Full Text Available 2DPCA, which is one of the most important face recognition methods, is relatively sensitive to substantial variations in light direction, face pose, and facial expression. In order to improve the recognition performance of the traditional 2DPCA, a new 2DPCA algorithm based on the fuzzy theory is proposed in this paper, namely, the fuzzy 2DPCA (F2DPCA. In this method, applying fuzzy K-nearest neighbor (FKNN, the membership degree matrix of the training samples is calculated, which is used to get the fuzzy means of each class. The average of fuzzy means is then incorporated into the definition of the general scatter matrix with anticipation that it can improve classification result. The comprehensive experiments on the ORL, the YALE, and the FERET face database show that the proposed method can improve the classification rates and reduce the sensitivity to variations between face images caused by changes in illumination, face expression, and face pose.
Full Text Available We present a fast and robust object tracking algorithm by using 2DPCA and l2-regularization in a Bayesian inference framework. Firstly, we model the challenging appearance of the tracked object using 2DPCA bases, which exploit the strength of subspace representation. Secondly, we adopt the l2-regularization to solve the proposed presentation model and remove the trivial templates from the sparse tracking method which can provide a more fast tracking performance. Finally, we present a novel likelihood function that considers the reconstruction error, which is concluded from the orthogonal left-projection matrix and the orthogonal right-projection matrix. Experimental results on several challenging image sequences demonstrate that the proposed method can achieve more favorable performance against state-of-the-art tracking algorithms.
Full Text Available For modern synthetic aperture radar (SAR, it has much more urgent demands on ground moving target indication (GMTI, which includes not only the point moving targets like cars, truck or tanks but also the distributed moving targets like river or ocean surfaces. Among the existing GMTI methods, displaced phase center antenna (DPCA can effectively cancel the strong ground clutter and has been widely used. However, its detection performance is closely related to the target’s signal-to-clutter ratio (SCR as well as radial velocity, and it cannot effectively detect the weak large-sized river surfaces in strong ground clutter due to their low SCR caused by specular scattering. This paper proposes a novel method called relative residue of DPCA (RR-DPCA, which jointly utilizes the DPCA cancellation outputs and the multi-look images to improve the detection performance of weak river surfaces. Furthermore, based on the statistics analysis of the RR-DPCA outputs on the homogenous background, the cell average (CA method can be well applied for subsequent constant false alarm rate (CFAR detection. The proposed RR-DPCA method can well detect the point moving targets and distributed moving targets simultaneously. Finally, the results of both simulated and real data are provided to demonstrate the effectiveness of the proposed SAR/GMTI method.
Huang, Jun; Ma, Yong; Mei, Xiaoguang; Fan, Fan
The traditional noise reduction methods for 3-D infrared hyperspectral images typically operate independently in either the spatial or spectral domain, and such methods overlook the relationship between the two domains. To address this issue, we propose a hybrid spatial-spectral method in this paper to link both domains. First, principal component analysis and bivariate wavelet shrinkage are performed in the 2-D spatial domain. Second, 2-D principal component analysis transformation is conducted in the 1-D spectral domain to separate the basic components from detail ones. The energy distribution of noise is unaffected by orthogonal transformation; therefore, the signal-to-noise ratio of each component is used as a criterion to determine whether a component should be protected from over-denoising or denoised with certain 1-D denoising methods. This study implements the 1-D wavelet shrinking threshold method based on Stein's unbiased risk estimator, and the quantitative results on publicly available datasets demonstrate that our method can improve denoising performance more effectively than other state-of-the-art methods can.
... guests. Boat launch adjacent to Officer's Club Beach on American Lake/Beachwood area Cat Lake Picnic and Fishing Area—Training Area 19 Chambers Lake Picnic and *Fishing Area—Training Area 12 (See para 2 below) Ecology Park Hiking Path—North Fort, CTA A West Fiander Lake Picnic and Fishing Area—Training Area 20...
... launch adjacent to Officer's Club Beach on American Lake—Beachwood area Cat Lake Picnic and Fishing Area—Training Area 19 Chambers Lake Picnic and Fishing Area—Training Area 12 (See Para 3 below) Fiander lake Picnic and Fishing Area—Training Area 20 Johnson Marsh—Training Area 10 Lewis Lake Picnic and Fishing...
Tilma, Jens; Nørgaard, Mette; Mikkelsen, Kim Lyngby; Johnsen, Søren Paaske
Any patient in the Danish health care system who experiences a treatment injury can make a compensation claim to the Danish Patient Compensation Association (DPCA) free of charge. The aim of this paper is to describe the DPCA database as a source of data for epidemiological research. Data to DPCA are collected prospectively on all claims and include information on patient factors and health records, system factors, and administrative data. Approval of claims is based on injury due to the principle of treatment below experienced specialist standard or intolerable, unexpected extensiveness of injury. Average processing time of a compensation claim is 6-8 months. Data collection is nationwide and started in 1992. The patient's central registration system number, a unique personal identifier, allows for data linkage to other registries such as the Danish National Patient Registry. The DPCA data are accessible for research following data usage permission and make it possible to analyze all claims or specific subgroups to identify predictors, outcomes, etc. DPCA data have until now been used only in few studies but could be a useful data source in future studies of health care-related injuries.
Full Text Available Jens Tilma,1 Mette Nørgaard,1 Kim Lyngby Mikkelsen,2 Søren Paaske Johnsen1 1Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, 2Danish Patient Compensation Association, Copenhagen, Denmark Abstract: Any patient in the Danish health care system who experiences a treatment injury can make a compensation claim to the Danish Patient Compensation Association (DPCA free of charge. The aim of this paper is to describe the DPCA database as a source of data for epidemiological research. Data to DPCA are collected prospectively on all claims and include information on patient factors and health records, system factors, and administrative data. Approval of claims is based on injury due to the principle of treatment below experienced specialist standard or intolerable, unexpected extensiveness of injury. Average processing time of a compensation claim is 6–8 months. Data collection is nationwide and started in 1992. The patient's central registration system number, a unique personal identifier, allows for data linkage to other registries such as the Danish National Patient Registry. The DPCA data are accessible for research following data usage permission and make it possible to analyze all claims or specific subgroups to identify predictors, outcomes, etc. DPCA data have until now been used only in few studies but could be a useful data source in future studies of health care-related injuries. Keywords: public health care, treatment injuries, no-fault compensation, registries, research, Denmark
Kobak, Dmitry; Brendel, Wieland; Constantinidis, Christos; Feierstein, Claudia E; Kepecs, Adam; Mainen, Zachary F; Qi, Xue-Lian; Romo, Ranulfo; Uchida, Naoshige; Machens, Christian K
Neurons in higher cortical areas, such as the prefrontal cortex, are often tuned to a variety of sensory and motor variables, and are therefore said to display mixed selectivity. This complexity of single neuron responses can obscure what information these areas represent and how it is represented. Here we demonstrate the advantages of a new dimensionality reduction technique, demixed principal component analysis (dPCA), that decomposes population activity into a few components. In addition to systematically capturing the majority of the variance of the data, dPCA also exposes the dependence of the neural representation on task parameters such as stimuli, decisions, or rewards. To illustrate our method we reanalyze population data from four datasets comprising different species, different cortical areas and different experimental tasks. In each case, dPCA provides a concise way of visualizing the data that summarizes the task-dependent features of the population response in a single figure.
García Martos, P.; Gil de Sola, F.; Marín, P.; García-Agudo, L.; García-Agudo, R.; Tejuca, F.; Calle, L.
Antecedentes: La peritonitis fúngica es una complicación infrecuente pero grave en pacientes en diálisis peritoneal continua ambulatoria (DPCA). Métodos: Durante un período de 10 años (1999-2008), de un total de 175 pacientes con insuficiencia renal crónica en tratamiento con DPCA, estudiamos retrospectivamente 10 casos de peritonitis fúngica, analizando los factores predisponentes, aspectos clínicos, agentes etiológicos y tratamiento. El diagnóstico se estableció por la presencia de efluente...
Rivera-Ledesma, Armando; Montero-López Lena, María; Sandoval-Ávila, Rosalba
Se ha reportado que el impacto del procedimiento de diálisis peritoneal continua ambulatoria (DPCA) en la calidad de vida del paciente suele presentar profundas consecuencias psicosociales para él y su familia; dicha sustitución de la función renal promueve la supervivencia pero no un completo bienestar físico, emocional y social. Estos últimos aspectos pueden ser determinantes para el éxito del tratamiento con DPCA por su influencia positiva o negativa en la adherencia del paciente al mismo....
Principal components analysis (PCA) has been intensively studied and used in monitoring industrial systems. However, data generated from chemical processes are usually correlated in time due to process dynamics, which makes the fault detection based on PCA approach a challenging task. Accounting for the dynamic nature of data can also reflect the performance of the designed fault detection approaches. In PCA-based methods, this dynamic characteristic of the data can be accounted for by using dynamic PCA (DPCA), in which lagged variables are used in the PCA model to capture the time evolution of the process. This paper presents a new approach that combines the DPCA to account for autocorrelation in data and generalized likelihood ratio (GLR) test to detect faults. A DPCA model is applied to perform dimension reduction while appropriately considering the temporal relationships in the data. Specifically, the proposed approach uses the DPCA to generate residuals, and then apply GLR test to reveal any abnormality. The performances of the proposed method are evaluated through a continuous stirred tank heater system.
Wasser, M.N.; Schultze Kool, L.J.; Roos, A. de [Leiden Univ. Hospital (Netherlands)] [and others
Our goal was to assess the value of MRA for detecting stenoses in the celiac (CA) and superior mesenteric (SMA) arteries in patients suspected of having chronic mesenteric ischemia, using an optimized systolically gated 3D phase contrast technique. In an initial study in 24 patients who underwent conventional angiography of the abdominal vessels for different clinical indications, a 3D phase contrast MRA technique (3D-PCA) was evaluated and optimized to image the CAs and SMAs. Subsequently, a prospective study was performed to assess the value of systolically gated 3D-PCA in evaluation of the mesenteric arteries in 10 patients with signs and symptoms of chronic mesenteric ischemia. Intraarterial digital subtraction angiography and surgical findings were used as the reference standard. In the initial study, systolic gating appeared to be essential in imaging the SMA on 3D-PCA. In 10 patients suspected of mesenteric ischemia, systolically gated 3D-PCA identified significant proximal disease in the two mesenteric vessels in 4 patients. These patients underwent successful reconstruction of their stenotic vessels. Cardiac-gated MRA may become a useful tool in selection of patients suspected of having mesenteric ischemia who may benefit from surgery. 16 refs., 6 figs., 4 tabs.
Farajzadehbibalan, Saber; Ramezani, Mohammad Hossein; Nielsen, Peter
In this study, we derive an eigenvector-based multivariate model of a power grid from the wind farm's standpoint using dynamic principal component analysis (DPCA). The main advantages of our model over previously developed models are being more realistic and having low complexity. We show that th...
Vanhatalo, Erik; Kulahci, Murat; Bergquist, Bjarne
When principal component analysis (PCA) is used for statistical process monitoring it relies on the assumption that data are time independent. However, industrial data will often exhibit serial correlation. Dynamic PCA (DPCA) has been suggested as a remedy for high-dimensional and time...... for determining the number of principal components to retain. The number of retained principal components is determined by visual inspection of the serial correlation in the squared prediction error statistic, Q (SPE), together with the cumulative explained variance of the model. The methods are illustrated using...... driven method to determine the maximum number of lags in DPCA with a foundation in multivariate time series analysis. The method is based on the behavior of the eigenvalues of the lagged autocorrelation and partial autocorrelation matrices. Given a specific lag structure we also propose a method...
Full Text Available The difference between adjacent frames of human walking contains useful information for human gait identification. Based on the previous idea a silhouettes difference based human gait recognition method named as average gait differential image (AGDI is proposed in this paper. The AGDI is generated by the accumulation of the silhouettes difference between adjacent frames. The advantage of this method lies in that as a feature image it can preserve both the kinetic and static information of walking. Comparing to gait energy image (GEI, AGDI is more fit to representation the variation of silhouettes during walking. Two-dimensional principal component analysis (2DPCA is used to extract features from the AGDI. Experiments on CASIA dataset show that AGDI has better identification and verification performance than GEI. Comparing to PCA, 2DPCA is a more efficient and less memory storage consumption feature extraction method in gait based recognition.
Fábio Engel de Camargo
Full Text Available In this work, the Verhulst model and the Perron-Frobenius theorem are applied on the power control problem which is a concern in multiple access communication networks due to the multiple access interference. This paper deals with the performance versus complexity tradeoff of both power control algorithm (PCA, as well as highlights the computational cost aspects regarding the implementability of distributed PCA (DPCA version for both algorithms. As a proof-of-concept the DPCA implementation is carried out deploying a commercial point-floating DSP platform. Numerical results in terms of DSP cycles and computational time as well indicate a feasibility of implementing the PCA-Verhulst model in 2G and 3G cellular systems; b high computational cost for the PCA-Perron-Frobenius model.
Full Text Available To overcome the shortcomings of traditional dimensionality reduction algorithms, incremental tensor principal component analysis (ITPCA based on updated-SVD technique algorithm is proposed in this paper. This paper proves the relationship between PCA, 2DPCA, MPCA, and the graph embedding framework theoretically and derives the incremental learning procedure to add single sample and multiple samples in detail. The experiments on handwritten digit recognition have demonstrated that ITPCA has achieved better recognition performance than that of vector-based principal component analysis (PCA, incremental principal component analysis (IPCA, and multilinear principal component analysis (MPCA algorithms. At the same time, ITPCA also has lower time and space complexity.
Full Text Available OBJETIVO: Analisó el costo-efectividad en intervenciones para pacientes con insuficiencia renal crónica terminal (IRCT en términos de los costos económicos de cada intervención, los años de vida ganados y la calidad de vida que generan tres alternativas comparables y mutuamente excluyentes: diálisis peritoneal contínua ambulatoria (DPCA, la hemodiálisis (HD y el trasplante renal (TR. MÉTODO: El diseño del estudio fue de tipo longitudinal. Los costos de cada intervención se determinaron mediante la técnica de manejo de caso promedio. Las medidas para evaluar los criterios de efectividad elegidos fueron la probabilidad de sobrevida y el Año de Vida Ajustado por Calidad (QALY, Quality Adjusted Life Year medido por el Indice de Rosser. RSULTADOS: Los costos de manejo anual de caso fueron: diálisis peritoneal $5,643.07, hemodiálisis $9,631.60 y trasplante $3,021.63. En cuanto a la efectividad, la sobrevida del injerto de trasplante renal resultó de 89,9% y 79,6% a uno y tres años respectivamente, mientras que los pacientes sometidos a DPCA tienen una sobrevida de 86,2% y 66,9% a un año y a tres años respectivamente. En cuanto a los QALY's, los resultados para cada intervención fueron: DPCA 0,879; HD 0,864; y para el TR 0,978. CONCLUSIÓN: La intervención más costo-efectiva resultó el trasplante renal con un coeficiente de 3,088.69, seguido de la DPCA y la hemodiálisis, cuyos coeficientes fueron de 6,416.95 y 11,147.68 respectivamente. Por lo tanto se recomienda promover y utilizar el trasplante renal como la intervención más costo-efectiva para pacientes con IRCT.
Ahrendt, Peter; Meng, Anders; Larsen, Jan
In this paper music genre classification has been explored with special emphasis on the decision time horizon and ranking of tapped-delay-line short-time features. Late information fusion as e.g. majority voting is compared with techniques of early information fusion such as dynamic PCA (DPCA......). The most frequently suggested features in the literature were employed including mel-frequency cepstral coefficients (MFCC), linear prediction coefficients (LPC), zero-crossing rate (ZCR), and MPEG-7 features. To rank the importance of the short time features consensus sensitivity analysis is applied...
Love, Ryan J; Jones, Kim S
Connective tissue rapidly proliferates on and around biomaterials implanted in vivo, which impairs the function of the engineered tissues, biosensors, and devices. Glucocorticoids can be utilized to suppress tissue ingrowth, but can only be used for a limited time because they nonselectively arrest cell proliferation in the local environment. The present study examined use of a prolyl-4-hydroxylase inhibitor, 1,4-dihydrophenonthrolin-4-one-3-carboxylic acid (1,4-DPCA), to suppress connective tissue ingrowth in porous PLGA discs implanted in the peritoneal cavity for 28 days. The prolyl-4-hydroxylase inhibitor was found to be effective at inhibiting collagen deposition within and on the outer surface of the disc, and also limited connective tissue ingrowth, but not to the extent of glucocorticoid inhibition. Finally, it was discovered that 1,4-DPCA suppressed Scavenger Receptor A expression on a macrophage-like cell culture, which may account for the drug's ability to limit connective tissue ingrowth in vivo. Copyright © 2013 Wiley Periodicals, Inc., a Wiley Company.
Hsu, Chih-Bin; Hao, Shu-Sheng; Lee, Jen-Chun
Biometric identification is an emerging technology that can solve security problems in our networked society. A reliable and robust personal verification approach using dorsal hand vein patterns is proposed in this paper. The characteristic of the approach needs less computational and memory requirements and has a higher recognition accuracy. In our work, the near-infrared charge-coupled device (CCD) camera is adopted as an input device for capturing dorsal hand vein images, it has the advantages of the low-cost and noncontact imaging. In the proposed approach, two finger-peaks are automatically selected as the datum points to define the region of interest (ROI) in the dorsal hand vein images. The modified two-directional two-dimensional principal component analysis, which performs an alternate two-dimensional PCA (2DPCA) in the column direction of images in the 2DPCA subspace, is proposed to exploit the correlation of vein features inside the ROI between images. The major advantage of the proposed method is that it requires fewer coefficients for efficient dorsal hand vein image representation and recognition. The experimental results on our large dorsal hand vein database show that the presented schema achieves promising performance (false reject rate: 0.97% and false acceptance rate: 0.05%) and is feasible for dorsal hand vein recognition.
Paul Jean Etienne Jeszensky
Full Text Available In this paper the continuous dynamic model of Verhulst is used. It had been initially elaborated to describe the population growth of biological species with food restriction and physical space, in order to synthesize a new distributed power control algorithm (DPCA, applicable in direct sequence code division multiple access (DS-CDMA systems. The discretization of the corresponding differential equation is accomplished via numeric integration Euler method (NIE. The properties of the proposed recursive algorithm, as Euclidian distance from optimum vector after convergence, convergence speed, normalized mean squared error (NSE, average power consumption per user and implementation complexity, are investigated through simulations. The simulation results are confronted with the other DPCAs: the classic algorithm derived by Foschini and Miljanic and the sigmoidal by Uykan and Koivo. With estimate errors, the proposed DPCA showed smaller discrepancy from the optimum power vector allocation after convergence and better convergence. Additionally, the Gerchgorin Circles theory (GC is applied for the feasibility of the power allocation problem.
Full Text Available Two-dimensional ionospheric total electron content (TEC data were collected during the time period from 00:00 on 2 July to 12:00 UT on 08 July 2013. This period spanned 5 days before to 1 day after a deep earthquake (378.8 km in Papua New Guinea at 18:35:30 on 7 July 2013 UT (Mw=7.2. Data were examined by two-dimensional principal component analysis (2DPCA to detect TEC precursors related to the earthquake because TEC precursors have usually appeared in earlier time periods (Liu et al. 2006. A TEC precursor was highly localized around the epicenter on 6 July for 5 minutes, from 06:00 to 06:05. Ionizing radiation from radon gas release could possibly have caused the anomalous TEC fluctuation through, for example, a density variance. The plasma might have experienced large damping to cause short-term TEC fluctuations, and the gas released in a small amount in a short time period. 2DPCA can also identify short-term TEC fluctuations, but this fluctuation lasted for a considerable length of time. Other background TEC anomalies caused by the geomagnetic storm, small earthquakes and non-earthquake activities, e.g., equatorial ionization anomaly (EIA, resulted in small principal eigenvalues. Therefore, the detection of TEC precursors through large eigenvalues was not due to these background TEC anomalies. Resumen Datos del contenido total de electrones ionosféricos en dos dimensiones (TEC fueron medidos durante el período del 2 de julio de 2013, a las 0:00:00 horas GMT., hasta las 12:00 GMT. del 8 de julio. En este lapso se abarcan cinco días antes y un día después de un terremoto profundo (378,8 kilómetros en Papúa Nueva Guinea, que se presentó a las 18:35:30 del 7 de julio (M w =7.2. Los datos fueron examina- dos a través de los componentes principales en dos dimensiones (2DPCA para detectar los precursores TEC relacionados al terremoto (Liu et al. 2006. Un precursor de los TEC fue localizado alrededor del epicentro el 6 de julio durante 5
Full Text Available Synthetic aperture radar (SAR equipped on the hypersonic air vehicle in near space has many advantages over the conventional airborne SAR. However, its high-speed maneuvering characteristics with curved trajectory result in serious range migration, and exacerbate the contradiction between the high resolution and wide swath. To solve this problem, this paper establishes the imaging geometrical model matched with the flight trajectory of the hypersonic platform and the multichannel azimuth sampling model based on the displaced phase center antenna (DPCA technology. Furthermore, based on the multichannel signal reconstruction theory, a more efficient spectrum reconstruction model using discrete Fourier transform is proposed to obtain the azimuth uniform sampling data. Due to the high complexity of the slant range model, it is difficult to deduce the processing algorithm for SAR imaging. Thus, an approximate range model is derived based on the minimax criterion, and the optimal second-order approximate coefficients of cosine function are obtained using the two-population coevolutionary algorithm. On this basis, aiming at the problem that the traditional Omega-K algorithm cannot compensate the residual phase with the difficulty of Stolt mapping along the range frequency axis, this paper proposes an Exact Transfer Function (ETF algorithm for SAR imaging, and presents a method of range division to achieve wide swath imaging. Simulation results verify the effectiveness of the ETF imaging algorithm.
Zhou, Rui; Sun, Jinping; Hu, Yuxin; Qi, Yaolong
Synthetic aperture radar (SAR) equipped on the hypersonic air vehicle in near space has many advantages over the conventional airborne SAR. However, its high-speed maneuvering characteristics with curved trajectory result in serious range migration, and exacerbate the contradiction between the high resolution and wide swath. To solve this problem, this paper establishes the imaging geometrical model matched with the flight trajectory of the hypersonic platform and the multichannel azimuth sampling model based on the displaced phase center antenna (DPCA) technology. Furthermore, based on the multichannel signal reconstruction theory, a more efficient spectrum reconstruction model using discrete Fourier transform is proposed to obtain the azimuth uniform sampling data. Due to the high complexity of the slant range model, it is difficult to deduce the processing algorithm for SAR imaging. Thus, an approximate range model is derived based on the minimax criterion, and the optimal second-order approximate coefficients of cosine function are obtained using the two-population coevolutionary algorithm. On this basis, aiming at the problem that the traditional Omega-K algorithm cannot compensate the residual phase with the difficulty of Stolt mapping along the range frequency axis, this paper proposes an Exact Transfer Function (ETF) algorithm for SAR imaging, and presents a method of range division to achieve wide swath imaging. Simulation results verify the effectiveness of the ETF imaging algorithm.
Full Text Available Conventional incremental PCA methods usually only discuss the situation of adding samples. In this paper, we consider two different cases: deleting samples and simultaneously adding and deleting samples. To avoid the NP-hard problem of downdating SVD without right singular vectors and specific position information, we choose to use EVD instead of SVD, which is used by most IPCA methods. First, we propose an EVD updating and downdating algorithm, called EVD dualdating, which permits simultaneous arbitrary adding and deleting operation, via transforming the EVD of the covariance matrix into a SVD updating problem plus an EVD of a small autocorrelation matrix. A comprehensive analysis is delivered to express the essence, expansibility, and computation complexity of EVD dualdating. A mathematical theorem proves that if the whole data matrix satisfies the low-rank-plus-shift structure, EVD dualdating is an optimal rank-k estimator under the sequential environment. A selection method based on eigenvalues is presented to determine the optimal rank k of the subspace. Then, we propose three incremental/decremental PCA methods: EVDD-IPCA, EVDD-DPCA, and EVDD-IDPCA, which are adaptive to the varying mean. Finally, plenty of comparative experiments demonstrate that EVDD-based methods outperform conventional incremental/decremental PCA methods in both efficiency and accuracy.
Full Text Available Reactive energetic plasticizers (REPs coupled with hydroxy-telechelic poly(glycidyl azide-co-tetrahydrofuran (PGT-based energetic polyurethane (PU binders for use in solid propellants and plastic-bonded explosives (PBXs were investigated. The generation of gem-dinitro REPs along with a terminal alkyne stemmed from a series of finely designed approaches to not only satisfy common demands as conventional energetic plasticizers, but also to prevent the migration of plasticizers. The miscibility and rheological behavior of a binary mixture of PGT/REP with various REP fractions were quantitatively determined by differential scanning calorimetry (DSC and rheometer, respectively, highlighting the promising performance of REPs in the formulation process. The kinetics on the distinct reactivity of propargyl vs. 3-butynyl species of REPs towards the azide group of the PGT prepolymer in terms of Cu-free azide-alkyne 1,3-dipolar cycloaddition (1,3-DPCA was studied by monitoring 1H nuclear magnetic resonance spectroscopy and analyzing the activation energies (Ea obtained using DSC. The thermal stability of the finally cured energetic binders with the incorporation of REPs indicated that the thermal stability of the REP/PGT-based PUs was maintained independently of the REP content. The tensile strength and modulus of the PUs increased with an increase in the REP content. In addition, the energetic performance and sensitivity of REP and REP triazole species was predicted.
Md Noor, Siti Salwa; Michael, Kaleena; Marshall, Stephen; Ren, Jinchang
Eye assessment is essential in preventing blindness. Currently, the existing methods to assess corneal epithelium injury are complex and require expert knowledge. Hence, we have introduced a non-invasive technique using hyperspectral imaging (HSI) and an image analysis algorithm of corneal epithelium injury. Three groups of images were compared and analyzed, namely healthy eyes, injured eyes, and injured eyes with stain. Dimensionality reduction using principal component analysis (PCA) was applied to reduce massive data and redundancies. The first 10 principal components (PCs) were selected for further processing. The mean vector of 10 PCs with 45 pairs of all combinations was computed and sent to two classifiers. A quadratic Bayes normal classifier (QDC) and a support vector classifier (SVC) were used in this study to discriminate the eleven eyes into three groups. As a result, the combined classifier of QDC and SVC showed optimal performance with 2D PCA features (2DPCA-QDSVC) and was utilized to classify normal and abnormal tissues, using color image segmentation. The result was compared with human segmentation. The outcome showed that the proposed algorithm produced extremely promising results to assist the clinician in quantifying a cornea injury.
Nayak, Deepak Ranjan; Dash, Ratnakar; Majhi, Banshidhar
Pathological brain detection has made notable stride in the past years, as a consequence many pathological brain detection systems (PBDSs) have been proposed. But, the accuracy of these systems still needs significant improvement in order to meet the necessity of real world diagnostic situations. In this paper, an efficient PBDS based on MR images is proposed that markedly improves the recent results. The proposed system makes use of contrast limited adaptive histogram equalization (CLAHE) to enhance the quality of the input MR images. Thereafter, two-dimensional PCA (2DPCA) strategy is employed to extract the features and subsequently, a PCA+LDA approach is used to generate a compact and discriminative feature set. Finally, a new learning algorithm called MDE-ELM is suggested that combines modified differential evolution (MDE) and extreme learning machine (ELM) for segregation of MR images as pathological or healthy. The MDE is utilized to optimize the input weights and hidden biases of single-hidden-layer feed-forward neural networks (SLFN), whereas an analytical method is used for determining the output weights. The proposed algorithm performs optimization based on both the root mean squared error (RMSE) and norm of the output weights of SLFNs. The suggested scheme is benchmarked on three standard datasets and the results are compared against other competent schemes. The experimental outcomes show that the proposed scheme offers superior results compared to its counterparts. Further, it has been noticed that the proposed MDE-ELM classifier obtains better accuracy with compact network architecture than conventional algorithms.
Pour, A B; Hashim, M
The area under investigation is the Bau gold mining district in the State of Sarawak, East Malaysia, on the island of Borneo. It has tropical climate with limited bedrock exposures. Bau is a gold field similar to Carlin style gold deposits. Geological analyses coupled with remote sensing data were used to detect hydrothermally altered rocks associated with gold mineralization. The Landsat Enhanced Thematic Mapper + (ETM + ) and Hyperion data were used to carry out mineral mapping of mineralized zones in the study area and surrounding terrain. Directed Principal Components Analysis (DPCA) transformation of four appropriate ETM+ band ratios were applied to produce DPC images, allowing the removal of the effects of vegetation from ETM+ data and the detection of separate mineral images at a regional scale. Linear Spectral Unmixing (LSU) was used to produce image maps of hydroxyl-bearing minerals using Hyperion data at a district scale. Results derived from the visible and near infrared and shortwave infrared bands of Hyperion represented iron oxide/hydroxide and clay minerals rich zones associated with the known gold prospects in the Bau district. The results show that the known gold prospects and potentially interesting areas are recognizable by the methods used, despite limited bedrock exposure in this region and the constraints imposed by the tropical environment. The approach used in this study can be more broadly applicable to provide an opportunity for detecting potentially interesting areas of gold mineralization using the ETM + and Hyperion data in the tropical/sub-tropical regions
Pour, A. B.; Hashim, M.
The area under investigation is the Bau gold mining district in the State of Sarawak, East Malaysia, on the island of Borneo. It has tropical climate with limited bedrock exposures. Bau is a gold field similar to Carlin style gold deposits. Geological analyses coupled with remote sensing data were used to detect hydrothermally altered rocks associated with gold mineralization. The Landsat Enhanced Thematic Mapper+ (ETM+) and Hyperion data were used to carry out mineral mapping of mineralized zones in the study area and surrounding terrain. Directed Principal Components Analysis (DPCA) transformation of four appropriate ETM+ band ratios were applied to produce DPC images, allowing the removal of the effects of vegetation from ETM+ data and the detection of separate mineral images at a regional scale. Linear Spectral Unmixing (LSU) was used to produce image maps of hydroxyl-bearing minerals using Hyperion data at a district scale. Results derived from the visible and near infrared and shortwave infrared bands of Hyperion represented iron oxide/hydroxide and clay minerals rich zones associated with the known gold prospects in the Bau district. The results show that the known gold prospects and potentially interesting areas are recognizable by the methods used, despite limited bedrock exposure in this region and the constraints imposed by the tropical environment. The approach used in this study can be more broadly applicable to provide an opportunity for detecting potentially interesting areas of gold mineralization using the ETM+ and Hyperion data in the tropical/sub-tropical regions.
Andrzejewski, Matthew E; Ryals, Curtis
Pavlovian conditioning is an elementary form of reward-related behavioral adaptation. The mesolimbic dopamine system is widely considered to mediate critical aspects of reward-related learning. For example, initial acquisition of positively-reinforced operant behavior requires dopamine (DA) D1 receptor (D1R) activation in the basolateral amygdala (BLA), central nucleus of the amygdala (CeA), and the ventral subiculum (vSUB). However, the role of D1R activation in these areas on appetitive, non-drug-related, Pavlovian learning is not currently known. In separate experiments, microinfusions of the D1-like receptor antagonist SCH-23390 (3.0 nmol/0.5 μL per side) into the amygdala and subiculum preceded discriminated Pavlovian conditioned approach (dPCA) training sessions. D1-like antagonism in all three structures impaired the acquisition of discriminated approach, but had no effect on performance after conditioning was asymptotic. Moreover, dissociable effects of D1-like antagonism in the three structures on components of discriminated responding were obtained. Lastly, the lack of latent inhibition in drug-treated groups may elucidate the role of D1-like in reward-related Pavlovian conditioning. The present data suggest a role for the D1 receptors in the amygdala and hippocampus in learning the significance of conditional stimuli, but not in the expression of conditional responses. PMID:26632336
Chen, Jinyan; Wu, Rongteng
It is very necessary to recognize person through visual surveillance automatically for public security reason. Human gait based identification focus on recognizing human by his walking video automatically using computer vision and image processing approaches. As a potential biometric measure, human gait identification has attracted more and more researchers. Current human gait identification methods can be divided into two categories: model-based methods and motion-based methods. In this paper a two-Dimensional Principal Component Analysis and temporal-space analysis based human gait identification method is proposed. Using background estimation and image subtraction we can get a binary images sequence from the surveillance video. By comparing the difference of two adjacent images in the gait images sequence, we can get a difference binary images sequence. Every binary difference image indicates the body moving mode during a person walking. We use the following steps to extract the temporal-space features from the difference binary images sequence: Projecting one difference image to Y axis or X axis we can get two vectors. Project every difference image in the difference binary images sequence to Y axis or X axis difference binary images sequence we can get two matrixes. These two matrixes indicate the styles of one walking. Then Two-Dimensional Principal Component Analysis(2DPCA) is used to transform these two matrixes to two vectors while at the same time keep the maximum separability. Finally the similarity of two human gait images is calculated by the Euclidean distance of the two vectors. The performance of our methods is illustrated using the CASIA Gait Database.