved by the alternating direction method.Experimental results show that the reconstruction algorithm is compared with the traditional classical algorithm based on TV has great advantages, under the low measurement rate can be quickly and accurately recovers target image.

  19. An improved optimum-path forest clustering algorithm for remote sensing image segmentation

    Science.gov (United States)

    Chen, Siya; Sun, Tieli; Yang, Fengqin; Sun, Hongguang; Guan, Yu

    2018-03-01

    Remote sensing image segmentation is a key technology for processing remote sensing images. The image segmentation results can be used for feature extraction, target identification and object description. Thus, image segmentation directly affects the subsequent processing results. This paper proposes a novel Optimum-Path Forest (OPF) clustering algorithm that can be used for remote sensing segmentation. The method utilizes the principle that the cluster centres are characterized based on their densities and the distances between the centres and samples with higher densities. A new OPF clustering algorithm probability density function is defined based on this principle and applied to remote sensing image segmentation. Experiments are conducted using five remote sensing land cover images. The experimental results illustrate that the proposed method can outperform the original OPF approach.

  20. A Plane Target Detection Algorithm in Remote Sensing Images based on Deep Learning Network Technology

    Science.gov (United States)

    Shuxin, Li; Zhilong, Zhang; Biao, Li

    2018-01-01

    Plane is an important target category in remote sensing targets and it is of great value to detect the plane targets automatically. As remote imaging technology developing continuously, the resolution of the remote sensing image has been very high and we can get more detailed information for detecting the remote sensing targets automatically. Deep learning network technology is the most advanced technology in image target detection and recognition, which provided great performance improvement in the field of target detection and recognition in the everyday scenes. We combined the technology with the application in the remote sensing target detection and proposed an algorithm with end to end deep network, which can learn from the remote sensing images to detect the targets in the new images automatically and robustly. Our experiments shows that the algorithm can capture the feature information of the plane target and has better performance in target detection with the old methods.

  1. Distributed optical fiber vibration sensing using phase-generated carrier demodulation algorithm

    Science.gov (United States)

    Yu, Zhihua; Zhang, Qi; Zhang, Mingyu; Dai, Haolong; Zhang, Jingjing; Liu, Li; Zhang, Lijun; Jin, Xing; Wang, Gaifang; Qi, Guang

    2018-05-01

    A novel optical fiber-distributed vibration-sensing system is proposed, which is based on self-interference of Rayleigh backscattering with phase-generated carrier (PGC) demodulation algorithm. Pulsed lights are sent into the sensing fiber and the Rayleigh backscattering light from a certain position along the sensing fiber would interfere through an unbalanced Michelson interferometry to generate the interference light. An improved PGC demodulation algorithm is carried out to recover the phase information of the interference signal, which carries the sensing information. Three vibration events were applied simultaneously to different positions over 2000 m sensing fiber and demodulated correctly. The spatial resolution is 10 m, and the noise level of the Φ-OTDR system we proposed is about 10-3 rad/\\surd {Hz}, and the signal-to-noise ratio is about 30.34 dB.

  2. A structure preserving Lanczos algorithm for computing the optical absorption spectrum

    Energy Technology Data Exchange (ETDEWEB)

    Shao, Meiyue [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Computational Research Div.; Jornada, Felipe H. da [Univ. of California, Berkeley, CA (United States). Dept. of Physics; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Materials Science Div.; Lin, Lin [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Computational Research Div.; Univ. of California, Berkeley, CA (United States). Dept. of Mathematics; Yang, Chao [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Computational Research Div.; Deslippe, Jack [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); Louie, Steven G. [Univ. of California, Berkeley, CA (United States). Dept. of Physics; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Materials Science Div.

    2016-11-16

    We present a new structure preserving Lanczos algorithm for approximating the optical absorption spectrum in the context of solving full Bethe-Salpeter equation without Tamm-Dancoff approximation. The new algorithm is based on a structure preserving Lanczos procedure, which exploits the special block structure of Bethe-Salpeter Hamiltonian matrices. A recently developed technique of generalized averaged Gauss quadrature is incorporated to accelerate the convergence. We also establish the connection between our structure preserving Lanczos procedure with several existing Lanczos procedures developed in different contexts. Numerical examples are presented to demonstrate the effectiveness of our Lanczos algorithm.

  3. Model-based remote sensing algorithms for particulate organic carbon

    Indian Academy of Sciences (India)

    PCA algorithms based on the first three, four, and five modes accounted for 90, 95, and 98% of total variance and yielded significant correlations with POC with 2 = 0.89, 0.92, and 0.93. These full waveband approaches provided robust estimates of POC in various water types. Three different analyses (root mean square ...

  4. Historical feature pattern extraction based network attack situation sensing algorithm.

    Science.gov (United States)

    Zeng, Yong; Liu, Dacheng; Lei, Zhou

    2014-01-01

    The situation sequence contains a series of complicated and multivariate random trends, which are very sudden, uncertain, and difficult to recognize and describe its principle by traditional algorithms. To solve the above questions, estimating parameters of super long situation sequence is essential, but very difficult, so this paper proposes a situation prediction method based on historical feature pattern extraction (HFPE). First, HFPE algorithm seeks similar indications from the history situation sequence recorded and weighs the link intensity between occurred indication and subsequent effect. Then it calculates the probability that a certain effect reappears according to the current indication and makes a prediction after weighting. Meanwhile, HFPE method gives an evolution algorithm to derive the prediction deviation from the views of pattern and accuracy. This algorithm can continuously promote the adaptability of HFPE through gradual fine-tuning. The method preserves the rules in sequence at its best, does not need data preprocessing, and can track and adapt to the variation of situation sequence continuously.

  5. Historical Feature Pattern Extraction Based Network Attack Situation Sensing Algorithm

    Directory of Open Access Journals (Sweden)

    Yong Zeng

    2014-01-01

    Full Text Available The situation sequence contains a series of complicated and multivariate random trends, which are very sudden, uncertain, and difficult to recognize and describe its principle by traditional algorithms. To solve the above questions, estimating parameters of super long situation sequence is essential, but very difficult, so this paper proposes a situation prediction method based on historical feature pattern extraction (HFPE. First, HFPE algorithm seeks similar indications from the history situation sequence recorded and weighs the link intensity between occurred indication and subsequent effect. Then it calculates the probability that a certain effect reappears according to the current indication and makes a prediction after weighting. Meanwhile, HFPE method gives an evolution algorithm to derive the prediction deviation from the views of pattern and accuracy. This algorithm can continuously promote the adaptability of HFPE through gradual fine-tuning. The method preserves the rules in sequence at its best, does not need data preprocessing, and can track and adapt to the variation of situation sequence continuously.

  6. Equal gain combining for cooperative spectrum sensing in cognitive radio networks

    KAUST Repository

    Hamza, Doha R.

    2014-08-01

    Sensing with equal gain combining (SEGC), a novel cooperative spectrum sensing technique for cognitive radio networks, is proposed. Cognitive radios simultaneously transmit their sensing results to the fusion center (FC) over multipath fading reporting channels. The cognitive radios estimate the phases of the reporting channels and use those estimates for coherent combining of the sensing results at the FC. A global decision is made at the FC by comparing the received signal with a threshold. We obtain the global detection probabilities and secondary throughput exactly through a moment generating function approach. We verify our solution via system simulation and demonstrate that the Chernoff bound and central limit theory approximation are not tight. The cases of hard sensing and soft sensing are considered and we provide examples in which hard sensing is advantageous to soft sensing. We contrast the performance of SEGC with maximum ratio combining of the sensors\\' results and provide examples where the former is superior. Furthermore, we evaluate the performance of SEGC against existing orthogonal reporting techniques such as time division multiple access (TDMA). SEGC performance always dominates that of TDMA in terms of secondary throughput. We also study the impact of phase and synchronization errors and demonstrate the robustness of the SEGC technique against such imperfections. © 2002-2012 IEEE.

  7. Hybrid Accuracy-Time Trade-off Solution for Spectrum Sensing in Cognitive Radio Networks

    DEFF Research Database (Denmark)

    Ivanov, Antoni Stefkov; Mihovska, Albena Dimitrova; Poulkov, Vladimir

    2018-01-01

    parameters, therefore, a suitable trade-off is necessary for an optimal efficiency. We propose a dual-approach solution. The decision about the spectrum occupancy is made using the measured signal-to-noise ratio (SNR) and the received signal levels as inputs in a fuzzy logic algorithm. The result...

  8. Method of T2 spectrum inversion with conjugate gradient algorithm from NMR data

    International Nuclear Information System (INIS)

    Li Pengju; Shi Shangming; Song Yanjie

    2010-01-01

    Based on the optimization techniques, the T 2 spectrum inversion method of conjugate gradient that is easy to realize non-negativity constraint of T2 spectrum is proposed. The method transforms the linear mixed-determined problem of T2 spectrum inversion into the typical optimization problem of searching the minimum of objective function by building up the objective function according to the basic idea of geophysics modeling. The optimization problem above is solved with the conjugate gradient algorithm that has quick convergence rate and quadratic termination. The method has been applied to the inversion of noise free echo train generated from artificial spectrum, artificial echo train with signal-to-noise ratio (SNR)=25 and NMR experimental data of drilling core. The comparison between the inversion results of this paper and artificial spectrum or the result of software imported in NMR laboratory shows that the method can correctly invert T 2 spectrum from artificial NMR relaxation data even though SNR=25 and that inversion T 2 spectrum with good continuity and smoothness from core NMR experimental data accords perfectly with that of laboratory software imported, and moreover,the absolute error between the NMR porosity computed from T 2 spectrum and helium (He) porosity in laboratory is 0.65%. (authors)

  9. BONDI-97 A novel neutron energy spectrum unfolding tool using a genetic algorithm

    CERN Document Server

    Mukherjee, B

    1999-01-01

    The neutron spectrum unfolding procedure using the count rate data obtained from a set of Bonner sphere neutron detectors requires the solution of the Fredholm integral equation of the first kind by using complex mathematical methods. This paper reports a new approach for the unfolding of neutron spectra using the Genetic Algorithm tool BONDI-97 (BOnner sphere Neutron DIfferentiation). The BONDI-97 was used as the input for Genetic Algorithm engine EVOLVER to search for a globally optimised solution vector from a population of randomly generated solutions. This solution vector corresponds to the unfolded neutron energy spectrum. The Genetic Algorithm engine emulates the Darwinian 'Survival of the Fittest' strategy, the key ingredient of the 'Theory of Evolution'. The spectra of sup 2 sup 4 sup 1 Am/Be (alpha,n) and sup 2 sup 3 sup 9 Pu/Be (alpha,n) neutron sources were unfolded using the BONDI-97 tool. (author)

  10. [An operational remote sensing algorithm of land surface evapotranspiration based on NOAA PAL dataset].

    Science.gov (United States)

    Hou, Ying-Yu; He, Yan-Bo; Wang, Jian-Lin; Tian, Guo-Liang

    2009-10-01

    Based on the time series 10-day composite NOAA Pathfinder AVHRR Land (PAL) dataset (8 km x 8 km), and by using land surface energy balance equation and "VI-Ts" (vegetation index-land surface temperature) method, a new algorithm of land surface evapotranspiration (ET) was constructed. This new algorithm did not need the support from meteorological observation data, and all of its parameters and variables were directly inversed or derived from remote sensing data. A widely accepted ET model of remote sensing, i. e., SEBS model, was chosen to validate the new algorithm. The validation test showed that both the ET and its seasonal variation trend estimated by SEBS model and our new algorithm accorded well, suggesting that the ET estimated from the new algorithm was reliable, being able to reflect the actual land surface ET. The new ET algorithm of remote sensing was practical and operational, which offered a new approach to study the spatiotemporal variation of ET in continental scale and global scale based on the long-term time series satellite remote sensing images.

  11. Reconstruction algorithm in compressed sensing based on maximum a posteriori estimation

    International Nuclear Information System (INIS)

    Takeda, Koujin; Kabashima, Yoshiyuki

    2013-01-01

    We propose a systematic method for constructing a sparse data reconstruction algorithm in compressed sensing at a relatively low computational cost for general observation matrix. It is known that the cost of ℓ 1 -norm minimization using a standard linear programming algorithm is O(N 3 ). We show that this cost can be reduced to O(N 2 ) by applying the approach of posterior maximization. Furthermore, in principle, the algorithm from our approach is expected to achieve the widest successful reconstruction region, which is evaluated from theoretical argument. We also discuss the relation between the belief propagation-based reconstruction algorithm introduced in preceding works and our approach

  12. Performance analysis of spectrum sensing with multiple status changes in primary user traffic

    KAUST Repository

    Tang, Liang

    2012-06-01

    In this letter, the impact of primary user traffic with multiple status changes on the spectrum sensing performance is analyzed. Closed-form expressions for the probabilities of false alarm and detection are derived. Numerical results show that the multiple status changes of the primary user cause considerable degradation in the sensing performance. This degradation depends on the number of changes, the primary user traffic model, the primary user traffic intensity and the signal-to-noise ratio of the received signal. Numerical results also show that the amount of degradation decreases when the number of changes increases, and converges to a minimum sensing performance due to the limited sensing period and primary holding time. © 2012 IEEE.

  13. An Uneven Illumination Correction Algorithm for Optical Remote Sensing Images Covered with Thin Clouds

    Directory of Open Access Journals (Sweden)

    Xiaole Shen

    2015-09-01

    Full Text Available The uneven illumination phenomenon caused by thin clouds will reduce the quality of remote sensing images, and bring adverse effects to the image interpretation. To remove the effect of thin clouds on images, an uneven illumination correction can be applied. In this paper, an effective uneven illumination correction algorithm is proposed to remove the effect of thin clouds and to restore the ground information of the optical remote sensing image. The imaging model of remote sensing images covered by thin clouds is analyzed. Due to the transmission attenuation, reflection, and scattering, the thin cloud cover usually increases region brightness and reduces saturation and contrast of the image. As a result, a wavelet domain enhancement is performed for the image in Hue-Saturation-Value (HSV color space. We use images with thin clouds in Wuhan area captured by QuickBird and ZiYuan-3 (ZY-3 satellites for experiments. Three traditional uneven illumination correction algorithms, i.e., multi-scale Retinex (MSR algorithm, homomorphic filtering (HF-based algorithm, and wavelet transform-based MASK (WT-MASK algorithm are performed for comparison. Five indicators, i.e., mean value, standard deviation, information entropy, average gradient, and hue deviation index (HDI are used to analyze the effect of the algorithms. The experimental results show that the proposed algorithm can effectively eliminate the influences of thin clouds and restore the real color of ground objects under thin clouds.

  14. Genetic algorithms - A new technique for solving the neutron spectrum unfolding problem

    International Nuclear Information System (INIS)

    Freeman, David W.; Edwards, D. Ray; Bolon, Albert E.

    1999-01-01

    A new technique utilizing genetic algorithms has been applied to the Bonner sphere neutron spectrum unfolding problem. Genetic algorithms are part of a relatively new field of 'evolutionary' solution techniques that mimic living systems with computer-simulated 'chromosome' solutions. Solutions mate and mutate to create better solutions. Several benchmark problems, considered representative of radiation protection environments, have been evaluated using the newly developed UMRGA code which implements the genetic algorithm unfolding technique. The results are compared with results from other well-established unfolding codes. The genetic algorithm technique works remarkably well and produces solutions with relatively high spectral qualities. UMRGA appears to be a superior technique in the absence of a priori data - it does not rely on 'lucky' guesses of input spectra. Calculated personnel doses associated with the unfolded spectra match benchmark values within a few percent

  15. A brief comparison between grid based real space algorithms and spectrum algorithms for electronic structure calculations

    International Nuclear Information System (INIS)

    Wang, Lin-Wang

    2006-01-01

    Quantum mechanical ab initio calculation constitutes the biggest portion of the computer time in material science and chemical science simulations. As a computer center like NERSC, to better serve these communities, it will be very useful to have a prediction for the future trends of ab initio calculations in these areas. Such prediction can help us to decide what future computer architecture can be most useful for these communities, and what should be emphasized on in future supercomputer procurement. As the size of the computer and the size of the simulated physical systems increase, there is a renewed interest in using the real space grid method in electronic structure calculations. This is fueled by two factors. First, it is generally assumed that the real space grid method is more suitable for parallel computation for its limited communication requirement, compared with spectrum method where a global FFT is required. Second, as the size N of the calculated system increases together with the computer power, O(N) scaling approaches become more favorable than the traditional direct O(N 3 ) scaling methods. These O(N) methods are usually based on localized orbital in real space, which can be described more naturally by the real space basis. In this report, the author compares the real space methods versus the traditional plane wave (PW) spectrum methods, for their technical pros and cons, and the possible of future trends. For the real space method, the author focuses on the regular grid finite different (FD) method and the finite element (FE) method. These are the methods used mostly in material science simulation. As for chemical science, the predominant methods are still Gaussian basis method, and sometime the atomic orbital basis method. These two basis sets are localized in real space, and there is no indication that their roles in quantum chemical simulation will change anytime soon. The author focuses on the density functional theory (DFT), which is the

  16. EMD self-adaptive selecting relevant modes algorithm for FBG spectrum signal

    Science.gov (United States)

    Chen, Yong; Wu, Chun-ting; Liu, Huan-lin

    2017-07-01

    Noise may reduce the demodulation accuracy of fiber Bragg grating (FBG) sensing signal so as to affect the quality of sensing detection. Thus, the recovery of a signal from observed noisy data is necessary. In this paper, a precise self-adaptive algorithm of selecting relevant modes is proposed to remove the noise of signal. Empirical mode decomposition (EMD) is first used to decompose a signal into a set of modes. The pseudo modes cancellation is introduced to identify and eliminate false modes, and then the Mutual Information (MI) of partial modes is calculated. MI is used to estimate the critical point of high and low frequency components. Simulation results show that the proposed algorithm estimates the critical point more accurately than the traditional algorithms for FBG spectral signal. While, compared to the similar algorithms, the signal noise ratio of the signal can be improved more than 10 dB after processing by the proposed algorithm, and correlation coefficient can be increased by 0.5, so it demonstrates better de-noising effect.

  17. On the Impact of User Distribution on Cooperative Spectrum Sensing and Data Transmission with Multiuser Diversity

    KAUST Repository

    Rao, Anlei

    2011-07-01

    In this thesis, we investigate the independent but not identically distributed (i.n.i.d.) situations for spectrum sensing and data transmission. In particular, we derive the false-alarm probability and the detection probability of cooperative spectrum sensing with the scheme of energy fusion over i.n.i.d. Nakagami fading channels. Then, the performance of adaptive modulation with single-cell multiuser scheduling over i.n.i.d. Nakagami fading channels is analyzed. Closed-form expressions are derived for the average channel capacity, spectral efficiency, and bit-error-rate (BER) for both constant-power variable-rate and variable-power variable-rate uncoded M- ary quadrature amplitude modulation (M-QAM) schemes. In addition, we study the impact of time delay on the average BER of adaptive M-QAM. From the selected numerical results, we can see that cooperative spectrum sensing and multiuser diversity brings considerably better performance even over i.n.i.d. fading environments.

  18. RF Spectrum Sensing Based on an Overdamped Nonlinear Oscillator Ring for Cognitive Radios

    Directory of Open Access Journals (Sweden)

    Zhi-Ling Tang

    2016-06-01

    Full Text Available Existing spectrum-sensing techniques for cognitive radios require an analog-to-digital converter (ADC to work at high dynamic range and a high sampling rate, resulting in high cost. Therefore, in this paper, a spectrum-sensing method based on a unidirectionally coupled, overdamped nonlinear oscillator ring is proposed. First, the numerical model of such a system is established based on the circuit of the nonlinear oscillator. Through numerical analysis of the model, the critical condition of the system’s starting oscillation is determined, and the simulation results of the system’s response to Gaussian white noise and periodic signal are presented. The results show that once the radio signal is input into the system, it starts oscillating when in the critical region, and the oscillating frequency of each element is fo/N, where fo is the frequency of the radio signal and N is the number of elements in the ring. The oscillation indicates that the spectrum resources at fo are occupied. At the same time, the sampling rate required for an ADC is reduced to the original value, 1/N. A prototypical circuit to verify the functionality of the system is designed, and the sensing bandwidth of the system is measured.

  19. Implementation Aspects of a Flexible Frequency Spectrum Usage Algorithm for Cognitive OFDM Systems

    DEFF Research Database (Denmark)

    Sacchi, Claudio; Tonelli, Oscar; Cattoni, Andrea Fabio

    2011-01-01

    time on a shared spectrum chunk, emphasizes the role of resource allocation as a critical system design issue. This work is aimed at analyzing the practical issues related to the Software Defined Radio (SDR)-based implementation of a dynamic spectrum allocation algorithm, designed for OFDM...... on a Xilinx ML506 development board is performed. The main novelty proposed in this paper consists in the SDR-based implementation of a computationally-sustainable resource allocation algorithm for FSU on low-cost commercial FPGA platforms. The proposed implementation is competitive with respect to other ones...... on a Virtex 5 FPGA. Experimental results will illustrate that the selected core functionalities are effectively implementable with around 3% or less of the total FPGA computing resources....

  20. Exact performance of cooperative spectrum sensing for cognitive radios with quantized information under imperfect reporting channels

    KAUST Repository

    Ben Ghorbel, Mahdi

    2013-09-01

    Spectrum sensing is the first and main step for cognitive radio systems to achieve an efficient use of the spectrum. Cooperation among cognitive radio users is a technique employed to improve the sensing performance by exploiting the diversity between the sensing channels to overcome the fading and shadowing effects which allows reduction of miss-detection and false alarm probabilities. Information can be exchanged between cooperating users in different formats from the binary hard information to the full soft information. Quantized information has shown its efficiency as a trade-off between binary hard and full soft for other cooperative schemes, in this paper, we investigate the use of quantized information between cooperating cognitive users. We derive closed-form expressions of the cooperative average false alarm and detection probabilities over fading channels for a generalized system model with not necessarily identical average sensing Signal-to-Noise Ratio (SNR) and imperfect reporting channels. Numerical simulations allow us to conclude a tradeoff between the quantization size and the reporting energy in order to achieve the optimal cooperative error probability. Copyright © 2013 by the Institute of Electrical and Electronic Engineers, Inc.

  1. Neutron spectrum unfolding using genetic algorithm in a Monte Carlo simulation

    Energy Technology Data Exchange (ETDEWEB)

    Suman, Vitisha [Health Physics Division, Bhabha Atomic Research Centre, Mumbai 400085 (India); Sarkar, P.K., E-mail: pksarkar02@gmail.com [Manipal Centre for Natural Sciences, Manipal University, Manipal 576104 (India)

    2014-02-11

    A spectrum unfolding technique GAMCD (Genetic Algorithm and Monte Carlo based spectrum Deconvolution) has been developed using the genetic algorithm methodology within the framework of Monte Carlo simulations. Each Monte Carlo history starts with initial solution vectors (population) as randomly generated points in the hyper dimensional solution space that are related to the measured data by the response matrix of the detection system. The transition of the solution points in the solution space from one generation to another are governed by the genetic algorithm methodology using the techniques of cross-over (mating) and mutation in a probabilistic manner adding new solution points to the population. The population size is kept constant by discarding solutions having lesser fitness values (larger differences between measured and calculated results). Solutions having the highest fitness value at the end of each Monte Carlo history are averaged over all histories to obtain the final spectral solution. The present method shows promising results in neutron spectrum unfolding for both under-determined and over-determined problems with simulated test data as well as measured data when compared with some existing unfolding codes. An attractive advantage of the present method is the independence of the final spectra from the initial guess spectra.

  2. Neutron spectrum unfolding using genetic algorithm in a Monte Carlo simulation

    International Nuclear Information System (INIS)

    Suman, Vitisha; Sarkar, P.K.

    2014-01-01

    A spectrum unfolding technique GAMCD (Genetic Algorithm and Monte Carlo based spectrum Deconvolution) has been developed using the genetic algorithm methodology within the framework of Monte Carlo simulations. Each Monte Carlo history starts with initial solution vectors (population) as randomly generated points in the hyper dimensional solution space that are related to the measured data by the response matrix of the detection system. The transition of the solution points in the solution space from one generation to another are governed by the genetic algorithm methodology using the techniques of cross-over (mating) and mutation in a probabilistic manner adding new solution points to the population. The population size is kept constant by discarding solutions having lesser fitness values (larger differences between measured and calculated results). Solutions having the highest fitness value at the end of each Monte Carlo history are averaged over all histories to obtain the final spectral solution. The present method shows promising results in neutron spectrum unfolding for both under-determined and over-determined problems with simulated test data as well as measured data when compared with some existing unfolding codes. An attractive advantage of the present method is the independence of the final spectra from the initial guess spectra

  3. A Parallel Processing Algorithm for Remote Sensing Classification

    Science.gov (United States)

    Gualtieri, J. Anthony

    2005-01-01

    A current thread in parallel computation is the use of cluster computers created by networking a few to thousands of commodity general-purpose workstation-level commuters using the Linux operating system. For example on the Medusa cluster at NASA/GSFC, this provides for super computing performance, 130 G(sub flops) (Linpack Benchmark) at moderate cost, $370K. However, to be useful for scientific computing in the area of Earth science, issues of ease of programming, access to existing scientific libraries, and portability of existing code need to be considered. In this paper, I address these issues in the context of tools for rendering earth science remote sensing data into useful products. In particular, I focus on a problem that can be decomposed into a set of independent tasks, which on a serial computer would be performed sequentially, but with a cluster computer can be performed in parallel, giving an obvious speedup. To make the ideas concrete, I consider the problem of classifying hyperspectral imagery where some ground truth is available to train the classifier. In particular I will use the Support Vector Machine (SVM) approach as applied to hyperspectral imagery. The approach will be to introduce notions about parallel computation and then to restrict the development to the SVM problem. Pseudocode (an outline of the computation) will be described and then details specific to the implementation will be given. Then timing results will be reported to show what speedups are possible using parallel computation. The paper will close with a discussion of the results.

  4. Quantification of whispering gallery mode spectrum variability in application to sensing nanobiophotonics

    Science.gov (United States)

    Saetchnikov, Anton; Skakun, Victor; Saetchnikov, Vladimir; Tcherniavskaia, Elina; Ostendorf, Andreas

    2017-10-01

    An approach for the automated whispering gallery mode (WGM) signal decomposition and its parameter estimation is discussed. The algorithm is based on the peak picking and can be applied for the preprocessing of the raw signal acquired from the multiplied WGM-based biosensing chips. Quantitative estimations representing physically meaningful parameters of the external disturbing factors on the WGM spectral shape are the output values. Derived parameters can be directly applied to the further deep qualitative and quantitative interpretations of the sensed disturbing factors. The algorithm is tested on both simulated and experimental data taken from the bovine serum albumin biosensing task. The proposed solution is expected to be a useful contribution to the preprocessing phase of the complete data analysis engine and is expected to push the WGM technology toward the real-live sensing nanobiophotonics.

  5. A novel start algorithm for CNG engines using ion sense technology

    NARCIS (Netherlands)

    Bie, T. de; Ericsson, M.; Rask, P.

    2000-01-01

    This paper presents a start algorithm that is able to control the air/fuel ratio (AFR) during the cranking phase and immediately hereafter, where the ordinary ?-control is not yet enabled. The control is based on the ion sense principle, which means that a current through the spark plug is measured

  6. An Orthogonal Learning Differential Evolution Algorithm for Remote Sensing Image Registration

    Directory of Open Access Journals (Sweden)

    Wenping Ma

    2014-01-01

    Full Text Available We introduce an area-based method for remote sensing image registration. We use orthogonal learning differential evolution algorithm to optimize the similarity metric between the reference image and the target image. Many local and global methods have been used to achieve the optimal similarity metric in the last few years. Because remote sensing images are usually influenced by large distortions and high noise, local methods will fail in some cases. For this reason, global methods are often required. The orthogonal learning (OL strategy is efficient when searching in complex problem spaces. In addition, it can discover more useful information via orthogonal experimental design (OED. Differential evolution (DE is a heuristic algorithm. It has shown to be efficient in solving the remote sensing image registration problem. So orthogonal learning differential evolution algorithm (OLDE is efficient for many optimization problems. The OLDE method uses the OL strategy to guide the DE algorithm to discover more useful information. Experiments show that the OLDE method is more robust and efficient for registering remote sensing images.

  7. A Compressed Sensing-based Image Reconstruction Algorithm for Solar Flare X-Ray Observations

    Energy Technology Data Exchange (ETDEWEB)

    Felix, Simon; Bolzern, Roman; Battaglia, Marina, E-mail: simon.felix@fhnw.ch, E-mail: roman.bolzern@fhnw.ch, E-mail: marina.battaglia@fhnw.ch [University of Applied Sciences and Arts Northwestern Switzerland FHNW, 5210 Windisch (Switzerland)

    2017-11-01

    One way of imaging X-ray emission from solar flares is to measure Fourier components of the spatial X-ray source distribution. We present a new compressed sensing-based algorithm named VIS-CS, which reconstructs the spatial distribution from such Fourier components. We demonstrate the application of the algorithm on synthetic and observed solar flare X-ray data from the Reuven Ramaty High Energy Solar Spectroscopic Imager satellite and compare its performance with existing algorithms. VIS-CS produces competitive results with accurate photometry and morphology, without requiring any algorithm- and X-ray-source-specific parameter tuning. Its robustness and performance make this algorithm ideally suited for the generation of quicklook images or large image cubes without user intervention, such as for imaging spectroscopy analysis.

  8. A Compressed Sensing-based Image Reconstruction Algorithm for Solar Flare X-Ray Observations

    Science.gov (United States)

    Felix, Simon; Bolzern, Roman; Battaglia, Marina

    2017-11-01

    One way of imaging X-ray emission from solar flares is to measure Fourier components of the spatial X-ray source distribution. We present a new compressed sensing-based algorithm named VIS_CS, which reconstructs the spatial distribution from such Fourier components. We demonstrate the application of the algorithm on synthetic and observed solar flare X-ray data from the Reuven Ramaty High Energy Solar Spectroscopic Imager satellite and compare its performance with existing algorithms. VIS_CS produces competitive results with accurate photometry and morphology, without requiring any algorithm- and X-ray-source-specific parameter tuning. Its robustness and performance make this algorithm ideally suited for the generation of quicklook images or large image cubes without user intervention, such as for imaging spectroscopy analysis.

  9. Detection of Defective Sensors in Phased Array Using Compressed Sensing and Hybrid Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Shafqat Ullah Khan

    2016-01-01

    Full Text Available A compressed sensing based array diagnosis technique has been presented. This technique starts from collecting the measurements of the far-field pattern. The system linking the difference between the field measured using the healthy reference array and the field radiated by the array under test is solved using a genetic algorithm (GA, parallel coordinate descent (PCD algorithm, and then a hybridized GA with PCD algorithm. These algorithms are applied for fully and partially defective antenna arrays. The simulation results indicate that the proposed hybrid algorithm outperforms in terms of localization of element failure with a small number of measurements. In the proposed algorithm, the slow and early convergence of GA has been avoided by combining it with PCD algorithm. It has been shown that the hybrid GA-PCD algorithm provides an accurate diagnosis of fully and partially defective sensors as compared to GA or PCD alone. Different simulations have been provided to validate the performance of the designed algorithms in diversified scenarios.

  10. Energy/bandwidth-Saving Cooperative Spectrum Sensing for Two-hopWRAN

    Directory of Open Access Journals (Sweden)

    Ming-Tuo Zhou

    2014-07-01

    Full Text Available A two-hop wireless regional area network (WRAN providing monitoring services operating in Television White Space (TVWS, i.e., IEEE P802.22b, may employ a great number of subscriber customer-premises equipments (S-CPEs possibly without mains power supply, leading to requirement of cost-effective and power-saving design. This paper proposes a framework of cooperative spectrum sensing (CSS and an energy/bandwidth saving CSS scheme to P802.22b. In each round of sensing, S-CPEs with SNRs lower than a predefined threshold are excluded from reporting sensing results. Numerical results show that the fused missed-detection probability and false alarmprobability could remainmeeting sensing requirements, and the overall fused error probability changes very little. With 10 S-CPEs, it is possible to save more than 40% of the energy/bandwidth on a Rayleigh channel. The principle proposed can apply to other advanced sensing technologies capable of detecting primary signals with low average SNR.

  11. An Unsupervised Algorithm for Change Detection in Hyperspectral Remote Sensing Data Using Synthetically Fused Images and Derivative Spectral Profiles

    Directory of Open Access Journals (Sweden)

    Youkyung Han

    2017-01-01

    Full Text Available Multitemporal hyperspectral remote sensing data have the potential to detect altered areas on the earth’s surface. However, dissimilar radiometric and geometric properties between the multitemporal data due to the acquisition time or position of the sensors should be resolved to enable hyperspectral imagery for detecting changes in natural and human-impacted areas. In addition, data noise in the hyperspectral imagery spectrum decreases the change-detection accuracy when general change-detection algorithms are applied to hyperspectral images. To address these problems, we present an unsupervised change-detection algorithm based on statistical analyses of spectral profiles; the profiles are generated from a synthetic image fusion method for multitemporal hyperspectral images. This method aims to minimize the noise between the spectra corresponding to the locations of identical positions by increasing the change-detection rate and decreasing the false-alarm rate without reducing the dimensionality of the original hyperspectral data. Using a quantitative comparison of an actual dataset acquired by airborne hyperspectral sensors, we demonstrate that the proposed method provides superb change-detection results relative to the state-of-the-art unsupervised change-detection algorithms.

  12. Secondary access based on sensing and primary ARQ feedback in spectrum sharing systems

    KAUST Repository

    Hamza, Doha R.

    2012-04-01

    In the context of primary/secondary spectrum sharing, we propose a randomized secondary access strategy with access probabilities that are a function of both the primary automatic repeat request (ARQ) feedback and the spectrum sensing outcome. The primary terminal operates in a time slotted fashion and is active only when it has a packet to send. The primary receiver can send a positive acknowledgment (ACK) when the received packet is decoded correctly. Lack of ARQ feedback is interpreted as erroneous reception or inactivity. We call this the explicit ACK scheme. The primary receiver may also send a negative acknowledgment (NACK) when the packet is received in error. Lack of ARQ feedback is interpreted as an ACK or no-transmission. This is called the explicit NACK scheme. Under both schemes, when the primary feedback is interpreted as a NACK, the secondary user assumes that there will be retransmission in the next slot and accesses the channel with a certain probability. When the primary feedback is interpreted as an ACK, the secondary user accesses the channel with either one of two probabilities based on the sensing outcome. Under these settings, we find the three optimal access probabilities via maximizing the secondary throughput given a constraint on the primary throughput. We compare the performance of the explicit ACK and explicit NACK schemes and contrast them with schemes based on either sensing or primary ARQ feedback only. © 2012 IEEE.

  13. A low complexity based spectrum management algorithm for ‘Near–Far’ problem in VDSL environment

    Directory of Open Access Journals (Sweden)

    Sunil Sharma

    2015-10-01

    Full Text Available In digital subscriber line (DSL system, crosstalk created by electromagnetic interference among twisted pairs degrades the system performance. Very high bit rate DSL (VDSL, utilizes higher bandwidth of copper cable for data transmission. During upstream transmission, a ‘Near–Far’ problem occurs in VDSL system. In this problem the far end crosstalk (FEXT is produced from near end user degrades the data rate achieved at the far end user. The effect of FEXT can be reduced by properly managing power spectral densities (PSD of transmitters of near and far users. This kind of power allocation is called dynamic spectrum management (DSM. In this paper, a new distributed DSM algorithm is proposed in which power from only those sub channels of near end user are reduced which create interference to far end user. This power back off strategy takes place with the help of power spectral density (PSD masks at interference creating sub channels of near end user. The simulation results of the proposed algorithm show an improvement in terms of data rate and approaches near to that of optimal spectrum balancing (OSB algorithm.

  14. Algorithm for removing the noise from γ energy spectrum by analyzing the evolution of the wavelet transform maxima across scales

    International Nuclear Information System (INIS)

    Li Tianduo; Xiao Gang; Di Yuming; Han Feng; Qiu Xiaoling

    1999-01-01

    The γ energy spectrum is expanded in allied energy-frequency space. By the different characterization of the evolution of wavelet transform modulus maxima across scales between energy spectrum and noise, the algorithm for removing the noise from γ energy spectrum by analyzing the evolution of the wavelet transform maxima across scales is presented. The results show, in contrast to the methods in energy space or in frequency space, the method has the advantages that the peak of energy spectrum can be indicated accurately and the energy spectrum can be reconstructed with a good approximation

  15. Multispectral remote-sensing algorithms for particulate organic carbon (POC): The Gulf of Mexico

    OpenAIRE

    Son, Young Baek; Gardner, Wilford D.; Mishonov, Alexey V.; Richardson, Mary Jo

    2009-01-01

    To greatly increase the spatial and temporal resolution for studying carbon dynamics in the marine environment, we have developed remote-sensing algorithms for particulate organic carbon (POC) by matching in situ POC measurements in the Gulf of Mexico with matching SeaWiFS remote-sensing reflectance. Data on total particulate matter (PM) as well as POC collected during nine cruises in spring, summer and early winter from 1997-2000 as part of the Northeastern Gulf of Mexico (NEGOM) study were ...

  16. A research of road centerline extraction algorithm from high resolution remote sensing images

    Science.gov (United States)

    Zhang, Yushan; Xu, Tingfa

    2017-09-01

    Satellite remote sensing technology has become one of the most effective methods for land surface monitoring in recent years, due to its advantages such as short period, large scale and rich information. Meanwhile, road extraction is an important field in the applications of high resolution remote sensing images. An intelligent and automatic road extraction algorithm with high precision has great significance for transportation, road network updating and urban planning. The fuzzy c-means (FCM) clustering segmentation algorithms have been used in road extraction, but the traditional algorithms did not consider spatial information. An improved fuzzy C-means clustering algorithm combined with spatial information (SFCM) is proposed in this paper, which is proved to be effective for noisy image segmentation. Firstly, the image is segmented using the SFCM. Secondly, the segmentation result is processed by mathematical morphology to remover the joint region. Thirdly, the road centerlines are extracted by morphology thinning and burr trimming. The average integrity of the centerline extraction algorithm is 97.98%, the average accuracy is 95.36% and the average quality is 93.59%. Experimental results show that the proposed method in this paper is effective for road centerline extraction.

  17. Distortion correction algorithm for UAV remote sensing image based on CUDA

    International Nuclear Information System (INIS)

    Wenhao, Zhang; Yingcheng, Li; Delong, Li; Changsheng, Teng; Jin, Liu

    2014-01-01

    In China, natural disasters are characterized by wide distribution, severe destruction and high impact range, and they cause significant property damage and casualties every year. Following a disaster, timely and accurate acquisition of geospatial information can provide an important basis for disaster assessment, emergency relief, and reconstruction. In recent years, Unmanned Aerial Vehicle (UAV) remote sensing systems have played an important role in major natural disasters, with UAVs becoming an important technique of obtaining disaster information. UAV is equipped with a non-metric digital camera with lens distortion, resulting in larger geometric deformation for acquired images, and affecting the accuracy of subsequent processing. The slow speed of the traditional CPU-based distortion correction algorithm cannot meet the requirements of disaster emergencies. Therefore, we propose a Compute Unified Device Architecture (CUDA)-based image distortion correction algorithm for UAV remote sensing, which takes advantage of the powerful parallel processing capability of the GPU, greatly improving the efficiency of distortion correction. Our experiments show that, compared with traditional CPU algorithms and regardless of image loading and saving times, the maximum acceleration ratio using our proposed algorithm reaches 58 times that using the traditional algorithm. Thus, data processing time can be reduced by one to two hours, thereby considerably improving disaster emergency response capability

  18. Laser Sensing of Vegetation Based on Dual Spectrum Measurements of Reflection Coefficients

    Directory of Open Access Journals (Sweden)

    M. L. Belov

    2017-01-01

    Full Text Available Currently, a promising trend in remote sensing of environment is to monitor the vegetative cover: evaluate the productivity of agricultural crops; evaluate the moisture content of soils and the state of ecosystems; provide mapping the sites of bogging, desertification, drought, etc.; control the phases of vegetation of crops, etc.Development of monitoring systems for remote detection of vegetation sites being under unfavorable conditions (low or high temperature, excess or lack of water, soil salinity, disease, etc. is of relevance. Optical methods are the most effective for this task. These methods are based on the physical features of reflection spectra in the visible and near infrared spectral range for vegetation under unfavorable conditions and vegetation under normal conditions.One of the options of optoelectronic equipment for monitoring vegetation condition is laser equipment that allows remote sensing of vegetation from the aircraft and mapping of vegetation sites with abnormal (inactive periods of vegetation reflection spectra with a high degree of spatial resolution.The paper deals with development of a promising dual-spectrum method for laser remote sensing of vegetation. Using the experimentally measured reflection spectra of different vegetation types, mathematical modeling of probability for appropriate detection and false alarms to solve a problem of detecting the vegetation under unfavorable conditions (with abnormal reflection spectra is performed based on the results of dual-spectrum measurements of the reflection coefficient.In mathematical modeling, the lidar system was supposed to provide sensing at wavelengths of 0.532 μm and 0.85 μm. The noise of the measurement was supposed to be normal with zero mean value and mean-square value of 1% -10%.It is shown that the method of laser sensing of vegetation condition based on the results of dual-spectrum measurement of the reflection coefficient at wavelengths of 0.532 μm and 0

  19. Development of an Algorithm for Automatic Analysis of the Impedance Spectrum Based on a Measurement Model

    Science.gov (United States)

    Kobayashi, Kiyoshi; Suzuki, Tohru S.

    2018-03-01

    A new algorithm for the automatic estimation of an equivalent circuit and the subsequent parameter optimization is developed by combining the data-mining concept and complex least-squares method. In this algorithm, the program generates an initial equivalent-circuit model based on the sampling data and then attempts to optimize the parameters. The basic hypothesis is that the measured impedance spectrum can be reproduced by the sum of the partial-impedance spectra presented by the resistor, inductor, resistor connected in parallel to a capacitor, and resistor connected in parallel to an inductor. The adequacy of the model is determined by using a simple artificial-intelligence function, which is applied to the output function of the Levenberg-Marquardt module. From the iteration of model modifications, the program finds an adequate equivalent-circuit model without any user input to the equivalent-circuit model.

  20. Smart Soft-Sensing for the Feedwater Flowrate at PWRs Using a GMDH Algorithm

    Science.gov (United States)

    Lim, Dong Hyuk; Lee, Sung Han; Na, Man Gyun

    2010-02-01

    The thermal reactor power in pressurized water reactors (PWRs) is typically assessed using secondary system calorimetric calculations based on accurate measurements of the feedwater flowrate. Therefore, precise measurements of the feedwater flowrate are essential. In most PWRs, Venturi meters are used to measure the feedwater flowrate. However, the fouling phenomena of the Venturi meter deteriorate the accuracy of the existing hardware sensors. Consequently, it is essential to resolve the inaccurate measurements of the feedwater flowrate. In this study, in order to estimate the feedwater flowrate online with high precision, a smart soft sensing model for monitoring the feedwater flowrate was developed using a group method of data handling (GMDH) algorithm combined with a sequential probability ratio test (SPRT). The uncertainty of the GMDH model was also analyzed. The proposed sensing and monitoring algorithm was verified using the acquired real plant data from Yonggwang Nuclear Power Plant Unit 3.

  1. Advanced Dispersed Fringe Sensing Algorithm for Coarse Phasing Segmented Mirror Telescopes

    Science.gov (United States)

    Spechler, Joshua A.; Hoppe, Daniel J.; Sigrist, Norbert; Shi, Fang; Seo, Byoung-Joon; Bikkannavar, Siddarayappa A.

    2013-01-01

    Segment mirror phasing, a critical step of segment mirror alignment, requires the ability to sense and correct the relative pistons between segments from up to a few hundred microns to a fraction of wavelength in order to bring the mirror system to its full diffraction capability. When sampling the aperture of a telescope, using auto-collimating flats (ACFs) is more economical. The performance of a telescope with a segmented primary mirror strongly depends on how well those primary mirror segments can be phased. One such process to phase primary mirror segments in the axial piston direction is dispersed fringe sensing (DFS). DFS technology can be used to co-phase the ACFs. DFS is essentially a signal fitting and processing operation. It is an elegant method of coarse phasing segmented mirrors. DFS performance accuracy is dependent upon careful calibration of the system as well as other factors such as internal optical alignment, system wavefront errors, and detector quality. Novel improvements to the algorithm have led to substantial enhancements in DFS performance. The Advanced Dispersed Fringe Sensing (ADFS) Algorithm is designed to reduce the sensitivity to calibration errors by determining the optimal fringe extraction line. Applying an angular extraction line dithering procedure and combining this dithering process with an error function while minimizing the phase term of the fitted signal, defines in essence the ADFS algorithm.

  2. Development of radio frequency interference detection algorithms for passive microwave remote sensing

    Science.gov (United States)

    Misra, Sidharth

    Radio Frequency Interference (RFI) signals are man-made sources that are increasingly plaguing passive microwave remote sensing measurements. RFI is of insidious nature, with some signals low power enough to go undetected but large enough to impact science measurements and their results. With the launch of the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite in November 2009 and the upcoming launches of the new NASA sea-surface salinity measuring Aquarius mission in June 2011 and soil-moisture measuring Soil Moisture Active Passive (SMAP) mission around 2015, active steps are being taken to detect and mitigate RFI at L-band. An RFI detection algorithm was designed for the Aquarius mission. The algorithm performance was analyzed using kurtosis based RFI ground-truth. The algorithm has been developed with several adjustable location dependant parameters to control the detection statistics (false-alarm rate and probability of detection). The kurtosis statistical detection algorithm has been compared with the Aquarius pulse detection method. The comparative study determines the feasibility of the kurtosis detector for the SMAP radiometer, as a primary RFI detection algorithm in terms of detectability and data bandwidth. The kurtosis algorithm has superior detection capabilities for low duty-cycle radar like pulses, which are more prevalent according to analysis of field campaign data. Most RFI algorithms developed have generally been optimized for performance with individual pulsed-sinusoidal RFI sources. A new RFI detection model is developed that takes into account multiple RFI sources within an antenna footprint. The performance of the kurtosis detection algorithm under such central-limit conditions is evaluated. The SMOS mission has a unique hardware system, and conventional RFI detection techniques cannot be applied. Instead, an RFI detection algorithm for SMOS is developed and applied in the angular domain. This algorithm compares

  3. Collaborative spectrum sensing based on the ratio between largest eigenvalue and Geometric mean of eigenvalues

    KAUST Repository

    Shakir, Muhammad

    2011-12-01

    In this paper, we introduce a new detector referred to as Geometric mean detector (GEMD) which is based on the ratio of the largest eigenvalue to the Geometric mean of the eigenvalues for collaborative spectrum sensing. The decision threshold has been derived by employing Gaussian approximation approach. In this approach, the two random variables, i.e. The largest eigenvalue and the Geometric mean of the eigenvalues are considered as independent Gaussian random variables such that their cumulative distribution functions (CDFs) are approximated by a univariate Gaussian distribution function for any number of cooperating secondary users and received samples. The approximation approach is based on the calculation of exact analytical moments of the largest eigenvalue and the Geometric mean of the eigenvalues of the received covariance matrix. The decision threshold has been calculated by exploiting the CDF of the ratio of two Gaussian distributed random variables. In this context, we exchange the analytical moments of the two random variables with the moments of the Gaussian distribution function. The performance of the detector is compared with the performance of the energy detector and eigenvalue ratio detector. Analytical and simulation results show that our newly proposed detector yields considerable performance advantage in realistic spectrum sensing scenarios. Moreover, our results based on proposed approximation approach are in perfect agreement with the empirical results. © 2011 IEEE.

  4. Joint Spectrum Sensing and Resource Allocation for OFDM-based Transmission with a Cognitive Relay

    Directory of Open Access Journals (Sweden)

    S. Eman Mahmoodi

    2014-04-01

    Full Text Available In this paper, we investigate the joint spectrum sensing and resource allocation problem to maximize throughput capacity of an OFDM-based cognitive radio link with a cognitive relay. By applying a cognitive relay that uses decode and forward (D&F, we achieve more reliable communications, generating less interference (by needing less transmit power and more diversity gain. In order to account for imperfections in spectrum sensing, the proposed schemes jointly modify energy detector thresholds and allocates transmit powers to all cognitive radio (CR subcarriers, while simultaneously assigning subcarrier pairs for secondary users (SU and the cognitive relay. This problem is cast as a constrained optimization problem with constraints on (1 interference introduced by the SU and the cognitive relay to the PUs; (2 miss-detection and false alarm probabilities and (3 subcarrier pairing for transmission on the SU transmitter and the cognitive relay and (4 minimum Quality of Service (QoS for each CR subcarrier. We propose one optimal and two suboptimal schemes all of which are compared to other schemes in the literature. Simulation results show that the proposed schemes achieve significantly higher throughput than other schemes in the literature for different relay situations.

  5. Predictors of sense of coherence in typically developing adolescent siblings of individuals with autism spectrum disorder.

    Science.gov (United States)

    Smith, L O; Elder, J H; Storch, E A; Rowe, M A

    2015-01-01

    Children with autism spectrum disorder (ASD) may be a stressor for family members yet there is little published research on the impact of having a child with ASD on their typically developing (TD) adolescent siblings. According to Antonovsky's salutogenic model, a strong sense of coherence leads to the view that the stressor is a manageable challenge rather than a burden and promotes healthier adaptation. This study examines the relationship between stress, TD sibling resources and the sense of coherence in TD siblings. This quantitative mail-based study uses a survey methodology, analysing the responses of TD adolescent siblings (n = 96) of individuals with autism, Asperger's syndrome, or pervasive developmental disorder - not otherwise specified to several rating scales. Adolescent siblings, ages 11 to 18 years, completed the Adolescent Coping Orientation for Problem Experience (ACOPE), Network of Relationship Inventory - Social Provision Version (NRI-SPV), Youth Self Report (YSR), and Sense of Coherence (SOC) instruments; parents completed the Child Autism Rating Scale - 2nd Edition (CARS-2). The salutogenesis model was used to guide and inform this research. Findings suggested the following: (a) the stress of ASD severity and resource of adjustment are related in TD adolescent siblings; (b) TD sibling adjustment has a strong relationship with sense of coherence levels; and (c) a greater number of positive coping strategies buffer TD sibling coherence levels when ASD severity scores are high. ASD severity and TD adolescent sibling resources influence sense of coherence in adolescent TD siblings of individuals with ASD. © 2014 MENCAP and International Association of the Scientific Study of Intellectual and Developmental Disabilities and John Wiley & Sons Ltd.

  6. Performance comparison between total variation (TV)-based compressed sensing and statistical iterative reconstruction algorithms

    International Nuclear Information System (INIS)

    Tang Jie; Nett, Brian E; Chen Guanghong

    2009-01-01

    Of all available reconstruction methods, statistical iterative reconstruction algorithms appear particularly promising since they enable accurate physical noise modeling. The newly developed compressive sampling/compressed sensing (CS) algorithm has shown the potential to accurately reconstruct images from highly undersampled data. The CS algorithm can be implemented in the statistical reconstruction framework as well. In this study, we compared the performance of two standard statistical reconstruction algorithms (penalized weighted least squares and q-GGMRF) to the CS algorithm. In assessing the image quality using these iterative reconstructions, it is critical to utilize realistic background anatomy as the reconstruction results are object dependent. A cadaver head was scanned on a Varian Trilogy system at different dose levels. Several figures of merit including the relative root mean square error and a quality factor which accounts for the noise performance and the spatial resolution were introduced to objectively evaluate reconstruction performance. A comparison is presented between the three algorithms for a constant undersampling factor comparing different algorithms at several dose levels. To facilitate this comparison, the original CS method was formulated in the framework of the statistical image reconstruction algorithms. Important conclusions of the measurements from our studies are that (1) for realistic neuro-anatomy, over 100 projections are required to avoid streak artifacts in the reconstructed images even with CS reconstruction, (2) regardless of the algorithm employed, it is beneficial to distribute the total dose to more views as long as each view remains quantum noise limited and (3) the total variation-based CS method is not appropriate for very low dose levels because while it can mitigate streaking artifacts, the images exhibit patchy behavior, which is potentially harmful for medical diagnosis.

  7. Spectrum correction algorithm for detectors in airborne radioactivity monitoring equipment NH-UAV based on a ratio processing method

    International Nuclear Information System (INIS)

    Cao, Ye; Tang, Xiao-Bin; Wang, Peng; Meng, Jia; Huang, Xi; Wen, Liang-Sheng; Chen, Da

    2015-01-01

    The unmanned aerial vehicle (UAV) radiation monitoring method plays an important role in nuclear accidents emergency. In this research, a spectrum correction algorithm about the UAV airborne radioactivity monitoring equipment NH-UAV was studied to measure the radioactive nuclides within a small area in real time and in a fixed place. The simulation spectra of the high-purity germanium (HPGe) detector and the lanthanum bromide (LaBr 3 ) detector in the equipment were obtained using the Monte Carlo technique. Spectrum correction coefficients were calculated after performing ratio processing techniques about the net peak areas between the double detectors on the detection spectrum of the LaBr 3 detector according to the accuracy of the detection spectrum of the HPGe detector. The relationship between the spectrum correction coefficient and the size of the source term was also investigated. A good linear relation exists between the spectrum correction coefficient and the corresponding energy (R 2 =0.9765). The maximum relative deviation from the real condition reduced from 1.65 to 0.035. The spectrum correction method was verified as feasible. - Highlights: • An airborne radioactivity monitoring equipment NH-UAV was developed to measure radionuclide after a nuclear accident. • A spectrum correction algorithm was proposed to obtain precise information on the detected radioactivity within a small area. • The spectrum correction method was verified as feasible. • The corresponding spectrum correction coefficients increase first and then stay constant

  8. Spectrum correction algorithm for detectors in airborne radioactivity monitoring equipment NH-UAV based on a ratio processing method

    Energy Technology Data Exchange (ETDEWEB)

    Cao, Ye [Department of Nuclear Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016 (China); Tang, Xiao-Bin, E-mail: tangxiaobin@nuaa.edu.cn [Department of Nuclear Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016 (China); Jiangsu Key Laboratory of Nuclear Energy Equipment Materials Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016 (China); Wang, Peng; Meng, Jia; Huang, Xi; Wen, Liang-Sheng [Department of Nuclear Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016 (China); Chen, Da [Department of Nuclear Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016 (China); Jiangsu Key Laboratory of Nuclear Energy Equipment Materials Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016 (China)

    2015-10-11

    The unmanned aerial vehicle (UAV) radiation monitoring method plays an important role in nuclear accidents emergency. In this research, a spectrum correction algorithm about the UAV airborne radioactivity monitoring equipment NH-UAV was studied to measure the radioactive nuclides within a small area in real time and in a fixed place. The simulation spectra of the high-purity germanium (HPGe) detector and the lanthanum bromide (LaBr{sub 3}) detector in the equipment were obtained using the Monte Carlo technique. Spectrum correction coefficients were calculated after performing ratio processing techniques about the net peak areas between the double detectors on the detection spectrum of the LaBr{sub 3} detector according to the accuracy of the detection spectrum of the HPGe detector. The relationship between the spectrum correction coefficient and the size of the source term was also investigated. A good linear relation exists between the spectrum correction coefficient and the corresponding energy (R{sup 2}=0.9765). The maximum relative deviation from the real condition reduced from 1.65 to 0.035. The spectrum correction method was verified as feasible. - Highlights: • An airborne radioactivity monitoring equipment NH-UAV was developed to measure radionuclide after a nuclear accident. • A spectrum correction algorithm was proposed to obtain precise information on the detected radioactivity within a small area. • The spectrum correction method was verified as feasible. • The corresponding spectrum correction coefficients increase first and then stay constant.

  9. A compressed sensing based 3D resistivity inversion algorithm for hydrogeological applications

    Science.gov (United States)

    Ranjan, Shashi; Kambhammettu, B. V. N. P.; Peddinti, Srinivasa Rao; Adinarayana, J.

    2018-04-01

    Image reconstruction from discrete electrical responses pose a number of computational and mathematical challenges. Application of smoothness constrained regularized inversion from limited measurements may fail to detect resistivity anomalies and sharp interfaces separated by hydro stratigraphic units. Under favourable conditions, compressed sensing (CS) can be thought of an alternative to reconstruct the image features by finding sparse solutions to highly underdetermined linear systems. This paper deals with the development of a CS assisted, 3-D resistivity inversion algorithm for use with hydrogeologists and groundwater scientists. CS based l1-regularized least square algorithm was applied to solve the resistivity inversion problem. Sparseness in the model update vector is introduced through block oriented discrete cosine transformation, with recovery of the signal achieved through convex optimization. The equivalent quadratic program was solved using primal-dual interior point method. Applicability of the proposed algorithm was demonstrated using synthetic and field examples drawn from hydrogeology. The proposed algorithm has outperformed the conventional (smoothness constrained) least square method in recovering the model parameters with much fewer data, yet preserving the sharp resistivity fronts separated by geologic layers. Resistivity anomalies represented by discrete homogeneous blocks embedded in contrasting geologic layers were better imaged using the proposed algorithm. In comparison to conventional algorithm, CS has resulted in an efficient (an increase in R2 from 0.62 to 0.78; a decrease in RMSE from 125.14 Ω-m to 72.46 Ω-m), reliable, and fast converging (run time decreased by about 25%) solution.

  10. Comparative Study on a Solving Model and Algorithm for a Flush Air Data Sensing System

    Directory of Open Access Journals (Sweden)

    Yanbin Liu

    2014-05-01

    Full Text Available With the development of high-performance aircraft, precise air data are necessary to complete challenging tasks such as flight maneuvering with large angles of attack and high speed. As a result, the flush air data sensing system (FADS was developed to satisfy the stricter control demands. In this paper, comparative stuides on the solving model and algorithm for FADS are conducted. First, the basic principles of FADS are given to elucidate the nonlinear relations between the inputs and the outputs. Then, several different solving models and algorithms of FADS are provided to compute the air data, including the angle of attck, sideslip angle, dynamic pressure and static pressure. Afterwards, the evaluation criteria of the resulting models and algorithms are discussed to satisfy the real design demands. Futhermore, a simulation using these algorithms is performed to identify the properites of the distinct models and algorithms such as the measuring precision and real-time features. The advantages of these models and algorithms corresponding to the different flight conditions are also analyzed, furthermore, some suggestions on their engineering applications are proposed to help future research.

  11. Gaussian mixture models-based ship target recognition algorithm in remote sensing infrared images

    Science.gov (United States)

    Yao, Shoukui; Qin, Xiaojuan

    2018-02-01

    Since the resolution of remote sensing infrared images is low, the features of ship targets become unstable. The issue of how to recognize ships with fuzzy features is an open problem. In this paper, we propose a novel ship target recognition algorithm based on Gaussian mixture models (GMMs). In the proposed algorithm, there are mainly two steps. At the first step, the Hu moments of these ship target images are calculated, and the GMMs are trained on the moment features of ships. At the second step, the moment feature of each ship image is assigned to the trained GMMs for recognition. Because of the scale, rotation, translation invariance property of Hu moments and the power feature-space description ability of GMMs, the GMMs-based ship target recognition algorithm can recognize ship reliably. Experimental results of a large simulating image set show that our approach is effective in distinguishing different ship types, and obtains a satisfactory ship recognition performance.

  12. Research on fully distributed optical fiber sensing security system localization algorithm

    Science.gov (United States)

    Wu, Xu; Hou, Jiacheng; Liu, Kun; Liu, Tiegen

    2013-12-01

    A new fully distributed optical fiber sensing and location technology based on the Mach-Zehnder interferometers is studied. In this security system, a new climbing point locating algorithm based on short-time average zero-crossing rate is presented. By calculating the zero-crossing rates of the multiple grouped data separately, it not only utilizes the advantages of the frequency analysis method to determine the most effective data group more accurately, but also meets the requirement of the real-time monitoring system. Supplemented with short-term energy calculation group signal, the most effective data group can be quickly picked out. Finally, the accurate location of the climbing point can be effectively achieved through the cross-correlation localization algorithm. The experimental results show that the proposed algorithm can realize the accurate location of the climbing point and meanwhile the outside interference noise of the non-climbing behavior can be effectively filtered out.

  13. Combined diversity and improved energy detection in cooperative spectrum sensing with faded reporting channels

    Directory of Open Access Journals (Sweden)

    Srinivas Nallagonda

    2016-04-01

    Full Text Available In this paper we evaluate the performance of cooperative spectrum sensing (CSS where each cognitive radio (CR employs an improved energy detector (IED with multiple antennas and uses selection combining (SC for detecting the primary user (PU in noisy and faded sensing (S channels. We derive an expression for the probability of false alarm and expressions for probability of missed detection in non-faded (AWGN and Rayleigh faded sensing environments in terms of cumulative distribution function (CDF. Each CR transmits its decision about PU via noisy and faded reporting (R channel to fusion center (FC. In this paper we assume that S-channels are noisy and Rayleigh faded while several cases of fading are considered for R-channels such as: (i Hoyt (or Nakagami-q, (ii Rayleigh, (iii Rician (or Nakagami-n, and (iv Weibull. A Binary Symmetric channel (BSC with a fixed error probability (r in the R-channel is also considered. The impact of fading in R-channel, S-channel and several network parameters such as IED parameter, normalized detection threshold, number of CRs, and number of antennas on missed detection and total error probability is assessed. The effects of Hoyt, Rician, and Weibull fading parameters on overall performance of IED-CSS are also highlighted.

  14. Electronic Health Record Based Algorithm to Identify Patients with Autism Spectrum Disorder.

    Directory of Open Access Journals (Sweden)

    Todd Lingren

    Full Text Available Cohort selection is challenging for large-scale electronic health record (EHR analyses, as International Classification of Diseases 9th edition (ICD-9 diagnostic codes are notoriously unreliable disease predictors. Our objective was to develop, evaluate, and validate an automated algorithm for determining an Autism Spectrum Disorder (ASD patient cohort from EHR. We demonstrate its utility via the largest investigation to date of the co-occurrence patterns of medical comorbidities in ASD.We extracted ICD-9 codes and concepts derived from the clinical notes. A gold standard patient set was labeled by clinicians at Boston Children's Hospital (BCH (N = 150 and Cincinnati Children's Hospital and Medical Center (CCHMC (N = 152. Two algorithms were created: (1 rule-based implementing the ASD criteria from Diagnostic and Statistical Manual of Mental Diseases 4th edition, (2 predictive classifier. The positive predictive values (PPV achieved by these algorithms were compared to an ICD-9 code baseline. We clustered the patients based on grouped ICD-9 codes and evaluated subgroups.The rule-based algorithm produced the best PPV: (a BCH: 0.885 vs. 0.273 (baseline; (b CCHMC: 0.840 vs. 0.645 (baseline; (c combined: 0.864 vs. 0.460 (baseline. A validation at Children's Hospital of Philadelphia yielded 0.848 (PPV. Clustering analyses of comorbidities on the three-site large cohort (N = 20,658 ASD patients identified psychiatric, developmental, and seizure disorder clusters.In a large cross-institutional cohort, co-occurrence patterns of comorbidities in ASDs provide further hypothetical evidence for distinct courses in ASD. The proposed automated algorithms for cohort selection open avenues for other large-scale EHR studies and individualized treatment of ASD.

  15. A Multi-Band Analytical Algorithm for Deriving Absorption and Backscattering Coefficients from Remote-Sensing Reflectance of Optically Deep Waters

    Science.gov (United States)

    Lee, Zhong-Ping; Carder, Kendall L.

    2001-01-01

    A multi-band analytical (MBA) algorithm is developed to retrieve absorption and backscattering coefficients for optically deep waters, which can be applied to data from past and current satellite sensors, as well as data from hyperspectral sensors. This MBA algorithm applies a remote-sensing reflectance model derived from the Radiative Transfer Equation, and values of absorption and backscattering coefficients are analytically calculated from values of remote-sensing reflectance. There are only limited empirical relationships involved in the algorithm, which implies that this MBA algorithm could be applied to a wide dynamic range of waters. Applying the algorithm to a simulated non-"Case 1" data set, which has no relation to the development of the algorithm, the percentage error for the total absorption coefficient at 440 nm a (sub 440) is approximately 12% for a range of 0.012 - 2.1 per meter (approximately 6% for a (sub 440) less than approximately 0.3 per meter), while a traditional band-ratio approach returns a percentage error of approximately 30%. Applying it to a field data set ranging from 0.025 to 2.0 per meter, the result for a (sub 440) is very close to that using a full spectrum optimization technique (9.6% difference). Compared to the optimization approach, the MBA algorithm cuts the computation time dramatically with only a small sacrifice in accuracy, making it suitable for processing large data sets such as satellite images. Significant improvements over empirical algorithms have also been achieved in retrieving the optical properties of optically deep waters.

  16. Evaluation of Clear-Sky Incoming Radiation Estimating Equations Typically Used in Remote Sensing Evapotranspiration Algorithms

    Directory of Open Access Journals (Sweden)

    Ted W. Sammis

    2013-09-01

    Full Text Available Net radiation is a key component of the energy balance, whose estimation accuracy has an impact on energy flux estimates from satellite data. In typical remote sensing evapotranspiration (ET algorithms, the outgoing shortwave and longwave components of net radiation are obtained from remote sensing data, while the incoming shortwave (RS and longwave (RL components are typically estimated from weather data using empirical equations. This study evaluates the accuracy of empirical equations commonly used in remote sensing ET algorithms for estimating RS and RL radiation. Evaluation is carried out through comparison of estimates and observations at five sites that represent different climatic regions from humid to arid. Results reveal (1 both RS and RL estimates from all evaluated equations well correlate with observations (R2 ≥ 0.92, (2 RS estimating equations tend to overestimate, especially at higher values, (3 RL estimating equations tend to give more biased values in arid and semi-arid regions, (4 a model that parameterizes the diffuse component of radiation using two clearness indices and a simple model that assumes a linear increase of atmospheric transmissivity with elevation give better RS estimates, and (5 mean relative absolute errors in the net radiation (Rn estimates caused by the use of RS and RL estimating equations varies from 10% to 22%. This study suggests that Rn estimates using recommended incoming radiation estimating equations could improve ET estimates.

  17. A stereo remote sensing feature selection method based on artificial bee colony algorithm

    Science.gov (United States)

    Yan, Yiming; Liu, Pigang; Zhang, Ye; Su, Nan; Tian, Shu; Gao, Fengjiao; Shen, Yi

    2014-05-01

    To improve the efficiency of stereo information for remote sensing classification, a stereo remote sensing feature selection method is proposed in this paper presents, which is based on artificial bee colony algorithm. Remote sensing stereo information could be described by digital surface model (DSM) and optical image, which contain information of the three-dimensional structure and optical characteristics, respectively. Firstly, three-dimensional structure characteristic could be analyzed by 3D-Zernike descriptors (3DZD). However, different parameters of 3DZD could descript different complexity of three-dimensional structure, and it needs to be better optimized selected for various objects on the ground. Secondly, features for representing optical characteristic also need to be optimized. If not properly handled, when a stereo feature vector composed of 3DZD and image features, that would be a lot of redundant information, and the redundant information may not improve the classification accuracy, even cause adverse effects. To reduce information redundancy while maintaining or improving the classification accuracy, an optimized frame for this stereo feature selection problem is created, and artificial bee colony algorithm is introduced for solving this optimization problem. Experimental results show that the proposed method can effectively improve the computational efficiency, improve the classification accuracy.

  18. A wavelet-based regularized reconstruction algorithm for SENSE parallel MRI with applications to neuroimaging

    International Nuclear Information System (INIS)

    Chaari, L.; Pesquet, J.Ch.; Chaari, L.; Ciuciu, Ph.; Benazza-Benyahia, A.

    2011-01-01

    To reduce scanning time and/or improve spatial/temporal resolution in some Magnetic Resonance Imaging (MRI) applications, parallel MRI acquisition techniques with multiple coils acquisition have emerged since the early 1990's as powerful imaging methods that allow a faster acquisition process. In these techniques, the full FOV image has to be reconstructed from the resulting acquired under sampled k-space data. To this end, several reconstruction techniques have been proposed such as the widely-used Sensitivity Encoding (SENSE) method. However, the reconstructed image generally presents artifacts when perturbations occur in both the measured data and the estimated coil sensitivity profiles. In this paper, we aim at achieving accurate image reconstruction under degraded experimental conditions (low magnetic field and high reduction factor), in which neither the SENSE method nor the Tikhonov regularization in the image domain give convincing results. To this end, we present a novel method for SENSE-based reconstruction which proceeds with regularization in the complex wavelet domain by promoting sparsity. The proposed approach relies on a fast algorithm that enables the minimization of regularized non-differentiable criteria including more general penalties than a classical l 1 term. To further enhance the reconstructed image quality, local convex constraints are added to the regularization process. In vivo human brain experiments carried out on Gradient-Echo (GRE) anatomical and Echo Planar Imaging (EPI) functional MRI data at 1.5 T indicate that our algorithm provides reconstructed images with reduced artifacts for high reduction factors. (authors)

  19. a New Graduation Algorithm for Color Balance of Remote Sensing Image

    Science.gov (United States)

    Zhou, G.; Liu, X.; Yue, T.; Wang, Q.; Sha, H.; Huang, S.; Pan, Q.

    2018-05-01

    In order to expand the field of view to obtain more data and information when doing research on remote sensing image, workers always need to mosaicking images together. However, the image after mosaic always has the large color differences and produces the gap line. This paper based on the graduation algorithm of tarigonometric function proposed a new algorithm of Two Quarter-rounds Curves (TQC). The paper uses the Gaussian filter to solve the program about the image color noise and the gap line. The paper used one of Greenland compiled data acquired in 1963 from Declassified Intelligence Photography Project (DISP) by ARGON KH-5 satellite, and used the photography of North Gulf, China, by Landsat satellite to experiment. The experimental results show that the proposed method has improved the accuracy of the results in two parts: on the one hand, for the large color differences remote sensing image will become more balanced. On the other hands, the remote sensing image will achieve more smooth transition.

  20. Incorporation of local dependent reliability information into the Prior Image Constrained Compressed Sensing (PICCS) reconstruction algorithm

    International Nuclear Information System (INIS)

    Vaegler, Sven; Sauer, Otto; Stsepankou, Dzmitry; Hesser, Juergen

    2015-01-01

    The reduction of dose in cone beam computer tomography (CBCT) arises from the decrease of the tube current for each projection as well as from the reduction of the number of projections. In order to maintain good image quality, sophisticated image reconstruction techniques are required. The Prior Image Constrained Compressed Sensing (PICCS) incorporates prior images into the reconstruction algorithm and outperforms the widespread used Feldkamp-Davis-Kress-algorithm (FDK) when the number of projections is reduced. However, prior images that contain major variations are not appropriately considered so far in PICCS. We therefore propose the partial-PICCS (pPICCS) algorithm. This framework is a problem-specific extension of PICCS and enables the incorporation of the reliability of the prior images additionally. We assumed that the prior images are composed of areas with large and small deviations. Accordingly, a weighting matrix considered the assigned areas in the objective function. We applied our algorithm to the problem of image reconstruction from few views by simulations with a computer phantom as well as on clinical CBCT projections from a head-and-neck case. All prior images contained large local variations. The reconstructed images were compared to the reconstruction results by the FDK-algorithm, by Compressed Sensing (CS) and by PICCS. To show the gain of image quality we compared image details with the reference image and used quantitative metrics (root-mean-square error (RMSE), contrast-to-noise-ratio (CNR)). The pPICCS reconstruction framework yield images with substantially improved quality even when the number of projections was very small. The images contained less streaking, blurring and inaccurately reconstructed structures compared to the images reconstructed by FDK, CS and conventional PICCS. The increased image quality is also reflected in large RMSE differences. We proposed a modification of the original PICCS algorithm. The pPICCS algorithm

  1. Cognitive communication and cooperative hetnet coexistence selected advances on spectrum sensing, learning, and security approaches

    CERN Document Server

    Bader, Faouzi

    2014-01-01

    This book, written by experts from universities and major industrial research laboratories, is devoted to the very hot topic of cognitive radio and networking for cooperative coexistence of heterogeneous wireless networks. Selected highly relevant advanced research is presented on spectrum sensing and progress toward the realization of accurate radio environment mapping, biomimetic learning for self-organizing networks, security threats (with a special focus on primary user emulation attack), and cognition as a tool for green next-generation networks. The research activities covered include work undertaken within the framework of the European COST Action IC0902, which is geared towards the definition of a European platform for cognitive radio and networks. Communications engineers, R&D engineers, researchers, and students will all benefit from this complete reference on recent advances in wireless communications and the design and implementation of cognitive radio systems and networks.

  2. Water Extraction in High Resolution Remote Sensing Image Based on Hierarchical Spectrum and Shape Features

    International Nuclear Information System (INIS)

    Li, Bangyu; Zhang, Hui; Xu, Fanjiang

    2014-01-01

    This paper addresses the problem of water extraction from high resolution remote sensing images (including R, G, B, and NIR channels), which draws considerable attention in recent years. Previous work on water extraction mainly faced two difficulties. 1) It is difficult to obtain accurate position of water boundary because of using low resolution images. 2) Like all other image based object classification problems, the phenomena of ''different objects same image'' or ''different images same object'' affects the water extraction. Shadow of elevated objects (e.g. buildings, bridges, towers and trees) scattered in the remote sensing image is a typical noise objects for water extraction. In many cases, it is difficult to discriminate between water and shadow in a remote sensing image, especially in the urban region. We propose a water extraction method with two hierarchies: the statistical feature of spectral characteristic based on image segmentation and the shape feature based on shadow removing. In the first hierarchy, the Statistical Region Merging (SRM) algorithm is adopted for image segmentation. The SRM includes two key steps: one is sorting adjacent regions according to a pre-ascertained sort function, and the other one is merging adjacent regions based on a pre-ascertained merging predicate. The sort step is done one time during the whole processing without considering changes caused by merging which may cause imprecise results. Therefore, we modify the SRM with dynamic sort processing, which conducts sorting step repetitively when there is large adjacent region changes after doing merging. To achieve robust segmentation, we apply the merging region with six features (four remote sensing image bands, Normalized Difference Water Index (NDWI), and Normalized Saturation-value Difference Index (NSVDI)). All these features contribute to segment image into region of object. NDWI and NSVDI are discriminate between water and

  3. Development of a computationally efficient algorithm for attitude estimation of a remote sensing satellite

    Science.gov (United States)

    Labibian, Amir; Bahrami, Amir Hossein; Haghshenas, Javad

    2017-09-01

    This paper presents a computationally efficient algorithm for attitude estimation of remote a sensing satellite. In this study, gyro, magnetometer, sun sensor and star tracker are used in Extended Kalman Filter (EKF) structure for the purpose of Attitude Determination (AD). However, utilizing all of the measurement data simultaneously in EKF structure increases computational burden. Specifically, assuming n observation vectors, an inverse of a 3n×3n matrix is required for gain calculation. In order to solve this problem, an efficient version of EKF, namely Murrell's version, is employed. This method utilizes measurements separately at each sampling time for gain computation. Therefore, an inverse of a 3n×3n matrix is replaced by an inverse of a 3×3 matrix for each measurement vector. Moreover, gyro drifts during the time can reduce the pointing accuracy. Therefore, a calibration algorithm is utilized for estimation of the main gyro parameters.

  4. [A quick algorithm of dynamic spectrum photoelectric pulse wave detection based on LabVIEW].

    Science.gov (United States)

    Lin, Ling; Li, Na; Li, Gang

    2010-02-01

    Dynamic spectrum (DS) detection is attractive among the numerous noninvasive blood component detection methods because of the elimination of the main interference of the individual discrepancy and measure conditions. DS is a kind of spectrum extracted from the photoelectric pulse wave and closely relative to the artery blood. It can be used in a noninvasive blood component concentration examination. The key issues in DS detection are high detection precision and high operation speed. The precision of measure can be advanced by making use of over-sampling and lock-in amplifying on the pick-up of photoelectric pulse wave in DS detection. In the present paper, the theory expression formula of the over-sampling and lock-in amplifying method was deduced firstly. Then in order to overcome the problems of great data and excessive operation brought on by this technology, a quick algorithm based on LabVIEW and a method of using external C code applied in the pick-up of photoelectric pulse wave were presented. Experimental verification was conducted in the environment of LabVIEW. The results show that by the method pres ented, the speed of operation was promoted rapidly and the data memory was reduced largely.

  5. Online identification algorithms for integrated dielectric electroactive polymer sensors and self-sensing concepts

    International Nuclear Information System (INIS)

    Hoffstadt, Thorben; Griese, Martin; Maas, Jürgen

    2014-01-01

    Transducers based on dielectric electroactive polymers (DEAP) use electrostatic pressure to convert electric energy into strain energy or vice versa. Besides this, they are also designed for sensor applications in monitoring the actual stretch state on the basis of the deformation dependent capacitive–resistive behavior of the DEAP. In order to enable an efficient and proper closed loop control operation of these transducers, e.g. in positioning or energy harvesting applications, on the one hand, sensors based on DEAP material can be integrated into the transducers and evaluated externally, and on the other hand, the transducer itself can be used as a sensor, also in terms of self-sensing. For this purpose the characteristic electrical behavior of the transducer has to be evaluated in order to determine the mechanical state. Also, adequate online identification algorithms with sufficient accuracy and dynamics are required, independent from the sensor concept utilized, in order to determine the electrical DEAP parameters in real time. Therefore, in this contribution, algorithms are developed in the frequency domain for identifications of the capacitance as well as the electrode and polymer resistance of a DEAP, which are validated by measurements. These algorithms are designed for self-sensing applications, especially if the power electronics utilized is operated at a constant switching frequency, and parasitic harmonic oscillations are induced besides the desired DC value. These oscillations can be used for the online identification, so an additional superimposed excitation is no longer necessary. For this purpose a dual active bridge (DAB) is introduced to drive the DEAP transducer. The capabilities of the real-time identification algorithm in combination with the DAB are presented in detail and discussed, finally. (paper)

  6. A Region-Based GeneSIS Segmentation Algorithm for the Classification of Remotely Sensed Images

    Directory of Open Access Journals (Sweden)

    Stelios K. Mylonas

    2015-03-01

    Full Text Available This paper proposes an object-based segmentation/classification scheme for remotely sensed images, based on a novel variant of the recently proposed Genetic Sequential Image Segmentation (GeneSIS algorithm. GeneSIS segments the image in an iterative manner, whereby at each iteration a single object is extracted via a genetic-based object extraction algorithm. Contrary to the previous pixel-based GeneSIS where the candidate objects to be extracted were evaluated through the fuzzy content of their included pixels, in the newly developed region-based GeneSIS algorithm, a watershed-driven fine segmentation map is initially obtained from the original image, which serves as the basis for the forthcoming GeneSIS segmentation. Furthermore, in order to enhance the spatial search capabilities, we introduce a more descriptive encoding scheme in the object extraction algorithm, where the structural search modules are represented by polygonal shapes. Our objectives in the new framework are posed as follows: enhance the flexibility of the algorithm in extracting more flexible object shapes, assure high level classification accuracies, and reduce the execution time of the segmentation, while at the same time preserving all the inherent attributes of the GeneSIS approach. Finally, exploiting the inherent attribute of GeneSIS to produce multiple segmentations, we also propose two segmentation fusion schemes that operate on the ensemble of segmentations generated by GeneSIS. Our approaches are tested on an urban and two agricultural images. The results show that region-based GeneSIS has considerably lower computational demands compared to the pixel-based one. Furthermore, the suggested methods achieve higher classification accuracies and good segmentation maps compared to a series of existing algorithms.

  7. A novel remote sensing algorithm to quantify phycocyanin in cyanobacterial algal blooms

    International Nuclear Information System (INIS)

    Mishra, S; Mishra, D R

    2014-01-01

    We present a novel three-band algorithm (PC 3 ) to retrieve phycocyanin (PC) pigment concentration in cyanobacteria laden inland waters. The water sample and remote sensing reflectance data used for PC 3 calibration and validation were acquired from highly turbid productive catfish aquaculture ponds. Since the characteristic PC absorption feature at 620 nm is contaminated with residual chlorophyll-a (Chl-a) absorption, we propose a coefficient (ψ) for isolating the PC absorption component at 620 nm. Results show that inclusion of the model coefficient relating Chl-a absorption at 620 nm–665 nm enables PC 3 to compensate for the confounding effect of Chl-a at the PC absorption band and considerably increases the accuracy of the PC prediction algorithm. In the current dataset, PC 3 produced the lowest mean relative error of prediction among all PC algorithms considered in this research. Moreover, PC 3 eliminates the nonlinear sensitivity issue of PC algorithms particularly at high PC range (>100 μg L −1 ). Therefore, introduction of PC 3 will have an immediate positive impact on studies monitoring inland and coastal cyanobacterial harmful algal blooms. (letter)

  8. Defect-detection algorithm for noncontact acoustic inspection using spectrum entropy

    Science.gov (United States)

    Sugimoto, Kazuko; Akamatsu, Ryo; Sugimoto, Tsuneyoshi; Utagawa, Noriyuki; Kuroda, Chitose; Katakura, Kageyoshi

    2015-07-01

    In recent years, the detachment of concrete from bridges or tunnels and the degradation of concrete structures have become serious social problems. The importance of inspection, repair, and updating is recognized in measures against degradation. We have so far studied the noncontact acoustic inspection method using airborne sound and the laser Doppler vibrometer. In this method, depending on the surface state (reflectance, dirt, etc.), the quantity of the light of the returning laser decreases and optical noise resulting from the leakage of light reception arises. Some influencing factors are the stability of the output of the laser Doppler vibrometer, the low reflective characteristic of the measurement surface, the diffused reflection characteristic, measurement distance, and laser irradiation angle. If defect detection depends only on the vibration energy ratio since the frequency characteristic of the optical noise resembles white noise, the detection of optical noise resulting from the leakage of light reception may indicate a defective part. Therefore, in this work, the combination of the vibrational energy ratio and spectrum entropy is used to judge whether a measured point is healthy or defective or an abnormal measurement point. An algorithm that enables more vivid detection of a defective part is proposed. When our technique was applied in an experiment with real concrete structures, the defective part could be extracted more vividly and the validity of our proposed algorithm was confirmed.

  9. Development of a Machine Learning Algorithm for the Surveillance of Autism Spectrum Disorder.

    Directory of Open Access Journals (Sweden)

    Matthew J Maenner

    Full Text Available The Autism and Developmental Disabilities Monitoring (ADDM Network conducts population-based surveillance of autism spectrum disorder (ASD among 8-year old children in multiple US sites. To classify ASD, trained clinicians review developmental evaluations collected from multiple health and education sources to determine whether the child meets the ASD surveillance case criteria. The number of evaluations collected has dramatically increased since the year 2000, challenging the resources and timeliness of the surveillance system. We developed and evaluated a machine learning approach to classify case status in ADDM using words and phrases contained in children's developmental evaluations. We trained a random forest classifier using data from the 2008 Georgia ADDM site which included 1,162 children with 5,396 evaluations (601 children met ADDM ASD criteria using standard ADDM methods. The classifier used the words and phrases from the evaluations to predict ASD case status. We evaluated its performance on the 2010 Georgia ADDM surveillance data (1,450 children with 9,811 evaluations; 754 children met ADDM ASD criteria. We also estimated ASD prevalence using predictions from the classification algorithm. Overall, the machine learning approach predicted ASD case statuses that were 86.5% concordant with the clinician-determined case statuses (84.0% sensitivity, 89.4% predictive value positive. The area under the resulting receiver-operating characteristic curve was 0.932. Algorithm-derived ASD "prevalence" was 1.46% compared to the published (clinician-determined estimate of 1.55%. Using only the text contained in developmental evaluations, a machine learning algorithm was able to discriminate between children that do and do not meet ASD surveillance criteria at one surveillance site.

  10. Spectrum

    DEFF Research Database (Denmark)

    Høgfeldt Hansen, Leif

    2016-01-01

    The publication functions as a proces description of the development and construction of an urban furniture SPECTRUM in the city of Gwangju, Republic of Korea. It is used as the cataloque for the exhibition of Spectrum.......The publication functions as a proces description of the development and construction of an urban furniture SPECTRUM in the city of Gwangju, Republic of Korea. It is used as the cataloque for the exhibition of Spectrum....

  11. Research on fast Fourier transforms algorithm of huge remote sensing image technology with GPU and partitioning technology.

    Science.gov (United States)

    Yang, Xue; Li, Xue-You; Li, Jia-Guo; Ma, Jun; Zhang, Li; Yang, Jan; Du, Quan-Ye

    2014-02-01

    Fast Fourier transforms (FFT) is a basic approach to remote sensing image processing. With the improvement of capacity of remote sensing image capture with the features of hyperspectrum, high spatial resolution and high temporal resolution, how to use FFT technology to efficiently process huge remote sensing image becomes the critical step and research hot spot of current image processing technology. FFT algorithm, one of the basic algorithms of image processing, can be used for stripe noise removal, image compression, image registration, etc. in processing remote sensing image. CUFFT function library is the FFT algorithm library based on CPU and FFTW. FFTW is a FFT algorithm developed based on CPU in PC platform, and is currently the fastest CPU based FFT algorithm function library. However there is a common problem that once the available memory or memory is less than the capacity of image, there will be out of memory or memory overflow when using the above two methods to realize image FFT arithmetic. To address this problem, a CPU and partitioning technology based Huge Remote Fast Fourier Transform (HRFFT) algorithm is proposed in this paper. By improving the FFT algorithm in CUFFT function library, the problem of out of memory and memory overflow is solved. Moreover, this method is proved rational by experiment combined with the CCD image of HJ-1A satellite. When applied to practical image processing, it improves effect of the image processing, speeds up the processing, which saves the time of computation and achieves sound result.

  12. Pyramidal Watershed Segmentation Algorithm for High-Resolution Remote Sensing Images Using Discrete Wavelet Transforms

    Directory of Open Access Journals (Sweden)

    K. Parvathi

    2009-01-01

    Full Text Available The watershed transformation is a useful morphological segmentation tool for a variety of grey-scale images. However, over segmentation and under segmentation have become the key problems for the conventional algorithm. In this paper, an efficient segmentation method for high-resolution remote sensing image analysis is presented. Wavelet analysis is one of the most popular techniques that can be used to detect local intensity variation and hence the wavelet transformation is used to analyze the image. Wavelet transform is applied to the image, producing detail (horizontal, vertical, and diagonal and Approximation coefficients. The image gradient with selective regional minima is estimated with the grey-scale morphology for the Approximation image at a suitable resolution, and then the watershed is applied to the gradient image to avoid over segmentation. The segmented image is projected up to high resolutions using the inverse wavelet transform. The watershed segmentation is applied to small subset size image, demanding less computational time. We have applied our new approach to analyze remote sensing images. The algorithm was implemented in MATLAB. Experimental results demonstrated the method to be effective.

  13. Surpassing the Theoretical 1-Norm Phase Transition in Compressive Sensing by Tuning the Smoothed L0 Algorithm

    DEFF Research Database (Denmark)

    Oxvig, Christian Schou; Pedersen, Patrick Steffen; Arildsen, Thomas

    2013-01-01

    Reconstruction of an undersampled signal is at the root of compressive sensing: when is an algorithm capable of reconstructing the signal? what quality is achievable? and how much time does reconstruction require? We have considered the worst-case performance of the smoothed ℓ0 norm reconstruction...... algorithm in a noiseless setup. Through an empirical tuning of its parameters, we have improved the phase transition (capabilities) of the algorithm for fixed quality and required time. In this paper, we present simulation results that show a phase transition surpassing that of the theoretical ℓ1 approach......: the proposed modified algorithm obtains 1-norm phase transition with greatly reduced required computation time....

  14. Resource-Efficient Fusion with Pre-Compensated Transmissions for Cooperative Spectrum Sensing

    Directory of Open Access Journals (Sweden)

    Dayan Adionel Guimarães

    2015-05-01

    Full Text Available Recently, a novel fusion scheme for cooperative spectrum sensing was proposed for saving resources in the control channel. Secondary users (SUs simultaneously report their decisions using binary modulations with the same carrier frequencies. The transmitted symbols add incoherently at the fusion centre (FC, leading to a larger set of symbols in which a subset is associated with the presence of the primary user (PU signal, and another subset is associated with the absence of such a signal. The decision criterion applied for discriminating these subsets works under the assumption that the channel gains are known at the FC. In this paper, we propose a new simultaneous transmission and decision scheme in which the task of channel estimation is shifted from the FC to the SUs, without the need for feeding-back of the estimates to the FC. The estimates are used at the SUs to pre-compensate for the reporting channel phase rotations and to partially compensate for the channel gains. This partial compensation is the result of signal clipping for peak-to-average power ratio (PAPR control. We show, analytically and with simulations, that this new scheme can produce large performance improvements, yet reduces the implementation complexity when compared with the original one.

  15. Broad spectrum pro-quorum-sensing molecules as inhibitors of virulence in vibrios.

    Directory of Open Access Journals (Sweden)

    Wai-Leung Ng

    Full Text Available Quorum sensing (QS is a bacterial cell-cell communication process that relies on the production and detection of extracellular signal molecules called autoinducers. QS allows bacteria to perform collective activities. Vibrio cholerae, a pathogen that causes an acute disease, uses QS to repress virulence factor production and biofilm formation. Thus, molecules that activate QS in V. cholerae have the potential to control pathogenicity in this globally important bacterium. Using a whole-cell high-throughput screen, we identified eleven molecules that activate V. cholerae QS: eight molecules are receptor agonists and three molecules are antagonists of LuxO, the central NtrC-type response regulator that controls the global V. cholerae QS cascade. The LuxO inhibitors act by an uncompetitive mechanism by binding to the pre-formed LuxO-ATP complex to inhibit ATP hydrolysis. Genetic analyses suggest that the inhibitors bind in close proximity to the Walker B motif. The inhibitors display broad-spectrum capability in activation of QS in Vibrio species that employ LuxO. To the best of our knowledge, these are the first molecules identified that inhibit the ATPase activity of a NtrC-type response regulator. Our discovery supports the idea that exploiting pro-QS molecules is a promising strategy for the development of novel anti-infectives.

  16. The Sense of Agency in Autism Spectrum Disorders: a Dissociation between Prospective and Retrospective Mechanisms?

    Directory of Open Access Journals (Sweden)

    Tiziana eZalla

    2015-09-01

    Full Text Available While a large number of studies have reported impairments in social and interpersonal abilities in individuals with autism spectrum disorder (ASD, relatively few studies have focused on self-related knowledge in this population. One of the processes implicated in the physical dimension of the Self is the sense of agency (SoA, i.e., the experience of initiating and controlling one’s own actions and producing desired changes in the world via these actions. So far, the few studies investigating SoA in ASD have reported contrasting results, with some showing spared, others impaired SoA. Here, we review the existing literature and suggest that the distinction between prospective and retrospective mechanisms of the SoA might help reconcile the existing findings. In the light of a multi-componential model of SoA, we propose the view that a specific impairment at the level of prospective mechanisms acting on internal agency signals (i.e., the intention, action selection, or command produced to achieve the goal may be responsible for the reduced SoA in ASD, along with spared retrospective mechanisms. Future research should shed light on the impact of abnormal SoA on social and self-related dysfunctions in ASD.

  17. Equal gain combining for cooperative spectrum sensing in cognitive radio networks

    KAUST Repository

    Hamza, Doha R.; Aï ssa, Sonia; Aniba, Ghassane

    2014-01-01

    are not tight. The cases of hard sensing and soft sensing are considered and we provide examples in which hard sensing is advantageous to soft sensing. We contrast the performance of SEGC with maximum ratio combining of the sensors' results and provide examples

  18. A Novel Object Tracking Algorithm Based on Compressed Sensing and Entropy of Information

    Directory of Open Access Journals (Sweden)

    Ding Ma

    2015-01-01

    Full Text Available Object tracking has always been a hot research topic in the field of computer vision; its purpose is to track objects with specific characteristics or representation and estimate the information of objects such as their locations, sizes, and rotation angles in the current frame. Object tracking in complex scenes will usually encounter various sorts of challenges, such as location change, dimension change, illumination change, perception change, and occlusion. This paper proposed a novel object tracking algorithm based on compressed sensing and information entropy to address these challenges. First, objects are characterized by the Haar (Haar-like and ORB features. Second, the dimensions of computation space of the Haar and ORB features are effectively reduced through compressed sensing. Then the above-mentioned features are fused based on information entropy. Finally, in the particle filter framework, an object location was obtained by selecting candidate object locations in the current frame from the local context neighboring the optimal locations in the last frame. Our extensive experimental results demonstrated that this method was able to effectively address the challenges of perception change, illumination change, and large area occlusion, which made it achieve better performance than existing approaches such as MIL and CT.

  19. Comparison of remote sensing algorithms for retrieval of suspended particulate matter concentration from reflectance in coastal waters

    Science.gov (United States)

    Freeman, Lauren A.; Ackleson, Steven G.; Rhea, William Joseph

    2017-10-01

    Suspended particulate matter (SPM) is a key environmental indicator for rivers, estuaries, and coastal waters, which can be calculated from remote sensing reflectance obtained by an airborne or satellite imager. Here, algorithms from prior studies are applied to a dataset of in-situ at surface hyperspectral remote sensing reflectance, collected in three geographic regions representing different water types. These data show the optically inherent exponential nature of the relationship between reflectance and sediment concentration. However, linear models are also shown to provide a reasonable estimate of sediment concentration when utilized with care in similar conditions to those under which the algorithms were developed, particularly at lower SPM values (0 to 20 mg/L). Fifteen published SPM algorithms are tested, returning strong correlations of R2>0.7, and in most cases, R2>0.8. Very low SPM values show weaker correlation with algorithm calculated SPM that is not wavelength dependent. None of the tested algorithms performs well for high SPM values (>30 mg/L), with most algorithms underestimating SPM. A shift toward a smaller number of simple exponential or linear models relating satellite remote sensing reflectance to suspended sediment concentration with regional consideration will greatly aid larger spatiotemporal studies of suspended sediment trends.

  20. ISTA-Net: Iterative Shrinkage-Thresholding Algorithm Inspired Deep Network for Image Compressive Sensing

    KAUST Repository

    Zhang, Jian

    2017-06-24

    Traditional methods for image compressive sensing (CS) reconstruction solve a well-defined inverse problem that is based on a predefined CS model, which defines the underlying structure of the problem and is generally solved by employing convergent iterative solvers. These optimization-based CS methods face the challenge of choosing optimal transforms and tuning parameters in their solvers, while also suffering from high computational complexity in most cases. Recently, some deep network based CS algorithms have been proposed to improve CS reconstruction performance, while dramatically reducing time complexity as compared to optimization-based methods. Despite their impressive results, the proposed networks (either with fully-connected or repetitive convolutional layers) lack any structural diversity and they are trained as a black box, void of any insights from the CS domain. In this paper, we combine the merits of both types of CS methods: the structure insights of optimization-based method and the performance/speed of network-based ones. We propose a novel structured deep network, dubbed ISTA-Net, which is inspired by the Iterative Shrinkage-Thresholding Algorithm (ISTA) for optimizing a general $l_1$ norm CS reconstruction model. ISTA-Net essentially implements a truncated form of ISTA, where all ISTA-Net parameters are learned end-to-end to minimize a reconstruction error in training. Borrowing more insights from the optimization realm, we propose an accelerated version of ISTA-Net, dubbed FISTA-Net, which is inspired by the fast iterative shrinkage-thresholding algorithm (FISTA). Interestingly, this acceleration naturally leads to skip connections in the underlying network design. Extensive CS experiments demonstrate that the proposed ISTA-Net and FISTA-Net outperform existing optimization-based and network-based CS methods by large margins, while maintaining a fast runtime.

  1. Green Cooperative Spectrum Sensing and Scheduling in Heterogeneous Cognitive Radio Networks

    KAUST Repository

    Celik, Abdulkadir; Kamal, Ahmed E.

    2016-01-01

    the homogeneity assumption. Based on these, a prioritized ordering heuristic is developed to order channels under the spectrum, energy, and spectrum-energy limited regimes. After that, a scheduling and assignment heuristic is proposed and is shown to perform very

  2. A new stellar spectrum interpolation algorithm and its application to Yunnan-III evolutionary population synthesis models

    Science.gov (United States)

    Cheng, Liantao; Zhang, Fenghui; Kang, Xiaoyu; Wang, Lang

    2018-05-01

    In evolutionary population synthesis (EPS) models, we need to convert stellar evolutionary parameters into spectra via interpolation in a stellar spectral library. For theoretical stellar spectral libraries, the spectrum grid is homogeneous on the effective-temperature and gravity plane for a given metallicity. It is relatively easy to derive stellar spectra. For empirical stellar spectral libraries, stellar parameters are irregularly distributed and the interpolation algorithm is relatively complicated. In those EPS models that use empirical stellar spectral libraries, different algorithms are used and the codes are often not released. Moreover, these algorithms are often complicated. In this work, based on a radial basis function (RBF) network, we present a new spectrum interpolation algorithm and its code. Compared with the other interpolation algorithms that are used in EPS models, it can be easily understood and is highly efficient in terms of computation. The code is written in MATLAB scripts and can be used on any computer system. Using it, we can obtain the interpolated spectra from a library or a combination of libraries. We apply this algorithm to several stellar spectral libraries (such as MILES, ELODIE-3.1 and STELIB-3.2) and give the integrated spectral energy distributions (ISEDs) of stellar populations (with ages from 1 Myr to 14 Gyr) by combining them with Yunnan-III isochrones. Our results show that the differences caused by the adoption of different EPS model components are less than 0.2 dex. All data about the stellar population ISEDs in this work and the RBF spectrum interpolation code can be obtained by request from the first author or downloaded from http://www1.ynao.ac.cn/˜zhangfh.

  3. Incorporating a constrained optimization algorithm into remote sensing/precision agriculture methodology

    Science.gov (United States)

    Moreenthaler, George W.; Khatib, Nader; Kim, Byoungsoo

    2003-08-01

    Optimization Algorithm" to further improve these processes will be presented. The objective function of the model will used to maximize the farmer's profit via increasing yields while decreasing environmental damage and decreasing applications of costly treatments. This model will incorporate information from Remote Sensing, from in-situ weather sources, from soil history, and from tacit farmer knowledge of the relative productivity of selected "Management Zones" of the farm, to provide incremental advice throughout the growing season on the optimum usage of water and chemical treatments.

  4. AHIMSA - Ad hoc histogram information measure sensing algorithm for feature selection in the context of histogram inspired clustering techniques

    Science.gov (United States)

    Dasarathy, B. V.

    1976-01-01

    An algorithm is proposed for dimensionality reduction in the context of clustering techniques based on histogram analysis. The approach is based on an evaluation of the hills and valleys in the unidimensional histograms along the different features and provides an economical means of assessing the significance of the features in a nonparametric unsupervised data environment. The method has relevance to remote sensing applications.

  5. Analysis of the moderate resolution imaging spectroradiometer contextual algorithm for small fire detection, Journal of Applied Remote Sensing Vol.3

    Science.gov (United States)

    W. Wang; J.J. Qu; X. Hao; Y. Liu

    2009-01-01

    In the southeastern United States, most wildland fires are of low intensity. A substantial number of these fires cannot be detected by the MODIS contextual algorithm. To improve the accuracy of fire detection for this region, the remote-sensed characteristics of these fires have to be...

  6. Correlation Wave-Front Sensing Algorithms for Shack-Hartmann-Based Adaptive Optics using a Point Source

    International Nuclear Information System (INIS)

    Poynee, L A

    2003-01-01

    Shack-Hartmann based Adaptive Optics system with a point-source reference normally use a wave-front sensing algorithm that estimates the centroid (center of mass) of the point-source image 'spot' to determine the wave-front slope. The centroiding algorithm suffers for several weaknesses. For a small number of pixels, the algorithm gain is dependent on spot size. The use of many pixels on the detector leads to significant propagation of read noise. Finally, background light or spot halo aberrations can skew results. In this paper an alternative algorithm that suffers from none of these problems is proposed: correlation of the spot with a ideal reference spot. The correlation method is derived and a theoretical analysis evaluates its performance in comparison with centroiding. Both simulation and data from real AO systems are used to illustrate the results. The correlation algorithm is more robust than centroiding, but requires more computation

  7. A Dynamic Spectrum Allocation Algorithm for a Maritime Cognitive Radio Communication System Based on a Queuing Model

    Directory of Open Access Journals (Sweden)

    Jingbo Zhang

    2017-09-01

    Full Text Available With the rapid development of maritime digital communication, the demand for spectrum resources is increasing, and building a maritime cognitive radio communication system is an effective solution. In this paper, the problem of how to effectively allocate the spectrum for secondary users (SUs with different priorities in a maritime cognitive radio communication system is studied. According to the characteristics of a maritime cognitive radio and existing research about cognitive radio systems, this paper establishes a centralized maritime cognitive radio communication model and creates a simplified queuing model with two queues for the communication model. In the view of the behaviors of SUs and primary users (PUs, we propose a dynamic spectrum allocation (DSA algorithm based on the system status, and analyze it with a two-dimensional Markov chain. Simulation results show that, when different types of SUs have similar arrival rates, the algorithm can vary the priority factor according to the change of users’ status in the system, so as to adjust the channel allocation, decreasing system congestion. The improvement of the algorithm is about 7–26%, and the specific improvement is negatively correlated with the SU arrival rate.

  8. Algorithms

    Indian Academy of Sciences (India)

    polynomial) division have been found in Vedic Mathematics which are dated much before Euclid's algorithm. A programming language Is used to describe an algorithm for execution on a computer. An algorithm expressed using a programming.

  9. Secure Cooperative Spectrum Sensing via a Novel User-Classification Scheme in Cognitive Radios for Future Communication Technologies

    Directory of Open Access Journals (Sweden)

    Muhammad Usman

    2015-05-01

    Full Text Available Future communication networks would be required to deliver data on a far greater scale than is known to us today, thus mandating the maximal utilization of the available radio spectrum using cognitive radios. In this paper, we have proposed a novel cooperative spectrum sensing approach for cognitive radios. In cooperative spectrum sensing, the fusion center relies on reports of the cognitive users to make a global decision. The global decision is obtained by assigning weights to the reports received from cognitive users. Computation of such weights requires prior information of the probability of detection and the probability of false alarms, which are not readily available in real scenarios. Further, the cognitive users are divided into reliable and unreliable categories based on their weighted energy by using some empirical threshold. In this paper, we propose a method to classify the cognitive users into reliable, neutral and unreliable categories without using any pre-defined or empirically-obtained threshold. Moreover, the computation of weights does not require the detection, or false alarm probabilities, or an estimate of these probabilities. Reliable cognitive users are assigned the highest weights; neutral cognitive users are assigned medium weights (less than the reliable and higher than the unreliable cognitive users’ weights; and unreliable users are assigned the least weights. We show the performance improvement of our proposed method through simulations by comparing it with the conventional cooperative spectrum sensing scheme through different metrics, like receiver operating characteristic (ROC curve and mean square error. For clarity, we also show the effect of malicious users on detection probability and false alarm probability individually through simulations.

  10. Incorporating a Constrained Optimization Algorithm into Remote- Sensing/Precision Agriculture Methodology

    Science.gov (United States)

    Morgenthaler, George; Khatib, Nader; Kim, Byoungsoo

    application of costly treatments. This model will incorporate information from remote sensing, in-situ weather sources, soil measurements, crop models, and tacit farmer knowledge of the relative productivity of the selected control regions of the farm to provide incremental advice throughout the growing season on water and chemical treatments. Genetic and meta-heuristic algorithms will be used to solve the constrained optimization problem that possesses complex constraints and a non-linear objective function. *

  11. An algorithm for hyperspectral remote sensing of aerosols: 1. Development of theoretical framework

    International Nuclear Information System (INIS)

    Hou, Weizhen; Wang, Jun; Xu, Xiaoguang; Reid, Jeffrey S.; Han, Dong

    2016-01-01

    This paper describes the first part of a series of investigations to develop algorithms for simultaneous retrieval of aerosol parameters and surface reflectance from a newly developed hyperspectral instrument, the GEOstationary Trace gas and Aerosol Sensor Optimization (GEO-TASO), by taking full advantage of available hyperspectral measurement information in the visible bands. We describe the theoretical framework of an inversion algorithm for the hyperspectral remote sensing of the aerosol optical properties, in which major principal components (PCs) for surface reflectance is assumed known, and the spectrally dependent aerosol refractive indices are assumed to follow a power-law approximation with four unknown parameters (two for real and two for imaginary part of refractive index). New capabilities for computing the Jacobians of four Stokes parameters of reflected solar radiation at the top of the atmosphere with respect to these unknown aerosol parameters and the weighting coefficients for each PC of surface reflectance are added into the UNified Linearized Vector Radiative Transfer Model (UNL-VRTM), which in turn facilitates the optimization in the inversion process. Theoretical derivations of the formulas for these new capabilities are provided, and the analytical solutions of Jacobians are validated against the finite-difference calculations with relative error less than 0.2%. Finally, self-consistency check of the inversion algorithm is conducted for the idealized green-vegetation and rangeland surfaces that were spectrally characterized by the U.S. Geological Survey digital spectral library. It shows that the first six PCs can yield the reconstruction of spectral surface reflectance with errors less than 1%. Assuming that aerosol properties can be accurately characterized, the inversion yields a retrieval of hyperspectral surface reflectance with an uncertainty of 2% (and root-mean-square error of less than 0.003), which suggests self-consistency in the

  12. A novel algorithm for image representation using discrete spectrum of the Schrödinger operator

    KAUST Repository

    Kaisserli, Zineb; Laleg-Kirati, Taous-Meriem; Lahmar-Benbernou, Amina

    2015-01-01

    This paper extends the recent signal analysis method based on the spectral analysis of the semi-classical Schrödinger operator to two dimensions. An algorithm based on the tensor product approach when writing the eigenfunctions of the semi-classical Schrödinger operator is proposed. The algorithm is described and the effect of some parameters on the convergence of this method are numerically studied. The performance of the algorithm is illustrated through some examples.

  13. A novel algorithm for image representation using discrete spectrum of the Schrödinger operator

    KAUST Repository

    Kaisserli, Zineb

    2015-05-01

    This paper extends the recent signal analysis method based on the spectral analysis of the semi-classical Schrödinger operator to two dimensions. An algorithm based on the tensor product approach when writing the eigenfunctions of the semi-classical Schrödinger operator is proposed. The algorithm is described and the effect of some parameters on the convergence of this method are numerically studied. The performance of the algorithm is illustrated through some examples.

  14. Partial Discharge Detection Using Low Cost RTL-SDR Model for Wideband Spectrum Sensing

    DEFF Research Database (Denmark)

    Mohamed, H.; Lazaridis, Pavlos; Upton, D.

    2016-01-01

    an optimal approach for PD signal analysis, and are very costly. In this paper an RTLSDR (Software Defined Radio) based spectrum analyser has been proposed in order to provide a potentially low cost solution for PD detection and monitoring. Initially, a portable spectrum analyser has been used for PD...

  15. Robust frequency diversity based algorithm for clutter noise reduction of ultrasonic signals using multiple sub-spectrum phase coherence

    Energy Technology Data Exchange (ETDEWEB)

    Gongzhang, R.; Xiao, B.; Lardner, T.; Gachagan, A. [Centre for Ultrasonic Engineering, University of Strathclyde, Glasgow, G1 1XW (United Kingdom); Li, M. [School of Engineering, University of Glasgow, Glasgow, G12 8QQ (United Kingdom)

    2014-02-18

    This paper presents a robust frequency diversity based algorithm for clutter reduction in ultrasonic A-scan waveforms. The performance of conventional spectral-temporal techniques like Split Spectrum Processing (SSP) is highly dependent on the parameter selection, especially when the signal to noise ratio (SNR) is low. Although spatial beamforming offers noise reduction with less sensitivity to parameter variation, phased array techniques are not always available. The proposed algorithm first selects an ascending series of frequency bands. A signal is reconstructed for each selected band in which a defect is present when all frequency components are in uniform sign. Combining all reconstructed signals through averaging gives a probability profile of potential defect position. To facilitate data collection and validate the proposed algorithm, Full Matrix Capture is applied on the austenitic steel and high nickel alloy (HNA) samples with 5MHz transducer arrays. When processing A-scan signals with unrefined parameters, the proposed algorithm enhances SNR by 20dB for both samples and consequently, defects are more visible in B-scan images created from the large amount of A-scan traces. Importantly, the proposed algorithm is considered robust, while SSP is shown to fail on the austenitic steel data and achieves less SNR enhancement on the HNA data.

  16. Energy Efficiency and SINR Maximization Beamformers for Spectrum Sharing With Sensing Information

    KAUST Repository

    Alabbasi, AbdulRahman; Rezki, Zouheir; Shihada, Basem

    2014-01-01

    an underlaying communication using adaptive beamforming schemes combined with sensing information to achieve optimal energy-efficient systems. The proposed schemes maximize EE and SINR metrics subject to cognitive radio and quality-of-service constraints

  17. Efficient Error Detection in Soft Data Fusion for Cooperative Spectrum Sensing

    KAUST Repository

    Saqib Bhatti, Dost Muhammad; Ahmed, Saleem; Saeed, Nasir; Shaikh, Bushra

    2018-01-01

    . For CSS, all SUs report their sensing information through reporting channel to the central base station called fusion center (FC). During transmission, some of the SUs are subjected to fading and shadowing, due to which the overall performance of CSS

  18. Exact performance of cooperative spectrum sensing for cognitive radios with quantized information under imperfect reporting channels

    KAUST Repository

    Ben Ghorbel, Mahdi; Nam, Haewoon; Alouini, Mohamed-Slim

    2013-01-01

    between the sensing channels to overcome the fading and shadowing effects which allows reduction of miss-detection and false alarm probabilities. Information can be exchanged between cooperating users in different formats from the binary hard information

  19. Multi-Objective Clustering Optimization for Multi-Channel Cooperative Spectrum Sensing in Heterogeneous Green CRNs

    KAUST Repository

    Celik, Abdulkadir; Kamal, Ahmed E.

    2016-01-01

    ) with heterogeneous sensing and reporting channel qualities. We approach this issue from macro and micro perspectives. Macro perspective groups SUs into clusters with the objectives: 1) total energy consumption minimization; 2) total throughput maximization; and 3

  20. Validation and Algorithms Comparative Study for Microwave Remote Sensing of Snow Depth over China

    International Nuclear Information System (INIS)

    Bin, C J; Qiu, Y B; Shi, L J

    2014-01-01

    In this study, five different snow algorithms (Chang algorithm, GSFC 96 algorithm, AMSR-E SWE algorithm, Improved Tibetan Plateau algorithm and Savoie algorithm) were selected to validate the accuracy of snow algorithms over China. These algorithms were compared for the accuracy of snow depth algorithms with AMSR-E brightness temperature data and ground measurements on February 10-12, 2010. Results showed that the GSFC 96 algorithm was more suitable in Xinjiang with the RMSE range from 6.85cm to 7.48 cm; in Inner Mongolia and Northeast China. Improved Tibetan Plateau algorithm is superior to the other four algorithms with the RMSE of 5.46cm∼6.11cm and 6.21cm∼7.83cm respectively; due to the lack of ground measurements, we couldn't get valid statistical results over the Tibetan Plateau. However, the mean relative error (MRE) of the selected algorithms was ranging from 37.95% to 189.13% in four study areas, which showed that the accuracy of the five snow depth algorithms is limited over China

  1. A novel power harmonic analysis method based on Nuttall-Kaiser combination window double spectrum interpolated FFT algorithm

    Science.gov (United States)

    Jin, Tao; Chen, Yiyang; Flesch, Rodolfo C. C.

    2017-11-01

    Harmonics pose a great threat to safe and economical operation of power grids. Therefore, it is critical to detect harmonic parameters accurately to design harmonic compensation equipment. The fast Fourier transform (FFT) is widely used for electrical popular power harmonics analysis. However, the barrier effect produced by the algorithm itself and spectrum leakage caused by asynchronous sampling often affects the harmonic analysis accuracy. This paper examines a new approach for harmonic analysis based on deducing the modifier formulas of frequency, phase angle, and amplitude, utilizing the Nuttall-Kaiser window double spectrum line interpolation method, which overcomes the shortcomings in traditional FFT harmonic calculations. The proposed approach is verified numerically and experimentally to be accurate and reliable.

  2. Algorithms

    Indian Academy of Sciences (India)

    to as 'divide-and-conquer'. Although there has been a large effort in realizing efficient algorithms, there are not many universally accepted algorithm design paradigms. In this article, we illustrate algorithm design techniques such as balancing, greedy strategy, dynamic programming strategy, and backtracking or traversal of ...

  3. A Proposed Algorithm for Improved Recognition and Treatment of the Depression/Anxiety Spectrum in Primary Care

    Science.gov (United States)

    Ballenger, James C.; Davidson, Jonathan R. T.; Lecrubier, Yves; Nutt, David J.

    2001-01-01

    The International Consensus Group on Depression and Anxiety has held 7 meetings over the last 3 years that focused on depression and specific anxiety disorders. During the course of the meeting series, a number of common themes have developed. At the last meeting of the Consensus Group, we reviewed these areas of commonality across the spectrum of depression and anxiety disorders. With the aim of improving the recognition and management of depression and anxiety in the primary care setting, we developed an algorithm that is presented in this article. We attempted to balance currently available scientific knowledge about the treatment of these disorders and to reformat it to provide an acceptable algorithm that meets the practical aspects of recognizing and treating these disorders in primary care. PMID:15014615

  4. Testing the performance of empirical remote sensing algorithms in the Baltic Sea waters with modelled and in situ reflectance data

    Directory of Open Access Journals (Sweden)

    Martin Ligi

    2017-01-01

    Full Text Available Remote sensing studies published up to now show that the performance of empirical (band-ratio type algorithms in different parts of the Baltic Sea is highly variable. Best performing algorithms are different in the different regions of the Baltic Sea. Moreover, there is indication that the algorithms have to be seasonal as the optical properties of phytoplankton assemblages dominating in spring and summer are different. We modelled 15,600 reflectance spectra using HydroLight radiative transfer model to test 58 previously published empirical algorithms. 7200 of the spectra were modelled using specific inherent optical properties (SIOPs of the open parts of the Baltic Sea in summer and 8400 with SIOPs of spring season. Concentration range of chlorophyll-a, coloured dissolved organic matter (CDOM and suspended matter used in the model simulations were based on the actually measured values available in literature. For each optically active constituent we added one concentration below actually measured minimum and one concentration above the actually measured maximum value in order to test the performance of the algorithms in wider range. 77 in situ reflectance spectra from rocky (Sweden and sandy (Estonia, Latvia coastal areas were used to evaluate the performance of the algorithms also in coastal waters. Seasonal differences in the algorithm performance were confirmed but we found also algorithms that can be used in both spring and summer conditions. The algorithms that use bands available on OLCI, launched in February 2016, are highlighted as this sensor will be available for Baltic Sea monitoring for coming decades.

  5. Balancing Inverted Pendulum by Angle Sensing Using Fuzzy Logic Supervised PID Controller Optimized by Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Ashutosh K. AGARWAL

    2011-10-01

    Full Text Available Genetic algorithms are robust search techniques based on the principles of evolution. A genetic algorithm maintains a population of encoded solutions and guides the population towards the optimum solution. This important property of genetic algorithm is used in this paper to stabilize the Inverted pendulum system. This paper highlights the application and stability of inverted pendulum using PID controller with fuzzy logic genetic algorithm supervisor . There are a large number of well established search techniques in use within the information technology industry. We propose a method to control inverted pendulum steady state error and overshoot using genetic algorithm technique.

  6. Experimental validation of a distributed algorithm for dynamic spectrum access in local area networks

    DEFF Research Database (Denmark)

    Tonelli, Oscar; Berardinelli, Gilberto; Tavares, Fernando Menezes Leitão

    2013-01-01

    Next generation wireless networks aim at a significant improvement of the spectral efficiency in order to meet the dramatic increase in data service demand. In local area scenarios user-deployed base stations are expected to take place, thus making the centralized planning of frequency resources...... activities with the Autonomous Component Carrier Selection (ACCS) algorithm, a distributed solution for interference management among small neighboring cells. A preliminary evaluation of the algorithm performance is provided considering its live execution on a software defined radio network testbed...

  7. Teaching Students with Autism Spectrum Disorders: Technology, Curriculum, and Common Sense

    Science.gov (United States)

    Ennis-Cole, Demetria

    2012-01-01

    Autism is a spectrum of disorders which comprises Asperger's Syndrome, Pervasive Developmental Delay-Not Otherwise Specified (PDD-NOS), Rett's Syndrome, Childhood Disintegrative Disorder, and Autistic Disorder. It affects 1 in 110 children (Center for Disease Control and Prevention, [CDC], 2011), and it is a complex neurological disorder that is…

  8. Outage Analysis of Spectrum-Sharing over M-Block Fading with Sensing Information

    KAUST Repository

    Alabbasi, Abdulrahman; Rezki, Zouheir; Shihada, Basem

    2016-01-01

    on the outage probability with tractable expressions. These bounds allow us to derive the exact diversity order of the secondary user’s outage probability. To further enhance the system’s performance, we also investigate the impact of including the sensing

  9. Vertical Jump Height Estimation Algorithm Based on Takeoff and Landing Identification Via Foot-Worn Inertial Sensing.

    Science.gov (United States)

    Wang, Jianren; Xu, Junkai; Shull, Peter B

    2018-03-01

    Vertical jump height is widely used for assessing motor development, functional ability, and motor capacity. Traditional methods for estimating vertical jump height rely on force plates or optical marker-based motion capture systems limiting assessment to people with access to specialized laboratories. Current wearable designs need to be attached to the skin or strapped to an appendage which can potentially be uncomfortable and inconvenient to use. This paper presents a novel algorithm for estimating vertical jump height based on foot-worn inertial sensors. Twenty healthy subjects performed countermovement jumping trials and maximum jump height was determined via inertial sensors located above the toe and under the heel and was compared with the gold standard maximum jump height estimation via optical marker-based motion capture. Average vertical jump height estimation errors from inertial sensing at the toe and heel were -2.2±2.1 cm and -0.4±3.8 cm, respectively. Vertical jump height estimation with the presented algorithm via inertial sensing showed excellent reliability at the toe (ICC(2,1)=0.98) and heel (ICC(2,1)=0.97). There was no significant bias in the inertial sensing at the toe, but proportional bias (b=1.22) and fixed bias (a=-10.23cm) were detected in inertial sensing at the heel. These results indicate that the presented algorithm could be applied to foot-worn inertial sensors to estimate maximum jump height enabling assessment outside of traditional laboratory settings, and to avoid bias errors, the toe may be a more suitable location for inertial sensor placement than the heel.

  10. Sensing across large-scale cognitive radio networks: Data processing, algorithms, and testbed for wireless tomography and moving target tracking

    Science.gov (United States)

    Bonior, Jason David

    As the use of wireless devices has become more widespread so has the potential for utilizing wireless networks for remote sensing applications. Regular wireless communication devices are not typically designed for remote sensing. Remote sensing techniques must be carefully tailored to the capabilities of these networks before they can be applied. Experimental verification of these techniques and algorithms requires robust yet flexible testbeds. In this dissertation, two experimental testbeds for the advancement of research into sensing across large-scale cognitive radio networks are presented. System architectures, implementations, capabilities, experimental verification, and performance are discussed. One testbed is designed for the collection of scattering data to be used in RF and wireless tomography research. This system is used to collect full complex scattering data using a vector network analyzer (VNA) and amplitude-only data using non-synchronous software-defined radios (SDRs). Collected data is used to experimentally validate a technique for phase reconstruction using semidefinite relaxation and demonstrate the feasibility of wireless tomography. The second testbed is a SDR network for the collection of experimental data. The development of tools for network maintenance and data collection is presented and discussed. A novel recursive weighted centroid algorithm for device-free target localization using the variance of received signal strength for wireless links is proposed. The signal variance resulting from a moving target is modeled as having contours related to Cassini ovals. This model is used to formulate recursive weights which reduce the influence of wireless links that are farther from the target location estimate. The algorithm and its implementation on this testbed are presented and experimental results discussed.

  11. Concept for a hyperspectral remote sensing algorithm for floating marine macro plastics

    NARCIS (Netherlands)

    Goddijn-Murphy, Lonneke; Peters, Steef; van Sebille, Erik; James, Neil A.; Gibb, Stuart

    2018-01-01

    There is growing global concern over the chemical, biological and ecological impact of plastics in the ocean. Remote sensing has the potential to provide long-term, global monitoring but for marine plastics it is still in its early stages. Some progress has been made in hyperspectral remote sensing

  12. Study on the effects of sample selection on spectral reflectance reconstruction based on the algorithm of compressive sensing

    International Nuclear Information System (INIS)

    Zhang, Leihong; Liang, Dong

    2016-01-01

    In order to solve the problem that reconstruction efficiency and precision is not high, in this paper different samples are selected to reconstruct spectral reflectance, and a new kind of spectral reflectance reconstruction method based on the algorithm of compressive sensing is provided. Four different color numbers of matte color cards such as the ColorChecker Color Rendition Chart and Color Checker SG, the copperplate paper spot color card of Panton, and the Munsell colors card are chosen as training samples, the spectral image is reconstructed respectively by the algorithm of compressive sensing and pseudo-inverse and Wiener, and the results are compared. These methods of spectral reconstruction are evaluated by root mean square error and color difference accuracy. The experiments show that the cumulative contribution rate and color difference of the Munsell colors card are better than those of the other three numbers of color cards in the same conditions of reconstruction, and the accuracy of the spectral reconstruction will be affected by the training sample of different numbers of color cards. The key technology of reconstruction means that the uniformity and representation of the training sample selection has important significance upon reconstruction. In this paper, the influence of the sample selection on the spectral image reconstruction is studied. The precision of the spectral reconstruction based on the algorithm of compressive sensing is higher than that of the traditional algorithm of spectral reconstruction. By the MATLAB simulation results, it can be seen that the spectral reconstruction precision and efficiency are affected by the different color numbers of the training sample. (paper)

  13. Recovering stellar population parameters via two full-spectrum fitting algorithms in the absence of model uncertainties

    Science.gov (United States)

    Ge, Junqiang; Yan, Renbin; Cappellari, Michele; Mao, Shude; Li, Hongyu; Lu, Youjun

    2018-05-01

    Using mock spectra based on Vazdekis/MILES library fitted within the wavelength region 3600-7350Å, we analyze the bias and scatter on the resulting physical parameters induced by the choice of fitting algorithms and observational uncertainties, but avoid effects of those model uncertainties. We consider two full-spectrum fitting codes: pPXF and STARLIGHT, in fitting for stellar population age, metallicity, mass-to-light ratio, and dust extinction. With pPXF we find that both the bias μ in the population parameters and the scatter σ in the recovered logarithmic values follows the expected trend μ ∝ σ ∝ 1/(S/N). The bias increases for younger ages and systematically makes recovered ages older, M*/Lr larger and metallicities lower than the true values. For reference, at S/N=30, and for the worst case (t = 108yr), the bias is 0.06 dex in M/Lr, 0.03 dex in both age and [M/H]. There is no significant dependence on either E(B-V) or the shape of the error spectrum. Moreover, the results are consistent for both our 1-SSP and 2-SSP tests. With the STARLIGHT algorithm, we find trends similar to pPXF, when the input E(B-V)values, with significantly underestimated dust extinction and [M/H], and larger ages and M*/Lr. Results degrade when moving from our 1-SSP to the 2-SSP tests. The STARLIGHT convergence to the true values can be improved by increasing Markov Chains and annealing loops to the "slow mode". For the same input spectrum, pPXF is about two order of magnitudes faster than STARLIGHT's "default mode" and about three order of magnitude faster than STARLIGHT's "slow mode".

  14. Optimization of Selected Remote Sensing Algorithms for Embedded NVIDIA Kepler GPU Architecture

    Science.gov (United States)

    Riha, Lubomir; Le Moigne, Jacqueline; El-Ghazawi, Tarek

    2015-01-01

    This paper evaluates the potential of embedded Graphic Processing Units in the Nvidias Tegra K1 for onboard processing. The performance is compared to a general purpose multi-core CPU and full fledge GPU accelerator. This study uses two algorithms: Wavelet Spectral Dimension Reduction of Hyperspectral Imagery and Automated Cloud-Cover Assessment (ACCA) Algorithm. Tegra K1 achieved 51 for ACCA algorithm and 20 for the dimension reduction algorithm, as compared to the performance of the high-end 8-core server Intel Xeon CPU with 13.5 times higher power consumption.

  15. A compressed sensing based reconstruction algorithm for synchrotron source propagation-based X-ray phase contrast computed tomography

    Energy Technology Data Exchange (ETDEWEB)

    Melli, Seyed Ali, E-mail: sem649@mail.usask.ca [Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK (Canada); Wahid, Khan A. [Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK (Canada); Babyn, Paul [Department of Medical Imaging, University of Saskatchewan, Saskatoon, SK (Canada); Montgomery, James [College of Medicine, University of Saskatchewan, Saskatoon, SK (Canada); Snead, Elisabeth [Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, SK (Canada); El-Gayed, Ali [College of Medicine, University of Saskatchewan, Saskatoon, SK (Canada); Pettitt, Murray; Wolkowski, Bailey [College of Agriculture and Bioresources, University of Saskatchewan, Saskatoon, SK (Canada); Wesolowski, Michal [Department of Medical Imaging, University of Saskatchewan, Saskatoon, SK (Canada)

    2016-01-11

    Synchrotron source propagation-based X-ray phase contrast computed tomography is increasingly used in pre-clinical imaging. However, it typically requires a large number of projections, and subsequently a large radiation dose, to produce high quality images. To improve the applicability of this imaging technique, reconstruction algorithms that can reduce the radiation dose and acquisition time without degrading image quality are needed. The proposed research focused on using a novel combination of Douglas–Rachford splitting and randomized Kaczmarz algorithms to solve large-scale total variation based optimization in a compressed sensing framework to reconstruct 2D images from a reduced number of projections. Visual assessment and quantitative performance evaluations of a synthetic abdomen phantom and real reconstructed image of an ex-vivo slice of canine prostate tissue demonstrate that the proposed algorithm is competitive in reconstruction process compared with other well-known algorithms. An additional potential benefit of reducing the number of projections would be reduction of time for motion artifact to occur if the sample moves during image acquisition. Use of this reconstruction algorithm to reduce the required number of projections in synchrotron source propagation-based X-ray phase contrast computed tomography is an effective form of dose reduction that may pave the way for imaging of in-vivo samples.

  16. A sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image

    Science.gov (United States)

    Li, Jing; Xie, Weixin; Pei, Jihong

    2018-03-01

    Sea-land segmentation is one of the key technologies of sea target detection in remote sensing images. At present, the existing algorithms have the problems of low accuracy, low universality and poor automatic performance. This paper puts forward a sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image removing island. Firstly, the coastline data is extracted and all of land area is labeled by using the geographic information in large-field remote sensing image. Secondly, three features (local entropy, local texture and local gradient mean) is extracted in the sea-land border area, and the three features combine a 3D feature vector. And then the MultiGaussian model is adopted to describe 3D feature vectors of sea background in the edge of the coastline. Based on this multi-gaussian sea background model, the sea pixels and land pixels near coastline are classified more precise. Finally, the coarse segmentation result and the fine segmentation result are fused to obtain the accurate sea-land segmentation. Comparing and analyzing the experimental results by subjective vision, it shows that the proposed method has high segmentation accuracy, wide applicability and strong anti-disturbance ability.

  17. Compressive power spectrum sensing for vibration-based output-only system identification of structural systems in the presence of noise

    Science.gov (United States)

    Tau Siesakul, Bamrung; Gkoktsi, Kyriaki; Giaralis, Agathoklis

    2015-05-01

    Motivated by the need to reduce monetary and energy consumption costs of wireless sensor networks in undertaking output-only/operational modal analysis of engineering structures, this paper considers a multi-coset analog-toinformation converter for structural system identification from acceleration response signals of white noise excited linear damped structures sampled at sub-Nyquist rates. The underlying natural frequencies, peak gains in the frequency domain, and critical damping ratios of the vibrating structures are estimated directly from the sub-Nyquist measurements and, therefore, the computationally demanding signal reconstruction step is by-passed. This is accomplished by first employing a power spectrum blind sampling (PSBS) technique for multi-band wide sense stationary stochastic processes in conjunction with deterministic non-uniform multi-coset sampling patterns derived from solving a weighted least square optimization problem. Next, modal properties are derived by the standard frequency domain peak picking algorithm. Special attention is focused on assessing the potential of the adopted PSBS technique, which poses no sparsity requirements to the sensed signals, to derive accurate estimates of modal structural system properties from noisy sub- Nyquist measurements. To this aim, sub-Nyquist sampled acceleration response signals corrupted by various levels of additive white noise pertaining to a benchmark space truss structure with closely spaced natural frequencies are obtained within an efficient Monte Carlo simulation-based framework. Accurate estimates of natural frequencies and reasonable estimates of local peak spectral ordinates and critical damping ratios are derived from measurements sampled at about 70% below the Nyquist rate and for SNR as low as 0db demonstrating that the adopted approach enjoys noise immunity.

  18. Density-independent algorithm for sensing moisture content of sawdust based on reflection measurements

    Science.gov (United States)

    A density-independent algorithm for moisture content determination in sawdust, based on a one-port reflection measurement technique is proposed for the first time. Performance of this algorithm is demonstrated through measurement of the dielectric properties of sawdust with an open-ended haft-mode s...

  19. Study on algorithm of process neural network for soft sensing in sewage disposal system

    Science.gov (United States)

    Liu, Zaiwen; Xue, Hong; Wang, Xiaoyi; Yang, Bin; Lu, Siying

    2006-11-01

    A new method of soft sensing based on process neural network (PNN) for sewage disposal system is represented in the paper. PNN is an extension of traditional neural network, in which the inputs and outputs are time-variation. An aggregation operator is introduced to process neuron, and it makes the neuron network has the ability to deal with the information of space-time two dimensions at the same time, so the data processing enginery of biological neuron is imitated better than traditional neuron. Process neural network with the structure of three layers in which hidden layer is process neuron and input and output are common neurons for soft sensing is discussed. The intelligent soft sensing based on PNN may be used to fulfill measurement of the effluent BOD (Biochemical Oxygen Demand) from sewage disposal system, and a good training result of soft sensing was obtained by the method.

  20. High efficient optical remote sensing images acquisition for nano-satellite: reconstruction algorithms

    Science.gov (United States)

    Liu, Yang; Li, Feng; Xin, Lei; Fu, Jie; Huang, Puming

    2017-10-01

    Large amount of data is one of the most obvious features in satellite based remote sensing systems, which is also a burden for data processing and transmission. The theory of compressive sensing(CS) has been proposed for almost a decade, and massive experiments show that CS has favorable performance in data compression and recovery, so we apply CS theory to remote sensing images acquisition. In CS, the construction of classical sensing matrix for all sparse signals has to satisfy the Restricted Isometry Property (RIP) strictly, which limits applying CS in practical in image compression. While for remote sensing images, we know some inherent characteristics such as non-negative, smoothness and etc.. Therefore, the goal of this paper is to present a novel measurement matrix that breaks RIP. The new sensing matrix consists of two parts: the standard Nyquist sampling matrix for thumbnails and the conventional CS sampling matrix. Since most of sun-synchronous based satellites fly around the earth 90 minutes and the revisit cycle is also short, lots of previously captured remote sensing images of the same place are available in advance. This drives us to reconstruct remote sensing images through a deep learning approach with those measurements from the new framework. Therefore, we propose a novel deep convolutional neural network (CNN) architecture which takes in undersampsing measurements as input and outputs an intermediate reconstruction image. It is well known that the training procedure to the network costs long time, luckily, the training step can be done only once, which makes the approach attractive for a host of sparse recovery problems.

  1. 抑制扩频系统中窄带干扰的新卡尔曼滤波算法%New Kalman Filtering Algorithm for Narrowband Interference Suppression in Spread Spectrum Systems

    Institute of Scientific and Technical Information of China (English)

    许光辉; 胡光锐

    2005-01-01

    A new Kalman filtering algorithm based on estimation of spread spectrum signal before suppression of narrowband interference (NBI) in spread spectrum systems, using the dependence of autoregressive (AR) interference, is presented compared with performance of the ACM nonlinear filtering algorithm, simulation results show that the proposed algorithm has preferable performance, there is about 5 dB SNR improvement in average.

  2. Implementation of an Optical-Wireless Network with Spectrum Sensing and Dynamic Resource Allocation Using Optically Controlled Reconfigurable Antennas

    Directory of Open Access Journals (Sweden)

    E. Raimundo-Neto

    2014-01-01

    Full Text Available This work proposes the concept and reports the implementation of an adaptive and cognitive radio over fiber architecture. It is aimed at dealing with the new demands for convergent networks by means of simultaneously providing the functionalities of multiband radiofrequency spectrum sensing, dynamic resource allocation, and centralized processing capability, as well as the use of optically controlled reconfigurable antennas and radio over fiber technology. The performance of this novel and innovative architecture has been evaluated in a geographically distributed optical-wireless network under real conditions and for different fiber lengths. Experimental results demonstrate reach extension of more than 40 times and an enhancement of more than 30 dB in the carrier to interference plus noise ratio parameter.

  3. Fast mapping algorithm of lighting spectrum and GPS coordinates for a large area

    Science.gov (United States)

    Lin, Chih-Wei; Hsu, Ke-Fang; Hwang, Jung-Min

    2016-09-01

    In this study, we propose a fast rebuild technology for evaluating light quality in large areas. Outdoor light quality, which is measured by illuminance uniformity and the color rendering index, is difficult to conform after improvement. We develop an algorithm for a lighting quality mapping system and coordinates using a micro spectrometer and GPS tracker integrated with a quadcopter or unmanned aerial vehicle. After cruising at a constant altitude, lighting quality data is transmitted and immediately mapped to evaluate the light quality in a large area.

  4. Algorithms

    Indian Academy of Sciences (India)

    ticians but also forms the foundation of computer science. Two ... with methods of developing algorithms for solving a variety of problems but ... applications of computers in science and engineer- ... numerical calculus are as important. We will ...

  5. Developing an eye-tracking algorithm as a potential tool for early diagnosis of autism spectrum disorder in children.

    Directory of Open Access Journals (Sweden)

    Natalia I Vargas-Cuentas

    Full Text Available Autism spectrum disorder (ASD currently affects nearly 1 in 160 children worldwide. In over two-thirds of evaluations, no validated diagnostics are used and gold standard diagnostic tools are used in less than 5% of evaluations. Currently, the diagnosis of ASD requires lengthy and expensive tests, in addition to clinical confirmation. Therefore, fast, cheap, portable, and easy-to-administer screening instruments for ASD are required. Several studies have shown that children with ASD have a lower preference for social scenes compared with children without ASD. Based on this, eye-tracking and measurement of gaze preference for social scenes has been used as a screening tool for ASD. Currently available eye-tracking software requires intensive calibration, training, or holding of the head to prevent interference with gaze recognition limiting its use in children with ASD.In this study, we designed a simple eye-tracking algorithm that does not require calibration or head holding, as a platform for future validation of a cost-effective ASD potential screening instrument. This system operates on a portable and inexpensive tablet to measure gaze preference of children for social compared to abstract scenes. A child watches a one-minute stimulus video composed of a social scene projected on the left side and an abstract scene projected on the right side of the tablet's screen. We designed five stimulus videos by changing the social/abstract scenes. Every child observed all the five videos in random order. We developed an eye-tracking algorithm that calculates the child's gaze preference for the social and abstract scenes, estimated as the percentage of the accumulated time that the child observes the left or right side of the screen, respectively. Twenty-three children without a prior history of ASD and 8 children with a clinical diagnosis of ASD were evaluated. The recorded video of the child´s eye movement was analyzed both manually by an observer

  6. Energy Efficiency and SINR Maximization Beamformers for Spectrum Sharing With Sensing Information

    KAUST Repository

    Alabbasi, Abdulrahman

    2014-09-01

    In this paper, we consider a cognitive radio multi-input-multi-output environment, in which we adapt our beamformer to maximize both energy efficiency (EE) and signal-to-interference-plus-noise ratio (SINR) metrics. Our design considers an underlaying communication using adaptive beamforming schemes combined with sensing information to achieve optimal energy-efficient systems. The proposed schemes maximize EE and SINR metrics subject to cognitive radio and quality-of-service constraints. The analysis of the proposed schemes is classified into two categories based on knowledge of the secondary-transmitter-to-primary-receiver channel. Since the optimizations of EE and SINR problems are not convex problems, we transform them into a standard semidefinite programming (SDP) form to guarantee that the optimal solutions are global. An analytical solution is provided for one scheme, while the second scheme is left in a standard SDP form. Selected numerical results are used to quantify the impact of the sensing information on the proposed schemes compared to the benchmark ones.

  7. A Novel Compressed Sensing Method for Magnetic Resonance Imaging: Exponential Wavelet Iterative Shrinkage-Thresholding Algorithm with Random Shift

    Directory of Open Access Journals (Sweden)

    Yudong Zhang

    2016-01-01

    Full Text Available Aim. It can help improve the hospital throughput to accelerate magnetic resonance imaging (MRI scanning. Patients will benefit from less waiting time. Task. In the last decade, various rapid MRI techniques on the basis of compressed sensing (CS were proposed. However, both computation time and reconstruction quality of traditional CS-MRI did not meet the requirement of clinical use. Method. In this study, a novel method was proposed with the name of exponential wavelet iterative shrinkage-thresholding algorithm with random shift (abbreviated as EWISTARS. It is composed of three successful components: (i exponential wavelet transform, (ii iterative shrinkage-thresholding algorithm, and (iii random shift. Results. Experimental results validated that, compared to state-of-the-art approaches, EWISTARS obtained the least mean absolute error, the least mean-squared error, and the highest peak signal-to-noise ratio. Conclusion. EWISTARS is superior to state-of-the-art approaches.

  8. A Novel Compressed Sensing Method for Magnetic Resonance Imaging: Exponential Wavelet Iterative Shrinkage-Thresholding Algorithm with Random Shift

    Science.gov (United States)

    Zhang, Yudong; Yang, Jiquan; Yang, Jianfei; Liu, Aijun; Sun, Ping

    2016-01-01

    Aim. It can help improve the hospital throughput to accelerate magnetic resonance imaging (MRI) scanning. Patients will benefit from less waiting time. Task. In the last decade, various rapid MRI techniques on the basis of compressed sensing (CS) were proposed. However, both computation time and reconstruction quality of traditional CS-MRI did not meet the requirement of clinical use. Method. In this study, a novel method was proposed with the name of exponential wavelet iterative shrinkage-thresholding algorithm with random shift (abbreviated as EWISTARS). It is composed of three successful components: (i) exponential wavelet transform, (ii) iterative shrinkage-thresholding algorithm, and (iii) random shift. Results. Experimental results validated that, compared to state-of-the-art approaches, EWISTARS obtained the least mean absolute error, the least mean-squared error, and the highest peak signal-to-noise ratio. Conclusion. EWISTARS is superior to state-of-the-art approaches. PMID:27066068

  9. Improved discrete swarm intelligence algorithms for endmember extraction from hyperspectral remote sensing images

    Science.gov (United States)

    Su, Yuanchao; Sun, Xu; Gao, Lianru; Li, Jun; Zhang, Bing

    2016-10-01

    Endmember extraction is a key step in hyperspectral unmixing. A new endmember extraction framework is proposed for hyperspectral endmember extraction. The proposed approach is based on the swarm intelligence (SI) algorithm, where discretization is used to solve the SI algorithm because pixels in a hyperspectral image are naturally defined within a discrete space. Moreover, a "distance" factor is introduced into the objective function to limit the endmember numbers which is generally limited in real scenarios, while traditional SI algorithms likely produce superabundant spectral signatures, which generally belong to the same classes. Three endmember extraction methods are proposed based on the artificial bee colony, ant colony optimization, and particle swarm optimization algorithms. Experiments with both simulated and real hyperspectral images indicate that the proposed framework can improve the accuracy of endmember extraction.

  10. A robust cooperative spectrum sensing scheme based on Dempster-Shafer theory and trustworthiness degree calculation in cognitive radio networks

    Science.gov (United States)

    Wang, Jinlong; Feng, Shuo; Wu, Qihui; Zheng, Xueqiang; Xu, Yuhua; Ding, Guoru

    2014-12-01

    Cognitive radio (CR) is a promising technology that brings about remarkable improvement in spectrum utilization. To tackle the hidden terminal problem, cooperative spectrum sensing (CSS) which benefits from the spatial diversity has been studied extensively. Since CSS is vulnerable to the attacks initiated by malicious secondary users (SUs), several secure CSS schemes based on Dempster-Shafer theory have been proposed. However, the existing works only utilize the current difference of SUs, such as the difference in SNR or similarity degree, to evaluate the trustworthiness of each SU. As the current difference is only one-sided and sometimes inaccurate, the statistical information contained in each SU's historical behavior should not be overlooked. In this article, we propose a robust CSS scheme based on Dempster-Shafer theory and trustworthiness degree calculation. It is carried out in four successive steps, which are basic probability assignment (BPA), trustworthiness degree calculation, selection and adjustment of BPA, and combination by Dempster-Shafer rule, respectively. Our proposed scheme evaluates the trustworthiness degree of SUs from both current difference aspect and historical behavior aspect and exploits Dempster-Shafer theory's potential to establish a `soft update' approach for the reputation value maintenance. It can not only differentiate malicious SUs from honest ones based on their historical behaviors but also reserve the current difference for each SU to achieve a better real-time performance. Abundant simulation results have validated that the proposed scheme outperforms the existing ones under the impact of different attack patterns and different number of malicious SUs.

  11. Dense sampled transmission matrix for high resolution angular spectrum imaging through turbid media via compressed sensing (Conference Presentation)

    Science.gov (United States)

    Jang, Hwanchol; Yoon, Changhyeong; Choi, Wonshik; Eom, Tae Joong; Lee, Heung-No

    2016-03-01

    We provide an approach to improve the quality of image reconstruction in wide-field imaging through turbid media (WITM). In WITM, a calibration stage which measures the transmission matrix (TM), the set of responses of turbid medium to a set of plane waves with different incident angles, is preceded to the image recovery. Then, the TM is used for estimation of object image in image recovery stage. In this work, we aim to estimate highly resolved angular spectrum and use it for high quality image reconstruction. To this end, we propose to perform a dense sampling for TM measurement in calibration stage with finer incident angle spacing. In conventional approaches, incident angle spacing is made to be large enough so that the columns in TM are out of memory effect of turbid media. Otherwise, the columns in TM are correlated and the inversion becomes difficult. We employ compressed sensing (CS) for a successful high resolution angular spectrum recovery with dense sampled TM. CS is a relatively new information acquisition and reconstruction framework and has shown to provide superb performance in ill-conditioned inverse problems. We observe that the image quality metrics such as contrast-to-noise ratio and mean squared error are improved and the perceptual image quality is improved with reduced speckle noise in the reconstructed image. This results shows that the WITM performance can be improved only by executing dense sampling in the calibration stage and with an efficient signal reconstruction framework without elaborating the overall optical imaging systems.

  12. Efficient Retrieval of Massive Ocean Remote Sensing Images via a Cloud-Based Mean-Shift Algorithm.

    Science.gov (United States)

    Yang, Mengzhao; Song, Wei; Mei, Haibin

    2017-07-23

    The rapid development of remote sensing (RS) technology has resulted in the proliferation of high-resolution images. There are challenges involved in not only storing large volumes of RS images but also in rapidly retrieving the images for ocean disaster analysis such as for storm surges and typhoon warnings. In this paper, we present an efficient retrieval of massive ocean RS images via a Cloud-based mean-shift algorithm. Distributed construction method via the pyramid model is proposed based on the maximum hierarchical layer algorithm and used to realize efficient storage structure of RS images on the Cloud platform. We achieve high-performance processing of massive RS images in the Hadoop system. Based on the pyramid Hadoop distributed file system (HDFS) storage method, an improved mean-shift algorithm for RS image retrieval is presented by fusion with the canopy algorithm via Hadoop MapReduce programming. The results show that the new method can achieve better performance for data storage than HDFS alone and WebGIS-based HDFS. Speedup and scaleup are very close to linear changes with an increase of RS images, which proves that image retrieval using our method is efficient.

  13. Discrimination of liver cancer in cellular level based on backscatter micro-spectrum with PCA algorithm and BP neural network

    Science.gov (United States)

    Yang, Jing; Wang, Cheng; Cai, Gan; Dong, Xiaona

    2016-10-01

    The incidence and mortality rate of the primary liver cancer are very high and its postoperative metastasis and recurrence have become important factors to the prognosis of patients. Circulating tumor cells (CTC), as a new tumor marker, play important roles in the early diagnosis and individualized treatment. This paper presents an effective method to distinguish liver cancer based on the cellular scattering spectrum, which is a non-fluorescence technique based on the fiber confocal microscopic spectrometer. Combining the principal component analysis (PCA) with back propagation (BP) neural network were utilized to establish an automatic recognition model for backscatter spectrum of the liver cancer cells from blood cell. PCA was applied to reduce the dimension of the scattering spectral data which obtained by the fiber confocal microscopic spectrometer. After dimensionality reduction by PCA, a neural network pattern recognition model with 2 input layer nodes, 11 hidden layer nodes, 3 output nodes was established. We trained the network with 66 samples and also tested it. Results showed that the recognition rate of the three types of cells is more than 90%, the relative standard deviation is only 2.36%. The experimental results showed that the fiber confocal microscopic spectrometer combining with the algorithm of PCA and BP neural network can automatically identify the liver cancer cell from the blood cells. This will provide a better tool for investigating the metastasis of liver cancers in vivo, the biology metabolic characteristics of liver cancers and drug transportation. Additionally, it is obviously referential in practical application.

  14. Development and Implementation of an Advanced Power Management Algorithm for Electronic Load Sensing on a Telehandler

    DEFF Research Database (Denmark)

    Hansen, Rico Hjerm; Andersen, Torben Ole; Pedersen, Henrik C.

    2010-01-01

    The relevance of electronic control of mobile hydraulic systems is increasing as hydraulic components are implemented with more electrical sensors and actuators. This paper presents how the traditional Hydro-mechanical Load Sensing (HLS) control of a specific mobile hydraulic application......, a telehandler, can be replaced with electronic control, i.e. Electronic Load Sensing (ELS). The motivation is the potential of improved dynamic performance and power utilization, along with reducing the mechanical complexity by moving traditional hydro-mechanical implemented features such as pressure control...

  15. Using remote sensing in support of environmental management: A framework for selecting products, algorithms and methods

    CSIR Research Space (South Africa)

    De Klerk, HM

    2016-11-01

    Full Text Available Traditionally, to map environmental features using remote sensing, practitioners will use training data to develop models on various satellite data sets using a number of classification approaches and use test data to select a single ‘best performer...

  16. Algorithms

    Indian Academy of Sciences (India)

    algorithm design technique called 'divide-and-conquer'. One of ... Turtle graphics, September. 1996. 5. ... whole list named 'PO' is a pointer to the first element of the list; ..... Program for computing matrices X and Y and placing the result in C *).

  17. Algorithms

    Indian Academy of Sciences (India)

    algorithm that it is implicitly understood that we know how to generate the next natural ..... Explicit comparisons are made in line (1) where maximum and minimum is ... It can be shown that the function T(n) = 3/2n -2 is the solution to the above ...

  18. Remote Sensing of Coral Bleaching Using Temperature and Light: Progress towards an Operational Algorithm

    Directory of Open Access Journals (Sweden)

    William Skirving

    2017-12-01

    Full Text Available The National Oceanic and Atmospheric Administration’s Coral Reef Watch program developed and operates several global satellite products to monitor bleaching-level heat stress. While these products have a proven ability to predict the onset of most mass coral bleaching events, they occasionally miss events; inaccurately predict the severity of some mass coral bleaching events; or report false alarms. These products are based solely on temperature and yet coral bleaching is known to result from both temperature and light stress. This study presents a novel methodology (still under development, which combines temperature and light into a single measure of stress to predict the onset and severity of mass coral bleaching. We describe here the biological basis of the Light Stress Damage (LSD algorithm under development. Then by using empirical relationships derived in separate experiments conducted in mesocosm facilities in the Mexican Caribbean we parameterize the LSD algorithm and demonstrate that it is able to describe three past bleaching events from the Great Barrier Reef (GBR. For this limited example, the LSD algorithm was able to better predict differences in the severity of the three past GBR bleaching events, quantifying the contribution of light to reduce or exacerbate the impact of heat stress. The new Light Stress Damage algorithm we present here is potentially a significant step forward in the evolution of satellite-based bleaching products.

  19. ISTA-Net: Iterative Shrinkage-Thresholding Algorithm Inspired Deep Network for Image Compressive Sensing

    KAUST Repository

    Zhang, Jian; Ghanem, Bernard

    2017-01-01

    and the performance/speed of network-based ones. We propose a novel structured deep network, dubbed ISTA-Net, which is inspired by the Iterative Shrinkage-Thresholding Algorithm (ISTA) for optimizing a general $l_1$ norm CS reconstruction model. ISTA-Net essentially

  20. Sensing the Worst: Neurophenomenological Perspectives on Neutral Stimuli Misperception in Schizophrenia Spectrum

    Directory of Open Access Journals (Sweden)

    Mariateresa Sestito

    2017-06-01

    Full Text Available While investigating social cognitive impairments in schizophrenia, prominent evidence has been found that patients with schizophrenia show a tendency to misclassify neutral stimuli as negatively valenced. Within this population, patients presenting delusions are more prone to this phenomenon. In a previous study, Schizophrenia spectrum (SzSp patients rated positive, negative and neutral stimuli that were multimodally presented, while assessed with a checklist exploring anomalous subjective experiences and evaluated for positive and negative symptomatology. In the present work, we aimed to further explore the relationship between neutral stimuli misperception, anomalous experiences and positive/negative symptoms in SzSp patients. To this end, we adopted a dimensional approach by reconstructing from available data: (1 four a priori scales representing essential dimensions of SzSp experiential pathology following Parnas et al. (2005; and (2 five clinically meaningful factors to describe illness severity derived by Toomey et al. (1997. Results showed that although overall patients correctly recognized the target emotions, those who misinterpreted neutral auditory cues as negatively valenced also presented higher scores in Perplexity (PY, Bizarre Delusions (BD and Disorganization (Di dimensions. Moreover, a positive association between BD and both PY and Self-Disorder (SD dimensions emerged, suggesting that psychotic symptoms may be directly linked to patients’ subjectivity. In an attempt to comprehensively capture the multilayered neutral stimuli misperception phenomenon in SzSp, we aimed at bridging phenomenology and neurobiology by connecting the levels of molecular neurochemistry (i.e., altered dopaminergic neurotransmission, system neuroscience (aberrant salience of perceptual details and psychopathology (the chain involving hyper-reflexivity, self-disorders and the emergence of delusions.

  1. Algorithms

    Indian Academy of Sciences (India)

    will become clear in the next article when we discuss a simple logo like programming language. ... Rod B may be used as an auxiliary store. The problem is to find an algorithm which performs this task. ... No disks are moved from A to Busing C as auxiliary rod. • move _disk (A, C);. (No + l)th disk is moved from A to C directly ...

  2. Development of algorithms for building inventory compilation through remote sensing and statistical inferencing

    Science.gov (United States)

    Sarabandi, Pooya

    Building inventories are one of the core components of disaster vulnerability and loss estimations models, and as such, play a key role in providing decision support for risk assessment, disaster management and emergency response efforts. In may parts of the world inclusive building inventories, suitable for the use in catastrophe models cannot be found. Furthermore, there are serious shortcomings in the existing building inventories that include incomplete or out-dated information on critical attributes as well as missing or erroneous values for attributes. In this dissertation a set of methodologies for updating spatial and geometric information of buildings from single and multiple high-resolution optical satellite images are presented. Basic concepts, terminologies and fundamentals of 3-D terrain modeling from satellite images are first introduced. Different sensor projection models are then presented and sources of optical noise such as lens distortions are discussed. An algorithm for extracting height and creating 3-D building models from a single high-resolution satellite image is formulated. The proposed algorithm is a semi-automated supervised method capable of extracting attributes such as longitude, latitude, height, square footage, perimeter, irregularity index and etc. The associated errors due to the interactive nature of the algorithm are quantified and solutions for minimizing the human-induced errors are proposed. The height extraction algorithm is validated against independent survey data and results are presented. The validation results show that an average height modeling accuracy of 1.5% can be achieved using this algorithm. Furthermore, concept of cross-sensor data fusion for the purpose of 3-D scene reconstruction using quasi-stereo images is developed in this dissertation. The developed algorithm utilizes two or more single satellite images acquired from different sensors and provides the means to construct 3-D building models in a more

  3. Remote Sensing of Cloud Top Height from SEVIRI: Analysis of Eleven Current Retrieval Algorithms

    Science.gov (United States)

    Hamann, U.; Walther, A.; Baum, B.; Bennartz, R.; Bugliaro, L.; Derrien, M.; Francis, P. N.; Heidinger, A.; Joro, S.; Kniffka, A.; hide

    2014-01-01

    The role of clouds remains the largest uncertainty in climate projections. They influence solar and thermal radiative transfer and the earth's water cycle. Therefore, there is an urgent need for accurate cloud observations to validate climate models and to monitor climate change. Passive satellite imagers measuring radiation at visible to thermal infrared (IR) wavelengths provide a wealth of information on cloud properties. Among others, the cloud top height (CTH) - a crucial parameter to estimate the thermal cloud radiative forcing - can be retrieved. In this paper we investigate the skill of ten current retrieval algorithms to estimate the CTH using observations from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard Meteosat Second Generation (MSG). In the first part we compare ten SEVIRI cloud top pressure (CTP) data sets with each other. The SEVIRI algorithms catch the latitudinal variation of the CTP in a similar way. The agreement is better in the extratropics than in the tropics. In the tropics multi-layer clouds and thin cirrus layers complicate the CTP retrieval, whereas a good agreement among the algorithms is found for trade wind cumulus, marine stratocumulus and the optically thick cores of the deep convective system. In the second part of the paper the SEVIRI retrievals are compared to CTH observations from the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) and Cloud Profiling Radar (CPR) instruments. It is important to note that the different measurement techniques cause differences in the retrieved CTH data. SEVIRI measures a radiatively effective CTH, while the CTH of the active instruments is derived from the return time of the emitted radar or lidar signal. Therefore, some systematic differences are expected. On average the CTHs detected by the SEVIRI algorithms are 1.0 to 2.5 kilometers lower than CALIOP observations, and the correlation coefficients between the SEVIRI and the CALIOP data sets range between 0.77 and 0

  4. Hybrid wavefront sensing and image correction algorithm for imaging through turbulent media

    Science.gov (United States)

    Wu, Chensheng; Robertson Rzasa, John; Ko, Jonathan; Davis, Christopher C.

    2017-09-01

    It is well known that passive image correction of turbulence distortions often involves using geometry-dependent deconvolution algorithms. On the other hand, active imaging techniques using adaptive optic correction should use the distorted wavefront information for guidance. Our work shows that a hybrid hardware-software approach is possible to obtain accurate and highly detailed images through turbulent media. The processing algorithm also takes much fewer iteration steps in comparison with conventional image processing algorithms. In our proposed approach, a plenoptic sensor is used as a wavefront sensor to guide post-stage image correction on a high-definition zoomable camera. Conversely, we show that given the ground truth of the highly detailed image and the plenoptic imaging result, we can generate an accurate prediction of the blurred image on a traditional zoomable camera. Similarly, the ground truth combined with the blurred image from the zoomable camera would provide the wavefront conditions. In application, our hybrid approach can be used as an effective way to conduct object recognition in a turbulent environment where the target has been significantly distorted or is even unrecognizable.

  5. Improved Wallis Dodging Algorithm for Large-Scale Super-Resolution Reconstruction Remote Sensing Images

    Directory of Open Access Journals (Sweden)

    Chong Fan

    2017-03-01

    Full Text Available A sub-block algorithm is usually applied in the super-resolution (SR reconstruction of images because of limitations in computer memory. However, the sub-block SR images can hardly achieve a seamless image mosaicking because of the uneven distribution of brightness and contrast among these sub-blocks. An effectively improved weighted Wallis dodging algorithm is proposed, aiming at the characteristic that SR reconstructed images are gray images with the same size and overlapping region. This algorithm can achieve consistency of image brightness and contrast. Meanwhile, a weighted adjustment sequence is presented to avoid the spatial propagation and accumulation of errors and the loss of image information caused by excessive computation. A seam line elimination method can share the partial dislocation in the seam line to the entire overlapping region with a smooth transition effect. Subsequently, the improved method is employed to remove the uneven illumination for 900 SR reconstructed images of ZY-3. Then, the overlapping image mosaic method is adopted to accomplish a seamless image mosaic based on the optimal seam line.

  6. Parallel Algorithm for GPU Processing; for use in High Speed Machine Vision Sensing of Cotton Lint Trash

    Directory of Open Access Journals (Sweden)

    Mathew G. Pelletier

    2008-02-01

    Full Text Available One of the main hurdles standing in the way of optimal cleaning of cotton lint isthe lack of sensing systems that can react fast enough to provide the control system withreal-time information as to the level of trash contamination of the cotton lint. This researchexamines the use of programmable graphic processing units (GPU as an alternative to thePC’s traditional use of the central processing unit (CPU. The use of the GPU, as analternative computation platform, allowed for the machine vision system to gain asignificant improvement in processing time. By improving the processing time, thisresearch seeks to address the lack of availability of rapid trash sensing systems and thusalleviate a situation in which the current systems view the cotton lint either well before, orafter, the cotton is cleaned. This extended lag/lead time that is currently imposed on thecotton trash cleaning control systems, is what is responsible for system operators utilizing avery large dead-band safety buffer in order to ensure that the cotton lint is not undercleaned.Unfortunately, the utilization of a large dead-band buffer results in the majority ofthe cotton lint being over-cleaned which in turn causes lint fiber-damage as well assignificant losses of the valuable lint due to the excessive use of cleaning machinery. Thisresearch estimates that upwards of a 30% reduction in lint loss could be gained through theuse of a tightly coupled trash sensor to the cleaning machinery control systems. Thisresearch seeks to improve processing times through the development of a new algorithm forcotton trash sensing that allows for implementation on a highly parallel architecture.Additionally, by moving the new parallel algorithm onto an alternative computing platform,the graphic processing unit “GPU”, for processing of the cotton trash images, a speed up ofover 6.5 times, over optimized code running on the PC’s central processing

  7. 3D noise power spectrum applied on clinical MDCT scanners: effects of reconstruction algorithms and reconstruction filters

    Science.gov (United States)

    Miéville, Frédéric A.; Bolard, Gregory; Benkreira, Mohamed; Ayestaran, Paul; Gudinchet, François; Bochud, François; Verdun, Francis R.

    2011-03-01

    The noise power spectrum (NPS) is the reference metric for understanding the noise content in computed tomography (CT) images. To evaluate the noise properties of clinical multidetector (MDCT) scanners, local 2D and 3D NPSs were computed for different acquisition reconstruction parameters. A 64- and a 128-MDCT scanners were employed. Measurements were performed on a water phantom in axial and helical acquisition modes. CT dose index was identical for both installations. Influence of parameters such as the pitch, the reconstruction filter (soft, standard and bone) and the reconstruction algorithm (filtered-back projection (FBP), adaptive statistical iterative reconstruction (ASIR)) were investigated. Images were also reconstructed in the coronal plane using a reformat process. Then 2D and 3D NPS methods were computed. In axial acquisition mode, the 2D axial NPS showed an important magnitude variation as a function of the z-direction when measured at the phantom center. In helical mode, a directional dependency with lobular shape was observed while the magnitude of the NPS was kept constant. Important effects of the reconstruction filter, pitch and reconstruction algorithm were observed on 3D NPS results for both MDCTs. With ASIR, a reduction of the NPS magnitude and a shift of the NPS peak to the low frequency range were visible. 2D coronal NPS obtained from the reformat images was impacted by the interpolation when compared to 2D coronal NPS obtained from 3D measurements. The noise properties of volume measured in last generation MDCTs was studied using local 3D NPS metric. However, impact of the non-stationarity noise effect may need further investigations.

  8. Improved ocean-color remote sensing in the Arctic using the POLYMER algorithm

    Science.gov (United States)

    Frouin, Robert; Deschamps, Pierre-Yves; Ramon, Didier; Steinmetz, François

    2012-10-01

    Atmospheric correction of ocean-color imagery in the Arctic brings some specific challenges that the standard atmospheric correction algorithm does not address, namely low solar elevation, high cloud frequency, multi-layered polar clouds, presence of ice in the field-of-view, and adjacency effects from highly reflecting surfaces covered by snow and ice and from clouds. The challenges may be addressed using a flexible atmospheric correction algorithm, referred to as POLYMER (Steinmetz and al., 2011). This algorithm does not use a specific aerosol model, but fits the atmospheric reflectance by a polynomial with a non spectral term that accounts for any non spectral scattering (clouds, coarse aerosol mode) or reflection (glitter, whitecaps, small ice surfaces within the instrument field of view), a spectral term with a law in wavelength to the power -1 (fine aerosol mode), and a spectral term with a law in wavelength to the power -4 (molecular scattering, adjacency effects from clouds and white surfaces). Tests are performed on selected MERIS imagery acquired over Arctic Seas. The derived ocean properties, i.e., marine reflectance and chlorophyll concentration, are compared with those obtained with the standard MEGS algorithm. The POLYMER estimates are more realistic in regions affected by the ice environment, e.g., chlorophyll concentration is higher near the ice edge, and spatial coverage is substantially increased. Good retrievals are obtained in the presence of thin clouds, with ocean-color features exhibiting spatial continuity from clear to cloudy regions. The POLYMER estimates of marine reflectance agree better with in situ measurements than the MEGS estimates. Biases are 0.001 or less in magnitude, except at 412 and 443 nm, where they reach 0.005 and 0.002, respectively, and root-mean-squared difference decreases from 0.006 at 412 nm to less than 0.001 at 620 and 665 nm. A first application to MODIS imagery is presented, revealing that the POLYMER algorithm is

  9. A Comparison of Compressed Sensing and Sparse Recovery Algorithms Applied to Simulation Data

    Directory of Open Access Journals (Sweden)

    Ya Ju Fan

    2016-08-01

    Full Text Available The move toward exascale computing for scientific simulations is placing new demands on compression techniques. It is expected that the I/O system will not be able to support the volume of data that is expected to be written out. To enable quantitative analysis and scientific discovery, we are interested in techniques that compress high-dimensional simulation data and can provide perfect or near-perfect reconstruction.  In this paper, we explore the use of compressed sensing (CS techniques to reduce the size of the data before they are written out. Using large-scale simulation data, we investigate how the sufficient sparsity condition and the contrast in the data affect the quality of reconstruction and the degree of compression.  We provide suggestions for the practical implementation of CS techniques and compare them with other sparse recovery methods. Our results show that despite longer times for reconstruction, compressed sensing techniques can provide near perfect reconstruction over a range of data with varying sparsity.

  10. CHAM: a fast algorithm of modelling non-linear matter power spectrum in the sCreened HAlo Model

    Science.gov (United States)

    Hu, Bin; Liu, Xue-Wen; Cai, Rong-Gen

    2018-05-01

    We present a fast numerical screened halo model algorithm (CHAM, which stands for the sCreened HAlo Model) for modelling non-linear power spectrum for the alternative models to Λ cold dark matter. This method has three obvious advantages. First of all, it is not being restricted to a specific dark energy/modified gravity model. In principle, all of the screened scalar-tensor theories can be applied. Secondly, the least assumptions are made in the calculation. Hence, the physical picture is very easily understandable. Thirdly, it is very predictable and does not rely on the calibration from N-body simulation. As an example, we show the case of the Hu-Sawicki f(R) gravity. In this case, the typical CPU time with the current parallel PYTHON script (eight threads) is roughly within 10 min. The resulting spectra are in a good agreement with N-body data within a few percentage accuracy up to k ˜ 1 h Mpc-1.

  11. [Study of building quantitative analysis model for chlorophyll in winter wheat with reflective spectrum using MSC-ANN algorithm].

    Science.gov (United States)

    Liang, Xue; Ji, Hai-yan; Wang, Peng-xin; Rao, Zhen-hong; Shen, Bing-hui

    2010-01-01

    Preprocess method of multiplicative scatter correction (MSC) was used to reject noises in the original spectra produced by the environmental physical factor effectively, then the principal components of near-infrared spectroscopy were calculated by nonlinear iterative partial least squares (NIPALS) before building the back propagation artificial neural networks method (BP-ANN), and the numbers of principal components were calculated by the method of cross validation. The calculated principal components were used as the inputs of the artificial neural networks model, and the artificial neural networks model was used to find the relation between chlorophyll in winter wheat and reflective spectrum, which can predict the content of chlorophyll in winter wheat. The correlation coefficient (r) of calibration set was 0.9604, while the standard deviation (SD) and relative standard deviation (RSD) was 0.187 and 5.18% respectively. The correlation coefficient (r) of predicted set was 0.9600, and the standard deviation (SD) and relative standard deviation (RSD) was 0.145 and 4.21% respectively. It means that the MSC-ANN algorithm can reject noises in the original spectra produced by the environmental physical factor effectively and set up an exact model to predict the contents of chlorophyll in living leaves veraciously to replace the classical method and meet the needs of fast analysis of agricultural products.

  12. Remote sensing algorithm for surface evapotranspiration considering landscape and statistical effects on mixed pixels

    Directory of Open Access Journals (Sweden)

    Z. Q. Peng

    2016-11-01

    Full Text Available Evapotranspiration (ET plays an important role in surface–atmosphere interactions and can be monitored using remote sensing data. However, surface heterogeneity, including the inhomogeneity of landscapes and surface variables, significantly affects the accuracy of ET estimated from satellite data. The objective of this study is to assess and reduce the uncertainties resulting from surface heterogeneity in remotely sensed ET using Chinese HJ-1B satellite data, which is of 30 m spatial resolution in VIS/NIR bands and 300 m spatial resolution in the thermal-infrared (TIR band. A temperature-sharpening and flux aggregation scheme (TSFA was developed to obtain accurate heat fluxes from the HJ-1B satellite data. The IPUS (input parameter upscaling and TRFA (temperature resampling and flux aggregation methods were used to compare with the TSFA in this study. The three methods represent three typical schemes used to handle mixed pixels from the simplest to the most complex. IPUS handles all surface variables at coarse resolution of 300 m in this study, TSFA handles them at 30 m resolution, and TRFA handles them at 30 and 300 m resolution, which depends on the actual spatial resolution. Analyzing and comparing the three methods can help us to get a better understanding of spatial-scale errors in remote sensing of surface heat fluxes. In situ data collected during HiWATER-MUSOEXE (Multi-Scale Observation Experiment on Evapotranspiration over heterogeneous land surfaces of the Heihe Watershed Allied Telemetry Experimental Research were used to validate and analyze the methods. ET estimated by TSFA exhibited the best agreement with in situ observations, and the footprint validation results showed that the R2, MBE, and RMSE values of the sensible heat flux (H were 0.61, 0.90, and 50.99 W m−2, respectively, and those for the latent heat flux (LE were 0.82, −20.54, and 71.24 W m−2, respectively. IPUS yielded the largest errors

  13. Creating Aerosol Types from CHemistry (CATCH): A New Algorithm to Extend the Link Between Remote Sensing and Models

    Science.gov (United States)

    Dawson, K. W.; Meskhidze, N.; Burton, S. P.; Johnson, M. S.; Kacenelenbogen, M. S.; Hostetler, C. A.; Hu, Y.

    2017-11-01

    Current remote sensing methods can identify aerosol types within an atmospheric column, presenting an opportunity to incrementally bridge the gap between remote sensing and models. Here a new algorithm was designed for Creating Aerosol Types from CHemistry (CATCH). CATCH-derived aerosol types—dusty mix, maritime, urban, smoke, and fresh smoke—are based on first-generation airborne High Spectral Resolution Lidar (HSRL-1) retrievals during the Ship-Aircraft Bio-Optical Research (SABOR) campaign, July/August 2014. CATCH is designed to derive aerosol types from model output of chemical composition. CATCH-derived aerosol types are determined by multivariate clustering of model-calculated variables that have been trained using retrievals of aerosol types from HSRL-1. CATCH-derived aerosol types (with the exception of smoke) compare well with HSRL-1 retrievals during SABOR with an average difference in aerosol optical depth (AOD) methods. In the future, spaceborne HSRL-1 and CATCH can be used to gain insight into chemical composition of aerosol types, reducing uncertainties in estimates of aerosol radiative forcing.

  14. A General Algorithm for Robot Formations Using Local Sensing and Minimal Communication

    DEFF Research Database (Denmark)

    Fredslund, Jakob; Matarić, Maja J

    2002-01-01

    the friend in the sensor's field of view. We also present a general analytical measure for evaluating formations and apply it to the position data from both simulation and physical robot experiments. We used two lasers to track the physical robots to obtain ground truth validation data....... simulation exper- iments, and 40+ experiments with four physical robots, showing the viability of our approach. The key idea is that each robot keeps a single friend at a desired angle , using some appropriate sensor. By panning the sensor by degrees, the goal for all formations be- comes simply to center......We study the problem of achieving global behavior in a group of distributed robots using only local sensing and minimal communication, in the context of formations. The goal is to have mobile robots establish and maintain some predetermined geo- metric shape. We report results from extensive...

  15. A Real-Time Smooth Weighted Data Fusion Algorithm for Greenhouse Sensing Based on Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Tengyue Zou

    2017-11-01

    Full Text Available Wireless sensor networks are widely used to acquire environmental parameters to support agricultural production. However, data variation and noise caused by actuators often produce complex measurement conditions. These factors can lead to nonconformity in reporting samples from different nodes and cause errors when making a final decision. Data fusion is well suited to reduce the influence of actuator-based noise and improve automation accuracy. A key step is to identify the sensor nodes disturbed by actuator noise and reduce their degree of participation in the data fusion results. A smoothing value is introduced and a searching method based on Prim’s algorithm is designed to help obtain stable sensing data. A voting mechanism with dynamic weights is then proposed to obtain the data fusion result. The dynamic weighting process can sharply reduce the influence of actuator noise in data fusion and gradually condition the data to normal levels over time. To shorten the data fusion time in large networks, an acceleration method with prediction is also presented to reduce the data collection time. A real-time system is implemented on STMicroelectronics STM32F103 and NORDIC nRF24L01 platforms and the experimental results verify the improvement provided by these new algorithms.

  16. An algorithm for sensing venous oxygenation using ultrasound-modulated light enhanced by microbubbles

    Science.gov (United States)

    Honeysett, Jack E.; Stride, Eleanor; Deng, Jing; Leung, Terence S.

    2012-02-01

    Near-infrared spectroscopy (NIRS) can provide an estimate of the mean oxygen saturation in tissue. This technique is limited by optical scattering, which reduces the spatial resolution of the measurement, and by absorption, which makes the measurement insensitive to oxygenation changes in larger deep blood vessels relative to that in the superficial tissue. Acousto-optic (AO) techniques which combine focused ultrasound (US) with diffuse light have been shown to improve the spatial resolution as a result of US-modulation of the light signal, however this technique still suffers from low signal-to-noise when detecting a signal from regions of high optical absorption. Combining an US contrast agent with this hybrid technique has been proposed to amplify an AO signal. Microbubbles are a clinical contrast agent used in diagnostic US for their ability to resonate in a sound field: in this work we also make use of their optical scattering properties (modelled using Mie theory). A perturbation Monte Carlo (pMC) model of light transport in a highly absorbing blood vessel containing microbubbles surrounded by tissue is used to calculate the AO signal detected on the top surface of the tissue. An algorithm based on the modified Beer-Lambert law is derived which expresses intravenous oxygen saturation in terms of an AO signal. This is used to determine the oxygen saturation in the blood vessel from a dual wavelength microbubble-contrast AO measurement. Applying this algorithm to the simulation data shows that the venous oxygen saturation is accurately recovered, and this measurement is robust to changes in the oxygenation of the superficial tissue layer.

  17. Molecular genetic analysis of the calcium sensing receptor gene in patients clinically suspected to have familial hypocalciuric hypercalcemia: phenotypic variation and mutation spectrum in a Danish population

    DEFF Research Database (Denmark)

    Nissen, Peter H; Christensen, Signe E; Heickendorff, Lene

    2007-01-01

    CONTEXT: The autosomal dominantly inherited condition familial hypocalciuric hypercalcemia (FHH) is characterized by elevated plasma calcium levels, relative or absolute hypocalciuria, and normal to moderately elevated plasma PTH. The condition is difficult to distinguish clinically from primary...... hyperparathyroidism and is caused by inactivating mutations in the calcium sensing receptor (CASR) gene. OBJECTIVE: We sought to define the mutation spectrum of the CASR gene in a Danish FHH population and to establish genotype-phenotype relationships regarding the different mutations. DESIGN AND PARTICIPANTS...

  18. New Quality Control Algorithm Based on GNSS Sensing Data for a Bridge Health Monitoring System

    Directory of Open Access Journals (Sweden)

    Jae Kang Lee

    2016-05-01

    Full Text Available This research introduces an improvement plan for the reliability of Global Navigation Satellite System (GNSS positioning solutions. It should be considered the most suitable methodology in terms of the adjustment and positioning of GNSS in order to maximize the utilization of GNSS applications. Though various studies have been conducted with regards to Bridge Health Monitoring System (BHMS based on GNSS, the outliers which depend on the signal reception environment could not be considered until now. Since these outliers may be connected to GNSS data collected from major bridge members, which can reduce the reliability of a whole monitoring system through the delivery of false information, they should be detected and eliminated in the previous adjustment stage. In this investigation, the Detection, Identification, Adaptation (DIA technique was applied and implemented through an algorithm. Moreover, it can be directly applied to GNSS data collected from long span cable stayed bridges and most of outliers were efficiently detected and eliminated simultaneously. By these effects, the reliability of GNSS should be enormously improved. Improvement on GNSS positioning accuracy is directly linked to the safety of bridges itself, and at the same time, the reliability of monitoring systems in terms of the system operation can also be increased.

  19. Remote sensing algorithm for sea surface CO2 in the Baltic Sea

    Science.gov (United States)

    Parard, G.; Charantonis, A. A.; Rutgerson, A.

    2014-08-01

    Studies of coastal seas in Europe have brought forth the high variability in the CO2 system. This high variability, generated by the complex mechanisms driving the CO2 fluxes makes their accurate estimation an arduous task. This is more pronounced in the Baltic Sea, where the mechanisms driving the fluxes have not been as highly detailed as in the open oceans. In adition, the joint availability of in-situ measurements of CO2 and of sea-surface satellite data is limited in the area. In this paper, a combination of two existing methods (Self-Organizing-Maps and Multiple Linear regression) is used to estimate ocean surface pCO2 in the Baltic Sea from remotely sensed surface temperature, chlorophyll, coloured dissolved organic matter, net primary production and mixed layer depth. The outputs of this research have an horizontal resolution of 4 km, and cover the period from 1998 to 2011. The reconstructed pCO2 values over the validation data set have a correlation of 0.93 with the in-situ measurements, and a root mean square error is of 38 μatm. The removal of any of the satellite parameters degraded this reconstruction of the CO2 flux, and we chose therefore to complete any missing data through statistical imputation. The CO2 maps produced by this method also provide a confidence level of the reconstruction at each grid point. The results obtained are encouraging given the sparsity of available data and we expect to be able to produce even more accurate reconstructions in the coming years, in view of the predicted acquisitions of new data.

  20. A coupled remote sensing and the Surface Energy Balance with Topography Algorithm (SEBTA to estimate actual evapotranspiration over heterogeneous terrain

    Directory of Open Access Journals (Sweden)

    Z. Q. Gao

    2011-01-01

    Full Text Available Evapotranspiration (ET may be used as an ecological indicator to address the ecosystem complexity. The accurate measurement of ET is of great significance for studying environmental sustainability, global climate changes, and biodiversity. Remote sensing technologies are capable of monitoring both energy and water fluxes on the surface of the Earth. With this advancement, existing models, such as SEBAL, S_SEBI and SEBS, enable us to estimate the regional ET with limited temporal and spatial coverage in the study areas. This paper extends the existing modeling efforts with the inclusion of new components for ET estimation at different temporal and spatial scales under heterogeneous terrain with varying elevations, slopes and aspects. Following a coupled remote sensing and surface energy balance approach, this study emphasizes the structure and function of the Surface Energy Balance with Topography Algorithm (SEBTA. With the aid of the elevation and landscape information, such as slope and aspect parameters derived from the digital elevation model (DEM, and the vegetation cover derived from satellite images, the SEBTA can account for the dynamic impacts of heterogeneous terrain and changing land cover with some varying kinetic parameters (i.e., roughness and zero-plane displacement. Besides, the dry and wet pixels can be recognized automatically and dynamically in image processing thereby making the SEBTA more sensitive to derive the sensible heat flux for ET estimation. To prove the application potential, the SEBTA was carried out to present the robust estimates of 24 h solar radiation over time, which leads to the smooth simulation of the ET over seasons in northern China where the regional climate and vegetation cover in different seasons compound the ET calculations. The SEBTA was validated by the measured data at the ground level. During validation, it shows that the consistency index reached 0.92 and the correlation coefficient was 0.87.

  1. Comparison of RF spectrum prediction methods for dynamic spectrum access

    Science.gov (United States)

    Kovarskiy, Jacob A.; Martone, Anthony F.; Gallagher, Kyle A.; Sherbondy, Kelly D.; Narayanan, Ram M.

    2017-05-01

    Dynamic spectrum access (DSA) refers to the adaptive utilization of today's busy electromagnetic spectrum. Cognitive radio/radar technologies require DSA to intelligently transmit and receive information in changing environments. Predicting radio frequency (RF) activity reduces sensing time and energy consumption for identifying usable spectrum. Typical spectrum prediction methods involve modeling spectral statistics with Hidden Markov Models (HMM) or various neural network structures. HMMs describe the time-varying state probabilities of Markov processes as a dynamic Bayesian network. Neural Networks model biological brain neuron connections to perform a wide range of complex and often non-linear computations. This work compares HMM, Multilayer Perceptron (MLP), and Recurrent Neural Network (RNN) algorithms and their ability to perform RF channel state prediction. Monte Carlo simulations on both measured and simulated spectrum data evaluate the performance of these algorithms. Generalizing spectrum occupancy as an alternating renewal process allows Poisson random variables to generate simulated data while energy detection determines the occupancy state of measured RF spectrum data for testing. The results suggest that neural networks achieve better prediction accuracy and prove more adaptable to changing spectral statistics than HMMs given sufficient training data.

  2. The Spatio-Temporal Modeling of Urban Growth Using Remote Sensing and Intelligent Algorithms, Case of Mahabad, Iran

    Directory of Open Access Journals (Sweden)

    Alì Soltani

    2013-06-01

    Full Text Available The simulation of urban growth can be considered as a useful way for analyzing the complex process of urban physical evolution. The aim of this study is to model and simulate the complex patterns of land use change by utilizing remote sensing and artificial intelligence techniques in the fast growing city of Mahabad, north-west of Iran which encountered with several environmental subsequences. The key subject is how to allocate optimized weight into effective parameters upon urban growth and subsequently achieving an improved simulation. Artificial Neural Networks (ANN algorithm was used to allocate the weight via an iteration approach. In this way, weight allocation was carried out by the ANN training accomplishing through time-series satellite images representing urban growth process. Cellular Automata (CA was used as the principal motor of the model and then ANN applied to find suitable scale of parameters and relations between potential factors affecting urban growth. The general accuracy of the suggested model and obtained Fuzzy Kappa Coefficient confirms achieving better results than classic CA models in simulating nonlinear urban evolution process.

  3. The Spectrum Analysis Solution (SAS) System: Theoretical Analysis, Hardware Design and Implementation.

    Science.gov (United States)

    Narayanan, Ram M; Pooler, Richard K; Martone, Anthony F; Gallagher, Kyle A; Sherbondy, Kelly D

    2018-02-22

    This paper describes a multichannel super-heterodyne signal analyzer, called the Spectrum Analysis Solution (SAS), which performs multi-purpose spectrum sensing to support spectrally adaptive and cognitive radar applications. The SAS operates from ultrahigh frequency (UHF) to the S-band and features a wideband channel with eight narrowband channels. The wideband channel acts as a monitoring channel that can be used to tune the instantaneous band of the narrowband channels to areas of interest in the spectrum. The data collected from the SAS has been utilized to develop spectrum sensing algorithms for the budding field of spectrum sharing (SS) radar. Bandwidth (BW), average total power, percent occupancy (PO), signal-to-interference-plus-noise ratio (SINR), and power spectral entropy (PSE) have been examined as metrics for the characterization of the spectrum. These metrics are utilized to determine a contiguous optimal sub-band (OSB) for a SS radar transmission in a given spectrum for different modalities. Three OSB algorithms are presented and evaluated: the spectrum sensing multi objective (SS-MO), the spectrum sensing with brute force PSE (SS-BFE), and the spectrum sensing multi-objective with brute force PSE (SS-MO-BFE).

  4. A Spectrum-Matching and Look-Up-Table Approach to Interpretation of Hyperspectral Remote-Sensing Data

    National Research Council Canada - National Science Library

    Mobley, Curtis

    2004-01-01

    .... The LUT methodology works as follows. First, a database of remote-sensing reflectance (R(sub rs) spectra corresponding to various water depths, bottom reflectance spectra, and water-column inherent optical properties...

  5. An empirical algorithm to estimate spectral average cosine of underwater light field from remote sensing data in coastal oceanic waters.

    Digital Repository Service at National Institute of Oceanography (India)

    Talaulika, M.; Suresh, T.; Desa, E.S.; Inamdar, A.

    parameters from the coastal waters off Goa, India, and eastern Arabian Sea and the optical parameters derived using the radiative transfer code using these measured data. The algorithm was compared with two earlier reported empirical algorithms of Haltrin...

  6. Comparison of Different Machine Learning Algorithms for Lithological Mapping Using Remote Sensing Data and Morphological Features: A Case Study in Kurdistan Region, NE Iraq

    Science.gov (United States)

    Othman, Arsalan; Gloaguen, Richard

    2015-04-01

    Topographic effects and complex vegetation cover hinder lithology classification in mountain regions based not only in field, but also in reflectance remote sensing data. The area of interest "Bardi-Zard" is located in the NE of Iraq. It is part of the Zagros orogenic belt, where seven lithological units outcrop and is known for its chromite deposit. The aim of this study is to compare three machine learning algorithms (MLAs): Maximum Likelihood (ML), Support Vector Machines (SVM), and Random Forest (RF) in the context of a supervised lithology classification task using Advanced Space-borne Thermal Emission and Reflection radiometer (ASTER) satellite, its derived, spatial information (spatial coordinates) and geomorphic data. We emphasize the enhancement in remote sensing lithological mapping accuracy that arises from the integration of geomorphic features and spatial information (spatial coordinates) in classifications. This study identifies that RF is better than ML and SVM algorithms in almost the sixteen combination datasets, which were tested. The overall accuracy of the best dataset combination with the RF map for the all seven classes reach ~80% and the producer and user's accuracies are ~73.91% and 76.09% respectively while the kappa coefficient is ~0.76. TPI is more effective with SVM algorithm than an RF algorithm. This paper demonstrates that adding geomorphic indices such as TPI and spatial information in the dataset increases the lithological classification accuracy.

  7. SEBAL-A: A Remote Sensing ET Algorithm that Accounts for Advection with Limited Data. Part I: Development and Validation

    Directory of Open Access Journals (Sweden)

    Mcebisi Mkhwanazi

    2015-11-01

    Full Text Available The Surface Energy Balance Algorithm for Land (SEBAL is one of the remote sensing (RS models that are increasingly being used to determine evapotranspiration (ET. SEBAL is a widely used model, mainly due to the fact that it requires minimum weather data, and also no prior knowledge of surface characteristics is needed. However, it has been observed that it underestimates ET under advective conditions due to its disregard of advection as another source of energy available for evaporation. A modified SEBAL model was therefore developed in this study. An advection component, which is absent in the original SEBAL, was introduced such that the energy available for evapotranspiration was a sum of net radiation and advected heat energy. The improved SEBAL model was termed SEBAL-Advection or SEBAL-A. An important aspect of the improved model is the estimation of advected energy using minimal weather data. While other RS models would require hourly weather data to be able to account for advection (e.g., METRIC, SEBAL-A only requires daily averages of limited weather data, making it appropriate even in areas where weather data at short time steps may not be available. In this study, firstly, the original SEBAL model was evaluated under advective and non-advective conditions near Rocky Ford in southeastern Colorado, a semi-arid area where afternoon advection is common occurrence. The SEBAL model was found to incur large errors when there was advection (which was indicated by higher wind speed and warm and dry air. SEBAL-A was then developed and validated in the same area under standard surface conditions, which were described as healthy alfalfa with height of 40–60 cm, without water-stress. ET values estimated using the original and modified SEBAL were compared to large weighing lysimeter-measured ET values. When the SEBAL ET was compared to SEBAL-A ET values, the latter showed improved performance, with the ET Mean Bias Error (MBE reduced from −17

  8. Different Responses to Trauma in Two Children with Autistic Spectrum Disorder: The Mouth as Crossroads for the Sense of Self

    Science.gov (United States)

    Rhode, Maria

    2004-01-01

    Two contrasting cases are discussed of boys with autistic spectrum disorder who had suffered cumulative trauma. Although their material was similar in many respects, the 9-year-old made excellent progress during therapy, while the 4-year-old developed much less in spite of being in intensive treatment. This contrast is discussed with regard to…

  9. Dual-sensing porphyrin-containing copolymer nanosensor as full-spectrum colorimeter and ultra-sensitive thermometer.

    Science.gov (United States)

    Yan, Qiang; Yuan, Jinying; Kang, Yan; Cai, Zhinan; Zhou, Lilin; Yin, Yingwu

    2010-04-28

    A porphyrin-containing copolymer has dual-sensing in response to metal ions and temperature as a novel nanosensor. Triggered by ions, the sensor exhibits full-color tunable behavior as a cationic detector and colorimeter. Responding to temperature, the sensor displays an "isothermal" thermochromic point as an ultra-sensitive thermometer.

  10. Using GRAPPA to improve autocalibrated coil sensitivity estimation for the SENSE family of parallel imaging reconstruction algorithms.

    Science.gov (United States)

    Hoge, W Scott; Brooks, Dana H

    2008-08-01

    Two strategies are widely used in parallel MRI to reconstruct subsampled multicoil image data. SENSE and related methods employ explicit receiver coil spatial response estimates to reconstruct an image. In contrast, coil-by-coil methods such as GRAPPA leverage correlations among the acquired multicoil data to reconstruct missing k-space lines. In self-referenced scenarios, both methods employ Nyquist-rate low-frequency k-space data to identify the reconstruction parameters. Because GRAPPA does not require explicit coil sensitivities estimates, it needs considerably fewer autocalibration signals than SENSE. However, SENSE methods allow greater opportunity to control reconstruction quality though regularization and thus may outperform GRAPPA in some imaging scenarios. Here, we employ GRAPPA to improve self-referenced coil sensitivity estimation in SENSE and related methods using very few auto-calibration signals. This enables one to leverage each methods' inherent strength and produce high quality self-referenced SENSE reconstructions. (c) 2008 Wiley-Liss, Inc.

  11. Compressive Sensing in Communication Systems

    DEFF Research Database (Denmark)

    Fyhn, Karsten

    2013-01-01

    . The need for cheaper, smarter and more energy efficient wireless devices is greater now than ever. This thesis addresses this problem and concerns the application of the recently developed sampling theory of compressive sensing in communication systems. Compressive sensing is the merging of signal...... acquisition and compression. It allows for sampling a signal with a rate below the bound dictated by the celebrated Shannon-Nyquist sampling theorem. In some communication systems this necessary minimum sample rate, dictated by the Shannon-Nyquist sampling theorem, is so high it is at the limit of what...... with using compressive sensing in communication systems. The main contribution of this thesis is two-fold: 1) a new compressive sensing hardware structure for spread spectrum signals, which is simpler than the current state-of-the-art, and 2) a range of algorithms for parameter estimation for the class...

  12. Advanced sensing techniques for cognitive radio

    CERN Document Server

    Zhao, Guodong; Li, Shaoqian

    2017-01-01

    This SpringerBrief investigates advanced sensing techniques to detect and estimate the primary receiver for cognitive radio systems. Along with a comprehensive overview of existing spectrum sensing techniques, this brief focuses on the design of new signal processing techniques, including the region-based sensing, jamming-based probing, and relay-based probing. The proposed sensing techniques aim to detect the nearby primary receiver and estimate the cross-channel gain between the cognitive transmitter and primary receiver. The performance of the proposed algorithms is evaluated by simulations in terms of several performance parameters, including detection probability, interference probability, and estimation error. The results show that the proposed sensing techniques can effectively sense the primary receiver and improve the cognitive transmission throughput. Researchers and postgraduate students in electrical engineering will find this an exceptional resource.

  13. Speech Enhancement by Multichannel Crosstalk Resistant ANC and Improved Spectrum Subtraction

    Directory of Open Access Journals (Sweden)

    Zeng Qingning

    2006-01-01

    Full Text Available A scheme combining multichannel crosstalk resistant adaptive noise cancellation (MCRANC algorithm and improved spectrum subtraction (ISS algorithm is presented to enhance noise carrying speech signals. The scheme would permit locating the microphones in close proximity by virtue of using MCRANC which has the capability of removing the crosstalk effect. MCRANC would also permit canceling out nonstationary noise and making the residual noise more stationary for further treatment by ISS algorithm. Experimental results have indicated that this scheme outperforms many commonly used techniques in the sense of SNR improvement and music effect reduction which is an inevitable byproduct of the spectrum subtraction algorithm.

  14. LOCAL ALGORITHM FOR MONITORING TOTAL SUSPENDED SEDIMENTS IN MICRO-WATERSHEDS USIN DRONES AND REMOTE SENSING APPLICATIONS. CASE STUDY: TEUSACÁ RIVER, LA CALERA, COLOMBIA

    Directory of Open Access Journals (Sweden)

    N. A. Sáenz

    2015-08-01

    Full Text Available An empirical relationship of Total Suspended Sediments (TSS concentrations and reflectance values obtained with Drones’ aerial photos and processed using remote sensing tools was set up as the main objective of this research. A local mathematic algorithm for the micro-watershed of the Teusacá River at La Calera, Colombia, was developed based on the computing of four component of bands from consumed-grade cameras obtaining from each their corresponding reflectance values from procedures for correcting digital camera imagery and using statistical analysis for study the fit and RMSE of 25 regressions. The assessment was characterized by the comparison of reflectance values and 34 in-situ data measurements concentrations between 1.6 and 33 mg L−1 taken from the superficial layer of the river in two campaigns. A large data set of empirical and referenced algorithm from literature were used to evaluate the accuracy and precision of the relationship. For estimation of TSS, a higher accuracy was achieved using the Tassan’s algorithm with the BAND X/ BANDX ratio. The correlation coefficient with R2 = X demonstrate the feasibility of use remote sensed data with consumed-grade cameras as an effective tool for a frequent monitoring and controlling of water quality parameters such as Total Suspended Solids of watersheds, these being the most vulnerable and less compliance with environmental regulations.

  15. The WACMOS-ET project – Part 1: Tower-scale evaluation of four remote sensing-based evapotranspiration algorithms

    KAUST Repository

    Michel, D.; Jimé nez, C.; Miralles, Diego G.; Jung, M.; Hirschi, M.; Ershadi, A.; Martens, B.; McCabe, Matthew; Fisher, J. B.; Mu, Q.; Seneviratne, S. I.; Wood, E. F.; Ferná ndez-Prieto, D.

    2015-01-01

    algorithms: the Priestley–Taylor Jet Propulsion Laboratory model (PT-JPL), the Penman–Monteith algorithm from the MODIS evaporation product (PM-MOD), the Surface Energy Balance System (SEBS) and the Global Land Evaporation Amsterdam Model (GLEAM). In addition

  16. Improved Tensor-Based Singular Spectrum Analysis Based on Single Channel Blind Source Separation Algorithm and Its Application to Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Dan Yang

    2017-04-01

    Full Text Available To solve the problem of multi-fault blind source separation (BSS in the case that the observed signals are under-determined, a novel approach for single channel blind source separation (SCBSS based on the improved tensor-based singular spectrum analysis (TSSA is proposed. As the most natural representation of high-dimensional data, tensor can preserve the intrinsic structure of the data to the maximum extent. Thus, TSSA method can be employed to extract the multi-fault features from the measured single-channel vibration signal. However, SCBSS based on TSSA still has some limitations, mainly including unsatisfactory convergence of TSSA in many cases and the number of source signals is hard to accurately estimate. Therefore, the improved TSSA algorithm based on canonical decomposition and parallel factors (CANDECOMP/PARAFAC weighted optimization, namely CP-WOPT, is proposed in this paper. CP-WOPT algorithm is applied to process the factor matrix using a first-order optimization approach instead of the original least square method in TSSA, so as to improve the convergence of this algorithm. In order to accurately estimate the number of the source signals in BSS, EMD-SVD-BIC (empirical mode decomposition—singular value decomposition—Bayesian information criterion method, instead of the SVD in the conventional TSSA, is introduced. To validate the proposed method, we applied it to the analysis of the numerical simulation signal and the multi-fault rolling bearing signals.

  17. A new bio-optical algorithm for the remote sensing of algal blooms in complex ocean waters

    Science.gov (United States)

    Shanmugam, Palanisamy

    2011-04-01

    A new bio-optical algorithm has been developed to provide accurate assessments of chlorophyll a (Chl a) concentration for detection and mapping of algal blooms from satellite data in optically complex waters, where the presence of suspended sediments and dissolved substances can interfere with phytoplankton signal and thus confound conventional band ratio algorithms. A global data set of concurrent measurements of pigment concentration and radiometric reflectance was compiled and used to develop this algorithm that uses the normalized water-leaving radiance ratios along with an algal bloom index (ABI) between three visible bands to determine Chl a concentrations. The algorithm is derived using Sea-viewing Wide Field-of-view Sensor bands, and it is subsequently tuned to be applicable to Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua data. When compared with large in situ data sets and satellite matchups in a variety of coastal and ocean waters the present algorithm makes good retrievals of the Chl a concentration and shows statistically significant improvement over current global algorithms (e.g., OC3 and OC4v4). An examination of the performance of these algorithms on several MODIS/Aqua images in complex waters of the Arabian Sea and west Florida shelf shows that the new algorithm provides a better means for detecting and differentiating algal blooms from other turbid features, whereas the OC3 algorithm has significant errors although yielding relatively consistent results in clear waters. These findings imply that, provided that an accurate atmospheric correction scheme is available to deal with complex waters, the current MODIS/Aqua, MERIS and OCM data could be extensively used for quantitative and operational monitoring of algal blooms in various regional and global waters.

  18. Development of a Semi-Analytical Algorithm for the Retrieval of Suspended Particulate Matter from Remote Sensing over Clear to Very Turbid Waters

    Directory of Open Access Journals (Sweden)

    Bing Han

    2016-03-01

    Full Text Available Remote sensing of suspended particulate matter, SPM, from space has long been used to assess its spatio-temporal variability in various coastal areas. The associated algorithms were generally site specific or developed over a relatively narrow range of concentration, which make them inappropriate for global applications (or at least over broad SPM range. In the frame of the GlobCoast project, a large in situ data set of SPM and remote sensing reflectance, Rrs(λ, has been built gathering together measurements from various coastal areas around Europe, French Guiana, North Canada, Vietnam, and China. This data set covers various contrasting coastal environments diversely affected by different biogeochemical and physical processes such as sediment resuspension, phytoplankton bloom events, and rivers discharges (Amazon, Mekong, Yellow river, MacKenzie, etc.. The SPM concentration spans about four orders of magnitude, from 0.15 to 2626 g·m−3. Different empirical and semi-analytical approaches developed to assess SPM from Rrs(λ were tested over this in situ data set. As none of them provides satisfactory results over the whole SPM range, a generic semi-analytical approach has been developed. This algorithm is based on two standard semi-analytical equations calibrated for low-to-medium and highly turbid waters, respectively. A mixing law has also been developed for intermediate environments. Sources of uncertainties in SPM retrieval such as the bio-optical variability, atmospheric correction errors, and spectral bandwidth have been evaluated. The coefficients involved in these different algorithms have been calculated for ocean color (SeaWiFS, MODIS-A/T, MERIS/OLCI, VIIRS and high spatial resolution (LandSat8-OLI, and Sentinel2-MSI sensors. The performance of the proposed algorithm varies only slightly from one sensor to another demonstrating the great potential applicability of the proposed approach over global and contrasting coastal waters.

  19. Satellite remote sensing of harmful algal blooms: A new multi-algorithm method for detecting the Florida Red Tide (Karenia brevis)

    Science.gov (United States)

    Carvalho, Gustavo A.; Minnett, Peter J.; Fleming, Lora E.; Banzon, Viva F.; Baringer, Warner

    2010-01-01

    In a continuing effort to develop suitable methods for the surveillance of Harmful Algal Blooms (HABs) of Karenia brevis using satellite radiometers, a new multi-algorithm method was developed to explore whether improvements in the remote sensing detection of the Florida Red Tide was possible. A Hybrid Scheme was introduced that sequentially applies the optimized versions of two pre-existing satellite-based algorithms: an Empirical Approach (using water-leaving radiance as a function of chlorophyll concentration) and a Bio-optical Technique (using particulate backscatter along with chlorophyll concentration). The long-term evaluation of the new multi-algorithm method was performed using a multi-year MODIS dataset (2002 to 2006; during the boreal Summer-Fall periods – July to December) along the Central West Florida Shelf between 25.75°N and 28.25°N. Algorithm validation was done with in situ measurements of the abundances of K. brevis; cell counts ≥1.5×104 cells l−1 defined a detectable HAB. Encouraging statistical results were derived when either or both algorithms correctly flagged known samples. The majority of the valid match-ups were correctly identified (~80% of both HABs and non-blooming conditions) and few false negatives or false positives were produced (~20% of each). Additionally, most of the HAB-positive identifications in the satellite data were indeed HAB samples (positive predictive value: ~70%) and those classified as HAB-negative were almost all non-bloom cases (negative predictive value: ~86%). These results demonstrate an excellent detection capability, on average ~10% more accurate than the individual algorithms used separately. Thus, the new Hybrid Scheme could become a powerful tool for environmental monitoring of K. brevis blooms, with valuable consequences including leading to the more rapid and efficient use of ships to make in situ measurements of HABs. PMID:21037979

  20. Biogenic synthesis of Zinc oxide nanostructures from Nigella sativa seed: Prospective role as food packaging material inhibiting broad-spectrum quorum sensing and biofilm.

    Science.gov (United States)

    Al-Shabib, Nasser A; Husain, Fohad Mabood; Ahmed, Faheem; Khan, Rais Ahmad; Ahmad, Iqbal; Alsharaeh, Edreese; Khan, Mohd Shahnawaz; Hussain, Afzal; Rehman, Md Tabish; Yusuf, Mohammad; Hassan, Iftekhar; Khan, Javed Masood; Ashraf, Ghulam Md; Alsalme, Ali Mohammed; Al-Ajmi, Mohamed F; Tarasov, Vadim V; Aliev, Gjumrakch

    2016-12-05

    Bacterial spoilage of food products is regulated by density dependent communication system called quorum sensing (QS). QS control biofilm formation in numerous food pathogens and Biofilms formed on food surfaces act as carriers of bacterial contamination leading to spoilage of food and health hazards. Agents inhibiting or interfering with bacterial QS and biofilm are gaining importance as a novel class of next-generation food preservatives/packaging material. In the present study, Zinc nanostructures were synthesised using Nigella sativa seed extract (NS-ZnNPs). Synthesized nanostructures were characterized hexagonal wurtzite structure of size ~24 nm by UV-visible, XRD, FTIR and TEM. NS-ZnNPs demonstrated broad-spectrum QS inhibition in C. violaceum and P. aeruginosa biosensor strains. Synthesized nanostructures inhibited QS regulated functions of C. violaceum CVO26 (violacein) and elastase, protease, pyocyanin and alginate production in PAO1 significantly. NS-ZnNPs at sub-inhibitory concentrations inhibited the biofilm formation of four-food pathogens viz. C. violaceum 12472, PAO1, L. monocytogenes, E. coli. Moreover, NS-ZnNPs was found effective in inhibiting pre-formed mature biofilms of the four pathogens. Therefore, the broad-spectrum inhibition of QS and biofilm by biogenic Zinc oxide nanoparticles and it is envisaged that these nontoxic bioactive nanostructures can be used as food packaging material and/or as food preservative.

  1. Distributed Schemes for Crowdsourcing-Based Sensing Task Assignment in Cognitive Radio Networks

    Directory of Open Access Journals (Sweden)

    Linbo Zhai

    2017-01-01

    Full Text Available Spectrum sensing is an important issue in cognitive radio networks. The unlicensed users can access the licensed wireless spectrum only when the licensed wireless spectrum is sensed to be idle. Since mobile terminals such as smartphones and tablets are popular among people, spectrum sensing can be assigned to these mobile intelligent terminals, which is called crowdsourcing method. Based on the crowdsourcing method, this paper studies the distributed scheme to assign spectrum sensing task to mobile terminals such as smartphones and tablets. Considering the fact that mobile terminals’ positions may influence the sensing results, a precise sensing effect function is designed for the crowdsourcing-based sensing task assignment. We aim to maximize the sensing effect function and cast this optimization problem to address crowdsensing task assignment in cognitive radio networks. This problem is difficult to be solved because the complexity of this problem increases exponentially with the growth in mobile terminals. To assign crowdsensing task, we propose four distributed algorithms with different transition probabilities and use a Markov chain to analyze the approximation gap of our proposed schemes. Simulation results evaluate the average performance of our proposed algorithms and validate the algorithm’s convergence.

  2. Evaluation of the image quality in digital breast tomosynthesis (DBT) employed with a compressed-sensing (CS)-based reconstruction algorithm by using the mammographic accreditation phantom

    Energy Technology Data Exchange (ETDEWEB)

    Park, Yeonok; Cho, Heemoon; Je, Uikyu; Cho, Hyosung, E-mail: hscho1@yonsei.ac.kr; Park, Chulkyu; Lim, Hyunwoo; Kim, Kyuseok; Kim, Guna; Park, Soyoung; Woo, Taeho; Choi, Sungil

    2015-12-21

    In this work, we have developed a prototype digital breast tomosynthesis (DBT) system which mainly consists of an x-ray generator (28 kV{sub p}, 7 mA s), a CMOS-type flat-panel detector (70-μm pixel size, 230.5×339 mm{sup 2} active area), and a rotational arm to move the x-ray generator in an arc. We employed a compressed-sensing (CS)-based reconstruction algorithm, rather than a common filtered-backprojection (FBP) one, for more accurate DBT reconstruction. Here the CS is a state-of-the-art mathematical theory for solving the inverse problems, which exploits the sparsity of the image with substantially high accuracy. We evaluated the reconstruction quality in terms of the detectability, the contrast-to-noise ratio (CNR), and the slice-sensitive profile (SSP) by using the mammographic accreditation phantom (Model 015, CIRS Inc.) and compared it to the FBP-based quality. The CS-based algorithm yielded much better image quality, preserving superior image homogeneity, edge sharpening, and cross-plane resolution, compared to the FBP-based one. - Highlights: • A prototype digital breast tomosynthesis (DBT) system is developed. • Compressed-sensing (CS) based reconstruction framework is employed. • We reconstructed high-quality DBT images by using the proposed reconstruction framework.

  3. Will algorithms modified with soil and weather information improve in-field reflectance-sensing corn nitrogen applications?

    Science.gov (United States)

    Nitrogen (N) needs to support corn (Zea mays L.) production can be highly variable within fields. Canopy reflectance sensing for assessing crop N health has been implemented on many farmers’ fields to side-dress or top-dress variable-rate N application, but at times farmers report the performance of...

  4. Optical Remote Sensing Algorithm Validation using High-Frequency Underway Biogeochemical Measurements in Three Large Global River Systems

    Science.gov (United States)

    Kuhn, C.; Richey, J. E.; Striegl, R. G.; Ward, N.; Sawakuchi, H. O.; Crawford, J.; Loken, L. C.; Stadler, P.; Dornblaser, M.; Butman, D. E.

    2017-12-01

    More than 93% of the world's river-water volume occurs in basins impacted by large dams and about 43% of river water discharge is impacted by flow regulation. Human land use also alters nutrient and carbon cycling and the emission of carbon dioxide from inland reservoirs. Increased water residence times and warmer temperatures in reservoirs fundamentally alter the physical settings for biogeochemical processing in large rivers, yet river biogeochemistry for many large systems remains undersampled. Satellite remote sensing holds promise as a methodology for responsive regional and global water resources management. Decades of ocean optics research has laid the foundation for the use of remote sensing reflectance in optical wavelengths (400 - 700 nm) to produce satellite-derived, near-surface estimates of phytoplankton chlorophyll concentration. Significant improvements between successive generations of ocean color sensors have enabled the scientific community to document changes in global ocean productivity (NPP) and estimate ocean biomass with increasing accuracy. Despite large advances in ocean optics, application of optical methods to inland waters has been limited to date due to their optical complexity and small spatial scale. To test this frontier, we present a study evaluating the accuracy and suitability of empirical inversion approaches for estimating chlorophyll-a, turbidity and temperature for the Amazon, Columbia and Mississippi rivers using satellite remote sensing. We demonstrate how riverine biogeochemical measurements collected at high frequencies from underway vessels can be used as in situ matchups to evaluate remotely-sensed, near-surface temperature, turbidity, chlorophyll-a derived from the Landsat 8 (NASA) and Sentinel 2 (ESA) satellites. We investigate the use of remote sensing water reflectance to infer trophic status as well as tributary influences on the optical characteristics of the Amazon, Mississippi and Columbia rivers.

  5. Data processing of remotely sensed airborne hyperspectral data using the Airborne Processing Library (APL): Geocorrection algorithm descriptions and spatial accuracy assessment

    Science.gov (United States)

    Warren, Mark A.; Taylor, Benjamin H.; Grant, Michael G.; Shutler, Jamie D.

    2014-03-01

    Remote sensing airborne hyperspectral data are routinely used for applications including algorithm development for satellite sensors, environmental monitoring and atmospheric studies. Single flight lines of airborne hyperspectral data are often in the region of tens of gigabytes in size. This means that a single aircraft can collect terabytes of remotely sensed hyperspectral data during a single year. Before these data can be used for scientific analyses, they need to be radiometrically calibrated, synchronised with the aircraft's position and attitude and then geocorrected. To enable efficient processing of these large datasets the UK Airborne Research and Survey Facility has recently developed a software suite, the Airborne Processing Library (APL), for processing airborne hyperspectral data acquired from the Specim AISA Eagle and Hawk instruments. The APL toolbox allows users to radiometrically calibrate, geocorrect, reproject and resample airborne data. Each stage of the toolbox outputs data in the common Band Interleaved Lines (BILs) format, which allows its integration with other standard remote sensing software packages. APL was developed to be user-friendly and suitable for use on a workstation PC as well as for the automated processing of the facility; to this end APL can be used under both Windows and Linux environments on a single desktop machine or through a Grid engine. A graphical user interface also exists. In this paper we describe the Airborne Processing Library software, its algorithms and approach. We present example results from using APL with an AISA Eagle sensor and we assess its spatial accuracy using data from multiple flight lines collected during a campaign in 2008 together with in situ surveyed ground control points.

  6. Finite Element Modelling of a Field-Sensed Magnetic Suspended System for Accurate Proximity Measurement Based on a Sensor Fusion Algorithm with Unscented Kalman Filter.

    Science.gov (United States)

    Chowdhury, Amor; Sarjaš, Andrej

    2016-09-15

    The presented paper describes accurate distance measurement for a field-sensed magnetic suspension system. The proximity measurement is based on a Hall effect sensor. The proximity sensor is installed directly on the lower surface of the electro-magnet, which means that it is very sensitive to external magnetic influences and disturbances. External disturbances interfere with the information signal and reduce the usability and reliability of the proximity measurements and, consequently, the whole application operation. A sensor fusion algorithm is deployed for the aforementioned reasons. The sensor fusion algorithm is based on the Unscented Kalman Filter, where a nonlinear dynamic model was derived with the Finite Element Modelling approach. The advantage of such modelling is a more accurate dynamic model parameter estimation, especially in the case when the real structure, materials and dimensions of the real-time application are known. The novelty of the paper is the design of a compact electro-magnetic actuator with a built-in low cost proximity sensor for accurate proximity measurement of the magnetic object. The paper successively presents a modelling procedure with the finite element method, design and parameter settings of a sensor fusion algorithm with Unscented Kalman Filter and, finally, the implementation procedure and results of real-time operation.

  7. A coupled remote sensing and the Surface Energy Balance with Topography Algorithm (SEBTA) to estimate actual evapotranspiration under complex terrain

    OpenAIRE

    Z. Q. Gao; C. S. Liu; W. Gao; N. B. Chang

    2010-01-01

    Evapotranspiration (ET) may be used as an ecological indicator to address the ecosystem complexity. The accurate measurement of ET is of great significance for studying environmental sustainability, global climate changes, and biodiversity. Remote sensing technologies are capable of monitoring both energy and water fluxes on the surface of the Earth. With this advancement, existing models, such as SEBAL, S_SEBI and SEBS, enable us to estimate the regional ET with limited temporal and spa...

  8. A coupled remote sensing and the Surface Energy Balance with Topography Algorithm (SEBTA) to estimate actual evapotranspiration over heterogeneous terrain

    OpenAIRE

    Gao, Z. Q.; Liu, C. S.; Gao, W.; Chang, N.-B.

    2011-01-01

    Evapotranspiration (ET) may be used as an ecological indicator to address the ecosystem complexity. The accurate measurement of ET is of great significance for studying environmental sustainability, global climate changes, and biodiversity. Remote sensing technologies are capable of monitoring both energy and water fluxes on the surface of the Earth. With this advancement, existing models, such as SEBAL, S_SEBI and SEBS, enable us to estimate the regional ET with limited temporal and spatial ...

  9. The WACMOS-ET project – Part 1: Tower-scale evaluation of four remote sensing-based evapotranspiration algorithms

    KAUST Repository

    Michel, D.

    2015-10-20

    The WACMOS-ET project has compiled a forcing data set covering the period 2005–2007 that aims to maximize the exploitation of European Earth Observations data sets for evapotranspiration (ET) estimation. The data set was used to run 4 established ET algorithms: the Priestley–Taylor Jet Propulsion Laboratory model (PT-JPL), the Penman–Monteith algorithm from the MODIS evaporation product (PM-MOD), the Surface Energy Balance System (SEBS) and the Global Land Evaporation Amsterdam Model (GLEAM). In addition, in-situ meteorological data from 24 FLUXNET towers was used to force the models, with results from both forcing sets compared to tower-based flux observations. Model performance was assessed across several time scales using both sub-daily and daily forcings. The PT-JPL model and GLEAM provide the best performance for both satellite- and tower-based forcing as well as for the considered temporal resolutions. Simulations using the PM-MOD were mostly underestimated, while the SEBS performance was characterized by a systematic overestimation. In general, all four algorithms produce the best results in wet and moderately wet climate regimes. In dry regimes, the correlation and the absolute agreement to the reference tower ET observations were consistently lower. While ET derived with in situ forcing data agrees best with the tower measurements (R2 = 0.67), the agreement of the satellite-based ET estimates is only marginally lower (R2 = 0.58). Results also show similar model performance at daily and sub-daily (3-hourly) resolutions. Overall, our validation experiments against in situ measurements indicate that there is no single best-performing algorithm across all biome and forcing types. An extension of the evaluation to a larger selection of 85 towers (model inputs re-sampled to a common grid to facilitate global estimates) confirmed the original findings.

  10. MAPSM: A Spatio-Temporal Algorithm for Merging Soil Moisture from Active and Passive Microwave Remote Sensing

    Directory of Open Access Journals (Sweden)

    Sat Kumar Tomer

    2016-12-01

    Full Text Available Availability of soil moisture observations at a high spatial and temporal resolution is a prerequisite for various hydrological, agricultural and meteorological applications. In the current study, a novel algorithm for merging soil moisture from active microwave (SAR and passive microwave is presented. The MAPSM algorithm—Merge Active and Passive microwave Soil Moisture—uses a spatio-temporal approach based on the concept of the Water Change Capacity (WCC which represents the amplitude and direction of change in the soil moisture at the fine spatial resolution. The algorithm is applied and validated during a period of 3 years spanning from 2010 to 2013 over the Berambadi watershed which is located in a semi-arid tropical region in the Karnataka state of south India. Passive microwave products are provided from ESA Level 2 soil moisture products derived from Soil Moisture and Ocean Salinity (SMOS satellite (3 days temporal resolution and 40 km nominal spatial resolution. Active microwave are based on soil moisture retrievals from 30 images of RADARSAT-2 data (24 days temporal resolution and 20 m spatial resolution. The results show that MAPSM is able to provide a good estimate of soil moisture at a spatial resolution of 500 m with an RMSE of 0.025 m3/m3 and 0.069 m3/m3 when comparing it to soil moisture from RADARSAT-2 and in-situ measurements, respectively. The use of Sentinel-1 and RISAT products in MAPSM algorithm is envisioned over other areas where high number of revisits is available. This will need an update of the algorithm to take into account the angle sampling and resolution of Sentinel-1 and RISAT data.

  11. Identification of cultivated land using remote sensing images based on object-oriented artificial bee colony algorithm

    Science.gov (United States)

    Li, Nan; Zhu, Xiufang

    2017-04-01

    Cultivated land resources is the key to ensure food security. Timely and accurate access to cultivated land information is conducive to a scientific planning of food production and management policies. The GaoFen 1 (GF-1) images have high spatial resolution and abundant texture information and thus can be used to identify fragmentized cultivated land. In this paper, an object-oriented artificial bee colony algorithm was proposed for extracting cultivated land from GF-1 images. Firstly, the GF-1 image was segmented by eCognition software and some samples from the segments were manually identified into 2 types (cultivated land and non-cultivated land). Secondly, the artificial bee colony (ABC) algorithm was used to search for classification rules based on the spectral and texture information extracted from the image objects. Finally, the extracted classification rules were used to identify the cultivated land area on the image. The experiment was carried out in Hongze area, Jiangsu Province using wide field-of-view sensor on the GF-1 satellite image. The total precision of classification result was 94.95%, and the precision of cultivated land was 92.85%. The results show that the object-oriented ABC algorithm can overcome the defect of insufficient spectral information in GF-1 images and obtain high precision in cultivated identification.

  12. Autism spectrum disorders and fetal hypoxia in a population-based cohort: Accounting for missing exposures via Estimation-Maximization algorithm

    Directory of Open Access Journals (Sweden)

    Yasui Yutaka

    2011-01-01

    Full Text Available Abstract Background Autism spectrum disorders (ASD are associated with complications of pregnancy that implicate fetal hypoxia (FH; the excess of ASD in male gender is poorly understood. We tested the hypothesis that risk of ASD is related to fetal hypoxia and investigated whether this effect is greater among males. Methods Provincial delivery records (PDR identified the cohort of all 218,890 singleton live births in the province of Alberta, Canada, between 01-01-98 and 12-31-04. These were followed-up for ASD via ICD-9 diagnostic codes assigned by physician billing until 03-31-08. Maternal and obstetric risk factors, including FH determined from blood tests of acidity (pH, were extracted from PDR. The binary FH status was missing in approximately half of subjects. Assuming that characteristics of mothers and pregnancies would be correlated with FH, we used an Estimation-Maximization algorithm to estimate HF-ASD association, allowing for both missing-at-random (MAR and specific not-missing-at-random (NMAR mechanisms. Results Data indicated that there was excess risk of ASD among males who were hypoxic at birth, not materially affected by adjustment for potential confounding due to birth year and socio-economic status: OR 1.13, 95%CI: 0.96, 1.33 (MAR assumption. Limiting analysis to full-term males, the adjusted OR under specific NMAR assumptions spanned 95%CI of 1.0 to 1.6. Conclusion Our results are consistent with a weak effect of fetal hypoxia on risk of ASD among males. E-M algorithm is an efficient and flexible tool for modeling missing data in the studied setting.

  13. Design of Compressed Sensing Algorithm for Coal Mine IoT Moving Measurement Data Based on a Multi-Hop Network and Total Variation

    Directory of Open Access Journals (Sweden)

    Gang Wang

    2018-05-01

    Full Text Available As the application of a coal mine Internet of Things (IoT, mobile measurement devices, such as intelligent mine lamps, cause moving measurement data to be increased. How to transmit these large amounts of mobile measurement data effectively has become an urgent problem. This paper presents a compressed sensing algorithm for the large amount of coal mine IoT moving measurement data based on a multi-hop network and total variation. By taking gas data in mobile measurement data as an example, two network models for the transmission of gas data flow, namely single-hop and multi-hop transmission modes, are investigated in depth, and a gas data compressed sensing collection model is built based on a multi-hop network. To utilize the sparse characteristics of gas data, the concept of total variation is introduced and a high-efficiency gas data compression and reconstruction method based on Total Variation Sparsity based on Multi-Hop (TVS-MH is proposed. According to the simulation results, by using the proposed method, the moving measurement data flow from an underground distributed mobile network can be acquired and transmitted efficiently.

  14. Design of Compressed Sensing Algorithm for Coal Mine IoT Moving Measurement Data Based on a Multi-Hop Network and Total Variation.

    Science.gov (United States)

    Wang, Gang; Zhao, Zhikai; Ning, Yongjie

    2018-05-28

    As the application of a coal mine Internet of Things (IoT), mobile measurement devices, such as intelligent mine lamps, cause moving measurement data to be increased. How to transmit these large amounts of mobile measurement data effectively has become an urgent problem. This paper presents a compressed sensing algorithm for the large amount of coal mine IoT moving measurement data based on a multi-hop network and total variation. By taking gas data in mobile measurement data as an example, two network models for the transmission of gas data flow, namely single-hop and multi-hop transmission modes, are investigated in depth, and a gas data compressed sensing collection model is built based on a multi-hop network. To utilize the sparse characteristics of gas data, the concept of total variation is introduced and a high-efficiency gas data compression and reconstruction method based on Total Variation Sparsity based on Multi-Hop (TVS-MH) is proposed. According to the simulation results, by using the proposed method, the moving measurement data flow from an underground distributed mobile network can be acquired and transmitted efficiently.

  15. A method for the evaluation of image quality according to the recognition effectiveness of objects in the optical remote sensing image using machine learning algorithm.

    Directory of Open Access Journals (Sweden)

    Tao Yuan

    Full Text Available Objective and effective image quality assessment (IQA is directly related to the application of optical remote sensing images (ORSI. In this study, a new IQA method of standardizing the target object recognition rate (ORR is presented to reflect quality. First, several quality degradation treatments with high-resolution ORSIs are implemented to model the ORSIs obtained in different imaging conditions; then, a machine learning algorithm is adopted for recognition experiments on a chosen target object to obtain ORRs; finally, a comparison with commonly used IQA indicators was performed to reveal their applicability and limitations. The results showed that the ORR of the original ORSI was calculated to be up to 81.95%, whereas the ORR ratios of the quality-degraded images to the original images were 65.52%, 64.58%, 71.21%, and 73.11%. The results show that these data can more accurately reflect the advantages and disadvantages of different images in object identification and information extraction when compared with conventional digital image assessment indexes. By recognizing the difference in image quality from the application effect perspective, using a machine learning algorithm to extract regional gray scale features of typical objects in the image for analysis, and quantitatively assessing quality of ORSI according to the difference, this method provides a new approach for objective ORSI assessment.

  16. A method for the evaluation of image quality according to the recognition effectiveness of objects in the optical remote sensing image using machine learning algorithm.

    Science.gov (United States)

    Yuan, Tao; Zheng, Xinqi; Hu, Xuan; Zhou, Wei; Wang, Wei

    2014-01-01

    Objective and effective image quality assessment (IQA) is directly related to the application of optical remote sensing images (ORSI). In this study, a new IQA method of standardizing the target object recognition rate (ORR) is presented to reflect quality. First, several quality degradation treatments with high-resolution ORSIs are implemented to model the ORSIs obtained in different imaging conditions; then, a machine learning algorithm is adopted for recognition experiments on a chosen target object to obtain ORRs; finally, a comparison with commonly used IQA indicators was performed to reveal their applicability and limitations. The results showed that the ORR of the original ORSI was calculated to be up to 81.95%, whereas the ORR ratios of the quality-degraded images to the original images were 65.52%, 64.58%, 71.21%, and 73.11%. The results show that these data can more accurately reflect the advantages and disadvantages of different images in object identification and information extraction when compared with conventional digital image assessment indexes. By recognizing the difference in image quality from the application effect perspective, using a machine learning algorithm to extract regional gray scale features of typical objects in the image for analysis, and quantitatively assessing quality of ORSI according to the difference, this method provides a new approach for objective ORSI assessment.

  17. Spectrum Hole Identification in IEEE 802.22 WRAN using Unsupervised Learning

    OpenAIRE

    V. Balaji; S. Anand; C.R. Hota; G. Raghurama

    2016-01-01

    In this paper we present a Cooperative Spectrum Sensing (CSS) algorithm for Cognitive Radios (CR) based on IEEE 802.22Wireless Regional Area Network (WRAN) standard. The core objective is to improve cooperative sensing efficiency which specifies how fast a decision can be reached in each round of cooperation (iteration) to sense an appropriate number of channels/bands (i.e. 86 channels of 7MHz bandwidth as per IEEE 802.22) within a time constraint (channel sensing time). To meet this objectiv...

  18. A Multi-Sensor RSS Spatial Sensing-Based Robust Stochastic Optimization Algorithm for Enhanced Wireless Tethering

    CERN Document Server

    Parasuraman, Ramviyas; Molinari, Luca; Kershaw, Keith; Di Castro, Mario; Masi, Alessandro; Ferre, Manuel

    2014-01-01

    The reliability of wireless communication in a network of mobile wireless robot nodes depends on the received radio signal strength (RSS). When the robot nodes are deployed in hostile environments with ionizing radiations (such as in some scientific facilities), there is a possibility that some electronic components may fail randomly (due to radiation effects), which causes problems in wireless connectivity. The objective of this paper is to maximize robot mission capabilities by maximizing the wireless network capacity and to reduce the risk of communication failure. Thus, in this paper, we consider a multi-node wireless tethering structure called the “server-relay-client” framework that uses (multiple) relay nodes in between a server and a client node. We propose a robust stochastic optimization (RSO) algorithm using a multi-sensor-based RSS sampling method at the relay nodes to efficiently improve and balance the RSS between the source and client nodes to improve the network capacity and to provide red...

  19. The remote sensing of ocean primary productivity - Use of a new data compilation to test satellite algorithms

    Science.gov (United States)

    Balch, William; Evans, Robert; Brown, Jim; Feldman, Gene; Mcclain, Charles; Esaias, Wayne

    1992-01-01

    Global pigment and primary productivity algorithms based on a new data compilation of over 12,000 stations occupied mostly in the Northern Hemisphere, from the late 1950s to 1988, were tested. The results showed high variability of the fraction of total pigment contributed by chlorophyll, which is required for subsequent predictions of primary productivity. Two models, which predict pigment concentration normalized to an attenuation length of euphotic depth, were checked against 2,800 vertical profiles of pigments. Phaeopigments consistently showed maxima at about one optical depth below the chlorophyll maxima. CZCS data coincident with the sea truth data were also checked. A regression of satellite-derived pigment vs ship-derived pigment had a coefficient of determination. The satellite underestimated the true pigment concentration in mesotrophic and oligotrophic waters and overestimated the pigment concentration in eutrophic waters. The error in the satellite estimate showed no trends with time between 1978 and 1986.

  20. A Multi-Sensor RSS Spatial Sensing-Based Robust Stochastic Optimization Algorithm for Enhanced Wireless Tethering

    Science.gov (United States)

    Parasuraman, Ramviyas; Fabry, Thomas; Molinari, Luca; Kershaw, Keith; Di Castro, Mario; Masi, Alessandro; Ferre, Manuel

    2014-01-01

    The reliability of wireless communication in a network of mobile wireless robot nodes depends on the received radio signal strength (RSS). When the robot nodes are deployed in hostile environments with ionizing radiations (such as in some scientific facilities), there is a possibility that some electronic components may fail randomly (due to radiation effects), which causes problems in wireless connectivity. The objective of this paper is to maximize robot mission capabilities by maximizing the wireless network capacity and to reduce the risk of communication failure. Thus, in this paper, we consider a multi-node wireless tethering structure called the “server-relay-client” framework that uses (multiple) relay nodes in between a server and a client node. We propose a robust stochastic optimization (RSO) algorithm using a multi-sensor-based RSS sampling method at the relay nodes to efficiently improve and balance the RSS between the source and client nodes to improve the network capacity and to provide redundant networking abilities. We use pre-processing techniques, such as exponential moving averaging and spatial averaging filters on the RSS data for smoothing. We apply a receiver spatial diversity concept and employ a position controller on the relay node using a stochastic gradient ascent method for self-positioning the relay node to achieve the RSS balancing task. The effectiveness of the proposed solution is validated by extensive simulations and field experiments in CERN facilities. For the field trials, we used a youBot mobile robot platform as the relay node, and two stand-alone Raspberry Pi computers as the client and server nodes. The algorithm has been proven to be robust to noise in the radio signals and to work effectively even under non-line-of-sight conditions. PMID:25615734

  1. A Multi-Sensor RSS Spatial Sensing-Based Robust Stochastic Optimization Algorithm for Enhanced Wireless Tethering

    Directory of Open Access Journals (Sweden)

    Ramviyas Parasuraman

    2014-12-01

    Full Text Available The reliability of wireless communication in a network of mobile wireless robot nodes depends on the received radio signal strength (RSS. When the robot nodes are deployed in hostile environments with ionizing radiations (such as in some scientific facilities, there is a possibility that some electronic components may fail randomly (due to radiation effects, which causes problems in wireless connectivity. The objective of this paper is to maximize robot mission capabilities by maximizing the wireless network capacity and to reduce the risk of communication failure. Thus, in this paper, we consider a multi-node wireless tethering structure called the “server-relay-client” framework that uses (multiple relay nodes in between a server and a client node. We propose a robust stochastic optimization (RSO algorithm using a multi-sensor-based RSS sampling method at the relay nodes to efficiently improve and balance the RSS between the source and client nodes to improve the network capacity and to provide redundant networking abilities. We use pre-processing techniques, such as exponential moving averaging and spatial averaging filters on the RSS data for smoothing. We apply a receiver spatial diversity concept and employ a position controller on the relay node using a stochastic gradient ascent method for self-positioning the relay node to achieve the RSS balancing task. The effectiveness of the proposed solution is validated by extensive simulations and field experiments in CERN facilities. For the field trials, we used a youBot mobile robot platform as the relay node, and two stand-alone Raspberry Pi computers as the client and server nodes. The algorithm has been proven to be robust to noise in the radio signals and to work effectively even under non-line-of-sight conditions.

  2. An Improved Algorithm Based on Minimum Spanning Tree for Multi-scale Segmentation of Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    LI Hui

    2015-07-01

    Full Text Available As the basis of object-oriented information extraction from remote sensing imagery,image segmentation using multiple image features,exploiting spatial context information, and by a multi-scale approach are currently the research focuses. Using an optimization approach of the graph theory, an improved multi-scale image segmentation method is proposed. In this method, the image is applied with a coherent enhancement anisotropic diffusion filter followed by a minimum spanning tree segmentation approach, and the resulting segments are merged with reference to a minimum heterogeneity criterion.The heterogeneity criterion is defined as a function of the spectral characteristics and shape parameters of segments. The purpose of the merging step is to realize the multi-scale image segmentation. Tested on two images, the proposed method was visually and quantitatively compared with the segmentation method employed in the eCognition software. The results show that the proposed method is effective and outperforms the latter on areas with subtle spectral differences.

  3. Classification of remotely sensed images

    CSIR Research Space (South Africa)

    Dudeni, N

    2008-10-01

    Full Text Available For this research, the researchers examine various existing image classification algorithms with the aim of demonstrating how these algorithms can be applied to remote sensing images. These algorithms are broadly divided into supervised...

  4. Pentocin MQ1: A Novel, Broad-Spectrum, Pore-Forming Bacteriocin From Lactobacillus pentosus CS2 With Quorum Sensing Regulatory Mechanism and Biopreservative Potential

    Directory of Open Access Journals (Sweden)

    Samson B. Wayah

    2018-03-01

    Full Text Available Micrococcus luteus, Listeria monocytogenes, and Bacillus cereus are major food-borne pathogenic and spoilage bacteria. Emergence of antibiotic resistance and consumer demand for foods containing less of chemical preservatives led to a search for natural antimicrobials. A study aimed at characterizing, investigating the mechanism of action and regulation of biosynthesis and evaluating the biopreservative potential of pentocin from Lactobacillus pentosus CS2 was conducted. Pentocin MQ1 is a novel bacteriocin isolated from L. pentosus CS2 of coconut shake origin. The purification strategy involved adsorption-desorption of bacteriocin followed by RP-HPLC. It has a molecular weight of 2110.672 Da as determined by MALDI-TOF mass spectrometry and a molar extinction value of 298.82 M−1 cm−1. Pentocin MQ1 is not plasmid-borne and its biosynthesis is regulated by a quorum sensing mechanism. It has a broad spectrum of antibacterial activity, exhibited high chemical, thermal and pH stability but proved sensitive to proteolytic enzymes. It is potent against M. luteus, B. cereus, and L. monocytogenes at micromolar concentrations. It is quick-acting and exhibited a bactericidal mode of action against its targets. Target killing was mediated by pore formation. We report for the first time membrane permeabilization as a mechanism of action of the pentocin from the study against Gram-positive bacteria. Pentocin MQ1 is a cell wall-associated bacteriocin. Application of pentocin MQ1 improved the microbiological quality and extended the shelf life of fresh banana. This is the first report on the biopreservation of banana using bacteriocin. These findings place pentocin MQ1 as a potential biopreservative for further evaluation in food and medical applications.

  5. Generalized eigenvalue based spectrum sensing

    KAUST Repository

    Shakir, Muhammad; Alouini, Mohamed-Slim

    2012-01-01

    of the decision threshold of the respective detectors. The decision threshold has been calculated in a closed form which is based on the approximation of Cumulative Distribution Functions (CDFs) of the respective test statistics. In this context, we exchange

  6. Spectrum estimation method based on marginal spectrum

    International Nuclear Information System (INIS)

    Cai Jianhua; Hu Weiwen; Wang Xianchun

    2011-01-01

    FFT method can not meet the basic requirements of power spectrum for non-stationary signal and short signal. A new spectrum estimation method based on marginal spectrum from Hilbert-Huang transform (HHT) was proposed. The procession of obtaining marginal spectrum in HHT method was given and the linear property of marginal spectrum was demonstrated. Compared with the FFT method, the physical meaning and the frequency resolution of marginal spectrum were further analyzed. Then the Hilbert spectrum estimation algorithm was discussed in detail, and the simulation results were given at last. The theory and simulation shows that under the condition of short data signal and non-stationary signal, the frequency resolution and estimation precision of HHT method is better than that of FFT method. (authors)

  7. Automatic Tracking Of Remote Sensing Precipitation Data Using Genetic Algorithm Image Registration Based Automatic Morphing: September 1999 Storm Floyd Case Study

    Science.gov (United States)

    Chiu, L.; Vongsaard, J.; El-Ghazawi, T.; Weinman, J.; Yang, R.; Kafatos, M.

    U Due to the poor temporal sampling by satellites, data gaps exist in satellite derived time series of precipitation. This poses a challenge for assimilating rain- fall data into forecast models. To yield a continuous time series, the classic image processing technique of digital image morphing has been used. However, the digital morphing technique was applied manually and that is time consuming. In order to avoid human intervention in the process, an automatic procedure for image morphing is needed for real-time operations. For this purpose, Genetic Algorithm Based Image Registration Automatic Morphing (GRAM) model was developed and tested in this paper. Specifically, automatic morphing technique was integrated with Genetic Algo- rithm and Feature Based Image Metamorphosis technique to fill in data gaps between satellite coverage. The technique was tested using NOWRAD data which are gener- ated from the network of NEXRAD radars. Time series of NOWRAD data from storm Floyd that occurred at the US eastern region on September 16, 1999 for 00:00, 01:00, 02:00,03:00, and 04:00am were used. The GRAM technique was applied to data col- lected at 00:00 and 04:00am. These images were also manually morphed. Images at 01:00, 02:00 and 03:00am were interpolated from the GRAM and manual morphing and compared with the original NOWRAD rainrates. The results show that the GRAM technique outperforms manual morphing. The correlation coefficients between the im- ages generated using manual morphing are 0.905, 0.900, and 0.905 for the images at 01:00, 02:00,and 03:00 am, while the corresponding correlation coefficients are 0.946, 0.911, and 0.913, respectively, based on the GRAM technique. Index terms ­ Remote Sensing, Image Registration, Hydrology, Genetic Algorithm, Morphing, NEXRAD

  8. SEBAL-A: A Remote Sensing ET Algorithm that Accounts for Advection with Limited Data. Part II: Test for Transferability

    Directory of Open Access Journals (Sweden)

    Mcebisi Mkhwanazi

    2015-11-01

    Full Text Available Because the Surface Energy Balance Algorithm for Land (SEBAL tends to underestimate ET when there is advection, the model was modified by incorporating an advection component as part of the energy usable for crop evapotranspiration (ET. The modification involved the estimation of advected energy, which required the development of a wind function. In Part I, the modified SEBAL model (SEBAL-A was developed and validated on well-watered alfalfa of a standard height of 40–60 cm. In this Part II, SEBAL-A was tested on different crops and irrigation treatments in order to determine its performance under varying conditions. The crops used for the transferability test were beans (Phaseolus vulgaris L., wheat (Triticum aestivum L. and corn (Zea mays L.. The estimated ET using SEBAL-A was compared to actual ET measured using a Bowen Ratio Energy Balance (BREB system. Results indicated that SEBAL-A estimated ET fairly well for beans and wheat, only showing some slight underestimation of a Mean Bias Error (MBE of −0.7 mm·d−1 (−11.3%, a Root Mean Square Error (RMSE of 0.82 mm·d−1 (13.9% and a Nash Sutcliffe Coefficient of Efficiency (NSCE of 0.64. On corn, SEBAL-A resulted in an ET estimation error MBE of −0.7 mm·d−1 (−9.9%, a RMSE of 1.59 mm·d−1 (23.1% and NSCE = 0.24. This result shows an improvement on the original SEBAL model, which for the same data resulted in an ET MBE of −1.4 mm·d−1 (−20.4%, a RMSE of 1.97 mm·d−1 (28.8% and a NSCE of −0.18. When SEBAL-A was tested on only fully irrigated corn, it performed well, resulting in no bias, i.e., MBE of 0.0 mm·d−1; RMSE of 0.78 mm·d−1 (10.7% and NSCE of 0.82. The SEBAL-A model showed less or no improvement on corn that was either water-stressed or at early stages of growth. The errors incurred under these conditions were not due to advection not accounted for but rather were due to the nature of SEBAL and SEBAL-A being single-source energy balance models and

  9. Deterministic Compressed Sensing

    Science.gov (United States)

    2011-11-01

    39 4.3 Digital Communications . . . . . . . . . . . . . . . . . . . . . . . . . 40 4.4 Group Testing ...deterministic de - sign matrices. All bounds ignore the O() constants. . . . . . . . . . . 131 xvi List of Algorithms 1 Iterative Hard Thresholding Algorithm...sensing is information theoretically possible using any (2k, )-RIP sensing matrix . The following celebrated results of Candès, Romberg and Tao [54

  10. A Robust Algorithm of Multiquadric Method Based on an Improved Huber Loss Function for Interpolating Remote-Sensing-Derived Elevation Data Sets

    Directory of Open Access Journals (Sweden)

    Chuanfa Chen

    2015-03-01

    Full Text Available Remote-sensing-derived elevation data sets often suffer from noise and outliers due to various reasons, such as the physical limitations of sensors, multiple reflectance, occlusions and low contrast of texture. Outliers generally have a seriously negative effect on DEM construction. Some interpolation methods like ordinary kriging (OK are capable of smoothing noise inherent in sample points, but are sensitive to outliers. In this paper, a robust algorithm of multiquadric method (MQ based on an Improved Huber loss function (MQ-IH has been developed to decrease the impact of outliers on DEM construction. Theoretically, the improved Huber loss function is null for outliers, quadratic for small errors, and linear for others. Simulated data sets drawn from a mathematical surface with different error distributions were employed to analyze the robustness of MQ-IH. Results indicate that MQ-IH obtains a good balance between efficiency and robustness. Namely, the performance of MQ-IH is comparative to those of the classical MQ and MQ based on the Classical Huber loss function (MQ-CH when sample points follow a normal distribution, and the former outperforms the latter two when sample points are subject to outliers. For example, for the Cauchy error distribution with the location parameter of 0 and scale parameter of 1, the root mean square errors (RMSEs of MQ-CH and the classical MQ are 0.3916 and 1.4591, respectively, whereas that of MQ-IH is 0.3698. The performance of MQ-IH is further evaluated by qualitative and quantitative analysis through a real-world example of DEM construction with the stereo-images-derived elevation points. Results demonstrate that compared with the classical interpolation methods, including natural neighbor (NN, OK and ANUDEM (a program that calculates regular grid digital elevation models (DEMs with sensible shape and drainage structure from arbitrarily large topographic data sets, and two versions of MQ, including the

  11. Atmospheric Corrections and Multi-Conditional Algorithm for Multi-Sensor Remote Sensing of Suspended Particulate Matter in Low-to-High Turbidity Levels Coastal Waters

    Directory of Open Access Journals (Sweden)

    Stéfani Novoa

    2017-01-01

    Full Text Available The accurate measurement of suspended particulate matter (SPM concentrations in coastal waters is of crucial importance for ecosystem studies, sediment transport monitoring, and assessment of anthropogenic impacts in the coastal ocean. Ocean color remote sensing is an efficient tool to monitor SPM spatio-temporal variability in coastal waters. However, near-shore satellite images are complex to correct for atmospheric effects due to the proximity of land and to the high level of reflectance caused by high SPM concentrations in the visible and near-infrared spectral regions. The water reflectance signal (ρw tends to saturate at short visible wavelengths when the SPM concentration increases. Using a comprehensive dataset of high-resolution satellite imagery and in situ SPM and water reflectance data, this study presents (i an assessment of existing atmospheric correction (AC algorithms developed for turbid coastal waters; and (ii a switching method that automatically selects the most sensitive SPM vs. ρw relationship, to avoid saturation effects when computing the SPM concentration. The approach is applied to satellite data acquired by three medium-high spatial resolution sensors (Landsat-8/Operational Land Imager, National Polar-Orbiting Partnership/Visible Infrared Imaging Radiometer Suite and Aqua/Moderate Resolution Imaging Spectrometer to map the SPM concentration in some of the most turbid areas of the European coastal ocean, namely the Gironde and Loire estuaries as well as Bourgneuf Bay on the French Atlantic coast. For all three sensors, AC methods based on the use of short-wave infrared (SWIR spectral bands were tested, and the consistency of the retrieved water reflectance was examined along transects from low- to high-turbidity waters. For OLI data, we also compared a SWIR-based AC (ACOLITE with a method based on multi-temporal analyses of atmospheric constituents (MACCS. For the selected scenes, the ACOLITE-MACCS difference was

  12. Enhancing Sensing and Channel Access in Cognitive Radio Networks

    KAUST Repository

    Hamza, Doha R.

    2014-06-18

    Cognitive radio technology is a promising technology to solve the wireless spectrum scarcity problem by intelligently allowing secondary, or unlicensed, users access to the primary, licensed, users\\' frequency bands. Cognitive technology involves two main tasks: 1) sensing the wireless medium to assess the presence of the primary users and 2) designing secondary spectrum access techniques that maximize the secondary users\\' benefits while maintaining the primary users\\' privileged status. On the spectrum sensing side, we make two contributions. First, we maximize a utility function representing the secondary throughput while constraining the collision probability with the primary below a certain value. We optimize therein the channel sensing time, the sensing decision threshold, the channel probing time, together with the channel sensing order for wideband primary channels. Second, we design a cooperative spectrum sensing technique termed sensing with equal gain combining whereby cognitive radios simultaneously transmit their sensing results to the fusion center over multipath fading reporting channels. The proposed scheme is shown to outperform orthogonal reporting systems in terms of achievable secondary throughput and to be robust against phase and synchronization errors. On the spectrum access side, we make four contributions. First, we design a secondary scheduling scheme with the goal of minimizing the secondary queueing delay under constraints on the average secondary transmit power and the maximum tolerable primary outage probability. Second, we design another secondary scheduling scheme based on the spectrum sensing results and the primary automatic repeat request feedback. The optimal medium access probabilities are obtained via maximizing the secondary throughput subject to constraints that guarantee quality of service parameters for the primary. Third, we propose a three-message superposition coding scheme to maximize the secondary throughput without

  13. Compressed-sensing wavenumber-scanning interferometry

    Science.gov (United States)

    Bai, Yulei; Zhou, Yanzhou; He, Zhaoshui; Ye, Shuangli; Dong, Bo; Xie, Shengli

    2018-01-01

    The Fourier transform (FT), the nonlinear least-squares algorithm (NLSA), and eigenvalue decomposition algorithm (EDA) are used to evaluate the phase field in depth-resolved wavenumber-scanning interferometry (DRWSI). However, because the wavenumber series of the laser's output is usually accompanied by nonlinearity and mode-hop, FT, NLSA, and EDA, which are only suitable for equidistant interference data, often lead to non-negligible phase errors. In this work, a compressed-sensing method for DRWSI (CS-DRWSI) is proposed to resolve this problem. By using the randomly spaced inverse Fourier matrix and solving the underdetermined equation in the wavenumber domain, CS-DRWSI determines the nonuniform sampling and spectral leakage of the interference spectrum. Furthermore, it can evaluate interference data without prior knowledge of the object. The experimental results show that CS-DRWSI improves the depth resolution and suppresses sidelobes. It can replace the FT as a standard algorithm for DRWSI.

  14. Multimedia over cognitive radio networks algorithms, protocols, and experiments

    CERN Document Server

    Hu, Fei

    2014-01-01

    PrefaceAbout the EditorsContributorsNetwork Architecture to Support Multimedia over CRNA Management Architecture for Multimedia Communication in Cognitive Radio NetworksAlexandru O. Popescu, Yong Yao, Markus Fiedler , and Adrian P. PopescuPaving a Wider Way for Multimedia over Cognitive Radios: An Overview of Wideband Spectrum Sensing AlgorithmsBashar I. Ahmad, Hongjian Sun, Cong Ling, and Arumugam NallanathanBargaining-Based Spectrum Sharing for Broadband Multimedia Services in Cognitive Radio NetworkYang Yan, Xiang Chen, Xiaofeng Zhong, Ming Zhao, and Jing WangPhysical Layer Mobility Challen

  15. Accurate estimation of motion blur parameters in noisy remote sensing image

    Science.gov (United States)

    Shi, Xueyan; Wang, Lin; Shao, Xiaopeng; Wang, Huilin; Tao, Zhong

    2015-05-01

    The relative motion between remote sensing satellite sensor and objects is one of the most common reasons for remote sensing image degradation. It seriously weakens image data interpretation and information extraction. In practice, point spread function (PSF) should be estimated firstly for image restoration. Identifying motion blur direction and length accurately is very crucial for PSF and restoring image with precision. In general, the regular light-and-dark stripes in the spectrum can be employed to obtain the parameters by using Radon transform. However, serious noise existing in actual remote sensing images often causes the stripes unobvious. The parameters would be difficult to calculate and the error of the result relatively big. In this paper, an improved motion blur parameter identification method to noisy remote sensing image is proposed to solve this problem. The spectrum characteristic of noisy remote sensing image is analyzed firstly. An interactive image segmentation method based on graph theory called GrabCut is adopted to effectively extract the edge of the light center in the spectrum. Motion blur direction is estimated by applying Radon transform on the segmentation result. In order to reduce random error, a method based on whole column statistics is used during calculating blur length. Finally, Lucy-Richardson algorithm is applied to restore the remote sensing images of the moon after estimating blur parameters. The experimental results verify the effectiveness and robustness of our algorithm.

  16. Evaluation of the Revised Algorithm of Autism Diagnostic Observation Schedule (ADOS) in the Diagnostic Investigation of High-Functioning Children and Adolescents with Autism Spectrum Disorders

    Science.gov (United States)

    Kamp-Becker, Inge; Ghahreman, Mardjan; Heinzel-Gutenbrunner, Monika; Peters, Mira; Remschmidt, Helmut; Becker, Katja

    2013-01-01

    The Autism Diagnostic Observation Schedule (ADOS) is a semi-structured, standardized assessment designed for use in diagnostic evaluation of individuals with suspected autism spectrum disorder (ASD). The ADOS has been effective in categorizing children who definitely have autism or not, but has lower specificity and sometimes sensitivity for…

  17. Edge Detection from High Resolution Remote Sensing Images using Two-Dimensional log Gabor Filter in Frequency Domain

    International Nuclear Information System (INIS)

    Wang, K; Yu, T; Meng, Q Y; Wang, G K; Li, S P; Liu, S H

    2014-01-01

    Edges are vital features to describe the structural information of images, especially high spatial resolution remote sensing images. Edge features can be used to define the boundaries between different ground objects in high spatial resolution remote sensing images. Thus edge detection is important in the remote sensing image processing. Even though many different edge detection algorithms have been proposed, it is difficult to extract the edge features from high spatial resolution remote sensing image including complex ground objects. This paper introduces a novel method to detect edges from the high spatial resolution remote sensing image based on frequency domain. Firstly, the high spatial resolution remote sensing images are Fourier transformed to obtain the magnitude spectrum image (frequency image) by FFT. Then, the frequency spectrum is analyzed by using the radius and angle sampling. Finally, two-dimensional log Gabor filter with optimal parameters is designed according to the result of spectrum analysis. Finally, dot product between the result of Fourier transform and the log Gabor filter is inverse Fourier transformed to obtain the detections. The experimental result shows that the proposed algorithm can detect edge features from the high resolution remote sensing image commendably

  18. Distributed opportunistic spectrum sharing in cognitive radio networks

    KAUST Repository

    Hawa, Mohammed

    2016-05-19

    In cases where the licensed radio spectrum is underutilized, cognitive radio technology enables cognitive devices to sense and then dynamically access this scarce resource making the most out of it. In this work, we introduce a simple and intuitive, yet powerful and efficient, technique that allows opportunistic channel access in cognitive radio systems in a completely distributed fashion. Our proposed method achieves very high values of spectrum utilization and throughput. It also minimizes interference between cognitive base stations and the primary users licensed to use the spectrum. The algorithm responds quickly and efficiently to variations in the network parameters and also achieves a high degree of fairness between cognitive base stations. © 2016 John Wiley & Sons, Ltd.

  19. Optical remote sensing

    CERN Document Server

    Prasad, Saurabh; Chanussot, Jocelyn

    2011-01-01

    Optical remote sensing relies on exploiting multispectral and hyper spectral imagery possessing high spatial and spectral resolutions respectively. These modalities, although useful for most remote sensing tasks, often present challenges that must be addressed for their effective exploitation. This book presents current state-of-the-art algorithms that address the following key challenges encountered in representation and analysis of such optical remotely sensed data: challenges in pre-processing images, storing and representing high dimensional data, fusing different sensor modalities, patter

  20. A Multi-Channel Spectrum Sensing Fusion Mechanism for Cognitive Radio Networks: Design and Application to IEEE 802.22 WRANs

    OpenAIRE

    Tadayon, Navid; Aissa, Sonia

    2016-01-01

    The IEEE 802.22 is a new cognitive radio standard that is aimed at extending wireless outreach to rural areas. Known as wireless regional area networks, and designed based on the not-to-interfere spectrum sharing model, WRANs are channelized and centrally-controlled networks working on the under-utilized UHF/VHF TV bands to establish communication with remote users, so-called customer premises equipment (CPEs). Despite the importance of reliable and interference-free operation in these freque...

  1. Research on an uplink carrier sense multiple access algorithm of large indoor visible light communication networks based on an optical hard core point process.

    Science.gov (United States)

    Nan, Zhufen; Chi, Xuefen

    2016-12-20

    The IEEE 802.15.7 protocol suggests that it could coordinate the channel access process based on the competitive method of carrier sensing. However, the directionality of light and randomness of diffuse reflection would give rise to a serious imperfect carrier sense (ICS) problem [e.g., hidden node (HN) problem and exposed node (EN) problem], which brings great challenges in realizing the optical carrier sense multiple access (CSMA) mechanism. In this paper, the carrier sense process implemented by diffuse reflection light is modeled as the choice of independent sets. We establish an ICS model with the presence of ENs and HNs for the multi-point to multi-point visible light communication (VLC) uplink communications system. Considering the severe optical ICS problem, an optical hard core point process (OHCPP) is developed, which characterizes the optical CSMA for the indoor VLC uplink communications system. Due to the limited coverage of the transmitted optical signal, in our OHCPP, the ENs within the transmitters' carrier sense region could be retained provided that they could not corrupt the ongoing communications. Moreover, because of the directionality of both light emitting diode (LED) transmitters and receivers, theoretical analysis of the HN problem becomes difficult. In this paper, we derive the closed-form expression for approximating the outage probability and transmission capacity of VLC networks with the presence of HNs and ENs. Simulation results validate the analysis and also show the existence of an optimal physical carrier-sensing threshold that maximizes the transmission capacity for a given emission angle of LED.

  2. Intelligent environmental sensing

    CERN Document Server

    Mukhopadhyay, Subhas

    2015-01-01

    Developing environmental sensing and monitoring technologies become essential especially for industries that may cause severe contamination. Intelligent environmental sensing uses novel sensor techniques, intelligent signal and data processing algorithms, and wireless sensor networks to enhance environmental sensing and monitoring. It finds applications in many environmental problems such as oil and gas, water quality, and agriculture. This book addresses issues related to three main approaches to intelligent environmental sensing and discusses their latest technological developments. Key contents of the book include:   Agricultural monitoring Classification, detection, and estimation Data fusion Geological monitoring Motor monitoring Multi-sensor systems Oil reservoirs monitoring Sensor motes Water quality monitoring Wireless sensor network protocol  

  3. Urban Growth Modeling Using Cellular Automata with Multi-Temporal Remote Sensing Images Calibrated by the Artificial Bee Colony Optimization Algorithm.

    Science.gov (United States)

    Naghibi, Fereydoun; Delavar, Mahmoud Reza; Pijanowski, Bryan

    2016-12-14

    Cellular Automata (CA) is one of the most common techniques used to simulate the urbanization process. CA-based urban models use transition rules to deliver spatial patterns of urban growth and urban dynamics over time. Determining the optimum transition rules of the CA is a critical step because of the heterogeneity and nonlinearities existing among urban growth driving forces. Recently, new CA models integrated with optimization methods based on swarm intelligence algorithms were proposed to overcome this drawback. The Artificial Bee Colony (ABC) algorithm is an advanced meta-heuristic swarm intelligence-based algorithm. Here, we propose a novel CA-based urban change model that uses the ABC algorithm to extract optimum transition rules. We applied the proposed ABC-CA model to simulate future urban growth in Urmia (Iran) with multi-temporal Landsat images from 1997, 2006 and 2015. Validation of the simulation results was made through statistical methods such as overall accuracy, the figure of merit and total operating characteristics (TOC). Additionally, we calibrated the CA model by ant colony optimization (ACO) to assess the performance of our proposed model versus similar swarm intelligence algorithm methods. We showed that the overall accuracy and the figure of merit of the ABC-CA model are 90.1% and 51.7%, which are 2.9% and 8.8% higher than those of the ACO-CA model, respectively. Moreover, the allocation disagreement of the simulation results for the ABC-CA model is 9.9%, which is 2.9% less than that of the ACO-CA model. Finally, the ABC-CA model also outperforms the ACO-CA model with fewer quantity and allocation errors and slightly more hits.

  4. Multifractal signal reconstruction based on singularity power spectrum

    International Nuclear Information System (INIS)

    Xiong, Gang; Yu, Wenxian; Xia, Wenxiang; Zhang, Shuning

    2016-01-01

    Highlights: • We propose a novel multifractal reconstruction method based on singularity power spectrum analysis (MFR-SPS). • The proposed MFR-SPS method has better power characteristic than the algorithm in Fraclab. • Further, the SPS-ISE algorithm performs better than the SPS-MFS algorithm. • Based on the proposed MFR-SPS method, we can restructure singularity white fractal noise (SWFN) and linear singularity modulation (LSM) multifractal signal, in equivalent sense, similar with the linear frequency modulation(LFM) signal and WGN in the Fourier domain. - Abstract: Fractal reconstruction (FR) and multifractal reconstruction (MFR) can be considered as the inverse problem of singularity spectrum analysis, and it is challenging to reconstruct fractal signal in accord with multifractal spectrum (MFS). Due to the multiple solutions of fractal reconstruction, the traditional methods of FR/MFR, such as FBM based method, wavelet based method, random wavelet series, fail to reconstruct fractal signal deterministically, and besides, those methods neglect the power spectral distribution in the singular domain. In this paper, we propose a novel MFR method based singularity power spectrum (SPS). Supposing the consistent uniform covering of multifractal measurement, we control the traditional power law of each scale of wavelet coefficients based on the instantaneous singularity exponents (ISE) or MFS, simultaneously control the singularity power law based on the SPS, and deduce the principle and algorithm of MFR based on SPS. Reconstruction simulation and error analysis of estimated ISE, MFS and SPS show the effectiveness and the improvement of the proposed methods compared to those obtained by the Fraclab package.

  5. The WACMOS-ET project – Part 1: Tower-scale evaluation of four remote-sensing-based evapotranspiration algorithms

    KAUST Repository

    Michel, D.; Jimé nez, C.; Miralles, Diego G.; Jung, M.; Hirschi, M.; Ershadi, Ali; Martens, B.; McCabe, Matthew; Fisher, J. B.; Mu, Q.; Seneviratne, S. I.; Wood, E. F.; Ferná ndez-Prieto, D.

    2016-01-01

    The WAter Cycle Multi-mission Observation Strategy – EvapoTranspiration (WACMOS-ET) project has compiled a forcing data set covering the period 2005–2007 that aims to maximize the exploitation of European Earth Observations data sets for evapotranspiration (ET) estimation. The data set was used to run four established ET algorithms: the Priestley–Taylor Jet Propulsion Laboratory model (PT-JPL), the Penman–Monteith algorithm from the MODerate resolution Imaging Spectroradiometer (MODIS) evaporation product (PM-MOD), the Surface Energy Balance System (SEBS) and the Global Land Evaporation Amsterdam Model (GLEAM). In addition, in situ meteorological data from 24 FLUXNET towers were used to force the models, with results from both forcing sets compared to tower-based flux observations. Model performance was assessed on several timescales using both sub-daily and daily forcings. The PT-JPL model and GLEAM provide the best performance for both satellite- and tower-based forcing as well as for the considered temporal resolutions. Simulations using the PM-MOD were mostly underestimated, while the SEBS performance was characterized by a systematic overestimation. In general, all four algorithms produce the best results in wet and moderately wet climate regimes. In dry regimes, the correlation and the absolute agreement with the reference tower ET observations were consistently lower. While ET derived with in situ forcing data agrees best with the tower measurements (R2  =  0.67), the agreement of the satellite-based ET estimates is only marginally lower (R2  =  0.58). Results also show similar model performance at daily and sub-daily (3-hourly) resolutions. Overall, our validation experiments against in situ measurements indicate that there is no single best-performing algorithm across all biome and forcing types. An extension of the evaluation to a larger selection of 85 towers (model inputs resampled to a

  6. The WACMOS-ET project – Part 1: Tower-scale evaluation of four remote-sensing-based evapotranspiration algorithms

    KAUST Repository

    Michel, D.

    2016-02-23

    The WAter Cycle Multi-mission Observation Strategy – EvapoTranspiration (WACMOS-ET) project has compiled a forcing data set covering the period 2005–2007 that aims to maximize the exploitation of European Earth Observations data sets for evapotranspiration (ET) estimation. The data set was used to run four established ET algorithms: the Priestley–Taylor Jet Propulsion Laboratory model (PT-JPL), the Penman–Monteith algorithm from the MODerate resolution Imaging Spectroradiometer (MODIS) evaporation product (PM-MOD), the Surface Energy Balance System (SEBS) and the Global Land Evaporation Amsterdam Model (GLEAM). In addition, in situ meteorological data from 24 FLUXNET towers were used to force the models, with results from both forcing sets compared to tower-based flux observations. Model performance was assessed on several timescales using both sub-daily and daily forcings. The PT-JPL model and GLEAM provide the best performance for both satellite- and tower-based forcing as well as for the considered temporal resolutions. Simulations using the PM-MOD were mostly underestimated, while the SEBS performance was characterized by a systematic overestimation. In general, all four algorithms produce the best results in wet and moderately wet climate regimes. In dry regimes, the correlation and the absolute agreement with the reference tower ET observations were consistently lower. While ET derived with in situ forcing data agrees best with the tower measurements (R2  =  0.67), the agreement of the satellite-based ET estimates is only marginally lower (R2  =  0.58). Results also show similar model performance at daily and sub-daily (3-hourly) resolutions. Overall, our validation experiments against in situ measurements indicate that there is no single best-performing algorithm across all biome and forcing types. An extension of the evaluation to a larger selection of 85 towers (model inputs resampled to a

  7. Application of Near-Surface Remote Sensing and computer algorithms in evaluating impacts of agroecosystem management on Zea mays (corn) phenological development in the Platte River - High Plains Aquifer Long Term Agroecosystem Research Network field sites.

    Science.gov (United States)

    Okalebo, J. A.; Das Choudhury, S.; Awada, T.; Suyker, A.; LeBauer, D.; Newcomb, M.; Ward, R.

    2017-12-01

    The Long-term Agroecosystem Research (LTAR) network is a USDA-ARS effort that focuses on conducting research that addresses current and emerging issues in agriculture related to sustainability and profitability of agroecosystems in the face of climate change and population growth. There are 18 sites across the USA covering key agricultural production regions. In Nebraska, a partnership between the University of Nebraska - Lincoln and ARD/USDA resulted in the establishment of the Platte River - High Plains Aquifer LTAR site in 2014. The site conducts research to sustain multiple ecosystem services focusing specifically on Nebraska's main agronomic production agroecosystems that comprise of abundant corn, soybeans, managed grasslands and beef production. As part of the national LTAR network, PR-HPA participates and contributes near-surface remotely sensed imagery of corn, soybean and grassland canopy phenology to the PhenoCam Network through high-resolution digital cameras. This poster highlights the application, advantages and usefulness of near-surface remotely sensed imagery in agroecosystem studies and management. It demonstrates how both Infrared and Red-Green-Blue imagery may be applied to monitor phenological events as well as crop abiotic stresses. Computer-based algorithms and analytic techniques proved very instrumental in revealing crop phenological changes such as green-up and tasseling in corn. This poster also reports the suitability and applicability of corn-derived computer based algorithms for evaluating phenological development of sorghum since both crops have similarities in their phenology; with sorghum panicles being similar to corn tassels. This later assessment was carried out using a sorghum dataset obtained from the Transportation Energy Resources from Renewable Agriculture Phenotyping Reference Platform project, Maricopa Agricultural Center, Arizona.

  8. Elaborate analysis and design of filter-bank-based sensing for wideband cognitive radios

    Science.gov (United States)

    Maliatsos, Konstantinos; Adamis, Athanasios; Kanatas, Athanasios G.

    2014-12-01

    The successful operation of a cognitive radio system strongly depends on its ability to sense the radio environment. With the use of spectrum sensing algorithms, the cognitive radio is required to detect co-existing licensed primary transmissions and to protect them from interference. This paper focuses on filter-bank-based sensing and provides a solid theoretical background for the design of these detectors. Optimum detectors based on the Neyman-Pearson theorem are developed for uniform discrete Fourier transform (DFT) and modified DFT filter banks with root-Nyquist filters. The proposed sensing framework does not require frequency alignment between the filter bank of the sensor and the primary signal. Each wideband primary channel is spanned and monitored by several sensor subchannels that analyse it in narrowband signals. Filter-bank-based sensing is proved to be robust and efficient under coloured noise. Moreover, the performance of the weighted energy detector as a sensing technique is evaluated. Finally, based on the Locally Most Powerful and the Generalized Likelihood Ratio test, real-world sensing algorithms that do not require a priori knowledge are proposed and tested.

  9. Cognitive Radio for Smart Grid: Theory, Algorithms, and Security

    Directory of Open Access Journals (Sweden)

    Raghuram Ranganathan

    2011-01-01

    Full Text Available Recently, cognitive radio and smart grid are two areas which have received considerable research impetus. Cognitive radios are intelligent software defined radios (SDRs that efficiently utilize the unused regions of the spectrum, to achieve higher data rates. The smart grid is an automated electric power system that monitors and controls grid activities. In this paper, the novel concept of incorporating a cognitive radio network as the communications infrastructure for the smart grid is presented. A brief overview of the cognitive radio, IEEE 802.22 standard and smart grid, is provided. Experimental results obtained by using dimensionality reduction techniques such as principal component analysis (PCA, kernel PCA, and landmark maximum variance unfolding (LMVU on Wi-Fi signal measurements are presented in a spectrum sensing context. Furthermore, compressed sensing algorithms such as Bayesian compressed sensing and the compressed sensing Kalman filter is employed for recovering the sparse smart meter transmissions. From the power system point of view, a supervised learning method called support vector machine (SVM is used for the automated classification of power system disturbances. The impending problem of securing the smart grid is also addressed, in addition to the possibility of applying FPGA-based fuzzy logic intrusion detection for the smart grid.

  10. An algorithm for hyperspectral remote sensing of aerosols: 2. Information content analysis for aerosol parameters and principal components of surface spectra

    Science.gov (United States)

    Hou, Weizhen; Wang, Jun; Xu, Xiaoguang; Reid, Jeffrey S.

    2017-05-01

    This paper describes the second part of a series of investigation to develop algorithms for simultaneous retrieval of aerosol parameters and surface reflectance from the future hyperspectral and geostationary satellite sensors such as Tropospheric Emissions: Monitoring of POllution (TEMPO). The information content in these hyperspectral measurements is analyzed for 6 principal components (PCs) of surface spectra and a total of 14 aerosol parameters that describe the columnar aerosol volume Vtotal, fine-mode aerosol volume fraction, and the size distribution and wavelength-dependent index of refraction in both coarse and fine mode aerosols. Forward simulations of atmospheric radiative transfer are conducted for 5 surface types (green vegetation, bare soil, rangeland, concrete and mixed surface case) and a wide range of aerosol mixtures. It is shown that the PCs of surface spectra in the atmospheric window channel could be derived from the top-of-the-atmosphere reflectance in the conditions of low aerosol optical depth (AOD ≤ 0.2 at 550 nm), with a relative error of 1%. With degree freedom for signal analysis and the sequential forward selection method, the common bands for different aerosol mixture types and surface types can be selected for aerosol retrieval. The first 20% of our selected bands accounts for more than 90% of information content for aerosols, and only 4 PCs are needed to reconstruct surface reflectance. However, the information content in these common bands from each TEMPO individual observation is insufficient for the simultaneous retrieval of surface's PC weight coefficients and multiple aerosol parameters (other than Vtotal). In contrast, with multiple observations for the same location from TEMPO in multiple consecutive days, 1-3 additional aerosol parameters could be retrieved. Consequently, a self-adjustable aerosol retrieval algorithm to account for surface types, AOD conditions, and multiple-consecutive observations is recommended to derive

  11. Compressed sensing & sparse filtering

    CERN Document Server

    Carmi, Avishy Y; Godsill, Simon J

    2013-01-01

    This book is aimed at presenting concepts, methods and algorithms ableto cope with undersampled and limited data. One such trend that recently gained popularity and to some extent revolutionised signal processing is compressed sensing. Compressed sensing builds upon the observation that many signals in nature are nearly sparse (or compressible, as they are normally referred to) in some domain, and consequently they can be reconstructed to within high accuracy from far fewer observations than traditionally held to be necessary. Apart from compressed sensing this book contains other related app

  12. Feature Selection for Object-Based Classification of High-Resolution Remote Sensing Images Based on the Combination of a Genetic Algorithm and Tabu Search

    Directory of Open Access Journals (Sweden)

    Lei Shi

    2018-01-01

    Full Text Available In object-based image analysis of high-resolution images, the number of features can reach hundreds, so it is necessary to perform feature reduction prior to classification. In this paper, a feature selection method based on the combination of a genetic algorithm (GA and tabu search (TS is presented. The proposed GATS method aims to reduce the premature convergence of the GA by the use of TS. A prematurity index is first defined to judge the convergence situation during the search. When premature convergence does take place, an improved mutation operator is executed, in which TS is performed on individuals with higher fitness values. As for the other individuals with lower fitness values, mutation with a higher probability is carried out. Experiments using the proposed GATS feature selection method and three other methods, a standard GA, the multistart TS method, and ReliefF, were conducted on WorldView-2 and QuickBird images. The experimental results showed that the proposed method outperforms the other methods in terms of the final classification accuracy.

  13. Feature Selection for Object-Based Classification of High-Resolution Remote Sensing Images Based on the Combination of a Genetic Algorithm and Tabu Search

    Science.gov (United States)

    Shi, Lei; Wan, Youchuan; Gao, Xianjun

    2018-01-01

    In object-based image analysis of high-resolution images, the number of features can reach hundreds, so it is necessary to perform feature reduction prior to classification. In this paper, a feature selection method based on the combination of a genetic algorithm (GA) and tabu search (TS) is presented. The proposed GATS method aims to reduce the premature convergence of the GA by the use of TS. A prematurity index is first defined to judge the convergence situation during the search. When premature convergence does take place, an improved mutation operator is executed, in which TS is performed on individuals with higher fitness values. As for the other individuals with lower fitness values, mutation with a higher probability is carried out. Experiments using the proposed GATS feature selection method and three other methods, a standard GA, the multistart TS method, and ReliefF, were conducted on WorldView-2 and QuickBird images. The experimental results showed that the proposed method outperforms the other methods in terms of the final classification accuracy. PMID:29581721

  14. Algorithming the Algorithm

    DEFF Research Database (Denmark)

    Mahnke, Martina; Uprichard, Emma

    2014-01-01

    Imagine sailing across the ocean. The sun is shining, vastness all around you. And suddenly [BOOM] you’ve hit an invisible wall. Welcome to the Truman Show! Ever since Eli Pariser published his thoughts on a potential filter bubble, this movie scenario seems to have become reality, just with slight...... changes: it’s not the ocean, it’s the internet we’re talking about, and it’s not a TV show producer, but algorithms that constitute a sort of invisible wall. Building on this assumption, most research is trying to ‘tame the algorithmic tiger’. While this is a valuable and often inspiring approach, we...

  15. Spectrum Recombination.

    Science.gov (United States)

    Greenslade, Thomas B., Jr.

    1984-01-01

    Describes several methods of executing lecture demonstrations involving the recombination of the spectrum. Groups the techniques into two general classes: bringing selected portions of the spectrum together using lenses or mirrors and blurring the colors by rapid movement or foreshortening. (JM)

  16. Spectrum Hole Identification in IEEE 802.22 WRAN using Unsupervised Learning

    Directory of Open Access Journals (Sweden)

    V. Balaji

    2016-01-01

    Full Text Available In this paper we present a Cooperative Spectrum Sensing (CSS algorithm for Cognitive Radios (CR based on IEEE 802.22Wireless Regional Area Network (WRAN standard. The core objective is to improve cooperative sensing efficiency which specifies how fast a decision can be reached in each round of cooperation (iteration to sense an appropriate number of channels/bands (i.e. 86 channels of 7MHz bandwidth as per IEEE 802.22 within a time constraint (channel sensing time. To meet this objective, we have developed CSS algorithm using unsupervised K-means clustering classification approach. The received energy level of each Secondary User (SU is considered as the parameter for determining channel availability. The performance of proposed algorithm is quantified in terms of detection accuracy, training and classification delay time. Further, the detection accuracy of our proposed scheme meets the requirement of IEEE 802.22 WRAN with the target probability of falsealrm as 0.1. All the simulations are carried out using Matlab tool.

  17. Resource management for energy and spectrum harvesting sensor networks

    CERN Document Server

    Zhang, Deyu; Zhou, Haibo; Shen, Xuemin (Sherman)

    2017-01-01

    This SpringerBrief offers a comprehensive review and in-depth discussion of the current research on resource management. The authors explain how to best utilize harvested energy and temporally available licensed spectrum. Throughout the brief, the primary focus is energy and spectrum harvesting sensor networks (ESHNs) including energy harvesting (EH)-powered spectrum sensing and dynamic spectrum access. To efficiently collect data through the available licensed spectrum, this brief examines the joint management of energy and spectrum. An EH-powered spectrum sensing and management scheme for Heterogeneous Spectrum Harvesting Sensor Networks (HSHSNs) is presented in this brief. The scheme dynamically schedules the data sensing and spectrum access of sensors in ESHSNs to optimize the network utility, while considering the stochastic nature of EH process, PU activities and channel conditions. This brief also provides useful insights for the practical resource management scheme design for ESHSNs and motivates a ne...

  18. Decision Analysis of Dynamic Spectrum Access Rules

    Energy Technology Data Exchange (ETDEWEB)

    Juan D. Deaton; Luiz A. DaSilva; Christian Wernz

    2011-12-01

    A current trend in spectrum regulation is to incorporate spectrum sharing through the design of spectrum access rules that support Dynamic Spectrum Access (DSA). This paper develops a decision-theoretic framework for regulators to assess the impacts of different decision rules on both primary and secondary operators. We analyze access rules based on sensing and exclusion areas, which in practice can be enforced through geolocation databases. Our results show that receiver-only sensing provides insufficient protection for primary and co-existing secondary users and overall low social welfare. On the other hand, using sensing information between the transmitter and receiver of a communication link, provides dramatic increases in system performance. The performance of using these link end points is relatively close to that of using many cooperative sensing nodes associated to the same access point and large link exclusion areas. These results are useful to regulators and network developers in understanding in developing rules for future DSA regulation.

  19. Approximate equiangular tight frames for compressed sensing and CDMA applications

    Science.gov (United States)

    Tsiligianni, Evaggelia; Kondi, Lisimachos P.; Katsaggelos, Aggelos K.

    2017-12-01

    Performance guarantees for recovery algorithms employed in sparse representations, and compressed sensing highlights the importance of incoherence. Optimal bounds of incoherence are attained by equiangular unit norm tight frames (ETFs). Although ETFs are important in many applications, they do not exist for all dimensions, while their construction has been proven extremely difficult. In this paper, we construct frames that are close to ETFs. According to results from frame and graph theory, the existence of an ETF depends on the existence of its signature matrix, that is, a symmetric matrix with certain structure and spectrum consisting of two distinct eigenvalues. We view the construction of a signature matrix as an inverse eigenvalue problem and propose a method that produces frames of any dimensions that are close to ETFs. Due to the achieved equiangularity property, the so obtained frames can be employed as spreading sequences in synchronous code-division multiple access (s-CDMA) systems, besides compressed sensing.

  20. Optimal Pricing of Spectrum Resources in Wireless Opportunistic Access

    Directory of Open Access Journals (Sweden)

    Hanna Bogucka

    2012-01-01

    Full Text Available We consider opportunistic access to spectrum resources in cognitive wireless networks. The users equipment, or the network nodes in general are able to sense the spectrum and adopt a subset of available resources (the spectrum and the power individually and independently in a distributed manner, that is, based on their local channel quality information and not knowing the Channel State Information (CSI of the other nodes' links in the considered network area. In such a network scenery, the competition of nodes for available resources is observed, which can be modeled as a game. To obtain spectrally efficient and fair spectrum allocation in this competitive environment with the nodes having no information on the other players, taxation of resources is applied to coerce desired behavior of the competitors. In the paper, we present mathematical formulation of the problem of finding the optimal taxation rate (common for all nodes and propose a reduced-complexity algorithm for this optimization. Simulation results for these derived optimal values in various scenarios are also provided.

  1. Micro-Doppler Ambiguity Resolution Based on Short-Time Compressed Sensing

    Directory of Open Access Journals (Sweden)

    Jing-bo Zhuang

    2015-01-01

    Full Text Available When using a long range radar (LRR to track a target with micromotion, the micro-Doppler embodied in the radar echoes may suffer from ambiguity problem. In this paper, we propose a novel method based on compressed sensing (CS to solve micro-Doppler ambiguity. According to the RIP requirement, a sparse probing pulse train with its transmitting time random is designed. After matched filtering, the slow-time echo signals of the micromotion target can be viewed as randomly sparse sampling of Doppler spectrum. Select several successive pulses to form a short-time window and the CS sensing matrix can be built according to the time stamps of these pulses. Then performing Orthogonal Matching Pursuit (OMP, the unambiguous micro-Doppler spectrum can be obtained. The proposed algorithm is verified using the echo signals generated according to the theoretical model and the signals with micro-Doppler signature produced using the commercial electromagnetic simulation software FEKO.

  2. Sound algorithms

    OpenAIRE

    De Götzen , Amalia; Mion , Luca; Tache , Olivier

    2007-01-01

    International audience; We call sound algorithms the categories of algorithms that deal with digital sound signal. Sound algorithms appeared in the very infancy of computer. Sound algorithms present strong specificities that are the consequence of two dual considerations: the properties of the digital sound signal itself and its uses, and the properties of auditory perception.

  3. Genetic algorithms

    Science.gov (United States)

    Wang, Lui; Bayer, Steven E.

    1991-01-01

    Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology.

  4. Proceedings of the 1986 international geoscience and remote sensing symposium (IGARSS '86) on remote sensing: today's solutions for tomorrow's information needs, volume 1

    Energy Technology Data Exchange (ETDEWEB)

    Guyenne, T.D.; Hunt, J.J.

    1986-08-01

    New instruments with enormous information gathering abilities are being planned to provide data from all parts of the spectrum. New data processing and storage hardware, combined with fundamental advances in information systems concepts and algorithms are awaiting the research efforts to mold them for special use. Some topics covered in the proceedings are: Optical and infrared remote sensing systems; information transfer and Third World development; wave target interaction mechanisms; microwave remote sensing of sea ice; ERS-1 sensor performance, calibration, and data validation; geophysics; imaging spectrometry; image analysis systems; ocean radar scattering; marginal ice zone remote sensing; geomorphology; SAR applications; geology; multispectral image analysis; ocean wind scatterometry; passive microwave sensing; radar mapping and land use; meteorology and atmospheric sounding; and radar instrumentation.

  5. Randomized Filtering Algorithms

    DEFF Research Database (Denmark)

    Katriel, Irit; Van Hentenryck, Pascal

    2008-01-01

    of AllDifferent and is generalization, the Global Cardinality Constraint. The first delayed filtering scheme is a Monte Carlo algorithm: its running time is superior, in the worst case, to that of enforcing are consistency after every domain event, while its filtering effectiveness is analyzed...... in the expected sense. The second scheme is a Las Vegas algorithm using filtering triggers: Its effectiveness is the same as enforcing are consistency after every domain event, while in the expected case it is faster by a factor of m/n, where n and m are, respectively, the number of nodes and edges...

  6. Algorithmic cryptanalysis

    CERN Document Server

    Joux, Antoine

    2009-01-01

    Illustrating the power of algorithms, Algorithmic Cryptanalysis describes algorithmic methods with cryptographically relevant examples. Focusing on both private- and public-key cryptographic algorithms, it presents each algorithm either as a textual description, in pseudo-code, or in a C code program.Divided into three parts, the book begins with a short introduction to cryptography and a background chapter on elementary number theory and algebra. It then moves on to algorithms, with each chapter in this section dedicated to a single topic and often illustrated with simple cryptographic applic

  7. New Theory and Algorithms for Compressive Sensing

    National Research Council Canada - National Science Library

    Baraniuk, Richard G

    2009-01-01

    .... We first demonstrated the information scalability of CS. We applied CS principles to analog-to-digital conversion, showing ADC can be accomplished on structured high rate signals with sub-Nyquist sampling...

  8. Algorithmic mathematics

    CERN Document Server

    Hougardy, Stefan

    2016-01-01

    Algorithms play an increasingly important role in nearly all fields of mathematics. This book allows readers to develop basic mathematical abilities, in particular those concerning the design and analysis of algorithms as well as their implementation. It presents not only fundamental algorithms like the sieve of Eratosthenes, the Euclidean algorithm, sorting algorithms, algorithms on graphs, and Gaussian elimination, but also discusses elementary data structures, basic graph theory, and numerical questions. In addition, it provides an introduction to programming and demonstrates in detail how to implement algorithms in C++. This textbook is suitable for students who are new to the subject and covers a basic mathematical lecture course, complementing traditional courses on analysis and linear algebra. Both authors have given this "Algorithmic Mathematics" course at the University of Bonn several times in recent years.

  9. Remote Sensing

    CERN Document Server

    Khorram, Siamak; Koch, Frank H; van der Wiele, Cynthia F

    2012-01-01

    Remote Sensing provides information on how remote sensing relates to the natural resources inventory, management, and monitoring, as well as environmental concerns. It explains the role of this new technology in current global challenges. "Remote Sensing" will discuss remotely sensed data application payloads and platforms, along with the methodologies involving image processing techniques as applied to remotely sensed data. This title provides information on image classification techniques and image registration, data integration, and data fusion techniques. How this technology applies to natural resources and environmental concerns will also be discussed.

  10. Zellweger Spectrum

    Science.gov (United States)

    ... severe defect, resulting in essentially nonfunctional peroxisomes. This phenomenon produces the range of severity of the disorders. How is the Zellweger Spectrum Diagnosed? The distinctive shape of the head and face of a child born with one of the diseases of the ...

  11. Introducing molecular selectivity in rapid impedimetric sensing of phthalates

    KAUST Repository

    Zia, Asif I.

    2014-05-01

    This research article reports a real-time and non-invasive detection technique for phthalates in liquids by Electrochemical Impedance Spectroscopy (EIS), incorporating molecular imprinting technique to introduce selectivity for the phthalate molecule in the detection system. A functional polymer with Bis (2-ethylhexyl) phthalate (DEHP) template was immobilized on the sensing surface of the inter-digital (ID) capacitive sensor with sputtered gold sensing electrodes fabricated over a native layer of silicon dioxide on a single crystal silicon substrate. Various concentrations (10 to 200 ppm) of DEHP in deionized MilliQ water were exposed to the sensor surface functionalized with molecular imprinted polymer (MIP) in order to capture the analyte molecule, hence introducing molecular selectivity to the testing system. Impedance spectra were obtained using EIS in order to determine sample conductance for evaluation of phthalate concentration in the solution. Electrochemical Spectrum Analyzer algorithm was used to deduce equivalent circuit and equivalent component parameters from the experimentally obtained impedance spectra employing Randle\\'s cell model curve fitting technique. Experimental results confirmed that the immobilization of the functional polymer on sensing surface introduces selectivity for phthalates in the sensing system. The results were validated by testing the samples using High Performance Liquid Chromatography (HPLC-DAD). © 2014 IEEE.

  12. Total algorithms

    NARCIS (Netherlands)

    Tel, G.

    We define the notion of total algorithms for networks of processes. A total algorithm enforces that a "decision" is taken by a subset of the processes, and that participation of all processes is required to reach this decision. Total algorithms are an important building block in the design of

  13. Cognitive Spectrum Efficient Multiple Access Technique using Relay Systems

    DEFF Research Database (Denmark)

    Frederiksen, Flemming Bjerge; Prasad, Ramjee

    2007-01-01

    Methods to enhance the use of the frequency spectrum by automatical spectrum sensing plus spectrum sharing in a cognitive radio technology context will be presented and discussed in this paper. Ideas to increase the coverage of cellular systems by relay channels, relay stations and collaborate...

  14. Word Domain Disambiguation via Word Sense Disambiguation

    Energy Technology Data Exchange (ETDEWEB)

    Sanfilippo, Antonio P.; Tratz, Stephen C.; Gregory, Michelle L.

    2006-06-04

    Word subject domains have been widely used to improve the perform-ance of word sense disambiguation al-gorithms. However, comparatively little effort has been devoted so far to the disambiguation of word subject do-mains. The few existing approaches have focused on the development of al-gorithms specific to word domain dis-ambiguation. In this paper we explore an alternative approach where word domain disambiguation is achieved via word sense disambiguation. Our study shows that this approach yields very strong results, suggesting that word domain disambiguation can be ad-dressed in terms of word sense disam-biguation with no need for special purpose algorithms.

  15. Compressed sensing for distributed systems

    CERN Document Server

    Coluccia, Giulio; Magli, Enrico

    2015-01-01

    This book presents a survey of the state-of-the art in the exciting and timely topic of compressed sensing for distributed systems. It has to be noted that, while compressed sensing has been studied for some time now, its distributed applications are relatively new. Remarkably, such applications are ideally suited to exploit all the benefits that compressed sensing can provide. The objective of this book is to provide the reader with a comprehensive survey of this topic, from the basic concepts to different classes of centralized and distributed reconstruction algorithms, as well as a comparison of these techniques. This book collects different contributions on these aspects. It presents the underlying theory in a complete and unified way for the first time, presenting various signal models and their use cases. It contains a theoretical part collecting latest results in rate-distortion analysis of distributed compressed sensing, as well as practical implementations of algorithms obtaining performance close to...

  16. Glucose Sensing

    CERN Document Server

    Geddes, Chris D

    2006-01-01

    Topics in Fluorescence Spectroscopy, Glucose Sensing is the eleventh volume in the popular series Topics in Fluorescence Spectroscopy, edited by Drs. Chris D. Geddes and Joseph R. Lakowicz. This volume incorporates authoritative analytical fluorescence-based glucose sensing reviews specialized enough to be attractive to professional researchers, yet also appealing to the wider audience of scientists in related disciplines of fluorescence. Glucose Sensing is an essential reference for any lab working in the analytical fluorescence glucose sensing field. All academics, bench scientists, and industry professionals wishing to take advantage of the latest and greatest in the continuously emerging field of glucose sensing, and diabetes care & management, will find this volume an invaluable resource. Topics in Fluorescence Spectroscopy Volume 11, Glucose Sensing Chapters include: Implantable Sensors for Interstitial Fluid Smart Tattoo Glucose Sensors Optical Enzyme-based Glucose Biosensors Plasmonic Glucose Sens...

  17. Make Sense?

    DEFF Research Database (Denmark)

    Gyrd-Jones, Richard; Törmälä, Minna

    Purpose: An important part of how we sense a brand is how we make sense of a brand. Sense-making is naturally strongly connected to how we cognize about the brand. But sense-making is concerned with multiple forms of knowledge that arise from our interpretation of the brand-related stimuli......: Declarative, episodic, procedural and sensory. Knowledge is given meaning through mental association (Keller, 1993) and / or symbolic interaction (Blumer, 1969). These meanings are centrally related to individuals’ sense of identity or “identity needs” (Wallpach & Woodside, 2009). The way individuals make...... sense of brands is related to who people think they are in their context and this shapes what they enact and how they interpret the brand (Currie & Brown, 2003; Weick, Sutcliffe, & Obstfeld, 2005; Weick, 1993). Our subject of interest in this paper is how stakeholders interpret and ascribe meaning...

  18. Algorithms and Public Service Media

    OpenAIRE

    Sørensen, Jannick Kirk; Hutchinson, Jonathon

    2018-01-01

    When Public Service Media (PSM) organisations introduce algorithmic recommender systems to suggest media content to users, fundamental values of PSM are challenged. Beyond being confronted with ubiquitous computer ethics problems of causality and transparency, also the identity of PSM as curator and agenda-setter is challenged. The algorithms represents rules for which content to present to whom, and in this sense they may discriminate and bias the exposure of diversity. Furthermore, on a pra...

  19. New algorithms for parallel MRI

    International Nuclear Information System (INIS)

    Anzengruber, S; Ramlau, R; Bauer, F; Leitao, A

    2008-01-01

    Magnetic Resonance Imaging with parallel data acquisition requires algorithms for reconstructing the patient's image from a small number of measured lines of the Fourier domain (k-space). In contrast to well-known algorithms like SENSE and GRAPPA and its flavors we consider the problem as a non-linear inverse problem. However, in order to avoid cost intensive derivatives we will use Landweber-Kaczmarz iteration and in order to improve the overall results some additional sparsity constraints.

  20. Ship detection using STFT sea background statistical modeling for large-scale oceansat remote sensing image

    Science.gov (United States)

    Wang, Lixia; Pei, Jihong; Xie, Weixin; Liu, Jinyuan

    2018-03-01

    Large-scale oceansat remote sensing images cover a big area sea surface, which fluctuation can be considered as a non-stationary process. Short-Time Fourier Transform (STFT) is a suitable analysis tool for the time varying nonstationary signal. In this paper, a novel ship detection method using 2-D STFT sea background statistical modeling for large-scale oceansat remote sensing images is proposed. First, the paper divides the large-scale oceansat remote sensing image into small sub-blocks, and 2-D STFT is applied to each sub-block individually. Second, the 2-D STFT spectrum of sub-blocks is studied and the obvious different characteristic between sea background and non-sea background is found. Finally, the statistical model for all valid frequency points in the STFT spectrum of sea background is given, and the ship detection method based on the 2-D STFT spectrum modeling is proposed. The experimental result shows that the proposed algorithm can detect ship targets with high recall rate and low missing rate.

  1. Autonomous Coral Reef Survey in Support of Remote Sensing

    Directory of Open Access Journals (Sweden)

    Steven G. Ackleson

    2017-10-01

    Full Text Available An autonomous surface vehicle instrumented with optical and acoustical sensors was deployed in Kane'ohe Bay, HI, U.S.A., to provide high-resolution, in situ observations of coral reef reflectance with minimal human presence. The data represented a wide range in bottom type, water depth, and illumination and supported more thorough investigations of remote sensing methods for identifying and mapping shallow reef features. The in situ data were used to compute spectral bottom reflectance and remote sensing reflectance, Rrs,λ, as a function of water depth and benthic features. The signals were used to distinguish between live coral and uncolonized sediment within the depth range of the measurements (2.5–5 m. In situRrs, λ were found to compare well with remotely sensed measurements from an imaging spectrometer, the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS, deployed on an aircraft at high altitude. Cloud cover and in situ sensor orientation were found to have minimal impact on in situRrs, λ, suggesting that valid reflectance data may be collected using autonomous surveys even when atmospheric conditions are not favorable for remote sensing operations. The use of reflectance in the red and near infrared portions of the spectrum, expressed as the red edge height, REHλ, was investigated for detecting live aquatic vegetative biomass, including coral symbionts and turf algae. The REHλ signal from live coral was detected in Kane'ohe Bay to a depth of approximately 4 m with in situ measurements. A remote sensing algorithm based on the REHλ signal was defined and applied to AVIRIS imagery of the entire bay and was found to reveal areas of shallow, dense coral and algal cover. The peak wavelength of REHλ decreased with increasing water depth, indicating that a more complete examination of the red edge signal may potentially yield a remote sensing approach to simultaneously estimate vegetative biomass and bathymetry in shallow water.

  2. Overview of remote sensing of chlorophyll flourescene in ocean waters

    African Journals Online (AJOL)

    Overview of remote sensing of chlorophyll flourescene in ocean waters. ... Besides empirical algorithms with the blue-green ratio, the algorithms based on ... between fluorescence and chlorophyll concentration and the red shift phenomena.

  3. REMOTE SENSING FOR ENVIRONMENTAL COMPLIANCE MONITORING

    Science.gov (United States)

    I. Remote Sensing Basics A. The electromagnetic spectrum demonstrates what we can see both in the visible and beyond the visible part of the spectrum through the use of various types of sensors. B. Resolution refers to what a remote sensor can see and how often. 1. Sp...

  4. Operational Automatic Remote Sensing Image Understanding Systems: Beyond Geographic Object-Based and Object-Oriented Image Analysis (GEOBIA/GEOOIA. Part 2: Novel system Architecture, Information/Knowledge Representation, Algorithm Design and Implementation

    Directory of Open Access Journals (Sweden)

    Luigi Boschetti

    2012-09-01

    Full Text Available According to literature and despite their commercial success, state-of-the-art two-stage non-iterative geographic object-based image analysis (GEOBIA systems and three-stage iterative geographic object-oriented image analysis (GEOOIA systems, where GEOOIA/GEOBIA, remain affected by a lack of productivity, general consensus and research. To outperform the Quality Indexes of Operativeness (OQIs of existing GEOBIA/GEOOIA systems in compliance with the Quality Assurance Framework for Earth Observation (QA4EO guidelines, this methodological work is split into two parts. Based on an original multi-disciplinary Strengths, Weaknesses, Opportunities and Threats (SWOT analysis of the GEOBIA/GEOOIA approaches, the first part of this work promotes a shift of learning paradigm in the pre-attentive vision first stage of a remote sensing (RS image understanding system (RS-IUS, from sub-symbolic statistical model-based (inductive image segmentation to symbolic physical model-based (deductive image preliminary classification capable of accomplishing image sub-symbolic segmentation and image symbolic pre-classification simultaneously. In the present second part of this work, a novel hybrid (combined deductive and inductive RS-IUS architecture featuring a symbolic deductive pre-attentive vision first stage is proposed and discussed in terms of: (a computational theory (system design, (b information/knowledge representation, (c algorithm design and (d implementation. As proof-of-concept of symbolic physical model-based pre-attentive vision first stage, the spectral knowledge-based, operational, near real-time, multi-sensor, multi-resolution, application-independent Satellite Image Automatic Mapper™ (SIAM™ is selected from existing literature. To the best of these authors’ knowledge, this is the first time a symbolic syntactic inference system, like SIAM™, is made available to the RS community for operational use in a RS-IUS pre-attentive vision first stage

  5. Spectrum sharing in cognitive radio networks medium access control protocol based approach

    CERN Document Server

    Pandit, Shweta

    2017-01-01

    This book discusses the use of the spectrum sharing techniques in cognitive radio technology, in order to address the problem of spectrum scarcity for future wireless communications. The authors describe a cognitive radio medium access control (MAC) protocol, with which throughput maximization has been achieved. The discussion also includes use of this MAC protocol for imperfect sensing scenarios and its effect on the performance of cognitive radio systems. The authors also discuss how energy efficiency has been maximized in this system, by applying a simple algorithm for optimizing the transmit power of the cognitive user. The study about the channel fading in the cognitive user and licensed user and power adaption policy in this scenario under peak transmit power and interference power constraint is also present in this book.

  6. SENSOR++: Simulation of Remote Sensing Systems from Visible to Thermal Infrared

    Science.gov (United States)

    Paproth, C.; Schlüßler, E.; Scherbaum, P.; Börner, A.

    2012-07-01

    During the development process of a remote sensing system, the optimization and the verification of the sensor system are important tasks. To support these tasks, the simulation of the sensor and its output is valuable. This enables the developers to test algorithms, estimate errors, and evaluate the capabilities of the whole sensor system before the final remote sensing system is available and produces real data. The presented simulation concept, SENSOR++, consists of three parts. The first part is the geometric simulation which calculates where the sensor looks at by using a ray tracing algorithm. This also determines whether the observed part of the scene is shadowed or not. The second part describes the radiometry and results in the spectral at-sensor radiance from the visible spectrum to the thermal infrared according to the simulated sensor type. In the case of earth remote sensing, it also includes a model of the radiative transfer through the atmosphere. The final part uses the at-sensor radiance to generate digital images by using an optical and an electronic sensor model. Using SENSOR++ for an optimization requires the additional application of task-specific data processing algorithms. The principle of the simulation approach is explained, all relevant concepts of SENSOR++ are discussed, and first examples of its use are given, for example a camera simulation for a moon lander. Finally, the verification of SENSOR++ is demonstrated.

  7. Compressive sensing in medical imaging.

    Science.gov (United States)

    Graff, Christian G; Sidky, Emil Y

    2015-03-10

    The promise of compressive sensing, exploitation of compressibility to achieve high quality image reconstructions with less data, has attracted a great deal of attention in the medical imaging community. At the Compressed Sensing Incubator meeting held in April 2014 at OSA Headquarters in Washington, DC, presentations were given summarizing some of the research efforts ongoing in compressive sensing for x-ray computed tomography and magnetic resonance imaging systems. This article provides an expanded version of these presentations. Sparsity-exploiting reconstruction algorithms that have gained popularity in the medical imaging community are studied, and examples of clinical applications that could benefit from compressive sensing ideas are provided. The current and potential future impact of compressive sensing on the medical imaging field is discussed.

  8. Cooperative Convex Optimization in Networked Systems: Augmented Lagrangian Algorithms With Directed Gossip Communication

    Science.gov (United States)

    Jakovetic, Dusan; Xavier, João; Moura, José M. F.

    2011-08-01

    We study distributed optimization in networked systems, where nodes cooperate to find the optimal quantity of common interest, x=x^\\star. The objective function of the corresponding optimization problem is the sum of private (known only by a node,) convex, nodes' objectives and each node imposes a private convex constraint on the allowed values of x. We solve this problem for generic connected network topologies with asymmetric random link failures with a novel distributed, decentralized algorithm. We refer to this algorithm as AL-G (augmented Lagrangian gossiping,) and to its variants as AL-MG (augmented Lagrangian multi neighbor gossiping) and AL-BG (augmented Lagrangian broadcast gossiping.) The AL-G algorithm is based on the augmented Lagrangian dual function. Dual variables are updated by the standard method of multipliers, at a slow time scale. To update the primal variables, we propose a novel, Gauss-Seidel type, randomized algorithm, at a fast time scale. AL-G uses unidirectional gossip communication, only between immediate neighbors in the network and is resilient to random link failures. For networks with reliable communication (i.e., no failures,) the simplified, AL-BG (augmented Lagrangian broadcast gossiping) algorithm reduces communication, computation and data storage cost. We prove convergence for all proposed algorithms and demonstrate by simulations the effectiveness on two applications: l_1-regularized logistic regression for classification and cooperative spectrum sensing for cognitive radio networks.

  9. Spectrum 101: An Introduction to Spectrum Management

    Science.gov (United States)

    2004-03-01

    produces a Joint Restricted Frequency List (JRFL). The JFRL consolidates and classifies the spectrum uses that are most critical to operations and to...Management Office JRFL Joint Restricted Frequency List JSC Joint Spectrum Center JSIR Joint Spectrum Interference Resolution JSME Joint Spectrum...Multifunctional Information Distribution System MILSATCOM Military Satellite Communications MOA Memorandum of Agreement MRFL Master Radio Frequency

  10. Adaptive MCS selection and resource planning for energy-efficient communication in LTE-M based IoT sensing platform.

    Science.gov (United States)

    Dao, Nhu-Ngoc; Park, Minho; Kim, Joongheon; Cho, Sungrae

    2017-01-01

    As an important part of IoTization trends, wireless sensing technologies have been involved in many fields of human life. In cellular network evolution, the long term evolution advanced (LTE-A) networks including machine-type communication (MTC) features (named LTE-M) provide a promising infrastructure for a proliferation of Internet of things (IoT) sensing platform. However, LTE-M may not be optimally exploited for directly supporting such low-data-rate devices in terms of energy efficiency since it depends on core technologies of LTE that are originally designed for high-data-rate services. Focusing on this circumstance, we propose a novel adaptive modulation and coding selection (AMCS) algorithm to address the energy consumption problem in the LTE-M based IoT-sensing platform. The proposed algorithm determines the optimal pair of MCS and the number of primary resource blocks (#PRBs), at which the transport block size is sufficient to packetize the sensing data within the minimum transmit power. In addition, a quantity-oriented resource planning (QORP) technique that utilizes these optimal MCS levels as main criteria for spectrum allocation has been proposed for better adapting to the sensing node requirements. The simulation results reveal that the proposed approach significantly reduces the energy consumption of IoT sensing nodes and #PRBs up to 23.09% and 25.98%, respectively.

  11. Spectral Compressive Sensing with Polar Interpolation

    DEFF Research Database (Denmark)

    Fyhn, Karsten; Dadkhahi, Hamid; F. Duarte, Marco

    2013-01-01

    . In this paper, we introduce a greedy recovery algorithm that leverages a band-exclusion function and a polar interpolation function to address these two issues in spectral compressive sensing. Our algorithm is geared towards line spectral estimation from compressive measurements and outperforms most existing...

  12. Compressive sensing sectional imaging for single-shot in-line self-interference incoherent holography

    Science.gov (United States)

    Weng, Jiawen; Clark, David C.; Kim, Myung K.

    2016-05-01

    A numerical reconstruction method based on compressive sensing (CS) for self-interference incoherent digital holography (SIDH) is proposed to achieve sectional imaging by single-shot in-line self-interference incoherent hologram. The sensing operator is built up based on the physical mechanism of SIDH according to CS theory, and a recovery algorithm is employed for image restoration. Numerical simulation and experimental studies employing LEDs as discrete point-sources and resolution targets as extended sources are performed to demonstrate the feasibility and validity of the method. The intensity distribution and the axial resolution along the propagation direction of SIDH by angular spectrum method (ASM) and by CS are discussed. The analysis result shows that compared to ASM the reconstruction by CS can improve the axial resolution of SIDH, and achieve sectional imaging. The proposed method may be useful to 3D analysis of dynamic systems.

  13. Distributed opportunistic spectrum sharing in cognitive radio networks

    KAUST Repository

    Hawa, Mohammed; Alammouri, Ahmad; Alhiary, Ala; Alhamad, Nidal

    2016-01-01

    In cases where the licensed radio spectrum is underutilized, cognitive radio technology enables cognitive devices to sense and then dynamically access this scarce resource making the most out of it. In this work, we introduce a simple and intuitive

  14. Development of airborne remote sensing data assimilation system

    International Nuclear Information System (INIS)

    Gudu, B R; Bi, H Y; Wang, H Y; Qin, S X; Ma, J W

    2014-01-01

    In this paper, an airborne remote sensing data assimilation system for China Airborne Remote Sensing System is introduced. This data assimilation system is composed of a land surface model, data assimilation algorithms, observation data and fundamental parameters forcing the land surface model. In this data assimilation system, Variable Infiltration Capacity hydrologic model is selected as the land surface model, which also serves as the main framework of the system. Three-dimensional variation algorithm, four-dimensional variation algorithms, ensemble Kalman filter and Particle filter algorithms are integrated in this system. Observation data includes ground observations and remotely sensed data. The fundamental forcing parameters include soil parameters, vegetation parameters and the meteorological data

  15. Algorithmic alternatives

    International Nuclear Information System (INIS)

    Creutz, M.

    1987-11-01

    A large variety of Monte Carlo algorithms are being used for lattice gauge simulations. For purely bosonic theories, present approaches are generally adequate; nevertheless, overrelaxation techniques promise savings by a factor of about three in computer time. For fermionic fields the situation is more difficult and less clear. Algorithms which involve an extrapolation to a vanishing step size are all quite closely related. Methods which do not require such an approximation tend to require computer time which grows as the square of the volume of the system. Recent developments combining global accept/reject stages with Langevin or microcanonical updatings promise to reduce this growth to V/sup 4/3/

  16. Combinatorial algorithms

    CERN Document Server

    Hu, T C

    2002-01-01

    Newly enlarged, updated second edition of a valuable text presents algorithms for shortest paths, maximum flows, dynamic programming and backtracking. Also discusses binary trees, heuristic and near optimums, matrix multiplication, and NP-complete problems. 153 black-and-white illus. 23 tables.Newly enlarged, updated second edition of a valuable, widely used text presents algorithms for shortest paths, maximum flows, dynamic programming and backtracking. Also discussed are binary trees, heuristic and near optimums, matrix multiplication, and NP-complete problems. New to this edition: Chapter 9

  17. Fast Dictionary-Based Reconstruction for Diffusion Spectrum Imaging

    Science.gov (United States)

    Bilgic, Berkin; Chatnuntawech, Itthi; Setsompop, Kawin; Cauley, Stephen F.; Yendiki, Anastasia; Wald, Lawrence L.; Adalsteinsson, Elfar

    2015-01-01

    Diffusion Spectrum Imaging (DSI) reveals detailed local diffusion properties at the expense of substantially long imaging times. It is possible to accelerate acquisition by undersampling in q-space, followed by image reconstruction that exploits prior knowledge on the diffusion probability density functions (pdfs). Previously proposed methods impose this prior in the form of sparsity under wavelet and total variation (TV) transforms, or under adaptive dictionaries that are trained on example datasets to maximize the sparsity of the representation. These compressed sensing (CS) methods require full-brain processing times on the order of hours using Matlab running on a workstation. This work presents two dictionary-based reconstruction techniques that use analytical solutions, and are two orders of magnitude faster than the previously proposed dictionary-based CS approach. The first method generates a dictionary from the training data using Principal Component Analysis (PCA), and performs the reconstruction in the PCA space. The second proposed method applies reconstruction using pseudoinverse with Tikhonov regularization with respect to a dictionary. This dictionary can either be obtained using the K-SVD algorithm, or it can simply be the training dataset of pdfs without any training. All of the proposed methods achieve reconstruction times on the order of seconds per imaging slice, and have reconstruction quality comparable to that of dictionary-based CS algorithm. PMID:23846466

  18. Fast dictionary-based reconstruction for diffusion spectrum imaging.

    Science.gov (United States)

    Bilgic, Berkin; Chatnuntawech, Itthi; Setsompop, Kawin; Cauley, Stephen F; Yendiki, Anastasia; Wald, Lawrence L; Adalsteinsson, Elfar

    2013-11-01

    Diffusion spectrum imaging reveals detailed local diffusion properties at the expense of substantially long imaging times. It is possible to accelerate acquisition by undersampling in q-space, followed by image reconstruction that exploits prior knowledge on the diffusion probability density functions (pdfs). Previously proposed methods impose this prior in the form of sparsity under wavelet and total variation transforms, or under adaptive dictionaries that are trained on example datasets to maximize the sparsity of the representation. These compressed sensing (CS) methods require full-brain processing times on the order of hours using MATLAB running on a workstation. This work presents two dictionary-based reconstruction techniques that use analytical solutions, and are two orders of magnitude faster than the previously proposed dictionary-based CS approach. The first method generates a dictionary from the training data using principal component analysis (PCA), and performs the reconstruction in the PCA space. The second proposed method applies reconstruction using pseudoinverse with Tikhonov regularization with respect to a dictionary. This dictionary can either be obtained using the K-SVD algorithm, or it can simply be the training dataset of pdfs without any training. All of the proposed methods achieve reconstruction times on the order of seconds per imaging slice, and have reconstruction quality comparable to that of dictionary-based CS algorithm.

  19. Studies on Five Senses Treatment

    Science.gov (United States)

    Sato, Sadaka; Miao, Tiejun; Oyama-Higa, Mayumi

    2011-06-01

    This study proposed a therapy from complementary and alternative medicine to treat mental disorder by through interactions of five senses between therapist and patient. In this method sounding a certain six voices play an important role in healing and recovery. First, we studied effects of speaking using scalp- EEG measurement. Chaos analysis of EEG showed a largely enhanced largest Lyapunov exponent (LLE) during the speaking. In addition, EEG power spectrum showed an increase over most frequencies. Second, we performed case studies on mental disorder using the therapy. Running power spectrum of EEG of patients indicated decreasing power at end of treatment, implying five senses therapy induced relaxed and lowered energy in central neural system. The results agreed with patient's reports that there were considerable decline in anxiety and improvements in mood.

  20. Pervasive sensing

    Science.gov (United States)

    Nagel, David J.

    2000-11-01

    The coordinated exploitation of modern communication, micro- sensor and computer technologies makes it possible to give global reach to our senses. Web-cameras for vision, web- microphones for hearing and web-'noses' for smelling, plus the abilities to sense many factors we cannot ordinarily perceive, are either available or will be soon. Applications include (1) determination of weather and environmental conditions on dense grids or over large areas, (2) monitoring of energy usage in buildings, (3) sensing the condition of hardware in electrical power distribution and information systems, (4) improving process control and other manufacturing, (5) development of intelligent terrestrial, marine, aeronautical and space transportation systems, (6) managing the continuum of routine security monitoring, diverse crises and military actions, and (7) medicine, notably the monitoring of the physiology and living conditions of individuals. Some of the emerging capabilities, such as the ability to measure remotely the conditions inside of people in real time, raise interesting social concerns centered on privacy issues. Methods for sensor data fusion and designs for human-computer interfaces are both crucial for the full realization of the potential of pervasive sensing. Computer-generated virtual reality, augmented with real-time sensor data, should be an effective means for presenting information from distributed sensors.

  1. The Improved Adaptive Silence Period Algorithm over Time-Variant Channels in the Cognitive Radio System

    Directory of Open Access Journals (Sweden)

    Jingbo Zhang

    2018-01-01

    Full Text Available In the field of cognitive radio spectrum sensing, the adaptive silence period management mechanism (ASPM has improved the problem of the low time-resource utilization rate of the traditional silence period management mechanism (TSPM. However, in the case of the low signal-to-noise ratio (SNR, the ASPM algorithm will increase the probability of missed detection for the primary user (PU. Focusing on this problem, this paper proposes an improved adaptive silence period management (IA-SPM algorithm which can adaptively adjust the sensing parameters of the current period in combination with the feedback information from the data communication with the sensing results of the previous period. The feedback information in the channel is achieved with frequency resources rather than time resources in order to adapt to the parameter change in the time-varying channel. The Monte Carlo simulation results show that the detection probability of the IA-SPM is 10–15% higher than that of the ASPM under low SNR conditions.

  2. Subsampling for graph power spectrum estimation

    KAUST Repository

    Chepuri, Sundeep Prabhakar; Leus, Geert

    2016-01-01

    In this paper we focus on subsampling stationary random signals that reside on the vertices of undirected graphs. Second-order stationary graph signals are obtained by filtering white noise and they admit a well-defined power spectrum. Estimating the graph power spectrum forms a central component of stationary graph signal processing and related inference tasks. We show that by sampling a significantly smaller subset of vertices and using simple least squares, we can reconstruct the power spectrum of the graph signal from the subsampled observations, without any spectral priors. In addition, a near-optimal greedy algorithm is developed to design the subsampling scheme.

  3. Subsampling for graph power spectrum estimation

    KAUST Repository

    Chepuri, Sundeep Prabhakar

    2016-10-06

    In this paper we focus on subsampling stationary random signals that reside on the vertices of undirected graphs. Second-order stationary graph signals are obtained by filtering white noise and they admit a well-defined power spectrum. Estimating the graph power spectrum forms a central component of stationary graph signal processing and related inference tasks. We show that by sampling a significantly smaller subset of vertices and using simple least squares, we can reconstruct the power spectrum of the graph signal from the subsampled observations, without any spectral priors. In addition, a near-optimal greedy algorithm is developed to design the subsampling scheme.

  4. Autodriver algorithm

    Directory of Open Access Journals (Sweden)

    Anna Bourmistrova

    2011-02-01

    Full Text Available The autodriver algorithm is an intelligent method to eliminate the need of steering by a driver on a well-defined road. The proposed method performs best on a four-wheel steering (4WS vehicle, though it is also applicable to two-wheel-steering (TWS vehicles. The algorithm is based on coinciding the actual vehicle center of rotation and road center of curvature, by adjusting the kinematic center of rotation. The road center of curvature is assumed prior information for a given road, while the dynamic center of rotation is the output of dynamic equations of motion of the vehicle using steering angle and velocity measurements as inputs. We use kinematic condition of steering to set the steering angles in such a way that the kinematic center of rotation of the vehicle sits at a desired point. At low speeds the ideal and actual paths of the vehicle are very close. With increase of forward speed the road and tire characteristics, along with the motion dynamics of the vehicle cause the vehicle to turn about time-varying points. By adjusting the steering angles, our algorithm controls the dynamic turning center of the vehicle so that it coincides with the road curvature center, hence keeping the vehicle on a given road autonomously. The position and orientation errors are used as feedback signals in a closed loop control to adjust the steering angles. The application of the presented autodriver algorithm demonstrates reliable performance under different driving conditions.

  5. Rapid and molecular selective electrochemical sensing of phthalates in aqueous solution

    KAUST Repository

    Zia, Asif I.

    2015-05-01

    Reported research work presents real time non-invasive detection of phthalates in spiked aqueous samples by employing electrochemical impedance spectroscopy (EIS) technique incorporating a novel interdigital capacitive sensor with multiple sensing thin film gold micro-electrodes fabricated on native silicon dioxide layer grown on semiconducting single crystal silicon wafer. The sensing surface was functionalized by a self-assembled monolayer of 3-aminopropyltrietoxysilane (APTES) with embedded molecular imprinted polymer (MIP) to introduce selectivity for the di(2-ethylhexyl) phthalate (DEHP) molecule. Various concentrations (1-100. ppm) of DEHP in deionized MilliQ water were tested using the functionalized sensing surface to capture the analyte. Frequency response analyzer (FRA) algorithm was used to obtain impedance spectra so as to determine sample conductance and capacitance for evaluation of phthalate concentration in the sample solution. Spectrum analysis algorithm interpreted the experimentally obtained impedance spectra by applying complex nonlinear least square (CNLS) curve fitting in order to obtain electrochemical equivalent circuit and corresponding circuit parameters describing the kinetics of the electrochemical cell. Principal component analysis was applied to deduce the effects of surface immobilized molecular imprinted polymer layer on the evaluated circuit parameters and its electrical response. The results obtained by the testing system were validated using commercially available high performance liquid chromatography diode array detector system.

  6. Quantum-circuit model of Hamiltonian search algorithms

    International Nuclear Information System (INIS)

    Roland, Jeremie; Cerf, Nicolas J.

    2003-01-01

    We analyze three different quantum search algorithms, namely, the traditional circuit-based Grover's algorithm, its continuous-time analog by Hamiltonian evolution, and the quantum search by local adiabatic evolution. We show that these algorithms are closely related in the sense that they all perform a rotation, at a constant angular velocity, from a uniform superposition of all states to the solution state. This makes it possible to implement the two Hamiltonian-evolution algorithms on a conventional quantum circuit, while keeping the quadratic speedup of Grover's original algorithm. It also clarifies the link between the adiabatic search algorithm and Grover's algorithm

  7. Limit sets for the discrete spectrum of complex Jacobi matrices

    International Nuclear Information System (INIS)

    Golinskii, L B; Egorova, I E

    2005-01-01

    The discrete spectrum of complex Jacobi matrices that are compact perturbations of the discrete Laplacian is studied. The precise stabilization rate (in the sense of order) of the matrix elements ensuring the finiteness of the discrete spectrum is found. An example of a Jacobi matrix with discrete spectrum having a unique limit point is constructed. These results are discrete analogues of Pavlov's well-known results on Schroedinger operators with complex potential on a half-axis.

  8. Library correlation nuclide identification algorithm

    International Nuclear Information System (INIS)

    Russ, William R.

    2007-01-01

    A novel nuclide identification algorithm, Library Correlation Nuclide Identification (LibCorNID), is proposed. In addition to the spectrum, LibCorNID requires the standard energy, peak shape and peak efficiency calibrations. Input parameters include tolerances for some expected variations in the calibrations, a minimum relative nuclide peak area threshold, and a correlation threshold. Initially, the measured peak spectrum is obtained as the residual after baseline estimation via peak erosion, removing the continuum. Library nuclides are filtered by examining the possible nuclide peak areas in terms of the measured peak spectrum and applying the specified relative area threshold. Remaining candidates are used to create a set of theoretical peak spectra based on the calibrations and library entries. These candidate spectra are then simultaneously fit to the measured peak spectrum while also optimizing the calibrations within the bounds of the specified tolerances. Each candidate with optimized area still exceeding the area threshold undergoes a correlation test. The normalized Pearson's correlation value is calculated as a comparison of the optimized nuclide peak spectrum to the measured peak spectrum with the other optimized peak spectra subtracted. Those candidates with correlation values that exceed the specified threshold are identified and their optimized activities are output. An evaluation of LibCorNID was conducted to verify identification performance in terms of detection probability and false alarm rate. LibCorNID has been shown to perform well compared to standard peak-based analyses

  9. Switch and examine transmit diversity for spectrum sharing systems

    KAUST Repository

    Abdallah, Mohamed M.; Alouini, Mohamed-Slim; Qaraqe, Khalid A.

    2011-01-01

    In this paper, we develop a switch and examine transmit diversity algorithm for spectrum sharing cognitive networks. We consider a cognitive network composed of a primary link that employs constant rate and constant power transmission scheme

  10. Algorithmic Self

    DEFF Research Database (Denmark)

    Markham, Annette

    This paper takes an actor network theory approach to explore some of the ways that algorithms co-construct identity and relational meaning in contemporary use of social media. Based on intensive interviews with participants as well as activity logging and data tracking, the author presents a richly...... layered set of accounts to help build our understanding of how individuals relate to their devices, search systems, and social network sites. This work extends critical analyses of the power of algorithms in implicating the social self by offering narrative accounts from multiple perspectives. It also...... contributes an innovative method for blending actor network theory with symbolic interaction to grapple with the complexity of everyday sensemaking practices within networked global information flows....

  11. Speed testing of Sliding spectrum analysis

    International Nuclear Information System (INIS)

    Frenski, Emil; Manolev, Dimitar

    2013-01-01

    The standard method for spectrum analysis in DSP is the Discrete Fourier transform (DFT), typically implemented using a Fast Fourier transform (FFT) algorithm. The reconstruction of the time-domain signal is then performed by the IFFT (Inverse Fast Fourier transform) algorithm. The FFT calculates the spectral components in a window, on a block-by-block basis. If that window is move by one sample, it is obvious that most of the information will remain the same. This article shows how to measure execution time of scripts realizing SDFT algorithm written for MatLab

  12. Economic optimization and evolutionary programming when using remote sensing data

    OpenAIRE

    Shamin Roman; Alberto Gabriel Enrike; Uryngaliyeva Ayzhana; Semenov Aleksandr

    2018-01-01

    The article considers the issues of optimizing the use of remote sensing data. Built a mathematical model to describe the economic effect of the use of remote sensing data. It is shown that this model is incorrect optimisation task. Given a numerical method of solving this problem. Also discusses how to optimize organizational structure by using genetic algorithm based on remote sensing. The methods considered allow the use of remote sensing data in an optimal way. The proposed mathematical m...

  13. Conversational sensing

    Science.gov (United States)

    Preece, Alun; Gwilliams, Chris; Parizas, Christos; Pizzocaro, Diego; Bakdash, Jonathan Z.; Braines, Dave

    2014-05-01

    Recent developments in sensing technologies, mobile devices and context-aware user interfaces have made it pos- sible to represent information fusion and situational awareness for Intelligence, Surveillance and Reconnaissance (ISR) activities as a conversational process among actors at or near the tactical edges of a network. Motivated by use cases in the domain of Company Intelligence Support Team (CoIST) tasks, this paper presents an approach to information collection, fusion and sense-making based on the use of natural language (NL) and controlled nat- ural language (CNL) to support richer forms of human-machine interaction. The approach uses a conversational protocol to facilitate a ow of collaborative messages from NL to CNL and back again in support of interactions such as: turning eyewitness reports from human observers into actionable information (from both soldier and civilian sources); fusing information from humans and physical sensors (with associated quality metadata); and assisting human analysts to make the best use of available sensing assets in an area of interest (governed by man- agement and security policies). CNL is used as a common formal knowledge representation for both machine and human agents to support reasoning, semantic information fusion and generation of rationale for inferences, in ways that remain transparent to human users. Examples are provided of various alternative styles for user feedback, including NL, CNL and graphical feedback. A pilot experiment with human subjects shows that a prototype conversational agent is able to gather usable CNL information from untrained human subjects.

  14. Multidimensional Signal Processing for Sensing & Communications

    Science.gov (United States)

    2015-07-29

    Spectrum Sensing,” submitted to IEEE Intl. Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Cancun, Mexico , 13-16 Dec. 2015...Sensing,” submitted to IEEE Intl. Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Cancun, Mexico , 13-16 Dec. 2015...diversity in echolocating mammals ,” IEEE Signal Processing Magazine, vol. 26, no. 1, pp. 65- 75, Jan. 2009. DISTRIBUTION A: Distribution approved for

  15. Machine vision theory, algorithms, practicalities

    CERN Document Server

    Davies, E R

    2005-01-01

    In the last 40 years, machine vision has evolved into a mature field embracing a wide range of applications including surveillance, automated inspection, robot assembly, vehicle guidance, traffic monitoring and control, signature verification, biometric measurement, and analysis of remotely sensed images. While researchers and industry specialists continue to document their work in this area, it has become increasingly difficult for professionals and graduate students to understand the essential theory and practicalities well enough to design their own algorithms and systems. This book directl

  16. Remote RemoteRemoteRemote sensing potential for sensing ...

    African Journals Online (AJOL)

    Remote RemoteRemoteRemote sensing potential for sensing potential for sensing potential for sensing potential for sensing potential for sensing potential for sensing potential for sensing potential for sensing potential for sensing potential for sensing p. A Ngie, F Ahmed, K Abutaleb ...

  17. The light absorption by suspended particles, phytoplankton and dissolved organic matter in deep-and coastal waiters of the Black Sea impact on algorithms for remote sensing of chlorophyll -a-.

    Science.gov (United States)

    Churilova, T.; Suslin, V.; Berseneva, G.; Georgieva, L.

    At present time for the analysis and prediction of marine ecosystem state Chlorophyll and Primary production models based on optical satellite data are widely used. However, the SeaWiFS algorithms providing the transformation of color images to chlorophyll maps give inaccurate estimation of chlorophyll "a" (Chl "a") concentration in the Black Sea - an overestimation approximately two times in summer and an underestimation - ~1,5 times during the large diatom bloom in winter-spring. A development of the regional Chl "a" algorithm requires an estimation of spectral characteristics of all light absorbing components and their relationships with Chl "a" concentration. With this aim bio-optical monitoring was organized in two fixed stations in deep-water central western part of the Black Sea and in shelf waters near the Crimea. The weekly monitoring in deep-waters region allowed to determine phytoplankton community succession: seasonal dynamics of size and taxonomic structure, development of large diatoms blooming in March and coccolithophores - in June. The significant variability in pigment concentration and species content of phytoplankton is accompanied by high variability in shape of the phytoplankton absorption spectra and in values of chl a-specific absorption coefficients. This variability had seasonal character depending mostly on the optical status of phytoplankton cells and partly on taxonomic structure of phytoplankton. The pigment packaging parameter fluctuated from 0.64-0.68 (October-December) to 0.95-0.97 (April-May). The package effect depended on intracellular pigment concentration and the size and geometry of cells, which change significantly over the year, because of extremely different environmental conditions. The relationships between phytoplankton specific absorption coefficients (at 412, 443, 490, 510, 555, 678 nm) and Chl "a" concentration have been described by power functions. The contribution of detritus to total particulate absorption

  18. Market-driven spectrum sharing in cognitive radio

    CERN Document Server

    Yi, Changyan

    2016-01-01

    This brief focuses on the current research on mechanism design for dynamic spectrum sharing in cognitive radio (CR) networks. Along with a review of CR architectures and characteristics, this brief presents the motivations, significances and unique challenges of implementing algorithmic mechanism design for encouraging both primary spectrum owners and secondary spectrum users to participate in dynamic spectrum sharing. The brief then focuses on recent advances in mechanism design in CR networks. With an emphasis on dealing with the uncertain spectrum availabilities, mechanisms based on spectrum recall, two-stage spectrum sharing and online spectrum allocation are introduced with the support of theoretic analyses and numerical illustrations. The brief concludes with a discussion of potential research directions and interests, which will motivate further studies on mechanism design for wireless communications. This brief is concise and approachable for researchers, professionals and advanced-level students in w...

  19. HORIZON SENSING

    Energy Technology Data Exchange (ETDEWEB)

    Larry G. Stolarczyk

    2003-03-18

    With the aid of a DOE grant (No. DE-FC26-01NT41050), Stolar Research Corporation (Stolar) developed the Horizon Sensor (HS) to distinguish between the different layers of a coal seam. Mounted on mining machine cutter drums, HS units can detect or sense the horizon between the coal seam and the roof and floor rock, providing the opportunity to accurately mine the section of the seam most desired. HS also enables accurate cutting of minimum height if that is the operator's objective. Often when cutting is done out-of-seam, the head-positioning function facilitates a fixed mining height to minimize dilution. With this technology, miners can still be at a remote location, yet cut only the clean coal, resulting in a much more efficient overall process. The objectives of this project were to demonstrate the feasibility of horizon sensing on mining machines and demonstrate that Horizon Sensing can allow coal to be cut cleaner and more efficiently. Stolar's primary goal was to develop the Horizon Sensor (HS) into an enabling technology for full or partial automation or ''agile mining''. This technical innovation (R&D 100 Award Winner) is quickly demonstrating improvements in productivity and miner safety at several prominent coal mines in the United States. In addition, the HS system can enable the cutting of cleaner coal. Stolar has driven the HS program on the philosophy that cutting cleaner coal means burning cleaner coal. The sensor, located inches from the cutting bits, is based upon the physics principles of a Resonant Microstrip Patch Antenna (RMPA). When it is in proximity of the rock-coal interface, the RMPA impedance varies depending on the thickness of uncut coal. The impedance is measured by the computer-controlled electronics and then sent by radio waves to the mining machine. The worker at the machine can read the data via a Graphical User Interface, displaying a color-coded image of the coal being cut, and direct the machine

  20. Parallel algorithms

    CERN Document Server

    Casanova, Henri; Robert, Yves

    2008-01-01

    ""…The authors of the present book, who have extensive credentials in both research and instruction in the area of parallelism, present a sound, principled treatment of parallel algorithms. … This book is very well written and extremely well designed from an instructional point of view. … The authors have created an instructive and fascinating text. The book will serve researchers as well as instructors who need a solid, readable text for a course on parallelism in computing. Indeed, for anyone who wants an understandable text from which to acquire a current, rigorous, and broad vi

  1. Algorithm 865

    DEFF Research Database (Denmark)

    Gustavson, Fred G.; Reid, John K.; Wasniewski, Jerzy

    2007-01-01

    We present subroutines for the Cholesky factorization of a positive-definite symmetric matrix and for solving corresponding sets of linear equations. They exploit cache memory by using the block hybrid format proposed by the authors in a companion article. The matrix is packed into n(n + 1)/2 real...... variables, and the speed is usually better than that of the LAPACK algorithm that uses full storage (n2 variables). Included are subroutines for rearranging a matrix whose upper or lower-triangular part is packed by columns to this format and for the inverse rearrangement. Also included is a kernel...

  2. The possibilities of compressed sensing based migration

    KAUST Repository

    Aldawood, Ali; Hoteit, Ibrahim; Alkhalifah, Tariq Ali

    2013-01-01

    Linearized waveform inversion or Least-square migration helps reduce migration artifacts caused by limited acquisition aperture, coarse sampling of sources and receivers, and low subsurface illumination. However, leastsquare migration, based on L2-norm minimization of the misfit function, tends to produce a smeared (smoothed) depiction of the true subsurface reflectivity. Assuming that the subsurface reflectivity distribution is a sparse signal, we use a compressed-sensing (Basis Pursuit) algorithm to retrieve this sparse distribution from a small number of linear measurements. We applied a compressed-sensing algorithm to image a synthetic fault model using dense and sparse acquisition geometries. Tests on synthetic data demonstrate the ability of compressed-sensing to produce highly resolved migrated images. We, also, studied the robustness of the Basis Pursuit algorithm in the presence of Gaussian random noise.

  3. The possibilities of compressed sensing based migration

    KAUST Repository

    Aldawood, Ali

    2013-09-22

    Linearized waveform inversion or Least-square migration helps reduce migration artifacts caused by limited acquisition aperture, coarse sampling of sources and receivers, and low subsurface illumination. However, leastsquare migration, based on L2-norm minimization of the misfit function, tends to produce a smeared (smoothed) depiction of the true subsurface reflectivity. Assuming that the subsurface reflectivity distribution is a sparse signal, we use a compressed-sensing (Basis Pursuit) algorithm to retrieve this sparse distribution from a small number of linear measurements. We applied a compressed-sensing algorithm to image a synthetic fault model using dense and sparse acquisition geometries. Tests on synthetic data demonstrate the ability of compressed-sensing to produce highly resolved migrated images. We, also, studied the robustness of the Basis Pursuit algorithm in the presence of Gaussian random noise.

  4. Geological remote sensing

    Science.gov (United States)

    Bishop, Charlotte; Rivard, Benoit; de Souza Filho, Carlos; van der Meer, Freek

    2018-02-01

    Geology is defined as the 'study of the planet Earth - the materials of which it is made, the processes that act on these materials, the products formed, and the history of the planet and its life forms since its origin' (Bates and Jackson, 1976). Remote sensing has seen a number of variable definitions such as those by Sabins and Lillesand and Kiefer in their respective textbooks (Sabins, 1996; Lillesand and Kiefer, 2000). Floyd Sabins (Sabins, 1996) defined it as 'the science of acquiring, processing and interpreting images that record the interaction between electromagnetic energy and matter' while Lillesand and Kiefer (Lillesand and Kiefer, 2000) defined it as 'the science and art of obtaining information about an object, area, or phenomenon through the analysis of data acquired by a device that is not in contact with the object, area, or phenomenon under investigation'. Thus Geological Remote Sensing can be considered the study of, not just Earth given the breadth of work undertaken in planetary science, geological features and surfaces and their interaction with the electromagnetic spectrum using technology that is not in direct contact with the features of interest.

  5. Extraction Method for Earthquake-Collapsed Building Information Based on High-Resolution Remote Sensing

    International Nuclear Information System (INIS)

    Chen, Peng; Wu, Jian; Liu, Yaolin; Wang, Jing

    2014-01-01

    At present, the extraction of earthquake disaster information from remote sensing data relies on visual interpretation. However, this technique cannot effectively and quickly obtain precise and efficient information for earthquake relief and emergency management. Collapsed buildings in the town of Zipingpu after the Wenchuan earthquake were used as a case study to validate two kinds of rapid extraction methods for earthquake-collapsed building information based on pixel-oriented and object-oriented theories. The pixel-oriented method is based on multi-layer regional segments that embody the core layers and segments of the object-oriented method. The key idea is to mask layer by layer all image information, including that on the collapsed buildings. Compared with traditional techniques, the pixel-oriented method is innovative because it allows considerably rapid computer processing. As for the object-oriented method, a multi-scale segment algorithm was applied to build a three-layer hierarchy. By analyzing the spectrum, texture, shape, location, and context of individual object classes in different layers, the fuzzy determined rule system was established for the extraction of earthquake-collapsed building information. We compared the two sets of results using three variables: precision assessment, visual effect, and principle. Both methods can extract earthquake-collapsed building information quickly and accurately. The object-oriented method successfully overcomes the pepper salt noise caused by the spectral diversity of high-resolution remote sensing data and solves the problem of same object, different spectrums and that of same spectrum, different objects. With an overall accuracy of 90.38%, the method achieves more scientific and accurate results compared with the pixel-oriented method (76.84%). The object-oriented image analysis method can be extensively applied in the extraction of earthquake disaster information based on high-resolution remote sensing

  6. Privacy Preservation in Distributed Subgradient Optimization Algorithms

    OpenAIRE

    Lou, Youcheng; Yu, Lean; Wang, Shouyang

    2015-01-01

    Privacy preservation is becoming an increasingly important issue in data mining and machine learning. In this paper, we consider the privacy preserving features of distributed subgradient optimization algorithms. We first show that a well-known distributed subgradient synchronous optimization algorithm, in which all agents make their optimization updates simultaneously at all times, is not privacy preserving in the sense that the malicious agent can learn other agents' subgradients asymptotic...

  7. Plasmonic sensing

    DEFF Research Database (Denmark)

    Mogensen, Klaus Bo

    2015-01-01

    Plasmonic sensors typically rely on detection of changes in the refractive index of the surrounding medium. Here, an alternative approach is reported based on electrical surface screening and controlled dissolution of ultrasmall silver nanoparticles (NPs; R ... in the plasmon band. This is demonstrated by using the strong nucleophiles, cyanide and cysteamine, as ligands. The “dissolution paths” in terms of peak wavelength and amplitude shifts differ significantly between different types of analytes, which are suggested as a means to obtain selectivity of the detection...... that cannot be obtained by traditional refractive index sensing, without the use of bioprobes. A simple modified Drude model is used to account for shifts in the plasmon band position due to electrical charging. Here, a screening parameter is introduced in the expression for the free electron density...

  8. Concurrent communication and sensing in cognitive radio devices: challenges and an enabling solution

    DEFF Research Database (Denmark)

    Tsakalaki, Elpiniki; Alrabadi, Osama; Tatomirescu, Alexandru

    2014-01-01

    Cognitive Radios (CRs) need to continuously monitor the availability of unoccupied spectrum. Prior work on spectrum sensing mainly focused on time-slotted schemes where sensing and communication take place on different time periods in the same frequency. This however leads to a) limited CR...

  9. Many channel spectrum unfolding

    International Nuclear Information System (INIS)

    Najzer, M.; Glumac, B.; Pauko, M.

    1980-01-01

    The principle of the ITER unfolding code as used for the many channel spectrum unfolding is described. Its unfolding ability is tested on seven typical neutron spectra. The effect of the initial spectrum approximation upon the solution is discussed

  10. Pulsar Emission Spectrum

    OpenAIRE

    Gruzinov, Andrei

    2013-01-01

    Emission spectrum is calculated for a weak axisymmetric pulsar. Also calculated are the observed spectrum, efficiency, and the observed efficiency. The underlying flow of electrons and positrons turns out to be curiously intricate.

  11. Autism Spectrum Disorder

    Science.gov (United States)

    ... Caregiver Education » Fact Sheets Autism Spectrum Disorder Fact Sheet What is autism spectrum disorder? What are some ... of mutations in individual genes but rather spontaneous coding mutations across many genes. De novo mutations may ...

  12. Estimation error algorithm at analysis of beta-spectra

    International Nuclear Information System (INIS)

    Bakovets, N.V.; Zhukovskij, A.I.; Zubarev, V.N.; Khadzhinov, E.M.

    2005-01-01

    This work describes the estimation error algorithm at the operations with beta-spectrums, as well as compares the theoretical and experimental errors by the processing of beta-channel's data. (authors)

  13. A novel approach in recognizing magnetic material with simplified algorithm

    KAUST Repository

    Talukdar, Abdul Hafiz Ibne; Sultana, Mahbuba Q.; Useinov, Arthur

    2011-01-01

    . This signal was further analyzed (recognized) in frequency domain creating the Fourier frequency spectrum which is easily used to detect the response of magnetic sample. The novel algorithm in detecting magnetic field is presented here with both simulation

  14. An algorithm to determine backscattering ratio and single scattering albedo

    Digital Repository Service at National Institute of Oceanography (India)

    Suresh, T.; Desa, E.; Matondkar, S.G.P.; Mascarenhas, A.A.M.Q.; Nayak, S.R.; Naik, P.

    Algorithms to determine the inherent optical properties of water, backscattering probability and single scattering albedo at 490 and 676 nm from the apparent optical property, remote sensing reflectance are presented here. The measured scattering...

  15. Concept of an advanced hyperspectral remote sensing system for pipeline monitoring

    Science.gov (United States)

    Keskin, Göksu; Teutsch, Caroline D.; Lenz, Andreas; Middelmann, Wolfgang

    2015-10-01

    Areas occupied by oil pipelines and storage facilities are prone to severe contamination due to leaks caused by natural forces, poor maintenance or third parties. These threats have to be detected as quickly as possible in order to prevent serious environmental damage. Periodical and emergency monitoring activities need to be carried out for successful disaster management and pollution minimization. Airborne remote sensing stands out as an appropriate choice to operate either in an emergency or periodically. Hydrocarbon Index (HI) and Hydrocarbon Detection Index (HDI) utilize the unique absorption features of hydrocarbon based materials at SWIR spectral region. These band ratio based methods require no a priori knowledge of the reference spectrum and can be calculated in real time. This work introduces a flexible airborne pipeline monitoring system based on the online quasi-operational hyperspectral remote sensing system developed at Fraunhofer IOSB, utilizing HI and HDI for oil leak detection on the data acquired by an SWIR imaging sensor. Robustness of HI and HDI compared to state of the art detection algorithms is evaluated in an experimental setup using a synthetic dataset, which was prepared in a systematic way to simulate linear mixtures of selected background and oil spectra consisting of gradually decreasing percentages of oil content. Real airborne measurements in Ettlingen, Germany are used to gather background data while the crude oil spectrum was measured with a field spectrometer. The results indicate that the system can be utilized for online and offline monitoring activities.

  16. Decision tree approach for classification of remotely sensed satellite

    Indian Academy of Sciences (India)

    DTC) algorithm for classification of remotely sensed satellite data (Landsat TM) using open source support. The decision tree is constructed by recursively partitioning the spectral distribution of the training dataset using WEKA, open source ...

  17. An optical water type framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal waters.

    Science.gov (United States)

    Moore, Timothy S; Dowell, Mark D; Bradt, Shane; Verdu, Antonio Ruiz

    2014-03-05

    Bio-optical models are based on relationships between the spectral remote sensing reflectance and optical properties of in-water constituents. The wavelength range where this information can be exploited changes depending on the water characteristics. In low chlorophyll- a waters, the blue/green region of the spectrum is more sensitive to changes in chlorophyll- a concentration, whereas the red/NIR region becomes more important in turbid and/or eutrophic waters. In this work we present an approach to manage the shift from blue/green ratios to red/NIR-based chlorophyll- a algorithms for optically complex waters. Based on a combined in situ data set of coastal and inland waters, measures of overall algorithm uncertainty were roughly equal for two chlorophyll- a algorithms-the standard NASA OC4 algorithm based on blue/green bands and a MERIS 3-band algorithm based on red/NIR bands-with RMS error of 0.416 and 0.437 for each in log chlorophyll- a units, respectively. However, it is clear that each algorithm performs better at different chlorophyll- a ranges. When a blending approach is used based on an optical water type classification, the overall RMS error was reduced to 0.320. Bias and relative error were also reduced when evaluating the blended chlorophyll- a product compared to either of the single algorithm products. As a demonstration for ocean color applications, the algorithm blending approach was applied to MERIS imagery over Lake Erie. We also examined the use of this approach in several coastal marine environments, and examined the long-term frequency of the OWTs to MODIS-Aqua imagery over Lake Erie.

  18. An empirical line-by-line model for the infrared solar transmittance spectrum from 700 to 5000cm{sup -1}

    Energy Technology Data Exchange (ETDEWEB)

    Hase, F. [Institut fuer Meteorologie und Klimaforschung, Forschungszentrum Karlsruhe, Postfach 3640, D-76021 Karlsruhe (Germany)]. E-mail: frank.hase@imk.fzk.de; Demoulin, P. [Institut d' Astrophysique et de Geophysique, allee du VI aout, 17, batiment B5a, B-4000, Liege (Belgium); Sauval, A.J. [Observatoire Royal de Belgique, avenue circulaire, 3, B-1180, Bruxelles (Belgium); Toon, G.C. [Jet Propulsion Laboratory, 4800 Oak Grove Drive, Pasadena, CA 91109 (United States); Bernath, P.F. [Department of Chemistry, University of Waterloo, Waterloo, Ont., Canada N2L3G1 (Canada); Goldman, A. [Department of Physics, University of Denver, Denver, CO 80208 (United States); Hannigan, J.W. [Atmospheric Chemistry Division, National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80303 (United States); Rinsland, C.P. [NASA Langley Research Center, Hampton, VA 23681-2199 (United States)

    2006-12-15

    An empirical line-by-line model for the infrared solar transmittance spectrum is presented. The model can be incorporated into radiative transfer codes to allow fast calculation of all relevant emission and absorption features in the solar spectrum in the mid-infrared region from 700 to 5000cm{sup -1}. The transmittance is modelled as a function of the diameter of the field-of-view centered on the solar disk: the line broadening due to solar rotation as well as center-to-limb variations in strength and width are taken into account for stronger lines. Applications of the model presented here are in the fields of terrestrial remote sensing in the mid-infrared spectral region when the sun is used as radiation source or scattered solar radiation contributes to the measured signal and in the fields of atmospheric radiative transfer algorithms which compute the propagation of infrared solar radiation in the terrestrial atmosphere.

  19. Algorithmic chemistry

    Energy Technology Data Exchange (ETDEWEB)

    Fontana, W.

    1990-12-13

    In this paper complex adaptive systems are defined by a self- referential loop in which objects encode functions that act back on these objects. A model for this loop is presented. It uses a simple recursive formal language, derived from the lambda-calculus, to provide a semantics that maps character strings into functions that manipulate symbols on strings. The interaction between two functions, or algorithms, is defined naturally within the language through function composition, and results in the production of a new function. An iterated map acting on sets of functions and a corresponding graph representation are defined. Their properties are useful to discuss the behavior of a fixed size ensemble of randomly interacting functions. This function gas'', or Turning gas'', is studied under various conditions, and evolves cooperative interaction patterns of considerable intricacy. These patterns adapt under the influence of perturbations consisting in the addition of new random functions to the system. Different organizations emerge depending on the availability of self-replicators.

  20. Application of Reinforcement Learning in Cognitive Radio Networks: Models and Algorithms

    Directory of Open Access Journals (Sweden)

    Kok-Lim Alvin Yau

    2014-01-01

    Full Text Available Cognitive radio (CR enables unlicensed users to exploit the underutilized spectrum in licensed spectrum whilst minimizing interference to licensed users. Reinforcement learning (RL, which is an artificial intelligence approach, has been applied to enable each unlicensed user to observe and carry out optimal actions for performance enhancement in a wide range of schemes in CR, such as dynamic channel selection and channel sensing. This paper presents new discussions of RL in the context of CR networks. It provides an extensive review on how most schemes have been approached using the traditional and enhanced RL algorithms through state, action, and reward representations. Examples of the enhancements on RL, which do not appear in the traditional RL approach, are rules and cooperative learning. This paper also reviews performance enhancements brought about by the RL algorithms and open issues. This paper aims to establish a foundation in order to spark new research interests in this area. Our discussion has been presented in a tutorial manner so that it is comprehensive to readers outside the specialty of RL and CR.

  1. Soil moisture and temperature algorithms and validation

    Science.gov (United States)

    Passive microwave remote sensing of soil moisture has matured over the past decade as a result of the Advanced Microwave Scanning Radiometer (AMSR) program of JAXA. This program has resulted in improved algorithms that have been supported by rigorous validation. Access to the products and the valida...

  2. Remote-sensing image encryption in hybrid domains

    Science.gov (United States)

    Zhang, Xiaoqiang; Zhu, Guiliang; Ma, Shilong

    2012-04-01

    Remote-sensing technology plays an important role in military and industrial fields. Remote-sensing image is the main means of acquiring information from satellites, which always contain some confidential information. To securely transmit and store remote-sensing images, we propose a new image encryption algorithm in hybrid domains. This algorithm makes full use of the advantages of image encryption in both spatial domain and transform domain. First, the low-pass subband coefficients of image DWT (discrete wavelet transform) decomposition are sorted by a PWLCM system in transform domain. Second, the image after IDWT (inverse discrete wavelet transform) reconstruction is diffused with 2D (two-dimensional) Logistic map and XOR operation in spatial domain. The experiment results and algorithm analyses show that the new algorithm possesses a large key space and can resist brute-force, statistical and differential attacks. Meanwhile, the proposed algorithm has the desirable encryption efficiency to satisfy requirements in practice.

  3. Dynamic fair node spectrum allocation for ad hoc networks using random matrices

    Science.gov (United States)

    Rahmes, Mark; Lemieux, George; Chester, Dave; Sonnenberg, Jerry

    2015-05-01

    Dynamic Spectrum Access (DSA) is widely seen as a solution to the problem of limited spectrum, because of its ability to adapt the operating frequency of a radio. Mobile Ad Hoc Networks (MANETs) can extend high-capacity mobile communications over large areas where fixed and tethered-mobile systems are not available. In one use case with high potential impact, cognitive radio employs spectrum sensing to facilitate the identification of allocated frequencies not currently accessed by their primary users. Primary users own the rights to radiate at a specific frequency and geographic location, while secondary users opportunistically attempt to radiate at a specific frequency when the primary user is not using it. We populate a spatial radio environment map (REM) database with known information that can be leveraged in an ad hoc network to facilitate fair path use of the DSA-discovered links. Utilization of high-resolution geospatial data layers in RF propagation analysis is directly applicable. Random matrix theory (RMT) is useful in simulating network layer usage in nodes by a Wishart adjacency matrix. We use the Dijkstra algorithm for discovering ad hoc network node connection patterns. We present a method for analysts to dynamically allocate node-node path and link resources using fair division. User allocation of limited resources as a function of time must be dynamic and based on system fairness policies. The context of fair means that first available request for an asset is not envied as long as it is not yet allocated or tasked in order to prevent cycling of the system. This solution may also save money by offering a Pareto efficient repeatable process. We use a water fill queue algorithm to include Shapley value marginal contributions for allocation.

  4. Evolutionary algorithms for mobile ad hoc networks

    CERN Document Server

    Dorronsoro, Bernabé; Danoy, Grégoire; Pigné, Yoann; Bouvry, Pascal

    2014-01-01

    Describes how evolutionary algorithms (EAs) can be used to identify, model, and minimize day-to-day problems that arise for researchers in optimization and mobile networking. Mobile ad hoc networks (MANETs), vehicular networks (VANETs), sensor networks (SNs), and hybrid networks—each of these require a designer’s keen sense and knowledge of evolutionary algorithms in order to help with the common issues that plague professionals involved in optimization and mobile networking. This book introduces readers to both mobile ad hoc networks and evolutionary algorithms, presenting basic concepts as well as detailed descriptions of each. It demonstrates how metaheuristics and evolutionary algorithms (EAs) can be used to help provide low-cost operations in the optimization process—allowing designers to put some “intelligence” or sophistication into the design. It also offers efficient and accurate information on dissemination algorithms topology management, and mobility models to address challenges in the ...

  5. Searching spectrum points of difference initial-boundary value problems with using GAS

    International Nuclear Information System (INIS)

    Mazepa, N.E.

    1989-01-01

    A new algorithm for searching spectrum points is proposed. The difference schemes which approximate systems of linear differential equations of hyperbolic type with constant coefficients and in one space dimension are considered. For important class of practiclas problems this algorithm reduces the hard spectrum calculation problem to the polynomial equation solution. For complicated analytic manipulations connected with realization of this algorithm the computation algebraic system REDUCE is used. 28 refs

  6. Compact Microwave Fourier Spectrum Analyzer

    Science.gov (United States)

    Savchenkov, Anatoliy; Matsko, Andrey; Strekalov, Dmitry

    2009-01-01

    A compact photonic microwave Fourier spectrum analyzer [a Fourier-transform microwave spectrometer, (FTMWS)] with no moving parts has been proposed for use in remote sensing of weak, natural microwave emissions from the surfaces and atmospheres of planets to enable remote analysis and determination of chemical composition and abundances of critical molecular constituents in space. The instrument is based on a Bessel beam (light modes with non-zero angular momenta) fiber-optic elements. It features low power consumption, low mass, and high resolution, without a need for any cryogenics, beyond what is achievable by the current state-of-the-art in space instruments. The instrument can also be used in a wide-band scatterometer mode in active radar systems.

  7. Clustered K nearest neighbor algorithm for daily inflow forecasting

    NARCIS (Netherlands)

    Akbari, M.; Van Overloop, P.J.A.T.M.; Afshar, A.

    2010-01-01

    Instance based learning (IBL) algorithms are a common choice among data driven algorithms for inflow forecasting. They are based on the similarity principle and prediction is made by the finite number of similar neighbors. In this sense, the similarity of a query instance is estimated according to

  8. Operational remote sensing of aerosols over land to account for directional effects

    International Nuclear Information System (INIS)

    Ramon, Didier; Santer, Richard

    2001-01-01

    The assumption that the ground is a Lambertian reflector is commonly adopted in operational atmospheric corrections of spaceborne sensors. Through a simple modeling of directional effects in radiative transfer following the second simulation of the satellite signal in the solar spectrum (6S) approach, we propose an operational method to account for the departure from Lambertian behavior of a reflector covered by a scattering medium. This method relies on the computation of coupling terms between the reflecting and the scattering media and is able to deal with a two-layer atmosphere. We focus on the difficult problem of aerosol remote sensing over land. One popular sensing method relies on observations over dense dark vegetation, for which the surface reflectance is low and quite well defined in the blue and in the red. Therefore a study was made for three cases: (1) dark vegetation covered by atmospheric aerosols, (2) atmospheric aerosols covered by molecules, and finally (3) dark vegetation covered by atmospheric aerosols covered by molecules. Comparisons of top-of-the-atmosphere reflectances computed with our modeling and reference computations made with the successive-order-of-scattering code show the robustness of the modeling in the blue and in the red for aerosol optical thicknesses as great as 0.6 and solar zenith angles as large as 60 deg. . The model begins to fail only in the blue for large solar zenith angles. The benefits expected for aerosol remote sensing over land are evaluated with an aerosol retrieval scheme developed for the Medium-Resolution Imaging Spectrometer. The main result is a better constraint on the aerosol model with inclusion of directional effects and a weaker effect on the optical thickness of the retrieval aerosol. The directional scheme is then applied to the aerosol remote-sensing problem in actual Indian Remote Sensing Satellite P3/Modular Optoelectronic Scanner images over land and shows significant improvement compared with a

  9. The application of particle swarm optimization to identify gamma spectrum with neural network

    International Nuclear Information System (INIS)

    Shi Dongsheng; Di Yuming; Zhou Chunlin

    2006-01-01

    Aiming at the shortcomings that BP algorithm is usually trapped to a local optimum and it has a low speed of convergence in the application of neural network to identify gamma spectrum, according to the advantage of the globe optimal searching of particle swarm optimization, this paper put forward a new algorithm for neural network training by combining BP algorithm and Particle Swarm Optimization-mixed PSO-BP algorithm. In the application to identify gamma spectrum, the new algorithm overcomes the shortcoming that BP algorithm is usually trapped to a local optimum and the neural network trained by it has a high ability of generalization with identification result of one hundred percent correct. Practical example shows that the mixed PSO-BP algorithm can effectively and reliably be used to identify gamma spectrum. (authors)

  10. Nuclear spectrum analysis by using microcomputer

    International Nuclear Information System (INIS)

    Sanyal, M.K.; Mukhopadhyay, P.K.; Rao, A.D.; Pethe, V.A.

    1984-01-01

    A method is presented for analysis of nuclear spectra by using microcomputer. A nonlinear least square fit of a mathematical model with observed spectrum is performed with variable metric method. The linear search procedure of the variable metric method has been modified so that the algorithm needs less program space and computational time both of which are important for microcomputer implementation. This widely used peak analysis method can now be made available in microcomputer based multichannel analysers. (author)

  11. Suppression of vegetation in LANDSAT ETM+ remote sensing images

    Science.gov (United States)

    Yu, Le; Porwal, Alok; Holden, Eun-Jung; Dentith, Michael

    2010-05-01

    Vegetation cover is an impediment to the interpretation of multispectral remote sensing images for geological applications, especially in densely vegetated terrains. In order to enhance the underlying geological information in such terrains, it is desirable to suppress the reflectance component of vegetation. One form of spectral unmixing that has been successfully used for vegetation reflectance suppression in multispectral images is called "forced invariance". It is based on segregating components of the reflectance spectrum that are invariant with respect to a specific spectral index such as the NDVI. The forced invariance method uses algorithms such as software defoliation. However, the outputs of software defoliation are single channel data, which are not amenable to geological interpretations. Crippen and Blom (2001) proposed a new forced invariance algorithm that utilizes band statistics, rather than band ratios. The authors demonstrated the effectiveness of their algorithms on a LANDSAT TM scene from Nevada, USA, especially in open canopy areas in mixed and semi-arid terrains. In this presentation, we report the results of our experimentation with this algorithm on a densely to sparsely vegetated Landsat ETM+ scene. We selected a scene (Path 119, Row 39) acquired on 18th July, 2004. Two study areas located around the city of Hangzhou, eastern China were tested. One of them covers uninhabited hilly terrain characterized by low rugged topography, parts of the hills are densely vegetated; another one covers both inhabited urban areas and uninhabited hilly terrain, which is densely vegetated. Crippen and Blom's algorithm is implemented in the following sequential steps: (1) dark pixel correction; (2) vegetation index calculation; (3) estimation of statistical relationship between vegetation index and digital number (DN) values for each band; (4) calculation of a smooth best-fit curve for the above relationships; and finally, (5) selection of a target average DN

  12. Supervised remote sensing image classification: An example of a ...

    African Journals Online (AJOL)

    These conventional multi-class classifiers/algorithms are usually written in programming languages such as C, C++, and python. The objective of this research is to experiment the use of a binary classifier/algorithm for multi-class remote sensing task, implemented in MATLAB. MATLAB is a programming language just like C ...

  13. Harvesting the electromagnetic spectrum: from communications to renewables

    OpenAIRE

    Gremont, Boris

    2011-01-01

    The talk will give a unified perspective on one of the most precious commodities underpinning the globalised world: the electromagnetic spectrum. In particular, we will describe how electromagnetic waves have been used over the years to create the global village and the modern world as we know it. How waves can be used to help fight global warming will be discussed along with how waves and remote sensing help in saving lives. Finally, how can the electromagnetic spectrum be used to create the...

  14. Noninvasive hemoglobin measurement using dynamic spectrum

    Science.gov (United States)

    Yi, Xiaoqing; Li, Gang; Lin, Ling

    2017-08-01

    Spectroscopy methods for noninvasive hemoglobin (Hgb) measurement are interfered by individual difference and particular weak signal. In order to address these problems, we have put forward a series of improvement methods based on dynamic spectrum (DS), including instrument design, spectrum extraction algorithm, and modeling approach. The instrument adopts light sources composed of eight laser diodes with the wavelength range from 600 nm to 1100 nm and records photoplethysmography signals at eight wavelengths synchronously. In order to simplify the optical design, we modulate the light sources with orthogonal square waves and design the corresponding demodulation algorithm, instead of adopting a beam-splitting system. A newly designed algorithm named difference accumulation has been proved to be effective in improving the accuracy of dynamic spectrum extraction. 220 subjects are involved in the clinical experiment. An extreme learning machine calibration model between the DS data and the Hgb levels is established. Correlation coefficient and root-mean-square error of prediction sets are 0.8645 and 8.48 g/l, respectively. The results indicate that the Hgb level can be derived by this approach noninvasively with acceptable precision and accuracy. It is expected to achieve a clinic application in the future.

  15. Working Through the Senses: Art Therapy for Autism Spectrum ...

    African Journals Online (AJOL)

    ... and successfully within their social context within which they belong. By using Art Therapy action programme ASD children are expected to develop social and communication skills through emotions and artistic creations as a way to train conversational abilities, anticipate situations and understand emotions and actions.

  16. Pseudo-deterministic Algorithms

    OpenAIRE

    Goldwasser , Shafi

    2012-01-01

    International audience; In this talk we describe a new type of probabilistic algorithm which we call Bellagio Algorithms: a randomized algorithm which is guaranteed to run in expected polynomial time, and to produce a correct and unique solution with high probability. These algorithms are pseudo-deterministic: they can not be distinguished from deterministic algorithms in polynomial time by a probabilistic polynomial time observer with black box access to the algorithm. We show a necessary an...

  17. Real-world experimentation of distributed DSA network algorithms

    DEFF Research Database (Denmark)

    Tonelli, Oscar; Berardinelli, Gilberto; Tavares, Fernando Menezes Leitão

    2013-01-01

    such as a dynamic propagation environment, human presence impact and terminals mobility. This chapter focuses on the practical aspects related to the real world-experimentation with distributed DSA network algorithms over a testbed network. Challenges and solutions are extensively discussed, from the testbed design......The problem of spectrum scarcity in uncoordinated and/or heterogeneous wireless networks is the key aspect driving the research in the field of flexible management of frequency resources. In particular, distributed dynamic spectrum access (DSA) algorithms enable an efficient sharing...... to the setup of experiments. A practical example of experimentation process with a DSA algorithm is also provided....

  18. An adaptive inverse kinematics algorithm for robot manipulators

    Science.gov (United States)

    Colbaugh, R.; Glass, K.; Seraji, H.

    1990-01-01

    An adaptive algorithm for solving the inverse kinematics problem for robot manipulators is presented. The algorithm is derived using model reference adaptive control (MRAC) theory and is computationally efficient for online applications. The scheme requires no a priori knowledge of the kinematics of the robot if Cartesian end-effector sensing is available, and it requires knowledge of only the forward kinematics if joint position sensing is used. Computer simulation results are given for the redundant seven-DOF robotics research arm, demonstrating that the proposed algorithm yields accurate joint angle trajectories for a given end-effector position/orientation trajectory.

  19. A Polygon Model for Wireless Sensor Network Deployment with Directional Sensing Areas

    Science.gov (United States)

    Wu, Chun-Hsien; Chung, Yeh-Ching

    2009-01-01

    The modeling of the sensing area of a sensor node is essential for the deployment algorithm of wireless sensor networks (WSNs). In this paper, a polygon model is proposed for the sensor node with directional sensing area. In addition, a WSN deployment algorithm is presented with topology control and scoring mechanisms to maintain network connectivity and improve sensing coverage rate. To evaluate the proposed polygon model and WSN deployment algorithm, a simulation is conducted. The simulation results show that the proposed polygon model outperforms the existed disk model and circular sector model in terms of the maximum sensing coverage rate. PMID:22303159

  20. An Innovative Thinking-Based Intelligent Information Fusion Algorithm

    Directory of Open Access Journals (Sweden)

    Huimin Lu

    2013-01-01

    Full Text Available This study proposes an intelligent algorithm that can realize information fusion in reference to the relative research achievements in brain cognitive theory and innovative computation. This algorithm treats knowledge as core and information fusion as a knowledge-based innovative thinking process. Furthermore, the five key parts of this algorithm including information sense and perception, memory storage, divergent thinking, convergent thinking, and evaluation system are simulated and modeled. This algorithm fully develops innovative thinking skills of knowledge in information fusion and is a try to converse the abstract conception of brain cognitive science to specific and operable research routes and strategies. Furthermore, the influences of each parameter of this algorithm on algorithm performance are analyzed and compared with those of classical intelligent algorithms trough test. Test results suggest that the algorithm proposed in this study can obtain the optimum problem solution by less target evaluation times, improve optimization effectiveness, and achieve the effective fusion of information.

  1. Remote Sensing of Ecology, Biodiversity and Conservation: A Review from the Perspective of Remote Sensing Specialists

    Directory of Open Access Journals (Sweden)

    Marc Cattet

    2010-11-01

    Full Text Available Remote sensing, the science of obtaining information via noncontact recording, has swept the fields of ecology, biodiversity and conservation (EBC. Several quality review papers have contributed to this field. However, these papers often discuss the issues from the standpoint of an ecologist or a biodiversity specialist. This review focuses on the spaceborne remote sensing of EBC from the perspective of remote sensing specialists, i.e., it is organized in the context of state-of-the-art remote sensing technology, including instruments and techniques. Herein, the instruments to be discussed consist of high spatial resolution, hyperspectral, thermal infrared, small-satellite constellation, and LIDAR sensors; and the techniques refer to image classification, vegetation index (VI, inversion algorithm, data fusion, and the integration of remote sensing (RS and geographic information system (GIS.

  2. Remote sensing of ecology, biodiversity and conservation: a review from the perspective of remote sensing specialists.

    Science.gov (United States)

    Wang, Kai; Franklin, Steven E; Guo, Xulin; Cattet, Marc

    2010-01-01

    Remote sensing, the science of obtaining information via noncontact recording, has swept the fields of ecology, biodiversity and conservation (EBC). Several quality review papers have contributed to this field. However, these papers often discuss the issues from the standpoint of an ecologist or a biodiversity specialist. This review focuses on the spaceborne remote sensing of EBC from the perspective of remote sensing specialists, i.e., it is organized in the context of state-of-the-art remote sensing technology, including instruments and techniques. Herein, the instruments to be discussed consist of high spatial resolution, hyperspectral, thermal infrared, small-satellite constellation, and LIDAR sensors; and the techniques refer to image classification, vegetation index (VI), inversion algorithm, data fusion, and the integration of remote sensing (RS) and geographic information system (GIS).

  3. 5G Spectrum Sharing

    OpenAIRE

    Nekovee, Maziar; Rudd, Richard

    2017-01-01

    In this paper an overview is given of the current status of 5G industry standards, spectrum allocation and use cases, followed by initial investigations of new opportunities for spectrum sharing in 5G using cognitive radio techniques, considering both licensed and unlicensed scenarios. A particular attention is given to sharing millimeter-wave frequencies, which are of prominent importance for 5G.

  4. Autism Spectrum Disorder (ASD)

    Science.gov (United States)

    ... Español (Spanish) Recommend on Facebook Tweet Share Compartir Autism spectrum disorder (ASD) is a developmental disability that can cause ... work. Autism: What's New MMWR article: Prevalence of Autism Spectrum Disorder Data Community Report Press release: Autism Prevalence Slightly ...

  5. The VEGA Assembly Spectrum Code

    International Nuclear Information System (INIS)

    Milosevic, M.

    1997-01-01

    The VEGA is assembly spectrum code, developed as a design tool for producing a few-group averaged cross section data for a wide range of reactor types including both thermal and fast reactors. It belongs to a class of codes, which may be characterized by the separate stages for micro group, spectrum and macro group assembly calculations. The theoretical foundation for the development of the VEGA code was integral transport theory in the first-flight collision probability formulation. Two versions of VEGA are now in use, VEGA-1 established on standard equivalence theory and VEGA-2 based on new subgroup method applicable for any geometry for which a flux solution is possible. This paper describes a features which are unique to the VEGA codes with concentration on the basic principles and algorithms used in the proposed subgroup method. Presented validation of this method, comprise the results for a homogenous uranium-plutonium mixture and a PWR cell containing a recycled uranium-plutonium oxide. Example application for a realistic fuel dissolver benchmark problem , which was extensive analyzed in the international calculations, is also included. (author)

  6. Algorithmic and user study of an autocompletion algorithm on a large medical vocabulary.

    Science.gov (United States)

    Sevenster, Merlijn; van Ommering, Rob; Qian, Yuechen

    2012-02-01

    Autocompletion supports human-computer interaction in software applications that let users enter textual data. We will be inspired by the use case in which medical professionals enter ontology concepts, catering the ongoing demand for structured and standardized data in medicine. Goal is to give an algorithmic analysis of one particular autocompletion algorithm, called multi-prefix matching algorithm, which suggests terms whose words' prefixes contain all words in the string typed by the user, e.g., in this sense, opt ner me matches optic nerve meningioma. Second we aim to investigate how well it supports users entering concepts from a large and comprehensive medical vocabulary (snomed ct). We give a concise description of the multi-prefix algorithm, and sketch how it can be optimized to meet required response time. Performance will be compared to a baseline algorithm, which gives suggestions that extend the string typed by the user to the right, e.g. optic nerve m gives optic nerve meningioma, but opt ner me does not. We conduct a user experiment in which 12 participants are invited to complete 40 snomed ct terms with the baseline algorithm and another set of 40 snomed ct terms with the multi-prefix algorithm. Our results show that users need significantly fewer keystrokes when supported by the multi-prefix algorithm than when supported by the baseline algorithm. The proposed algorithm is a competitive candidate for searching and retrieving terms from a large medical ontology. Copyright © 2011 Elsevier Inc. All rights reserved.

  7. Strategies for improvement of spectrum capacity for WiMax cellular systems by Cognitive Radio Technology supported by Relay Stations

    DEFF Research Database (Denmark)

    Frederiksen, Flemming Bjerge; Prasad, Ramjee

    2007-01-01

    Methods to enhance the use of the frequency spectrum by automatical spectrum sensing plus spectrum sharing in a cognitive radio technology context will be presented and discussed in this paper. Ideas to improve the wireless transmission by orthogonal OFDM-based communication and to increase...... the coverage of cellular systems by relay stations will be presented as well.   ...

  8. 76 FR 32993 - Toward Innovative Spectrum-Sharing Technologies: A Technical Workshop on Coordinating Federal...

    Science.gov (United States)

    2011-06-07

    .../WSRD/ . Background: The dramatic rise of radio frequency-based applications has sparked a new sense of urgency among federal users, commercial service providers, equipment developers, and spectrum management...

  9. Polarimetric Remote Sensing of Atmospheric Particulate Pollutants

    Science.gov (United States)

    Li, Z.; Zhang, Y.; Hong, J.

    2018-04-01

    Atmospheric particulate pollutants not only reduce atmospheric visibility, change the energy balance of the troposphere, but also affect human and vegetation health. For monitoring the particulate pollutants, we establish and develop a series of inversion algorithms based on polarimetric remote sensing technology which has unique advantages in dealing with atmospheric particulates. A solution is pointed out to estimate the near surface PM2.5 mass concentrations from full remote sensing measurements including polarimetric, active and infrared remote sensing technologies. It is found that the mean relative error of PM2.5 retrieved by full remote sensing measurements is 35.5 % in the case of October 5th 2013, improved to a certain degree compared to previous studies. A systematic comparison with the ground-based observations further indicates the effectiveness of the inversion algorithm and reliability of results. A new generation of polarized sensors (DPC and PCF), whose observation can support these algorithms, will be onboard GF series satellites and launched by China in the near future.

  10. POLARIMETRIC REMOTE SENSING OF ATMOSPHERIC PARTICULATE POLLUTANTS

    Directory of Open Access Journals (Sweden)

    Z. Li

    2018-04-01

    Full Text Available Atmospheric particulate pollutants not only reduce atmospheric visibility, change the energy balance of the troposphere, but also affect human and vegetation health. For monitoring the particulate pollutants, we establish and develop a series of inversion algorithms based on polarimetric remote sensing technology which has unique advantages in dealing with atmospheric particulates. A solution is pointed out to estimate the near surface PM2.5 mass concentrations from full remote sensing measurements including polarimetric, active and infrared remote sensing technologies. It is found that the mean relative error of PM2.5 retrieved by full remote sensing measurements is 35.5 % in the case of October 5th 2013, improved to a certain degree compared to previous studies. A systematic comparison with the ground-based observations further indicates the effectiveness of the inversion algorithm and reliability of results. A new generation of polarized sensors (DPC and PCF, whose observation can support these algorithms, will be onboard GF series satellites and launched by China in the near future.

  11. Spectrum recovery method based on sparse representation for segmented multi-Gaussian model

    Science.gov (United States)

    Teng, Yidan; Zhang, Ye; Ti, Chunli; Su, Nan

    2016-09-01

    Hyperspectral images can realize crackajack features discriminability for supplying diagnostic characteristics with high spectral resolution. However, various degradations may generate negative influence on the spectral information, including water absorption, bands-continuous noise. On the other hand, the huge data volume and strong redundancy among spectrums produced intense demand on compressing HSIs in spectral dimension, which also leads to the loss of spectral information. The reconstruction of spectral diagnostic characteristics has irreplaceable significance for the subsequent application of HSIs. This paper introduces a spectrum restoration method for HSIs making use of segmented multi-Gaussian model (SMGM) and sparse representation. A SMGM is established to indicating the unsymmetrical spectral absorption and reflection characteristics, meanwhile, its rationality and sparse property are discussed. With the application of compressed sensing (CS) theory, we implement sparse representation to the SMGM. Then, the degraded and compressed HSIs can be reconstructed utilizing the uninjured or key bands. Finally, we take low rank matrix recovery (LRMR) algorithm for post processing to restore the spatial details. The proposed method was tested on the spectral data captured on the ground with artificial water absorption condition and an AVIRIS-HSI data set. The experimental results in terms of qualitative and quantitative assessments demonstrate that the effectiveness on recovering the spectral information from both degradations and loss compression. The spectral diagnostic characteristics and the spatial geometry feature are well preserved.

  12. Hamiltonian Algorithm Sound Synthesis

    OpenAIRE

    大矢, 健一

    2013-01-01

    Hamiltonian Algorithm (HA) is an algorithm for searching solutions is optimization problems. This paper introduces a sound synthesis technique using Hamiltonian Algorithm and shows a simple example. "Hamiltonian Algorithm Sound Synthesis" uses phase transition effect in HA. Because of this transition effect, totally new waveforms are produced.

  13. Progressive geometric algorithms

    NARCIS (Netherlands)

    Alewijnse, S.P.A.; Bagautdinov, T.M.; de Berg, M.T.; Bouts, Q.W.; ten Brink, Alex P.; Buchin, K.A.; Westenberg, M.A.

    2015-01-01

    Progressive algorithms are algorithms that, on the way to computing a complete solution to the problem at hand, output intermediate solutions that approximate the complete solution increasingly well. We present a framework for analyzing such algorithms, and develop efficient progressive algorithms

  14. Progressive geometric algorithms

    NARCIS (Netherlands)

    Alewijnse, S.P.A.; Bagautdinov, T.M.; Berg, de M.T.; Bouts, Q.W.; Brink, ten A.P.; Buchin, K.; Westenberg, M.A.

    2014-01-01

    Progressive algorithms are algorithms that, on the way to computing a complete solution to the problem at hand, output intermediate solutions that approximate the complete solution increasingly well. We present a framework for analyzing such algorithms, and develop efficient progressive algorithms

  15. Big Spectrum Data: The New Resource for Cognitive Wireless Networking

    OpenAIRE

    Ding, Guoru; Wu, Qihui; Wang, Jinlong; Yao, Yu-Dong

    2014-01-01

    The era of Big Data is here now, which has brought both unprecedented opportunities and critical challenges. In this article, from a perspective of cognitive wireless networking, we start with a definition of Big Spectrum Data by analyzing its characteristics in terms of six Vs, i.e., volume, variety, velocity, veracity, viability, and value. We then present a high-level tutorial on research frontiers in Big Spectrum Data analytics to guide the development of practical algorithms. We also hig...

  16. The Algorithmic Imaginary

    DEFF Research Database (Denmark)

    Bucher, Taina

    2017-01-01

    the notion of the algorithmic imaginary. It is argued that the algorithmic imaginary – ways of thinking about what algorithms are, what they should be and how they function – is not just productive of different moods and sensations but plays a generative role in moulding the Facebook algorithm itself...... of algorithms affect people's use of these platforms, if at all? To help answer these questions, this article examines people's personal stories about the Facebook algorithm through tweets and interviews with 25 ordinary users. To understand the spaces where people and algorithms meet, this article develops...

  17. The BR eigenvalue algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Geist, G.A. [Oak Ridge National Lab., TN (United States). Computer Science and Mathematics Div.; Howell, G.W. [Florida Inst. of Tech., Melbourne, FL (United States). Dept. of Applied Mathematics; Watkins, D.S. [Washington State Univ., Pullman, WA (United States). Dept. of Pure and Applied Mathematics

    1997-11-01

    The BR algorithm, a new method for calculating the eigenvalues of an upper Hessenberg matrix, is introduced. It is a bulge-chasing algorithm like the QR algorithm, but, unlike the QR algorithm, it is well adapted to computing the eigenvalues of the narrowband, nearly tridiagonal matrices generated by the look-ahead Lanczos process. This paper describes the BR algorithm and gives numerical evidence that it works well in conjunction with the Lanczos process. On the biggest problems run so far, the BR algorithm beats the QR algorithm by a factor of 30--60 in computing time and a factor of over 100 in matrix storage space.

  18. Quantum game application to spectrum scarcity problems

    Science.gov (United States)

    Zabaleta, O. G.; Barrangú, J. P.; Arizmendi, C. M.

    2017-01-01

    Recent spectrum-sharing research has produced a strategy to address spectrum scarcity problems. This novel idea, named cognitive radio, considers that secondary users can opportunistically exploit spectrum holes left temporarily unused by primary users. This presents a competitive scenario among cognitive users, making it suitable for game theory treatment. In this work, we show that the spectrum-sharing benefits of cognitive radio can be increased by designing a medium access control based on quantum game theory. In this context, we propose a model to manage spectrum fairly and effectively, based on a multiple-users multiple-choice quantum minority game. By taking advantage of quantum entanglement and quantum interference, it is possible to reduce the probability of collision problems commonly associated with classic algorithms. Collision avoidance is an essential property for classic and quantum communications systems. In our model, two different scenarios are considered, to meet the requirements of different user strategies. The first considers sensor networks where the rational use of energy is a cornerstone; the second focuses on installations where the quality of service of the entire network is a priority.

  19. Empirical wind retrieval model based on SAR spectrum measurements

    Science.gov (United States)

    Panfilova, Maria; Karaev, Vladimir; Balandina, Galina; Kanevsky, Mikhail; Portabella, Marcos; Stoffelen, Ad

    The present paper considers polarimetric SAR wind vector applications. Remote-sensing measurements of the near-surface wind over the ocean are of great importance for the understanding of atmosphere-ocean interaction. In recent years investigations for wind vector retrieval using Synthetic Aperture Radar (SAR) data have been performed. In contrast with scatterometers, a SAR has a finer spatial resolution that makes it a more suitable microwave instrument to explore wind conditions in the marginal ice zones, coastal regions and lakes. The wind speed retrieval procedure from scatterometer data matches the measured radar backscattering signal with the geophysical model function (GMF). The GMF determines the radar cross section dependence on the wind speed and direction with respect to the azimuthal angle of the radar beam. Scatterometers provide information on wind speed and direction simultaneously due to the fact that each wind vector cell (WVC) is observed at several azimuth angles. However, SAR is not designed to be used as a high resolution scatterometer. In this case, each WVC is observed at only one single azimuth angle. That is why for wind vector determination additional information such as wind streak orientation over the sea surface is required. It is shown that the wind vector can be obtained using polarimetric SAR without additional information. The main idea is to analyze the spectrum of a homogeneous SAR image area instead of the backscattering normalized radar cross section. Preliminary numerical simulations revealed that SAR image spectral maxima positions depend on the wind vector. Thus the following method for wind speed retrieval is proposed. In the first stage of the algorithm, the SAR spectrum maxima are determined. This procedure is carried out to estimate the wind speed and direction with ambiguities separated by 180 degrees due to the SAR spectrum symmetry. The second stage of the algorithm allows us to select the correct wind direction

  20. Algorithmically specialized parallel computers

    CERN Document Server

    Snyder, Lawrence; Gannon, Dennis B

    1985-01-01

    Algorithmically Specialized Parallel Computers focuses on the concept and characteristics of an algorithmically specialized computer.This book discusses the algorithmically specialized computers, algorithmic specialization using VLSI, and innovative architectures. The architectures and algorithms for digital signal, speech, and image processing and specialized architectures for numerical computations are also elaborated. Other topics include the model for analyzing generalized inter-processor, pipelined architecture for search tree maintenance, and specialized computer organization for raster

  1. Travel Advice for Higher Functioning Individuals on the Autism Spectrum

    Science.gov (United States)

    VanBergeijk, Ernst

    2009-01-01

    While travel training on local mass transit makes intuitive sense, the thought of larger scale travel training does not occur to most people. Possible benefits that could be gained from long distance or more involved traveling with individuals on the autism spectrum are vast. In this article, the author presents 11 essential skills that are a…

  2. Mobile Sensing Systems

    Science.gov (United States)

    Macias, Elsa; Suarez, Alvaro; Lloret, Jaime

    2013-01-01

    Rich-sensor smart phones have made possible the recent birth of the mobile sensing research area as part of ubiquitous sensing which integrates other areas such as wireless sensor networks and web sensing. There are several types of mobile sensing: individual, participatory, opportunistic, crowd, social, etc. The object of sensing can be people-centered or environment-centered. The sensing domain can be home, urban, vehicular… Currently there are barriers that limit the social acceptance of mobile sensing systems. Examples of social barriers are privacy concerns, restrictive laws in some countries and the absence of economic incentives that might encourage people to participate in a sensing campaign. Several technical barriers are phone energy savings and the variety of sensors and software for their management. Some existing surveys partially tackle the topic of mobile sensing systems. Published papers theoretically or partially solve the above barriers. We complete the above surveys with new works, review the barriers of mobile sensing systems and propose some ideas for efficiently implementing sensing, fusion, learning, security, privacy and energy saving for any type of mobile sensing system, and propose several realistic research challenges. The main objective is to reduce the learning curve in mobile sensing systems where the complexity is very high. PMID:24351637

  3. Mobile sensing systems.

    Science.gov (United States)

    Macias, Elsa; Suarez, Alvaro; Lloret, Jaime

    2013-12-16

    Rich-sensor smart phones have made possible the recent birth of the mobile sensing research area as part of ubiquitous sensing which integrates other areas such as wireless sensor networks and web sensing. There are several types of mobile sensing: individual, participatory, opportunistic, crowd, social, etc. The object of sensing can be people-centered or environment-centered. The sensing domain can be home, urban, vehicular… Currently there are barriers that limit the social acceptance of mobile sensing systems. Examples of social barriers are privacy concerns, restrictive laws in some countries and the absence of economic incentives that might encourage people to participate in a sensing campaign. Several technical barriers are phone energy savings and the variety of sensors and software for their management. Some existing surveys partially tackle the topic of mobile sensing systems. Published papers theoretically or partially solve the above barriers. We complete the above surveys with new works, review the barriers of mobile sensing systems and propose some ideas for efficiently implementing sensing, fusion, learning, security, privacy and energy saving for any type of mobile sensing system, and propose several realistic research challenges. The main objective is to reduce the learning curve in mobile sensing systems where the complexity is very high.

  4. Mobile Sensing Systems

    Directory of Open Access Journals (Sweden)

    Elsa Macias

    2013-12-01

    Full Text Available Rich-sensor smart phones have made possible the recent birth of the mobile sensing research area as part of ubiquitous sensing which integrates other areas such as wireless sensor networks and web sensing. There are several types of mobile sensing: individual, participatory, opportunistic, crowd, social, etc. The object of sensing can be people-centered or environment-centered. The sensing domain can be home, urban, vehicular… Currently there are barriers that limit the social acceptance of mobile sensing systems. Examples of social barriers are privacy concerns, restrictive laws in some countries and the absence of economic incentives that might encourage people to participate in a sensing campaign. Several technical barriers are phone energy savings and the variety of sensors and software for their management. Some existing surveys partially tackle the topic of mobile sensing systems. Published papers theoretically or partially solve the above barriers. We complete the above surveys with new works, review the barriers of mobile sensing systems and propose some ideas for efficiently implementing sensing, fusion, learning, security, privacy and energy saving for any type of mobile sensing system, and propose several realistic research challenges. The main objective is to reduce the learning curve in mobile sensing systems where the complexity is very high.

  5. Hybrid Arrays for Chemical Sensing

    Science.gov (United States)

    Kramer, Kirsten E.; Rose-Pehrsson, Susan L.; Johnson, Kevin J.; Minor, Christian P.

    In recent years, multisensory approaches to environment monitoring for chemical detection as well as other forms of situational awareness have become increasingly popular. A hybrid sensor is a multimodal system that incorporates several sensing elements and thus produces data that are multivariate in nature and may be significantly increased in complexity compared to data provided by single-sensor systems. Though a hybrid sensor is itself an array, hybrid sensors are often organized into more complex sensing systems through an assortment of network topologies. Part of the reason for the shift to hybrid sensors is due to advancements in sensor technology and computational power available for processing larger amounts of data. There is also ample evidence to support the claim that a multivariate analytical approach is generally superior to univariate measurements because it provides additional redundant and complementary information (Hall, D. L.; Linas, J., Eds., Handbook of Multisensor Data Fusion, CRC, Boca Raton, FL, 2001). However, the benefits of a multisensory approach are not automatically achieved. Interpretation of data from hybrid arrays of sensors requires the analyst to develop an application-specific methodology to optimally fuse the disparate sources of data generated by the hybrid array into useful information characterizing the sample or environment being observed. Consequently, multivariate data analysis techniques such as those employed in the field of chemometrics have become more important in analyzing sensor array data. Depending on the nature of the acquired data, a number of chemometric algorithms may prove useful in the analysis and interpretation of data from hybrid sensor arrays. It is important to note, however, that the challenges posed by the analysis of hybrid sensor array data are not unique to the field of chemical sensing. Applications in electrical and process engineering, remote sensing, medicine, and of course, artificial

  6. Spectrum optimization-based chaotification using time-delay feedback control

    International Nuclear Information System (INIS)

    Zhou Jiaxi; Xu Daolin; Zhang Jing; Liu Chunrong

    2012-01-01

    Highlights: ► A time-delay feedback controller is designed for chaotification. ► A spectrum optimization method is proposed to determine chaotification parameters. ► Numerical examples verify the spectrum optimization- based chaotification method. ► Engineering application in line spectrum reconfiguration is demonstrated. - Abstract: In this paper, a spectrum optimization method is developed for chaotification in conjunction with an application in line spectrum reconfiguration. A key performance index (the objective function) based on Fourier spectrum is specially devised with the idea of suppressing spectrum spikes and broadening frequency band. Minimization of the index empowered by a genetic algorithm enables to locate favorable parameters of the time-delay feedback controller, by which a line spectrum of harmonic vibration can be transformed into a broad-band continuous spectrum of chaotic motion. Numerical simulations are carried out to verify the feasibility of the method and to demonstrate its effectiveness of chaotifying a 2-DOFs linear mechanical system.

  7. The marine diversity spectrum

    DEFF Research Database (Denmark)

    Reuman, Daniel C.; Gislason, Henrik; Barnes, Carolyn

    2014-01-01

    of taxonomy (all the species in a region regardless of clade) are much less studied but are equally important and will illuminate a different set of ecological and evolutionary processes. We develop and test a mechanistic model of how diversity varies with body mass in marine ecosystems. The model predicts...... the form of the diversity spectrum', which quantifies the distribution of species' asymptotic body masses, is a species analogue of the classic size spectrum of individuals, and which we have found to be a new and widely applicable description of diversity patterns. The marine diversity spectrum...... is predicted to be approximately linear across an asymptotic mass range spanning seven orders of magnitude. Slope -0 center dot 5 is predicted for the global marine diversity spectrum for all combined pelagic zones of continental shelf seas, and slopes for large regions are predicted to lie between -0 center...

  8. Autism Spectrum Disorder

    Centers for Disease Control (CDC) Podcasts

    This podcast discusses autism spectrum disorder (ASD), a developmental disability that causes problems with social, communication, and behavioral skills. CDC estimates that one in 68 children has been identified as having ASD.

  9. Fetal Alcohol Spectrum Disorders

    Science.gov (United States)

    Alcohol can harm your baby at any stage during a pregnancy. That includes the earliest stages, before ... can cause a group of conditions called fetal alcohol spectrum disorders (FASDs). Children who are born with ...

  10. Fast Spectrum Reactors

    CERN Document Server

    Todd, Donald; Tsvetkov, Pavel

    2012-01-01

    Fast Spectrum Reactors presents a detailed overview of world-wide technology contributing to the development of fast spectrum reactors. With a unique focus on the capabilities of fast spectrum reactors to address nuclear waste transmutation issues, in addition to the well-known capabilities of breeding new fuel, this volume describes how fast spectrum reactors contribute to the wide application of nuclear power systems to serve the global nuclear renaissance while minimizing nuclear proliferation concerns. Readers will find an introduction to the sustainable development of nuclear energy and the role of fast reactors, in addition to an economic analysis of nuclear reactors. A section devoted to neutronics offers the current trends in nuclear design, such as performance parameters and the optimization of advanced power systems. The latest findings on fuel management, partitioning and transmutation include the physics, efficiency and strategies of transmutation, homogeneous and heterogeneous recycling, in addit...

  11. Gluonium spectrum in QCD

    International Nuclear Information System (INIS)

    Dominguez, C.A.

    1987-02-01

    The scalar (0 ++ ) and the tensor (2 ++ ) gluonium spectrum is analyzed in the framework of QCD sum rules. Stable eigenvalue solutions, consistent with duality and low energy theorems, are obtained for the mass and width of these glueballs. (orig.)

  12. Nano-bio-sensing

    CERN Document Server

    Carrara, Sandro

    2011-01-01

    This book examines state-of-the-art applications of nano-bio-sensing. It brings together researchers from nano-electronics and bio-technology, providing multidisciplinary content from nano-structures fabrication to bio-sensing applications.

  13. Spectrum and network measurements

    CERN Document Server

    Witte, Robert A

    2014-01-01

    This new edition of Spectrum and Network Measurements enables readers to understand the basic theory, relate it to measured results, and apply it when creating new designs. This comprehensive treatment of frequency domain measurements successfully consolidates all the pertinent theory into one text. It covers the theory and practice of spectrum and network measurements in electronic systems. It also provides thorough coverage of Fourier analysis, transmission lines, intermodulation distortion, signal-to-noise ratio and S-parameters.

  14. Internet of Things for Sensing: A Case Study in the Healthcare System

    Directory of Open Access Journals (Sweden)

    Syed Aziz Shah

    2018-03-01

    Full Text Available Medical healthcare is one of the fascinating applications using Internet of Things (IoTs. The pervasive smart environment in IoTs has the potential to monitor various human activities by deploying smart devices. In our pilot study, we look at narcolepsy, a disorder in which individuals lose the ability to regulate their sleep-wake cycle. An imbalance in the brain chemical called orexin makes the sleep pattern irregular. This sleep disorder in patients suffering from narcolepsy results in them experience irrepressible sleep episodes while performing daily routine activities. This study presents a novel method for detecting sleep attacks or sleepiness due to immune system attacks and affecting daily activities measured using the S-band sensing technique. The S-Band sensing technique is channel sensing based on frequency spectrum sensing using the orthogonal frequency division multiplexing transmission at a 2 to 4 GHz frequency range leveraging amplitude and calibrated phase information of different frequencies obtained using wireless devices such as card, and omni-directional antenna. Each human behavior induces a unique channel information (CI signature contained in amplitude and phase information. By linearly transforming raw phase measurements into calibrated phase information, we ascertain phase coherence. Classification and validation of various human activities such as walking, sitting on a chair, push-ups, and narcolepsy sleep episodes are done using support vector machine, K-nearest neighbor, and random forest algorithms. The measurement and evaluation were carried out several times with classification values of accuracy, precision, recall, specificity, Kappa, and F-measure of more than 90% that were achieved when delineating sleep attacks.

  15. Local multiplicative Schwarz algorithms for convection-diffusion equations

    Science.gov (United States)

    Cai, Xiao-Chuan; Sarkis, Marcus

    1995-01-01

    We develop a new class of overlapping Schwarz type algorithms for solving scalar convection-diffusion equations discretized by finite element or finite difference methods. The preconditioners consist of two components, namely, the usual two-level additive Schwarz preconditioner and the sum of some quadratic terms constructed by using products of ordered neighboring subdomain preconditioners. The ordering of the subdomain preconditioners is determined by considering the direction of the flow. We prove that the algorithms are optimal in the sense that the convergence rates are independent of the mesh size, as well as the number of subdomains. We show by numerical examples that the new algorithms are less sensitive to the direction of the flow than either the classical multiplicative Schwarz algorithms, and converge faster than the additive Schwarz algorithms. Thus, the new algorithms are more suitable for fluid flow applications than the classical additive or multiplicative Schwarz algorithms.

  16. Introduction to remote sensing

    CERN Document Server

    Cracknell, Arthur P

    2007-01-01

    Addressing the need for updated information in remote sensing, Introduction to Remote Sensing, Second Edition provides a full and authoritative introduction for scientists who need to know the scope, potential, and limitations in the field. The authors discuss the physical principles of common remote sensing systems and examine the processing, interpretation, and applications of data. This new edition features updated and expanded material, including greater coverage of applications from across earth, environmental, atmospheric, and oceanographic sciences. Illustrated with remotely sensed colo

  17. Autism spectrum disorder - Asperger syndrome

    Science.gov (United States)

    ... part of the larger developmental disorder category of autism spectrum disorder . ... American Psychiatric Association. Autism spectrum disorder. ... VA: American Psychiatric Publishing: 2013;50-59. Raviola GJ, ...

  18. Sense of moving

    DEFF Research Database (Denmark)

    Christensen, Mark Schram; Grünbaum, Thor

    2017-01-01

    In this chapter, we assume the existence of a sense of “movement activity” that arises when a person actively moves a body part. This sense is usually supposed to be part of sense of agency (SoA). The purpose of the chapter is to determine whether the already existing experimental paradigms can...

  19. Quantum Computation and Algorithms

    International Nuclear Information System (INIS)

    Biham, O.; Biron, D.; Biham, E.; Grassi, M.; Lidar, D.A.

    1999-01-01

    It is now firmly established that quantum algorithms provide a substantial speedup over classical algorithms for a variety of problems, including the factorization of large numbers and the search for a marked element in an unsorted database. In this talk I will review the principles of quantum algorithms, the basic quantum gates and their operation. The combination of superposition and interference, that makes these algorithms efficient, will be discussed. In particular, Grover's search algorithm will be presented as an example. I will show that the time evolution of the amplitudes in Grover's algorithm can be found exactly using recursion equations, for any initial amplitude distribution

  20. Neural Network Back-Propagation Algorithm for Sensing Hypergols

    Science.gov (United States)

    Perotti, Jose; Lewis, Mark; Medelius, Pedro; Bastin, Gary

    2013-01-01

    Fast, continuous detection of a wide range of hazardous substances simultaneously is needed to achieve improved safety for personnel working with hypergolic fuels and oxidizers, as well as other hazardous substances, with a requirement for such detection systems to warn personnel immediately upon the sudden advent of hazardous conditions, with a high probability of detection and a low false alarm rate. The primary purpose of this software is to read the voltage outputs from voltage dividers containing carbon nano - tube sensors as a variable resistance leg, and to recognize quickly when a leak has occurred through recognizing that a generalized pattern change in resistivity of a carbon nanotube sensor has occurred upon exposure to dangerous substances, and, further, to identify quickly just what substance is present through detailed pattern recognition of the shape of the response provided by the carbon nanotube sensor.