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Sample records for mouse distal convoluted

  1. Direct physical contact between intercalated cells in the distal convoluted tubule and the afferent arteriole in mouse kidneys.

    Directory of Open Access Journals (Sweden)

    Hao Ren

    Full Text Available Recent physiological studies in the kidney proposed the existence of a secondary feedback mechanism termed 'crosstalk' localized after the macula densa. This newly discovered crosstalk contact between the nephron tubule and its own afferent arteriole may potentially revolutionize our understanding of renal vascular resistance and electrolyte regulation. However, the nature of such a crosstalk mechanism is still debated due to a lack of direct and comprehensive morphological evidence. Its exact location along the nephron, its prevalence among the different types of nephrons, and the type of cells involved are yet unknown. To address these issues, computer assisted 3-dimensional nephron tracing was applied in combination with direct immunohistochemistry on plastic sections and electron microscopy. 'Random' contacts in the cortex were identified by the tracing and excluded. We investigated a total of 168 nephrons from all cortical regions. The results demonstrated that the crosstalk contact existed, and that it was only present in certain nephrons (90% of the short-looped and 75% of the long-looped nephrons. The crosstalk contacts always occurred at a specific position--the last 10% of the distal convoluted tubule. Importantly, we demonstrated, for the first time, that the cells found in the tubule wall at the contact site were always type nonA-nonB intercalated cells. In conclusion, the present work confirmed the existence of a post macula densa physical crosstalk contact.

  2. Direct physical contact between intercalated cells in the distal convoluted tubule and the afferent arteriole in mouse kidneys.

    Science.gov (United States)

    Ren, Hao; Liu, Ning-Yu; Andreasen, Arne; Thomsen, Jesper S; Cao, Liu; Christensen, Erik I; Zhai, Xiao-Yue

    2013-01-01

    Recent physiological studies in the kidney proposed the existence of a secondary feedback mechanism termed 'crosstalk' localized after the macula densa. This newly discovered crosstalk contact between the nephron tubule and its own afferent arteriole may potentially revolutionize our understanding of renal vascular resistance and electrolyte regulation. However, the nature of such a crosstalk mechanism is still debated due to a lack of direct and comprehensive morphological evidence. Its exact location along the nephron, its prevalence among the different types of nephrons, and the type of cells involved are yet unknown. To address these issues, computer assisted 3-dimensional nephron tracing was applied in combination with direct immunohistochemistry on plastic sections and electron microscopy. 'Random' contacts in the cortex were identified by the tracing and excluded. We investigated a total of 168 nephrons from all cortical regions. The results demonstrated that the crosstalk contact existed, and that it was only present in certain nephrons (90% of the short-looped and 75% of the long-looped nephrons). The crosstalk contacts always occurred at a specific position--the last 10% of the distal convoluted tubule. Importantly, we demonstrated, for the first time, that the cells found in the tubule wall at the contact site were always type nonA-nonB intercalated cells. In conclusion, the present work confirmed the existence of a post macula densa physical crosstalk contact.

  3. Cystogenesis and elongated primary cilia in Tsc1-deficient distal convoluted tubules.

    Science.gov (United States)

    Armour, Eric A; Carson, Robert P; Ess, Kevin C

    2012-08-15

    Tuberous sclerosis complex (TSC) is a multiorgan hamartomatous disease caused by loss of function mutations of either the TSC1 or TSC2 genes. Neurological symptoms of TSC predominate in younger patients, but renal pathologies are a serious aspect of the disease in older children and adults. To study TSC pathogenesis in the kidney, we inactivated the mouse Tsc1 gene in the distal convoluted tubules (DCT). At young ages, Tsc1 conditional knockout (CKO) mice have enlarged kidneys and mild cystogenesis with increased mammalian target of rapamycin complex (mTORC)1 but decreased mTORC2 signaling. Treatment with the mTORC1 inhibitor rapamycin reduces kidney size and cystogenesis. Rapamycin withdrawal led to massive cystogenesis involving both distal as well as proximal tubules. To assess the contribution of decreased mTORC2 signaling in kidney pathogenesis, we also generated Rictor CKO mice. These animals did not have any detectable kidney pathology. Finally, we examined primary cilia in the DCT. Cilia were longer in Tsc1 CKO mice, and rapamycin treatment returned cilia length to normal. Rictor CKO mice had normal cilia in the DCT. Overall, our findings suggest that loss of the Tsc1 gene in the DCT is sufficient for renal cystogenesis. This cytogenesis appears to be mTORC1 but not mTORC2 dependent. Intriguingly, the mechanism may be cell autonomous as well as non-cell autonomous and possibly involves the length and function of primary cilia.

  4. Cystogenesis and elongated primary cilia in Tsc1-deficient distal convoluted tubules

    Science.gov (United States)

    Armour, Eric A.; Carson, Robert P.

    2012-01-01

    Tuberous sclerosis complex (TSC) is a multiorgan hamartomatous disease caused by loss of function mutations of either the TSC1 or TSC2 genes. Neurological symptoms of TSC predominate in younger patients, but renal pathologies are a serious aspect of the disease in older children and adults. To study TSC pathogenesis in the kidney, we inactivated the mouse Tsc1 gene in the distal convoluted tubules (DCT). At young ages, Tsc1 conditional knockout (CKO) mice have enlarged kidneys and mild cystogenesis with increased mammalian target of rapamycin complex (mTORC)1 but decreased mTORC2 signaling. Treatment with the mTORC1 inhibitor rapamycin reduces kidney size and cystogenesis. Rapamycin withdrawal led to massive cystogenesis involving both distal as well as proximal tubules. To assess the contribution of decreased mTORC2 signaling in kidney pathogenesis, we also generated Rictor CKO mice. These animals did not have any detectable kidney pathology. Finally, we examined primary cilia in the DCT. Cilia were longer in Tsc1 CKO mice, and rapamycin treatment returned cilia length to normal. Rictor CKO mice had normal cilia in the DCT. Overall, our findings suggest that loss of the Tsc1 gene in the DCT is sufficient for renal cystogenesis. This cytogenesis appears to be mTORC1 but not mTORC2 dependent. Intriguingly, the mechanism may be cell autonomous as well as non-cell autonomous and possibly involves the length and function of primary cilia. PMID:22674026

  5. Comparative proteomic analysis of kidney distal convoluted tubule and cortical collecting duct cells following long-term hormonal stimulation

    DEFF Research Database (Denmark)

    Wu, Qi; Moller, Hanne; Rosenbaek, Lena Lindtoft

    2017-01-01

    The distal convoluted tubule (DCT) and the cortical collecting ducts (CCD) are portions of renal tubule that are partly responsible for maintaining the systemic concentrations of potassium, sodium, calcium and magnesium. Despite being structurally similar, DCT and CCD cells have different transpo...... FDR threshold in one cell type plus the unique proteins in this cell type. These 1025 mpkDCT specific proteins and 1211 mpkCCD specific proteins under the three conditions were subjected to further bioinformatics analyses including Panther and DAVID gene ontology analyses, E3 ligase...

  6. Deep convolutional neural networks for annotating gene expression patterns in the mouse brain.

    Science.gov (United States)

    Zeng, Tao; Li, Rongjian; Mukkamala, Ravi; Ye, Jieping; Ji, Shuiwang

    2015-05-07

    Profiling gene expression in brain structures at various spatial and temporal scales is essential to understanding how genes regulate the development of brain structures. The Allen Developing Mouse Brain Atlas provides high-resolution 3-D in situ hybridization (ISH) gene expression patterns in multiple developing stages of the mouse brain. Currently, the ISH images are annotated with anatomical terms manually. In this paper, we propose a computational approach to annotate gene expression pattern images in the mouse brain at various structural levels over the course of development. We applied deep convolutional neural network that was trained on a large set of natural images to extract features from the ISH images of developing mouse brain. As a baseline representation, we applied invariant image feature descriptors to capture local statistics from ISH images and used the bag-of-words approach to build image-level representations. Both types of features from multiple ISH image sections of the entire brain were then combined to build 3-D, brain-wide gene expression representations. We employed regularized learning methods for discriminating gene expression patterns in different brain structures. Results show that our approach of using convolutional model as feature extractors achieved superior performance in annotating gene expression patterns at multiple levels of brain structures throughout four developing ages. Overall, we achieved average AUC of 0.894 ± 0.014, as compared with 0.820 ± 0.046 yielded by the bag-of-words approach. Deep convolutional neural network model trained on natural image sets and applied to gene expression pattern annotation tasks yielded superior performance, demonstrating its transfer learning property is applicable to such biological image sets.

  7. Genome-wide analysis of murine renal distal convoluted tubular cells for the target genes of mineralocorticoid receptor

    Energy Technology Data Exchange (ETDEWEB)

    Ueda, Kohei [Department of Nephrology and Endocrinology, The University of Tokyo, Tokyo (Japan); Fujiki, Katsunori; Shirahige, Katsuhiko [Research Center for Epigenetic Disease, Institute of Molecular and Cellular Biosciences, The University of Tokyo, Tokyo (Japan); Gomez-Sanchez, Celso E. [Endocrine Section, G.V. (Sonny) Montgomery VA Medical Center, MS (United States); Endocrinology, University of Mississippi Medical Center, MS (United States); Fujita, Toshiro [Division of Clinical Epigenetics, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo (Japan); Nangaku, Masaomi [Department of Nephrology and Endocrinology, The University of Tokyo, Tokyo (Japan); Nagase, Miki, E-mail: mnagase-tky@umin.ac.jp [Department of Nephrology and Endocrinology, The University of Tokyo, Tokyo (Japan); Department of Anatomy and Life Structure, School of Medicine Juntendo University, Tokyo (Japan)

    2014-02-28

    Highlights: • We define a target gene of MR as that with MR-binding to the adjacent region of DNA. • We use ChIP-seq analysis in combination with microarray. • We, for the first time, explore the genome-wide binding profile of MR. • We reveal 5 genes as the direct target genes of MR in the renal epithelial cell-line. - Abstract: Background and objective: Mineralocorticoid receptor (MR) is a member of nuclear receptor family proteins and contributes to fluid homeostasis in the kidney. Although aldosterone-MR pathway induces several gene expressions in the kidney, it is often unclear whether the gene expressions are accompanied by direct regulations of MR through its binding to the regulatory region of each gene. The purpose of this study is to identify the direct target genes of MR in a murine distal convoluted tubular epithelial cell-line (mDCT). Methods: We analyzed the DNA samples of mDCT cells overexpressing 3xFLAG-hMR after treatment with 10{sup −7} M aldosterone for 1 h by chromatin immunoprecipitation with deep-sequence (ChIP-seq) and mRNA of the cell-line with treatment of 10{sup −7} M aldosterone for 3 h by microarray. Results: 3xFLAG-hMR overexpressed in mDCT cells accumulated in the nucleus in response to 10{sup −9} M aldosterone. Twenty-five genes were indicated as the candidate target genes of MR by ChIP-seq and microarray analyses. Five genes, Sgk1, Fkbp5, Rasl12, Tns1 and Tsc22d3 (Gilz), were validated as the direct target genes of MR by quantitative RT-qPCR and ChIP-qPCR. MR binding regions adjacent to Ctgf and Serpine1 were also validated. Conclusions: We, for the first time, captured the genome-wide distribution of MR in mDCT cells and, furthermore, identified five MR target genes in the cell-line. These results will contribute to further studies on the mechanisms of kidney diseases.

  8. Differential roles of stretch-sensitive pelvic nerve afferents innervating mouse distal colon and rectum

    OpenAIRE

    Feng, Bin; Brumovsky, Pablo R.; Gebhart, Gerald F.

    2010-01-01

    Information about colorectal distension (i.e., colorectal dilation by increased intraluminal pressure) is primarily encoded by stretch-sensitive colorectal afferents in the pelvic nerve (PN). Despite anatomic differences between rectum and distal colon, little is known about the functional roles of colonic vs. rectal afferents in the PN pathway or the quantitative nature of mechanosensory encoding. We utilized an in vitro mouse colorectum-PN preparation to investigate pressure-encoding charac...

  9. TRPV3, a thermosensitive channel is expressed in mouse distal colon epithelium

    International Nuclear Information System (INIS)

    Ueda, Takashi; Yamada, Takahiro; Ugawa, Shinya; Ishida, Yusuke; Shimada, Shoichi

    2009-01-01

    The thermo-transient receptor potential (thermoTRP) subfamily is composed of channels that are important in nociception and thermo-sensing. Here, we show a selective expression of TRPV3 channel in the distal colon throughout the gastrointestinal tract. Expression analyses clearly revealed that TRPV3 mRNA and proteins were expressed in the superficial epithelial cells of the distal colon, but not in those of the stomach, duodenum or proximal colon. In a subset of primary epithelial cells cultured from the distal colon, carvacrol, an agonist for TRPV3, elevated cytosolic Ca 2+ concentration in a concentration-dependent manner. This response was inhibited by ruthenium red, a TRPV channel antagonist. Organotypic culture supported that the carvacrol-responsive cells were present in superficial epithelial cells. Moreover, application of carvacrol evoked ATP release in primary colonic epithelial cells. We conclude that TRPV3 is present in absorptive cells in the distal colon and may be involved in a variety of cellular functions.

  10. Establishment of a molecular genetic map of distal mouse chromosome 1: further definition of a conserved linkage group syntenic with human chromosome 1q.

    Science.gov (United States)

    Seldin, M F; Morse, H C; LeBoeuf, R C; Steinberg, A D

    1988-01-01

    A linkage map of distal mouse chromosome 1 was constructed by restriction fragment length polymorphism analysis of DNAs from seven sets of recombinant inbred (RI) strains. The data obtained with seven probes on Southern hybridization combined with data from previous studies suggest the gene order Cfh, Pep-3/Ren-1,2, Ly-5, Lamb-2, At-3, Apoa-2/Ly-17,Spna-1. These results confirm and extend analyses of a large linkage group which includes genes present on a 20-30 cM span of mouse chromosome 1 and those localized to human chromosome 1q21-32. Moreover, the data indicate similar relative positions of human and mouse complement receptor-related genes REN, CD45, LAMB2, AT3, APOA2, and SPTA. These results suggest that mouse gene analyses may help in detailed mapping of human genes within such a syntenic group.

  11. Fundamentals of convolutional coding

    CERN Document Server

    Johannesson, Rolf

    2015-01-01

    Fundamentals of Convolutional Coding, Second Edition, regarded as a bible of convolutional coding brings you a clear and comprehensive discussion of the basic principles of this field * Two new chapters on low-density parity-check (LDPC) convolutional codes and iterative coding * Viterbi, BCJR, BEAST, list, and sequential decoding of convolutional codes * Distance properties of convolutional codes * Includes a downloadable solutions manual

  12. Fast Convolution Module (Fast Convolution Module)

    National Research Council Canada - National Science Library

    Bierens, L

    1997-01-01

    This report describes the design and realisation of a real-time range azimuth compression module, the so-called 'Fast Convolution Module', based on the fast convolution algorithm developed at TNO-FEL...

  13. Tissue-specific expression of the human laminin alpha5-chain, and mapping of the gene to human chromosome 20q13.2-13.3 and to distal mouse chromosome 2 near the locus for the ragged (Ra) mutation

    DEFF Research Database (Denmark)

    Durkin, M E; Loechel, F; Mattei, M G

    1997-01-01

    , heart, lung, skeletal muscle, kidney, and pancreas. The human laminin alpha5-chain gene (LAMA5) was assigned to chromosome 20q13.2-q13.3 by in situ hybridization, and the mouse gene (Lama5) was mapped by linkage analysis to a syntonic region of distal chromosome 2, close to the locus for the ragged (Ra...

  14. A knock-in/knock-out mouse model of HSPB8-associated distal hereditary motor neuropathy and myopathy reveals toxic gain-of-function of mutant Hspb8.

    Science.gov (United States)

    Bouhy, Delphine; Juneja, Manisha; Katona, Istvan; Holmgren, Anne; Asselbergh, Bob; De Winter, Vicky; Hochepied, Tino; Goossens, Steven; Haigh, Jody J; Libert, Claude; Ceuterick-de Groote, Chantal; Irobi, Joy; Weis, Joachim; Timmerman, Vincent

    2018-01-01

    Mutations in the small heat shock protein B8 gene (HSPB8/HSP22) have been associated with distal hereditary motor neuropathy, Charcot-Marie-Tooth disease, and recently distal myopathy. It is so far not clear how mutant HSPB8 induces the neuronal and muscular phenotypes and if a common pathogenesis lies behind these diseases. Growing evidence points towards a role of HSPB8 in chaperone-associated autophagy, which has been shown to be a determinant for the clearance of poly-glutamine aggregates in neurodegenerative diseases but also for the maintenance of skeletal muscle myofibrils. To test this hypothesis and better dissect the pathomechanism of mutant HSPB8, we generated a new transgenic mouse model leading to the expression of the mutant protein (knock-in lines) or the loss-of-function (functional knock-out lines) of the endogenous protein Hspb8. While the homozygous knock-in mice developed motor deficits associated with degeneration of peripheral nerves and severe muscle atrophy corroborating patient data, homozygous knock-out mice had locomotor performances equivalent to those of wild-type animals. The distal skeletal muscles of the post-symptomatic homozygous knock-in displayed Z-disk disorganisation, granulofilamentous material accumulation along with Hspb8, αB-crystallin (HSPB5/CRYAB), and desmin aggregates. The presence of the aggregates correlated with reduced markers of effective autophagy. The sciatic nerve of the homozygous knock-in mice was characterized by low autophagy potential in pre-symptomatic and Hspb8 aggregates in post-symptomatic animals. On the other hand, the sciatic nerve of the homozygous knock-out mice presented a normal morphology and their distal muscle displayed accumulation of abnormal mitochondria but intact myofiber and Z-line organisation. Our data, therefore, suggest that toxic gain-of-function of mutant Hspb8 aggregates is a major contributor to the peripheral neuropathy and the myopathy. In addition, mutant Hspb8 induces

  15. CRISPR reveals a distal super-enhancer required for Sox2 expression in mouse embryonic stem cells.

    Directory of Open Access Journals (Sweden)

    Yan Li

    Full Text Available The pluripotency of embryonic stem cells (ESCs is maintained by a small group of master transcription factors including Oct4, Sox2 and Nanog. These core factors form a regulatory circuit controlling the transcription of a number of pluripotency factors including themselves. Although previous studies have identified transcriptional regulators of this core network, the cis-regulatory DNA sequences required for the transcription of these key pluripotency factors remain to be defined. We analyzed epigenomic data within the 1.5 Mb gene-desert regions around the Sox2 gene and identified a 13kb-long super-enhancer (SE located 100kb downstream of Sox2 in mouse ESCs. This SE is occupied by Oct4, Sox2, Nanog, and the mediator complex, and physically interacts with the Sox2 locus via DNA looping. Using a simple and highly efficient double-CRISPR genome editing strategy we deleted the entire 13-kb SE and characterized transcriptional defects in the resulting monoallelic and biallelic deletion clones with RNA-seq. We showed that the SE is responsible for over 90% of Sox2 expression, and Sox2 is the only target gene along the chromosome. Our results support the functional significance of a SE in maintaining the pluripotency transcription program in mouse ESCs.

  16. DISTAL MYOPATHIES

    Science.gov (United States)

    Dimachkie, Mazen M.; Barohn, Richard J.

    2014-01-01

    Over a century ago, Gowers described two young patients in whom distal muscles weakness involved the hand, foot, sternocleidomastoid, and facial muscles in the other case the shoulder and distal leg musculature. Soon after, , similar distal myopathy cases were reported whereby the absence of sensory symptoms and of pathologic changes in the peripheral nerves and spinal cord at postmortem examination allowed differentiation from Charcot-Marie-Tooth disease. In 1951, Welander described autosomal dominant (AD) distal arm myopathy in a large Scandanavian cohort. Since then the number of well-characterized distal myopathies has continued to grow such that the distal myopathies have formed a clinically and genetically heterogeneous group of disorders. Affected kindred commonly manifest weakness that is limited to foot and toe muscles even in advanced stages of the disease, with variable mild proximal leg, distal arm, neck and laryngeal muscle involvement in selected individuals. An interesting consequence of the molecular characterization of the distal myopathies has been the recognition that mutation in a single gene can lead to more than one clinical disorder. For example, Myoshi myopathy (MM) and limb girdle muscular dystrophy (LGMD) type 2B are allelic disorders due to defects in the gene that encodes dysferlin. The six well described distal myopathy syndromes are shown in Table 1. Table 2 lists advances in our understanding of the myofibrillar myopathy group and Table 3 includes more recently delineated and less common distal myopathies. In the same manner, the first section of this review pertains to the more traditional six distal myopathies followed by discussion of the myofibrillar myopathies. In the third section, we review other clinically and genetically distinctive distal myopathy syndromes usually based upon single or smaller family cohorts. The fourth section considers other neuromuscular disorders that are important to recognize as they display prominent

  17. Hypoxia inducible factor 3α plays a critical role in alveolarization and distal epithelial cell differentiation during mouse lung development.

    Directory of Open Access Journals (Sweden)

    Yadi Huang

    Full Text Available Lung development occurs under relative hypoxia and the most important oxygen-sensitive response pathway is driven by Hypoxia Inducible Factors (HIF. HIFs are heterodimeric transcription factors of an oxygen-sensitive subunit, HIFα, and a constitutively expressed subunit, HIF1β. HIF1α and HIF2α, encoded by two separate genes, contribute to the activation of hypoxia inducible genes. A third HIFα gene, HIF3α, is subject to alternative promoter usage and splicing, leading to three major isoforms, HIF3α, NEPAS and IPAS. HIF3α gene products add to the complexity of the hypoxia response as they function as dominant negative inhibitors (IPAS or weak transcriptional activators (HIF3α/NEPAS. Previously, we and others have shown the importance of the Hif1α and Hif2α factors in lung development, and here we investigated the role of Hif3α during pulmonary development. Therefore, HIF3α was conditionally expressed in airway epithelial cells during gestation and although HIF3α transgenic mice were born alive and appeared normal, their lungs showed clear abnormalities, including a post-pseudoglandular branching defect and a decreased number of alveoli. The HIF3α expressing lungs displayed reduced numbers of Clara cells, alveolar epithelial type I and type II cells. As a result of HIF3α expression, the level of Hif2α was reduced, but that of Hif1α was not affected. Two regulatory genes, Rarβ, involved in alveologenesis, and Foxp2, a transcriptional repressor of the Clara cell specific Ccsp gene, were significantly upregulated in the HIF3α expressing lungs. In addition, aberrant basal cells were observed distally as determined by the expression of Sox2 and p63. We show that Hif3α binds a conserved HRE site in the Sox2 promoter and weakly transactivated a reporter construct containing the Sox2 promoter region. Moreover, Hif3α affected the expression of genes not typically involved in the hypoxia response, providing evidence for a novel

  18. Supervised Convolutional Sparse Coding

    KAUST Repository

    Affara, Lama Ahmed

    2018-04-08

    Convolutional Sparse Coding (CSC) is a well-established image representation model especially suited for image restoration tasks. In this work, we extend the applicability of this model by proposing a supervised approach to convolutional sparse coding, which aims at learning discriminative dictionaries instead of purely reconstructive ones. We incorporate a supervised regularization term into the traditional unsupervised CSC objective to encourage the final dictionary elements to be discriminative. Experimental results show that using supervised convolutional learning results in two key advantages. First, we learn more semantically relevant filters in the dictionary and second, we achieve improved image reconstruction on unseen data.

  19. Convolution based profile fitting

    International Nuclear Information System (INIS)

    Kern, A.; Coelho, A.A.; Cheary, R.W.

    2002-01-01

    Full text: In convolution based profile fitting, profiles are generated by convoluting functions together to form the observed profile shape. For a convolution of 'n' functions this process can be written as, Y(2θ)=F 1 (2θ)x F 2 (2θ)x... x F i (2θ)x....xF n (2θ). In powder diffractometry the functions F i (2θ) can be interpreted as the aberration functions of the diffractometer, but in general any combination of appropriate functions for F i (2θ) may be used in this context. Most direct convolution fitting methods are restricted to combinations of F i (2θ) that can be convoluted analytically (e.g. GSAS) such as Lorentzians, Gaussians, the hat (impulse) function and the exponential function. However, software such as TOPAS is now available that can accurately convolute and refine a wide variety of profile shapes numerically, including user defined profiles, without the need to convolute analytically. Some of the most important advantages of modern convolution based profile fitting are: 1) virtually any peak shape and angle dependence can normally be described using minimal profile parameters in laboratory and synchrotron X-ray data as well as in CW and TOF neutron data. This is possible because numerical convolution and numerical differentiation is used within the refinement procedure so that a wide range of functions can easily be incorporated into the convolution equation; 2) it can use physically based diffractometer models by convoluting the instrument aberration functions. This can be done for most laboratory based X-ray powder diffractometer configurations including conventional divergent beam instruments, parallel beam instruments, and diffractometers used for asymmetric diffraction. It can also accommodate various optical elements (e.g. multilayers and monochromators) and detector systems (e.g. point and position sensitive detectors) and has already been applied to neutron powder diffraction systems (e.g. ANSTO) as well as synchrotron based

  20. The secretory KCa1.1 channel localises to crypts of distal mouse colon: functional and molecular evidence

    DEFF Research Database (Denmark)

    Sørensen, Mads Vaarby; Strandsby, Anne Bystrup; Larsen, Casper Kornbech

    2011-01-01

    The colonic epithelium absorbs and secretes electrolytes and water. Ion and water absorption occurs primarily in surface cells, whereas crypt cells perform secretion. Ion transport in distal colon is regulated by aldosterone, which stimulates both Na+ absorption and K+ secretion. The electrogenic...

  1. Convolution copula econometrics

    CERN Document Server

    Cherubini, Umberto; Mulinacci, Sabrina

    2016-01-01

    This book presents a novel approach to time series econometrics, which studies the behavior of nonlinear stochastic processes. This approach allows for an arbitrary dependence structure in the increments and provides a generalization with respect to the standard linear independent increments assumption of classical time series models. The book offers a solution to the problem of a general semiparametric approach, which is given by a concept called C-convolution (convolution of dependent variables), and the corresponding theory of convolution-based copulas. Intended for econometrics and statistics scholars with a special interest in time series analysis and copula functions (or other nonparametric approaches), the book is also useful for doctoral students with a basic knowledge of copula functions wanting to learn about the latest research developments in the field.

  2. Efficient convolutional sparse coding

    Science.gov (United States)

    Wohlberg, Brendt

    2017-06-20

    Computationally efficient algorithms may be applied for fast dictionary learning solving the convolutional sparse coding problem in the Fourier domain. More specifically, efficient convolutional sparse coding may be derived within an alternating direction method of multipliers (ADMM) framework that utilizes fast Fourier transforms (FFT) to solve the main linear system in the frequency domain. Such algorithms may enable a significant reduction in computational cost over conventional approaches by implementing a linear solver for the most critical and computationally expensive component of the conventional iterative algorithm. The theoretical computational cost of the algorithm may be reduced from O(M.sup.3N) to O(MN log N), where N is the dimensionality of the data and M is the number of elements in the dictionary. This significant improvement in efficiency may greatly increase the range of problems that can practically be addressed via convolutional sparse representations.

  3. Multithreaded implicitly dealiased convolutions

    Science.gov (United States)

    Roberts, Malcolm; Bowman, John C.

    2018-03-01

    Implicit dealiasing is a method for computing in-place linear convolutions via fast Fourier transforms that decouples work memory from input data. It offers easier memory management and, for long one-dimensional input sequences, greater efficiency than conventional zero-padding. Furthermore, for convolutions of multidimensional data, the segregation of data and work buffers can be exploited to reduce memory usage and execution time significantly. This is accomplished by processing and discarding data as it is generated, allowing work memory to be reused, for greater data locality and performance. A multithreaded implementation of implicit dealiasing that accepts an arbitrary number of input and output vectors and a general multiplication operator is presented, along with an improved one-dimensional Hermitian convolution that avoids the loop dependency inherent in previous work. An alternate data format that can accommodate a Nyquist mode and enhance cache efficiency is also proposed.

  4. RNA sequencing of kidney distal tubule cells reveals multiple mediators of chronic aldosterone action

    DEFF Research Database (Denmark)

    Poulsen, Søren Brandt; Limbutara, Kavee; Fenton, Robert Andrew

    2018-01-01

    The renal aldosterone-sensitive distal tubule (ASDT) is crucial for sodium reabsorption and blood pressure regulation. The ASDT consists of the late distal convoluted tubule (DCT2), connecting tubule (CNT) and collecting duct. Due to difficulties in isolating epithelial cells from the ASDT in lar...

  5. High-intensity exercise training increases the diversity and metabolic capacity of the mouse distal gut microbiota during diet-induced obesity.

    Science.gov (United States)

    Denou, Emmanuel; Marcinko, Katarina; Surette, Michael G; Steinberg, Gregory R; Schertzer, Jonathan D

    2016-06-01

    Diet and exercise underpin the risk of obesity-related metabolic disease. Diet alters the gut microbiota, which contributes to aspects of metabolic disease during obesity. Repeated exercise provides metabolic benefits during obesity. We assessed whether exercise could oppose changes in the taxonomic and predicted metagenomic characteristics of the gut microbiota during diet-induced obesity. We hypothesized that high-intensity interval training (HIIT) would counteract high-fat diet (HFD)-induced changes in the microbiota without altering obesity in mice. Compared with chow-fed mice, an obesity-causing HFD decreased the Bacteroidetes-to-Firmicutes ratio and decreased the genetic capacity in the fecal microbiota for metabolic pathways such as the tricarboxylic acid (TCA) cycle. After HFD-induced obesity was established, a subset of mice were HIIT for 6 wk, which increased host aerobic capacity but did not alter body or adipose tissue mass. The effects of exercise training on the microbiota were gut segment dependent and more extensive in the distal gut. HIIT increased the alpha diversity and Bacteroidetes/Firmicutes ratio of the distal gut and fecal microbiota during diet-induced obesity. Exercise training increased the predicted genetic capacity related to the TCA cycle among other aspects of metabolism. Strikingly, the same microbial metabolism indexes that were increased by exercise were all decreased in HFD-fed vs. chow diet-fed mice. Therefore, exercise training directly opposed some of the obesity-related changes in gut microbiota, including lower metagenomic indexes of metabolism. Some host and microbial pathways appeared similarly affected by exercise. These exercise- and diet-induced microbiota interactions can be captured in feces. Copyright © 2016 the American Physiological Society.

  6. Convolutional coding techniques for data protection

    Science.gov (United States)

    Massey, J. L.

    1975-01-01

    Results of research on the use of convolutional codes in data communications are presented. Convolutional coding fundamentals are discussed along with modulation and coding interaction. Concatenated coding systems and data compression with convolutional codes are described.

  7. The convolution transform

    CERN Document Server

    Hirschman, Isidore Isaac

    2005-01-01

    In studies of general operators of the same nature, general convolution transforms are immediately encountered as the objects of inversion. The relation between differential operators and integral transforms is the basic theme of this work, which is geared toward upper-level undergraduates and graduate students. It may be read easily by anyone with a working knowledge of real and complex variable theory. Topics include the finite and non-finite kernels, variation diminishing transforms, asymptotic behavior of kernels, real inversion theory, representation theory, the Weierstrass transform, and

  8. Separating Underdetermined Convolutive Speech Mixtures

    DEFF Research Database (Denmark)

    Pedersen, Michael Syskind; Wang, DeLiang; Larsen, Jan

    2006-01-01

    a method for underdetermined blind source separation of convolutive mixtures. The proposed framework is applicable for separation of instantaneous as well as convolutive speech mixtures. It is possible to iteratively extract each speech signal from the mixture by combining blind source separation...

  9. Strongly-MDS convolutional codes

    NARCIS (Netherlands)

    Gluesing-Luerssen, H; Rosenthal, J; Smarandache, R

    Maximum-distance separable (MDS) convolutional codes have the property that their free distance is maximal among all codes of the same rate and the same degree. In this paper, a class of MDS convolutional codes is introduced whose column distances reach the generalized Singleton bound at the

  10. Consensus Convolutional Sparse Coding

    KAUST Repository

    Choudhury, Biswarup

    2017-12-01

    Convolutional sparse coding (CSC) is a promising direction for unsupervised learning in computer vision. In contrast to recent supervised methods, CSC allows for convolutional image representations to be learned that are equally useful for high-level vision tasks and low-level image reconstruction and can be applied to a wide range of tasks without problem-specific retraining. Due to their extreme memory requirements, however, existing CSC solvers have so far been limited to low-dimensional problems and datasets using a handful of low-resolution example images at a time. In this paper, we propose a new approach to solving CSC as a consensus optimization problem, which lifts these limitations. By learning CSC features from large-scale image datasets for the first time, we achieve significant quality improvements in a number of imaging tasks. Moreover, the proposed method enables new applications in high-dimensional feature learning that has been intractable using existing CSC methods. This is demonstrated for a variety of reconstruction problems across diverse problem domains, including 3D multispectral demosaicing and 4D light field view synthesis.

  11. Consensus Convolutional Sparse Coding

    KAUST Repository

    Choudhury, Biswarup

    2017-04-11

    Convolutional sparse coding (CSC) is a promising direction for unsupervised learning in computer vision. In contrast to recent supervised methods, CSC allows for convolutional image representations to be learned that are equally useful for high-level vision tasks and low-level image reconstruction and can be applied to a wide range of tasks without problem-specific retraining. Due to their extreme memory requirements, however, existing CSC solvers have so far been limited to low-dimensional problems and datasets using a handful of low-resolution example images at a time. In this paper, we propose a new approach to solving CSC as a consensus optimization problem, which lifts these limitations. By learning CSC features from large-scale image datasets for the first time, we achieve significant quality improvements in a number of imaging tasks. Moreover, the proposed method enables new applications in high dimensional feature learning that has been intractable using existing CSC methods. This is demonstrated for a variety of reconstruction problems across diverse problem domains, including 3D multispectral demosaickingand 4D light field view synthesis.

  12. Consensus Convolutional Sparse Coding

    KAUST Repository

    Choudhury, Biswarup; Swanson, Robin; Heide, Felix; Wetzstein, Gordon; Heidrich, Wolfgang

    2017-01-01

    Convolutional sparse coding (CSC) is a promising direction for unsupervised learning in computer vision. In contrast to recent supervised methods, CSC allows for convolutional image representations to be learned that are equally useful for high-level vision tasks and low-level image reconstruction and can be applied to a wide range of tasks without problem-specific retraining. Due to their extreme memory requirements, however, existing CSC solvers have so far been limited to low-dimensional problems and datasets using a handful of low-resolution example images at a time. In this paper, we propose a new approach to solving CSC as a consensus optimization problem, which lifts these limitations. By learning CSC features from large-scale image datasets for the first time, we achieve significant quality improvements in a number of imaging tasks. Moreover, the proposed method enables new applications in high-dimensional feature learning that has been intractable using existing CSC methods. This is demonstrated for a variety of reconstruction problems across diverse problem domains, including 3D multispectral demosaicing and 4D light field view synthesis.

  13. Dealiased convolutions for pseudospectral simulations

    International Nuclear Information System (INIS)

    Roberts, Malcolm; Bowman, John C

    2011-01-01

    Efficient algorithms have recently been developed for calculating dealiased linear convolution sums without the expense of conventional zero-padding or phase-shift techniques. For one-dimensional in-place convolutions, the memory requirements are identical with the zero-padding technique, with the important distinction that the additional work memory need not be contiguous with the input data. This decoupling of data and work arrays dramatically reduces the memory and computation time required to evaluate higher-dimensional in-place convolutions. The memory savings is achieved by computing the in-place Fourier transform of the data in blocks, rather than all at once. The technique also allows one to dealias the n-ary convolutions that arise on Fourier transforming cubic and higher powers. Implicitly dealiased convolutions can be built on top of state-of-the-art adaptive fast Fourier transform libraries like FFTW. Vectorized multidimensional implementations for the complex and centered Hermitian (pseudospectral) cases have already been implemented in the open-source software FFTW++. With the advent of this library, writing a high-performance dealiased pseudospectral code for solving nonlinear partial differential equations has now become a relatively straightforward exercise. New theoretical estimates of computational complexity and memory use are provided, including corrected timing results for 3D pruned convolutions and further consideration of higher-order convolutions.

  14. Distal renal tubular acidosis

    Science.gov (United States)

    ... this disorder. Alternative Names Renal tubular acidosis - distal; Renal tubular acidosis type I; Type I RTA; RTA - distal; Classical RTA Images Kidney anatomy Kidney - blood and urine flow References Bose A, Monk RD, Bushinsky DA. Kidney ...

  15. Potassium secretion in mammalian distal colon

    DEFF Research Database (Denmark)

    Sørensen, Mads Vaarby

    2009-01-01

    Epithelial organs adjust the „inner milieu“ of the body and are crucial for all homeostatic processes. Epithelial transport of different solutes and water is regulated phenomena. The regulation processes include both long term hormonal regulation and short term local agonist mediated regulation....... This research project is the summary of 3 original papers addressing the functional role of different regulating factors on ion transport in mouse distal colon. The first paper addresses the effect of luminal nucleotides on electrogenic Na+ absorption. The distal colon, like the distal nephron is an aldosterone......-sensitive tissue and participates in the regulation of Na+ excretion. In the distal nephron it was found that luminal nucleotides inhibit ENaC-mediated Na+ absorption. Here it was addressed whether luminal nucleotides regulate Na+ absorption and if so, which of the known luminal P2 receptors are involved. Using...

  16. Design of convolutional tornado code

    Science.gov (United States)

    Zhou, Hui; Yang, Yao; Gao, Hongmin; Tan, Lu

    2017-09-01

    As a linear block code, the traditional tornado (tTN) code is inefficient in burst-erasure environment and its multi-level structure may lead to high encoding/decoding complexity. This paper presents a convolutional tornado (cTN) code which is able to improve the burst-erasure protection capability by applying the convolution property to the tTN code, and reduce computational complexity by abrogating the multi-level structure. The simulation results show that cTN code can provide a better packet loss protection performance with lower computation complexity than tTN code.

  17. Distal digital replantation.

    Science.gov (United States)

    Jazayeri, Leila; Klausner, Jill Q; Chang, James

    2013-11-01

    Hand surgeons have been hesitant to perform distal digital replantation because of the technical challenges and the perception of a high cost-to-benefit ratio. Recent studies, however, have shown high survival rates and excellent functional and aesthetic results, providing renewed enthusiasm for distal replantation. The authors reviewed the literature and summarize key points regarding the surgical treatment, perioperative care, and outcomes of distal digital replantation. They describe specific techniques and considerations for surgical repair in each of four distal zones as described by Sebastin and Chung. Zone 1A replantation involves an artery-only anastomosis of a longitudinal pulp artery. Venous anastomosis first becomes possible in zone 1B. Zone 1C involves periarticular amputations where arthrodesis of the distal interphalangeal joint is usually indicated. Repair of the artery, vein, and nerve is technically optimal in zone 1D, where venous anastomosis should be performed. Overall, survival rates for distal digital replantation are similar to those reported for more proximal replantation. The literature reports good outcomes regarding nail salvage, fingertip sensibility, and range of motion, with restoration of length and aesthetic appearance. Distal replantation performed at institutions that specialize in microsurgery and specifically tailored to the level of injury is associated with good survival, function, and patient satisfaction and superior aesthetic outcome. More prospective data are needed to evaluate the cost of treatment, psychological outcomes, and functional outcomes of distal replantation compared with revision amputation.

  18. Solutions to Arithmetic Convolution Equations

    Czech Academy of Sciences Publication Activity Database

    Glöckner, H.; Lucht, L.G.; Porubský, Štefan

    2007-01-01

    Roč. 135, č. 6 (2007), s. 1619-1629 ISSN 0002-9939 R&D Projects: GA ČR GA201/04/0381 Institutional research plan: CEZ:AV0Z10300504 Keywords : arithmetic functions * Dirichlet convolution * polynomial equations * analytic equations * topological algebras * holomorphic functional calculus Subject RIV: BA - General Mathematics Impact factor: 0.520, year: 2007

  19. Adaptive Graph Convolutional Neural Networks

    OpenAIRE

    Li, Ruoyu; Wang, Sheng; Zhu, Feiyun; Huang, Junzhou

    2018-01-01

    Graph Convolutional Neural Networks (Graph CNNs) are generalizations of classical CNNs to handle graph data such as molecular data, point could and social networks. Current filters in graph CNNs are built for fixed and shared graph structure. However, for most real data, the graph structures varies in both size and connectivity. The paper proposes a generalized and flexible graph CNN taking data of arbitrary graph structure as input. In that way a task-driven adaptive graph is learned for eac...

  20. Convolution of Distribution-Valued Functions. Applications.

    OpenAIRE

    BARGETZ, CHRISTIAN

    2011-01-01

    In this article we examine products and convolutions of vector-valued functions. For nuclear normal spaces of distributions Proposition 25 in [31,p. 120] yields a vector-valued product or convolution if there is a continuous product or convolution mapping in the range of the vector-valued functions. For specific spaces, we generalize this result to hypocontinuous bilinear maps at the expense of generality with respect to the function space. We consider holomorphic, meromorphic and differentia...

  1. [Distal clavicle fracture].

    Science.gov (United States)

    Seppel, G; Lenich, A; Imhoff, A B

    2014-06-01

    Reposition and fixation of unstable distal clavicle fractures with a low profile locking plate (Acumed, Hempshire, UK) in conjunction with a button/suture augmentation cerclage (DogBone/FibreTape, Arthrex, Naples, FL, USA). Unstable fractures of the distal clavicle (Jäger and Breitner IIA) in adults. Unstable fractures of the distal clavicle (Jäger and Breitner IV) in children. Distal clavicle fractures (Jäger and Breitner I, IIB or III) with marked dislocation, injury of nerves and vessels, or high functional demand. Patients in poor general condition. Fractures of the distal clavicle (Jäger and Breitner I, IIB or III) without marked dislocation or vertical instability. Local soft-tissue infection. Combination procedure: Initially the lateral part of the clavicle is exposed by a 4 cm skin incision. After reduction of the fracture, stabilization is performed with a low profile locking distal clavicle plate. Using a special guiding device, a transclavicular-transcoracoidal hole is drilled under arthroscopic view. Additional vertical stabilization is arthroscopically achieved by shuttling the DogBone/FibreTape cerclage from the lateral portal cranially through the clavicular plate. The two ends of the FibreTape cerclage are brought cranially via adjacent holes of the locking plate while the DogBone button is placed under the coracoid process. Thus, plate bridging is achieved. Finally reduction is performed and the cerclage is secured by surgical knotting. Use of an arm sling for 6 weeks. Due to the fact that the described technique is a relatively new procedure, long-term results are lacking. In the short term, patients postoperatively report high subjective satisfaction without persistent pain.

  2. Incomplete convolutions in production and inventory models

    NARCIS (Netherlands)

    Houtum, van G.J.J.A.N.; Zijm, W.H.M.

    1997-01-01

    In this paper, we study incomplete convolutions of continuous distribution functions, as they appear in the analysis of (multi-stage) production and inventory systems. Three example systems are discussed where these incomplete convolutions naturally arise. We derive explicit, nonrecursive formulae

  3. A convolutional approach to reflection symmetry

    DEFF Research Database (Denmark)

    Cicconet, Marcelo; Birodkar, Vighnesh; Lund, Mads

    2017-01-01

    We present a convolutional approach to reflection symmetry detection in 2D. Our model, built on the products of complex-valued wavelet convolutions, simplifies previous edge-based pairwise methods. Being parameter-centered, as opposed to feature-centered, it has certain computational advantages w...

  4. Symbol synchronization in convolutionally coded systems

    Science.gov (United States)

    Baumert, L. D.; Mceliece, R. J.; Van Tilborg, H. C. A.

    1979-01-01

    Alternate symbol inversion is sometimes applied to the output of convolutional encoders to guarantee sufficient richness of symbol transition for the receiver symbol synchronizer. A bound is given for the length of the transition-free symbol stream in such systems, and those convolutional codes are characterized in which arbitrarily long transition free runs occur.

  5. The general theory of convolutional codes

    Science.gov (United States)

    Mceliece, R. J.; Stanley, R. P.

    1993-01-01

    This article presents a self-contained introduction to the algebraic theory of convolutional codes. This introduction is partly a tutorial, but at the same time contains a number of new results which will prove useful for designers of advanced telecommunication systems. Among the new concepts introduced here are the Hilbert series for a convolutional code and the class of compact codes.

  6. Convolution-deconvolution in DIGES

    International Nuclear Information System (INIS)

    Philippacopoulos, A.J.; Simos, N.

    1995-01-01

    Convolution and deconvolution operations is by all means a very important aspect of SSI analysis since it influences the input to the seismic analysis. This paper documents some of the convolution/deconvolution procedures which have been implemented into the DIGES code. The 1-D propagation of shear and dilatational waves in typical layered configurations involving a stack of layers overlying a rock is treated by DIGES in a similar fashion to that of available codes, e.g. CARES, SHAKE. For certain configurations, however, there is no need to perform such analyses since the corresponding solutions can be obtained in analytic form. Typical cases involve deposits which can be modeled by a uniform halfspace or simple layered halfspaces. For such cases DIGES uses closed-form solutions. These solutions are given for one as well as two dimensional deconvolution. The type of waves considered include P, SV and SH waves. The non-vertical incidence is given special attention since deconvolution can be defined differently depending on the problem of interest. For all wave cases considered, corresponding transfer functions are presented in closed-form. Transient solutions are obtained in the frequency domain. Finally, a variety of forms are considered for representing the free field motion both in terms of deterministic as well as probabilistic representations. These include (a) acceleration time histories, (b) response spectra (c) Fourier spectra and (d) cross-spectral densities

  7. Model structure selection in convolutive mixtures

    DEFF Research Database (Denmark)

    Dyrholm, Mads; Makeig, S.; Hansen, Lars Kai

    2006-01-01

    The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimonious represent......The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimonious...... representation in many practical mixtures. The new filter-CICAAR allows Bayesian model selection and can help answer questions like: ’Are we actually dealing with a convolutive mixture?’. We try to answer this question for EEG data....

  8. Convolutions

    Indian Academy of Sciences (India)

    President's Address to the Association of Mathematics Teachers of India, December 2011. I am expected to tell you, in 25 minutes, something that should interest you, excite you, pique your curiosity, and make you look for more. It is a tall order, but I will try. The word 'interactive' is in fashion these days. So I will leave a few ...

  9. Developmental immunolocalization of the Klotho protein in mouse kidney epithelial cells

    Directory of Open Access Journals (Sweden)

    J.H. Song

    2014-01-01

    Full Text Available A defect in Klotho gene expression in the mouse results in a syndrome that resembles rapid human aging. In this study, we investigated the detailed distribution and the time of the first appearance of Klotho in developing and adult mouse kidney. Kidneys from 16-(F16, 18-(F18 and 20-day-old (F20 fetuses, 1- (P1, 4- (P4, 7- (P7, 14- (P14, and 21-day-old (P21 pups and adults were processed for immunohistochemistry and immunoblot analyses. In the developing mouse kidney, Klotho immunoreactivity was initially observed in a few cells of the connecting tubules (CNT of 18-day-old fetus (F and in the medullary collecting duct (MCD and distal nephron of the F16 developing kidney. In F20, Klotho immunoreactivity was increased in CNT and additionally observed in the outer portion of MCD and tip of the renal papilla. During the first 3 weeks after birth, Klotho-positive cells gradually disappeared from the MCD due to apoptosis, but remained in the CNT and cortical collecting ducts (CCD. In the adult mouse, the Klotho protein was expressed only in a few cells of the CNT and CCD in cortical area. Also, Klotho immunoreactivity was observed in the aquaporin 2-positive CNT, CCD, and NaCl co-transporter-positive distal convoluted tubule (DCT cells and type B and nonA-nonB intercalated cells of CNT, DCT, and CCD. Collectively, our data indicate that immunolocalization of Klotho is closely correlated with proliferation in the intercalated cells of CNT and CCD from aging, and may be involved in the regulation of tubular proliferation.

  10. Distal finger replantation.

    Science.gov (United States)

    Scheker, Luis R; Becker, Giles W

    2011-03-01

    Reconstruction of the fingertip distal to the flexor tendon insertion by replantation remains controversial and technically challenging, but the anatomy of the fingertip has been well described and provides help in surgical planning. The open-book surgical technique is described with potential complications and is illustrated with clinical cases. Copyright © 2011 American Society for Surgery of the Hand. Published by Elsevier Inc. All rights reserved.

  11. Distal radioulnar joint injuries

    Directory of Open Access Journals (Sweden)

    Binu P Thomas

    2012-01-01

    Full Text Available Distal radioulnar joint is a trochoid joint relatively new in evolution. Along with proximal radioulnar joint , forearm bones and interosseous membrane, it allows pronosupination and load transmission across the wrist. Injuries around distal radioulnar joint are not uncommon, and are usually associated with distal radius fractures,fractures of the ulnar styloid and with the eponymous Galeazzi or Essex_Lopresti fractures. The injury can be purely involving the soft tissue especially the triangular fibrocartilage or the radioulnar ligaments.The patients usually present with ulnar sided wrist pain, features of instability, or restriction of rotation. Difficulty in carrying loads in the hand is a major constraint for these patients. Thorough clinical examination to localize point of tenderness and appropriate provocative tests help in diagnosis. Radiology and MRI are extremely useful, while arthroscopy is the gold standard for evaluation. The treatment protocols are continuously evolving and range from conservative, arthroscopic to open surgical methods. Isolated dislocation are uncommon. Basal fractures of the ulnar styloid tend to make the joint unstable and may require operative intervention. Chronic instability requires reconstruction of the stabilizing ligaments to avoid onset of arthritis. Prosthetic replacement in arthritis is gaining acceptance in the management of arthritis.

  12. Adaptive decoding of convolutional codes

    Directory of Open Access Journals (Sweden)

    K. Hueske

    2007-06-01

    Full Text Available Convolutional codes, which are frequently used as error correction codes in digital transmission systems, are generally decoded using the Viterbi Decoder. On the one hand the Viterbi Decoder is an optimum maximum likelihood decoder, i.e. the most probable transmitted code sequence is obtained. On the other hand the mathematical complexity of the algorithm only depends on the used code, not on the number of transmission errors. To reduce the complexity of the decoding process for good transmission conditions, an alternative syndrome based decoder is presented. The reduction of complexity is realized by two different approaches, the syndrome zero sequence deactivation and the path metric equalization. The two approaches enable an easy adaptation of the decoding complexity for different transmission conditions, which results in a trade-off between decoding complexity and error correction performance.

  13. Adaptive decoding of convolutional codes

    Science.gov (United States)

    Hueske, K.; Geldmacher, J.; Götze, J.

    2007-06-01

    Convolutional codes, which are frequently used as error correction codes in digital transmission systems, are generally decoded using the Viterbi Decoder. On the one hand the Viterbi Decoder is an optimum maximum likelihood decoder, i.e. the most probable transmitted code sequence is obtained. On the other hand the mathematical complexity of the algorithm only depends on the used code, not on the number of transmission errors. To reduce the complexity of the decoding process for good transmission conditions, an alternative syndrome based decoder is presented. The reduction of complexity is realized by two different approaches, the syndrome zero sequence deactivation and the path metric equalization. The two approaches enable an easy adaptation of the decoding complexity for different transmission conditions, which results in a trade-off between decoding complexity and error correction performance.

  14. The ClC-K2 Chloride Channel Is Critical for Salt Handling in the Distal Nephron.

    Science.gov (United States)

    Hennings, J Christopher; Andrini, Olga; Picard, Nicolas; Paulais, Marc; Huebner, Antje K; Cayuqueo, Irma Karen Lopez; Bignon, Yohan; Keck, Mathilde; Cornière, Nicolas; Böhm, David; Jentsch, Thomas J; Chambrey, Régine; Teulon, Jacques; Hübner, Christian A; Eladari, Dominique

    2017-01-01

    Chloride transport by the renal tubule is critical for blood pressure (BP), acid-base, and potassium homeostasis. Chloride uptake from the urinary fluid is mediated by various apical transporters, whereas basolateral chloride exit is thought to be mediated by ClC-Ka/K1 and ClC-Kb/K2, two chloride channels from the ClC family, or by KCl cotransporters from the SLC12 gene family. Nevertheless, the localization and role of ClC-K channels is not fully resolved. Because inactivating mutations in ClC-Kb/K2 cause Bartter syndrome, a disease that mimics the effects of the loop diuretic furosemide, ClC-Kb/K2 is assumed to have a critical role in salt handling by the thick ascending limb. To dissect the role of this channel in detail, we generated a mouse model with a targeted disruption of the murine ortholog ClC-K2. Mutant mice developed a Bartter syndrome phenotype, characterized by renal salt loss, marked hypokalemia, and metabolic alkalosis. Patch-clamp analysis of tubules isolated from knockout (KO) mice suggested that ClC-K2 is the main basolateral chloride channel in the thick ascending limb and in the aldosterone-sensitive distal nephron. Accordingly, ClC-K2 KO mice did not exhibit the natriuretic response to furosemide and exhibited a severely blunted response to thiazide. We conclude that ClC-Kb/K2 is critical for salt absorption not only by the thick ascending limb, but also by the distal convoluted tubule. Copyright © 2016 by the American Society of Nephrology.

  15. Convolutional Neural Network for Image Recognition

    CERN Document Server

    Seifnashri, Sahand

    2015-01-01

    The aim of this project is to use machine learning techniques especially Convolutional Neural Networks for image processing. These techniques can be used for Quark-Gluon discrimination using calorimeters data, but unfortunately I didn’t manage to get the calorimeters data and I just used the Jet data fromminiaodsim(ak4 chs). The Jet data was not good enough for Convolutional Neural Network which is designed for ’image’ recognition. This report is made of twomain part, part one is mainly about implementing Convolutional Neural Network on unphysical data such as MNIST digits and CIFAR-10 dataset and part 2 is about the Jet data.

  16. A Note on Cubic Convolution Interpolation

    OpenAIRE

    Meijering, E.; Unser, M.

    2003-01-01

    We establish a link between classical osculatory interpolation and modern convolution-based interpolation and use it to show that two well-known cubic convolution schemes are formally equivalent to two osculatory interpolation schemes proposed in the actuarial literature about a century ago. We also discuss computational differences and give examples of other cubic interpolation schemes not previously studied in signal and image processing.

  17. Distinct distribution of specific members of protein 4.1 genefamily in the mouse nephron

    Energy Technology Data Exchange (ETDEWEB)

    Ramez, Mohamed; Blot-Chabaud, Marcel; Cluzeaud, Francoise; Chanan, Sumita; Patterson, Michael; Walensky, Loren D.; Marfatia, Shirin; Baines, Anthony J.; Chasis, Joel A.; Conboy, John G.; Mohandas, Narla; Gascard, Philippe

    2002-12-11

    Background: Protein 4.1 is an adapter protein which linksthe actin cytoskeleton to various transmembrane proteins. 4.1 proteinsare encoded by four homologous genes, 4.1R, 4.1G, 4.1N, and 4.1B, whichundergo complex alternative splicing. Here we performed a detailedcharacterization of the expression of specific 4.1 proteins in the mousenephron. Methods: Distribution of renal 4.1 proteins was investigated bystaining of paraformaldehyde fixed mouse kidney sections with antibodieshighly specific for each 4.1 protein. Major 4.1 splice forms, amplifiedfrom mouse kidney marathon cDNA, were expressed in transfected COS-7cells in order to assign species of known exon composition to proteinsdetected in kidney. Results: A 105kDa4.1R splice form, initiating atATG-2 translation initiation site and lacking exon 16, but including exon17B, was restricted to thick ascending limb of Henle's loop. A 95kDa 4.1Nspliceform,lacking exons 15 and 17D, was expressed in either descendingor ascending thin limb of Henle'sloop, distal convoluted tubule and allregions of the collecting duct system. A major 108kDa 4.1B spliceform,initiating at a newly characterized ATG translation initiation site, andlacking exons 15, 17B, and 21, was present only in Bowman's capsule andproximal convoluted tubule (PCT). There was no expression of 4.1G inkidney. Conclusion: Distinct distribution of 4.1 proteins along thenephron suggests their involvement in targeting of selected transmembraneproteins in kidney epithelium andtherefore in regulation of specifickidney functions.

  18. Distal corporoplasty for distal cylinders extrusion after penile prosthesis implantation.

    Science.gov (United States)

    Carrino, Maurizio; Chiancone, Francesco; Battaglia, Gaetano; Pucci, Luigi; Fedelini, Paolo

    2017-02-03

    Distal extrusion of cylinders is a potential complication of the penile prosthesis implantation. Several methods have been proposed for repairing a distal penile erosion. We present our preliminary experience in "Distal corporoplasty" technique. We enrolled 18 consecutive patients whose underwent a distal corporoplasty with simultaneous reimplantation of an "AMS 700 inflatable penile prosthesis (LGX)" from January 2013 to November 2015 at our hospital. All procedures were performed by a single surgical team. Intraoperative and postoperative complications have been classified and reported according to Satava6 and Clavien-Dindo (CD) system.7 Mean values with standard deviations (±SD) were computed and reported for all items. Mean age of the patients was 53.61 (±11.90) years. Mean body max index (BMI) was 24.22 (±2.51). Mean operative time was 85.2 (±13.1) minutes. Blood losses were minimal. No intraoperative complications are reported according to Satava classification. Four out of 18 patients (22.22%) experienced postoperative complications according to CD system. All patients had sexual intercourse for the first time postsurgery after a mean of 59.11 ± 2.08 days. Mean follow-up was 22.11 (±9.95). Distal extrusion of cylinders is a potential complication of the penile prosthesis implantation. Distal corporoplasty was first described by Mulcahy. He reported a series of 14 patients with a follow-up of about 2 years with optimal functional outcomes. Moreover, distal corporoplasty resulted in shorter operative time, better function, less pain, and fewer recurrences than Gortex windsock repair.10 In our experience, distal corporoplasty is a simple and safe procedure in the treatment of distal cylinders extrusion when the prosthetic material is not exposed to the exterior.

  19. Semantic segmentation of bioimages using convolutional neural networks

    CSIR Research Space (South Africa)

    Wiehman, S

    2016-07-01

    Full Text Available Convolutional neural networks have shown great promise in both general image segmentation problems as well as bioimage segmentation. In this paper, the application of different convolutional network architectures is explored on the C. elegans live...

  20. One weird trick for parallelizing convolutional neural networks

    OpenAIRE

    Krizhevsky, Alex

    2014-01-01

    I present a new way to parallelize the training of convolutional neural networks across multiple GPUs. The method scales significantly better than all alternatives when applied to modern convolutional neural networks.

  1. Vasopressin induces phosphorylation of the thiazide-sensitive sodium chloride cotransporter in the distal convoluted tubule

    DEFF Research Database (Denmark)

    Pedersen, Nis Borbye; Hofmeister, Marlene Vind; Rosenbaek, Lena L

    2010-01-01

    The thiazide-sensitive Na(+)-Cl(-) cotransporter (NCC) is important for renal electrolyte balance and its phosphorylation causes an increase in its transport activity and cellular localization. Here, we generated phospho-specific antibodies against two conserved N-terminal phosphorylation sites...

  2. Cisplatin-induced injury of the renal distal convoluted tubule is associated with hypomagnesaemia in mice

    NARCIS (Netherlands)

    Angelen, A.A. van; Glaudemans, B.; Kemp, A. van der; Hoenderop, J.G.J.; Bindels, R.J.M.

    2013-01-01

    Background Cisplatin is an effective anti-neoplastic drug, but its clinical use is limited due to dose-dependent nephrotoxicity. The majority of cisplatin-treated patients develop hypomagnesaemia, often associated with a reduced glomerular filtration rate (GFR), polyuria and other electrolyte

  3. Gradient Flow Convolutive Blind Source Separation

    DEFF Research Database (Denmark)

    Pedersen, Michael Syskind; Nielsen, Chinton Møller

    2004-01-01

    Experiments have shown that the performance of instantaneous gradient flow beamforming by Cauwenberghs et al. is reduced significantly in reverberant conditions. By expanding the gradient flow principle to convolutive mixtures, separation in a reverberant environment is possible. By use...... of a circular four microphone array with a radius of 5 mm, and applying convolutive gradient flow instead of just applying instantaneous gradient flow, experimental results show an improvement of up to around 14 dB can be achieved for simulated impulse responses and up to around 10 dB for a hearing aid...

  4. Human Face Recognition Using Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Răzvan-Daniel Albu

    2009-10-01

    Full Text Available In this paper, I present a novel hybrid face recognition approach based on a convolutional neural architecture, designed to robustly detect highly variable face patterns. The convolutional network extracts successively larger features in a hierarchical set of layers. With the weights of the trained neural networks there are created kernel windows used for feature extraction in a 3-stage algorithm. I present experimental results illustrating the efficiency of the proposed approach. I use a database of 796 images of 159 individuals from Reims University which contains quite a high degree of variability in expression, pose, and facial details.

  5. CMOS Compressed Imaging by Random Convolution

    OpenAIRE

    Jacques, Laurent; Vandergheynst, Pierre; Bibet, Alexandre; Majidzadeh, Vahid; Schmid, Alexandre; Leblebici, Yusuf

    2009-01-01

    We present a CMOS imager with built-in capability to perform Compressed Sensing. The adopted sensing strategy is the random Convolution due to J. Romberg. It is achieved by a shift register set in a pseudo-random configuration. It acts as a convolutive filter on the imager focal plane, the current issued from each CMOS pixel undergoing a pseudo-random redirection controlled by each component of the filter sequence. A pseudo-random triggering of the ADC reading is finally applied to comp...

  6. Feedback equivalence of convolutional codes over finite rings

    Directory of Open Access Journals (Sweden)

    DeCastro-García Noemí

    2017-12-01

    Full Text Available The approach to convolutional codes from the linear systems point of view provides us with effective tools in order to construct convolutional codes with adequate properties that let us use them in many applications. In this work, we have generalized feedback equivalence between families of convolutional codes and linear systems over certain rings, and we show that every locally Brunovsky linear system may be considered as a representation of a code under feedback convolutional equivalence.

  7. Discrete convolution-operators and radioactive disintegration. [Numerical solution

    Energy Technology Data Exchange (ETDEWEB)

    Kalla, S L; VALENTINUZZI, M E [UNIVERSIDAD NACIONAL DE TUCUMAN (ARGENTINA). FACULTAD DE CIENCIAS EXACTAS Y TECNOLOGIA

    1975-08-01

    The basic concepts of discrete convolution and discrete convolution-operators are briefly described. Then, using the discrete convolution - operators, the differential equations associated with the process of radioactive disintegration are numerically solved. The importance of the method is emphasized to solve numerically, differential and integral equations.

  8. Deformable image registration using convolutional neural networks

    NARCIS (Netherlands)

    Eppenhof, Koen A.J.; Lafarge, Maxime W.; Moeskops, Pim; Veta, Mitko; Pluim, Josien P.W.

    2018-01-01

    Deformable image registration can be time-consuming and often needs extensive parameterization to perform well on a specific application. We present a step towards a registration framework based on a three-dimensional convolutional neural network. The network directly learns transformations between

  9. Epileptiform spike detection via convolutional neural networks

    DEFF Research Database (Denmark)

    Johansen, Alexander Rosenberg; Jin, Jing; Maszczyk, Tomasz

    2016-01-01

    The EEG of epileptic patients often contains sharp waveforms called "spikes", occurring between seizures. Detecting such spikes is crucial for diagnosing epilepsy. In this paper, we develop a convolutional neural network (CNN) for detecting spikes in EEG of epileptic patients in an automated...

  10. Convolutional Neural Networks for SAR Image Segmentation

    DEFF Research Database (Denmark)

    Malmgren-Hansen, David; Nobel-Jørgensen, Morten

    2015-01-01

    Segmentation of Synthetic Aperture Radar (SAR) images has several uses, but it is a difficult task due to a number of properties related to SAR images. In this article we show how Convolutional Neural Networks (CNNs) can easily be trained for SAR image segmentation with good results. Besides...

  11. Convolutional Neural Networks - Generalizability and Interpretations

    DEFF Research Database (Denmark)

    Malmgren-Hansen, David

    from data despite it being limited in amount or context representation. Within Machine Learning this thesis focuses on Convolutional Neural Networks for Computer Vision. The research aims to answer how to explore a model's generalizability to the whole population of data samples and how to interpret...

  12. Towards dropout training for convolutional neural networks.

    Science.gov (United States)

    Wu, Haibing; Gu, Xiaodong

    2015-11-01

    Recently, dropout has seen increasing use in deep learning. For deep convolutional neural networks, dropout is known to work well in fully-connected layers. However, its effect in convolutional and pooling layers is still not clear. This paper demonstrates that max-pooling dropout is equivalent to randomly picking activation based on a multinomial distribution at training time. In light of this insight, we advocate employing our proposed probabilistic weighted pooling, instead of commonly used max-pooling, to act as model averaging at test time. Empirical evidence validates the superiority of probabilistic weighted pooling. We also empirically show that the effect of convolutional dropout is not trivial, despite the dramatically reduced possibility of over-fitting due to the convolutional architecture. Elaborately designing dropout training simultaneously in max-pooling and fully-connected layers, we achieve state-of-the-art performance on MNIST, and very competitive results on CIFAR-10 and CIFAR-100, relative to other approaches without data augmentation. Finally, we compare max-pooling dropout and stochastic pooling, both of which introduce stochasticity based on multinomial distributions at pooling stage. Copyright © 2015 Elsevier Ltd. All rights reserved.

  13. A locality aware convolutional neural networks accelerator

    NARCIS (Netherlands)

    Shi, R.; Xu, Z.; Sun, Z.; Peemen, M.C.J.; Li, A.; Corporaal, H.; Wu, D.

    2015-01-01

    The advantages of Convolutional Neural Networks (CNNs) with respect to traditional methods for visual pattern recognition have changed the field of machine vision. The main issue that hinders broad adoption of this technique is the massive computing workload in CNN that prevents real-time

  14. Reverse Distal Transverse Palmar Arch in Distal Digital Replantation.

    Science.gov (United States)

    Wei, Ching-Yueh; Orozco, Oscar; Vinagre, Gustavo; Shafarenko, Mark

    2017-11-01

    Refinements in microsurgery have made distal finger replantation an established technique with high success rates and good functional and aesthetic outcomes. However, it still represents a technically demanding procedure due to the small vessel caliber and frequent lack of vessel length, requiring the use of interpositional venous grafts in some instances. We describe a new technique for anastomosis in fingertip replantation, whereby the need for venous grafts is eliminated. Applying the reverse distal transverse palmar arch technique, 11 cases of distal digital replantation were performed between January 2011 and July 2016. The described procedure was used for arterial anastomosis in 10 cases and arteriovenous shunting for venous drainage in 1 case. A retrospective case review was conducted. The technical description and clinical outcome evaluations are presented. Ten of the 11 replanted digits survived, corresponding to an overall success rate of 91%. One replant failed due to venous insufficiency. Blood transfusions were not required for any of the patients. Follow-up (range, 1.5-5 months) revealed near-normal range of motion and good aesthetic results. All of the replanted digits developed protective sensation. The average length of hospital admission was 5 days. All patients were satisfied with the results and were able to return to their previous work. The use of the reverse distal transverse palmar arch is a novel and reliable technique in distal digital replantation when an increase in vessel length is required, allowing for a tension-free arterial repair without the need for vein grafts.

  15. Traumatic Distal Ulnar Artery Thrombosis

    Directory of Open Access Journals (Sweden)

    Ahmet A. Karaarslan

    2014-01-01

    Full Text Available This paper is about a posttraumatic distal ulnar artery thrombosis case that has occurred after a single blunt trauma. The ulnar artery thrombosis because of chronic trauma is a frequent condition (hypothenar hammer syndrome but an ulnar artery thrombosis because of a single direct blunt trauma is rare. Our patient who has been affected by a single blunt trauma to his hand and developed ulnar artery thrombosis has been treated by resection of the thrombosed ulnar artery segment. This report shows that a single blunt trauma can cause distal ulnar artery thrombosis in the hand and it can be treated merely by thrombosed segment resection in suitable cases.

  16. DCMDN: Deep Convolutional Mixture Density Network

    Science.gov (United States)

    D'Isanto, Antonio; Polsterer, Kai Lars

    2017-09-01

    Deep Convolutional Mixture Density Network (DCMDN) estimates probabilistic photometric redshift directly from multi-band imaging data by combining a version of a deep convolutional network with a mixture density network. The estimates are expressed as Gaussian mixture models representing the probability density functions (PDFs) in the redshift space. In addition to the traditional scores, the continuous ranked probability score (CRPS) and the probability integral transform (PIT) are applied as performance criteria. DCMDN is able to predict redshift PDFs independently from the type of source, e.g. galaxies, quasars or stars and renders pre-classification of objects and feature extraction unnecessary; the method is extremely general and allows the solving of any kind of probabilistic regression problems based on imaging data, such as estimating metallicity or star formation rate in galaxies.

  17. Gas Classification Using Deep Convolutional Neural Networks

    Science.gov (United States)

    Peng, Pai; Zhao, Xiaojin; Pan, Xiaofang; Ye, Wenbin

    2018-01-01

    In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP). PMID:29316723

  18. A convolutional neural network neutrino event classifier

    International Nuclear Information System (INIS)

    Aurisano, A.; Sousa, A.; Radovic, A.; Vahle, P.; Rocco, D.; Pawloski, G.; Himmel, A.; Niner, E.; Messier, M.D.; Psihas, F.

    2016-01-01

    Convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the core concepts of CNNs and recent innovations in CNN architectures related to the field of deep learning, we outline a specific application to the NOvA neutrino detector. This algorithm, CVN (Convolutional Visual Network) identifies neutrino interactions based on their topology without the need for detailed reconstruction and outperforms algorithms currently in use by the NOvA collaboration.

  19. Gas Classification Using Deep Convolutional Neural Networks.

    Science.gov (United States)

    Peng, Pai; Zhao, Xiaojin; Pan, Xiaofang; Ye, Wenbin

    2018-01-08

    In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP).

  20. Quasi-cyclic unit memory convolutional codes

    DEFF Research Database (Denmark)

    Justesen, Jørn; Paaske, Erik; Ballan, Mark

    1990-01-01

    Unit memory convolutional codes with generator matrices, which are composed of circulant submatrices, are introduced. This structure facilitates the analysis of efficient search for good codes. Equivalences among such codes and some of the basic structural properties are discussed. In particular......, catastrophic encoders and minimal encoders are characterized and dual codes treated. Further, various distance measures are discussed, and a number of good codes, some of which result from efficient computer search and some of which result from known block codes, are presented...

  1. Applying Gradient Descent in Convolutional Neural Networks

    Science.gov (United States)

    Cui, Nan

    2018-04-01

    With the development of the integrated circuit and computer science, people become caring more about solving practical issues via information technologies. Along with that, a new subject called Artificial Intelligent (AI) comes up. One popular research interest of AI is about recognition algorithm. In this paper, one of the most common algorithms, Convolutional Neural Networks (CNNs) will be introduced, for image recognition. Understanding its theory and structure is of great significance for every scholar who is interested in this field. Convolution Neural Network is an artificial neural network which combines the mathematical method of convolution and neural network. The hieratical structure of CNN provides it reliable computer speed and reasonable error rate. The most significant characteristics of CNNs are feature extraction, weight sharing and dimension reduction. Meanwhile, combining with the Back Propagation (BP) mechanism and the Gradient Descent (GD) method, CNNs has the ability to self-study and in-depth learning. Basically, BP provides an opportunity for backwardfeedback for enhancing reliability and GD is used for self-training process. This paper mainly discusses the CNN and the related BP and GD algorithms, including the basic structure and function of CNN, details of each layer, the principles and features of BP and GD, and some examples in practice with a summary in the end.

  2. Phylogenetic convolutional neural networks in metagenomics.

    Science.gov (United States)

    Fioravanti, Diego; Giarratano, Ylenia; Maggio, Valerio; Agostinelli, Claudio; Chierici, Marco; Jurman, Giuseppe; Furlanello, Cesare

    2018-03-08

    Convolutional Neural Networks can be effectively used only when data are endowed with an intrinsic concept of neighbourhood in the input space, as is the case of pixels in images. We introduce here Ph-CNN, a novel deep learning architecture for the classification of metagenomics data based on the Convolutional Neural Networks, with the patristic distance defined on the phylogenetic tree being used as the proximity measure. The patristic distance between variables is used together with a sparsified version of MultiDimensional Scaling to embed the phylogenetic tree in a Euclidean space. Ph-CNN is tested with a domain adaptation approach on synthetic data and on a metagenomics collection of gut microbiota of 38 healthy subjects and 222 Inflammatory Bowel Disease patients, divided in 6 subclasses. Classification performance is promising when compared to classical algorithms like Support Vector Machines and Random Forest and a baseline fully connected neural network, e.g. the Multi-Layer Perceptron. Ph-CNN represents a novel deep learning approach for the classification of metagenomics data. Operatively, the algorithm has been implemented as a custom Keras layer taking care of passing to the following convolutional layer not only the data but also the ranked list of neighbourhood of each sample, thus mimicking the case of image data, transparently to the user.

  3. Image quality assessment using deep convolutional networks

    Science.gov (United States)

    Li, Yezhou; Ye, Xiang; Li, Yong

    2017-12-01

    This paper proposes a method of accurately assessing image quality without a reference image by using a deep convolutional neural network. Existing training based methods usually utilize a compact set of linear filters for learning features of images captured by different sensors to assess their quality. These methods may not be able to learn the semantic features that are intimately related with the features used in human subject assessment. Observing this drawback, this work proposes training a deep convolutional neural network (CNN) with labelled images for image quality assessment. The ReLU in the CNN allows non-linear transformations for extracting high-level image features, providing a more reliable assessment of image quality than linear filters. To enable the neural network to take images of any arbitrary size as input, the spatial pyramid pooling (SPP) is introduced connecting the top convolutional layer and the fully-connected layer. In addition, the SPP makes the CNN robust to object deformations to a certain extent. The proposed method taking an image as input carries out an end-to-end learning process, and outputs the quality of the image. It is tested on public datasets. Experimental results show that it outperforms existing methods by a large margin and can accurately assess the image quality on images taken by different sensors of varying sizes.

  4. Cell dynamic morphology classification using deep convolutional neural networks.

    Science.gov (United States)

    Li, Heng; Pang, Fengqian; Shi, Yonggang; Liu, Zhiwen

    2018-05-15

    Cell morphology is often used as a proxy measurement of cell status to understand cell physiology. Hence, interpretation of cell dynamic morphology is a meaningful task in biomedical research. Inspired by the recent success of deep learning, we here explore the application of convolutional neural networks (CNNs) to cell dynamic morphology classification. An innovative strategy for the implementation of CNNs is introduced in this study. Mouse lymphocytes were collected to observe the dynamic morphology, and two datasets were thus set up to investigate the performances of CNNs. Considering the installation of deep learning, the classification problem was simplified from video data to image data, and was then solved by CNNs in a self-taught manner with the generated image data. CNNs were separately performed in three installation scenarios and compared with existing methods. Experimental results demonstrated the potential of CNNs in cell dynamic morphology classification, and validated the effectiveness of the proposed strategy. CNNs were successfully applied to the classification problem, and outperformed the existing methods in the classification accuracy. For the installation of CNNs, transfer learning was proved to be a promising scheme. © 2018 International Society for Advancement of Cytometry. © 2018 International Society for Advancement of Cytometry.

  5. Distal protection in cardiovascular medicine: current status.

    Science.gov (United States)

    Ali, Onn Akbar; Bhindi, Ravinay; McMahon, Aisling C; Brieger, David; Kritharides, Leonard; Lowe, Harry C

    2006-08-01

    Iatrogenic and spontaneous downstream microembolization of atheromatous material is increasingly recognized as a source of cardiovascular morbidity and mortality. Devising ways of reducing this distal embolization using a variety of mechanical means--distal protection--is currently under intense and diverse investigation. This review therefore summarizes the present status of distal protection. It examines the problem of distal embolization, describes the available distal protection devices, reviews those areas of cardiovascular medicine where distal protection devices are being investigated, and discusses potential future developments.

  6. An Algorithm for the Convolution of Legendre Series

    KAUST Repository

    Hale, Nicholas; Townsend, Alex

    2014-01-01

    An O(N2) algorithm for the convolution of compactly supported Legendre series is described. The algorithm is derived from the convolution theorem for Legendre polynomials and the recurrence relation satisfied by spherical Bessel functions. Combining with previous work yields an O(N 2) algorithm for the convolution of Chebyshev series. Numerical results are presented to demonstrate the improved efficiency over the existing algorithm. © 2014 Society for Industrial and Applied Mathematics.

  7. The Urbanik generalized convolutions in the non-commutative ...

    Indian Academy of Sciences (India)

    −sν(dx) < ∞. Now we apply this construction to the Kendall convolution case, starting with the weakly stable measure δ1. Example 1. Let △ be the Kendall convolution, i.e. the generalized convolution with the probability kernel: δ1△δa = (1 − a)δ1 + aπ2 for a ∈ [0, 1] and π2 be the Pareto distribution with the density π2(dx) =.

  8. On a Generalized Hankel Type Convolution of Generalized Functions

    Indian Academy of Sciences (India)

    Generalized Hankel type transformation; Parserval relation; generalized ... The classical generalized Hankel type convolution are defined and extended to a class of generalized functions. ... Proceedings – Mathematical Sciences | News.

  9. Enhanced online convolutional neural networks for object tracking

    Science.gov (United States)

    Zhang, Dengzhuo; Gao, Yun; Zhou, Hao; Li, Tianwen

    2018-04-01

    In recent several years, object tracking based on convolution neural network has gained more and more attention. The initialization and update of convolution filters can directly affect the precision of object tracking effective. In this paper, a novel object tracking via an enhanced online convolution neural network without offline training is proposed, which initializes the convolution filters by a k-means++ algorithm and updates the filters by an error back-propagation. The comparative experiments of 7 trackers on 15 challenging sequences showed that our tracker can perform better than other trackers in terms of AUC and precision.

  10. Cerebral vessels segmentation for light-sheet microscopy image using convolutional neural networks

    Science.gov (United States)

    Hu, Chaoen; Hui, Hui; Wang, Shuo; Dong, Di; Liu, Xia; Yang, Xin; Tian, Jie

    2017-03-01

    Cerebral vessel segmentation is an important step in image analysis for brain function and brain disease studies. To extract all the cerebrovascular patterns, including arteries and capillaries, some filter-based methods are used to segment vessels. However, the design of accurate and robust vessel segmentation algorithms is still challenging, due to the variety and complexity of images, especially in cerebral blood vessel segmentation. In this work, we addressed a problem of automatic and robust segmentation of cerebral micro-vessels structures in cerebrovascular images acquired by light-sheet microscope for mouse. To segment micro-vessels in large-scale image data, we proposed a convolutional neural networks (CNNs) architecture trained by 1.58 million pixels with manual label. Three convolutional layers and one fully connected layer were used in the CNNs model. We extracted a patch of size 32x32 pixels in each acquired brain vessel image as training data set to feed into CNNs for classification. This network was trained to output the probability that the center pixel of input patch belongs to vessel structures. To build the CNNs architecture, a series of mouse brain vascular images acquired from a commercial light sheet fluorescence microscopy (LSFM) system were used for training the model. The experimental results demonstrated that our approach is a promising method for effectively segmenting micro-vessels structures in cerebrovascular images with vessel-dense, nonuniform gray-level and long-scale contrast regions.

  11. Convolutional neural networks and face recognition task

    Science.gov (United States)

    Sochenkova, A.; Sochenkov, I.; Makovetskii, A.; Vokhmintsev, A.; Melnikov, A.

    2017-09-01

    Computer vision tasks are remaining very important for the last couple of years. One of the most complicated problems in computer vision is face recognition that could be used in security systems to provide safety and to identify person among the others. There is a variety of different approaches to solve this task, but there is still no universal solution that would give adequate results in some cases. Current paper presents following approach. Firstly, we extract an area containing face, then we use Canny edge detector. On the next stage we use convolutional neural networks (CNN) to finally solve face recognition and person identification task.

  12. Decoding LDPC Convolutional Codes on Markov Channels

    Directory of Open Access Journals (Sweden)

    Kashyap Manohar

    2008-01-01

    Full Text Available Abstract This paper describes a pipelined iterative technique for joint decoding and channel state estimation of LDPC convolutional codes over Markov channels. Example designs are presented for the Gilbert-Elliott discrete channel model. We also compare the performance and complexity of our algorithm against joint decoding and state estimation of conventional LDPC block codes. Complexity analysis reveals that our pipelined algorithm reduces the number of operations per time step compared to LDPC block codes, at the expense of increased memory and latency. This tradeoff is favorable for low-power applications.

  13. Decoding LDPC Convolutional Codes on Markov Channels

    Directory of Open Access Journals (Sweden)

    Chris Winstead

    2008-04-01

    Full Text Available This paper describes a pipelined iterative technique for joint decoding and channel state estimation of LDPC convolutional codes over Markov channels. Example designs are presented for the Gilbert-Elliott discrete channel model. We also compare the performance and complexity of our algorithm against joint decoding and state estimation of conventional LDPC block codes. Complexity analysis reveals that our pipelined algorithm reduces the number of operations per time step compared to LDPC block codes, at the expense of increased memory and latency. This tradeoff is favorable for low-power applications.

  14. Fourier transforms and convolutions for the experimentalist

    CERN Document Server

    Jennison, RC

    1961-01-01

    Fourier Transforms and Convolutions for the Experimentalist provides the experimentalist with a guide to the principles and practical uses of the Fourier transformation. It aims to bridge the gap between the more abstract account of a purely mathematical approach and the rule of thumb calculation and intuition of the practical worker. The monograph springs from a lecture course which the author has given in recent years and for which he has drawn upon a number of sources, including a set of notes compiled by the late Dr. I. C. Browne from a series of lectures given by Mr. J . A. Ratcliffe of t

  15. Target recognition based on convolutional neural network

    Science.gov (United States)

    Wang, Liqiang; Wang, Xin; Xi, Fubiao; Dong, Jian

    2017-11-01

    One of the important part of object target recognition is the feature extraction, which can be classified into feature extraction and automatic feature extraction. The traditional neural network is one of the automatic feature extraction methods, while it causes high possibility of over-fitting due to the global connection. The deep learning algorithm used in this paper is a hierarchical automatic feature extraction method, trained with the layer-by-layer convolutional neural network (CNN), which can extract the features from lower layers to higher layers. The features are more discriminative and it is beneficial to the object target recognition.

  16. QCDNUM: Fast QCD evolution and convolution

    Science.gov (United States)

    Botje, M.

    2011-02-01

    The QCDNUM program numerically solves the evolution equations for parton densities and fragmentation functions in perturbative QCD. Un-polarised parton densities can be evolved up to next-to-next-to-leading order in powers of the strong coupling constant, while polarised densities or fragmentation functions can be evolved up to next-to-leading order. Other types of evolution can be accessed by feeding alternative sets of evolution kernels into the program. A versatile convolution engine provides tools to compute parton luminosities, cross-sections in hadron-hadron scattering, and deep inelastic structure functions in the zero-mass scheme or in generalised mass schemes. Input to these calculations are either the QCDNUM evolved densities, or those read in from an external parton density repository. Included in the software distribution are packages to calculate zero-mass structure functions in un-polarised deep inelastic scattering, and heavy flavour contributions to these structure functions in the fixed flavour number scheme. Program summaryProgram title: QCDNUM version: 17.00 Catalogue identifier: AEHV_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEHV_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GNU Public Licence No. of lines in distributed program, including test data, etc.: 45 736 No. of bytes in distributed program, including test data, etc.: 911 569 Distribution format: tar.gz Programming language: Fortran-77 Computer: All Operating system: All RAM: Typically 3 Mbytes Classification: 11.5 Nature of problem: Evolution of the strong coupling constant and parton densities, up to next-to-next-to-leading order in perturbative QCD. Computation of observable quantities by Mellin convolution of the evolved densities with partonic cross-sections. Solution method: Parametrisation of the parton densities as linear or quadratic splines on a discrete grid, and evolution of the spline

  17. Enhancing Hi-C data resolution with deep convolutional neural network HiCPlus.

    Science.gov (United States)

    Zhang, Yan; An, Lin; Xu, Jie; Zhang, Bo; Zheng, W Jim; Hu, Ming; Tang, Jijun; Yue, Feng

    2018-02-21

    Although Hi-C technology is one of the most popular tools for studying 3D genome organization, due to sequencing cost, the resolution of most Hi-C datasets are coarse and cannot be used to link distal regulatory elements to their target genes. Here we develop HiCPlus, a computational approach based on deep convolutional neural network, to infer high-resolution Hi-C interaction matrices from low-resolution Hi-C data. We demonstrate that HiCPlus can impute interaction matrices highly similar to the original ones, while only using 1/16 of the original sequencing reads. We show that the models learned from one cell type can be applied to make predictions in other cell or tissue types. Our work not only provides a computational framework to enhance Hi-C data resolution but also reveals features underlying the formation of 3D chromatin interactions.

  18. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs.

    Science.gov (United States)

    Chen, Liang-Chieh; Papandreou, George; Kokkinos, Iasonas; Murphy, Kevin; Yuille, Alan L

    2018-04-01

    In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First, we highlight convolution with upsampled filters, or 'atrous convolution', as a powerful tool in dense prediction tasks. Atrous convolution allows us to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural Networks. It also allows us to effectively enlarge the field of view of filters to incorporate larger context without increasing the number of parameters or the amount of computation. Second, we propose atrous spatial pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales. Third, we improve the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models. The commonly deployed combination of max-pooling and downsampling in DCNNs achieves invariance but has a toll on localization accuracy. We overcome this by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF), which is shown both qualitatively and quantitatively to improve localization performance. Our proposed "DeepLab" system sets the new state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching 79.7 percent mIOU in the test set, and advances the results on three other datasets: PASCAL-Context, PASCAL-Person-Part, and Cityscapes. All of our code is made publicly available online.

  19. Convolutive ICA for Spatio-Temporal Analysis of EEG

    DEFF Research Database (Denmark)

    Dyrholm, Mads; Makeig, Scott; Hansen, Lars Kai

    2007-01-01

    in the convolutive model can be correctly detected using Bayesian model selection. We demonstrate a framework for deconvolving an EEG ICA subspace. Initial results suggest that in some cases convolutive mixing may be a more realistic model for EEG signals than the instantaneous ICA model....

  20. Modified Stieltjes Transform and Generalized Convolutions of Probability Distributions

    Directory of Open Access Journals (Sweden)

    Lev B. Klebanov

    2018-01-01

    Full Text Available The classical Stieltjes transform is modified in such a way as to generalize both Stieltjes and Fourier transforms. This transform allows the introduction of new classes of commutative and non-commutative generalized convolutions. A particular case of such a convolution for degenerate distributions appears to be the Wigner semicircle distribution.

  1. Nuclear norm regularized convolutional Max Pos@Top machine

    KAUST Repository

    Li, Qinfeng; Zhou, Xiaofeng; Gu, Aihua; Li, Zonghua; Liang, Ru-Ze

    2016-01-01

    , named as Pos@Top. Our proposed classification model has a convolutional structure that is composed by four layers, i.e., the convolutional layer, the activation layer, the max-pooling layer and the full connection layer. In this paper, we propose

  2. Spherical convolutions and their application in molecular modelling

    DEFF Research Database (Denmark)

    Boomsma, Wouter; Frellsen, Jes

    2017-01-01

    Convolutional neural networks are increasingly used outside the domain of image analysis, in particular in various areas of the natural sciences concerned with spatial data. Such networks often work out-of-the box, and in some cases entire model architectures from image analysis can be carried over...... to other problem domains almost unaltered. Unfortunately, this convenience does not trivially extend to data in non-euclidean spaces, such as spherical data. In this paper, we introduce two strategies for conducting convolutions on the sphere, using either a spherical-polar grid or a grid based...... of spherical convolutions in the context of molecular modelling, by considering structural environments within proteins. We show that the models are capable of learning non-trivial functions in these molecular environments, and that our spherical convolutions generally outperform standard 3D convolutions...

  3. Spleen-preserving distal pancreatectomy in trauma.

    Science.gov (United States)

    Schellenberg, Morgan; Inaba, Kenji; Cheng, Vincent; Bardes, James M; Lam, Lydia; Benjamin, Elizabeth; Matsushima, Kazuhide; Demetriades, Demetrios

    2018-01-01

    Traumatic injuries to the distal pancreas are infrequent. Universally accepted recommendations about the need for routine splenectomy with distal pancreatectomy do not exist. The aims of this study were to compare outcomes after distal pancreatectomy and splenectomy versus spleen-preserving distal pancreatectomy, and to define the appropriate patient population for splenic preservation. All patients who underwent distal pancreatectomy (January 1, 2007, to December 31, 2014) were identified from the National Trauma Data Bank. Patients with concomitant splenic injury and those who underwent partial splenectomy were excluded. Demographics, clinical data, procedures, and outcomes were collected. Study groups were defined by surgical procedure: distal pancreatectomy and splenectomy versus spleen-preserving distal pancreatectomy. Baseline characteristics between groups were compared with univariate analysis. Multivariate analysis was performed with logistic and linear regression to examine differences in outcomes. Over the 8-year study period, 2,223 patients underwent distal pancreatectomy. After excluding 1,381 patients with concomitant splenic injury (62%) and 8 (pancreatectomy and splenectomy, those who underwent spleen-preserving distal pancreatectomy were younger (p pancreatectomy (p = 0.017). Complications, mortality, and intensive care unit LOS were not significantly different. In young patients after blunt trauma who are not severely injured, a spleen-preserving distal pancreatectomy should be considered to allow for conservation of splenic function and a shorter hospital LOS. In all other patients, the surgeon should not hesitate to remove the spleen with the distal pancreas. Therapy, level IV.

  4. Deformable image registration using convolutional neural networks

    Science.gov (United States)

    Eppenhof, Koen A. J.; Lafarge, Maxime W.; Moeskops, Pim; Veta, Mitko; Pluim, Josien P. W.

    2018-03-01

    Deformable image registration can be time-consuming and often needs extensive parameterization to perform well on a specific application. We present a step towards a registration framework based on a three-dimensional convolutional neural network. The network directly learns transformations between pairs of three-dimensional images. The outputs of the network are three maps for the x, y, and z components of a thin plate spline transformation grid. The network is trained on synthetic random transformations, which are applied to a small set of representative images for the desired application. Training therefore does not require manually annotated ground truth deformation information. The methodology is demonstrated on public data sets of inspiration-expiration lung CT image pairs, which come with annotated corresponding landmarks for evaluation of the registration accuracy. Advantages of this methodology are its fast registration times and its minimal parameterization.

  5. Codeword Structure Analysis for LDPC Convolutional Codes

    Directory of Open Access Journals (Sweden)

    Hua Zhou

    2015-12-01

    Full Text Available The codewords of a low-density parity-check (LDPC convolutional code (LDPC-CC are characterised into structured and non-structured. The number of the structured codewords is dominated by the size of the polynomial syndrome former matrix H T ( D , while the number of the non-structured ones depends on the particular monomials or polynomials in H T ( D . By evaluating the relationship of the codewords between the mother code and its super codes, the low weight non-structured codewords in the super codes can be eliminated by appropriately choosing the monomials or polynomials in H T ( D , resulting in improved distance spectrum of the mother code.

  6. An Improved Convolutional Neural Network on Crowd Density Estimation

    Directory of Open Access Journals (Sweden)

    Pan Shao-Yun

    2016-01-01

    Full Text Available In this paper, a new method is proposed for crowd density estimation. An improved convolutional neural network is combined with traditional texture feature. The data calculated by the convolutional layer can be treated as a new kind of features.So more useful information of images can be extracted by different features.In the meantime, the size of image has little effect on the result of convolutional neural network. Experimental results indicate that our scheme has adequate performance to allow for its use in real world applications.

  7. High Order Tensor Formulation for Convolutional Sparse Coding

    KAUST Repository

    Bibi, Adel Aamer; Ghanem, Bernard

    2017-01-01

    Convolutional sparse coding (CSC) has gained attention for its successful role as a reconstruction and a classification tool in the computer vision and machine learning community. Current CSC methods can only reconstruct singlefeature 2D images

  8. Adversarial training and dilated convolutions for brain MRI segmentation

    NARCIS (Netherlands)

    Moeskops, P.; Veta, M.; Lafarge, M.W.; Eppenhof, K.A.J.; Pluim, J.P.W.

    2017-01-01

    Convolutional neural networks (CNNs) have been applied to various automatic image segmentation tasks in medical image analysis, including brain MRI segmentation. Generative adversarial networks have recently gained popularity because of their power in generating images that are difficult to

  9. Classification of urine sediment based on convolution neural network

    Science.gov (United States)

    Pan, Jingjing; Jiang, Cunbo; Zhu, Tiantian

    2018-04-01

    By designing a new convolution neural network framework, this paper breaks the constraints of the original convolution neural network framework requiring large training samples and samples of the same size. Move and cropping the input images, generate the same size of the sub-graph. And then, the generated sub-graph uses the method of dropout, increasing the diversity of samples and preventing the fitting generation. Randomly select some proper subset in the sub-graphic set and ensure that the number of elements in the proper subset is same and the proper subset is not the same. The proper subsets are used as input layers for the convolution neural network. Through the convolution layer, the pooling, the full connection layer and output layer, we can obtained the classification loss rate of test set and training set. In the red blood cells, white blood cells, calcium oxalate crystallization classification experiment, the classification accuracy rate of 97% or more.

  10. Convolution of second order linear recursive sequences II.

    Directory of Open Access Journals (Sweden)

    Szakács Tamás

    2017-12-01

    Full Text Available We continue the investigation of convolutions of second order linear recursive sequences (see the first part in [1]. In this paper, we focus on the case when the characteristic polynomials of the sequences have common root.

  11. FPGA-based digital convolution for wireless applications

    CERN Document Server

    Guan, Lei

    2017-01-01

    This book presents essential perspectives on digital convolutions in wireless communications systems and illustrates their corresponding efficient real-time field-programmable gate array (FPGA) implementations. Covering these digital convolutions from basic concept to vivid simulation/illustration, the book is also supplemented with MS PowerPoint presentations to aid in comprehension. FPGAs or generic all programmable devices will soon become widespread, serving as the “brains” of all types of real-time smart signal processing systems, like smart networks, smart homes and smart cities. The book examines digital convolution by bringing together the following main elements: the fundamental theory behind the mathematical formulae together with corresponding physical phenomena; virtualized algorithm simulation together with benchmark real-time FPGA implementations; and detailed, state-of-the-art case studies on wireless applications, including popular linear convolution in digital front ends (DFEs); nonlinear...

  12. Deep Recurrent Convolutional Neural Network: Improving Performance For Speech Recognition

    OpenAIRE

    Zhang, Zewang; Sun, Zheng; Liu, Jiaqi; Chen, Jingwen; Huo, Zhao; Zhang, Xiao

    2016-01-01

    A deep learning approach has been widely applied in sequence modeling problems. In terms of automatic speech recognition (ASR), its performance has significantly been improved by increasing large speech corpus and deeper neural network. Especially, recurrent neural network and deep convolutional neural network have been applied in ASR successfully. Given the arising problem of training speed, we build a novel deep recurrent convolutional network for acoustic modeling and then apply deep resid...

  13. Traffic sign recognition with deep convolutional neural networks

    OpenAIRE

    Karamatić, Boris

    2016-01-01

    The problem of detection and recognition of traffic signs is becoming an important problem when it comes to the development of self driving cars and advanced driver assistance systems. In this thesis we will develop a system for detection and recognition of traffic signs. For the problem of detection we will use aggregate channel features and for the problem of recognition we will use a deep convolutional neural network. We will describe how convolutional neural networks work, how they are co...

  14. Convolutional Codes with Maximum Column Sum Rank for Network Streaming

    OpenAIRE

    Mahmood, Rafid; Badr, Ahmed; Khisti, Ashish

    2015-01-01

    The column Hamming distance of a convolutional code determines the error correction capability when streaming over a class of packet erasure channels. We introduce a metric known as the column sum rank, that parallels column Hamming distance when streaming over a network with link failures. We prove rank analogues of several known column Hamming distance properties and introduce a new family of convolutional codes that maximize the column sum rank up to the code memory. Our construction invol...

  15. Efficient and Invariant Convolutional Neural Networks for Dense Prediction

    OpenAIRE

    Gao, Hongyang; Ji, Shuiwang

    2017-01-01

    Convolutional neural networks have shown great success on feature extraction from raw input data such as images. Although convolutional neural networks are invariant to translations on the inputs, they are not invariant to other transformations, including rotation and flip. Recent attempts have been made to incorporate more invariance in image recognition applications, but they are not applicable to dense prediction tasks, such as image segmentation. In this paper, we propose a set of methods...

  16. Prediction of Electricity Usage Using Convolutional Neural Networks

    OpenAIRE

    Hansen, Martin

    2017-01-01

    Master's thesis Information- and communication technology IKT590 - University of Agder 2017 Convolutional Neural Networks are overwhelmingly accurate when attempting to predict numbers using the famous MNIST-dataset. In this paper, we are attempting to transcend these results for time- series forecasting, and compare them with several regression mod- els. The Convolutional Neural Network model predicted the same value through the entire time lapse in contrast with the other ...

  17. Research of convolutional neural networks for traffic sign recognition

    OpenAIRE

    Stadalnikas, Kasparas

    2017-01-01

    In this thesis the convolutional neural networks application for traffic sign recognition is analyzed. Thesis describes the basic operations, techniques that are commonly used to apply in the image classification using convolutional neural networks. Also, this paper describes the data sets used for traffic sign recognition, their problems affecting the final training results. The paper reviews most popular existing technologies – frameworks for developing the solution for traffic sign recogni...

  18. On the Fresnel sine integral and the convolution

    Directory of Open Access Journals (Sweden)

    Adem Kılıçman

    2003-01-01

    Full Text Available The Fresnel sine integral S(x, the Fresnel cosine integral C(x, and the associated functions S+(x, S−(x, C+(x, and C−(x are defined as locally summable functions on the real line. Some convolutions and neutrix convolutions of the Fresnel sine integral and its associated functions with x+r, xr are evaluated.

  19. Radial Structure Scaffolds Convolution Patterns of Developing Cerebral Cortex

    Directory of Open Access Journals (Sweden)

    Mir Jalil Razavi

    2017-08-01

    Full Text Available Commonly-preserved radial convolution is a prominent characteristic of the mammalian cerebral cortex. Endeavors from multiple disciplines have been devoted for decades to explore the causes for this enigmatic structure. However, the underlying mechanisms that lead to consistent cortical convolution patterns still remain poorly understood. In this work, inspired by prior studies, we propose and evaluate a plausible theory that radial convolution during the early development of the brain is sculptured by radial structures consisting of radial glial cells (RGCs and maturing axons. Specifically, the regionally heterogeneous development and distribution of RGCs controlled by Trnp1 regulate the convex and concave convolution patterns (gyri and sulci in the radial direction, while the interplay of RGCs' effects on convolution and axons regulates the convex (gyral convolution patterns. This theory is assessed by observations and measurements in literature from multiple disciplines such as neurobiology, genetics, biomechanics, etc., at multiple scales to date. Particularly, this theory is further validated by multimodal imaging data analysis and computational simulations in this study. We offer a versatile and descriptive study model that can provide reasonable explanations of observations, experiments, and simulations of the characteristic mammalian cortical folding.

  20. Posttraumatic osteolysis of the distal clavicula end

    International Nuclear Information System (INIS)

    Hermanns, P.H.; Beeger, R.; Koetter, D.; Hamburg Univ.

    1981-01-01

    Posttraumatic osteolysis of bone is rare. Its etiology is unknown. A case of posttraumatic osteolysis of the distal clavicle end is reported. Differentialdiagnostical and ethiological relations are discussed. The literature of posttraumatic osteolysis especially of distal clavicle osteolysis is reported. (orig.) [de

  1. Contemporary Management of Primary Distal Urethral Cancer

    NARCIS (Netherlands)

    Traboulsi, S.L.; Witjes, J.A.; Kassouf, W.

    2016-01-01

    Primary urethral cancer is one of the rare urologic tumors. Distal urethral tumors are usually less advanced at diagnosis compared with proximal tumors and have a good prognosis if treated appropriately. Low-stage distal tumors can be managed successfully with a surgical approach in men or radiation

  2. ELHnet: a convolutional neural network for classifying cochlear endolymphatic hydrops imaged with optical coherence tomography.

    Science.gov (United States)

    Liu, George S; Zhu, Michael H; Kim, Jinkyung; Raphael, Patrick; Applegate, Brian E; Oghalai, John S

    2017-10-01

    Detection of endolymphatic hydrops is important for diagnosing Meniere's disease, and can be performed non-invasively using optical coherence tomography (OCT) in animal models as well as potentially in the clinic. Here, we developed ELHnet, a convolutional neural network to classify endolymphatic hydrops in a mouse model using learned features from OCT images of mice cochleae. We trained ELHnet on 2159 training and validation images from 17 mice, using only the image pixels and observer-determined labels of endolymphatic hydrops as the inputs. We tested ELHnet on 37 images from 37 mice that were previously not used, and found that the neural network correctly classified 34 of the 37 mice. This demonstrates an improvement in performance from previous work on computer-aided classification of endolymphatic hydrops. To the best of our knowledge, this is the first deep CNN designed for endolymphatic hydrops classification.

  3. Characterization of the distal promoter of the human pyruvate carboxylase gene in pancreatic beta cells.

    Directory of Open Access Journals (Sweden)

    Ansaya Thonpho

    Full Text Available Pyruvate carboxylase (PC is an enzyme that plays a crucial role in many biosynthetic pathways in various tissues including glucose-stimulated insulin secretion. In the present study, we identify promoter usage of the human PC gene in pancreatic beta cells. The data show that in the human, two alternative promoters, proximal and distal, are responsible for the production of multiple mRNA isoforms as in the rat and mouse. RT-PCR analysis performed with cDNA prepared from human liver and islets showed that the distal promoter, but not the proximal promoter, of the human PC gene is active in pancreatic beta cells. A 1108 bp fragment of the human PC distal promoter was cloned and analyzed. It contains no TATA box but possesses two CCAAT boxes, and other putative transcription factor binding sites, similar to those of the distal promoter of rat PC gene. To localize the positive regulatory region in the human PC distal promoter, 5'-truncated and the 25-bp and 15-bp internal deletion mutants of the human PC distal promoter were generated and used in transient transfections in INS-1 832/13 insulinoma and HEK293T (kidney cell lines. The results indicated that positions -340 to -315 of the human PC distal promoter serve as (an activator element(s for cell-specific transcription factor, while the CCAAT box at -71/-67, a binding site for nuclear factor Y (NF-Y, as well as a GC box at -54/-39 of the human PC distal promoter act as activator sequences for basal transcription.

  4. Metaheuristic Algorithms for Convolution Neural Network.

    Science.gov (United States)

    Rere, L M Rasdi; Fanany, Mohamad Ivan; Arymurthy, Aniati Murni

    2016-01-01

    A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent).

  5. Metaheuristic Algorithms for Convolution Neural Network

    Directory of Open Access Journals (Sweden)

    L. M. Rasdi Rere

    2016-01-01

    Full Text Available A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN, a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent.

  6. Do Convolutional Neural Networks Learn Class Hierarchy?

    Science.gov (United States)

    Bilal, Alsallakh; Jourabloo, Amin; Ye, Mao; Liu, Xiaoming; Ren, Liu

    2018-01-01

    Convolutional Neural Networks (CNNs) currently achieve state-of-the-art accuracy in image classification. With a growing number of classes, the accuracy usually drops as the possibilities of confusion increase. Interestingly, the class confusion patterns follow a hierarchical structure over the classes. We present visual-analytics methods to reveal and analyze this hierarchy of similar classes in relation with CNN-internal data. We found that this hierarchy not only dictates the confusion patterns between the classes, it furthermore dictates the learning behavior of CNNs. In particular, the early layers in these networks develop feature detectors that can separate high-level groups of classes quite well, even after a few training epochs. In contrast, the latter layers require substantially more epochs to develop specialized feature detectors that can separate individual classes. We demonstrate how these insights are key to significant improvement in accuracy by designing hierarchy-aware CNNs that accelerate model convergence and alleviate overfitting. We further demonstrate how our methods help in identifying various quality issues in the training data.

  7. Microaneurysm detection using fully convolutional neural networks.

    Science.gov (United States)

    Chudzik, Piotr; Majumdar, Somshubra; Calivá, Francesco; Al-Diri, Bashir; Hunter, Andrew

    2018-05-01

    Diabetic retinopathy is a microvascular complication of diabetes that can lead to sight loss if treated not early enough. Microaneurysms are the earliest clinical signs of diabetic retinopathy. This paper presents an automatic method for detecting microaneurysms in fundus photographies. A novel patch-based fully convolutional neural network with batch normalization layers and Dice loss function is proposed. Compared to other methods that require up to five processing stages, it requires only three. Furthermore, to the best of the authors' knowledge, this is the first paper that shows how to successfully transfer knowledge between datasets in the microaneurysm detection domain. The proposed method was evaluated using three publicly available and widely used datasets: E-Ophtha, DIARETDB1, and ROC. It achieved better results than state-of-the-art methods using the FROC metric. The proposed algorithm accomplished highest sensitivities for low false positive rates, which is particularly important for screening purposes. Performance, simplicity, and robustness of the proposed method demonstrates its suitability for diabetic retinopathy screening applications. Copyright © 2018 Elsevier B.V. All rights reserved.

  8. Multiscale Convolutional Neural Networks for Hand Detection

    Directory of Open Access Journals (Sweden)

    Shiyang Yan

    2017-01-01

    Full Text Available Unconstrained hand detection in still images plays an important role in many hand-related vision problems, for example, hand tracking, gesture analysis, human action recognition and human-machine interaction, and sign language recognition. Although hand detection has been extensively studied for decades, it is still a challenging task with many problems to be tackled. The contributing factors for this complexity include heavy occlusion, low resolution, varying illumination conditions, different hand gestures, and the complex interactions between hands and objects or other hands. In this paper, we propose a multiscale deep learning model for unconstrained hand detection in still images. Deep learning models, and deep convolutional neural networks (CNNs in particular, have achieved state-of-the-art performances in many vision benchmarks. Developed from the region-based CNN (R-CNN model, we propose a hand detection scheme based on candidate regions generated by a generic region proposal algorithm, followed by multiscale information fusion from the popular VGG16 model. Two benchmark datasets were applied to validate the proposed method, namely, the Oxford Hand Detection Dataset and the VIVA Hand Detection Challenge. We achieved state-of-the-art results on the Oxford Hand Detection Dataset and had satisfactory performance in the VIVA Hand Detection Challenge.

  9. Convolutional Dictionary Learning: Acceleration and Convergence

    Science.gov (United States)

    Chun, Il Yong; Fessler, Jeffrey A.

    2018-04-01

    Convolutional dictionary learning (CDL or sparsifying CDL) has many applications in image processing and computer vision. There has been growing interest in developing efficient algorithms for CDL, mostly relying on the augmented Lagrangian (AL) method or the variant alternating direction method of multipliers (ADMM). When their parameters are properly tuned, AL methods have shown fast convergence in CDL. However, the parameter tuning process is not trivial due to its data dependence and, in practice, the convergence of AL methods depends on the AL parameters for nonconvex CDL problems. To moderate these problems, this paper proposes a new practically feasible and convergent Block Proximal Gradient method using a Majorizer (BPG-M) for CDL. The BPG-M-based CDL is investigated with different block updating schemes and majorization matrix designs, and further accelerated by incorporating some momentum coefficient formulas and restarting techniques. All of the methods investigated incorporate a boundary artifacts removal (or, more generally, sampling) operator in the learning model. Numerical experiments show that, without needing any parameter tuning process, the proposed BPG-M approach converges more stably to desirable solutions of lower objective values than the existing state-of-the-art ADMM algorithm and its memory-efficient variant do. Compared to the ADMM approaches, the BPG-M method using a multi-block updating scheme is particularly useful in single-threaded CDL algorithm handling large datasets, due to its lower memory requirement and no polynomial computational complexity. Image denoising experiments show that, for relatively strong additive white Gaussian noise, the filters learned by BPG-M-based CDL outperform those trained by the ADMM approach.

  10. Lidar Cloud Detection with Fully Convolutional Networks

    Science.gov (United States)

    Cromwell, E.; Flynn, D.

    2017-12-01

    The vertical distribution of clouds from active remote sensing instrumentation is a widely used data product from global atmospheric measuring sites. The presence of clouds can be expressed as a binary cloud mask and is a primary input for climate modeling efforts and cloud formation studies. Current cloud detection algorithms producing these masks do not accurately identify the cloud boundaries and tend to oversample or over-represent the cloud. This translates as uncertainty for assessing the radiative impact of clouds and tracking changes in cloud climatologies. The Atmospheric Radiation Measurement (ARM) program has over 20 years of micro-pulse lidar (MPL) and High Spectral Resolution Lidar (HSRL) instrument data and companion automated cloud mask product at the mid-latitude Southern Great Plains (SGP) and the polar North Slope of Alaska (NSA) atmospheric observatory. Using this data, we train a fully convolutional network (FCN) with semi-supervised learning to segment lidar imagery into geometric time-height cloud locations for the SGP site and MPL instrument. We then use transfer learning to train a FCN for (1) the MPL instrument at the NSA site and (2) for the HSRL. In our semi-supervised approach, we pre-train the classification layers of the FCN with weakly labeled lidar data. Then, we facilitate end-to-end unsupervised pre-training and transition to fully supervised learning with ground truth labeled data. Our goal is to improve the cloud mask accuracy and precision for the MPL instrument to 95% and 80%, respectively, compared to the current cloud mask algorithms of 89% and 50%. For the transfer learning based FCN for the HSRL instrument, our goal is to achieve a cloud mask accuracy of 90% and a precision of 80%.

  11. Detecting atrial fibrillation by deep convolutional neural networks.

    Science.gov (United States)

    Xia, Yong; Wulan, Naren; Wang, Kuanquan; Zhang, Henggui

    2018-02-01

    Atrial fibrillation (AF) is the most common cardiac arrhythmia. The incidence of AF increases with age, causing high risks of stroke and increased morbidity and mortality. Efficient and accurate diagnosis of AF based on the ECG is valuable in clinical settings and remains challenging. In this paper, we proposed a novel method with high reliability and accuracy for AF detection via deep learning. The short-term Fourier transform (STFT) and stationary wavelet transform (SWT) were used to analyze ECG segments to obtain two-dimensional (2-D) matrix input suitable for deep convolutional neural networks. Then, two different deep convolutional neural network models corresponding to STFT output and SWT output were developed. Our new method did not require detection of P or R peaks, nor feature designs for classification, in contrast to existing algorithms. Finally, the performances of the two models were evaluated and compared with those of existing algorithms. Our proposed method demonstrated favorable performances on ECG segments as short as 5 s. The deep convolutional neural network using input generated by STFT, presented a sensitivity of 98.34%, specificity of 98.24% and accuracy of 98.29%. For the deep convolutional neural network using input generated by SWT, a sensitivity of 98.79%, specificity of 97.87% and accuracy of 98.63% was achieved. The proposed method using deep convolutional neural networks shows high sensitivity, specificity and accuracy, and, therefore, is a valuable tool for AF detection. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. Video Super-Resolution via Bidirectional Recurrent Convolutional Networks.

    Science.gov (United States)

    Huang, Yan; Wang, Wei; Wang, Liang

    2018-04-01

    Super resolving a low-resolution video, namely video super-resolution (SR), is usually handled by either single-image SR or multi-frame SR. Single-Image SR deals with each video frame independently, and ignores intrinsic temporal dependency of video frames which actually plays a very important role in video SR. Multi-Frame SR generally extracts motion information, e.g., optical flow, to model the temporal dependency, but often shows high computational cost. Considering that recurrent neural networks (RNNs) can model long-term temporal dependency of video sequences well, we propose a fully convolutional RNN named bidirectional recurrent convolutional network for efficient multi-frame SR. Different from vanilla RNNs, 1) the commonly-used full feedforward and recurrent connections are replaced with weight-sharing convolutional connections. So they can greatly reduce the large number of network parameters and well model the temporal dependency in a finer level, i.e., patch-based rather than frame-based, and 2) connections from input layers at previous timesteps to the current hidden layer are added by 3D feedforward convolutions, which aim to capture discriminate spatio-temporal patterns for short-term fast-varying motions in local adjacent frames. Due to the cheap convolutional operations, our model has a low computational complexity and runs orders of magnitude faster than other multi-frame SR methods. With the powerful temporal dependency modeling, our model can super resolve videos with complex motions and achieve well performance.

  13. Gitelman or Bartter type 3 syndrome? A case of distal convoluted tubulopathy caused by CLCNKB gene mutation.

    Science.gov (United States)

    Cruz, António José; Castro, Alexandra

    2013-01-22

    A 32-year-old woman with no significant medical history was sent to our consultation due to hypokalaemia (syndrome (GS) came negative. CLCNKB gene mutation analysis present in both GS and Bartter (BS) type 3 syndromes was positive. The patient is now being treated with potassium and magnesium oral supplements, ramipril and spironolactone with stable near-normal potassium and magnesium levels. This article presents the case of a patient with hypokalaemia caused by CLCNKB gene mutation hard to categorise as GS or BS type 3.

  14. Conceptualizing distal drivers in land use competition

    DEFF Research Database (Denmark)

    Niewhöner, Jörg; Nielsen, Jonas Ø; Gasparri, Gasparri

    2016-01-01

    This introductory chapter explores the notion of ‘distal drivers’ in land use competition. Research has moved beyond proximate causes of land cover and land use change to focus on the underlying drivers of these dynamics. We discuss the framework of telecoupling within human–environment systems...... as a first step to come to terms with the increasingly distal nature of driving forces behind land use practices. We then expand the notion of distal as mainly a measure of Euclidian space to include temporal, social, and institutional dimensions. This understanding of distal widens our analytical scope...... for the analysis of land use competition as a distributed process to consider the role of knowledge and power, technology, and different temporalities within a relational or systemic analysis of practices of land use competition. We conclude by pointing toward the historical and social contingency of land use...

  15. Distal technologies and type 1 diabetes management.

    Science.gov (United States)

    Duke, Danny C; Barry, Samantha; Wagner, David V; Speight, Jane; Choudhary, Pratik; Harris, Michael A

    2018-02-01

    Type 1 diabetes requires intensive self-management to avoid acute and long-term health complications. In the past two decades, substantial advances in technology have enabled more effective and convenient self-management of type 1 diabetes. Although proximal technologies (eg, insulin pumps, continuous glucose monitors, closed-loop and artificial pancreas systems) have been the subject of frequent systematic and narrative reviews, distal technologies have received scant attention. Distal technologies refer to electronic systems designed to provide a service remotely and include heterogeneous systems such as telehealth, mobile health applications, game-based support, social platforms, and patient portals. In this Review, we summarise the empirical literature to provide current information about the effectiveness of available distal technologies to improve type 1 diabetes management. We also discuss privacy, ethics, and regulatory considerations, issues of global adoption, knowledge gaps in distal technology, and recommendations for future directions. Copyright © 2018 Elsevier Ltd. All rights reserved.

  16. Genetics Home Reference: distal arthrogryposis type 1

    Science.gov (United States)

    ... 1 is a disorder characterized by joint deformities (contractures) that restrict movement in the hands and feet. ... distal arthrogryposis type 1 . However, researchers speculate that contractures may be related to problems with muscle contraction ...

  17. Normal distal pulmonary vein anatomy

    Directory of Open Access Journals (Sweden)

    Wiesława Klimek-Piotrowska

    2016-01-01

    Full Text Available Background. It is well known that the pulmonary veins (PVs, especially their myocardial sleeves play a critical role in the initiation and maintenance of atrial fibrillation. Understanding the PV anatomy is crucial for the safety and efficacy of all procedures performed on PVs. The aim of this study was to present normal distal PV anatomy and to create a juxtaposition of all PV ostium variants.Methods. A total of 130 randomly selected autopsied adult human hearts (Caucasian were examined. The number of PVs ostia was evaluated and their diameter was measured. The ostium-to-last-tributary distance and macroscopic presence of myocardial sleeves were also evaluated.Results. Five hundred forty-one PV ostia were identified. Four classical PV ostia patterns (two left and two right PVs were observed in 70.8% of all cases. The most common variant was the classical pattern with additional middle right PV (19.2%, followed by the common ostium for the left superior and the inferior PVs (4.44%. Mean diameters of PV ostia (for the classical pattern were: left superior = 13.8 ± 2.9 mm; left inferior = 13.3 ± 3.4 mm; right superior = 14.3 ± 2.9 mm; right inferior = 13.7 ± 3.3 mm. When present, the additional middle right PV ostium had the smallest PV ostium diameter in the heart (8.2 ± 4.1 mm. The mean ostium-to-last-tributary (closest to the atrium distances were: left superior = 15.1 ± 4.6 mm; left inferior = 13.5 ± 4.0 mm; right superior = 11.8 ± 4.0 mm; right inferior = 11.0 ± 3.7 mm. There were no statistically significant differences between sexes in ostia diameters and ostium-to-last-tributary distances.Conclusion. Only 71% of the cases have four standard pulmonary veins. The middle right pulmonary vein is present in almost 20% of patients. Presented data can provide useful information for the clinicians during interventional procedures or radiologic examinations of PVs.

  18. Invariant moments based convolutional neural networks for image analysis

    Directory of Open Access Journals (Sweden)

    Vijayalakshmi G.V. Mahesh

    2017-01-01

    Full Text Available The paper proposes a method using convolutional neural network to effectively evaluate the discrimination between face and non face patterns, gender classification using facial images and facial expression recognition. The novelty of the method lies in the utilization of the initial trainable convolution kernels coefficients derived from the zernike moments by varying the moment order. The performance of the proposed method was compared with the convolutional neural network architecture that used random kernels as initial training parameters. The multilevel configuration of zernike moments was significant in extracting the shape information suitable for hierarchical feature learning to carry out image analysis and classification. Furthermore the results showed an outstanding performance of zernike moment based kernels in terms of the computation time and classification accuracy.

  19. Single image super-resolution based on convolutional neural networks

    Science.gov (United States)

    Zou, Lamei; Luo, Ming; Yang, Weidong; Li, Peng; Jin, Liujia

    2018-03-01

    We present a deep learning method for single image super-resolution (SISR). The proposed approach learns end-to-end mapping between low-resolution (LR) images and high-resolution (HR) images. The mapping is represented as a deep convolutional neural network which inputs the LR image and outputs the HR image. Our network uses 5 convolution layers, which kernels size include 5×5, 3×3 and 1×1. In our proposed network, we use residual-learning and combine different sizes of convolution kernels at the same layer. The experiment results show that our proposed method performs better than the existing methods in reconstructing quality index and human visual effects on benchmarked images.

  20. Spacings and pair correlations for finite Bernoulli convolutions

    International Nuclear Information System (INIS)

    Benjamini, Itai; Solomyak, Boris

    2009-01-01

    We consider finite Bernoulli convolutions with a parameter 1/2 N . These sequences are uniformly distributed with respect to the infinite Bernoulli convolution measure ν λ , as N → ∞. Numerical evidence suggests that for a generic λ, the distribution of spacings between appropriately rescaled points is Poissonian. We obtain some partial results in this direction; for instance, we show that, on average, the pair correlations do not exhibit attraction or repulsion in the limit. On the other hand, for certain algebraic λ the behaviour is totally different

  1. A New Reverberator Based on Variable Sparsity Convolution

    DEFF Research Database (Denmark)

    Holm-Rasmussen, Bo; Lehtonen, Heidi-Maria; Välimäki, Vesa

    2013-01-01

    FIR filter coefficients are selected from a velvet noise sequence, which consists of ones, minus ones, and zeros only. In this application, it is sufficient perceptually to use very sparse velvet noise sequences having only about 0.1 to 0.2% non-zero elements, with increasing sparsity along...... the impulse response. The algorithm yields a parametric approximation of the late part of the impulse response, which is more than 100 times more efficient computationally than the direct convolution. The computational load of the proposed algorithm is comparable to that of FFT-based partitioned convolution...

  2. Convolutional cylinder-type block-circulant cycle codes

    Directory of Open Access Journals (Sweden)

    Mohammad Gholami

    2013-06-01

    Full Text Available In this paper, we consider a class of column-weight two quasi-cyclic low-density paritycheck codes in which the girth can be large enough, as an arbitrary multiple of 8. Then we devote a convolutional form to these codes, such that their generator matrix can be obtained by elementary row and column operations on the parity-check matrix. Finally, we show that the free distance of the convolutional codes is equal to the minimum distance of their block counterparts.

  3. Weed Growth Stage Estimator Using Deep Convolutional Neural Networks

    DEFF Research Database (Denmark)

    Teimouri, Nima; Dyrmann, Mads; Nielsen, Per Rydahl

    2018-01-01

    conditions with regards to soil types, resolution and light settings. Then, 9649 of these images were used for training the computer, which automatically divided the weeds into nine growth classes. The performance of this proposed convolutional neural network approach was evaluated on a further set of 2516...... in estimating the number of leaves and 96% accuracy when accepting a deviation of two leaves. These results show that this new method of using deep convolutional neural networks has a relatively high ability to estimate early growth stages across a wide variety of weed species....

  4. Deep Convolutional Neural Networks: Structure, Feature Extraction and Training

    Directory of Open Access Journals (Sweden)

    Namatēvs Ivars

    2017-12-01

    Full Text Available Deep convolutional neural networks (CNNs are aimed at processing data that have a known network like topology. They are widely used to recognise objects in images and diagnose patterns in time series data as well as in sensor data classification. The aim of the paper is to present theoretical and practical aspects of deep CNNs in terms of convolution operation, typical layers and basic methods to be used for training and learning. Some practical applications are included for signal and image classification. Finally, the present paper describes the proposed block structure of CNN for classifying crucial features from 3D sensor data.

  5. Very deep recurrent convolutional neural network for object recognition

    Science.gov (United States)

    Brahimi, Sourour; Ben Aoun, Najib; Ben Amar, Chokri

    2017-03-01

    In recent years, Computer vision has become a very active field. This field includes methods for processing, analyzing, and understanding images. The most challenging problems in computer vision are image classification and object recognition. This paper presents a new approach for object recognition task. This approach exploits the success of the Very Deep Convolutional Neural Network for object recognition. In fact, it improves the convolutional layers by adding recurrent connections. This proposed approach was evaluated on two object recognition benchmarks: Pascal VOC 2007 and CIFAR-10. The experimental results prove the efficiency of our method in comparison with the state of the art methods.

  6. Spectral interpolation - Zero fill or convolution. [image processing

    Science.gov (United States)

    Forman, M. L.

    1977-01-01

    Zero fill, or augmentation by zeros, is a method used in conjunction with fast Fourier transforms to obtain spectral spacing at intervals closer than obtainable from the original input data set. In the present paper, an interpolation technique (interpolation by repetitive convolution) is proposed which yields values accurate enough for plotting purposes and which lie within the limits of calibration accuracies. The technique is shown to operate faster than zero fill, since fewer operations are required. The major advantages of interpolation by repetitive convolution are that efficient use of memory is possible (thus avoiding the difficulties encountered in decimation in time FFTs) and that is is easy to implement.

  7. Efficient forward propagation of time-sequences in convolutional neural networks using Deep Shifting

    NARCIS (Netherlands)

    K.L. Groenland (Koen); S.M. Bohte (Sander)

    2016-01-01

    textabstractWhen a Convolutional Neural Network is used for on-the-fly evaluation of continuously updating time-sequences, many redundant convolution operations are performed. We propose the method of Deep Shifting, which remembers previously calculated results of convolution operations in order

  8. Mouse adhalin

    DEFF Research Database (Denmark)

    Liu, L; Vachon, P H; Kuang, W

    1997-01-01

    . To analyze the biological roles of adhalin, we cloned the mouse adhalin cDNA, raised peptide-specific antibodies to its cytoplasmic domain, and examined its expression and localization in vivo and in vitro. The mouse adhalin sequence was 80% identical to that of human, rabbit, and hamster. Adhalin...... was specifically expressed in striated muscle cells and their immediate precursors, and absent in many other cell types. Adhalin expression in embryonic mouse muscle was coincident with primary myogenesis. Its expression was found to be up-regulated at mRNA and protein levels during myogenic differentiation...

  9. Discrete singular convolution for the generalized variable-coefficient ...

    African Journals Online (AJOL)

    Numerical solutions of the generalized variable-coefficient Korteweg-de Vries equation are obtained using a discrete singular convolution and a fourth order singly diagonally implicit Runge-Kutta method for space and time discretisation, respectively. The theoretical convergence of the proposed method is rigorously ...

  10. Down image recognition based on deep convolutional neural network

    Directory of Open Access Journals (Sweden)

    Wenzhu Yang

    2018-06-01

    Full Text Available Since of the scale and the various shapes of down in the image, it is difficult for traditional image recognition method to correctly recognize the type of down image and get the required recognition accuracy, even for the Traditional Convolutional Neural Network (TCNN. To deal with the above problems, a Deep Convolutional Neural Network (DCNN for down image classification is constructed, and a new weight initialization method is proposed. Firstly, the salient regions of a down image were cut from the image using the visual saliency model. Then, these salient regions of the image were used to train a sparse autoencoder and get a collection of convolutional filters, which accord with the statistical characteristics of dataset. At last, a DCNN with Inception module and its variants was constructed. To improve the recognition accuracy, the depth of the network is deepened. The experiment results indicate that the constructed DCNN increases the recognition accuracy by 2.7% compared to TCNN, when recognizing the down in the images. The convergence rate of the proposed DCNN with the new weight initialization method is improved by 25.5% compared to TCNN. Keywords: Deep convolutional neural network, Weight initialization, Sparse autoencoder, Visual saliency model, Image recognition

  11. Face recognition: a convolutional neural-network approach.

    Science.gov (United States)

    Lawrence, S; Giles, C L; Tsoi, A C; Back, A D

    1997-01-01

    We present a hybrid neural-network for human face recognition which compares favourably with other methods. The system combines local image sampling, a self-organizing map (SOM) neural network, and a convolutional neural network. The SOM provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image sample, and the convolutional neural network provides partial invariance to translation, rotation, scale, and deformation. The convolutional network extracts successively larger features in a hierarchical set of layers. We present results using the Karhunen-Loeve transform in place of the SOM, and a multilayer perceptron (MLP) in place of the convolutional network for comparison. We use a database of 400 images of 40 individuals which contains quite a high degree of variability in expression, pose, and facial details. We analyze the computational complexity and discuss how new classes could be added to the trained recognizer.

  12. Training Convolutional Neural Networks for Translational Invariance on SAR ATR

    DEFF Research Database (Denmark)

    Malmgren-Hansen, David; Engholm, Rasmus; Østergaard Pedersen, Morten

    2016-01-01

    In this paper we present a comparison of the robustness of Convolutional Neural Networks (CNN) to other classifiers in the presence of uncertainty of the objects localization in SAR image. We present a framework for simulating simple SAR images, translating the object of interest systematically...

  13. An Interactive Graphics Program for Assistance in Learning Convolution.

    Science.gov (United States)

    Frederick, Dean K.; Waag, Gary L.

    1980-01-01

    A program has been written for the interactive computer graphics facility at Rensselaer Polytechnic Institute that is designed to assist the user in learning the mathematical technique of convolving two functions. Because convolution can be represented graphically by a sequence of steps involving folding, shifting, multiplying, and integration, it…

  14. Diffraction and Dirchlet problem for parameter-elliptic convolution ...

    African Journals Online (AJOL)

    In this paper we evaluate the difference between the inverse operators of a Dirichlet problem and of a diffraction problem for parameter-elliptic convolution operators with constant symbols. We prove that the inverse operator of a Dirichlet problem can be obtained as a limit case of such a diffraction problem. Quaestiones ...

  15. Review of the convolution algorithm for evaluating service integrated systems

    DEFF Research Database (Denmark)

    Iversen, Villy Bæk

    1997-01-01

    In this paper we give a review of the applicability of the convolution algorithm. By this we are able to evaluate communication networks end--to--end with e.g. BPP multi-ratetraffic models insensitive to the holding time distribution. Rearrangement, minimum allocation, and maximum allocation...

  16. A convolutional neural network to filter artifacts in spectroscopic MRI.

    Science.gov (United States)

    Gurbani, Saumya S; Schreibmann, Eduard; Maudsley, Andrew A; Cordova, James Scott; Soher, Brian J; Poptani, Harish; Verma, Gaurav; Barker, Peter B; Shim, Hyunsuk; Cooper, Lee A D

    2018-03-09

    Proton MRSI is a noninvasive modality capable of generating volumetric maps of in vivo tissue metabolism without the need for ionizing radiation or injected contrast agent. Magnetic resonance spectroscopic imaging has been shown to be a viable imaging modality for studying several neuropathologies. However, a key hurdle in the routine clinical adoption of MRSI is the presence of spectral artifacts that can arise from a number of sources, possibly leading to false information. A deep learning model was developed that was capable of identifying and filtering out poor quality spectra. The core of the model used a tiled convolutional neural network that analyzed frequency-domain spectra to detect artifacts. When compared with a panel of MRS experts, our convolutional neural network achieved high sensitivity and specificity with an area under the curve of 0.95. A visualization scheme was implemented to better understand how the convolutional neural network made its judgement on single-voxel or multivoxel MRSI, and the convolutional neural network was embedded into a pipeline capable of producing whole-brain spectroscopic MRI volumes in real time. The fully automated method for assessment of spectral quality provides a valuable tool to support clinical MRSI or spectroscopic MRI studies for use in fields such as adaptive radiation therapy planning. © 2018 International Society for Magnetic Resonance in Medicine.

  17. Deep convolutional neural networks for detection of rail surface defects

    NARCIS (Netherlands)

    Faghih Roohi, S.; Hajizadeh, S.; Nunez Vicencio, Alfredo; Babuska, R.; De Schutter, B.H.K.; Estevez, Pablo A.; Angelov, Plamen P.; Del Moral Hernandez, Emilio

    2016-01-01

    In this paper, we propose a deep convolutional neural network solution to the analysis of image data for the detection of rail surface defects. The images are obtained from many hours of automated video recordings. This huge amount of data makes it impossible to manually inspect the images and

  18. Symbol Stream Combining in a Convolutionally Coded System

    Science.gov (United States)

    Mceliece, R. J.; Pollara, F.; Swanson, L.

    1985-01-01

    Symbol stream combining has been proposed as a method for arraying signals received at different antennas. If convolutional coding and Viterbi decoding are used, it is shown that a Viterbi decoder based on the proposed weighted sum of symbol streams yields maximum likelihood decisions.

  19. Two-level convolution formula for nuclear structure function

    Science.gov (United States)

    Ma, Boqiang

    1990-05-01

    A two-level convolution formula for the nuclear structure function is derived in considering the nucleus as a composite system of baryon-mesons which are also composite systems of quark-gluons again. The results show that the European Muon Colaboration effect can not be explained by the nuclear effects as nucleon Fermi motion and nuclear binding contributions.

  20. Two-level convolution formula for nuclear structure function

    International Nuclear Information System (INIS)

    Ma Boqiang

    1990-01-01

    A two-level convolution formula for the nuclear structure function is derived in considering the nucleus as a composite system of baryon-mesons which are also composite systems of quark-gluons again. The results show that the European Muon Colaboration effect can not be explained by the nuclear effects as nucleon Fermi motion and nuclear binding contributions

  1. Plant species classification using deep convolutional neural network

    DEFF Research Database (Denmark)

    Dyrmann, Mads; Karstoft, Henrik; Midtiby, Henrik Skov

    2016-01-01

    Information on which weed species are present within agricultural fields is important for site specific weed management. This paper presents a method that is capable of recognising plant species in colour images by using a convolutional neural network. The network is built from scratch trained an...

  2. Distal splenorenal shunt with partial spleen resection

    Directory of Open Access Journals (Sweden)

    Gajin Predrag

    2007-01-01

    Full Text Available Introduction: Hypersplenism is a common complication of portal hypertension. Cytopenia in hypersplenism is predominantly caused by splenomegaly. Distal splenorenal shunt (Warren with partial spleen resection is an original surgical technique that regulates cytopenia by reduction of the enlarged spleen. Objective. The aim of our study was to present the advantages of distal splenorenal shunt (Warren with partial spleen resection comparing morbidity and mortality in a group of patients treated by distal splenorenal shunt with partial spleen resection with a group of patients treated only by a distal splenorenal shunt. Method. From 1995 to 2003, 41 patients with portal hypertension were surgically treated due to hypersplenism and oesophageal varices. The first group consisted of 20 patients (11 male, mean age 42.3 years who were treated by distal splenorenal shunt with partial spleen resection. The second group consisted of 21 patients (13 male, mean age 49.4 years that were treated by distal splenorenal shunt only. All patients underwent endoscopy and assessment of oesophageal varices. The size of the spleen was evaluated by ultrasound, CT or by scintigraphy. Angiography was performed in all patients. The platelet and white blood cell count and haemoglobin level were registered. Postoperatively, we noted blood transfusion, complications and total hospital stay. Follow-up period was 12 months, with first checkup after one month. Results In the first group, only one patient had splenomegaly postoperatively (5%, while in the second group there were 13 patients with splenomegaly (68%. Before surgery, the mean platelet count in the first group was 51.6±18.3x109/l, to 118.6±25.4x109/l postoperatively. The mean platelet count in the second group was 67.6±22.8x109/l, to 87.8±32.1x109/l postoperatively. Concerning postoperative splenomegaly, statistically significant difference was noted between the first and the second group (p<0.05. Comparing the

  3. Contemporary Management of Primary Distal Urethral Cancer.

    Science.gov (United States)

    Traboulsi, Samer L; Witjes, Johannes Alfred; Kassouf, Wassim

    2016-11-01

    Primary urethral cancer is one of the rare urologic tumors. Distal urethral tumors are usually less advanced at diagnosis compared with proximal tumors and have a good prognosis if treated appropriately. Low-stage distal tumors can be managed successfully with a surgical approach in men or radiation therapy in women. There are no clear-cut indications for the choice of the most appropriate treatment modality. Organ-preserving modalities have shown effective and should be used whenever they do not compromise the oncological safety to decrease the physical and psychological trauma of dismemberment or loss of sexual/urinary function. Copyright © 2016 Elsevier Inc. All rights reserved.

  4. Spontaneous distal rupture of the plantar fascia.

    Science.gov (United States)

    Gitto, Salvatore; Draghi, Ferdinando

    2018-07-01

    Spontaneous ruptures of the plantar fascia are uncommon injuries. They typically occur at its calcaneal insertion and usually represent a complication of plantar fasciitis and local treatment with steroid injections. In contrast, distal ruptures commonly result from traumatic injuries. We describe the case of a spontaneous distal rupture of the plantar fascia in a 48-year-old woman with a low level of physical activity and no history of direct injury to the foot, plantar fasciitis, or steroid injections. © 2017 Wiley Periodicals, Inc.

  5. Torsion of wandering spleen and distal pancreas

    International Nuclear Information System (INIS)

    Sheflin, J.R.; Lee, C.M.; Kretchmar, K.A.

    1984-01-01

    Wandering spleen is the term applied to the condition in which a long pedicle allows the spleen to lie in an abnormal location. Torsion of a wandering spleen is an unusual cause of an acute abdomen and is rarely diagnosed preoperatively. Associated torsion of the distal pancreas is even more uncommon. The authors describe a patient with torsion of a wandering spleen and distal pancreas, who was correctly diagnosed, and define the merits of the imaging methods used. The initial examination should be 99 /sup m/Tc-sulfur colloid liner-spleen scanning

  6. An Implementation of Error Minimization Data Transmission in OFDM using Modified Convolutional Code

    Directory of Open Access Journals (Sweden)

    Hendy Briantoro

    2016-04-01

    Full Text Available This paper presents about error minimization in OFDM system. In conventional system, usually using channel coding such as BCH Code or Convolutional Code. But, performance BCH Code or Convolutional Code is not good in implementation of OFDM System. Error bits of OFDM system without channel coding is 5.77%. Then, we used convolutional code with code rate 1/2, it can reduce error bitsonly up to 3.85%. So, we proposed OFDM system with Modified Convolutional Code. In this implementation, we used Software Define Radio (SDR, namely Universal Software Radio Peripheral (USRP NI 2920 as the transmitter and receiver. The result of OFDM system using Modified Convolutional Code with code rate is able recover all character received so can decrease until 0% error bit. Increasing performance of Modified Convolutional Code is about 1 dB in BER of 10-4 from BCH Code and Convolutional Code. So, performance of Modified Convolutional better than BCH Code or Convolutional Code. Keywords: OFDM, BCH Code, Convolutional Code, Modified Convolutional Code, SDR, USRP

  7. Endovascular treatment of ruptured distal posterior inferior ...

    African Journals Online (AJOL)

    2014-03-01

    Mar 1, 2014 ... there are lots of reports regarding the interventional therapy of the artery aneurysms (including proximal and distal), all of which are considered to be safe and effective. All the incidences of interventional-related complications are below 10% and there is no report of injury of lower cranial nerves [11, 12].

  8. Urethral mobilization and advancement for distal hypospadias ...

    African Journals Online (AJOL)

    Background/purpose Despite the existence of numerous techniques for the repair of distal penile hypospadias, none of them is completely satisfactory. Advancing the urethra without mobilization for repair of glanular hypospadias has the advantage of avoiding a common problem occurring with other techniques: ...

  9. Relative biological effectiveness (RBE) and distal edge effects of proton radiation on early damage in vivo

    DEFF Research Database (Denmark)

    Sørensen, Brita Singers; Bassler, Niels; Nielsen, Steffen

    2017-01-01

    of the SOBP to behind the distal dose fall-off. Irradiations were performed with the same dose plan at all positions, corresponding to a dose of 31.25 Gy in the middle of the SOBP. Endpoint of the study was early skin damage of the foot, assessed by a mouse foot skin scoring system. RESULTS: The MDD50 values......, where LETd,z =1 was 3.3 keV/μm. CONCLUSIONS: Although there is a need to expand the current study to be able to calculate an exact enhancement ratio, an enhanced biological effect in vivo for early skin damage in the distal edge was demonstrated....

  10. Combining morphometric features and convolutional networks fusion for glaucoma diagnosis

    Science.gov (United States)

    Perdomo, Oscar; Arevalo, John; González, Fabio A.

    2017-11-01

    Glaucoma is an eye condition that leads to loss of vision and blindness. Ophthalmoscopy exam evaluates the shape, color and proportion between the optic disc and physiologic cup, but the lack of agreement among experts is still the main diagnosis problem. The application of deep convolutional neural networks combined with automatic extraction of features such as: the cup-to-disc distance in the four quadrants, the perimeter, area, eccentricity, the major radio, the minor radio in optic disc and cup, in addition to all the ratios among the previous parameters may help with a better automatic grading of glaucoma. This paper presents a strategy to merge morphological features and deep convolutional neural networks as a novel methodology to support the glaucoma diagnosis in eye fundus images.

  11. Improving deep convolutional neural networks with mixed maxout units.

    Directory of Open Access Journals (Sweden)

    Hui-Zhen Zhao

    Full Text Available Motivated by insights from the maxout-units-based deep Convolutional Neural Network (CNN that "non-maximal features are unable to deliver" and "feature mapping subspace pooling is insufficient," we present a novel mixed variant of the recently introduced maxout unit called a mixout unit. Specifically, we do so by calculating the exponential probabilities of feature mappings gained by applying different convolutional transformations over the same input and then calculating the expected values according to their exponential probabilities. Moreover, we introduce the Bernoulli distribution to balance the maximum values with the expected values of the feature mappings subspace. Finally, we design a simple model to verify the pooling ability of mixout units and a Mixout-units-based Network-in-Network (NiN model to analyze the feature learning ability of the mixout models. We argue that our proposed units improve the pooling ability and that mixout models can achieve better feature learning and classification performance.

  12. Convolutional over Recurrent Encoder for Neural Machine Translation

    Directory of Open Access Journals (Sweden)

    Dakwale Praveen

    2017-06-01

    Full Text Available Neural machine translation is a recently proposed approach which has shown competitive results to traditional MT approaches. Standard neural MT is an end-to-end neural network where the source sentence is encoded by a recurrent neural network (RNN called encoder and the target words are predicted using another RNN known as decoder. Recently, various models have been proposed which replace the RNN encoder with a convolutional neural network (CNN. In this paper, we propose to augment the standard RNN encoder in NMT with additional convolutional layers in order to capture wider context in the encoder output. Experiments on English to German translation demonstrate that our approach can achieve significant improvements over a standard RNN-based baseline.

  13. Infimal Convolution Regularisation Functionals of BV and Lp Spaces

    KAUST Repository

    Burger, Martin

    2016-02-03

    We study a general class of infimal convolution type regularisation functionals suitable for applications in image processing. These functionals incorporate a combination of the total variation seminorm and Lp norms. A unified well-posedness analysis is presented and a detailed study of the one-dimensional model is performed, by computing exact solutions for the corresponding denoising problem and the case p=2. Furthermore, the dependency of the regularisation properties of this infimal convolution approach to the choice of p is studied. It turns out that in the case p=2 this regulariser is equivalent to the Huber-type variant of total variation regularisation. We provide numerical examples for image decomposition as well as for image denoising. We show that our model is capable of eliminating the staircasing effect, a well-known disadvantage of total variation regularisation. Moreover as p increases we obtain almost piecewise affine reconstructions, leading also to a better preservation of hat-like structures.

  14. 3D Medical Image Interpolation Based on Parametric Cubic Convolution

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    In the process of display, manipulation and analysis of biomedical image data, they usually need to be converted to data of isotropic discretization through the process of interpolation, while the cubic convolution interpolation is widely used due to its good tradeoff between computational cost and accuracy. In this paper, we present a whole concept for the 3D medical image interpolation based on cubic convolution, and the six methods, with the different sharp control parameter, which are formulated in details. Furthermore, we also give an objective comparison for these methods using data sets with the different slice spacing. Each slice in these data sets is estimated by each interpolation method and compared with the original slice using three measures: mean-squared difference, number of sites of disagreement, and largest difference. According to the experimental results, we present a recommendation for 3D medical images under the different situations in the end.

  15. Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks.

    Science.gov (United States)

    Yu, Haiyang; Wu, Zhihai; Wang, Shuqin; Wang, Yunpeng; Ma, Xiaolei

    2017-06-26

    Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs), for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs) and long short-term memory (LSTM) neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction.

  16. Deep learning for steganalysis via convolutional neural networks

    Science.gov (United States)

    Qian, Yinlong; Dong, Jing; Wang, Wei; Tan, Tieniu

    2015-03-01

    Current work on steganalysis for digital images is focused on the construction of complex handcrafted features. This paper proposes a new paradigm for steganalysis to learn features automatically via deep learning models. We novelly propose a customized Convolutional Neural Network for steganalysis. The proposed model can capture the complex dependencies that are useful for steganalysis. Compared with existing schemes, this model can automatically learn feature representations with several convolutional layers. The feature extraction and classification steps are unified under a single architecture, which means the guidance of classification can be used during the feature extraction step. We demonstrate the effectiveness of the proposed model on three state-of-theart spatial domain steganographic algorithms - HUGO, WOW, and S-UNIWARD. Compared to the Spatial Rich Model (SRM), our model achieves comparable performance on BOSSbase and the realistic and large ImageNet database.

  17. ID card number detection algorithm based on convolutional neural network

    Science.gov (United States)

    Zhu, Jian; Ma, Hanjie; Feng, Jie; Dai, Leiyan

    2018-04-01

    In this paper, a new detection algorithm based on Convolutional Neural Network is presented in order to realize the fast and convenient ID information extraction in multiple scenarios. The algorithm uses the mobile device equipped with Android operating system to locate and extract the ID number; Use the special color distribution of the ID card, select the appropriate channel component; Use the image threshold segmentation, noise processing and morphological processing to take the binary processing for image; At the same time, the image rotation and projection method are used for horizontal correction when image was tilting; Finally, the single character is extracted by the projection method, and recognized by using Convolutional Neural Network. Through test shows that, A single ID number image from the extraction to the identification time is about 80ms, the accuracy rate is about 99%, It can be applied to the actual production and living environment.

  18. Trajectory Generation Method with Convolution Operation on Velocity Profile

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Geon [Hanyang Univ., Seoul (Korea, Republic of); Kim, Doik [Korea Institute of Science and Technology, Daejeon (Korea, Republic of)

    2014-03-15

    The use of robots is no longer limited to the field of industrial robots and is now expanding into the fields of service and medical robots. In this light, a trajectory generation method that can respond instantaneously to the external environment is strongly required. Toward this end, this study proposes a method that enables a robot to change its trajectory in real-time using a convolution operation. The proposed method generates a trajectory in real time and satisfies the physical limits of the robot system such as acceleration and velocity limit. Moreover, a new way to improve the previous method, which generates inefficient trajectories in some cases owing to the characteristics of the trapezoidal shape of trajectories, is proposed by introducing a triangle shape. The validity and effectiveness of the proposed method is shown through a numerical simulation and a comparison with the previous convolution method.

  19. Airplane detection in remote sensing images using convolutional neural networks

    Science.gov (United States)

    Ouyang, Chao; Chen, Zhong; Zhang, Feng; Zhang, Yifei

    2018-03-01

    Airplane detection in remote sensing images remains a challenging problem and has also been taking a great interest to researchers. In this paper we propose an effective method to detect airplanes in remote sensing images using convolutional neural networks. Deep learning methods show greater advantages than the traditional methods with the rise of deep neural networks in target detection, and we give an explanation why this happens. To improve the performance on detection of airplane, we combine a region proposal algorithm with convolutional neural networks. And in the training phase, we divide the background into multi classes rather than one class, which can reduce false alarms. Our experimental results show that the proposed method is effective and robust in detecting airplane.

  20. Rock images classification by using deep convolution neural network

    Science.gov (United States)

    Cheng, Guojian; Guo, Wenhui

    2017-08-01

    Granularity analysis is one of the most essential issues in authenticate under microscope. To improve the efficiency and accuracy of traditional manual work, an convolutional neural network based method is proposed for granularity analysis from thin section image, which chooses and extracts features from image samples while build classifier to recognize granularity of input image samples. 4800 samples from Ordos basin are used for experiments under colour spaces of HSV, YCbCr and RGB respectively. On the test dataset, the correct rate in RGB colour space is 98.5%, and it is believable in HSV and YCbCr colour space. The results show that the convolution neural network can classify the rock images with high reliability.

  1. User-generated content curation with deep convolutional neural networks

    OpenAIRE

    Tous Liesa, Rubén; Wust, Otto; Gómez, Mauro; Poveda, Jonatan; Elena, Marc; Torres Viñals, Jordi; Makni, Mouna; Ayguadé Parra, Eduard

    2016-01-01

    In this paper, we report a work consisting in using deep convolutional neural networks (CNNs) for curating and filtering photos posted by social media users (Instagram and Twitter). The final goal is to facilitate searching and discovering user-generated content (UGC) with potential value for digital marketing tasks. The images are captured in real time and automatically annotated with multiple CNNs. Some of the CNNs perform generic object recognition tasks while others perform what we call v...

  2. A quantum algorithm for Viterbi decoding of classical convolutional codes

    OpenAIRE

    Grice, Jon R.; Meyer, David A.

    2014-01-01

    We present a quantum Viterbi algorithm (QVA) with better than classical performance under certain conditions. In this paper the proposed algorithm is applied to decoding classical convolutional codes, for instance; large constraint length $Q$ and short decode frames $N$. Other applications of the classical Viterbi algorithm where $Q$ is large (e.g. speech processing) could experience significant speedup with the QVA. The QVA exploits the fact that the decoding trellis is similar to the butter...

  3. Abnormality Detection in Mammography using Deep Convolutional Neural Networks

    OpenAIRE

    Xi, Pengcheng; Shu, Chang; Goubran, Rafik

    2018-01-01

    Breast cancer is the most common cancer in women worldwide. The most common screening technology is mammography. To reduce the cost and workload of radiologists, we propose a computer aided detection approach for classifying and localizing calcifications and masses in mammogram images. To improve on conventional approaches, we apply deep convolutional neural networks (CNN) for automatic feature learning and classifier building. In computer-aided mammography, deep CNN classifiers cannot be tra...

  4. Quantifying Translation-Invariance in Convolutional Neural Networks

    OpenAIRE

    Kauderer-Abrams, Eric

    2017-01-01

    A fundamental problem in object recognition is the development of image representations that are invariant to common transformations such as translation, rotation, and small deformations. There are multiple hypotheses regarding the source of translation invariance in CNNs. One idea is that translation invariance is due to the increasing receptive field size of neurons in successive convolution layers. Another possibility is that invariance is due to the pooling operation. We develop a simple ...

  5. Fast convolutional sparse coding using matrix inversion lemma

    Czech Academy of Sciences Publication Activity Database

    Šorel, Michal; Šroubek, Filip

    2016-01-01

    Roč. 55, č. 1 (2016), s. 44-51 ISSN 1051-2004 R&D Projects: GA ČR GA13-29225S Institutional support: RVO:67985556 Keywords : Convolutional sparse coding * Feature learning * Deconvolution networks * Shift-invariant sparse coding Subject RIV: JD - Computer Applications, Robotics Impact factor: 2.337, year: 2016 http://library.utia.cas.cz/separaty/2016/ZOI/sorel-0459332.pdf

  6. Learning Convolutional Text Representations for Visual Question Answering

    OpenAIRE

    Wang, Zhengyang; Ji, Shuiwang

    2017-01-01

    Visual question answering is a recently proposed artificial intelligence task that requires a deep understanding of both images and texts. In deep learning, images are typically modeled through convolutional neural networks, and texts are typically modeled through recurrent neural networks. While the requirement for modeling images is similar to traditional computer vision tasks, such as object recognition and image classification, visual question answering raises a different need for textual...

  7. Shallow and deep convolutional networks for saliency prediction

    OpenAIRE

    Pan, Junting; Sayrol Clols, Elisa; Giró Nieto, Xavier; McGuinness, Kevin; O'Connor, Noel

    2016-01-01

    The prediction of salient areas in images has been traditionally addressed with hand-crafted features based on neuroscience principles. This paper, however, addresses the problem with a completely data-driven approach by training a convolutional neural network (convnet). The learning process is formulated as a minimization of a loss function that measures the Euclidean distance of the predicted saliency map with the provided ground truth. The recent publication of large datasets of saliency p...

  8. Production and reception of meaningful sound in Foville's 'encompassing convolution'.

    Science.gov (United States)

    Schiller, F

    1999-04-01

    In the history of neurology. Achille Louis Foville (1799-1879) is a name deserving to be remembered. In the course of time, his circonvolution d'enceinte of 1844 (surrounding the Sylvian fissure) became the 'convolution encompassing' every aspect of aphasiology, including amusia, ie., the localization in a coherent semicircle of semicircle of cerebral cortext serving the production and perception of language, song and instrumental music in health and disease.

  9. Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks

    OpenAIRE

    Shen, Li; Lin, Zhouchen; Huang, Qingming

    2015-01-01

    Learning deeper convolutional neural networks becomes a tendency in recent years. However, many empirical evidences suggest that performance improvement cannot be gained by simply stacking more layers. In this paper, we consider the issue from an information theoretical perspective, and propose a novel method Relay Backpropagation, that encourages the propagation of effective information through the network in training stage. By virtue of the method, we achieved the first place in ILSVRC 2015...

  10. Maximum likelihood convolutional decoding (MCD) performance due to system losses

    Science.gov (United States)

    Webster, L.

    1976-01-01

    A model for predicting the computational performance of a maximum likelihood convolutional decoder (MCD) operating in a noisy carrier reference environment is described. This model is used to develop a subroutine that will be utilized by the Telemetry Analysis Program to compute the MCD bit error rate. When this computational model is averaged over noisy reference phase errors using a high-rate interpolation scheme, the results are found to agree quite favorably with experimental measurements.

  11. General Dirichlet Series, Arithmetic Convolution Equations and Laplace Transforms

    Czech Academy of Sciences Publication Activity Database

    Glöckner, H.; Lucht, L.G.; Porubský, Štefan

    2009-01-01

    Roč. 193, č. 2 (2009), s. 109-129 ISSN 0039-3223 R&D Projects: GA ČR GA201/07/0191 Institutional research plan: CEZ:AV0Z10300504 Keywords : arithmetic function * Dirichlet convolution * polynomial equation * analytic equation * topological algebra * holomorphic functional calculus * implicit function theorem * Laplace transform * semigroup * complex measure Subject RIV: BA - General Mathematics Impact factor: 0.645, year: 2009 http://arxiv.org/abs/0712.3172

  12. Solving singular convolution equations using the inverse fast Fourier transform

    Czech Academy of Sciences Publication Activity Database

    Krajník, E.; Montesinos, V.; Zizler, P.; Zizler, Václav

    2012-01-01

    Roč. 57, č. 5 (2012), s. 543-550 ISSN 0862-7940 R&D Projects: GA AV ČR IAA100190901 Institutional research plan: CEZ:AV0Z10190503 Keywords : singular convolution equations * fast Fourier transform * tempered distribution Subject RIV: BA - General Mathematics Impact factor: 0.222, year: 2012 http://www.springerlink.com/content/m8437t3563214048/

  13. CICAAR - Convolutive ICA with an Auto-Regressive Inverse Model

    DEFF Research Database (Denmark)

    Dyrholm, Mads; Hansen, Lars Kai

    2004-01-01

    We invoke an auto-regressive IIR inverse model for convolutive ICA and derive expressions for the likelihood and its gradient. We argue that optimization will give a stable inverse. When there are more sensors than sources the mixing model parameters are estimated in a second step by least square...... estimation. We demonstrate the method on synthetic data and finally separate speech and music in a real room recording....

  14. AFM tip-sample convolution effects for cylinder protrusions

    Science.gov (United States)

    Shen, Jian; Zhang, Dan; Zhang, Fei-Hu; Gan, Yang

    2017-11-01

    A thorough understanding about the AFM tip geometry dependent artifacts and tip-sample convolution effect is essential for reliable AFM topographic characterization and dimensional metrology. Using rigid sapphire cylinder protrusions (diameter: 2.25 μm, height: 575 nm) as the model system, a systematic and quantitative study about the imaging artifacts of four types of tips-two different pyramidal tips, one tetrahedral tip and one super sharp whisker tip-is carried out through comparing tip geometry dependent variations in AFM topography of cylinders and constructing the rigid tip-cylinder convolution models. We found that the imaging artifacts and the tip-sample convolution effect are critically related to the actual inclination of the working cantilever, the tip geometry, and the obstructive contacts between the working tip's planes/edges and the cylinder. Artifact-free images can only be obtained provided that all planes and edges of the working tip are steeper than the cylinder sidewalls. The findings reported here will contribute to reliable AFM characterization of surface features of micron or hundreds of nanometers in height that are frequently met in semiconductor, biology and materials fields.

  15. Edgeworth Expansion Based Model for the Convolutional Noise pdf

    Directory of Open Access Journals (Sweden)

    Yonatan Rivlin

    2014-01-01

    Full Text Available Recently, the Edgeworth expansion up to order 4 was used to represent the convolutional noise probability density function (pdf in the conditional expectation calculations where the source pdf was modeled with the maximum entropy density approximation technique. However, the applied Lagrange multipliers were not the appropriate ones for the chosen model for the convolutional noise pdf. In this paper we use the Edgeworth expansion up to order 4 and up to order 6 to model the convolutional noise pdf. We derive the appropriate Lagrange multipliers, thus obtaining new closed-form approximated expressions for the conditional expectation and mean square error (MSE as a byproduct. Simulation results indicate hardly any equalization improvement with Edgeworth expansion up to order 4 when using optimal Lagrange multipliers over a nonoptimal set. In addition, there is no justification for using the Edgeworth expansion up to order 6 over the Edgeworth expansion up to order 4 for the 16QAM and easy channel case. However, Edgeworth expansion up to order 6 leads to improved equalization performance compared to the Edgeworth expansion up to order 4 for the 16QAM and hard channel case as well as for the case where the 64QAM is sent via an easy channel.

  16. Traffic sign recognition based on deep convolutional neural network

    Science.gov (United States)

    Yin, Shi-hao; Deng, Ji-cai; Zhang, Da-wei; Du, Jing-yuan

    2017-11-01

    Traffic sign recognition (TSR) is an important component of automated driving systems. It is a rather challenging task to design a high-performance classifier for the TSR system. In this paper, we propose a new method for TSR system based on deep convolutional neural network. In order to enhance the expression of the network, a novel structure (dubbed block-layer below) which combines network-in-network and residual connection is designed. Our network has 10 layers with parameters (block-layer seen as a single layer): the first seven are alternate convolutional layers and block-layers, and the remaining three are fully-connected layers. We train our TSR network on the German traffic sign recognition benchmark (GTSRB) dataset. To reduce overfitting, we perform data augmentation on the training images and employ a regularization method named "dropout". The activation function we employ in our network adopts scaled exponential linear units (SELUs), which can induce self-normalizing properties. To speed up the training, we use an efficient GPU to accelerate the convolutional operation. On the test dataset of GTSRB, we achieve the accuracy rate of 99.67%, exceeding the state-of-the-art results.

  17. Face recognition via Gabor and convolutional neural network

    Science.gov (United States)

    Lu, Tongwei; Wu, Menglu; Lu, Tao

    2018-04-01

    In recent years, the powerful feature learning and classification ability of convolutional neural network have attracted widely attention. Compared with the deep learning, the traditional machine learning algorithm has a good explanatory which deep learning does not have. Thus, In this paper, we propose a method to extract the feature of the traditional algorithm as the input of convolution neural network. In order to reduce the complexity of the network, the kernel function of Gabor wavelet is used to extract the feature from different position, frequency and direction of target image. It is sensitive to edge of image which can provide good direction and scale selection. The extraction of the image from eight directions on a scale are as the input of network that we proposed. The network have the advantage of weight sharing and local connection and texture feature of the input image can reduce the influence of facial expression, gesture and illumination. At the same time, we introduced a layer which combined the results of the pooling and convolution can extract deeper features. The training network used the open source caffe framework which is beneficial to feature extraction. The experiment results of the proposed method proved that the network structure effectively overcame the barrier of illumination and had a good robustness as well as more accurate and rapid than the traditional algorithm.

  18. Nuclear norm regularized convolutional Max Pos@Top machine

    KAUST Repository

    Li, Qinfeng

    2016-11-18

    In this paper, we propose a novel classification model for the multiple instance data, which aims to maximize the number of positive instances ranked before the top-ranked negative instances. This method belongs to a recently emerged performance, named as Pos@Top. Our proposed classification model has a convolutional structure that is composed by four layers, i.e., the convolutional layer, the activation layer, the max-pooling layer and the full connection layer. In this paper, we propose an algorithm to learn the convolutional filters and the full connection weights to maximize the Pos@Top measure over the training set. Also, we try to minimize the rank of the filter matrix to explore the low-dimensional space of the instances in conjunction with the classification results. The rank minimization is conducted by the nuclear norm minimization of the filter matrix. In addition, we develop an iterative algorithm to solve the corresponding problem. We test our method on several benchmark datasets. The experimental results show the superiority of our method compared with other state-of-the-art Pos@Top maximization methods.

  19. Distal displacement of the maxilla and the upper first molar.

    Science.gov (United States)

    Baumrind, S; Molthen, R; West, E E; Miller, D M

    1979-06-01

    Data from a sample of 198 Class II cases treated with various appliances which deliver distally directed forces to the maxilla were examined to determine the frequency of absolute distal displacement of the upper first molar and of the maxilla. Analysis revealed that such distal displacement is possible and that it is, in fact, a frequent finding following treatment. Long-range stability of distal displacement was not assessed.

  20. Distal radioulnar joint: functional anatomy, including pathomechanics.

    Science.gov (United States)

    Haugstvedt, J R; Langer, M F; Berger, R A

    2017-05-01

    The distal radioulnar joint allows the human to rotate the forearm to place the hand in a desired position to perform different tasks, without interfering with the grasping function of the hand. The ulna is the stable part of the forearm around which the radius rotates; the stability of the distal radioulnar joint is provided by the interaction between ligaments, muscles and bones. The stabilizing structures are the triangular fibrocartilage complex, the ulnocarpal ligament complex, the extensor carpi ulnaris tendon and tendon sheath, the pronator quadratus, the interosseous membrane and ligament, the bone itself and the joint capsule. The purpose of this review article is to present and illustrate the current understanding of the functional anatomy and pathomechanics of this joint.

  1. Distal Stressors and Depression among Homeless Men

    OpenAIRE

    Coohey, Carol; Easton, Scott D.

    2016-01-01

    Depression is a common problem among homeless men that may interfere with functional tasks, such as securing stable housing, obtaining employment, and accessing health services. Previous research on depression among homeless men has largely focused on current psychosocial resources, substance abuse, and past victimization. Guided by Ensel and Lin’s life course stress process model, the authors examined whether distal stressors, including victimization and exposure to parent problems in childh...

  2. Recognition of prokaryotic and eukaryotic promoters using convolutional deep learning neural networks

    KAUST Repository

    Umarov, Ramzan

    2017-02-03

    Accurate computational identification of promoters remains a challenge as these key DNA regulatory regions have variable structures composed of functional motifs that provide gene-specific initiation of transcription. In this paper we utilize Convolutional Neural Networks (CNN) to analyze sequence characteristics of prokaryotic and eukaryotic promoters and build their predictive models. We trained a similar CNN architecture on promoters of five distant organisms: human, mouse, plant (Arabidopsis), and two bacteria (Escherichia coli and Bacillus subtilis). We found that CNN trained on sigma70 subclass of Escherichia coli promoter gives an excellent classification of promoters and non-promoter sequences (Sn = 0.90, Sp = 0.96, CC = 0.84). The Bacillus subtilis promoters identification CNN model achieves Sn = 0.91, Sp = 0.95, and CC = 0.86. For human, mouse and Arabidopsis promoters we employed CNNs for identification of two well-known promoter classes (TATA and non-TATA promoters). CNN models nicely recognize these complex functional regions. For human promoters Sn/Sp/CC accuracy of prediction reached 0.95/0.98/0,90 on TATA and 0.90/0.98/0.89 for non-TATA promoter sequences, respectively. For Arabidopsis we observed Sn/Sp/CC 0.95/0.97/0.91 (TATA) and 0.94/0.94/0.86 (non-TATA) promoters. Thus, the developed CNN models, implemented in CNNProm program, demonstrated the ability of deep learning approach to grasp complex promoter sequence characteristics and achieve significantly higher accuracy compared to the previously developed promoter prediction programs. We also propose random substitution procedure to discover positionally conserved promoter functional elements. As the suggested approach does not require knowledge of any specific promoter features, it can be easily extended to identify promoters and other complex functional regions in sequences of many other and especially newly sequenced genomes. The CNNProm program is available to run at web server http://www.softberry.com.

  3. Fractures of the distal phalanx in the horse

    International Nuclear Information System (INIS)

    Yovich, J.V.

    1989-01-01

    Fractures of the distal phalanx are an important cause of lameness referable to the foot. Depending on the fracture configuration and articular involvement, conservative or surgical treatment may be required. Fractures of the distal phalanx have been divided into six categories based on fracture configuration. Discussion of clinical features, management, and prognosis for horses with distal phalangeal fractures is presented for each fracture type

  4. Nonunions of the distal tibia treated by reamed intramedullary nailing

    NARCIS (Netherlands)

    Richmond, Jeffrey; Colleran, Kevin; Borens, Olivier; Kloen, Peter; Helfet, David L.

    2004-01-01

    The purpose of this study is to determine the efficacy of reamed intramedullary nailing in the treatment of nonunions of the distal one-fourth of the tibia. Nonunions of the distal tibia are particularly difficult to treat given the short distal segment, the proximity to the ankle joint, and the

  5. Management and treatment of distal ulcerative colitis

    Directory of Open Access Journals (Sweden)

    Andrea Calafiore

    2013-12-01

    Full Text Available Ulcerative colitis (UC is a chronic inflammatory condition that is confined to the colonic mucosa. Its main symptoms include diarrhea, rectal bleeding and abdominal pain. Approximately two-thirds of UC patients have disease confined distal to the splenic flexure, which can be treated effectively with topical therapy. This means the active drug can be delivered directly to the site of inflammation, limiting the systemic absorption and potential side effects. Topical treatment with aminosalicylates is the most effective approach in the treatment of these forms, provided that the formulation reaches the upper margin of the disease. Given this, the suppository formulation is the treatment of choice for proctitis and distal sigmoiditis. Thanks to their proximal spread, enemas, foams and gels represent the treatment of choice for proctosigmoiditis and for distal ulcerative colitis. Oral aminosalicylates are less effective than topical therapies in patients with active disease, while the combination of topical and oral treatment is more effective in patients refractory to topical or oral mono-therapy. Topically administered aminosalicylates play an important role in the maintenance of remission, but the long-term adhesion to therapy is poor. For this reason, the oral formulation is the first-line therapy in the maintenance of remission. Refractory patients can be treated with topical steroids or systemic steroids and TNF-alpha inhibitors in severe forms.

  6. Distal Embolic Protection for Renal Arterial Interventions

    International Nuclear Information System (INIS)

    Dubel, Gregory J.; Murphy, Timothy P.

    2008-01-01

    Distal or embolic protection has intuitive appeal for its potential to prevent embolization of materials generated during interventional procedures. Distal protection devices (DPDs) have been most widely used in the coronary and carotid vascular beds, where they have demonstrated the ability to trap embolic materials and, in some cases, to reduce complications. Given the frequency of chronic kidney disease in patients with renal artery stenosis undergoing stent placement, it is reasonable to propose that these devices may play an important role in limiting distal embolization in the renal vasculature. Careful review of the literature reveals that atheroembolization does occur during renal arterial interventions, although it often goes undetected. Early experience with DPDs in the renal arteries in patients with suitable anatomy suggests retrieval of embolic materials in approximately 71% of cases and renal functional improvement/stabilization in 98% of cases. The combination of platelet inhibition and a DPD may provide even greater benefit. Given the critical importance of renal functional preservation, it follows that everything that can be done to prevent atheroembolism should be undertaken including the use of DPDs when anatomically feasible. The data available at this time support a beneficial role for these devices

  7. Convolution equations on lattices: periodic solutions with values in a prime characteristic field

    OpenAIRE

    Zaidenberg, Mikhail

    2006-01-01

    These notes are inspired by the theory of cellular automata. A linear cellular automaton on a lattice of finite rank or on a toric grid is a discrete dinamical system generated by a convolution operator with kernel concentrated in the nearest neighborhood of the origin. In the present paper we deal with general convolution operators. We propose an approach via harmonic analysis which works over a field of positive characteristic. It occurs that a standard spectral problem for a convolution op...

  8. Performance Analysis of DPSK Signals with Selection Combining and Convolutional Coding in Fading Channel

    National Research Council Canada - National Science Library

    Ong, Choon

    1998-01-01

    The performance analysis of a differential phase shift keyed (DPSK) communications system, operating in a Rayleigh fading environment, employing convolutional coding and diversity processing is presented...

  9. Model selection for convolutive ICA with an application to spatiotemporal analysis of EEG

    DEFF Research Database (Denmark)

    Dyrholm, Mads; Makeig, S.; Hansen, Lars Kai

    2007-01-01

    We present a new algorithm for maximum likelihood convolutive independent component analysis (ICA) in which components are unmixed using stable autoregressive filters determined implicitly by estimating a convolutive model of the mixing process. By introducing a convolutive mixing model...... for the components, we show how the order of the filters in the model can be correctly detected using Bayesian model selection. We demonstrate a framework for deconvolving a subspace of independent components in electroencephalography (EEG). Initial results suggest that in some cases, convolutive mixing may...

  10. Maxillary molar distalization with the dual-force distalizer supported by mini-implants: a clinical study.

    Science.gov (United States)

    Oberti, Giovanni; Villegas, Carlos; Ealo, Martha; Palacio, John Camilo; Baccetti, Tiziano

    2009-03-01

    The objective of this prospective study was to describe the clinical effects of a bone-supported molar distalizing appliance, the dual-force distalizer. The study group included 16 patients (mean age, 14.3 years) with Class II molar relationships. Study models and lateral cephalograms were taken before and after the distalizing movement to record significant dental and skeletal changes (Wilcoxon test). The average distalization time was 5 months, with a movement rate of 1.2 mm per month; the distalization amounts were 5.9 +/- 1.72 mm at the crown level and 4.4 +/- 1.41 mm at the furcation level. The average molar inclination was 5.6 degrees +/- 3.7 degrees ; this was less than the amount of inclination generated by bone-supported appliances that use single distalizing forces. The correlation between inclination and distalization was not significant, indicating predominantly bodily movement. The teeth anterior to the first molar moved distally also; the second premolars distalized an average of 4.26 mm, and the incisors retruded by 0.53 mm. The dual-force distalizer is a valid alternative distalizing appliance that generates controlled molar distalization with a good rate of movement and no loss of anchorage.

  11. Robotic distal pancreatectomy versus conventional laparoscopic distal pancreatectomy: a comparative study for short-term outcomes.

    Science.gov (United States)

    Lai, Eric C H; Tang, Chung Ngai

    2015-09-01

    Robotic system has been increasingly used in pancreatectomy. However, the effectiveness of this method remains uncertain. This study compared the surgical outcomes between robot-assisted laparoscopic distal pancreatectomy and conventional laparoscopic distal pancreatectomy. During a 15-year period, 35 patients underwent minimally invasive approach of distal pancreatectomy in our center. Seventeen of these patients had robot-assisted laparoscopic approach, and the remaining 18 had conventional laparoscopic approach. Their operative parameters and perioperative outcomes were analyzed retrospectively in a prospective database. The mean operating time in the robotic group (221.4 min) was significantly longer than that in the laparoscopic group (173.6 min) (P = 0.026). Both robotic and conventional laparoscopic groups presented no significant difference in spleen-preservation rate (52.9% vs. 38.9%) (P = 0.505), operative blood loss (100.3 ml vs. 268.3 ml) (P = 0.29), overall morbidity rate (47.1% vs. 38.9%) (P = 0.73), and post-operative hospital stay (11.4 days vs. 14.2 days) (P = 0.46). Both groups also showed no perioperative mortality. Similar outcomes were observed in robotic distal pancreatectomy and conventional laparoscopic approach. However, robotic approach tended to have the advantages of less blood loss and shorter hospital stay. Further studies are necessary to determine the clinical position of robotic distal pancreatectomy.

  12. An innovative technique to distalize maxillary molar using microimplant supported rapid molar distalizer

    Directory of Open Access Journals (Sweden)

    Meenu Goel

    2013-01-01

    Full Text Available Introduction: In recent years, enhancements in implants have made their use possible as a mode of absolute anchorage in orthodontic patients. In this paper, the authors have introduced an innovative technique to unilaterally distalize the upper left 1 st molar to obtain an ideal Class I molar relationship from a Class II existing molar relationship with an indigenous designed distalizer. Clinical Innovation: For effective unilateral diatalization of molar, a novel cantilever sliding jig assembly was utilized with coil spring supported by a buccally placed single micro implant. The results showed 3 mm of bodily distalization with 1 mm of intrusion and 2° of distal tipping of upper left 1 st molar in 1.5 months. Discussion: This appliance is relatively easy to insert, well-tolerated, and requires minimal patient cooperation compared to other present techniques of molar distalization. Moreover, it is particularly useful in cases that are Class II on one side and Class I on the other, with a minor midline discrepancy and nominal overjet. Patient acceptance level was reported to be within patients physiological and comfort limits.

  13. [New anterolateral approach of distal femur for treatment of distal femoral fractures].

    Science.gov (United States)

    Zhang, Bin; Dai, Min; Zou, Fan; Luo, Song; Li, Binhua; Qiu, Ping; Nie, Tao

    2013-11-01

    To assess the effectiveness of the new anterolateral approach of the distal femur for the treatment of distal femoral fractures. Between July 2007 and December 2009, 58 patients with distal femoral fractures were treated by new anterolateral approach of the distal femur in 28 patients (new approach group) and by conventional approach in 30 patients (conventional approach group). There was no significant difference in gender, age, cause of injury, affected side, type of fracture, disease duration, complication, or preoperative intervention (P > 0.05). The operation time, intraoperative blood loss, intraoperative fluoroscopy frequency, hospitalization days, and Hospital for Special Surgery (HSS) score of knee were recorded. Operation was successfully completed in all patients of 2 groups, and healing of incision by first intention was obtained; no vascular and nerves injuries occurred. The operation time and intraoperative fluoroscopy frequency of new approach group were significantly less than those of conventional approach group (P 0.05). All patients were followed up 12-36 months (mean, 19.8 months). Bone union was shown on X-ray films; the fracture healing time was (12.62 +/- 2.34) weeks in the new approach group and was (13.78 +/- 1.94) weeks in the conventional approach group, showing no significant difference (t=2.78, P=0.10). The knee HSS score at last follow-up was 94.4 +/- 4.2 in the new approach group, and was 89.2 +/- 6.0 in the conventional approach group, showing significant difference between 2 groups (t=3.85, P=0.00). New anterolateral approach of the distal femur for distal femoral fractures has the advantages of exposure plenitude, minimal tissue trauma, and early function rehabilitation training so as to enhance the function recovery of knee joint.

  14. Finding strong lenses in CFHTLS using convolutional neural networks

    Science.gov (United States)

    Jacobs, C.; Glazebrook, K.; Collett, T.; More, A.; McCarthy, C.

    2017-10-01

    We train and apply convolutional neural networks, a machine learning technique developed to learn from and classify image data, to Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) imaging for the identification of potential strong lensing systems. An ensemble of four convolutional neural networks was trained on images of simulated galaxy-galaxy lenses. The training sets consisted of a total of 62 406 simulated lenses and 64 673 non-lens negative examples generated with two different methodologies. An ensemble of trained networks was applied to all of the 171 deg2 of the CFHTLS wide field image data, identifying 18 861 candidates including 63 known and 139 other potential lens candidates. A second search of 1.4 million early-type galaxies selected from the survey catalogue as potential deflectors, identified 2465 candidates including 117 previously known lens candidates, 29 confirmed lenses/high-quality lens candidates, 266 novel probable or potential lenses and 2097 candidates we classify as false positives. For the catalogue-based search we estimate a completeness of 21-28 per cent with respect to detectable lenses and a purity of 15 per cent, with a false-positive rate of 1 in 671 images tested. We predict a human astronomer reviewing candidates produced by the system would identify 20 probable lenses and 100 possible lenses per hour in a sample selected by the robot. Convolutional neural networks are therefore a promising tool for use in the search for lenses in current and forthcoming surveys such as the Dark Energy Survey and the Large Synoptic Survey Telescope.

  15. Fast space-varying convolution using matrix source coding with applications to camera stray light reduction.

    Science.gov (United States)

    Wei, Jianing; Bouman, Charles A; Allebach, Jan P

    2014-05-01

    Many imaging applications require the implementation of space-varying convolution for accurate restoration and reconstruction of images. Here, we use the term space-varying convolution to refer to linear operators whose impulse response has slow spatial variation. In addition, these space-varying convolution operators are often dense, so direct implementation of the convolution operator is typically computationally impractical. One such example is the problem of stray light reduction in digital cameras, which requires the implementation of a dense space-varying deconvolution operator. However, other inverse problems, such as iterative tomographic reconstruction, can also depend on the implementation of dense space-varying convolution. While space-invariant convolution can be efficiently implemented with the fast Fourier transform, this approach does not work for space-varying operators. So direct convolution is often the only option for implementing space-varying convolution. In this paper, we develop a general approach to the efficient implementation of space-varying convolution, and demonstrate its use in the application of stray light reduction. Our approach, which we call matrix source coding, is based on lossy source coding of the dense space-varying convolution matrix. Importantly, by coding the transformation matrix, we not only reduce the memory required to store it; we also dramatically reduce the computation required to implement matrix-vector products. Our algorithm is able to reduce computation by approximately factoring the dense space-varying convolution operator into a product of sparse transforms. Experimental results show that our method can dramatically reduce the computation required for stray light reduction while maintaining high accuracy.

  16. Distal protection filter device efficacy with carotid artery stenting: comparison between a distal protection filter and a distal protection balloon.

    Science.gov (United States)

    Iko, Minoru; Tsutsumi, Masanori; Aikawa, Hiroshi; Matsumoto, Yoshihisa; Go, Yoshinori; Nii, Kouhei; Abe, Gorou; Ye, Iwae; Nomoto, Yasuyuki; Kazekawa, Kiyoshi

    2013-01-01

    This retrospective study aimed to compare the effectiveness of the embolization prevention mechanism of two types of embolic protection device (EPD)-a distal protection balloon (DPB) and a distal protection filter (DPF). Subjects were 164 patients scheduled to undergo carotid artery stenting: a DPB was used in 82 cases (DPB group) from April 2007 until June 2010, and a DPF was used in 82 cases (DPF group) from July 2010 to July 2011. Rates of positive findings on postoperative diffusion-weighted imaging (DWI) and stroke incidence were compared. Positive postoperative DWI results were found in 34 cases in the DPB group (41.4 %), but in only 22 cases in the DPF group (26.8 %), and there was only a small significant difference within the DPF group. In the DPB group, there was one case of transient ischemic attack (TIA) (1.2 %) and four cases of brain infarction (2 minor strokes, 2 major strokes; 4.9 %), compared to the DFP group with one case of TIA (1.2 %) and no cases of minor or major strokes. In this study, significantly lower rates of occurrence of DWI ischemic lesions and intraoperative embolization were associated with use of the DPF compared to the DPB.

  17. Fast Convolutional Sparse Coding in the Dual Domain

    KAUST Repository

    Affara, Lama Ahmed

    2017-09-27

    Convolutional sparse coding (CSC) is an important building block of many computer vision applications ranging from image and video compression to deep learning. We present two contributions to the state of the art in CSC. First, we significantly speed up the computation by proposing a new optimization framework that tackles the problem in the dual domain. Second, we extend the original formulation to higher dimensions in order to process a wider range of inputs, such as color inputs, or HOG features. Our results show a significant speedup compared to the current state of the art in CSC.

  18. Phase transitions in glassy systems via convolutional neural networks

    Science.gov (United States)

    Fang, Chao

    Machine learning is a powerful approach commonplace in industry to tackle large data sets. Most recently, it has found its way into condensed matter physics, allowing for the first time the study of, e.g., topological phase transitions and strongly-correlated electron systems. The study of spin glasses is plagued by finite-size effects due to the long thermalization times needed. Here we use convolutional neural networks in an attempt to detect a phase transition in three-dimensional Ising spin glasses. Our results are compared to traditional approaches.

  19. Visualizing Vector Fields Using Line Integral Convolution and Dye Advection

    Science.gov (United States)

    Shen, Han-Wei; Johnson, Christopher R.; Ma, Kwan-Liu

    1996-01-01

    We present local and global techniques to visualize three-dimensional vector field data. Using the Line Integral Convolution (LIC) method to image the global vector field, our new algorithm allows the user to introduce colored 'dye' into the vector field to highlight local flow features. A fast algorithm is proposed that quickly recomputes the dyed LIC images. In addition, we introduce volume rendering methods that can map the LIC texture on any contour surface and/or translucent region defined by additional scalar quantities, and can follow the advection of colored dye throughout the volume.

  20. Fast Convolutional Sparse Coding in the Dual Domain

    KAUST Repository

    Affara, Lama Ahmed; Ghanem, Bernard; Wonka, Peter

    2017-01-01

    Convolutional sparse coding (CSC) is an important building block of many computer vision applications ranging from image and video compression to deep learning. We present two contributions to the state of the art in CSC. First, we significantly speed up the computation by proposing a new optimization framework that tackles the problem in the dual domain. Second, we extend the original formulation to higher dimensions in order to process a wider range of inputs, such as color inputs, or HOG features. Our results show a significant speedup compared to the current state of the art in CSC.

  1. Salient regions detection using convolutional neural networks and color volume

    Science.gov (United States)

    Liu, Guang-Hai; Hou, Yingkun

    2018-03-01

    Convolutional neural network is an important technique in machine learning, pattern recognition and image processing. In order to reduce the computational burden and extend the classical LeNet-5 model to the field of saliency detection, we propose a simple and novel computing model based on LeNet-5 network. In the proposed model, hue, saturation and intensity are utilized to extract depth cues, and then we integrate depth cues and color volume to saliency detection following the basic structure of the feature integration theory. Experimental results show that the proposed computing model outperforms some existing state-of-the-art methods on MSRA1000 and ECSSD datasets.

  2. Traffic sign classification with dataset augmentation and convolutional neural network

    Science.gov (United States)

    Tang, Qing; Kurnianggoro, Laksono; Jo, Kang-Hyun

    2018-04-01

    This paper presents a method for traffic sign classification using a convolutional neural network (CNN). In this method, firstly we transfer a color image into grayscale, and then normalize it in the range (-1,1) as the preprocessing step. To increase robustness of classification model, we apply a dataset augmentation algorithm and create new images to train the model. To avoid overfitting, we utilize a dropout module before the last fully connection layer. To assess the performance of the proposed method, the German traffic sign recognition benchmark (GTSRB) dataset is utilized. Experimental results show that the method is effective in classifying traffic signs.

  3. Tandem mass spectrometry data quality assessment by self-convolution

    Directory of Open Access Journals (Sweden)

    Tham Wai

    2007-09-01

    Full Text Available Abstract Background Many algorithms have been developed for deciphering the tandem mass spectrometry (MS data sets. They can be essentially clustered into two classes. The first performs searches on theoretical mass spectrum database, while the second based itself on de novo sequencing from raw mass spectrometry data. It was noted that the quality of mass spectra affects significantly the protein identification processes in both instances. This prompted the authors to explore ways to measure the quality of MS data sets before subjecting them to the protein identification algorithms, thus allowing for more meaningful searches and increased confidence level of proteins identified. Results The proposed method measures the qualities of MS data sets based on the symmetric property of b- and y-ion peaks present in a MS spectrum. Self-convolution on MS data and its time-reversal copy was employed. Due to the symmetric nature of b-ions and y-ions peaks, the self-convolution result of a good spectrum would produce a highest mid point intensity peak. To reduce processing time, self-convolution was achieved using Fast Fourier Transform and its inverse transform, followed by the removal of the "DC" (Direct Current component and the normalisation of the data set. The quality score was defined as the ratio of the intensity at the mid point to the remaining peaks of the convolution result. The method was validated using both theoretical mass spectra, with various permutations, and several real MS data sets. The results were encouraging, revealing a high percentage of positive prediction rates for spectra with good quality scores. Conclusion We have demonstrated in this work a method for determining the quality of tandem MS data set. By pre-determining the quality of tandem MS data before subjecting them to protein identification algorithms, spurious protein predictions due to poor tandem MS data are avoided, giving scientists greater confidence in the

  4. The Use of Finite Fields and Rings to Compute Convolutions

    Science.gov (United States)

    1975-06-06

    showed in Ref. 1 that the convolution of two finite sequences of integers (a, ) and (b, ) for k = 1, 2, . . ., d can be obtained as the inverse transform of...since the T.’S are all distinct. Thus T~ exists and (7) can be solved as a = T A the inverse " transform .𔃻 Next let us impose on (7) the...the inverse transform d-1 Cn= (d) I Cka k=0 If an a can be found so that multiplications by powers of a are simple in hardware, the

  5. Tandem mass spectrometry data quality assessment by self-convolution.

    Science.gov (United States)

    Choo, Keng Wah; Tham, Wai Mun

    2007-09-20

    Many algorithms have been developed for deciphering the tandem mass spectrometry (MS) data sets. They can be essentially clustered into two classes. The first performs searches on theoretical mass spectrum database, while the second based itself on de novo sequencing from raw mass spectrometry data. It was noted that the quality of mass spectra affects significantly the protein identification processes in both instances. This prompted the authors to explore ways to measure the quality of MS data sets before subjecting them to the protein identification algorithms, thus allowing for more meaningful searches and increased confidence level of proteins identified. The proposed method measures the qualities of MS data sets based on the symmetric property of b- and y-ion peaks present in a MS spectrum. Self-convolution on MS data and its time-reversal copy was employed. Due to the symmetric nature of b-ions and y-ions peaks, the self-convolution result of a good spectrum would produce a highest mid point intensity peak. To reduce processing time, self-convolution was achieved using Fast Fourier Transform and its inverse transform, followed by the removal of the "DC" (Direct Current) component and the normalisation of the data set. The quality score was defined as the ratio of the intensity at the mid point to the remaining peaks of the convolution result. The method was validated using both theoretical mass spectra, with various permutations, and several real MS data sets. The results were encouraging, revealing a high percentage of positive prediction rates for spectra with good quality scores. We have demonstrated in this work a method for determining the quality of tandem MS data set. By pre-determining the quality of tandem MS data before subjecting them to protein identification algorithms, spurious protein predictions due to poor tandem MS data are avoided, giving scientists greater confidence in the predicted results. We conclude that the algorithm performs well

  6. Classifying medical relations in clinical text via convolutional neural networks.

    Science.gov (United States)

    He, Bin; Guan, Yi; Dai, Rui

    2018-05-16

    Deep learning research on relation classification has achieved solid performance in the general domain. This study proposes a convolutional neural network (CNN) architecture with a multi-pooling operation for medical relation classification on clinical records and explores a loss function with a category-level constraint matrix. Experiments using the 2010 i2b2/VA relation corpus demonstrate these models, which do not depend on any external features, outperform previous single-model methods and our best model is competitive with the existing ensemble-based method. Copyright © 2018. Published by Elsevier B.V.

  7. Weed Growth Stage Estimator Using Deep Convolutional Neural Networks

    DEFF Research Database (Denmark)

    Teimouri, Nima; Dyrmann, Mads; Nielsen, Per Rydahl

    2018-01-01

    This study outlines a new method of automatically estimating weed species and growth stages (from cotyledon until eight leaves are visible) of in situ images covering 18 weed species or families. Images of weeds growing within a variety of crops were gathered across variable environmental conditi...... in estimating the number of leaves and 96% accuracy when accepting a deviation of two leaves. These results show that this new method of using deep convolutional neural networks has a relatively high ability to estimate early growth stages across a wide variety of weed species....

  8. Mini-implant-supported Molar Distalization

    Directory of Open Access Journals (Sweden)

    Amit Goyal

    2012-01-01

    Full Text Available Temporary anchorage devices popularly called mini-implants or miniscrews are the latest addition to an orthodontist′s armamentarium. The following case report describes the treatment of a 16-year-old girl with a pleasant profile, moderate crowding and Angle′s Class II molar relationship. Maxillary molar distalization was planned and mini-implants were used to preserve the anterior anchorage. After 13 months of treatment, Class I molar and canine relation was achieved bilaterally and there was no anterior proclination. Thus, mini-implants provide a viable option to the clinician to carry out difficult tooth movements without any side effects.

  9. Periosteal osteoblastoma of the distal femur

    Energy Technology Data Exchange (ETDEWEB)

    Nakatani, Tetsuya; Yamamoto, Tetsuji; Akisue, Toshihiro; Marui, Takashi; Hitora, Toshiaki; Kawamoto, Teruya; Nagira, Keiko; Yoshiya, Shinichi; Kurosaka, Masahiro [Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-cho, Chuo-ku, Kobe (Japan); Fujita, Ikuo; Matsumoto, Keiji [Department of Orthopaedic Surgery, Hyogo Medical Center for Adults, Akashi, Hyogo (Japan)

    2004-02-01

    Osteoblastomas located on the surface of the cortical bone, so-called periosteal osteoblastomas, are extremely rare. We report on a case of periosteal osteoblastoma arising from the posterior surface of the right distal femur in a 17-year-old man. Roentgenographic, computed tomographic, magnetic resonance imaging, and histologic features of the case are presented. Periosteal osteoblastoma should be radiologically and histologically differentiated from myositis ossificans, avulsive cortical irregularity syndrome, osteoid osteoma, parosteal osteosarcoma, periosteal osteosarcoma, and high-grade surface osteosarcoma. Although periosteal osteoblastoma is rare, this tumor should be included in the differential diagnosis of surface-type bone tumors. (orig.)

  10. Krypton laser-induced photothrombotic distal middle cerebral artery occlusion without craniectomy in mice.

    Science.gov (United States)

    Sugimori, Hiroshi; Yao, Hiroshi; Ooboshi, Hiroaki; Ibayashi, Setsuro; Iida, Mitsuo

    2004-08-01

    Recent advances in genetical engineering of the mouse have highlighted the importance of reproducible and less invasive models of cerebral ischemia in mice. In this paper, we developed minimally invasive and reproducible model of distal middle cerebral artery (MCA) occlusion in mice using krypton (Kr) laser-induced photothrombosis. C57BL/6 or BALB mice (n=8 each) were anesthetized with halothane. The skin was cut, the temporal muscle was retracted, and the right distal MCA was observed through the skull. A Kr laser beam of wavelength 568 nm was focused onto the MCA over the intact skull. Upon laser irradiation, intravenous administration of a rose bengal solution was begun. After 4 min of irradiation, the laser beam was refocused on the MCA just proximal to the first spot, and another 4-min irradiation was performed. Then, the right common carotid artery (CCA) was ligated. Three days later, the brain was removed, and infarct volume was determined. Infarction confined almost solely to the cortical area was produced in each mouse. Mean infarct volume in C57BL/6 mice was 25.2+/-13.7 mm3. The BALB mice group showed significantly larger and more reproducible infarction (44.1+/-5.2 mm3; the coefficient of variation was 12%) than did C57BL/6 mice (P<0.005). Our photothrombosis model of stroke in mice can be performed without craniectomy, and its reproducibility is satisfactory when using BALB mice.

  11. A MacWilliams Identity for Convolutional Codes: The General Case

    OpenAIRE

    Gluesing-Luerssen, Heide; Schneider, Gert

    2008-01-01

    A MacWilliams Identity for convolutional codes will be established. It makes use of the weight adjacency matrices of the code and its dual, based on state space realizations (the controller canonical form) of the codes in question. The MacWilliams Identity applies to various notions of duality appearing in the literature on convolutional coding theory.

  12. Isointense infant brain MRI segmentation with a dilated convolutional neural network

    NARCIS (Netherlands)

    Moeskops, P.; Pluim, J.P.W.

    2017-01-01

    Quantitative analysis of brain MRI at the age of 6 months is difficult because of the limited contrast between white matter and gray matter. In this study, we use a dilated triplanar convolutional neural network in combination with a non-dilated 3D convolutional neural network for the segmentation

  13. An upper bound on the number of errors corrected by a convolutional code

    DEFF Research Database (Denmark)

    Justesen, Jørn

    2000-01-01

    The number of errors that a convolutional codes can correct in a segment of the encoded sequence is upper bounded by the number of distinct syndrome sequences of the relevant length.......The number of errors that a convolutional codes can correct in a segment of the encoded sequence is upper bounded by the number of distinct syndrome sequences of the relevant length....

  14. Linear diffusion-wave channel routing using a discrete Hayami convolution method

    Science.gov (United States)

    Li Wang; Joan Q. Wu; William J. Elliot; Fritz R. Feidler; Sergey. Lapin

    2014-01-01

    The convolution of an input with a response function has been widely used in hydrology as a means to solve various problems analytically. Due to the high computation demand in solving the functions using numerical integration, it is often advantageous to use the discrete convolution instead of the integration of the continuous functions. This approach greatly reduces...

  15. Using convolutional decoding to improve time delay and phase estimation in digital communications

    Science.gov (United States)

    Ormesher, Richard C [Albuquerque, NM; Mason, John J [Albuquerque, NM

    2010-01-26

    The time delay and/or phase of a communication signal received by a digital communication receiver can be estimated based on a convolutional decoding operation that the communication receiver performs on the received communication signal. If the original transmitted communication signal has been spread according to a spreading operation, a corresponding despreading operation can be integrated into the convolutional decoding operation.

  16. Radiographic anatomy of the distal dural SAC

    International Nuclear Information System (INIS)

    Larsen, J.L.; Olsen, K.O.

    1991-01-01

    A radio-anatomical study was performed of the distal dural sac (DS) in 121 patients subjected to myelography. In 83.4% the termination of the DS was located from the upper half of the S1-segment to the lower half of the S2-segment. In the remaining patients the dural terminations were more distally located. The average location of the DS-termination was higher than that found in a previous anatomic study. The inference is that in patients with low-back pain and sciatica, the DS tends to terminate at a higher spinal level than in a non-selected anatomic material. The caudal reduction in sagittal diameter of the DS was less than that of the frontal diameter of the sac. The linear diminution in cross-sectional area of the DS from the level of L3 towards the lumbosacral junction was not correlated with the degree of caudal extension of the DS into the sacrum. Thus the length of the DS and its transverse diameters are independent of each other. These results supported the view that the location of the termination of the DS (and hence that of the spinal cord) is not related to stenosis of the central spinal canal. (orig.)

  17. Maxillary molar distalization with first class appliance.

    Science.gov (United States)

    Ramesh, Namitha; Palukunnu, Biswas; Ravindran, Nidhi; Nair, Preeti P

    2014-02-27

    Non-extraction treatment has gained popularity for corrections of mild-to-moderate class II malocclusion over the past few decades. The distalization of maxillary molars is of significant value for treatment of cases with minimal arch discrepancy and mild class II molar relation associated with a normal mandibular arch and acceptable profile. This paper describes our experience with a 16-year-old female patient who reported with irregularly placed upper front teeth and unpleasant smile. The patient was diagnosed to have angles class II malocclusion with moderate maxillary anterior crowding, deep bite of 4 mm on a skeletal class II base with an orthognathic maxilla and retrognathic mandible and normal growth pattern. She presented an ideal profile and so molar distalization was planned with the first-class appliance. Molars were distalised by 8 mm on the right and left quadrants and class I molar relation achieved within 4 months. The space gained was utilised effectively to align the arch and establish a class I molar and canine relation.

  18. Dermal pocketing following distal finger replantation.

    Science.gov (United States)

    Puhaindran, Mark E; Paavilainen, Pasi; Tan, David M K; Peng, Yeong Pin; Lim, Aymeric Y T

    2010-08-01

    Replantation is an ideal technique for reconstruction following fingertip amputation as it provides 'like for like' total reconstruction of the nail complex, bone pulp tissue and skin with no donor-site morbidity. However, fingertips are often not replanted because veins cannot be found or are thought to be too small to repair. Attempts at 'cap-plasty' or pocketing of replanted tips with and without microvascular anastomosis have been done in the past with varying degrees of success. We prospectively followed up a group of patients who underwent digital replantation and dermal pocketing in the palm to evaluate the outcome of this procedure. There were 10 patients with 14 amputated digits (two thumbs, five index, four middle, two ring and one little) who underwent dermal pocketing of the amputated digit following replantation. Among the 14 digits that were treated with dermal pocketing, 11 survived completely, one had partial atrophy and two were completely lost. Complications encountered included finger stiffness (two patients) and infection of the replanted fingertip with osteomyelitis of the distal phalanx (one patient). We believe that this technique can help increase the chance of survival for distal replantation with an acceptable salvage rate of 85% in our series. Copyright 2009 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights reserved.

  19. Osteoid osteoma of the distal clavicle

    Directory of Open Access Journals (Sweden)

    Bernardo Barcellos Terra

    Full Text Available ABSTRACT The osteoid osteoma is a bone tumor that accounts for 10% of benign tumors. It was described in 1935 by Jaffe, as a tumor that affects the young adult population, with a predominance of males. This study aims to present a case of late diagnosis of a patient with osteoid osteoma of the distal clavicle region. Female patient, 44 years old, non-professional volleyball player, reported pain in the anterior and superior region of the shoulder girdle, specifically in the acromioclavicular joint, which worsened at night and had been treated for nine months as tendinitis of the rotator cuff and acromioclavicular joint arthritis. After confirming the diagnosis, the patient underwent open surgery with resection of the distal clavicle. At two years of follow-up, the patient presents without local pain. In the radiographic evaluation, coracoclavicular distance is preserved and there are no signs of recurrence. Tumors of the shoulder girdle are rare and are often diagnosed late. A high degree of suspicion for the diagnosis of tumors of the shoulder girdle is needed in order to avoid late diagnosis.

  20. Biotin absorption by distal rat intestine

    International Nuclear Information System (INIS)

    Bowman, B.B.; Rosenberg, I.H.

    1987-01-01

    We used the in vivo intestinal loop approach, with short (10-min) and long (3-h) incubations, to examine biotin absorption in proximal jejunum, distal ileum, cecum and proximal colon. In short-term studies, luminal biotin disappearance from rat ileum was about half that observed in the jejunum, whereas absorption by proximal colon was about 12% of that in the jejunum. In 3-h closed-loop studies, the absorption of 1.0 microM biotin varied regionally. Biotin absorption was nearly complete in the small intestine after 3 h; however, only about 15% of the dose had been absorbed in the cecum and 27% in the proximal colon after 3 h. Independent of site of administration, the major fraction of absorbed biotin was recovered in the liver; measurable amounts of radioactive biotin were also present in kidney and plasma. The results support the potential nutritional significance for the rat of biotin synthesized by bacteria in the distal intestine, by demonstrating directly an absorptive capability of mammalian large bowel for this vitamin

  1. [Distal ureteral lithiasis. ESWL versus ambulatory URS].

    Science.gov (United States)

    Miján Ortiz, J L; Gutiérrez Tejero, F; López Carmona, F; Nogueras Ocaña, M; Arrabal Martín, M; Zuluaga Gómez, A

    2001-11-01

    To present the results achieved in the treatment of 1802 distal ureteral stones treated at the Lithotripsy Unit of the San Cecilio University Hospital over the last 10 years (1990-2000). Stones were treated by extracorporeal shock wave lithotripsy (ESWL) or ureteroscopy (URS). ESWL was the initial treatment in 81% of the cases (1460 calculi) and URS in the remaining 19% (342 stones). URS was performed for complication or failed ESWL (102 stones) and ESWL was performed for failed URS, basically due to stone migration (24 stones). Ureterolithotomy was required on 7 occasions. Sedation-analgesia with fentanyl and midazolam was routinely used in URS. Sedation was required in only 55% of the ESWL procedures. Elective ESWL resolved 93% of the cases, a percentage which is similar to that achieved with URS as first treatment. The ESWL retreatment rate was 1.3. URS was successful in 98% of the cases of failed ESWL. There are two treatment modalities for stones in the distal ureter: ESWL and URS. We advocate the use of outpatient URS with sedation preferably in the female patient, impacted stones, obstructive uropathy, stones larger than 2 cm and radiotransparent stones.

  2. A STUDY OF SURGICAL MANAGEMENT OF DISTAL FEMORAL FRACTURES BY DISTAL FEMORAL LOCKING COMPRESSION PLATE OSTEOSYNTHESIS

    Directory of Open Access Journals (Sweden)

    Dema Rajaiah

    2016-08-01

    Full Text Available AIMS AND OBJECTIVES To study the fractures of distal end of femur and the mechanism of injury in distal end femur fractures, the advantages and disadvantages of open reduction and internal fixation of distal end femur fractures by distal femoral locking compression plate osteosynthesis and to analyse the outcome in terms of range of Knee motion, time to union, and limb shortening. RESULTS The mean age of patient is 44 years, 85% are males, road traffic accidents account for majority (80%, right side involved in 70%, Muller’s type C fracture is common, good range of movements is seen 90% of cases and union occurred in 95% in 5 months. The results were assessed using Neer’s score, seven (35% patients had excellent results, eight (40% patients had good results, four (20% patients had fair results and one (5% patient had poor result. CONCLUSION From our study, we conclude that DF-LCP is a safe and reliable implant and has shown excellent to satisfactory results in majority of intra-articular fractures (AO type C. Fixation with locking compression plate showed more effectiveness in severely osteoporotic bones, shorter operative stay, faster recovery, faster union rates and excellent functional outcome.

  3. Linkage of genes for laminin B1 and B2 subunits on chromosome 1 in mouse.

    Science.gov (United States)

    Elliott, R W; Barlow, D; Hogan, B L

    1985-08-01

    We have used cDNA clones for the B1 and B2 subunits of laminin to find restriction fragment length DNA polymorphisms for the genes encoding these polypeptides in the mouse. Three alleles were found for LamB2 and two for LamB1 among the inbred mouse strains. The segregation of these polymorphisms among recombinant inbred strains showed that these genes are tightly linked in the central region of mouse Chromosome 1 between Sas-1 and Ly-m22, 7.4 +/- 3.2 cM distal to the Pep-3 locus. There is no evidence in the mouse for pseudogenes for these proteins.

  4. Classifying images using restricted Boltzmann machines and convolutional neural networks

    Science.gov (United States)

    Zhao, Zhijun; Xu, Tongde; Dai, Chenyu

    2017-07-01

    To improve the feature recognition ability of deep model transfer learning, we propose a hybrid deep transfer learning method for image classification based on restricted Boltzmann machines (RBM) and convolutional neural networks (CNNs). It integrates learning abilities of two models, which conducts subject classification by exacting structural higher-order statistics features of images. While the method transfers the trained convolutional neural networks to the target datasets, fully-connected layers can be replaced by restricted Boltzmann machine layers; then the restricted Boltzmann machine layers and Softmax classifier are retrained, and BP neural network can be used to fine-tuned the hybrid model. The restricted Boltzmann machine layers has not only fully integrated the whole feature maps, but also learns the statistical features of target datasets in the view of the biggest logarithmic likelihood, thus removing the effects caused by the content differences between datasets. The experimental results show that the proposed method has improved the accuracy of image classification, outperforming other methods on Pascal VOC2007 and Caltech101 datasets.

  5. Cloud Detection by Fusing Multi-Scale Convolutional Features

    Science.gov (United States)

    Li, Zhiwei; Shen, Huanfeng; Wei, Yancong; Cheng, Qing; Yuan, Qiangqiang

    2018-04-01

    Clouds detection is an important pre-processing step for accurate application of optical satellite imagery. Recent studies indicate that deep learning achieves best performance in image segmentation tasks. Aiming at boosting the accuracy of cloud detection for multispectral imagery, especially for those that contain only visible and near infrared bands, in this paper, we proposed a deep learning based cloud detection method termed MSCN (multi-scale cloud net), which segments cloud by fusing multi-scale convolutional features. MSCN was trained on a global cloud cover validation collection, and was tested in more than ten types of optical images with different resolution. Experiment results show that MSCN has obvious advantages over the traditional multi-feature combined cloud detection method in accuracy, especially when in snow and other areas covered by bright non-cloud objects. Besides, MSCN produced more detailed cloud masks than the compared deep cloud detection convolution network. The effectiveness of MSCN make it promising for practical application in multiple kinds of optical imagery.

  6. Real-Time Video Convolutional Face Finder on Embedded Platforms

    Directory of Open Access Journals (Sweden)

    Mamalet Franck

    2007-01-01

    Full Text Available A high-level optimization methodology is applied for implementing the well-known convolutional face finder (CFF algorithm for real-time applications on mobile phones, such as teleconferencing, advanced user interfaces, image indexing, and security access control. CFF is based on a feature extraction and classification technique which consists of a pipeline of convolutions and subsampling operations. The design of embedded systems requires a good trade-off between performance and code size due to the limited amount of available resources. The followed methodology copes with the main drawbacks of the original implementation of CFF such as floating-point computation and memory allocation, in order to allow parallelism exploitation and perform algorithm optimizations. Experimental results show that our embedded face detection system can accurately locate faces with less computational load and memory cost. It runs on a 275 MHz Starcore DSP at 35 QCIF images/s with state-of-the-art detection rates and very low false alarm rates.

  7. Enhancing neutron beam production with a convoluted moderator

    Energy Technology Data Exchange (ETDEWEB)

    Iverson, E.B., E-mail: iversoneb@ornl.gov [Spallation Neutron Source, Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States); Baxter, D.V. [Center for the Exploration of Energy and Matter, Indiana University, Bloomington, IN 47408 (United States); Muhrer, G. [Lujan Neutron Scattering Center, Los Alamos National Laboratory, P.O. Box 1663, Los Alamos, NM 87545 (United States); Ansell, S.; Dalgliesh, R. [ISIS Facility, Rutherford Appleton Laboratory, Chilton (United Kingdom); Gallmeier, F.X. [Spallation Neutron Source, Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States); Kaiser, H. [Center for the Exploration of Energy and Matter, Indiana University, Bloomington, IN 47408 (United States); Lu, W. [Spallation Neutron Source, Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States)

    2014-10-21

    We describe a new concept for a neutron moderating assembly resulting in the more efficient production of slow neutron beams. The Convoluted Moderator, a heterogeneous stack of interleaved moderating material and nearly transparent single-crystal spacers, is a directionally enhanced neutron beam source, improving beam emission over an angular range comparable to the range accepted by neutron beam lines and guides. We have demonstrated gains of 50% in slow neutron intensity for a given fast neutron production rate while simultaneously reducing the wavelength-dependent emission time dispersion by 25%, both coming from a geometric effect in which the neutron beam lines view a large surface area of moderating material in a relatively small volume. Additionally, we have confirmed a Bragg-enhancement effect arising from coherent scattering within the single-crystal spacers. We have not observed hypothesized refractive effects leading to additional gains at long wavelength. In addition to confirmation of the validity of the Convoluted Moderator concept, our measurements provide a series of benchmark experiments suitable for developing simulation and analysis techniques for practical optimization and eventual implementation at slow neutron source facilities.

  8. Photon beam convolution using polyenergetic energy deposition kernels

    International Nuclear Information System (INIS)

    Hoban, P.W.; Murray, D.C.; Round, W.H.

    1994-01-01

    In photon beam convolution calculations where polyenergetic energy deposition kernels (EDKs) are used, the primary photon energy spectrum should be correctly accounted for in Monte Carlo generation of EDKs. This requires the probability of interaction, determined by the linear attenuation coefficient, μ, to be taken into account when primary photon interactions are forced to occur at the EDK origin. The use of primary and scattered EDKs generated with a fixed photon spectrum can give rise to an error in the dose calculation due to neglecting the effects of beam hardening with depth. The proportion of primary photon energy that is transferred to secondary electrons increases with depth of interaction, due to the increase in the ratio μ ab /μ as the beam hardens. Convolution depth-dose curves calculated using polyenergetic EDKs generated for the primary photon spectra which exist at depths of 0, 20 and 40 cm in water, show a fall-off which is too steep when compared with EGS4 Monte Carlo results. A beam hardening correction factor applied to primary and scattered 0 cm EDKs, based on the ratio of kerma to terma at each depth, gives primary, scattered and total dose in good agreement with Monte Carlo results. (Author)

  9. Multi-Branch Fully Convolutional Network for Face Detection

    KAUST Repository

    Bai, Yancheng

    2017-07-20

    Face detection is a fundamental problem in computer vision. It is still a challenging task in unconstrained conditions due to significant variations in scale, pose, expressions, and occlusion. In this paper, we propose a multi-branch fully convolutional network (MB-FCN) for face detection, which considers both efficiency and effectiveness in the design process. Our MB-FCN detector can deal with faces at all scale ranges with only a single pass through the backbone network. As such, our MB-FCN model saves computation and thus is more efficient, compared to previous methods that make multiple passes. For each branch, the specific skip connections of the convolutional feature maps at different layers are exploited to represent faces in specific scale ranges. Specifically, small faces can be represented with both shallow fine-grained and deep powerful coarse features. With this representation, superior improvement in performance is registered for the task of detecting small faces. We test our MB-FCN detector on two public face detection benchmarks, including FDDB and WIDER FACE. Extensive experiments show that our detector outperforms state-of-the-art methods on all these datasets in general and by a substantial margin on the most challenging among them (e.g. WIDER FACE Hard subset). Also, MB-FCN runs at 15 FPS on a GPU for images of size 640 x 480 with no assumption on the minimum detectable face size.

  10. Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks

    Directory of Open Access Journals (Sweden)

    Haiyang Yu

    2017-06-01

    Full Text Available Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs, for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs and long short-term memory (LSTM neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction.

  11. Convolutional Neural Network for Histopathological Analysis of Osteosarcoma.

    Science.gov (United States)

    Mishra, Rashika; Daescu, Ovidiu; Leavey, Patrick; Rakheja, Dinesh; Sengupta, Anita

    2018-03-01

    Pathologists often deal with high complexity and sometimes disagreement over osteosarcoma tumor classification due to cellular heterogeneity in the dataset. Segmentation and classification of histology tissue in H&E stained tumor image datasets is a challenging task because of intra-class variations, inter-class similarity, crowded context, and noisy data. In recent years, deep learning approaches have led to encouraging results in breast cancer and prostate cancer analysis. In this article, we propose convolutional neural network (CNN) as a tool to improve efficiency and accuracy of osteosarcoma tumor classification into tumor classes (viable tumor, necrosis) versus nontumor. The proposed CNN architecture contains eight learned layers: three sets of stacked two convolutional layers interspersed with max pooling layers for feature extraction and two fully connected layers with data augmentation strategies to boost performance. The use of a neural network results in higher accuracy of average 92% for the classification. We compare the proposed architecture with three existing and proven CNN architectures for image classification: AlexNet, LeNet, and VGGNet. We also provide a pipeline to calculate percentage necrosis in a given whole slide image. We conclude that the use of neural networks can assure both high accuracy and efficiency in osteosarcoma classification.

  12. Multi-Input Convolutional Neural Network for Flower Grading

    Directory of Open Access Journals (Sweden)

    Yu Sun

    2017-01-01

    Full Text Available Flower grading is a significant task because it is extremely convenient for managing the flowers in greenhouse and market. With the development of computer vision, flower grading has become an interdisciplinary focus in both botany and computer vision. A new dataset named BjfuGloxinia contains three quality grades; each grade consists of 107 samples and 321 images. A multi-input convolutional neural network is designed for large scale flower grading. Multi-input CNN achieves a satisfactory accuracy of 89.6% on the BjfuGloxinia after data augmentation. Compared with a single-input CNN, the accuracy of multi-input CNN is increased by 5% on average, demonstrating that multi-input convolutional neural network is a promising model for flower grading. Although data augmentation contributes to the model, the accuracy is still limited by lack of samples diversity. Majority of misclassification is derived from the medium class. The image processing based bud detection is useful for reducing the misclassification, increasing the accuracy of flower grading to approximately 93.9%.

  13. Convolutional neural network architectures for predicting DNA–protein binding

    Science.gov (United States)

    Zeng, Haoyang; Edwards, Matthew D.; Liu, Ge; Gifford, David K.

    2016-01-01

    Motivation: Convolutional neural networks (CNN) have outperformed conventional methods in modeling the sequence specificity of DNA–protein binding. Yet inappropriate CNN architectures can yield poorer performance than simpler models. Thus an in-depth understanding of how to match CNN architecture to a given task is needed to fully harness the power of CNNs for computational biology applications. Results: We present a systematic exploration of CNN architectures for predicting DNA sequence binding using a large compendium of transcription factor datasets. We identify the best-performing architectures by varying CNN width, depth and pooling designs. We find that adding convolutional kernels to a network is important for motif-based tasks. We show the benefits of CNNs in learning rich higher-order sequence features, such as secondary motifs and local sequence context, by comparing network performance on multiple modeling tasks ranging in difficulty. We also demonstrate how careful construction of sequence benchmark datasets, using approaches that control potentially confounding effects like positional or motif strength bias, is critical in making fair comparisons between competing methods. We explore how to establish the sufficiency of training data for these learning tasks, and we have created a flexible cloud-based framework that permits the rapid exploration of alternative neural network architectures for problems in computational biology. Availability and Implementation: All the models analyzed are available at http://cnn.csail.mit.edu. Contact: gifford@mit.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307608

  14. Transforming Musical Signals through a Genre Classifying Convolutional Neural Network

    Science.gov (United States)

    Geng, S.; Ren, G.; Ogihara, M.

    2017-05-01

    Convolutional neural networks (CNNs) have been successfully applied on both discriminative and generative modeling for music-related tasks. For a particular task, the trained CNN contains information representing the decision making or the abstracting process. One can hope to manipulate existing music based on this 'informed' network and create music with new features corresponding to the knowledge obtained by the network. In this paper, we propose a method to utilize the stored information from a CNN trained on musical genre classification task. The network was composed of three convolutional layers, and was trained to classify five-second song clips into five different genres. After training, randomly selected clips were modified by maximizing the sum of outputs from the network layers. In addition to the potential of such CNNs to produce interesting audio transformation, more information about the network and the original music could be obtained from the analysis of the generated features since these features indicate how the network 'understands' the music.

  15. Siamese convolutional networks for tracking the spine motion

    Science.gov (United States)

    Liu, Yuan; Sui, Xiubao; Sun, Yicheng; Liu, Chengwei; Hu, Yong

    2017-09-01

    Deep learning models have demonstrated great success in various computer vision tasks such as image classification and object tracking. However, tracking the lumbar spine by digitalized video fluoroscopic imaging (DVFI), which can quantitatively analyze the motion mode of spine to diagnose lumbar instability, has not yet been well developed due to the lack of steady and robust tracking method. In this paper, we propose a novel visual tracking algorithm of the lumbar vertebra motion based on a Siamese convolutional neural network (CNN) model. We train a full-convolutional neural network offline to learn generic image features. The network is trained to learn a similarity function that compares the labeled target in the first frame with the candidate patches in the current frame. The similarity function returns a high score if the two images depict the same object. Once learned, the similarity function is used to track a previously unseen object without any adapting online. In the current frame, our tracker is performed by evaluating the candidate rotated patches sampled around the previous frame target position and presents a rotated bounding box to locate the predicted target precisely. Results indicate that the proposed tracking method can detect the lumbar vertebra steadily and robustly. Especially for images with low contrast and cluttered background, the presented tracker can still achieve good tracking performance. Further, the proposed algorithm operates at high speed for real time tracking.

  16. Real-Time Video Convolutional Face Finder on Embedded Platforms

    Directory of Open Access Journals (Sweden)

    Franck Mamalet

    2007-03-01

    Full Text Available A high-level optimization methodology is applied for implementing the well-known convolutional face finder (CFF algorithm for real-time applications on mobile phones, such as teleconferencing, advanced user interfaces, image indexing, and security access control. CFF is based on a feature extraction and classification technique which consists of a pipeline of convolutions and subsampling operations. The design of embedded systems requires a good trade-off between performance and code size due to the limited amount of available resources. The followed methodology copes with the main drawbacks of the original implementation of CFF such as floating-point computation and memory allocation, in order to allow parallelism exploitation and perform algorithm optimizations. Experimental results show that our embedded face detection system can accurately locate faces with less computational load and memory cost. It runs on a 275 MHz Starcore DSP at 35 QCIF images/s with state-of-the-art detection rates and very low false alarm rates.

  17. Digital image correlation based on a fast convolution strategy

    Science.gov (United States)

    Yuan, Yuan; Zhan, Qin; Xiong, Chunyang; Huang, Jianyong

    2017-10-01

    In recent years, the efficiency of digital image correlation (DIC) methods has attracted increasing attention because of its increasing importance for many engineering applications. Based on the classical affine optical flow (AOF) algorithm and the well-established inverse compositional Gauss-Newton algorithm, which is essentially a natural extension of the AOF algorithm under a nonlinear iterative framework, this paper develops a set of fast convolution-based DIC algorithms for high-efficiency subpixel image registration. Using a well-developed fast convolution technique, the set of algorithms establishes a series of global data tables (GDTs) over the digital images, which allows the reduction of the computational complexity of DIC significantly. Using the pre-calculated GDTs, the subpixel registration calculations can be implemented efficiently in a look-up-table fashion. Both numerical simulation and experimental verification indicate that the set of algorithms significantly enhances the computational efficiency of DIC, especially in the case of a dense data sampling for the digital images. Because the GDTs need to be computed only once, the algorithms are also suitable for efficiently coping with image sequences that record the time-varying dynamics of specimen deformations.

  18. Convolutional neural network features based change detection in satellite images

    Science.gov (United States)

    Mohammed El Amin, Arabi; Liu, Qingjie; Wang, Yunhong

    2016-07-01

    With the popular use of high resolution remote sensing (HRRS) satellite images, a huge research efforts have been placed on change detection (CD) problem. An effective feature selection method can significantly boost the final result. While hand-designed features have proven difficulties to design features that effectively capture high and mid-level representations, the recent developments in machine learning (Deep Learning) omit this problem by learning hierarchical representation in an unsupervised manner directly from data without human intervention. In this letter, we propose approaching the change detection problem from a feature learning perspective. A novel deep Convolutional Neural Networks (CNN) features based HR satellite images change detection method is proposed. The main guideline is to produce a change detection map directly from two images using a pretrained CNN. This method can omit the limited performance of hand-crafted features. Firstly, CNN features are extracted through different convolutional layers. Then, a concatenation step is evaluated after an normalization step, resulting in a unique higher dimensional feature map. Finally, a change map was computed using pixel-wise Euclidean distance. Our method has been validated on real bitemporal HRRS satellite images according to qualitative and quantitative analyses. The results obtained confirm the interest of the proposed method.

  19. Classification of stroke disease using convolutional neural network

    Science.gov (United States)

    Marbun, J. T.; Seniman; Andayani, U.

    2018-03-01

    Stroke is a condition that occurs when the blood supply stop flowing to the brain because of a blockage or a broken blood vessel. A symptoms that happen when experiencing stroke, some of them is a dropped consciousness, disrupted vision and paralyzed body. The general examination is being done to get a picture of the brain part that have stroke using Computerized Tomography (CT) Scan. The image produced from CT will be manually checked and need a proper lighting by doctor to get a type of stroke. That is why it needs a method to classify stroke from CT image automatically. A method proposed in this research is Convolutional Neural Network. CT image of the brain is used as the input for image processing. The stage before classification are image processing (Grayscaling, Scaling, Contrast Limited Adaptive Histogram Equalization, then the image being classified with Convolutional Neural Network. The result then showed that the method significantly conducted was able to be used as a tool to classify stroke disease in order to distinguish the type of stroke from CT image.

  20. Image Classification Based on Convolutional Denoising Sparse Autoencoder

    Directory of Open Access Journals (Sweden)

    Shuangshuang Chen

    2017-01-01

    Full Text Available Image classification aims to group images into corresponding semantic categories. Due to the difficulties of interclass similarity and intraclass variability, it is a challenging issue in computer vision. In this paper, an unsupervised feature learning approach called convolutional denoising sparse autoencoder (CDSAE is proposed based on the theory of visual attention mechanism and deep learning methods. Firstly, saliency detection method is utilized to get training samples for unsupervised feature learning. Next, these samples are sent to the denoising sparse autoencoder (DSAE, followed by convolutional layer and local contrast normalization layer. Generally, prior in a specific task is helpful for the task solution. Therefore, a new pooling strategy—spatial pyramid pooling (SPP fused with center-bias prior—is introduced into our approach. Experimental results on the common two image datasets (STL-10 and CIFAR-10 demonstrate that our approach is effective in image classification. They also demonstrate that none of these three components: local contrast normalization, SPP fused with center-prior, and l2 vector normalization can be excluded from our proposed approach. They jointly improve image representation and classification performance.

  1. Weed Growth Stage Estimator Using Deep Convolutional Neural Networks.

    Science.gov (United States)

    Teimouri, Nima; Dyrmann, Mads; Nielsen, Per Rydahl; Mathiassen, Solvejg Kopp; Somerville, Gayle J; Jørgensen, Rasmus Nyholm

    2018-05-16

    This study outlines a new method of automatically estimating weed species and growth stages (from cotyledon until eight leaves are visible) of in situ images covering 18 weed species or families. Images of weeds growing within a variety of crops were gathered across variable environmental conditions with regards to soil types, resolution and light settings. Then, 9649 of these images were used for training the computer, which automatically divided the weeds into nine growth classes. The performance of this proposed convolutional neural network approach was evaluated on a further set of 2516 images, which also varied in term of crop, soil type, image resolution and light conditions. The overall performance of this approach achieved a maximum accuracy of 78% for identifying Polygonum spp. and a minimum accuracy of 46% for blackgrass. In addition, it achieved an average 70% accuracy rate in estimating the number of leaves and 96% accuracy when accepting a deviation of two leaves. These results show that this new method of using deep convolutional neural networks has a relatively high ability to estimate early growth stages across a wide variety of weed species.

  2. Convolutional neural networks for vibrational spectroscopic data analysis.

    Science.gov (United States)

    Acquarelli, Jacopo; van Laarhoven, Twan; Gerretzen, Jan; Tran, Thanh N; Buydens, Lutgarde M C; Marchiori, Elena

    2017-02-15

    In this work we show that convolutional neural networks (CNNs) can be efficiently used to classify vibrational spectroscopic data and identify important spectral regions. CNNs are the current state-of-the-art in image classification and speech recognition and can learn interpretable representations of the data. These characteristics make CNNs a good candidate for reducing the need for preprocessing and for highlighting important spectral regions, both of which are crucial steps in the analysis of vibrational spectroscopic data. Chemometric analysis of vibrational spectroscopic data often relies on preprocessing methods involving baseline correction, scatter correction and noise removal, which are applied to the spectra prior to model building. Preprocessing is a critical step because even in simple problems using 'reasonable' preprocessing methods may decrease the performance of the final model. We develop a new CNN based method and provide an accompanying publicly available software. It is based on a simple CNN architecture with a single convolutional layer (a so-called shallow CNN). Our method outperforms standard classification algorithms used in chemometrics (e.g. PLS) in terms of accuracy when applied to non-preprocessed test data (86% average accuracy compared to the 62% achieved by PLS), and it achieves better performance even on preprocessed test data (96% average accuracy compared to the 89% achieved by PLS). For interpretability purposes, our method includes a procedure for finding important spectral regions, thereby facilitating qualitative interpretation of results. Copyright © 2016 Elsevier B.V. All rights reserved.

  3. sEMG-Based Gesture Recognition with Convolution Neural Networks

    Directory of Open Access Journals (Sweden)

    Zhen Ding

    2018-06-01

    Full Text Available The traditional classification methods for limb motion recognition based on sEMG have been deeply researched and shown promising results. However, information loss during feature extraction reduces the recognition accuracy. To obtain higher accuracy, the deep learning method was introduced. In this paper, we propose a parallel multiple-scale convolution architecture. Compared with the state-of-art methods, the proposed architecture fully considers the characteristics of the sEMG signal. Larger sizes of kernel filter than commonly used in other CNN-based hand recognition methods are adopted. Meanwhile, the characteristics of the sEMG signal, that is, muscle independence, is considered when designing the architecture. All the classification methods were evaluated on the NinaPro database. The results show that the proposed architecture has the highest recognition accuracy. Furthermore, the results indicate that parallel multiple-scale convolution architecture with larger size of kernel filter and considering muscle independence can significantly increase the classification accuracy.

  4. Development of a morphological convolution operator for bearing fault detection

    Science.gov (United States)

    Li, Yifan; Liang, Xihui; Liu, Weiwei; Wang, Yan

    2018-05-01

    This paper presents a novel signal processing scheme, namely morphological convolution operator (MCO) lifted morphological undecimated wavelet (MUDW), for rolling element bearing fault detection. In this scheme, a MCO is first designed to fully utilize the advantage of the closing & opening gradient operator and the closing-opening & opening-closing gradient operator for feature extraction as well as the merit of excellent denoising characteristics of the convolution operator. The MCO is then introduced into MUDW for the purpose of improving the fault detection ability of the reported MUDWs. Experimental vibration signals collected from a train wheelset test rig and the bearing data center of Case Western Reserve University are employed to evaluate the effectiveness of the proposed MCO lifted MUDW on fault detection of rolling element bearings. The results show that the proposed approach has a superior performance in extracting fault features of defective rolling element bearings. In addition, comparisons are performed between two reported MUDWs and the proposed MCO lifted MUDW. The MCO lifted MUDW outperforms both of them in detection of outer race faults and inner race faults of rolling element bearings.

  5. Fluence-convolution broad-beam (FCBB) dose calculation

    Energy Technology Data Exchange (ETDEWEB)

    Lu Weiguo; Chen Mingli, E-mail: wlu@tomotherapy.co [TomoTherapy Inc., 1240 Deming Way, Madison, WI 53717 (United States)

    2010-12-07

    IMRT optimization requires a fast yet relatively accurate algorithm to calculate the iteration dose with small memory demand. In this paper, we present a dose calculation algorithm that approaches these goals. By decomposing the infinitesimal pencil beam (IPB) kernel into the central axis (CAX) component and lateral spread function (LSF) and taking the beam's eye view (BEV), we established a non-voxel and non-beamlet-based dose calculation formula. Both LSF and CAX are determined by a commissioning procedure using the collapsed-cone convolution/superposition (CCCS) method as the standard dose engine. The proposed dose calculation involves a 2D convolution of a fluence map with LSF followed by ray tracing based on the CAX lookup table with radiological distance and divergence correction, resulting in complexity of O(N{sup 3}) both spatially and temporally. This simple algorithm is orders of magnitude faster than the CCCS method. Without pre-calculation of beamlets, its implementation is also orders of magnitude smaller than the conventional voxel-based beamlet-superposition (VBS) approach. We compared the presented algorithm with the CCCS method using simulated and clinical cases. The agreement was generally within 3% for a homogeneous phantom and 5% for heterogeneous and clinical cases. Combined with the 'adaptive full dose correction', the algorithm is well suitable for calculating the iteration dose during IMRT optimization.

  6. Deep multi-scale convolutional neural network for hyperspectral image classification

    Science.gov (United States)

    Zhang, Feng-zhe; Yang, Xia

    2018-04-01

    In this paper, we proposed a multi-scale convolutional neural network for hyperspectral image classification task. Firstly, compared with conventional convolution, we utilize multi-scale convolutions, which possess larger respective fields, to extract spectral features of hyperspectral image. We design a deep neural network with a multi-scale convolution layer which contains 3 different convolution kernel sizes. Secondly, to avoid overfitting of deep neural network, dropout is utilized, which randomly sleeps neurons, contributing to improve the classification accuracy a bit. In addition, new skills like ReLU in deep learning is utilized in this paper. We conduct experiments on University of Pavia and Salinas datasets, and obtained better classification accuracy compared with other methods.

  7. Object Detection Based on Fast/Faster RCNN Employing Fully Convolutional Architectures

    Directory of Open Access Journals (Sweden)

    Yun Ren

    2018-01-01

    Full Text Available Modern object detectors always include two major parts: a feature extractor and a feature classifier as same as traditional object detectors. The deeper and wider convolutional architectures are adopted as the feature extractor at present. However, many notable object detection systems such as Fast/Faster RCNN only consider simple fully connected layers as the feature classifier. In this paper, we declare that it is beneficial for the detection performance to elaboratively design deep convolutional networks (ConvNets of various depths for feature classification, especially using the fully convolutional architectures. In addition, this paper also demonstrates how to employ the fully convolutional architectures in the Fast/Faster RCNN. Experimental results show that a classifier based on convolutional layer is more effective for object detection than that based on fully connected layer and that the better detection performance can be achieved by employing deeper ConvNets as the feature classifier.

  8. Intraspecies Competition for Niches in the Distal Gut Dictate Transmission during Persistent Salmonella Infection

    Science.gov (United States)

    Lam, Lilian H.; Monack, Denise M.

    2014-01-01

    In order to be transmitted, a pathogen must first successfully colonize and multiply within a host. Ecological principles can be applied to study host-pathogen interactions to predict transmission dynamics. Little is known about the population biology of Salmonella during persistent infection. To define Salmonella enterica serovar Typhimurium population structure in this context, 129SvJ mice were oral gavaged with a mixture of eight wild-type isogenic tagged Salmonella (WITS) strains. Distinct subpopulations arose within intestinal and systemic tissues after 35 days, and clonal expansion of the cecal and colonic subpopulation was responsible for increases in Salmonella fecal shedding. A co-infection system utilizing differentially marked isogenic strains was developed in which each mouse received one strain orally and the other systemically by intraperitoneal (IP) injection. Co-infections demonstrated that the intestinal subpopulation exerted intraspecies priority effects by excluding systemic S. Typhimurium from colonizing an extracellular niche within the cecum and colon. Importantly, the systemic strain was excluded from these distal gut sites and was not transmitted to naïve hosts. In addition, S. Typhimurium required hydrogenase, an enzyme that mediates acquisition of hydrogen from the gut microbiota, during the first week of infection to exert priority effects in the gut. Thus, early inhibitory priority effects are facilitated by the acquisition of nutrients, which allow S. Typhimurium to successfully compete for a nutritional niche in the distal gut. We also show that intraspecies colonization resistance is maintained by Salmonella Pathogenicity Islands SPI1 and SPI2 during persistent distal gut infection. Thus, important virulence effectors not only modulate interactions with host cells, but are crucial for Salmonella colonization of an extracellular intestinal niche and thereby also shape intraspecies dynamics. We conclude that priority effects and

  9. Distal Communication by Chimpanzees (Pan troglodytes): Evidence for Common Ground?

    Science.gov (United States)

    Leavens, David A; Reamer, Lisa A; Mareno, Mary Catherine; Russell, Jamie L; Wilson, Daniel; Schapiro, Steven J; Hopkins, William D

    2015-01-01

    van der Goot et al. (2014) proposed that distal, deictic communication indexed the appreciation of the psychological state of a common ground between a signaler and a receiver. In their study, great apes did not signal distally, which they construed as evidence for the human uniqueness of a sense of common ground. This study exposed 166 chimpanzees to food and an experimenter, at an angular displacement, to ask, "Do chimpanzees display distal communication?" Apes were categorized as (a) proximal or (b) distal signalers on each of four trials. The number of chimpanzees who communicated proximally did not statistically differ from the number who signaled distally. Therefore, contrary to the claim by van der Goot et al., apes do communicate distally. © 2015 The Authors. Child Development © 2015 Society for Research in Child Development, Inc.

  10. Distal renal tubular acidosis and hepatic lipidosis in a cat.

    Science.gov (United States)

    Brown, S A; Spyridakis, L K; Crowell, W A

    1986-11-15

    Clinical and laboratory evidence of hepatic failure was found in a chronically anorectic cat. Simultaneous blood and urine pH determinations established a diagnosis of distal renal tubular acidosis. The cat did not respond to treatment. Necropsy revealed distal tubular nephrosis and hepatic lipidosis. The finding of distal renal tubular acidosis in a cat with hepatic lipidosis emphasizes the importance of complete evaluation of acid-base disorders in patients.

  11. Wrist Hypothermia Related to Continuous Work with a Computer Mouse: A Digital Infrared Imaging Pilot Study

    Directory of Open Access Journals (Sweden)

    Jelena Reste

    2015-08-01

    Full Text Available Computer work is characterized by sedentary static workload with low-intensity energy metabolism. The aim of our study was to evaluate the dynamics of skin surface temperature in the hand during prolonged computer mouse work under different ergonomic setups. Digital infrared imaging of the right forearm and wrist was performed during three hours of continuous computer work (measured at the start and every 15 minutes thereafter in a laboratory with controlled ambient conditions. Four people participated in the study. Three different ergonomic computer mouse setups were tested on three different days (horizontal computer mouse without mouse pad; horizontal computer mouse with mouse pad and padded wrist support; vertical computer mouse without mouse pad. The study revealed a significantly strong negative correlation between the temperature of the dorsal surface of the wrist and time spent working with a computer mouse. Hand skin temperature decreased markedly after one hour of continuous computer mouse work. Vertical computer mouse work preserved more stable and higher temperatures of the wrist (>30 °C, while continuous use of a horizontal mouse for more than two hours caused an extremely low temperature (<28 °C in distal parts of the hand. The preliminary observational findings indicate the significant effect of the duration and ergonomics of computer mouse work on the development of hand hypothermia.

  12. Post-transplant distal limb syndrome

    Directory of Open Access Journals (Sweden)

    María Florencia Borghi Torzillo

    2017-02-01

    Full Text Available The post-transplant distal limb syndrome is a not well known entity, with a prevalence of 5% in patients with renal transplant. Its diagnosis is based on clinical symptoms, bone scintigraphy and MRI, it has a benign course and the patient recovers without sequel. We present the case of a 37-year-old male, with medical history of hypertension, Berger's disease in 1999 that required dialysis three times a week for four years (2009-2013 and renal transplant in 2013. The patient consults on January 2014 referring severe pain in both feet, with sudden onset; he remembers the exact date of the beginning of the pain and denies trauma, pain prevents ambulation. The bone scintigraphy shows pathological uptake in both feet with no difference between the two. Although there is no treatment for this disease, it has a benign course

  13. Comparison of standard laparoscopic distal pancreatectomy with minimally invasive distal pancreatectomy using the da Vinci S system.

    Science.gov (United States)

    Ito, Masahiro; Asano, Yukio; Shimizu, Tomohiro; Uyama, Ichiro; Horiguchi, Akihiko

    2014-01-01

    Minimally invasive procedures for pancreatic pathologies are increasingly being used, including distal pancreatectomy. This study aimed to assess the indications for and outcomes of the da Vinci distal pancreatectomy procedure. We reviewed the medical records of patients who underwent pancreatic head resection from April 2009 to September 2013. Four patients (mean age, 52.7 years) underwent da Vinci distal pancreatectomy and 10 (mean age, 68.0 +/- 12.1 years) underwent laparoscopic distal pancreatectomy. The mean surgical duration was 292 +/- 153 min and 306 +/- 29 min, the mean blood loss was 153 +/- 71 mL and 61.7 +/- 72 mL, and the mean postoperative length of stay was 24 +/- 11 days and 14 +/- 3 days in the da Vinci distal pancreatectomy and laparoscopic distal pancreatectomy groups, respectively. One patient who underwent da Vinci distal pancreatectomy developed a pancreatic fistula, while 2 patients in the laparoscopic distal pancreatectomy group developed splenic ischemia and gastric torsion, respectively. Laparoscopic and robotic pancreatic resection were both safe and feasible in selected patients with distal pancreatic pathologies. Further studies are necessary to clarify the role of robotic surgery in the advanced laparoscopic era.

  14. [Comparison of laparoscopic distal pancreatectomy and open distal pancreatectomy in pancreatic ductal adenocarcinoma].

    Science.gov (United States)

    Xu, K; Su, J J; Su, M; Yan, L; Feng, J; Xin, X L; Chen, Y L

    2017-10-23

    Objective: To compare and evaluate the curative effect of laparoscopic distal pancreatectomy(LDP) and traditional open distal pancreatectomy(ODP) in pancreatic ductal adenocarcinoma. Methods: The clinical data of 15 patients treated by LDP and 87 contemporaneous cases treated by ODP from January 2010 to November 2015 was collected, and the curative effect and prognosis of these patients were retrospectively analyzed. Results: The operation time of LDP group was (286.5±48.1) min, significantly longer than that of OPD group(226.6±56.8) min ( P 0.05). In both LDP group and ODP group, none occurred percutaneous drainage, re-admissions, second operation or perioperative death. Conclusions: Compared to ODP, LDP is much safer and more steady in perioperative periodand operation. Patients of pancreatic ductal adenocarcinoma received LDP can acquire more benefit and recovery sooner, and LDP is a safe and effective operative method.

  15. Training strategy for convolutional neural networks in pedestrian gender classification

    Science.gov (United States)

    Ng, Choon-Boon; Tay, Yong-Haur; Goi, Bok-Min

    2017-06-01

    In this work, we studied a strategy for training a convolutional neural network in pedestrian gender classification with limited amount of labeled training data. Unsupervised learning by k-means clustering on pedestrian images was used to learn the filters to initialize the first layer of the network. As a form of pre-training, supervised learning for the related task of pedestrian classification was performed. Finally, the network was fine-tuned for gender classification. We found that this strategy improved the network's generalization ability in gender classification, achieving better test results when compared to random weights initialization and slightly more beneficial than merely initializing the first layer filters by unsupervised learning. This shows that unsupervised learning followed by pre-training with pedestrian images is an effective strategy to learn useful features for pedestrian gender classification.

  16. Accurate lithography simulation model based on convolutional neural networks

    Science.gov (United States)

    Watanabe, Yuki; Kimura, Taiki; Matsunawa, Tetsuaki; Nojima, Shigeki

    2017-07-01

    Lithography simulation is an essential technique for today's semiconductor manufacturing process. In order to calculate an entire chip in realistic time, compact resist model is commonly used. The model is established for faster calculation. To have accurate compact resist model, it is necessary to fix a complicated non-linear model function. However, it is difficult to decide an appropriate function manually because there are many options. This paper proposes a new compact resist model using CNN (Convolutional Neural Networks) which is one of deep learning techniques. CNN model makes it possible to determine an appropriate model function and achieve accurate simulation. Experimental results show CNN model can reduce CD prediction errors by 70% compared with the conventional model.

  17. An effective convolutional neural network model for Chinese sentiment analysis

    Science.gov (United States)

    Zhang, Yu; Chen, Mengdong; Liu, Lianzhong; Wang, Yadong

    2017-06-01

    Nowadays microblog is getting more and more popular. People are increasingly accustomed to expressing their opinions on Twitter, Facebook and Sina Weibo. Sentiment analysis of microblog has received significant attention, both in academia and in industry. So far, Chinese microblog exploration still needs lots of further work. In recent years CNN has also been used to deal with NLP tasks, and already achieved good results. However, these methods ignore the effective use of a large number of existing sentimental resources. For this purpose, we propose a Lexicon-based Sentiment Convolutional Neural Networks (LSCNN) model focus on Weibo's sentiment analysis, which combines two CNNs, trained individually base on sentiment features and word embedding, at the fully connected hidden layer. The experimental results show that our model outperforms the CNN model only with word embedding features on microblog sentiment analysis task.

  18. High Order Tensor Formulation for Convolutional Sparse Coding

    KAUST Repository

    Bibi, Adel Aamer

    2017-12-25

    Convolutional sparse coding (CSC) has gained attention for its successful role as a reconstruction and a classification tool in the computer vision and machine learning community. Current CSC methods can only reconstruct singlefeature 2D images independently. However, learning multidimensional dictionaries and sparse codes for the reconstruction of multi-dimensional data is very important, as it examines correlations among all the data jointly. This provides more capacity for the learned dictionaries to better reconstruct data. In this paper, we propose a generic and novel formulation for the CSC problem that can handle an arbitrary order tensor of data. Backed with experimental results, our proposed formulation can not only tackle applications that are not possible with standard CSC solvers, including colored video reconstruction (5D- tensors), but it also performs favorably in reconstruction with much fewer parameters as compared to naive extensions of standard CSC to multiple features/channels.

  19. Classification of decays involving variable decay chains with convolutional architectures

    CERN Multimedia

    CERN. Geneva

    2018-01-01

    Vidyo contribution We present a technique to perform classification of decays that exhibit decay chains involving a variable number of particles, which include a broad class of $B$ meson decays sensitive to new physics. The utility of such decays as a probe of the Standard Model is dependent upon accurate determination of the decay rate, which is challenged by the combinatorial background arising in high-multiplicity decay modes. In our model, each particle in the decay event is represented as a fixed-dimensional vector of feature attributes, forming an $n \\times k$ representation of the event, where $n$ is the number of particles in the event and $k$ is the dimensionality of the feature vector. A convolutional architecture is used to capture dependencies between the embedded particle representations and perform the final classification. The proposed model performs outperforms standard machine learning approaches based on Monte Carlo studies across a range of variable final-state decays with the Belle II det...

  20. CONEDEP: COnvolutional Neural network based Earthquake DEtection and Phase Picking

    Science.gov (United States)

    Zhou, Y.; Huang, Y.; Yue, H.; Zhou, S.; An, S.; Yun, N.

    2017-12-01

    We developed an automatic local earthquake detection and phase picking algorithm based on Fully Convolutional Neural network (FCN). The FCN algorithm detects and segments certain features (phases) in 3 component seismograms to realize efficient picking. We use STA/LTA algorithm and template matching algorithm to construct the training set from seismograms recorded 1 month before and after the Wenchuan earthquake. Precise P and S phases are identified and labeled to construct the training set. Noise data are produced by combining back-ground noise and artificial synthetic noise to form the equivalent scale of noise set as the signal set. Training is performed on GPUs to achieve efficient convergence. Our algorithm has significantly improved performance in terms of the detection rate and precision in comparison with STA/LTA and template matching algorithms.

  1. Computational optical tomography using 3-D deep convolutional neural networks

    Science.gov (United States)

    Nguyen, Thanh; Bui, Vy; Nehmetallah, George

    2018-04-01

    Deep convolutional neural networks (DCNNs) offer a promising performance for many image processing areas, such as super-resolution, deconvolution, image classification, denoising, and segmentation, with outstanding results. Here, we develop for the first time, to our knowledge, a method to perform 3-D computational optical tomography using 3-D DCNN. A simulated 3-D phantom dataset was first constructed and converted to a dataset of phase objects imaged on a spatial light modulator. For each phase image in the dataset, the corresponding diffracted intensity image was experimentally recorded on a CCD. We then experimentally demonstrate the ability of the developed 3-D DCNN algorithm to solve the inverse problem by reconstructing the 3-D index of refraction distributions of test phantoms from the dataset from their corresponding diffraction patterns.

  2. Drug-Drug Interaction Extraction via Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Shengyu Liu

    2016-01-01

    Full Text Available Drug-drug interaction (DDI extraction as a typical relation extraction task in natural language processing (NLP has always attracted great attention. Most state-of-the-art DDI extraction systems are based on support vector machines (SVM with a large number of manually defined features. Recently, convolutional neural networks (CNN, a robust machine learning method which almost does not need manually defined features, has exhibited great potential for many NLP tasks. It is worth employing CNN for DDI extraction, which has never been investigated. We proposed a CNN-based method for DDI extraction. Experiments conducted on the 2013 DDIExtraction challenge corpus demonstrate that CNN is a good choice for DDI extraction. The CNN-based DDI extraction method achieves an F-score of 69.75%, which outperforms the existing best performing method by 2.75%.

  3. Truncation Depth Rule-of-Thumb for Convolutional Codes

    Science.gov (United States)

    Moision, Bruce

    2009-01-01

    In this innovation, it is shown that a commonly used rule of thumb (that the truncation depth of a convolutional code should be five times the memory length, m, of the code) is accurate only for rate 1/2 codes. In fact, the truncation depth should be 2.5 m/(1 - r), where r is the code rate. The accuracy of this new rule is demonstrated by tabulating the distance properties of a large set of known codes. This new rule was derived by bounding the losses due to truncation as a function of the code rate. With regard to particular codes, a good indicator of the required truncation depth is the path length at which all paths that diverge from a particular path have accumulated the minimum distance of the code. It is shown that the new rule of thumb provides an accurate prediction of this depth for codes of varying rates.

  4. Finding Neutrinos in LArTPCs using Convolutional Neural Networks

    Science.gov (United States)

    Wongjirad, Taritree

    2017-09-01

    Deep learning algorithms, which have emerged over the last decade, are opening up new ways to analyze data for many particle physics experiments. MicroBooNE, which is a neutrino experiment at Fermilab, has been exploring the use of such algorithms, in particular, convolutional neural networks (CNNS). CNNs are the state-of-the-art method for a large class of problems involving the analysis of images. This makes CNNs an attractive approach for MicroBooNE, whose detector, a liquid argon time projection chamber (LArTPC), produces high-resolution images of particle interactions. In this talk, I will discuss the ways CNNs can be applied to tasks like neutrino interaction detection and particle identification in MicroBooNE and LArTPCs.

  5. Radio frequency interference mitigation using deep convolutional neural networks

    Science.gov (United States)

    Akeret, J.; Chang, C.; Lucchi, A.; Refregier, A.

    2017-01-01

    We propose a novel approach for mitigating radio frequency interference (RFI) signals in radio data using the latest advances in deep learning. We employ a special type of Convolutional Neural Network, the U-Net, that enables the classification of clean signal and RFI signatures in 2D time-ordered data acquired from a radio telescope. We train and assess the performance of this network using the HIDE &SEEK radio data simulation and processing packages, as well as early Science Verification data acquired with the 7m single-dish telescope at the Bleien Observatory. We find that our U-Net implementation is showing competitive accuracy to classical RFI mitigation algorithms such as SEEK's SUMTHRESHOLD implementation. We publish our U-Net software package on GitHub under GPLv3 license.

  6. Forecasting Flare Activity Using Deep Convolutional Neural Networks

    Science.gov (United States)

    Hernandez, T.

    2017-12-01

    Current operational flare forecasting relies on human morphological analysis of active regions and the persistence of solar flare activity through time (i.e. that the Sun will continue to do what it is doing right now: flaring or remaining calm). In this talk we present the results of applying deep Convolutional Neural Networks (CNNs) to the problem of solar flare forecasting. CNNs operate by training a set of tunable spatial filters that, in combination with neural layer interconnectivity, allow CNNs to automatically identify significant spatial structures predictive for classification and regression problems. We will start by discussing the applicability and success rate of the approach, the advantages it has over non-automated forecasts, and how mining our trained neural network provides a fresh look into the mechanisms behind magnetic energy storage and release.

  7. Convolutional neural networks with balanced batches for facial expressions recognition

    Science.gov (United States)

    Battini Sönmez, Elena; Cangelosi, Angelo

    2017-03-01

    This paper considers the issue of fully automatic emotion classification on 2D faces. In spite of the great effort done in recent years, traditional machine learning approaches based on hand-crafted feature extraction followed by the classification stage failed to develop a real-time automatic facial expression recognition system. The proposed architecture uses Convolutional Neural Networks (CNN), which are built as a collection of interconnected processing elements to simulate the brain of human beings. The basic idea of CNNs is to learn a hierarchical representation of the input data, which results in a better classification performance. In this work we present a block-based CNN algorithm, which uses noise, as data augmentation technique, and builds batches with a balanced number of samples per class. The proposed architecture is a very simple yet powerful CNN, which can yield state-of-the-art accuracy on the very competitive benchmark algorithm of the Extended Cohn Kanade database.

  8. Network Intrusion Detection through Stacking Dilated Convolutional Autoencoders

    Directory of Open Access Journals (Sweden)

    Yang Yu

    2017-01-01

    Full Text Available Network intrusion detection is one of the most important parts for cyber security to protect computer systems against malicious attacks. With the emergence of numerous sophisticated and new attacks, however, network intrusion detection techniques are facing several significant challenges. The overall objective of this study is to learn useful feature representations automatically and efficiently from large amounts of unlabeled raw network traffic data by using deep learning approaches. We propose a novel network intrusion model by stacking dilated convolutional autoencoders and evaluate our method on two new intrusion detection datasets. Several experiments were carried out to check the effectiveness of our approach. The comparative experimental results demonstrate that the proposed model can achieve considerably high performance which meets the demand of high accuracy and adaptability of network intrusion detection systems (NIDSs. It is quite potential and promising to apply our model in the large-scale and real-world network environments.

  9. Fully Convolutional Network Based Shadow Extraction from GF-2 Imagery

    Science.gov (United States)

    Li, Z.; Cai, G.; Ren, H.

    2018-04-01

    There are many shadows on the high spatial resolution satellite images, especially in the urban areas. Although shadows on imagery severely affect the information extraction of land cover or land use, they provide auxiliary information for building extraction which is hard to achieve a satisfactory accuracy through image classification itself. This paper focused on the method of building shadow extraction by designing a fully convolutional network and training samples collected from GF-2 satellite imagery in the urban region of Changchun city. By means of spatial filtering and calculation of adjacent relationship along the sunlight direction, the small patches from vegetation or bridges have been eliminated from the preliminary extracted shadows. Finally, the building shadows were separated. The extracted building shadow information from the proposed method in this paper was compared with the results from the traditional object-oriented supervised classification algorihtms. It showed that the deep learning network approach can improve the accuracy to a large extent.

  10. Finger vein recognition based on convolutional neural network

    Directory of Open Access Journals (Sweden)

    Meng Gesi

    2017-01-01

    Full Text Available Biometric Authentication Technology has been widely used in this information age. As one of the most important technology of authentication, finger vein recognition attracts our attention because of its high security, reliable accuracy and excellent performance. However, the current finger vein recognition system is difficult to be applied widely because its complicated image pre-processing and not representative feature vectors. To solve this problem, a finger vein recognition method based on the convolution neural network (CNN is proposed in the paper. The image samples are directly input into the CNN model to extract its feature vector so that we can make authentication by comparing the Euclidean distance between these vectors. Finally, the Deep Learning Framework Caffe is adopted to verify this method. The result shows that there are great improvements in both speed and accuracy rate compared to the previous research. And the model has nice robustness in illumination and rotation.

  11. Fully convolutional network with cluster for semantic segmentation

    Science.gov (United States)

    Ma, Xiao; Chen, Zhongbi; Zhang, Jianlin

    2018-04-01

    At present, image semantic segmentation technology has been an active research topic for scientists in the field of computer vision and artificial intelligence. Especially, the extensive research of deep neural network in image recognition greatly promotes the development of semantic segmentation. This paper puts forward a method based on fully convolutional network, by cluster algorithm k-means. The cluster algorithm using the image's low-level features and initializing the cluster centers by the super-pixel segmentation is proposed to correct the set of points with low reliability, which are mistakenly classified in great probability, by the set of points with high reliability in each clustering regions. This method refines the segmentation of the target contour and improves the accuracy of the image segmentation.

  12. Real Time Eye Detector with Cascaded Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Bin Li

    2018-01-01

    Full Text Available An accurate and efficient eye detector is essential for many computer vision applications. In this paper, we present an efficient method to evaluate the eye location from facial images. First, a group of candidate regions with regional extreme points is quickly proposed; then, a set of convolution neural networks (CNNs is adopted to determine the most likely eye region and classify the region as left or right eye; finally, the center of the eye is located with other CNNs. In the experiments using GI4E, BioID, and our datasets, our method attained a detection accuracy which is comparable to existing state-of-the-art methods; meanwhile, our method was faster and adaptable to variations of the images, including external light changes, facial occlusion, and changes in image modality.

  13. Convolution product construction of interactions in probabilistic physical models

    International Nuclear Information System (INIS)

    Ratsimbarison, H.M.; Raboanary, R.

    2007-01-01

    This paper aims to give a probabilistic construction of interactions which may be relevant for building physical theories such as interacting quantum field theories. We start with the path integral definition of partition function in quantum field theory which recall us the probabilistic nature of this physical theory. From a Gaussian law considered as free theory, an interacting theory is constructed by nontrivial convolution product between the free theory and an interacting term which is also a probability law. The resulting theory, again a probability law, exhibits two proprieties already present in nowadays theories of interactions such as Gauge theory : the interaction term does not depend on the free term, and two different free theories can be implemented with the same interaction.

  14. Plane-wave decomposition by spherical-convolution microphone array

    Science.gov (United States)

    Rafaely, Boaz; Park, Munhum

    2004-05-01

    Reverberant sound fields are widely studied, as they have a significant influence on the acoustic performance of enclosures in a variety of applications. For example, the intelligibility of speech in lecture rooms, the quality of music in auditoria, the noise level in offices, and the production of 3D sound in living rooms are all affected by the enclosed sound field. These sound fields are typically studied through frequency response measurements or statistical measures such as reverberation time, which do not provide detailed spatial information. The aim of the work presented in this seminar is the detailed analysis of reverberant sound fields. A measurement and analysis system based on acoustic theory and signal processing, designed around a spherical microphone array, is presented. Detailed analysis is achieved by decomposition of the sound field into waves, using spherical Fourier transform and spherical convolution. The presentation will include theoretical review, simulation studies, and initial experimental results.

  15. Deep learning with convolutional neural network in radiology.

    Science.gov (United States)

    Yasaka, Koichiro; Akai, Hiroyuki; Kunimatsu, Akira; Kiryu, Shigeru; Abe, Osamu

    2018-04-01

    Deep learning with a convolutional neural network (CNN) is gaining attention recently for its high performance in image recognition. Images themselves can be utilized in a learning process with this technique, and feature extraction in advance of the learning process is not required. Important features can be automatically learned. Thanks to the development of hardware and software in addition to techniques regarding deep learning, application of this technique to radiological images for predicting clinically useful information, such as the detection and the evaluation of lesions, etc., are beginning to be investigated. This article illustrates basic technical knowledge regarding deep learning with CNNs along the actual course (collecting data, implementing CNNs, and training and testing phases). Pitfalls regarding this technique and how to manage them are also illustrated. We also described some advanced topics of deep learning, results of recent clinical studies, and the future directions of clinical application of deep learning techniques.

  16. Static facial expression recognition with convolution neural networks

    Science.gov (United States)

    Zhang, Feng; Chen, Zhong; Ouyang, Chao; Zhang, Yifei

    2018-03-01

    Facial expression recognition is a currently active research topic in the fields of computer vision, pattern recognition and artificial intelligence. In this paper, we have developed a convolutional neural networks (CNN) for classifying human emotions from static facial expression into one of the seven facial emotion categories. We pre-train our CNN model on the combined FER2013 dataset formed by train, validation and test set and fine-tune on the extended Cohn-Kanade database. In order to reduce the overfitting of the models, we utilized different techniques including dropout and batch normalization in addition to data augmentation. According to the experimental result, our CNN model has excellent classification performance and robustness for facial expression recognition.

  17. DeepNAT: Deep convolutional neural network for segmenting neuroanatomy.

    Science.gov (United States)

    Wachinger, Christian; Reuter, Martin; Klein, Tassilo

    2018-04-15

    We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is an end-to-end learning-based approach to brain segmentation that jointly learns an abstract feature representation and a multi-class classification. We propose a 3D patch-based approach, where we do not only predict the center voxel of the patch but also neighbors, which is formulated as multi-task learning. To address a class imbalance problem, we arrange two networks hierarchically, where the first one separates foreground from background, and the second one identifies 25 brain structures on the foreground. Since patches lack spatial context, we augment them with coordinates. To this end, we introduce a novel intrinsic parameterization of the brain volume, formed by eigenfunctions of the Laplace-Beltrami operator. As network architecture, we use three convolutional layers with pooling, batch normalization, and non-linearities, followed by fully connected layers with dropout. The final segmentation is inferred from the probabilistic output of the network with a 3D fully connected conditional random field, which ensures label agreement between close voxels. The roughly 2.7million parameters in the network are learned with stochastic gradient descent. Our results show that DeepNAT compares favorably to state-of-the-art methods. Finally, the purely learning-based method may have a high potential for the adaptation to young, old, or diseased brains by fine-tuning the pre-trained network with a small training sample on the target application, where the availability of larger datasets with manual annotations may boost the overall segmentation accuracy in the future. Copyright © 2017 Elsevier Inc. All rights reserved.

  18. FULLY CONVOLUTIONAL NETWORKS FOR GROUND CLASSIFICATION FROM LIDAR POINT CLOUDS

    Directory of Open Access Journals (Sweden)

    A. Rizaldy

    2018-05-01

    Full Text Available Deep Learning has been massively used for image classification in recent years. The use of deep learning for ground classification from LIDAR point clouds has also been recently studied. However, point clouds need to be converted into an image in order to use Convolutional Neural Networks (CNNs. In state-of-the-art techniques, this conversion is slow because each point is converted into a separate image. This approach leads to highly redundant computation during conversion and classification. The goal of this study is to design a more efficient data conversion and ground classification. This goal is achieved by first converting the whole point cloud into a single image. The classification is then performed by a Fully Convolutional Network (FCN, a modified version of CNN designed for pixel-wise image classification. The proposed method is significantly faster than state-of-the-art techniques. On the ISPRS Filter Test dataset, it is 78 times faster for conversion and 16 times faster for classification. Our experimental analysis on the same dataset shows that the proposed method results in 5.22 % of total error, 4.10 % of type I error, and 15.07 % of type II error. Compared to the previous CNN-based technique and LAStools software, the proposed method reduces the total error and type I error (while type II error is slightly higher. The method was also tested on a very high point density LIDAR point clouds resulting in 4.02 % of total error, 2.15 % of type I error and 6.14 % of type II error.

  19. Fully Convolutional Networks for Ground Classification from LIDAR Point Clouds

    Science.gov (United States)

    Rizaldy, A.; Persello, C.; Gevaert, C. M.; Oude Elberink, S. J.

    2018-05-01

    Deep Learning has been massively used for image classification in recent years. The use of deep learning for ground classification from LIDAR point clouds has also been recently studied. However, point clouds need to be converted into an image in order to use Convolutional Neural Networks (CNNs). In state-of-the-art techniques, this conversion is slow because each point is converted into a separate image. This approach leads to highly redundant computation during conversion and classification. The goal of this study is to design a more efficient data conversion and ground classification. This goal is achieved by first converting the whole point cloud into a single image. The classification is then performed by a Fully Convolutional Network (FCN), a modified version of CNN designed for pixel-wise image classification. The proposed method is significantly faster than state-of-the-art techniques. On the ISPRS Filter Test dataset, it is 78 times faster for conversion and 16 times faster for classification. Our experimental analysis on the same dataset shows that the proposed method results in 5.22 % of total error, 4.10 % of type I error, and 15.07 % of type II error. Compared to the previous CNN-based technique and LAStools software, the proposed method reduces the total error and type I error (while type II error is slightly higher). The method was also tested on a very high point density LIDAR point clouds resulting in 4.02 % of total error, 2.15 % of type I error and 6.14 % of type II error.

  20. Color encoding in biologically-inspired convolutional neural networks.

    Science.gov (United States)

    Rafegas, Ivet; Vanrell, Maria

    2018-05-11

    Convolutional Neural Networks have been proposed as suitable frameworks to model biological vision. Some of these artificial networks showed representational properties that rival primate performances in object recognition. In this paper we explore how color is encoded in a trained artificial network. It is performed by estimating a color selectivity index for each neuron, which allows us to describe the neuron activity to a color input stimuli. The index allows us to classify whether they are color selective or not and if they are of a single or double color. We have determined that all five convolutional layers of the network have a large number of color selective neurons. Color opponency clearly emerges in the first layer, presenting 4 main axes (Black-White, Red-Cyan, Blue-Yellow and Magenta-Green), but this is reduced and rotated as we go deeper into the network. In layer 2 we find a denser hue sampling of color neurons and opponency is reduced almost to one new main axis, the Bluish-Orangish coinciding with the dataset bias. In layers 3, 4 and 5 color neurons are similar amongst themselves, presenting different type of neurons that detect specific colored objects (e.g., orangish faces), specific surrounds (e.g., blue sky) or specific colored or contrasted object-surround configurations (e.g. blue blob in a green surround). Overall, our work concludes that color and shape representation are successively entangled through all the layers of the studied network, revealing certain parallelisms with the reported evidences in primate brains that can provide useful insight into intermediate hierarchical spatio-chromatic representations. Copyright © 2018 Elsevier Ltd. All rights reserved.

  1. Fully convolutional neural networks improve abdominal organ segmentation

    Science.gov (United States)

    Bobo, Meg F.; Bao, Shunxing; Huo, Yuankai; Yao, Yuang; Virostko, Jack; Plassard, Andrew J.; Lyu, Ilwoo; Assad, Albert; Abramson, Richard G.; Hilmes, Melissa A.; Landman, Bennett A.

    2018-03-01

    Abdominal image segmentation is a challenging, yet important clinical problem. Variations in body size, position, and relative organ positions greatly complicate the segmentation process. Historically, multi-atlas methods have achieved leading results across imaging modalities and anatomical targets. However, deep learning is rapidly overtaking classical approaches for image segmentation. Recently, Zhou et al. showed that fully convolutional networks produce excellent results in abdominal organ segmentation of computed tomography (CT) scans. Yet, deep learning approaches have not been applied to whole abdomen magnetic resonance imaging (MRI) segmentation. Herein, we evaluate the applicability of an existing fully convolutional neural network (FCNN) designed for CT imaging to segment abdominal organs on T2 weighted (T2w) MRI's with two examples. In the primary example, we compare a classical multi-atlas approach with FCNN on forty-five T2w MRI's acquired from splenomegaly patients with five organs labeled (liver, spleen, left kidney, right kidney, and stomach). Thirty-six images were used for training while nine were used for testing. The FCNN resulted in a Dice similarity coefficient (DSC) of 0.930 in spleens, 0.730 in left kidneys, 0.780 in right kidneys, 0.913 in livers, and 0.556 in stomachs. The performance measures for livers, spleens, right kidneys, and stomachs were significantly better than multi-atlas (p < 0.05, Wilcoxon rank-sum test). In a secondary example, we compare the multi-atlas approach with FCNN on 138 distinct T2w MRI's with manually labeled pancreases (one label). On the pancreas dataset, the FCNN resulted in a median DSC of 0.691 in pancreases versus 0.287 for multi-atlas. The results are highly promising given relatively limited training data and without specific training of the FCNN model and illustrate the potential of deep learning approaches to transcend imaging modalities. 1

  2. Limitations of a convolution method for modeling geometric uncertainties in radiation therapy. I. The effect of shift invariance

    International Nuclear Information System (INIS)

    Craig, Tim; Battista, Jerry; Van Dyk, Jake

    2003-01-01

    Convolution methods have been used to model the effect of geometric uncertainties on dose delivery in radiation therapy. Convolution assumes shift invariance of the dose distribution. Internal inhomogeneities and surface curvature lead to violations of this assumption. The magnitude of the error resulting from violation of shift invariance is not well documented. This issue is addressed by comparing dose distributions calculated using the Convolution method with dose distributions obtained by Direct Simulation. A comparison of conventional Static dose distributions was also made with Direct Simulation. This analysis was performed for phantom geometries and several clinical tumor sites. A modification to the Convolution method to correct for some of the inherent errors is proposed and tested using example phantoms and patients. We refer to this modified method as the Corrected Convolution. The average maximum dose error in the calculated volume (averaged over different beam arrangements in the various phantom examples) was 21% with the Static dose calculation, 9% with Convolution, and reduced to 5% with the Corrected Convolution. The average maximum dose error in the calculated volume (averaged over four clinical examples) was 9% for the Static method, 13% for Convolution, and 3% for Corrected Convolution. While Convolution can provide a superior estimate of the dose delivered when geometric uncertainties are present, the violation of shift invariance can result in substantial errors near the surface of the patient. The proposed Corrected Convolution modification reduces errors near the surface to 3% or less

  3. Young Children's Sibling Relationship Quality: Distal and Proximal Correlates

    Science.gov (United States)

    Kretschmer, Tina; Pike, Alison

    2009-01-01

    Background: Relationships within families are interdependent and related to distal environmental factors. Low socioeconomic status (SES) and high household chaos (distal factors) have been linked to less positive marital and parent-child relationships, but have not yet been examined with regard to young children's sibling relationships. The…

  4. Distal clavicular osteolysis: MR evidence for subchondral fracture

    Energy Technology Data Exchange (ETDEWEB)

    Kassarjian, Ara; Palmer, William E. [Massachusetts General Hospital, Department of Radiology, Division of Musculoskeletal Radiology, Yawkey Center, Boston, MA (United States); Llopis, Eva [Hospital de la Ribera, Department of Radiology, Valencia (Spain)

    2007-01-15

    To investigate the association between distal clavicular osteolysis and subchondral fractures of the distal clavicle at MRI. This study was approved by the hospital human research committee, which waived the need for informed consent. Three radiologists retrospectively analyzed 36 shoulder MR examinations in 36 patients with imaging findings of distal clavicular osteolysis. The presence of a subchondral fracture of the distal clavicle, abnormalities of the acromioclavicular joint, rotator cuff tears and labral tears were assessed by MRI. These cases were then compared with 36 age-matched controls. At MRI, 31 of 36 patients (86%) had a subchondral line within the distal clavicular edema, consistent with a subchondral fracture. Of the 36 patients, 32 (89%) had fluid in the acromioclavicular joint, while 27 of 36 patients (75%) had cysts or erosions in the distal clavicle. There were 13 patients (36%) with associated labral tears, while eight patients (22%) had partial-thickness rotator cuff tears. In the control group one of 36 (3%) had a subchondral line (P<0.05), while ten of 36 (28%) had rotator cuff tears and 13 of 36 (36%) had labral tears. These latter two were not statistically significant between the groups. A distal clavicular subchondral fracture is a common finding in patients with imaging evidence of distal clavicular osteolysis. These subchondral fractures may be responsible for the propensity of findings occurring on the clavicular side of the acromioclavicular joint. (orig.)

  5. Distal clavicular osteolysis: MR evidence for subchondral fracture

    International Nuclear Information System (INIS)

    Kassarjian, Ara; Palmer, William E.; Llopis, Eva

    2007-01-01

    To investigate the association between distal clavicular osteolysis and subchondral fractures of the distal clavicle at MRI. This study was approved by the hospital human research committee, which waived the need for informed consent. Three radiologists retrospectively analyzed 36 shoulder MR examinations in 36 patients with imaging findings of distal clavicular osteolysis. The presence of a subchondral fracture of the distal clavicle, abnormalities of the acromioclavicular joint, rotator cuff tears and labral tears were assessed by MRI. These cases were then compared with 36 age-matched controls. At MRI, 31 of 36 patients (86%) had a subchondral line within the distal clavicular edema, consistent with a subchondral fracture. Of the 36 patients, 32 (89%) had fluid in the acromioclavicular joint, while 27 of 36 patients (75%) had cysts or erosions in the distal clavicle. There were 13 patients (36%) with associated labral tears, while eight patients (22%) had partial-thickness rotator cuff tears. In the control group one of 36 (3%) had a subchondral line (P<0.05), while ten of 36 (28%) had rotator cuff tears and 13 of 36 (36%) had labral tears. These latter two were not statistically significant between the groups. A distal clavicular subchondral fracture is a common finding in patients with imaging evidence of distal clavicular osteolysis. These subchondral fractures may be responsible for the propensity of findings occurring on the clavicular side of the acromioclavicular joint. (orig.)

  6. Intra-articular osteotomy for distal humerus malunion

    NARCIS (Netherlands)

    Marti, René K.; Doornberg, Job

    2009-01-01

    Intra-articular osteotomy is considered in the rare case of malunion after a fracture of the distal humerus to restore humeral alignment and gain a functional arc of elbow motion. Traumatic and iatrogenic disruption of the limited blood flow to the distal end of the humerus resulting in avascular

  7. Clinical relevance of distal biceps insertional and footprint anatomy

    NARCIS (Netherlands)

    van den Bekerom, Michel P J; Kodde, Izaäk F.; Aster, Asir; Bleys, Ronald L A W; Eygendaal, Denise

    2016-01-01

    Purpose: The aim of this review was to present an overview, based on a literature search, of surgical anatomy for distal biceps tendon repairs, based on the current literature. Methods: A narrative review was performed using Pubmed/Medline using key words: Search terms were distal biceps,

  8. Distal tibiofibular synostosis in a Nigerian: A case report | Owoeye ...

    African Journals Online (AJOL)

    X-ray of the bones showed an oblique fracture in the distal end of the shaft of fibula which is suggestive of post traumatic tibiofibular synostosis (TFS). Knowledge of distal TFS is important in resolving the puzzle of chronic shin pain of unknown origin and in accurate diagnosis of causes of ankle deformity and malformations.

  9. Radiographic study of distal radial physeal closure in thoroughbred horses

    International Nuclear Information System (INIS)

    Vulcano, L.C.; Mamprim, M.J.; Muniz, L.M.R.; Moreira, A.F.; Luna, S.P.L.

    1997-01-01

    Monthly radiography was performed to study distal radial physeal closure in ten male and ten female Throughbred horses. The height, thoracic circumference and metacarpus circumference were also measured, Distal radial physeal closure time was sooner in females than males, and took 701 +/- 37 and 748 +/- 55 days respectively

  10. A frequency bin-wise nonlinear masking algorithm in convolutive mixtures for speech segregation.

    Science.gov (United States)

    Chi, Tai-Shih; Huang, Ching-Wen; Chou, Wen-Sheng

    2012-05-01

    A frequency bin-wise nonlinear masking algorithm is proposed in the spectrogram domain for speech segregation in convolutive mixtures. The contributive weight from each speech source to a time-frequency unit of the mixture spectrogram is estimated by a nonlinear function based on location cues. For each sound source, a non-binary mask is formed from the estimated weights and is multiplied to the mixture spectrogram to extract the sound. Head-related transfer functions (HRTFs) are used to simulate convolutive sound mixtures perceived by listeners. Simulation results show our proposed method outperforms convolutive independent component analysis and degenerate unmixing and estimation technique methods in almost all test conditions.

  11. Development and application of deep convolutional neural network in target detection

    Science.gov (United States)

    Jiang, Xiaowei; Wang, Chunping; Fu, Qiang

    2018-04-01

    With the development of big data and algorithms, deep convolution neural networks with more hidden layers have more powerful feature learning and feature expression ability than traditional machine learning methods, making artificial intelligence surpass human level in many fields. This paper first reviews the development and application of deep convolutional neural networks in the field of object detection in recent years, then briefly summarizes and ponders some existing problems in the current research, and the future development of deep convolutional neural network is prospected.

  12. A FUNCTIONAL EVALUATION STUDY OF DISTAL FEMORAL FRACTURES FIXED WITH DISTAL FEMORAL LOCKING PLATE

    Directory of Open Access Journals (Sweden)

    Manikumar C. J

    2017-04-01

    Full Text Available BACKGROUND Fractures of the distal femur present considerable challenges in management. Older patients especially women sustain fractures due to osteoporosis. Supracondylar fractures of femur have a bimodal distribution. They account for 6% of all femur fractures and 31% if hip fractures were excluded. Nearly, 50% of distal femur intra-articular fractures are open fractures. Before 1970, most supracondylar fractures were treated nonoperatively; however, difficulties were often encountered including persistent angulatory deformity, knee joint incongruity, loss of knee motion and delayed mobilisation. The trend of open reduction and internal fixation has become evident in recent years with good results being obtained with AO blade plate, dynamic condylar screw, intramedullary supracondylar nail and locking compression plate. Elderly patients and osteoporosis pose difficulty in treating intra-articular fractures of the lower end of femur. Loss of stable fixation is of great concern in these cases. Hence, locking compression plate use has an advantage in these patients. MATERIALS AND METHODS In this study, 20 patients with closed fracture of distal femur were studied. All the cases were treated at the Department of Orthopaedics, Rangaraya Medical College/Government General Hospital, Kakinada, Andhra Pradesh, between November 2013 and November 2015. The method used for fracture fixation was open reduction and internal fixation with distal femoral locking plate. The duration of follow up ranged from 3 months to 24 months. All the fractures in this series were posttraumatic. The patients were functionally evaluated with Neer’s scoring system. 1 RESULTS Twenty distal femoral fractures were treated with distal femoral locking plates. 15 patients were males and 5 patients were females. The median age was 47 years ranging from 28-70 years. 16 of the fractures were caused by road traffic accidents and 2 were due to fall, 2 were due to assault. 12 patients

  13. Rehabilitation for distal radial fractures in adults.

    Science.gov (United States)

    Handoll, Helen H G; Elliott, Joanne

    2015-09-25

    Fracture of the distal radius is a common clinical problem, particularly in older people with osteoporosis. There is considerable variation in the management, including rehabilitation, of these fractures. This is an update of a Cochrane review first published in 2002 and last updated in 2006. To examine the effects of rehabilitation interventions in adults with conservatively or surgically treated distal radial fractures. We searched the Cochrane Bone, Joint and Muscle Trauma Group Specialised Register, the Cochrane Central Register of Controlled Trials (CENTRAL 2014; Issue 12), MEDLINE, EMBASE, CINAHL, AMED, PEDro, OTseeker and other databases, trial registers, conference proceedings and reference lists of articles. We did not apply any language restrictions. The date of the last search was 12 January 2015. Randomised controlled trials (RCTs) or quasi-RCTs evaluating rehabilitation as part of the management of fractures of the distal radius sustained by adults. Rehabilitation interventions such as active and passive mobilisation exercises, and training for activities of daily living, could be used on their own or in combination, and be applied in various ways by various clinicians. The review authors independently screened and selected trials, and reviewed eligible trials. We contacted study authors for additional information. We did not pool data. We included 26 trials, involving 1269 mainly female and older patients. With few exceptions, these studies did not include people with serious fracture or treatment-related complications, or older people with comorbidities and poor overall function that would have precluded trial participation or required more intensive treatment. Only four of the 23 comparisons covered by these 26 trials were evaluated by more than one trial. Participants of 15 trials were initially treated conservatively, involving plaster cast immobilisation. Initial treatment was surgery (external fixation or internal fixation) for all participants

  14. Distal Stressors and Depression among Homeless Men.

    Science.gov (United States)

    Coohey, Carol; Easton, Scott D

    2016-05-01

    Depression is a common problem among homeless men that may interfere with functional tasks, such as securing stable housing, obtaining employment, and accessing health services. Previous research on depression among homeless men has largely focused on current psychosocial resources, substance abuse, and past victimization. Guided by Ensel and Lin's life course stress process model, the authors examined whether distal stressors, including victimization and exposure to parent problems in childhood, contributed to men's depression above and beyond current (or proximal) stressors, such as substance abuse and health problems, and social resources. The sample consisted of 309 homeless men who had entered a federally funded emergency shelter. Using the Burns Depression Checklist, the authors found that one out of three men met the threshold for moderate to severe depression during the past week. The logistic regression showed that past exposure to parent problems was related to depression after accounting for current stressors and social resources (number of close adult relationships and whether their emotional support needs were met). Past victimization was not related to depression. To address men's depression, workers should concurrently provide services that meet men's basic needs (for example, housing) and address their relationship needs, including their need for emotional support.

  15. Does computer use affect the incidence of distal arm pain? A one-year prospective study using objective measures of computer use

    DEFF Research Database (Denmark)

    Mikkelsen, Sigurd; Lassen, Christina Funch; Vilstrup, Imogen

    2012-01-01

    PURPOSE: To study how objectively recorded mouse and keyboard activity affects distal arm pain among computer workers. METHODS: Computer activities were recorded among 2,146 computer workers. For 52 weeks mouse and keyboard time, sustained activity, speed and micropauses were recorded with a soft......PURPOSE: To study how objectively recorded mouse and keyboard activity affects distal arm pain among computer workers. METHODS: Computer activities were recorded among 2,146 computer workers. For 52 weeks mouse and keyboard time, sustained activity, speed and micropauses were recorded...... with a software program installed on the participants' computers. Participants reported weekly pain scores via the software program for elbow, forearm and wrist/hand as well as in a questionnaire at baseline and 1-year follow up. Associations between pain development and computer work were examined for three pain...... were not risk factors for acute pain, nor did they modify the effects of mouse or keyboard time. Computer usage parameters were not associated with prolonged or chronic pain. A major limitation of the study was low keyboard times. CONCLUSION: Computer work was not related to the development...

  16. Does computer use affect the incidence of distal arm pain? A one-year prospective study using objective measures of computer use

    DEFF Research Database (Denmark)

    Mikkelsen, S.; Lassen, C. F.; Vilstrup, Imogen

    2012-01-01

    PURPOSE: To study how objectively recorded mouse and keyboard activity affects distal arm pain among computer workers. METHODS: Computer activities were recorded among 2,146 computer workers. For 52 weeks mouse and keyboard time, sustained activity, speed and micropauses were recorded with a soft......PURPOSE: To study how objectively recorded mouse and keyboard activity affects distal arm pain among computer workers. METHODS: Computer activities were recorded among 2,146 computer workers. For 52 weeks mouse and keyboard time, sustained activity, speed and micropauses were recorded...... with a software program installed on the participants' computers. Participants reported weekly pain scores via the software program for elbow, forearm and wrist/hand as well as in a questionnaire at baseline and 1-year follow up. Associations between pain development and computer work were examined for three pain...... were not risk factors for acute pain, nor did they modify the effects of mouse or keyboard time. Computer usage parameters were not associated with prolonged or chronic pain. A major limitation of the study was low keyboard times. CONCLUSION: Computer work was not related to the development...

  17. Centralized mouse repositories.

    Science.gov (United States)

    Donahue, Leah Rae; Hrabe de Angelis, Martin; Hagn, Michael; Franklin, Craig; Lloyd, K C Kent; Magnuson, Terry; McKerlie, Colin; Nakagata, Naomi; Obata, Yuichi; Read, Stuart; Wurst, Wolfgang; Hörlein, Andreas; Davisson, Muriel T

    2012-10-01

    Because the mouse is used so widely for biomedical research and the number of mouse models being generated is increasing rapidly, centralized repositories are essential if the valuable mouse strains and models that have been developed are to be securely preserved and fully exploited. Ensuring the ongoing availability of these mouse strains preserves the investment made in creating and characterizing them and creates a global resource of enormous value. The establishment of centralized mouse repositories around the world for distributing and archiving these resources has provided critical access to and preservation of these strains. This article describes the common and specialized activities provided by major mouse repositories around the world.

  18. Subsidence feature discrimination using deep convolutional neral networks in synthetic aperture radar imagery

    CSIR Research Space (South Africa)

    Schwegmann, Colin P

    2017-07-01

    Full Text Available International Geoscience and Remote Sensing Symposium (IGARSS), 23-28 July 2017, Fort Worth, TX, USA SUBSIDENCE FEATURE DISCRIMINATION USING DEEP CONVOLUTIONAL NEURAL NETWORKS IN SYNTHETIC APERTURE RADAR IMAGERY Schwegmann, Colin P Kleynhans, Waldo...

  19. A Study of Recurrent and Convolutional Neural Networks in the Native Language Identification Task

    KAUST Repository

    Werfelmann, Robert

    2018-01-01

    around the world. The neural network models consisted of Long Short-Term Memory and Convolutional networks using the sentences of each document as the input. Additional statistical features were generated from the text to complement the predictions

  20. Convolution of large 3D images on GPU and its decomposition

    Science.gov (United States)

    Karas, Pavel; Svoboda, David

    2011-12-01

    In this article, we propose a method for computing convolution of large 3D images. The convolution is performed in a frequency domain using a convolution theorem. The algorithm is accelerated on a graphic card by means of the CUDA parallel computing model. Convolution is decomposed in a frequency domain using the decimation in frequency algorithm. We pay attention to keeping our approach efficient in terms of both time and memory consumption and also in terms of memory transfers between CPU and GPU which have a significant inuence on overall computational time. We also study the implementation on multiple GPUs and compare the results between the multi-GPU and multi-CPU implementations.

  1. Single Image Super-Resolution Based on Multi-Scale Competitive Convolutional Neural Network.

    Science.gov (United States)

    Du, Xiaofeng; Qu, Xiaobo; He, Yifan; Guo, Di

    2018-03-06

    Deep convolutional neural networks (CNNs) are successful in single-image super-resolution. Traditional CNNs are limited to exploit multi-scale contextual information for image reconstruction due to the fixed convolutional kernel in their building modules. To restore various scales of image details, we enhance the multi-scale inference capability of CNNs by introducing competition among multi-scale convolutional filters, and build up a shallow network under limited computational resources. The proposed network has the following two advantages: (1) the multi-scale convolutional kernel provides the multi-context for image super-resolution, and (2) the maximum competitive strategy adaptively chooses the optimal scale of information for image reconstruction. Our experimental results on image super-resolution show that the performance of the proposed network outperforms the state-of-the-art methods.

  2. Resting State fMRI Functional Connectivity-Based Classification Using a Convolutional Neural Network Architecture.

    Science.gov (United States)

    Meszlényi, Regina J; Buza, Krisztian; Vidnyánszky, Zoltán

    2017-01-01

    Machine learning techniques have become increasingly popular in the field of resting state fMRI (functional magnetic resonance imaging) network based classification. However, the application of convolutional networks has been proposed only very recently and has remained largely unexplored. In this paper we describe a convolutional neural network architecture for functional connectome classification called connectome-convolutional neural network (CCNN). Our results on simulated datasets and a publicly available dataset for amnestic mild cognitive impairment classification demonstrate that our CCNN model can efficiently distinguish between subject groups. We also show that the connectome-convolutional network is capable to combine information from diverse functional connectivity metrics and that models using a combination of different connectivity descriptors are able to outperform classifiers using only one metric. From this flexibility follows that our proposed CCNN model can be easily adapted to a wide range of connectome based classification or regression tasks, by varying which connectivity descriptor combinations are used to train the network.

  3. A Revised Piecewise Linear Recursive Convolution FDTD Method for Magnetized Plasmas

    International Nuclear Information System (INIS)

    Liu Song; Zhong Shuangying; Liu Shaobin

    2005-01-01

    The piecewise linear recursive convolution (PLRC) finite-different time-domain (FDTD) method improves accuracy over the original recursive convolution (RC) FDTD approach and current density convolution (JEC) but retains their advantages in speed and efficiency. This paper describes a revised piecewise linear recursive convolution PLRC-FDTD formulation for magnetized plasma which incorporates both anisotropy and frequency dispersion at the same time, enabling the transient analysis of magnetized plasma media. The technique is illustrated by numerical simulations of the reflection and transmission coefficients through a magnetized plasma layer. The results show that the revised PLRC-FDTD method has improved the accuracy over the original RC FDTD method and JEC FDTD method

  4. Deep Fully Convolutional Networks for the Detection of Informal Settlements in VHR Images

    NARCIS (Netherlands)

    Persello, Claudio; Stein, Alfred

    2017-01-01

    This letter investigates fully convolutional networks (FCNs) for the detection of informal settlements in very high resolution (VHR) satellite images. Informal settlements or slums are proliferating in developing countries and their detection and classification provides vital information for

  5. Fourier transform and mean quadratic variation of Bernoulli convolution on homogeneous Cantor set

    Energy Technology Data Exchange (ETDEWEB)

    Yu Zuguo E-mail: yuzg@hotmail.comz.yu

    2004-07-01

    For the Bernoulli convolution on homogeneous Cantor set, under some condition, it is proved that the mean quadratic variation and the average of Fourier transform of this measure are bounded above and below.

  6. Directional Radiometry and Radiative Transfer: the Convoluted Path From Centuries-old Phenomenology to Physical Optics

    Science.gov (United States)

    Mishchenko, Michael I.

    2014-01-01

    This Essay traces the centuries-long history of the phenomenological disciplines of directional radiometry and radiative transfer in turbid media, discusses their fundamental weaknesses, and outlines the convoluted process of their conversion into legitimate branches of physical optics.

  7. The neuro vector engine : flexibility to improve convolutional net efficiency for wearable vision

    NARCIS (Netherlands)

    Peemen, M.C.J.; Shi, R.; Lal, S.; Juurlink, B.H.H.; Mesman, B.; Corporaal, H.

    2016-01-01

    Deep Convolutional Networks (ConvNets) are currently superior in benchmark performance, but the associated demands on computation and data transfer prohibit straightforward mapping on energy constrained wearable platforms. The computational burden can be overcome by dedicated hardware accelerators,

  8. Method for assessing the probability of accumulated doses from an intermittent source using the convolution technique

    International Nuclear Information System (INIS)

    Coleman, J.H.

    1980-10-01

    A technique is discussed for computing the probability distribution of the accumulated dose received by an arbitrary receptor resulting from several single releases from an intermittent source. The probability density of the accumulated dose is the convolution of the probability densities of doses from the intermittent releases. Emissions are not assumed to be constant over the brief release period. The fast fourier transform is used in the calculation of the convolution

  9. Deep Galaxy: Classification of Galaxies based on Deep Convolutional Neural Networks

    OpenAIRE

    Khalifa, Nour Eldeen M.; Taha, Mohamed Hamed N.; Hassanien, Aboul Ella; Selim, I. M.

    2017-01-01

    In this paper, a deep convolutional neural network architecture for galaxies classification is presented. The galaxy can be classified based on its features into main three categories Elliptical, Spiral, and Irregular. The proposed deep galaxies architecture consists of 8 layers, one main convolutional layer for features extraction with 96 filters, followed by two principles fully connected layers for classification. It is trained over 1356 images and achieved 97.272% in testing accuracy. A c...

  10. Is Kinesio Taping to Generate Skin Convolutions Effective for Increasing Local Blood Circulation?

    OpenAIRE

    Yang, Jae-Man; Lee, Jung-Hoon

    2018-01-01

    Background It is unclear whether traditional application of Kinesio taping, which produces wrinkles in the skin, is effective for improving blood circulation. This study investigated local skin temperature changes after the application of an elastic therapeutic tape using convolution and non-convolution taping methods (CTM/NCTM). Material/Methods Twenty-eight pain-free men underwent CTM and NCTM randomly applied to the right and left sides of the lower back. Using infrared thermography, skin ...

  11. Isointense infant brain MRI segmentation with a dilated convolutional neural network

    OpenAIRE

    Moeskops, Pim; Pluim, Josien P. W.

    2017-01-01

    Quantitative analysis of brain MRI at the age of 6 months is difficult because of the limited contrast between white matter and gray matter. In this study, we use a dilated triplanar convolutional neural network in combination with a non-dilated 3D convolutional neural network for the segmentation of white matter, gray matter and cerebrospinal fluid in infant brain MR images, as provided by the MICCAI grand challenge on 6-month infant brain MRI segmentation.

  12. Experimental study of current loss and plasma formation in the Z machine post-hole convolute

    Directory of Open Access Journals (Sweden)

    M. R. Gomez

    2017-01-01

    Full Text Available The Z pulsed-power generator at Sandia National Laboratories drives high energy density physics experiments with load currents of up to 26 MA. Z utilizes a double post-hole convolute to combine the current from four parallel magnetically insulated transmission lines into a single transmission line just upstream of the load. Current loss is observed in most experiments and is traditionally attributed to inefficient convolute performance. The apparent loss current varies substantially for z-pinch loads with different inductance histories; however, a similar convolute impedance history is observed for all load types. This paper details direct spectroscopic measurements of plasma density, temperature, and apparent and actual plasma closure velocities within the convolute. Spectral measurements indicate a correlation between impedance collapse and plasma formation in the convolute. Absorption features in the spectra show the convolute plasma consists primarily of hydrogen, which likely forms from desorbed electrode contaminant species such as H_{2}O, H_{2}, and hydrocarbons. Plasma densities increase from 1×10^{16}  cm^{−3} (level of detectability just before peak current to over 1×10^{17}  cm^{−3} at stagnation (tens of ns later. The density seems to be highest near the cathode surface, with an apparent cathode to anode plasma velocity in the range of 35–50  cm/μs. Similar plasma conditions and convolute impedance histories are observed in experiments with high and low losses, suggesting that losses are driven largely by load dynamics, which determine the voltage on the convolute.

  13. The convolution integral for the forward-backward asymmetry in e+e- annihilation

    International Nuclear Information System (INIS)

    Bardin, D.; Bilenky, M.; Chizhov, A.; Sazonov, A.; Sedykh, Yu.; Riemann, T.; Sachwitz, M.

    1989-01-01

    The complete convolution integral for the forward-backward asymmetry in A FB in e + e - annihilation is obtained in order O(α) with soft photon exponentiation. The influence of these QED corrections on A FB in the vicinity of the Z peak is discussed. The results are used to comment on a recent ad hoc ansatz using convolution weights derived for the total cross section. (orig.)

  14. A Convolution Tree with Deconvolution Branches: Exploiting Geometric Relationships for Single Shot Keypoint Detection

    OpenAIRE

    Kumar, Amit; Chellappa, Rama

    2017-01-01

    Recently, Deep Convolution Networks (DCNNs) have been applied to the task of face alignment and have shown potential for learning improved feature representations. Although deeper layers can capture abstract concepts like pose, it is difficult to capture the geometric relationships among the keypoints in DCNNs. In this paper, we propose a novel convolution-deconvolution network for facial keypoint detection. Our model predicts the 2D locations of the keypoints and their individual visibility ...

  15. Learning text representation using recurrent convolutional neural network with highway layers

    OpenAIRE

    Wen, Ying; Zhang, Weinan; Luo, Rui; Wang, Jun

    2016-01-01

    Recently, the rapid development of word embedding and neural networks has brought new inspiration to various NLP and IR tasks. In this paper, we describe a staged hybrid model combining Recurrent Convolutional Neural Networks (RCNN) with highway layers. The highway network module is incorporated in the middle takes the output of the bi-directional Recurrent Neural Network (Bi-RNN) module in the first stage and provides the Convolutional Neural Network (CNN) module in the last stage with the i...

  16. Segmentation of Drosophila Heart in Optical Coherence Microscopy Images Using Convolutional Neural Networks

    OpenAIRE

    Duan, Lian; Qin, Xi; He, Yuanhao; Sang, Xialin; Pan, Jinda; Xu, Tao; Men, Jing; Tanzi, Rudolph E.; Li, Airong; Ma, Yutao; Zhou, Chao

    2018-01-01

    Convolutional neural networks are powerful tools for image segmentation and classification. Here, we use this method to identify and mark the heart region of Drosophila at different developmental stages in the cross-sectional images acquired by a custom optical coherence microscopy (OCM) system. With our well-trained convolutional neural network model, the heart regions through multiple heartbeat cycles can be marked with an intersection over union (IOU) of ~86%. Various morphological and dyn...

  17. A comparison between robotic-assisted laparoscopic distal pancreatectomy versus laparoscopic distal pancreatectomy.

    Science.gov (United States)

    Goh, Brian K P; Chan, Chung Yip; Soh, Hui-Ling; Lee, Ser Yee; Cheow, Peng-Chung; Chow, Pierce K H; Ooi, London L P J; Chung, Alexander Y F

    2017-03-01

    This study aims to compare the early perioperative outcomes of robotic-assisted laparoscopic distal pancreatectomy (RDP) versus laparoscopic distal pancreatectomy (LDP). The clinicopathologic features of 45 consecutive patients who underwent minimally-invasive distal pancreatectomy from 2006 to 2015 were retrospectively reviewed. Thirty-nine patients who met our study criteria were included. Eight patients underwent RDP and 31 had LDP. There were 10 (25.6%) open conversions. Six (15.4%) patients had major (> grade 2) morbidities and there was no in-hospital mortality. There were 14 (35.9%) grade A and 9 (23.1%) grade B pancreatic fistulas. Comparison between RDP and LDP demonstrated no significant difference between the patients' baseline characteristics except there was increased frequency of spleen-preserving pancreatectomies (3 (37.5%) vs 25 (80.6%), P=0.016) and splenic-vessel preservation (5 (62.5%) vs 4 (12.9%), P=0.003) in RDP. Comparison between outcomes demonstrated that RDP was associated with a longer median operation time (452.5 (range, 300-685) vs 245 min (range, 85-430), P=0.001) and increased frequency of the procedure completed purely laparoscopically (8 (100%) vs 18 (58.1%), P=0.025). RDP can be safely adopted and is equivalent to LDP in most perioperative outcomes. It is also associated with a decreased frequency of the need for hand-assistance laparoscopic surgery or open conversion but needed a longer operation time. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  18. Unilateral maxillary molar distalization with zygoma-gear appliance.

    Science.gov (United States)

    Kilkis, Dogan; Bayram, Mehmet; Celikoglu, Mevlut; Nur, Metin

    2012-08-01

    The aim of this study was to present the orthodontic treatment of a 15-year-old boy with a unilateral maxillary molar distalization system, called the zygoma-gear appliance. It consisted of a zygomatic anchorage miniplate, an inner bow, and a Sentalloy closed coil spring (GAC International, Bohemia, NY). A distalizing force of 350 g was used during the distalization period. The unilateral Class II malocclusion was corrected in 5 months with the zygoma-gear appliance. The maxillary left first molar showed distalization of 4 mm with an inclination of 3°. The maxillary premolars moved distally with the help of the transseptal fibers. In addition, there were slight decreases in overjet (-0.5 mm) and maxillary incisor inclination (-1°), indicating no anchorage loss from the zygoma-gear appliance. Preadjusted fixed appliances (0.022 × 0.028-in, MBT system; 3M Unitek, Monrovia, Calif) were placed in both arches to achieve leveling and alignment. After 14 months of unilateral distalization with the zygoma-gear appliance and fixed appliances, Class I molar and canine relationships were established with satisfactory interdigitation of the posterior teeth. Acceptable overjet and overbite were also achieved. This article shows that this new system, the zygoma-gear appliance, can be used for unilateral maxillary molar distalization without anchorage loss. Copyright © 2012 American Association of Orthodontists. Published by Mosby, Inc. All rights reserved.

  19. [Involvement of distal fragment of chromosome 13 in the regulation of sensitivity to ethanol in mice].

    Science.gov (United States)

    Bazovkina, D V; Kulikov, A V

    2015-01-01

    The role of the fragment 57-65 cM of mouse chromosome 13 was studied in the regulation of ethanol action on locomotor activity, anxiety and sensitivity to hypnotic and hypothermic effects of ethanol. We used male mice of recombinant lines AKR/J and AKR.CBA-D13Mit76C, differing only in this fragment. After acute administration of ethanol only AKR mice showed the increase in the length of traveled distance in the open-field test (p mice demonstrated the increase the time spent in the center of open-field arena (p mice. The results suggest the involvement of the distal fragment 57-65 cM of chromosome 13 in the mechanisms of ethanol action in mice.

  20. Minimal-memory realization of pearl-necklace encoders of general quantum convolutional codes

    International Nuclear Information System (INIS)

    Houshmand, Monireh; Hosseini-Khayat, Saied

    2011-01-01

    Quantum convolutional codes, like their classical counterparts, promise to offer higher error correction performance than block codes of equivalent encoding complexity, and are expected to find important applications in reliable quantum communication where a continuous stream of qubits is transmitted. Grassl and Roetteler devised an algorithm to encode a quantum convolutional code with a ''pearl-necklace'' encoder. Despite their algorithm's theoretical significance as a neat way of representing quantum convolutional codes, it is not well suited to practical realization. In fact, there is no straightforward way to implement any given pearl-necklace structure. This paper closes the gap between theoretical representation and practical implementation. In our previous work, we presented an efficient algorithm to find a minimal-memory realization of a pearl-necklace encoder for Calderbank-Shor-Steane (CSS) convolutional codes. This work is an extension of our previous work and presents an algorithm for turning a pearl-necklace encoder for a general (non-CSS) quantum convolutional code into a realizable quantum convolutional encoder. We show that a minimal-memory realization depends on the commutativity relations between the gate strings in the pearl-necklace encoder. We find a realization by means of a weighted graph which details the noncommutative paths through the pearl necklace. The weight of the longest path in this graph is equal to the minimal amount of memory needed to implement the encoder. The algorithm has a polynomial-time complexity in the number of gate strings in the pearl-necklace encoder.

  1. On the Reduction of Computational Complexity of Deep Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Partha Maji

    2018-04-01

    Full Text Available Deep convolutional neural networks (ConvNets, which are at the heart of many new emerging applications, achieve remarkable performance in audio and visual recognition tasks. Unfortunately, achieving accuracy often implies significant computational costs, limiting deployability. In modern ConvNets it is typical for the convolution layers to consume the vast majority of computational resources during inference. This has made the acceleration of these layers an important research area in academia and industry. In this paper, we examine the effects of co-optimizing the internal structures of the convolutional layers and underlying implementation of fundamental convolution operation. We demonstrate that a combination of these methods can have a big impact on the overall speedup of a ConvNet, achieving a ten-fold increase over baseline. We also introduce a new class of fast one-dimensional (1D convolutions for ConvNets using the Toom–Cook algorithm. We show that our proposed scheme is mathematically well-grounded, robust, and does not require any time-consuming retraining, while still achieving speedups solely from convolutional layers with no loss in baseline accuracy.

  2. A convolution method for predicting mean treatment dose including organ motion at imaging

    International Nuclear Information System (INIS)

    Booth, J.T.; Zavgorodni, S.F.; Royal Adelaide Hospital, SA

    2000-01-01

    Full text: The random treatment delivery errors (organ motion and set-up error) can be incorporated into the treatment planning software using a convolution method. Mean treatment dose is computed as the convolution of a static dose distribution with a variation kernel. Typically this variation kernel is Gaussian with variance equal to the sum of the organ motion and set-up error variances. We propose a novel variation kernel for the convolution technique that additionally considers the position of the mobile organ in the planning CT image. The systematic error of organ position in the planning CT image can be considered random for each patient over a population. Thus the variance of the variation kernel will equal the sum of treatment delivery variance and organ motion variance at planning for the population of treatments. The kernel is extended to deal with multiple pre-treatment CT scans to improve tumour localisation for planning. Mean treatment doses calculated with the convolution technique are compared to benchmark Monte Carlo (MC) computations. Calculations of mean treatment dose using the convolution technique agreed with MC results for all cases to better than ± 1 Gy in the planning treatment volume for a prescribed 60 Gy treatment. Convolution provides a quick method of incorporating random organ motion (captured in the planning CT image and during treatment delivery) and random set-up errors directly into the dose distribution. Copyright (2000) Australasian College of Physical Scientists and Engineers in Medicine

  3. Application of structured support vector machine backpropagation to a convolutional neural network for human pose estimation.

    Science.gov (United States)

    Witoonchart, Peerajak; Chongstitvatana, Prabhas

    2017-08-01

    In this study, for the first time, we show how to formulate a structured support vector machine (SSVM) as two layers in a convolutional neural network, where the top layer is a loss augmented inference layer and the bottom layer is the normal convolutional layer. We show that a deformable part model can be learned with the proposed structured SVM neural network by backpropagating the error of the deformable part model to the convolutional neural network. The forward propagation calculates the loss augmented inference and the backpropagation calculates the gradient from the loss augmented inference layer to the convolutional layer. Thus, we obtain a new type of convolutional neural network called an Structured SVM convolutional neural network, which we applied to the human pose estimation problem. This new neural network can be used as the final layers in deep learning. Our method jointly learns the structural model parameters and the appearance model parameters. We implemented our method as a new layer in the existing Caffe library. Copyright © 2017 Elsevier Ltd. All rights reserved.

  4. Potassium transport across guinea pig distal colon

    International Nuclear Information System (INIS)

    Rechkemmer, G.; Halm, D.R.; Frizzell, R.A.

    1986-01-01

    Active absorption and secretion of K was studied by measuring bidirectional 42 K fluxes across short-circuited guinea pig distal colon. Tissues were pretreated with mucosal (m) and serosal (s) indomethacin (1 μM) and amiloride (0.1 mM, m) to suppress spontaneous, electrogenic Cl secretion and Na absorption. Under these conditions, the short-circuit current (I/sub sc/) was 0.4 μeq/cm 2 h while electroneutral K absorption was 2.8 μeq/cm 2 h. Epinephrine (5 μM, s) stimulated electrogenic K secretion, reducing net K absorption to 1.3 μeq/cm 2 h. Bumetanide (0.1 mM, s) abolished this K secretion and restored K absorption to control values, suggesting mechanistic similarities between K and Cl secretion. K absorption was inhibited 40% by the gastric H/K ATPase inhibitor, omeprazole (0.1 mM, m), and was abolished by ouabain (0.1 mM, m). Neutral K absorption does not appear to be mediated by an apical membrane Na/K pump since: the effect of mucosal ouabain on K absorption does not require the presence of mucosal or serosal Na, unidirectional Na fluxes are not influenced by mucosal ouabain, and K absorption is not affected when Na absorption is abolished by amiloride. Net K transport is determined by the balance between electroneutral K absorption and electrogenic K secretion. The ouabain sensitivity of K absorption suggests that colonic H/K ATPase differs from its gastric counterpart

  5. Histology of the distal dural ring.

    Science.gov (United States)

    Graffeo, Christopher S; Perry, Avital; Copeland, William R; Raghunathan, Aditya; Link, Michael J

    2017-09-01

    The distal dural ring (DDR) is a conserved intracranial anatomic structure marking the boundary point at which the internal carotid artery (ICA) exits the cavernous sinus (CS) and enters the subarachnoid space. Although the CS has been well described in a range of anatomic studies, to our knowledge no prior study has analyzed the histologic relationship between the ICA and DDR. Correspondingly, our objective was to assess the relationship of the DDR to the ICA and determine whether the DDR can be dissected from the ICA and thus divided, or can only be circumferentially trimmed around the artery. The authors examined ten fresh-frozen, adult cadaveric specimens. A standard frontotemporal craniotomy, orbito-optic osteotomy, and extradural anterior clinoidectomy was performed bilaterally. The cavernous ICA, DDR, and supraclinoid ICA were harvested as an en bloc specimen. Specimens formalin-fixed and paraffin-embedded prior to routine histochemical staining with hematoxylin and eosin and Masson trichrome. In all specimens, marked microscopic investment of the DDR throughout the ICA adventitia was noted. Dural collagen fibers extensively permeated the arterial layers superficial to the muscularis propria, with no evidence of a clear separation between the DDR and arterial adventitia. Histologic analysis suggests that the ICA and DDR are highly interrelated, continuous structures, and therefore attempted intraoperative dissection between these structures may carry an elevated risk of injury to the ICA. We correspondingly recommend careful circumferential trimming of the DDR in lieu of direct dissection in cases requiring mobilization of the clinoidal ICA. Clin. Anat. 30:742-746, 2017. © 2017Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  6. Two-wave propagation in in vitro swine distal ulna

    Science.gov (United States)

    Mano, Isao; Horii, Kaoru; Matsukawa, Mami; Otani, Takahiko

    2015-07-01

    Ultrasonic transmitted waves were obtained in an in vitro swine distal ulna specimen, which mimics a human distal radius, that consists of interconnected cortical bone and cancellous bone. The transmitted waveforms appeared similar to the fast waves, slow waves, and overlapping fast and slow waves measured in the specimen after removing the surface cortical bone (only cancellous bone). In addition, the circumferential waves in the cortical bone and water did not affect the fast and slow waves. This suggests that the fast-and-slow-wave phenomenon can be observed in an in vivo human distal radius.

  7. Intra-Articular Osteotomy for Distal Humerus Malunion

    Directory of Open Access Journals (Sweden)

    René K. Marti

    2009-01-01

    Full Text Available Intra-articular osteotomy is considered in the rare case of malunion after a fracture of the distal humerus to restore humeral alignment and gain a functional arc of elbow motion. Traumatic and iatrogenic disruption of the limited blood flow to the distal end of the humerus resulting in avascular necrosis of capitellum or trochlea is a major pitfall of the this technically challenging procedure. Two cases are presented which illustrate the potential problems of intra-articular osteotomy for malunion of the distal humerus.

  8. Three-dimensional fingerprint recognition by using convolution neural network

    Science.gov (United States)

    Tian, Qianyu; Gao, Nan; Zhang, Zonghua

    2018-01-01

    With the development of science and technology and the improvement of social information, fingerprint recognition technology has become a hot research direction and been widely applied in many actual fields because of its feasibility and reliability. The traditional two-dimensional (2D) fingerprint recognition method relies on matching feature points. This method is not only time-consuming, but also lost three-dimensional (3D) information of fingerprint, with the fingerprint rotation, scaling, damage and other issues, a serious decline in robustness. To solve these problems, 3D fingerprint has been used to recognize human being. Because it is a new research field, there are still lots of challenging problems in 3D fingerprint recognition. This paper presents a new 3D fingerprint recognition method by using a convolution neural network (CNN). By combining 2D fingerprint and fingerprint depth map into CNN, and then through another CNN feature fusion, the characteristics of the fusion complete 3D fingerprint recognition after classification. This method not only can preserve 3D information of fingerprints, but also solves the problem of CNN input. Moreover, the recognition process is simpler than traditional feature point matching algorithm. 3D fingerprint recognition rate by using CNN is compared with other fingerprint recognition algorithms. The experimental results show that the proposed 3D fingerprint recognition method has good recognition rate and robustness.

  9. A quantum algorithm for Viterbi decoding of classical convolutional codes

    Science.gov (United States)

    Grice, Jon R.; Meyer, David A.

    2015-07-01

    We present a quantum Viterbi algorithm (QVA) with better than classical performance under certain conditions. In this paper, the proposed algorithm is applied to decoding classical convolutional codes, for instance, large constraint length and short decode frames . Other applications of the classical Viterbi algorithm where is large (e.g., speech processing) could experience significant speedup with the QVA. The QVA exploits the fact that the decoding trellis is similar to the butterfly diagram of the fast Fourier transform, with its corresponding fast quantum algorithm. The tensor-product structure of the butterfly diagram corresponds to a quantum superposition that we show can be efficiently prepared. The quantum speedup is possible because the performance of the QVA depends on the fanout (number of possible transitions from any given state in the hidden Markov model) which is in general much less than . The QVA constructs a superposition of states which correspond to all legal paths through the decoding lattice, with phase as a function of the probability of the path being taken given received data. A specialized amplitude amplification procedure is applied one or more times to recover a superposition where the most probable path has a high probability of being measured.

  10. On the Relationship between Visual Attributes and Convolutional Networks

    KAUST Repository

    Castillo, Victor; Ghanem, Bernard; Niebles, Juan Carlos

    2015-01-01

    One of the cornerstone principles of deep models is their abstraction capacity, i.e. their ability to learn abstract concepts from ‘simpler’ ones. Through extensive experiments, we characterize the nature of the relationship between abstract concepts (specifically objects in images) learned by popular and high performing convolutional networks (conv-nets) and established mid-level representations used in computer vision (specifically semantic visual attributes). We focus on attributes due to their impact on several applications, such as object description, retrieval and mining, and active (and zero-shot) learning. Among the findings we uncover, we show empirical evidence of the existence of Attribute Centric Nodes (ACNs) within a conv-net, which is trained to recognize objects (not attributes) in images. These special conv-net nodes (1) collectively encode information pertinent to visual attribute representation and discrimination, (2) are unevenly and sparsely distribution across all layers of the conv-net, and (3) play an important role in conv-net based object recognition.

  11. Generalization error analysis: deep convolutional neural network in mammography

    Science.gov (United States)

    Richter, Caleb D.; Samala, Ravi K.; Chan, Heang-Ping; Hadjiiski, Lubomir; Cha, Kenny

    2018-02-01

    We conducted a study to gain understanding of the generalizability of deep convolutional neural networks (DCNNs) given their inherent capability to memorize data. We examined empirically a specific DCNN trained for classification of masses on mammograms. Using a data set of 2,454 lesions from 2,242 mammographic views, a DCNN was trained to classify masses into malignant and benign classes using transfer learning from ImageNet LSVRC-2010. We performed experiments with varying amounts of label corruption and types of pixel randomization to analyze the generalization error for the DCNN. Performance was evaluated using the area under the receiver operating characteristic curve (AUC) with an N-fold cross validation. Comparisons were made between the convergence times, the inference AUCs for both the training set and the test set of the original image patches without corruption, and the root-mean-squared difference (RMSD) in the layer weights of the DCNN trained with different amounts and methods of corruption. Our experiments observed trends which revealed that the DCNN overfitted by memorizing corrupted data. More importantly, this study improved our understanding of DCNN weight updates when learning new patterns or new labels. Although we used a specific classification task with the ImageNet as example, similar methods may be useful for analysis of the DCNN learning processes, especially those that employ transfer learning for medical image analysis where sample size is limited and overfitting risk is high.

  12. On the Relationship between Visual Attributes and Convolutional Networks

    KAUST Repository

    Castillo, Victor

    2015-06-02

    One of the cornerstone principles of deep models is their abstraction capacity, i.e. their ability to learn abstract concepts from ‘simpler’ ones. Through extensive experiments, we characterize the nature of the relationship between abstract concepts (specifically objects in images) learned by popular and high performing convolutional networks (conv-nets) and established mid-level representations used in computer vision (specifically semantic visual attributes). We focus on attributes due to their impact on several applications, such as object description, retrieval and mining, and active (and zero-shot) learning. Among the findings we uncover, we show empirical evidence of the existence of Attribute Centric Nodes (ACNs) within a conv-net, which is trained to recognize objects (not attributes) in images. These special conv-net nodes (1) collectively encode information pertinent to visual attribute representation and discrimination, (2) are unevenly and sparsely distribution across all layers of the conv-net, and (3) play an important role in conv-net based object recognition.

  13. A discrete convolution kernel for No-DC MRI

    International Nuclear Information System (INIS)

    Zeng, Gengsheng L; Li, Ya

    2015-01-01

    An analytical inversion formula for the exponential Radon transform with an imaginary attenuation coefficient was developed in 2007 (2007 Inverse Problems 23 1963–71). The inversion formula in that paper suggested that it is possible to obtain an exact MRI (magnetic resonance imaging) image without acquiring low-frequency data. However, this un-measured low-frequency region (ULFR) in the k-space (which is the two-dimensional Fourier transform space in MRI terminology) must be very small. This current paper derives a FBP (filtered backprojection) algorithm based on You’s formula by suggesting a practical discrete convolution kernel. A point spread function is derived for this FBP algorithm. It is demonstrated that the derived FBP algorithm can have a larger ULFR than that in the 2007 paper. The significance of this paper is that we present a closed-form reconstruction algorithm for a special case of under-sampled MRI data. Usually, under-sampled MRI data requires iterative (instead of analytical) algorithms with L 1 -norm or total variation norm to reconstruct the image. (paper)

  14. A deep convolutional neural network for recognizing foods

    Science.gov (United States)

    Jahani Heravi, Elnaz; Habibi Aghdam, Hamed; Puig, Domenec

    2015-12-01

    Controlling the food intake is an efficient way that each person can undertake to tackle the obesity problem in countries worldwide. This is achievable by developing a smartphone application that is able to recognize foods and compute their calories. State-of-art methods are chiefly based on hand-crafted feature extraction methods such as HOG and Gabor. Recent advances in large-scale object recognition datasets such as ImageNet have revealed that deep Convolutional Neural Networks (CNN) possess more representation power than the hand-crafted features. The main challenge with CNNs is to find the appropriate architecture for each problem. In this paper, we propose a deep CNN which consists of 769; 988 parameters. Our experiments show that the proposed CNN outperforms the state-of-art methods and improves the best result of traditional methods 17%. Moreover, using an ensemble of two CNNs that have been trained two different times, we are able to improve the classification performance 21:5%.

  15. Forged Signature Distinction Using Convolutional Neural Network for Feature Extraction

    Directory of Open Access Journals (Sweden)

    Seungsoo Nam

    2018-01-01

    Full Text Available This paper proposes a dynamic verification scheme for finger-drawn signatures in smartphones. As a dynamic feature, the movement of a smartphone is recorded with accelerometer sensors in the smartphone, in addition to the moving coordinates of the signature. To extract high-level longitudinal and topological features, the proposed scheme uses a convolution neural network (CNN for feature extraction, and not as a conventional classifier. We assume that a CNN trained with forged signatures can extract effective features (called S-vector, which are common in forging activities such as hesitation and delay before drawing the complicated part. The proposed scheme also exploits an autoencoder (AE as a classifier, and the S-vector is used as the input vector to the AE. An AE has high accuracy for the one-class distinction problem such as signature verification, and is also greatly dependent on the accuracy of input data. S-vector is valuable as the input of AE, and, consequently, could lead to improved verification accuracy especially for distinguishing forged signatures. Compared to the previous work, i.e., the MLP-based finger-drawn signature verification scheme, the proposed scheme decreases the equal error rate by 13.7%, specifically, from 18.1% to 4.4%, for discriminating forged signatures.

  16. An improved multi-domain convolution tracking algorithm

    Science.gov (United States)

    Sun, Xin; Wang, Haiying; Zeng, Yingsen

    2018-04-01

    Along with the wide application of the Deep Learning in the field of Computer vision, Deep learning has become a mainstream direction in the field of object tracking. The tracking algorithm in this paper is based on the improved multidomain convolution neural network, and the VOT video set is pre-trained on the network by multi-domain training strategy. In the process of online tracking, the network evaluates candidate targets sampled from vicinity of the prediction target in the previous with Gaussian distribution, and the candidate target with the highest score is recognized as the prediction target of this frame. The Bounding Box Regression model is introduced to make the prediction target closer to the ground-truths target box of the test set. Grouping-update strategy is involved to extract and select useful update samples in each frame, which can effectively prevent over fitting. And adapt to changes in both target and environment. To improve the speed of the algorithm while maintaining the performance, the number of candidate target succeed in adjusting dynamically with the help of Self-adaption parameter Strategy. Finally, the algorithm is tested by OTB set, compared with other high-performance tracking algorithms, and the plot of success rate and the accuracy are drawn. which illustrates outstanding performance of the tracking algorithm in this paper.

  17. Convolutional networks for fast, energy-efficient neuromorphic computing.

    Science.gov (United States)

    Esser, Steven K; Merolla, Paul A; Arthur, John V; Cassidy, Andrew S; Appuswamy, Rathinakumar; Andreopoulos, Alexander; Berg, David J; McKinstry, Jeffrey L; Melano, Timothy; Barch, Davis R; di Nolfo, Carmelo; Datta, Pallab; Amir, Arnon; Taba, Brian; Flickner, Myron D; Modha, Dharmendra S

    2016-10-11

    Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on spiking neurons, low precision synapses, and a scalable communication network. Here, we demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that (i) approach state-of-the-art classification accuracy across eight standard datasets encompassing vision and speech, (ii) perform inference while preserving the hardware's underlying energy-efficiency and high throughput, running on the aforementioned datasets at between 1,200 and 2,600 frames/s and using between 25 and 275 mW (effectively >6,000 frames/s per Watt), and (iii) can be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning. This approach allows the algorithmic power of deep learning to be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer.

  18. Convolution neural-network-based detection of lung structures

    Science.gov (United States)

    Hasegawa, Akira; Lo, Shih-Chung B.; Freedman, Matthew T.; Mun, Seong K.

    1994-05-01

    Chest radiography is one of the most primary and widely used techniques in diagnostic imaging. Nowadays with the advent of digital radiology, the digital medical image processing techniques for digital chest radiographs have attracted considerable attention, and several studies on the computer-aided diagnosis (CADx) as well as on the conventional image processing techniques for chest radiographs have been reported. In the automatic diagnostic process for chest radiographs, it is important to outline the areas of the lungs, the heart, and the diaphragm. This is because the original chest radiograph is composed of important anatomic structures and, without knowing exact positions of the organs, the automatic diagnosis may result in unexpected detections. The automatic extraction of an anatomical structure from digital chest radiographs can be a useful tool for (1) the evaluation of heart size, (2) automatic detection of interstitial lung diseases, (3) automatic detection of lung nodules, and (4) data compression, etc. Based on the clearly defined boundaries of heart area, rib spaces, rib positions, and rib cage extracted, one should be able to use this information to facilitate the tasks of the CADx on chest radiographs. In this paper, we present an automatic scheme for the detection of lung field from chest radiographs by using a shift-invariant convolution neural network. A novel algorithm for smoothing boundaries of lungs is also presented.

  19. Real-time hybrid simulation using the convolution integral method

    International Nuclear Information System (INIS)

    Kim, Sung Jig; Christenson, Richard E; Wojtkiewicz, Steven F; Johnson, Erik A

    2011-01-01

    This paper proposes a real-time hybrid simulation method that will allow complex systems to be tested within the hybrid test framework by employing the convolution integral (CI) method. The proposed CI method is potentially transformative for real-time hybrid simulation. The CI method can allow real-time hybrid simulation to be conducted regardless of the size and complexity of the numerical model and for numerical stability to be ensured in the presence of high frequency responses in the simulation. This paper presents the general theory behind the proposed CI method and provides experimental verification of the proposed method by comparing the CI method to the current integration time-stepping (ITS) method. Real-time hybrid simulation is conducted in the Advanced Hazard Mitigation Laboratory at the University of Connecticut. A seismically excited two-story shear frame building with a magneto-rheological (MR) fluid damper is selected as the test structure to experimentally validate the proposed method. The building structure is numerically modeled and simulated, while the MR damper is physically tested. Real-time hybrid simulation using the proposed CI method is shown to provide accurate results

  20. A Hierarchical Convolutional Neural Network for vesicle fusion event classification.

    Science.gov (United States)

    Li, Haohan; Mao, Yunxiang; Yin, Zhaozheng; Xu, Yingke

    2017-09-01

    Quantitative analysis of vesicle exocytosis and classification of different modes of vesicle fusion from the fluorescence microscopy are of primary importance for biomedical researches. In this paper, we propose a novel Hierarchical Convolutional Neural Network (HCNN) method to automatically identify vesicle fusion events in time-lapse Total Internal Reflection Fluorescence Microscopy (TIRFM) image sequences. Firstly, a detection and tracking method is developed to extract image patch sequences containing potential fusion events. Then, a Gaussian Mixture Model (GMM) is applied on each image patch of the patch sequence with outliers rejected for robust Gaussian fitting. By utilizing the high-level time-series intensity change features introduced by GMM and the visual appearance features embedded in some key moments of the fusion process, the proposed HCNN architecture is able to classify each candidate patch sequence into three classes: full fusion event, partial fusion event and non-fusion event. Finally, we validate the performance of our method on 9 challenging datasets that have been annotated by cell biologists, and our method achieves better performances when comparing with three previous methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

  1. Village Building Identification Based on Ensemble Convolutional Neural Networks

    Science.gov (United States)

    Guo, Zhiling; Chen, Qi; Xu, Yongwei; Shibasaki, Ryosuke; Shao, Xiaowei

    2017-01-01

    In this study, we present the Ensemble Convolutional Neural Network (ECNN), an elaborate CNN frame formulated based on ensembling state-of-the-art CNN models, to identify village buildings from open high-resolution remote sensing (HRRS) images. First, to optimize and mine the capability of CNN for village mapping and to ensure compatibility with our classification targets, a few state-of-the-art models were carefully optimized and enhanced based on a series of rigorous analyses and evaluations. Second, rather than directly implementing building identification by using these models, we exploited most of their advantages by ensembling their feature extractor parts into a stronger model called ECNN based on the multiscale feature learning method. Finally, the generated ECNN was applied to a pixel-level classification frame to implement object identification. The proposed method can serve as a viable tool for village building identification with high accuracy and efficiency. The experimental results obtained from the test area in Savannakhet province, Laos, prove that the proposed ECNN model significantly outperforms existing methods, improving overall accuracy from 96.64% to 99.26%, and kappa from 0.57 to 0.86. PMID:29084154

  2. Classification of crystal structure using a convolutional neural network.

    Science.gov (United States)

    Park, Woon Bae; Chung, Jiyong; Jung, Jaeyoung; Sohn, Keemin; Singh, Satendra Pal; Pyo, Myoungho; Shin, Namsoo; Sohn, Kee-Sun

    2017-07-01

    A deep machine-learning technique based on a convolutional neural network (CNN) is introduced. It has been used for the classification of powder X-ray diffraction (XRD) patterns in terms of crystal system, extinction group and space group. About 150 000 powder XRD patterns were collected and used as input for the CNN with no handcrafted engineering involved, and thereby an appropriate CNN architecture was obtained that allowed determination of the crystal system, extinction group and space group. In sharp contrast with the traditional use of powder XRD pattern analysis, the CNN never treats powder XRD patterns as a deconvoluted and discrete peak position or as intensity data, but instead the XRD patterns are regarded as nothing but a pattern similar to a picture. The CNN interprets features that humans cannot recognize in a powder XRD pattern. As a result, accuracy levels of 81.14, 83.83 and 94.99% were achieved for the space-group, extinction-group and crystal-system classifications, respectively. The well trained CNN was then used for symmetry identification of unknown novel inorganic compounds.

  3. Noisy Ocular Recognition Based on Three Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Min Beom Lee

    2017-12-01

    Full Text Available In recent years, the iris recognition system has been gaining increasing acceptance for applications such as access control and smartphone security. When the images of the iris are obtained under unconstrained conditions, an issue of undermined quality is caused by optical and motion blur, off-angle view (the user’s eyes looking somewhere else, not into the front of the camera, specular reflection (SR and other factors. Such noisy iris images increase intra-individual variations and, as a result, reduce the accuracy of iris recognition. A typical iris recognition system requires a near-infrared (NIR illuminator along with an NIR camera, which are larger and more expensive than fingerprint recognition equipment. Hence, many studies have proposed methods of using iris images captured by a visible light camera without the need for an additional illuminator. In this research, we propose a new recognition method for noisy iris and ocular images by using one iris and two periocular regions, based on three convolutional neural networks (CNNs. Experiments were conducted by using the noisy iris challenge evaluation-part II (NICE.II training dataset (selected from the university of Beira iris (UBIRIS.v2 database, mobile iris challenge evaluation (MICHE database, and institute of automation of Chinese academy of sciences (CASIA-Iris-Distance database. As a result, the method proposed by this study outperformed previous methods.

  4. Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Martin Längkvist

    2016-04-01

    Full Text Available The availability of high-resolution remote sensing (HRRS data has opened up the possibility for new interesting applications, such as per-pixel classification of individual objects in greater detail. This paper shows how a convolutional neural network (CNN can be applied to multispectral orthoimagery and a digital surface model (DSM of a small city for a full, fast and accurate per-pixel classification. The predicted low-level pixel classes are then used to improve the high-level segmentation. Various design choices of the CNN architecture are evaluated and analyzed. The investigated land area is fully manually labeled into five categories (vegetation, ground, roads, buildings and water, and the classification accuracy is compared to other per-pixel classification works on other land areas that have a similar choice of categories. The results of the full classification and segmentation on selected segments of the map show that CNNs are a viable tool for solving both the segmentation and object recognition task for remote sensing data.

  5. Bilinear Convolutional Neural Networks for Fine-grained Visual Recognition.

    Science.gov (United States)

    Lin, Tsung-Yu; RoyChowdhury, Aruni; Maji, Subhransu

    2017-07-04

    We present a simple and effective architecture for fine-grained recognition called Bilinear Convolutional Neural Networks (B-CNNs). These networks represent an image as a pooled outer product of features derived from two CNNs and capture localized feature interactions in a translationally invariant manner. B-CNNs are related to orderless texture representations built on deep features but can be trained in an end-to-end manner. Our most accurate model obtains 84.1%, 79.4%, 84.5% and 91.3% per-image accuracy on the Caltech-UCSD birds [66], NABirds [63], FGVC aircraft [42], and Stanford cars [33] dataset respectively and runs at 30 frames-per-second on a NVIDIA Titan X GPU. We then present a systematic analysis of these networks and show that (1) the bilinear features are highly redundant and can be reduced by an order of magnitude in size without significant loss in accuracy, (2) are also effective for other image classification tasks such as texture and scene recognition, and (3) can be trained from scratch on the ImageNet dataset offering consistent improvements over the baseline architecture. Finally, we present visualizations of these models on various datasets using top activations of neural units and gradient-based inversion techniques. The source code for the complete system is available at http://vis-www.cs.umass.edu/bcnn.

  6. Pneumothorax detection in chest radiographs using convolutional neural networks

    Science.gov (United States)

    Blumenfeld, Aviel; Konen, Eli; Greenspan, Hayit

    2018-02-01

    This study presents a computer assisted diagnosis system for the detection of pneumothorax (PTX) in chest radiographs based on a convolutional neural network (CNN) for pixel classification. Using a pixel classification approach allows utilization of the texture information in the local environment of each pixel while training a CNN model on millions of training patches extracted from a relatively small dataset. The proposed system uses a pre-processing step of lung field segmentation to overcome the large variability in the input images coming from a variety of imaging sources and protocols. Using a CNN classification, suspected pixel candidates are extracted within each lung segment. A postprocessing step follows to remove non-physiological suspected regions and noisy connected components. The overall percentage of suspected PTX area was used as a robust global decision for the presence of PTX in each lung. The system was trained on a set of 117 chest x-ray images with ground truth segmentations of the PTX regions. The system was tested on a set of 86 images and reached diagnosis accuracy of AUC=0.95. Overall preliminary results are promising and indicate the growing ability of CAD based systems to detect findings in medical imaging on a clinical level accuracy.

  7. Classification of breast cancer histology images using Convolutional Neural Networks.

    Directory of Open Access Journals (Sweden)

    Teresa Araújo

    Full Text Available Breast cancer is one of the main causes of cancer death worldwide. The diagnosis of biopsy tissue with hematoxylin and eosin stained images is non-trivial and specialists often disagree on the final diagnosis. Computer-aided Diagnosis systems contribute to reduce the cost and increase the efficiency of this process. Conventional classification approaches rely on feature extraction methods designed for a specific problem based on field-knowledge. To overcome the many difficulties of the feature-based approaches, deep learning methods are becoming important alternatives. A method for the classification of hematoxylin and eosin stained breast biopsy images using Convolutional Neural Networks (CNNs is proposed. Images are classified in four classes, normal tissue, benign lesion, in situ carcinoma and invasive carcinoma, and in two classes, carcinoma and non-carcinoma. The architecture of the network is designed to retrieve information at different scales, including both nuclei and overall tissue organization. This design allows the extension of the proposed system to whole-slide histology images. The features extracted by the CNN are also used for training a Support Vector Machine classifier. Accuracies of 77.8% for four class and 83.3% for carcinoma/non-carcinoma are achieved. The sensitivity of our method for cancer cases is 95.6%.

  8. Image reconstruction in computerized tomography using the convolution method

    International Nuclear Information System (INIS)

    Oliveira Rebelo, A.M. de.

    1984-03-01

    In the present work an algoritin was derived, using the analytical convolution method (filtered back-projection) for two-dimensional or three-dimensional image reconstruction in computerized tomography applied to non-destructive testing and to the medical use. This mathematical model is based on the analytical Fourier transform method for image reconstruction. This model consists of a discontinuous system formed by an NxN array of cells (pixels). The attenuation in the object under study of a colimated gamma ray beam has been determined for various positions and incidence angles (projections) in terms of the interaction of the beam with the intercepted pixels. The contribution of each pixel to beam attenuation was determined using the weight function W ij which was used for simulated tests. Simulated tests using standard objects with attenuation coefficients in the range of 0,2 to 0,7 cm -1 were carried out using cell arrays of up to 25x25. One application was carried out in the medical area simulating image reconstruction of an arm phantom with attenuation coefficients in the range of 0,2 to 0,5 cm -1 using cell arrays of 41x41. The simulated results show that, in objects with a great number of interfaces and great variations of attenuation coefficients at these interfaces, a good reconstruction is obtained with the number of projections equal to the reconstruction matrix dimension. A good reconstruction is otherwise obtained with fewer projections. (author) [pt

  9. Classification of Two Comic Books based on Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Miki UENO

    2017-03-01

    Full Text Available Unphotographic images are the powerful representations described various situations. Thus, understanding intellectual products such as comics and picture books is one of the important topics in the field of artificial intelligence. Hence, stepwise analysis of a comic story, i.e., features of a part of the image, information features, features relating to continuous scene etc., was pursued. Especially, the length and each scene of four-scene comics are limited so as to ensure a clear interpretation of the contents.In this study, as the first step in this direction, the problem to classify two four-scene comics by the same artists were focused as the example. Several classifiers were constructed by utilizing a Convolutional Neural Network(CNN, and the results of classification by a human annotator and by a computational method were compared.From these experiments, we have clearly shown that CNN is efficient way to classify unphotographic gray scaled images and found that characteristic features of images to classify incorrectly.

  10. Bone age detection via carpogram analysis using convolutional neural networks

    Science.gov (United States)

    Torres, Felipe; Bravo, María. Alejandra; Salinas, Emmanuel; Triana, Gustavo; Arbeláez, Pablo

    2017-11-01

    Bone age assessment is a critical factor for determining delayed development in children, which can be a sign of pathologies such as endocrine diseases, growth abnormalities, chromosomal, neurological and congenital disorders among others. In this paper we present BoneNet, a methodology to assess automatically the skeletal maturity state in pediatric patients based on Convolutional Neural Networks. We train and evaluate our algorithm on a database of X-Ray images provided by the hospital Fundacion Santa Fe de Bogot ´ a with around 1500 images of patients between the ages 1 to 18. ´ We compare two different architectures to classify the given data in order to explore the generality of our method. To accomplish this, we define multiple binary age assessment problems, dividing the data by bone age and differentiating the patients by their gender. Thus, exploring several parameters, we develop BoneNet. Our approach is holistic, efficient, and modular, since it is possible for the specialists to use all the networks combined to determine how is the skeletal maturity of a patient. BoneNet achieves over 90% accuracy for most of the critical age thresholds, when differentiating the images between over or under a given age.

  11. Automated embolic signal detection using Deep Convolutional Neural Network.

    Science.gov (United States)

    Sombune, Praotasna; Phienphanich, Phongphan; Phuechpanpaisal, Sutanya; Muengtaweepongsa, Sombat; Ruamthanthong, Anuchit; Tantibundhit, Charturong

    2017-07-01

    This work investigated the potential of Deep Neural Network in detection of cerebral embolic signal (ES) from transcranial Doppler ultrasound (TCD). The resulting system is aimed to couple with TCD devices in diagnosing a risk of stroke in real-time with high accuracy. The Adaptive Gain Control (AGC) approach developed in our previous study is employed to capture suspected ESs in real-time. By using spectrograms of the same TCD signal dataset as that of our previous work as inputs and the same experimental setup, Deep Convolutional Neural Network (CNN), which can learn features while training, was investigated for its ability to bypass the traditional handcrafted feature extraction and selection process. Extracted feature vectors from the suspected ESs are later determined whether they are of an ES, artifact (AF) or normal (NR) interval. The effectiveness of the developed system was evaluated over 19 subjects going under procedures generating emboli. The CNN-based system could achieve in average of 83.0% sensitivity, 80.1% specificity, and 81.4% accuracy, with considerably much less time consumption in development. The certainly growing set of training samples and computational resources will contribute to high performance. Besides having potential use in various clinical ES monitoring settings, continuation of this promising study will benefit developments of wearable applications by leveraging learnable features to serve demographic differentials.

  12. Deep convolutional neural network for mammographic density segmentation

    Science.gov (United States)

    Wei, Jun; Li, Songfeng; Chan, Heang-Ping; Helvie, Mark A.; Roubidoux, Marilyn A.; Lu, Yao; Zhou, Chuan; Hadjiiski, Lubomir; Samala, Ravi K.

    2018-02-01

    Breast density is one of the most significant factors for cancer risk. In this study, we proposed a supervised deep learning approach for automated estimation of percentage density (PD) on digital mammography (DM). The deep convolutional neural network (DCNN) was trained to estimate a probability map of breast density (PMD). PD was calculated as the ratio of the dense area to the breast area based on the probability of each pixel belonging to dense region or fatty region at a decision threshold of 0.5. The DCNN estimate was compared to a feature-based statistical learning approach, in which gray level, texture and morphological features were extracted from each ROI and the least absolute shrinkage and selection operator (LASSO) was used to select and combine the useful features to generate the PMD. The reference PD of each image was provided by two experienced MQSA radiologists. With IRB approval, we retrospectively collected 347 DMs from patient files at our institution. The 10-fold cross-validation results showed a strong correlation r=0.96 between the DCNN estimation and interactive segmentation by radiologists while that of the feature-based statistical learning approach vs radiologists' segmentation had a correlation r=0.78. The difference between the segmentation by DCNN and by radiologists was significantly smaller than that between the feature-based learning approach and radiologists (p approach has the potential to replace radiologists' interactive thresholding in PD estimation on DMs.

  13. Vision-Based Fall Detection with Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Adrián Núñez-Marcos

    2017-01-01

    Full Text Available One of the biggest challenges in modern societies is the improvement of healthy aging and the support to older persons in their daily activities. In particular, given its social and economic impact, the automatic detection of falls has attracted considerable attention in the computer vision and pattern recognition communities. Although the approaches based on wearable sensors have provided high detection rates, some of the potential users are reluctant to wear them and thus their use is not yet normalized. As a consequence, alternative approaches such as vision-based methods have emerged. We firmly believe that the irruption of the Smart Environments and the Internet of Things paradigms, together with the increasing number of cameras in our daily environment, forms an optimal context for vision-based systems. Consequently, here we propose a vision-based solution using Convolutional Neural Networks to decide if a sequence of frames contains a person falling. To model the video motion and make the system scenario independent, we use optical flow images as input to the networks followed by a novel three-step training phase. Furthermore, our method is evaluated in three public datasets achieving the state-of-the-art results in all three of them.

  14. Reconstruction of Micropattern Detector Signals using Convolutional Neural Networks

    Science.gov (United States)

    Flekova, L.; Schott, M.

    2017-10-01

    Micropattern gaseous detector (MPGD) technologies, such as GEMs or MicroMegas, are particularly suitable for precision tracking and triggering in high rate environments. Given their relatively low production costs, MPGDs are an exemplary candidate for the next generation of particle detectors. Having acknowledged these advantages, both the ATLAS and CMS collaborations at the LHC are exploiting these new technologies for their detector upgrade programs in the coming years. When MPGDs are utilized for triggering purposes, the measured signals need to be precisely reconstructed within less than 200 ns, which can be achieved by the usage of FPGAs. In this work, we present a novel approach to identify reconstructed signals, their timing and the corresponding spatial position on the detector. In particular, we study the effect of noise and dead readout strips on the reconstruction performance. Our approach leverages the potential of convolutional neural network (CNNs), which have recently manifested an outstanding performance in a range of modeling tasks. The proposed neural network architecture of our CNN is designed simply enough, so that it can be modeled directly by an FPGA and thus provide precise information on reconstructed signals already in trigger level.

  15. Neonatal Seizure Detection Using Deep Convolutional Neural Networks.

    Science.gov (United States)

    Ansari, Amir H; Cherian, Perumpillichira J; Caicedo, Alexander; Naulaers, Gunnar; De Vos, Maarten; Van Huffel, Sabine

    2018-04-02

    Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published studies describing features and seizure classifiers, the features were hand-engineered, which may not be optimal. The main goal of the present paper is using deep convolutional neural networks (CNNs) and random forest to automatically optimize feature selection and classification. The input of the proposed classifier is raw multi-channel EEG and the output is the class label: seizure/nonseizure. By training this network, the required features are optimized, while fitting a nonlinear classifier on the features. After training the network with EEG recordings of 26 neonates, five end layers performing the classification were replaced with a random forest classifier in order to improve the performance. This resulted in a false alarm rate of 0.9 per hour and seizure detection rate of 77% using a test set of EEG recordings of 22 neonates that also included dubious seizures. The newly proposed CNN classifier outperformed three data-driven feature-based approaches and performed similar to a previously developed heuristic method.

  16. Chinese Sentence Classification Based on Convolutional Neural Network

    Science.gov (United States)

    Gu, Chengwei; Wu, Ming; Zhang, Chuang

    2017-10-01

    Sentence classification is one of the significant issues in Natural Language Processing (NLP). Feature extraction is often regarded as the key point for natural language processing. Traditional ways based on machine learning can not take high level features into consideration, such as Naive Bayesian Model. The neural network for sentence classification can make use of contextual information to achieve greater results in sentence classification tasks. In this paper, we focus on classifying Chinese sentences. And the most important is that we post a novel architecture of Convolutional Neural Network (CNN) to apply on Chinese sentence classification. In particular, most of the previous methods often use softmax classifier for prediction, we embed a linear support vector machine to substitute softmax in the deep neural network model, minimizing a margin-based loss to get a better result. And we use tanh as an activation function, instead of ReLU. The CNN model improve the result of Chinese sentence classification tasks. Experimental results on the Chinese news title database validate the effectiveness of our model.

  17. Convolutional neural network with transfer learning for rice type classification

    Science.gov (United States)

    Patel, Vaibhav Amit; Joshi, Manjunath V.

    2018-04-01

    Presently, rice type is identified manually by humans, which is time consuming and error prone. Therefore, there is a need to do this by machine which makes it faster with greater accuracy. This paper proposes a deep learning based method for classification of rice types. We propose two methods to classify the rice types. In the first method, we train a deep convolutional neural network (CNN) using the given segmented rice images. In the second method, we train a combination of a pretrained VGG16 network and the proposed method, while using transfer learning in which the weights of a pretrained network are used to achieve better accuracy. Our approach can also be used for classification of rice grain as broken or fine. We train a 5-class model for classifying rice types using 4000 training images and another 2- class model for the classification of broken and normal rice using 1600 training images. We observe that despite having distinct rice images, our architecture, pretrained on ImageNet data boosts classification accuracy significantly.

  18. Innervation of the renal proximal convoluted tubule of the rat

    International Nuclear Information System (INIS)

    Barajas, L.; Powers, K.

    1989-01-01

    Experimental data suggest the proximal tubule as a major site of neurogenic influence on tubular function. The functional and anatomical axial heterogeneity of the proximal tubule prompted this study of the distribution of innervation sites along the early, mid, and late proximal convoluted tubule (PCT) of the rat. Serial section autoradiograms, with tritiated norepinephrine serving as a marker for monoaminergic nerves, were used in this study. Freehand clay models and graphic reconstructions of proximal tubules permitted a rough estimation of the location of the innervation sites along the PCT. In the subcapsular nephrons, the early PCT (first third) was devoid of innervation sites with most of the innervation occurring in the mid (middle third) and in the late (last third) PCT. Innervation sites were found in the early PCT in nephrons located deeper in the cortex. In juxtamedullary nephrons, innervation sites could be observed on the PCT as it left the glomerulus. This gradient of PCT innervation can be explained by the different tubulovascular relationships of nephrons at different levels of the cortex. The absence of innervation sites in the early PCT of subcapsular nephrons suggests that any influence of the renal nerves on the early PCT might be due to an effect of neurotransmitter released from renal nerves reaching the early PCT via the interstitium and/or capillaries

  19. Classification of breast cancer cytological specimen using convolutional neural network

    Science.gov (United States)

    Żejmo, Michał; Kowal, Marek; Korbicz, Józef; Monczak, Roman

    2017-01-01

    The paper presents a deep learning approach for automatic classification of breast tumors based on fine needle cytology. The main aim of the system is to distinguish benign from malignant cases based on microscopic images. Experiment was carried out on cytological samples derived from 50 patients (25 benign cases + 25 malignant cases) diagnosed in Regional Hospital in Zielona Góra. To classify microscopic images, we used convolutional neural networks (CNN) of two types: GoogLeNet and AlexNet. Due to the very large size of images of cytological specimen (on average 200000 × 100000 pixels), they were divided into smaller patches of size 256 × 256 pixels. Breast cancer classification usually is based on morphometric features of nuclei. Therefore, training and validation patches were selected using Support Vector Machine (SVM) so that suitable amount of cell material was depicted. Neural classifiers were tuned using GPU accelerated implementation of gradient descent algorithm. Training error was defined as a cross-entropy classification loss. Classification accuracy was defined as the percentage ratio of successfully classified validation patches to the total number of validation patches. The best accuracy rate of 83% was obtained by GoogLeNet model. We observed that more misclassified patches belong to malignant cases.

  20. Processing of chromatic information in a deep convolutional neural network.

    Science.gov (United States)

    Flachot, Alban; Gegenfurtner, Karl R

    2018-04-01

    Deep convolutional neural networks are a class of machine-learning algorithms capable of solving non-trivial tasks, such as object recognition, with human-like performance. Little is known about the exact computations that deep neural networks learn, and to what extent these computations are similar to the ones performed by the primate brain. Here, we investigate how color information is processed in the different layers of the AlexNet deep neural network, originally trained on object classification of over 1.2M images of objects in their natural contexts. We found that the color-responsive units in the first layer of AlexNet learned linear features and were broadly tuned to two directions in color space, analogously to what is known of color responsive cells in the primate thalamus. Moreover, these directions are decorrelated and lead to statistically efficient representations, similar to the cardinal directions of the second-stage color mechanisms in primates. We also found, in analogy to the early stages of the primate visual system, that chromatic and achromatic information were segregated in the early layers of the network. Units in the higher layers of AlexNet exhibit on average a lower responsivity for color than units at earlier stages.

  1. Very Deep Convolutional Neural Networks for Morphologic Classification of Erythrocytes.

    Science.gov (United States)

    Durant, Thomas J S; Olson, Eben M; Schulz, Wade L; Torres, Richard

    2017-12-01

    Morphologic profiling of the erythrocyte population is a widely used and clinically valuable diagnostic modality, but one that relies on a slow manual process associated with significant labor cost and limited reproducibility. Automated profiling of erythrocytes from digital images by capable machine learning approaches would augment the throughput and value of morphologic analysis. To this end, we sought to evaluate the performance of leading implementation strategies for convolutional neural networks (CNNs) when applied to classification of erythrocytes based on morphology. Erythrocytes were manually classified into 1 of 10 classes using a custom-developed Web application. Using recent literature to guide architectural considerations for neural network design, we implemented a "very deep" CNN, consisting of >150 layers, with dense shortcut connections. The final database comprised 3737 labeled cells. Ensemble model predictions on unseen data demonstrated a harmonic mean of recall and precision metrics of 92.70% and 89.39%, respectively. Of the 748 cells in the test set, 23 misclassification errors were made, with a correct classification frequency of 90.60%, represented as a harmonic mean across the 10 morphologic classes. These findings indicate that erythrocyte morphology profiles could be measured with a high degree of accuracy with "very deep" CNNs. Further, these data support future efforts to expand classes and optimize practical performance in a clinical environment as a prelude to full implementation as a clinical tool. © 2017 American Association for Clinical Chemistry.

  2. Noisy Ocular Recognition Based on Three Convolutional Neural Networks.

    Science.gov (United States)

    Lee, Min Beom; Hong, Hyung Gil; Park, Kang Ryoung

    2017-12-17

    In recent years, the iris recognition system has been gaining increasing acceptance for applications such as access control and smartphone security. When the images of the iris are obtained under unconstrained conditions, an issue of undermined quality is caused by optical and motion blur, off-angle view (the user's eyes looking somewhere else, not into the front of the camera), specular reflection (SR) and other factors. Such noisy iris images increase intra-individual variations and, as a result, reduce the accuracy of iris recognition. A typical iris recognition system requires a near-infrared (NIR) illuminator along with an NIR camera, which are larger and more expensive than fingerprint recognition equipment. Hence, many studies have proposed methods of using iris images captured by a visible light camera without the need for an additional illuminator. In this research, we propose a new recognition method for noisy iris and ocular images by using one iris and two periocular regions, based on three convolutional neural networks (CNNs). Experiments were conducted by using the noisy iris challenge evaluation-part II (NICE.II) training dataset (selected from the university of Beira iris (UBIRIS).v2 database), mobile iris challenge evaluation (MICHE) database, and institute of automation of Chinese academy of sciences (CASIA)-Iris-Distance database. As a result, the method proposed by this study outperformed previous methods.

  3. Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields.

    Science.gov (United States)

    Wang, Sheng; Peng, Jian; Ma, Jianzhu; Xu, Jinbo

    2016-01-11

    Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility.

  4. Understanding the Convolutional Neural Networks with Gradient Descent and Backpropagation

    Science.gov (United States)

    Zhou, XueFei

    2018-04-01

    With the development of computer technology, the applications of machine learning are more and more extensive. And machine learning is providing endless opportunities to develop new applications. One of those applications is image recognition by using Convolutional Neural Networks (CNNs). CNN is one of the most common algorithms in image recognition. It is significant to understand its theory and structure for every scholar who is interested in this field. CNN is mainly used in computer identification, especially in voice, text recognition and other aspects of the application. It utilizes hierarchical structure with different layers to accelerate computing speed. In addition, the greatest features of CNNs are the weight sharing and dimension reduction. And all of these consolidate the high effectiveness and efficiency of CNNs with idea computing speed and error rate. With the help of other learning altruisms, CNNs could be used in several scenarios for machine learning, especially for deep learning. Based on the general introduction to the background and the core solution CNN, this paper is going to focus on summarizing how Gradient Descent and Backpropagation work, and how they contribute to the high performances of CNNs. Also, some practical applications will be discussed in the following parts. The last section exhibits the conclusion and some perspectives of future work.

  5. Classifying magnetic resonance image modalities with convolutional neural networks

    Science.gov (United States)

    Remedios, Samuel; Pham, Dzung L.; Butman, John A.; Roy, Snehashis

    2018-02-01

    Magnetic Resonance (MR) imaging allows the acquisition of images with different contrast properties depending on the acquisition protocol and the magnetic properties of tissues. Many MR brain image processing techniques, such as tissue segmentation, require multiple MR contrasts as inputs, and each contrast is treated differently. Thus it is advantageous to automate the identification of image contrasts for various purposes, such as facilitating image processing pipelines, and managing and maintaining large databases via content-based image retrieval (CBIR). Most automated CBIR techniques focus on a two-step process: extracting features from data and classifying the image based on these features. We present a novel 3D deep convolutional neural network (CNN)- based method for MR image contrast classification. The proposed CNN automatically identifies the MR contrast of an input brain image volume. Specifically, we explored three classification problems: (1) identify T1-weighted (T1-w), T2-weighted (T2-w), and fluid-attenuated inversion recovery (FLAIR) contrasts, (2) identify pre vs postcontrast T1, (3) identify pre vs post-contrast FLAIR. A total of 3418 image volumes acquired from multiple sites and multiple scanners were used. To evaluate each task, the proposed model was trained on 2137 images and tested on the remaining 1281 images. Results showed that image volumes were correctly classified with 97.57% accuracy.

  6. Convolutional networks for fast, energy-efficient neuromorphic computing

    Science.gov (United States)

    Esser, Steven K.; Merolla, Paul A.; Arthur, John V.; Cassidy, Andrew S.; Appuswamy, Rathinakumar; Andreopoulos, Alexander; Berg, David J.; McKinstry, Jeffrey L.; Melano, Timothy; Barch, Davis R.; di Nolfo, Carmelo; Datta, Pallab; Amir, Arnon; Taba, Brian; Flickner, Myron D.; Modha, Dharmendra S.

    2016-01-01

    Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on spiking neurons, low precision synapses, and a scalable communication network. Here, we demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that (i) approach state-of-the-art classification accuracy across eight standard datasets encompassing vision and speech, (ii) perform inference while preserving the hardware’s underlying energy-efficiency and high throughput, running on the aforementioned datasets at between 1,200 and 2,600 frames/s and using between 25 and 275 mW (effectively >6,000 frames/s per Watt), and (iii) can be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning. This approach allows the algorithmic power of deep learning to be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer. PMID:27651489

  7. Mobile Stride Length Estimation With Deep Convolutional Neural Networks.

    Science.gov (United States)

    Hannink, Julius; Kautz, Thomas; Pasluosta, Cristian F; Barth, Jens; Schulein, Samuel; GaBmann, Karl-Gunter; Klucken, Jochen; Eskofier, Bjoern M

    2018-03-01

    Accurate estimation of spatial gait characteristics is critical to assess motor impairments resulting from neurological or musculoskeletal disease. Currently, however, methodological constraints limit clinical applicability of state-of-the-art double integration approaches to gait patterns with a clear zero-velocity phase. We describe a novel approach to stride length estimation that uses deep convolutional neural networks to map stride-specific inertial sensor data to the resulting stride length. The model is trained on a publicly available and clinically relevant benchmark dataset consisting of 1220 strides from 101 geriatric patients. Evaluation is done in a tenfold cross validation and for three different stride definitions. Even though best results are achieved with strides defined from midstance to midstance with average accuracy and precision of , performance does not strongly depend on stride definition. The achieved precision outperforms state-of-the-art methods evaluated on the same benchmark dataset by . Due to the independence of stride definition, the proposed method is not subject to the methodological constrains that limit applicability of state-of-the-art double integration methods. Furthermore, it was possible to improve precision on the benchmark dataset. With more precise mobile stride length estimation, new insights to the progression of neurological disease or early indications might be gained. Due to the independence of stride definition, previously uncharted diseases in terms of mobile gait analysis can now be investigated by retraining and applying the proposed method.

  8. Transfer Learning with Convolutional Neural Networks for SAR Ship Recognition

    Science.gov (United States)

    Zhang, Di; Liu, Jia; Heng, Wang; Ren, Kaijun; Song, Junqiang

    2018-03-01

    Ship recognition is the backbone of marine surveillance systems. Recent deep learning methods, e.g. Convolutional Neural Networks (CNNs), have shown high performance for optical images. Learning CNNs, however, requires a number of annotated samples to estimate numerous model parameters, which prevents its application to Synthetic Aperture Radar (SAR) images due to the limited annotated training samples. Transfer learning has been a promising technique for applications with limited data. To this end, a novel SAR ship recognition method based on CNNs with transfer learning has been developed. In this work, we firstly start with a CNNs model that has been trained in advance on Moving and Stationary Target Acquisition and Recognition (MSTAR) database. Next, based on the knowledge gained from this image recognition task, we fine-tune the CNNs on a new task to recognize three types of ships in the OpenSARShip database. The experimental results show that our proposed approach can obviously increase the recognition rate comparing with the result of merely applying CNNs. In addition, compared to existing methods, the proposed method proves to be very competitive and can learn discriminative features directly from training data instead of requiring pre-specification or pre-selection manually.

  9. Combined open proximal and stent-graft distal repair for distal arch aneurysms: an alternative to total debranching.

    Science.gov (United States)

    Zierer, Andreas; Sanchez, Luis A; Moon, Marc R

    2009-07-01

    We present herein a novel, combined, simultaneous open proximal and stent-graft distal repair for complex distal aortic arch aneurysms involving the descending aorta. In the first surgical step, the transverse arch is opened during selective antegrade cerebral perfusion, and a Dacron graft (DuPont, Wilmington, DE) is positioned down the descending aorta in an elephant trunk-like fashion with its proximal free margin sutured circumferentially to the aorta just distal to the left subclavian or left common carotid artery. With the graft serving as the new proximal landing zone, subsequent endovascular repair is performed antegrade during rewarming through the ascending aorta.

  10. early functional outcome of distal femoral fractures at kenyatta

    African Journals Online (AJOL)

    The leading cause was RTA, followed by falls from a height. ... Distal femoral fractures cause considerable morbidity .... as means and standard deviations. .... Anaesthesia. Spinal. 37 (80). General Anaesthesia (GA). 9 (20). Transfusion.

  11. Subtrochanteric and Distal Femur Fractures in a Patient with ...

    African Journals Online (AJOL)

    This study reports the surgical management for this rare case and the treatment ... car accident and presented closed femoral shaft fracture associated with a ... to fix the distal femur fracture, enhancing the construction stability [Figures 4 and 5].

  12. Minimally invasive percutaneous plate fixation of distal tibia fractures.

    LENUS (Irish Health Repository)

    Bahari, Syah

    2007-10-01

    We report a series of 42 patients reviewed at a mean of 19.6 months after treatment of distal tibial and pilon fractures using the AO distal tibia locking plate with a minimally invasive percutaneous plate osteosynthesis (MIPPO) technique. Mean time to union was 22.4 weeks. All fractures united with acceptable alignment and angulation. Two cases of superficial infection were noted, with one case of deep infection. Mean SF36 score was 85 and mean AOFAS score was 90 at a mean of 19 months follow-up. We report satisfactory outcomes with the use of the AO distal tibia locking plate in treatment of unstable distal tibial fractures. Eighty-nine percent of the patients felt that they were back to their pre injury status and 95% back to their previous employment.

  13. Surgical treatment of distal biceps tendon rupture: a case report

    Directory of Open Access Journals (Sweden)

    Cristina N. Cozma

    2017-11-01

    Full Text Available Objectives. Distal biceps tendon rupture affects the functional upperextremity movement, impairing supination and flexion strength. According to age, profession and additional risks treatment might be nonoperative or surgical. Methods. We describe the case of a 43 years old male patient who sustained an injury to his right distal biceps and was diagnosed with acute right distal biceps rupture. Surgical treatment was decided and biceps tendon was reinserted to the radius tuberosity using a combination of a cortical button fixation associated with an interference screw. Results. Postoperative functional result was favorable with no complications and with no movement limitation after one month. Conclusions. When possible, distal biceps tendon repair should be realized surgically because this permits restoring of the muscle strength to near normal levels with no loss of motion. Nerve complications are common; therefore the surgery should be realized by experienced upper extremity surgeons.

  14. Cell-specific expression of the glucocorticoid receptor within granular convoluted tubules of the rat submaxillary gland

    International Nuclear Information System (INIS)

    Antakly, T.; Zhang, C.X.; Sarrieau, A.; Raquidan, D.

    1991-01-01

    The submaxillary gland, a heterogeneous tissue composed essentially of two functionally distinct cell types (tubular epithelial and acinar), offers an interesting system in which to study the mechanisms of steroid-dependent growth and differentiation. One cell type, the granular convoluted tubular (GCT) cell, secretes a large number of physiologically important polypeptides, including epidermal and nerve growth factors. Two steroids, androgens and glucocorticoids, greatly influence the growth, differentiation, and secretory activity of GCT cells. Because glucocorticoids can partially mimic or potentiate androgen effects, it has been thought that glucocorticoids act via androgen receptors. Since the presence of glucocorticoid receptors is a prerequisite for glucocorticoid action, we have investigated the presence and cellular distribution of glucocorticoid receptors within the rat submaxillary gland. Binding experiments using [3H]dexamethasone revealed the presence of high affinity binding sites in rat submaxillary tissue homogenates. Most of these sites were specifically competed by dexamethasone, corticosterone, and a pure glucocorticoid agonist RU 28362. Neither testosterone nor dihydrotestosterone competed for glucocorticoid binding. The cellular distribution of glucocorticoid receptors within the submaxillary gland was investigated by immunocytochemistry, using two highly specific glucocorticoid receptor antibodies. The receptor was localized in the GCT cells, but not in the acinar cells of rat and mouse submaxillary tissue sections. In GCT cells, the glucocorticoid receptor colocalized with several secretory polypeptides, including epidermal growth factor, nerve growth factor, alpha 2u-globulin, and atrial natriuretic factor

  15. Theory on the mechanism of distal action of transcription factors: looping of DNA versus tracking along DNA

    Energy Technology Data Exchange (ETDEWEB)

    Murugan, R, E-mail: rmurugan@gmail.co [Department of Biotechnology, Indian Institute of Technology Madras, Chennai 600036 (India)

    2010-10-15

    In this paper, we develop a theory on the mechanism of distal action of the transcription factors, which are bound at their respective cis-regulatory enhancer modules on the promoter-RNA polymerase II (PR) complexes to initiate the transcription event in eukaryotes. We consider both the looping and tracking modes of their distal communication and calculate the mean first passage time that is required for the distal interactions of the complex of enhancer and transcription factor with the PR via both these modes. We further investigate how this mean first passage time is dependent on the length of the DNA segment (L, base-pairs) that connects the cis-regulatory binding site and the respective promoter. When the radius of curvature of this connecting segment of DNA is R that was induced upon binding of the transcription factor at the cis-acting element and RNAPII at the promoter in cis-positions, our calculations indicate that the looping mode of distal action will dominate when L is such that L > 2{pi}R and the tracking mode of distal action will be favored when L < 2{pi}R. The time required for the distal action will be minimum when L = 2{pi}R where the typical value of R for the binding of histones will be R {approx} 16 bps and L {approx} 10{sup 2} bps. It seems that the free energy associated with the binding of the transcription factor with its cis-acting element and the distance of this cis-acting element from the corresponding promoter of the gene of interest is negatively correlated. Our results suggest that the looping and tracking modes of distal action are concurrently operating on the transcription activation and the physics that determines the timescales associated with the looping/tracking in the mechanism of action of these transcription factors on the initiation of the transcription event must put a selection pressure on the distribution of the distances of cis-regulatory modules from their respective promoters of the genes. The computational analysis

  16. Enclavado endomedular en fracturas del tercio distal de la tibia

    OpenAIRE

    Arroquy, Damian; Chahla, Jorge; Gomez Rodriguez, Gustavo; Cid Casteulani, Alberto; Svarzchtein, Santiago; Gomez, Diego; Pesciallo, Cesar

    2015-01-01

    Objetivo: Describir los resultados obtenidos con el enclavado endomedular acerrojado en pacientes con fractura del tercio distal de la tibia. Materiales y Métodos: Se incluyeron pacientes con fracturas desplazadas del tercio distal de la tibia, tratadas con clavo endomedular. La muestra incluyo 35 pacientes. El tiempo de seguimiento posoperatorio fue de 29.2 meses. Se evaluaron el tiempo de consolidacion, la consolidacion viciosa y las complicaciones. Los resultados funcionales se determinaro...

  17. Fractures of the Distal Tibia Treated with Polyaxial Locking Plating

    OpenAIRE

    Gao, Hong; Zhang, Chang-Qing; Luo, Cong-Feng; Zhou, Zu-Bin; Zeng, Bing-Fang

    2008-01-01

    We evaluated the healing rate, complications, and functional outcomes in 32 adult patients with very short metaphyseal fragments in fractures of the distal tibia treated with a polyaxial locking system. The average distance from the distal extent of the fracture to the tibial plafond was 11 mm. All fractures healed and the average time to union was 14 weeks. Six patients (19%) reported occasional local disturbance over the medial malleolus. There were two cases of postoperative superficial in...

  18. The role of imaging in diagnosing diseases of the distal radioulnar joint, triangular fibrocartilage complex, and distal ulna.

    Science.gov (United States)

    Squires, Judy H; England, Eric; Mehta, Kaushal; Wissman, Robert D

    2014-07-01

    The purpose of this article is to review the anatomy, biomechanics, and multimodality imaging findings of common and uncommon distal radioulnar joint (DRUJ), triangular fibrocartilage complex, and distal ulna abnormalities. The DRUJ is a common site for acute and chronic injuries and is frequently imaged to evaluate chronic wrist pain, forearm dysfunction, and traumatic forearm injury. Given the complex anatomy of the wrist, the radiologist plays a vital role in the diagnosis of wrist pain and dysfunction.

  19. Volar plating for distal radius fractures--do not trust the image intensifier when judging distal subchondral screw length.

    Science.gov (United States)

    Park, Derek H; Goldie, Boyd S

    2012-09-01

    The use of the volar plate to treat distal radius fractures is increasing but despite the theoretical advantages of a volar approach there have been reports of extensor tendon ruptures due to prominent screw tips protruding past the dorsal cortex. The valley in the intermediate column between Lister tubercle and the sigmoid notch of the distal radius makes it difficult to rely on fluoroscopy to judge screw length. Our aim was to quantify the dimensions of this valley and to demonstrate the danger of relying on intraoperative image intensification fluoroscopy to determine lengths of distal screws. We measured the depth of this valley in the intermediate column of the distal radius in 33 patients with computed tomographic (9 patients) or magnetic resonance image (24 patients) scans of the wrist. There was a consistent valley in all images examined [average 1.8 mm (95% confidence interval, 1.6-2.0 mm)]. Thirty-nine percent of wrists had a valley depth of at least 2 mm. Standard lateral views or rotation of the forearm to obtain oblique views does not identify prominent screw tips; and whatever the rotation of the forearm, screw tips protruding beyond dorsal cortex may look as if it is within the bone when in fact it is out. When drilling we suggest noting the depth at which the drill bit just penetrates dorsal cortex and routinely downsize the distal screw length by 2 mm. We caution against relying on flourosocopy when judging the length of the distal subchondral screws.

  20. Two-stream Convolutional Neural Network for Methane Emissions Quantification

    Science.gov (United States)

    Wang, J.; Ravikumar, A. P.; McGuire, M.; Bell, C.; Tchapmi, L. P.; Brandt, A. R.

    2017-12-01

    Methane, a key component of natural gas, has a 25x higher global warming potential than carbon dioxide on a 100-year basis. Accurately monitoring and mitigating methane emissions require cost-effective detection and quantification technologies. Optical gas imaging, one of the most commonly used leak detection technology, adopted by Environmental Protection Agency, cannot estimate leak-sizes. In this work, we harness advances in computer science to allow for rapid and automatic leak quantification. Particularly, we utilize two-stream deep Convolutional Networks (ConvNets) to estimate leak-size by capturing complementary spatial information from still plume frames, and temporal information from plume motion between frames. We build large leak datasets for training and evaluating purposes by collecting about 20 videos (i.e. 397,400 frames) of leaks. The videos were recorded at six distances from the source, covering 10 -60 ft. Leak sources included natural gas well-heads, separators, and tanks. All frames were labeled with a true leak size, which has eight levels ranging from 0 to 140 MCFH. Preliminary analysis shows that two-stream ConvNets provides significant accuracy advantage over single steam ConvNets. Spatial stream ConvNet can achieve an accuracy of 65.2%, by extracting important features, including texture, plume area, and pattern. Temporal stream, fed by the results of optical flow analysis, results in an accuracy of 58.3%. The integration of the two-stream ConvNets gives a combined accuracy of 77.6%. For future work, we will split the training and testing datasets in distinct ways in order to test the generalization of the algorithm for different leak sources. Several analytic metrics, including confusion matrix and visualization of key features, will be used to understand accuracy rates and occurrences of false positives. The quantification algorithm can help to find and fix super-emitters, and improve the cost-effectiveness of leak detection and repair

  1. A convolution-superposition dose calculation engine for GPUs

    Energy Technology Data Exchange (ETDEWEB)

    Hissoiny, Sami; Ozell, Benoit; Despres, Philippe [Departement de genie informatique et genie logiciel, Ecole polytechnique de Montreal, 2500 Chemin de Polytechnique, Montreal, Quebec H3T 1J4 (Canada); Departement de radio-oncologie, CRCHUM-Centre hospitalier de l' Universite de Montreal, 1560 rue Sherbrooke Est, Montreal, Quebec H2L 4M1 (Canada)

    2010-03-15

    Purpose: Graphic processing units (GPUs) are increasingly used for scientific applications, where their parallel architecture and unprecedented computing power density can be exploited to accelerate calculations. In this paper, a new GPU implementation of a convolution/superposition (CS) algorithm is presented. Methods: This new GPU implementation has been designed from the ground-up to use the graphics card's strengths and to avoid its weaknesses. The CS GPU algorithm takes into account beam hardening, off-axis softening, kernel tilting, and relies heavily on raytracing through patient imaging data. Implementation details are reported as well as a multi-GPU solution. Results: An overall single-GPU acceleration factor of 908x was achieved when compared to a nonoptimized version of the CS algorithm implemented in PlanUNC in single threaded central processing unit (CPU) mode, resulting in approximatively 2.8 s per beam for a 3D dose computation on a 0.4 cm grid. A comparison to an established commercial system leads to an acceleration factor of approximately 29x or 0.58 versus 16.6 s per beam in single threaded mode. An acceleration factor of 46x has been obtained for the total energy released per mass (TERMA) calculation and a 943x acceleration factor for the CS calculation compared to PlanUNC. Dose distributions also have been obtained for a simple water-lung phantom to verify that the implementation gives accurate results. Conclusions: These results suggest that GPUs are an attractive solution for radiation therapy applications and that careful design, taking the GPU architecture into account, is critical in obtaining significant acceleration factors. These results potentially can have a significant impact on complex dose delivery techniques requiring intensive dose calculations such as intensity-modulated radiation therapy (IMRT) and arc therapy. They also are relevant for adaptive radiation therapy where dose results must be obtained rapidly.

  2. Automated segmentation of geographic atrophy using deep convolutional neural networks

    Science.gov (United States)

    Hu, Zhihong; Wang, Ziyuan; Sadda, SriniVas R.

    2018-02-01

    Geographic atrophy (GA) is an end-stage manifestation of the advanced age-related macular degeneration (AMD), the leading cause of blindness and visual impairment in developed nations. Techniques to rapidly and precisely detect and quantify GA would appear to be of critical importance in advancing the understanding of its pathogenesis. In this study, we develop an automated supervised classification system using deep convolutional neural networks (CNNs) for segmenting GA in fundus autofluorescene (FAF) images. More specifically, to enhance the contrast of GA relative to the background, we apply the contrast limited adaptive histogram equalization. Blood vessels may cause GA segmentation errors due to similar intensity level to GA. A tensor-voting technique is performed to identify the blood vessels and a vessel inpainting technique is applied to suppress the GA segmentation errors due to the blood vessels. To handle the large variation of GA lesion sizes, three deep CNNs with three varying sized input image patches are applied. Fifty randomly chosen FAF images are obtained from fifty subjects with GA. The algorithm-defined GA regions are compared with manual delineation by a certified grader. A two-fold cross-validation is applied to evaluate the algorithm performance. The mean segmentation accuracy, true positive rate (i.e. sensitivity), true negative rate (i.e. specificity), positive predictive value, false discovery rate, and overlap ratio, between the algorithm- and manually-defined GA regions are 0.97 +/- 0.02, 0.89 +/- 0.08, 0.98 +/- 0.02, 0.87 +/- 0.12, 0.13 +/- 0.12, and 0.79 +/- 0.12 respectively, demonstrating a high level of agreement.

  3. Automatic Seismic-Event Classification with Convolutional Neural Networks.

    Science.gov (United States)

    Bueno Rodriguez, A.; Titos Luzón, M.; Garcia Martinez, L.; Benitez, C.; Ibáñez, J. M.

    2017-12-01

    Active volcanoes exhibit a wide range of seismic signals, providing vast amounts of unlabelled volcano-seismic data that can be analyzed through the lens of artificial intelligence. However, obtaining high-quality labelled data is time-consuming and expensive. Deep neural networks can process data in their raw form, compute high-level features and provide a better representation of the input data distribution. These systems can be deployed to classify seismic data at scale, enhance current early-warning systems and build extensive seismic catalogs. In this research, we aim to classify spectrograms from seven different seismic events registered at "Volcán de Fuego" (Colima, Mexico), during four eruptive periods. Our approach is based on convolutional neural networks (CNNs), a sub-type of deep neural networks that can exploit grid structure from the data. Volcano-seismic signals can be mapped into a grid-like structure using the spectrogram: a representation of the temporal evolution in terms of time and frequency. Spectrograms were computed from the data using Hamming windows with 4 seconds length, 2.5 seconds overlapping and 128 points FFT resolution. Results are compared to deep neural networks, random forest and SVMs. Experiments show that CNNs can exploit temporal and frequency information, attaining a classification accuracy of 93%, similar to deep networks 91% but outperforming SVM and random forest. These results empirically show that CNNs are powerful models to classify a wide range of volcano-seismic signals, and achieve good generalization. Furthermore, volcano-seismic spectrograms contains useful discriminative information for the CNN, as higher layers of the network combine high-level features computed for each frequency band, helping to detect simultaneous events in time. Being at the intersection of deep learning and geophysics, this research enables future studies of how CNNs can be used in volcano monitoring to accurately determine the detection and

  4. Aerial Images and Convolutional Neural Network for Cotton Bloom Detection.

    Science.gov (United States)

    Xu, Rui; Li, Changying; Paterson, Andrew H; Jiang, Yu; Sun, Shangpeng; Robertson, Jon S

    2017-01-01

    Monitoring flower development can provide useful information for production management, estimating yield and selecting specific genotypes of crops. The main goal of this study was to develop a methodology to detect and count cotton flowers, or blooms, using color images acquired by an unmanned aerial system. The aerial images were collected from two test fields in 4 days. A convolutional neural network (CNN) was designed and trained to detect cotton blooms in raw images, and their 3D locations were calculated using the dense point cloud constructed from the aerial images with the structure from motion method. The quality of the dense point cloud was analyzed and plots with poor quality were excluded from data analysis. A constrained clustering algorithm was developed to register the same bloom detected from different images based on the 3D location of the bloom. The accuracy and incompleteness of the dense point cloud were analyzed because they affected the accuracy of the 3D location of the blooms and thus the accuracy of the bloom registration result. The constrained clustering algorithm was validated using simulated data, showing good efficiency and accuracy. The bloom count from the proposed method was comparable with the number counted manually with an error of -4 to 3 blooms for the field with a single plant per plot. However, more plots were underestimated in the field with multiple plants per plot due to hidden blooms that were not captured by the aerial images. The proposed methodology provides a high-throughput method to continuously monitor the flowering progress of cotton.

  5. A deep convolutional neural network model to classify heartbeats.

    Science.gov (United States)

    Acharya, U Rajendra; Oh, Shu Lih; Hagiwara, Yuki; Tan, Jen Hong; Adam, Muhammad; Gertych, Arkadiusz; Tan, Ru San

    2017-10-01

    The electrocardiogram (ECG) is a standard test used to monitor the activity of the heart. Many cardiac abnormalities will be manifested in the ECG including arrhythmia which is a general term that refers to an abnormal heart rhythm. The basis of arrhythmia diagnosis is the identification of normal versus abnormal individual heart beats, and their correct classification into different diagnoses, based on ECG morphology. Heartbeats can be sub-divided into five categories namely non-ectopic, supraventricular ectopic, ventricular ectopic, fusion, and unknown beats. It is challenging and time-consuming to distinguish these heartbeats on ECG as these signals are typically corrupted by noise. We developed a 9-layer deep convolutional neural network (CNN) to automatically identify 5 different categories of heartbeats in ECG signals. Our experiment was conducted in original and noise attenuated sets of ECG signals derived from a publicly available database. This set was artificially augmented to even out the number of instances the 5 classes of heartbeats and filtered to remove high-frequency noise. The CNN was trained using the augmented data and achieved an accuracy of 94.03% and 93.47% in the diagnostic classification of heartbeats in original and noise free ECGs, respectively. When the CNN was trained with highly imbalanced data (original dataset), the accuracy of the CNN reduced to 89.07%% and 89.3% in noisy and noise-free ECGs. When properly trained, the proposed CNN model can serve as a tool for screening of ECG to quickly identify different types and frequency of arrhythmic heartbeats. Copyright © 2017 Elsevier Ltd. All rights reserved.

  6. Psoriasis skin biopsy image segmentation using Deep Convolutional Neural Network.

    Science.gov (United States)

    Pal, Anabik; Garain, Utpal; Chandra, Aditi; Chatterjee, Raghunath; Senapati, Swapan

    2018-06-01

    Development of machine assisted tools for automatic analysis of psoriasis skin biopsy image plays an important role in clinical assistance. Development of automatic approach for accurate segmentation of psoriasis skin biopsy image is the initial prerequisite for developing such system. However, the complex cellular structure, presence of imaging artifacts, uneven staining variation make the task challenging. This paper presents a pioneering attempt for automatic segmentation of psoriasis skin biopsy images. Several deep neural architectures are tried for segmenting psoriasis skin biopsy images. Deep models are used for classifying the super-pixels generated by Simple Linear Iterative Clustering (SLIC) and the segmentation performance of these architectures is compared with the traditional hand-crafted feature based classifiers built on popularly used classifiers like K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF). A U-shaped Fully Convolutional Neural Network (FCN) is also used in an end to end learning fashion where input is the original color image and the output is the segmentation class map for the skin layers. An annotated real psoriasis skin biopsy image data set of ninety (90) images is developed and used for this research. The segmentation performance is evaluated with two metrics namely, Jaccard's Coefficient (JC) and the Ratio of Correct Pixel Classification (RCPC) accuracy. The experimental results show that the CNN based approaches outperform the traditional hand-crafted feature based classification approaches. The present research shows that practical system can be developed for machine assisted analysis of psoriasis disease. Copyright © 2018 Elsevier B.V. All rights reserved.

  7. Classifying Radio Galaxies with the Convolutional Neural Network

    International Nuclear Information System (INIS)

    Aniyan, A. K.; Thorat, K.

    2017-01-01

    We present the application of a deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks (CNN). In this study, we have taken the case of the Fanaroff–Riley (FR) class of radio galaxies as well as radio galaxies with bent-tailed morphology. We have used archival data from the Very Large Array (VLA)—Faint Images of the Radio Sky at Twenty Centimeters survey and existing visually classified samples available in the literature to train a neural network for morphological classification of these categories of radio sources. Our training sample size for each of these categories is ∼200 sources, which has been augmented by rotated versions of the same. Our study shows that CNNs can classify images of the FRI and FRII and bent-tailed radio galaxies with high accuracy (maximum precision at 95%) using well-defined samples and a “fusion classifier,” which combines the results of binary classifications, while allowing for a mechanism to find sources with unusual morphologies. The individual precision is highest for bent-tailed radio galaxies at 95% and is 91% and 75% for the FRI and FRII classes, respectively, whereas the recall is highest for FRI and FRIIs at 91% each, while the bent-tailed class has a recall of 79%. These results show that our results are comparable to that of manual classification, while being much faster. Finally, we discuss the computational and data-related challenges associated with the morphological classification of radio galaxies with CNNs.

  8. Convolutional neural networks for prostate cancer recurrence prediction

    Science.gov (United States)

    Kumar, Neeraj; Verma, Ruchika; Arora, Ashish; Kumar, Abhay; Gupta, Sanchit; Sethi, Amit; Gann, Peter H.

    2017-03-01

    Accurate prediction of the treatment outcome is important for cancer treatment planning. We present an approach to predict prostate cancer (PCa) recurrence after radical prostatectomy using tissue images. We used a cohort whose case vs. control (recurrent vs. non-recurrent) status had been determined using post-treatment follow up. Further, to aid the development of novel biomarkers of PCa recurrence, cases and controls were paired based on matching of other predictive clinical variables such as Gleason grade, stage, age, and race. For this cohort, tissue resection microarray with up to four cores per patient was available. The proposed approach is based on deep learning, and its novelty lies in the use of two separate convolutional neural networks (CNNs) - one to detect individual nuclei even in the crowded areas, and the other to classify them. To detect nuclear centers in an image, the first CNN predicts distance transform of the underlying (but unknown) multi-nuclear map from the input HE image. The second CNN classifies the patches centered at nuclear centers into those belonging to cases or controls. Voting across patches extracted from image(s) of a patient yields the probability of recurrence for the patient. The proposed approach gave 0.81 AUC for a sample of 30 recurrent cases and 30 non-recurrent controls, after being trained on an independent set of 80 case-controls pairs. If validated further, such an approach might help in choosing between a combination of treatment options such as active surveillance, radical prostatectomy, radiation, and hormone therapy. It can also generalize to the prediction of treatment outcomes in other cancers.

  9. Classifying Radio Galaxies with the Convolutional Neural Network

    Energy Technology Data Exchange (ETDEWEB)

    Aniyan, A. K.; Thorat, K. [Department of Physics and Electronics, Rhodes University, Grahamstown (South Africa)

    2017-06-01

    We present the application of a deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks (CNN). In this study, we have taken the case of the Fanaroff–Riley (FR) class of radio galaxies as well as radio galaxies with bent-tailed morphology. We have used archival data from the Very Large Array (VLA)—Faint Images of the Radio Sky at Twenty Centimeters survey and existing visually classified samples available in the literature to train a neural network for morphological classification of these categories of radio sources. Our training sample size for each of these categories is ∼200 sources, which has been augmented by rotated versions of the same. Our study shows that CNNs can classify images of the FRI and FRII and bent-tailed radio galaxies with high accuracy (maximum precision at 95%) using well-defined samples and a “fusion classifier,” which combines the results of binary classifications, while allowing for a mechanism to find sources with unusual morphologies. The individual precision is highest for bent-tailed radio galaxies at 95% and is 91% and 75% for the FRI and FRII classes, respectively, whereas the recall is highest for FRI and FRIIs at 91% each, while the bent-tailed class has a recall of 79%. These results show that our results are comparable to that of manual classification, while being much faster. Finally, we discuss the computational and data-related challenges associated with the morphological classification of radio galaxies with CNNs.

  10. Classifying Radio Galaxies with the Convolutional Neural Network

    Science.gov (United States)

    Aniyan, A. K.; Thorat, K.

    2017-06-01

    We present the application of a deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks (CNN). In this study, we have taken the case of the Fanaroff-Riley (FR) class of radio galaxies as well as radio galaxies with bent-tailed morphology. We have used archival data from the Very Large Array (VLA)—Faint Images of the Radio Sky at Twenty Centimeters survey and existing visually classified samples available in the literature to train a neural network for morphological classification of these categories of radio sources. Our training sample size for each of these categories is ˜200 sources, which has been augmented by rotated versions of the same. Our study shows that CNNs can classify images of the FRI and FRII and bent-tailed radio galaxies with high accuracy (maximum precision at 95%) using well-defined samples and a “fusion classifier,” which combines the results of binary classifications, while allowing for a mechanism to find sources with unusual morphologies. The individual precision is highest for bent-tailed radio galaxies at 95% and is 91% and 75% for the FRI and FRII classes, respectively, whereas the recall is highest for FRI and FRIIs at 91% each, while the bent-tailed class has a recall of 79%. These results show that our results are comparable to that of manual classification, while being much faster. Finally, we discuss the computational and data-related challenges associated with the morphological classification of radio galaxies with CNNs.

  11. A staggered-grid convolutional differentiator for elastic wave modelling

    Science.gov (United States)

    Sun, Weijia; Zhou, Binzhong; Fu, Li-Yun

    2015-11-01

    The computation of derivatives in governing partial differential equations is one of the most investigated subjects in the numerical simulation of physical wave propagation. An analytical staggered-grid convolutional differentiator (CD) for first-order velocity-stress elastic wave equations is derived in this paper by inverse Fourier transformation of the band-limited spectrum of a first derivative operator. A taper window function is used to truncate the infinite staggered-grid CD stencil. The truncated CD operator is almost as accurate as the analytical solution, and as efficient as the finite-difference (FD) method. The selection of window functions will influence the accuracy of the CD operator in wave simulation. We search for the optimal Gaussian windows for different order CDs by minimizing the spectral error of the derivative and comparing the windows with the normal Hanning window function for tapering the CD operators. It is found that the optimal Gaussian window appears to be similar to the Hanning window function for tapering the same CD operator. We investigate the accuracy of the windowed CD operator and the staggered-grid FD method with different orders. Compared to the conventional staggered-grid FD method, a short staggered-grid CD operator achieves an accuracy equivalent to that of a long FD operator, with lower computational costs. For example, an 8th order staggered-grid CD operator can achieve the same accuracy of a 16th order staggered-grid FD algorithm but with half of the computational resources and time required. Numerical examples from a homogeneous model and a crustal waveguide model are used to illustrate the superiority of the CD operators over the conventional staggered-grid FD operators for the simulation of wave propagations.

  12. Deep convolutional networks for pancreas segmentation in CT imaging

    Science.gov (United States)

    Roth, Holger R.; Farag, Amal; Lu, Le; Turkbey, Evrim B.; Summers, Ronald M.

    2015-03-01

    Automatic organ segmentation is an important prerequisite for many computer-aided diagnosis systems. The high anatomical variability of organs in the abdomen, such as the pancreas, prevents many segmentation methods from achieving high accuracies when compared to state-of-the-art segmentation of organs like the liver, heart or kidneys. Recently, the availability of large annotated training sets and the accessibility of affordable parallel computing resources via GPUs have made it feasible for "deep learning" methods such as convolutional networks (ConvNets) to succeed in image classification tasks. These methods have the advantage that used classification features are trained directly from the imaging data. We present a fully-automated bottom-up method for pancreas segmentation in computed tomography (CT) images of the abdomen. The method is based on hierarchical coarse-to-fine classification of local image regions (superpixels). Superpixels are extracted from the abdominal region using Simple Linear Iterative Clustering (SLIC). An initial probability response map is generated, using patch-level confidences and a two-level cascade of random forest classifiers, from which superpixel regions with probabilities larger 0.5 are retained. These retained superpixels serve as a highly sensitive initial input of the pancreas and its surroundings to a ConvNet that samples a bounding box around each superpixel at different scales (and random non-rigid deformations at training time) in order to assign a more distinct probability of each superpixel region being pancreas or not. We evaluate our method on CT images of 82 patients (60 for training, 2 for validation, and 20 for testing). Using ConvNets we achieve maximum Dice scores of an average 68% +/- 10% (range, 43-80%) in testing. This shows promise for accurate pancreas segmentation, using a deep learning approach and compares favorably to state-of-the-art methods.

  13. Odf2-deficient mother centrioles lack distal/subdistal appendages and the ability to generate primary cilia.

    Science.gov (United States)

    Ishikawa, Hiroaki; Kubo, Akiharu; Tsukita, Shoichiro; Tsukita, Sachiko

    2005-05-01

    Outer dense fibre 2 (Odf2; also known as cenexin) was initially identified as a main component of the sperm tail cytoskeleton, but was later shown to be a general scaffold protein that is specifically localized at the distal/subdistal appendages of mother centrioles. Here we show that Odf2 expression is suppressed in mouse F9 cells when both alleles of Odf2 genes are deleted. Unexpectedly, the cell cycle of Odf2(-/-) cells does not seem to be affected. Immunofluorescence and ultrathin-section electron microscopy reveals that in Odf2(-/-) cells, distal/subdistal appendages disappear from mother centrioles, making it difficult to distinguish mother from daughter centrioles. In Odf2(-/-) cells, however, the formation of primary cilia is completely suppressed, although approximately 25% of wild-type F9 cells are ciliated under the steady-state cell cycle. The loss of primary cilia in Odf2(-/-) F9 cells can be rescued by exogenous Odf2 expression. These findings indicate that Odf2 is indispensable for the formation of distal/subdistal appendages and the generation of primary cilia, but not for other cell-cycle-related centriolar functions.

  14. Arthroplasty of the distal ulna distal in managing patients with post-traumatic disorders of the distal radioulnar joint: measurement of quality of life

    Directory of Open Access Journals (Sweden)

    Marcio Aurélio Aita

    2015-12-01

    Full Text Available ABSTRACT OBJECTIVE: To measure the quality of life and clinical-functional results from patients diagnosed with osteoarthrosis of the distal radioulnar joint who underwent surgical treatment using the technique of total arthroplasty of the ulna, with a total or partial Ascension(r prosthesis of the distal ulna. METHODS: Ten patients were evaluated after 12 months of follow-up subsequent to total or partial arthroplasty of the distal ulna. All of them presented post-traumatic osteoarthrosis and/or chronic symptomatic instability of the distal radioulnar joint. The study was prospective. Seven patients had previously undergone wrist procedures (two cases with Darrach, three with Sauvé-Kapandji and two with ligament reconstruction of the fibrocartilage complex and three presented fractures of the distal ulna that evolved with pain, instability and osteoarthrosis of the distal radioulnar joint. The following were assessed: quality of life (DASH scale; percentage degree of palm grip strength (kgf and pronosupination range of motion in relation to the unaffected side; pain (VAS; return to work; subjective evaluation of radiography; and complications. RESULTS: The patients presented a mean range of motion of 174.5° (normal side: 180°. Quality of life was analyzed by applying the DASH questionnaire and the mean value found was 5.9. The mean pain score using the VAS was 2.3. The mean degree of palm grip strength (kgf was 50.7, which represented 90.7% of the strength on the unaffected side. The complication rate was 10%: this patient presented slight dorsal instability of the ulna and persistent pain, and did not return to work. This patient is still being followed up in the outpatient clinic and occupational therapy sector, with little improvement. He does not wish to undergo a new procedure. The mean length of follow-up was 16.8 months, with a minimum of 10 and maximum of 36 months. CONCLUSION: This concept is subject to the test of time

  15. The alpha-spectrin gene is on chromosome 1 in mouse and man.

    Science.gov (United States)

    Huebner, K; Palumbo, A P; Isobe, M; Kozak, C A; Monaco, S; Rovera, G; Croce, C M; Curtis, P J

    1985-06-01

    By using alpha-spectrin cDNA clones of murine and human origin and somatic cell hybrids segregating either mouse or human chromosomes, the gene for alpha-spectrin has been mapped to chromosome 1 in both species. This assignment of the mouse alpha-spectrin gene to mouse chromosome 1 by DNA hybridization strengthens the previous identification of the alpha-spectrin locus in mouse with the sph locus, which previously was mapped by linkage analysis to mouse chromosome 1, distal to the Pep-3 locus. By in situ hybridization to human metaphase chromosomes, the human alpha-spectrin gene has been localized to 1q22-1q25; interestingly, the locus for a non-Rh-linked form of elliptocytosis has been provisionally mapped to band 1q2 by family linkage studies.

  16. Convoluted laminations in waterlain sediments:three examples from Eastern Canada and their relevance to neotectonics

    International Nuclear Information System (INIS)

    Macdougall, D.A.; Broster, B.E.

    1995-10-01

    The catastrophic disturbance of unconsolidated sediment produces a wide variety of deformation structures, particularly if the sediment is water-saturated at the time of disturbance. Layers, originally deposited as sub-horizontal, can become stretched or distended resulting in convoluted laminations. Faulted beds, slumped units, or dewatering structures may also occur in association with the disturbance. Convolutions were studied in five examples of Pleistocene glaciomarine deltas, at three locations in eastern Canada. Results from this study indicate that similar structures were produced in each of the sediment deposits, but some are especially common in specific facies (e.g. bottomset, foreset, topset). However, the particular cause of the convolutions varied within each deposit, and the origin could be better assessed when studied in relationship to other structures. None of the convolutions found could be attributed, categorically, to a seismic origin. However, neither could a seismic origin be dismissed for structures associated with convolutions occurring in deposits at: St. George, New Brunswick; Economy Point, Nova Scotia; and Lanark, Ontario. Of these deposits, the deformed structures at Economy Point are apparently post-glacial. (author). 24 refs., 58 figs

  17. High Performance Implementation of 3D Convolutional Neural Networks on a GPU

    Science.gov (United States)

    Wang, Zelong; Wen, Mei; Zhang, Chunyuan; Wang, Yijie

    2017-01-01

    Convolutional neural networks have proven to be highly successful in applications such as image classification, object tracking, and many other tasks based on 2D inputs. Recently, researchers have started to apply convolutional neural networks to video classification, which constitutes a 3D input and requires far larger amounts of memory and much more computation. FFT based methods can reduce the amount of computation, but this generally comes at the cost of an increased memory requirement. On the other hand, the Winograd Minimal Filtering Algorithm (WMFA) can reduce the number of operations required and thus can speed up the computation, without increasing the required memory. This strategy was shown to be successful for 2D neural networks. We implement the algorithm for 3D convolutional neural networks and apply it to a popular 3D convolutional neural network which is used to classify videos and compare it to cuDNN. For our highly optimized implementation of the algorithm, we observe a twofold speedup for most of the 3D convolution layers of our test network compared to the cuDNN version. PMID:29250109

  18. High Performance Implementation of 3D Convolutional Neural Networks on a GPU.

    Science.gov (United States)

    Lan, Qiang; Wang, Zelong; Wen, Mei; Zhang, Chunyuan; Wang, Yijie

    2017-01-01

    Convolutional neural networks have proven to be highly successful in applications such as image classification, object tracking, and many other tasks based on 2D inputs. Recently, researchers have started to apply convolutional neural networks to video classification, which constitutes a 3D input and requires far larger amounts of memory and much more computation. FFT based methods can reduce the amount of computation, but this generally comes at the cost of an increased memory requirement. On the other hand, the Winograd Minimal Filtering Algorithm (WMFA) can reduce the number of operations required and thus can speed up the computation, without increasing the required memory. This strategy was shown to be successful for 2D neural networks. We implement the algorithm for 3D convolutional neural networks and apply it to a popular 3D convolutional neural network which is used to classify videos and compare it to cuDNN. For our highly optimized implementation of the algorithm, we observe a twofold speedup for most of the 3D convolution layers of our test network compared to the cuDNN version.

  19. A New Orthodontic Appliance with a Mini Screw for Upper Molar Distalization.

    Science.gov (United States)

    Ozkalayci, Nurhat; Yetmez, Mehmet

    2016-01-01

    The aim of this study is to present a new upper molar distalization appliance called Cise distalizer designed as intraoral device supported with orthodontic mini screw for upper permanent molar distalization. The new appliance consists of eight main components. In order to understand the optimum force level, the appliance under static loading is tested by using strain gage measurement techniques. Results show that one of the open coils produces approximately 300 gr distalization force. Cise distalizer can provide totally 600 gr distalization force. This range of force level is enough for distalization of upper first and second molar teeth.

  20. A New Orthodontic Appliance with a Mini Screw for Upper Molar Distalization

    Directory of Open Access Journals (Sweden)

    Nurhat Ozkalayci

    2016-01-01

    Full Text Available The aim of this study is to present a new upper molar distalization appliance called Cise distalizer designed as intraoral device supported with orthodontic mini screw for upper permanent molar distalization. The new appliance consists of eight main components. In order to understand the optimum force level, the appliance under static loading is tested by using strain gage measurement techniques. Results show that one of the open coils produces approximately 300 gr distalization force. Cise distalizer can provide totally 600 gr distalization force. This range of force level is enough for distalization of upper first and second molar teeth.

  1. Limited distal organelles and synaptic function in extensive monoaminergic innervation.

    Science.gov (United States)

    Tao, Juan; Bulgari, Dinara; Deitcher, David L; Levitan, Edwin S

    2017-08-01

    Organelles such as neuropeptide-containing dense-core vesicles (DCVs) and mitochondria travel down axons to supply synaptic boutons. DCV distribution among en passant boutons in small axonal arbors is mediated by circulation with bidirectional capture. However, it is not known how organelles are distributed in extensive arbors associated with mammalian dopamine neuron vulnerability, and with volume transmission and neuromodulation by monoamines and neuropeptides. Therefore, we studied presynaptic organelle distribution in Drosophila octopamine neurons that innervate ∼20 muscles with ∼1500 boutons. Unlike in smaller arbors, distal boutons in these arbors contain fewer DCVs and mitochondria, although active zones are present. Absence of vesicle circulation is evident by proximal nascent DCV delivery, limited impact of retrograde transport and older distal DCVs. Traffic studies show that DCV axonal transport and synaptic capture are not scaled for extensive innervation, thus limiting distal delivery. Activity-induced synaptic endocytosis and synaptic neuropeptide release are also reduced distally. We propose that limits in organelle transport and synaptic capture compromise distal synapse maintenance and function in extensive axonal arbors, thereby affecting development, plasticity and vulnerability to neurodegenerative disease. © 2017. Published by The Company of Biologists Ltd.

  2. Fractures of the distal radius in children: A retrospective evaluation

    Directory of Open Access Journals (Sweden)

    Selma Yazıcı

    2012-06-01

    Full Text Available Objectives: This study designed to evaluate the resultsof treatment, closed reduction and percutaneous wires, ofthe distal radius fractures in children.Materials and methods: A retrospective analysis wascarried out in children aged between 5-15 years who presentedwith a displaced fracture of the distal radius to ourhospital. They were initially treated with closed reductionand cast immobilization. If the fractures redisplaced treatedby percutaneous Kirschner (K- wire with scope undera general anaesthesia.Results: Totally 104 patients, who have distal radius fractureswere treated by closed reduction and immobilizationin a plaster cast. 13 patient who have distal radiusfractures were treated by closed reduction under generalanaesthesia and fixed by percutaneous Kirschner (K-wire. Patients with impaired the alignment of the fracturein late period were usually completely displaced fractures.(n=5, 4,3%, in early period, completely displaced fractures(n=5, 4,3% are superior to partial displaced fractures(n=2, 1,7%.Conclusion: In our study, when children with distal radiusfracture first come, they were treated by closed reductionand immobilization in a plaster cast. We thought that inredisplaced fractures patients were suitable for the closedreduction with percutaneous wire treatment.

  3. No-reference image quality assessment based on statistics of convolution feature maps

    Science.gov (United States)

    Lv, Xiaoxin; Qin, Min; Chen, Xiaohui; Wei, Guo

    2018-04-01

    We propose a Convolutional Feature Maps (CFM) driven approach to accurately predict image quality. Our motivation bases on the finding that the Nature Scene Statistic (NSS) features on convolution feature maps are significantly sensitive to distortion degree of an image. In our method, a Convolutional Neural Network (CNN) is trained to obtain kernels for generating CFM. We design a forward NSS layer which performs on CFM to better extract NSS features. The quality aware features derived from the output of NSS layer is effective to describe the distortion type and degree an image suffered. Finally, a Support Vector Regression (SVR) is employed in our No-Reference Image Quality Assessment (NR-IQA) model to predict a subjective quality score of a distorted image. Experiments conducted on two public databases demonstrate the promising performance of the proposed method is competitive to state of the art NR-IQA methods.

  4. Design and Implementation of Behavior Recognition System Based on Convolutional Neural Network

    Directory of Open Access Journals (Sweden)

    Yu Bo

    2017-01-01

    Full Text Available We build a set of human behavior recognition system based on the convolution neural network constructed for the specific human behavior in public places. Firstly, video of human behavior data set will be segmented into images, then we process the images by the method of background subtraction to extract moving foreground characters of body. Secondly, the training data sets are trained into the designed convolution neural network, and the depth learning network is constructed by stochastic gradient descent. Finally, the various behaviors of samples are classified and identified with the obtained network model, and the recognition results are compared with the current mainstream methods. The result show that the convolution neural network can study human behavior model automatically and identify human’s behaviors without any manually annotated trainings.

  5. Efficient airport detection using region-based fully convolutional neural networks

    Science.gov (United States)

    Xin, Peng; Xu, Yuelei; Zhang, Xulei; Ma, Shiping; Li, Shuai; Lv, Chao

    2018-04-01

    This paper presents a model for airport detection using region-based fully convolutional neural networks. To achieve fast detection with high accuracy, we shared the conv layers between the region proposal procedure and the airport detection procedure and used graphics processing units (GPUs) to speed up the training and testing time. For lack of labeled data, we transferred the convolutional layers of ZF net pretrained by ImageNet to initialize the shared convolutional layers, then we retrained the model using the alternating optimization training strategy. The proposed model has been tested on an airport dataset consisting of 600 images. Experiments show that the proposed method can distinguish airports in our dataset from similar background scenes almost real-time with high accuracy, which is much better than traditional methods.

  6. Fast Automatic Airport Detection in Remote Sensing Images Using Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Fen Chen

    2018-03-01

    Full Text Available Fast and automatic detection of airports from remote sensing images is useful for many military and civilian applications. In this paper, a fast automatic detection method is proposed to detect airports from remote sensing images based on convolutional neural networks using the Faster R-CNN algorithm. This method first applies a convolutional neural network to generate candidate airport regions. Based on the features extracted from these proposals, it then uses another convolutional neural network to perform airport detection. By taking the typical elongated linear geometric shape of airports into consideration, some specific improvements to the method are proposed. These approaches successfully improve the quality of positive samples and achieve a better accuracy in the final detection results. Experimental results on an airport dataset, Landsat 8 images, and a Gaofen-1 satellite scene demonstrate the effectiveness and efficiency of the proposed method.

  7. Space-Time Convolutional Codes over Finite Fields and Rings for Systems with Large Diversity Order

    Directory of Open Access Journals (Sweden)

    B. F. Uchôa-Filho

    2008-06-01

    Full Text Available We propose a convolutional encoder over the finite ring of integers modulo pk,ℤpk, where p is a prime number and k is any positive integer, to generate a space-time convolutional code (STCC. Under this structure, we prove three properties related to the generator matrix of the convolutional code that can be used to simplify the code search procedure for STCCs over ℤpk. Some STCCs of large diversity order (≥4 designed under the trace criterion for n=2,3, and 4 transmit antennas are presented for various PSK signal constellations.

  8. a Novel Deep Convolutional Neural Network for Spectral-Spatial Classification of Hyperspectral Data

    Science.gov (United States)

    Li, N.; Wang, C.; Zhao, H.; Gong, X.; Wang, D.

    2018-04-01

    Spatial and spectral information are obtained simultaneously by hyperspectral remote sensing. Joint extraction of these information of hyperspectral image is one of most import methods for hyperspectral image classification. In this paper, a novel deep convolutional neural network (CNN) is proposed, which extracts spectral-spatial information of hyperspectral images correctly. The proposed model not only learns sufficient knowledge from the limited number of samples, but also has powerful generalization ability. The proposed framework based on three-dimensional convolution can extract spectral-spatial features of labeled samples effectively. Though CNN has shown its robustness to distortion, it cannot extract features of different scales through the traditional pooling layer that only have one size of pooling window. Hence, spatial pyramid pooling (SPP) is introduced into three-dimensional local convolutional filters for hyperspectral classification. Experimental results with a widely used hyperspectral remote sensing dataset show that the proposed model provides competitive performance.

  9. Autosomal dominant hypercalciuria in a mouse model due to a mutation of the epithelial calcium channel, TRPV5.

    Directory of Open Access Journals (Sweden)

    Nellie Y Loh

    Full Text Available Hypercalciuria is a major cause of nephrolithiasis, and is a common and complex disorder involving genetic and environmental factors. Identification of genetic factors for monogenic forms of hypercalciuria is hampered by the limited availability of large families, and to facilitate such studies, we screened for hypercalciuria in mice from an N-ethyl-N-nitrosourea mutagenesis programme. We identified a mouse with autosomal dominant hypercalciuria (HCALC1. Linkage studies mapped the Hcalc1 locus to a 11.94 Mb region on chromosome 6 containing the transient receptor potential cation channel, subfamily V, members 5 (Trpv5 and 6 (Trpv6 genes. DNA sequence analysis of coding regions, intron-exon boundaries and promoters of Trpv5 and Trpv6 identified a novel T to C transition in codon 682 of TRPV5, mutating a conserved serine to a proline (S682P. Compared to wild-type littermates, heterozygous (Trpv5(682P/+ and homozygous (Trpv5(682P/682P mutant mice had hypercalciuria, polyuria, hyperphosphaturia and a more acidic urine, and ∼10% of males developed tubulointerstitial nephritis. Trpv5(682P/682P mice also had normal plasma parathyroid hormone but increased 1,25-dihydroxyvitamin D(3 concentrations without increased bone resorption, consistent with a renal defect for the hypercalciuria. Expression of the S682P mutation in human embryonic kidney cells revealed that TRPV5-S682P-expressing cells had a lower baseline intracellular calcium concentration than wild-type TRPV5-expressing cells, suggesting an altered calcium permeability. Immunohistological studies revealed a selective decrease in TRPV5-expression from the renal distal convoluted tubules of Trpv5(682P/+ and Trpv5(682P/682P mice consistent with a trafficking defect. In addition, Trpv5(682P/682P mice had a reduction in renal expression of the intracellular calcium-binding protein, calbindin-D(28K, consistent with a specific defect in TRPV5-mediated renal calcium reabsorption. Thus, our findings

  10. Theory on the mechanism of distal action of transcription factors: looping of DNA versus tracking along DNA

    International Nuclear Information System (INIS)

    Murugan, R

    2010-01-01

    In this paper, we develop a theory on the mechanism of distal action of the transcription factors, which are bound at their respective cis-regulatory enhancer modules on the promoter-RNA polymerase II (PR) complexes to initiate the transcription event in eukaryotes. We consider both the looping and tracking modes of their distal communication and calculate the mean first passage time that is required for the distal interactions of the complex of enhancer and transcription factor with the PR via both these modes. We further investigate how this mean first passage time is dependent on the length of the DNA segment (L, base-pairs) that connects the cis-regulatory binding site and the respective promoter. When the radius of curvature of this connecting segment of DNA is R that was induced upon binding of the transcription factor at the cis-acting element and RNAPII at the promoter in cis-positions, our calculations indicate that the looping mode of distal action will dominate when L is such that L > 2πR and the tracking mode of distal action will be favored when L 2 bps. It seems that the free energy associated with the binding of the transcription factor with its cis-acting element and the distance of this cis-acting element from the corresponding promoter of the gene of interest is negatively correlated. Our results suggest that the looping and tracking modes of distal action are concurrently operating on the transcription activation and the physics that determines the timescales associated with the looping/tracking in the mechanism of action of these transcription factors on the initiation of the transcription event must put a selection pressure on the distribution of the distances of cis-regulatory modules from their respective promoters of the genes. The computational analysis of the upstream sequences of promoters of various genes in the human and mouse genomes for the presence of putative cis-regulatory elements for a set of known transcription factors using

  11. A digital pixel cell for address event representation image convolution processing

    Science.gov (United States)

    Camunas-Mesa, Luis; Acosta-Jimenez, Antonio; Serrano-Gotarredona, Teresa; Linares-Barranco, Bernabe

    2005-06-01

    Address Event Representation (AER) is an emergent neuromorphic interchip communication protocol that allows for real-time virtual massive connectivity between huge number of neurons located on different chips. By exploiting high speed digital communication circuits (with nano-seconds timings), synaptic neural connections can be time multiplexed, while neural activity signals (with mili-seconds timings) are sampled at low frequencies. Also, neurons generate events according to their information levels. Neurons with more information (activity, derivative of activities, contrast, motion, edges,...) generate more events per unit time, and access the interchip communication channel more frequently, while neurons with low activity consume less communication bandwidth. AER technology has been used and reported for the implementation of various type of image sensors or retinae: luminance with local agc, contrast retinae, motion retinae,... Also, there has been a proposal for realizing programmable kernel image convolution chips. Such convolution chips would contain an array of pixels that perform weighted addition of events. Once a pixel has added sufficient event contributions to reach a fixed threshold, the pixel fires an event, which is then routed out of the chip for further processing. Such convolution chips have been proposed to be implemented using pulsed current mode mixed analog and digital circuit techniques. In this paper we present a fully digital pixel implementation to perform the weighted additions and fire the events. This way, for a given technology, there is a fully digital implementation reference against which compare the mixed signal implementations. We have designed, implemented and tested a fully digital AER convolution pixel. This pixel will be used to implement a full AER convolution chip for programmable kernel image convolution processing.

  12. Tropical Cyclone Intensity Estimation Using Deep Convolutional Neural Networks

    Science.gov (United States)

    Maskey, Manil; Cecil, Dan; Ramachandran, Rahul; Miller, Jeffrey J.

    2018-01-01

    Estimating tropical cyclone intensity by just using satellite image is a challenging problem. With successful application of the Dvorak technique for more than 30 years along with some modifications and improvements, it is still used worldwide for tropical cyclone intensity estimation. A number of semi-automated techniques have been derived using the original Dvorak technique. However, these techniques suffer from subjective bias as evident from the most recent estimations on October 10, 2017 at 1500 UTC for Tropical Storm Ophelia: The Dvorak intensity estimates ranged from T2.3/33 kt (Tropical Cyclone Number 2.3/33 knots) from UW-CIMSS (University of Wisconsin-Madison - Cooperative Institute for Meteorological Satellite Studies) to T3.0/45 kt from TAFB (the National Hurricane Center's Tropical Analysis and Forecast Branch) to T4.0/65 kt from SAB (NOAA/NESDIS Satellite Analysis Branch). In this particular case, two human experts at TAFB and SAB differed by 20 knots in their Dvorak analyses, and the automated version at the University of Wisconsin was 12 knots lower than either of them. The National Hurricane Center (NHC) estimates about 10-20 percent uncertainty in its post analysis when only satellite based estimates are available. The success of the Dvorak technique proves that spatial patterns in infrared (IR) imagery strongly relate to tropical cyclone intensity. This study aims to utilize deep learning, the current state of the art in pattern recognition and image recognition, to address the need for an automated and objective tropical cyclone intensity estimation. Deep learning is a multi-layer neural network consisting of several layers of simple computational units. It learns discriminative features without relying on a human expert to identify which features are important. Our study mainly focuses on convolutional neural network (CNN), a deep learning algorithm, to develop an objective tropical cyclone intensity estimation. CNN is a supervised learning

  13. Deep convolutional neural networks as strong gravitational lens detectors

    Science.gov (United States)

    Schaefer, C.; Geiger, M.; Kuntzer, T.; Kneib, J.-P.

    2018-03-01

    Context. Future large-scale surveys with high-resolution imaging will provide us with approximately 105 new strong galaxy-scale lenses. These strong-lensing systems will be contained in large data amounts, however, which are beyond the capacity of human experts to visually classify in an unbiased way. Aim. We present a new strong gravitational lens finder based on convolutional neural networks (CNNs). The method was applied to the strong-lensing challenge organized by the Bologna Lens Factory. It achieved first and third place, respectively, on the space-based data set and the ground-based data set. The goal was to find a fully automated lens finder for ground-based and space-based surveys that minimizes human inspection. Methods: We compared the results of our CNN architecture and three new variations ("invariant" "views" and "residual") on the simulated data of the challenge. Each method was trained separately five times on 17 000 simulated images, cross-validated using 3000 images, and then applied to a test set with 100 000 images. We used two different metrics for evaluation, the area under the receiver operating characteristic curve (AUC) score, and the recall with no false positive (Recall0FP). Results: For ground-based data, our best method achieved an AUC score of 0.977 and a Recall0FP of 0.50. For space-based data, our best method achieved an AUC score of 0.940 and a Recall0FP of 0.32. Adding dihedral invariance to the CNN architecture diminished the overall score on space-based data, but achieved a higher no-contamination recall. We found that using committees of five CNNs produced the best recall at zero contamination and consistently scored better AUC than a single CNN. Conclusions: We found that for every variation of our CNN lensfinder, we achieved AUC scores close to 1 within 6%. A deeper network did not outperform simpler CNN models either. This indicates that more complex networks are not needed to model the simulated lenses. To verify this, more

  14. Convolutional Sparse Coding for Static and Dynamic Images Analysis

    Directory of Open Access Journals (Sweden)

    B. A. Knyazev

    2014-01-01

    Full Text Available The objective of this work is to improve performance of static and dynamic objects recognition. For this purpose a new image representation model and a transformation algorithm are proposed. It is examined and illustrated that limitations of previous methods make it difficult to achieve this objective. Static images, specifically handwritten digits of the widely used MNIST dataset, are the primary focus of this work. Nevertheless, preliminary qualitative results of image sequences analysis based on the suggested model are presented.A general analytical form of the Gabor function, often employed to generate filters, is described and discussed. In this research, this description is required for computing parameters of responses returned by our algorithm. The recursive convolution operator is introduced, which allows extracting free shape features of visual objects. The developed parametric representation model is compared with sparse coding based on energy function minimization.In the experimental part of this work, errors of estimating the parameters of responses are determined. Also, parameters statistics and their correlation coefficients for more than 106 responses extracted from the MNIST dataset are calculated. It is demonstrated that these data correspond well with previous research studies on Gabor filters as well as with works on visual cortex primary cells of mammals, in which similar responses were observed. A comparative test of the developed model with three other approaches is conducted; speed and accuracy scores of handwritten digits classification are presented. A support vector machine with a linear or radial basic function is used for classification of images and their representations while principal component analysis is used in some cases to prepare data beforehand. High accuracy is not attained due to the specific difficulties of combining our model with a support vector machine (a 3.99% error rate. However, another method is

  15. Laparoscopic versus open distal pancreatectomy for pancreatic cancer.

    Science.gov (United States)

    Riviere, Deniece; Gurusamy, Kurinchi Selvan; Kooby, David A; Vollmer, Charles M; Besselink, Marc G H; Davidson, Brian R; van Laarhoven, Cornelis J H M

    2016-04-04

    Surgical resection is currently the only treatment with the potential for long-term survival and cure of pancreatic cancer. Surgical resection is provided as distal pancreatectomy for cancers of the body and tail of the pancreas. It can be performed by laparoscopic or open surgery. In operations on other organs, laparoscopic surgery has been shown to reduce complications and length of hospital stay as compared with open surgery. However, concerns remain about the safety of laparoscopic distal pancreatectomy compared with open distal pancreatectomy in terms of postoperative complications and oncological clearance. To assess the benefits and harms of laparoscopic distal pancreatectomy versus open distal pancreatectomy for people undergoing distal pancreatectomy for pancreatic ductal adenocarcinoma of the body or tail of the pancreas, or both. We used search strategies to search the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, EMBASE, Science Citation Index Expanded and trials registers until June 2015 to identify randomised controlled trials (RCTs) and non-randomised studies. We also searched the reference lists of included trials to identify additional studies. We considered for inclusion in the review RCTs and non-randomised studies comparing laparoscopic versus open distal pancreatectomy in patients with resectable pancreatic cancer, irrespective of language, blinding or publication status.. Two review authors independently identified trials and independently extracted data. We calculated odds ratios (ORs), mean differences (MDs) or hazard ratios (HRs) along with 95% confidence intervals (CIs) using both fixed-effect and random-effects models with RevMan 5 on the basis of intention-to-treat analysis when possible. We found no RCTs on this topic. We included in this review 12 non-randomised studies that compared laparoscopic versus open distal pancreatectomy (1576 participants: 394 underwent laparoscopic distal pancreatectomy and 1182

  16. A Fast Numerical Method for Max-Convolution and the Application to Efficient Max-Product Inference in Bayesian Networks.

    Science.gov (United States)

    Serang, Oliver

    2015-08-01

    Observations depending on sums of random variables are common throughout many fields; however, no efficient solution is currently known for performing max-product inference on these sums of general discrete distributions (max-product inference can be used to obtain maximum a posteriori estimates). The limiting step to max-product inference is the max-convolution problem (sometimes presented in log-transformed form and denoted as "infimal convolution," "min-convolution," or "convolution on the tropical semiring"), for which no O(k log(k)) method is currently known. Presented here is an O(k log(k)) numerical method for estimating the max-convolution of two nonnegative vectors (e.g., two probability mass functions), where k is the length of the larger vector. This numerical max-convolution method is then demonstrated by performing fast max-product inference on a convolution tree, a data structure for performing fast inference given information on the sum of n discrete random variables in O(nk log(nk)log(n)) steps (where each random variable has an arbitrary prior distribution on k contiguous possible states). The numerical max-convolution method can be applied to specialized classes of hidden Markov models to reduce the runtime of computing the Viterbi path from nk(2) to nk log(k), and has potential application to the all-pairs shortest paths problem.

  17. Giant Cell Tumour of the Distal Ulna: A Rare Presentation

    Directory of Open Access Journals (Sweden)

    Ruben Jaya Kumar

    2011-07-01

    Full Text Available Giant-cell tumour (GCT of bone, a primary yet locally aggressive benign tumour, commonly affects patients between the ages of 20 and 40 years, with the peak incidence occurring in the third decade. Women are affected slightly more than men. The distal end of the ulna is an extremely uncommon site for primary bone tumours in general and giant cell tumours in particular. Wide resection of the distal ulna is the recommended treatment for GCT in such locations. Radio-ulna convergence and dorsal displacement of the ulna stump are known complications following ulna resection proximal to the insertion of the pronator quadratus. This leads to reduction in grip power and forearm rotatory motion. Stabilization of the ulna stump with extensor carpi ulnaris (ECU tendon after wide resection of the tumour has been described in the literature. We report a case of GCT of distal end of ulna treated with wide resection and stabilization with ECU tendon.

  18. Segmental reversal of distal small intestine in short bowel syndrome

    DEFF Research Database (Denmark)

    Grave, Pernille Kock; Thomsen, Sabrina Valentin; Clark, Pia Susanne

    2018-01-01

    were the influence on cell proliferation and mucosal architecture shown by histological analysis. Methods: Sixteen piglets underwent a 60% resection of the distal small intestine and were randomized into two groups. Group 1 short bowel syndrome alone (SBS) (n = 8) and group 2 with reversal of a distal...... small intestinal segment (SBS-RS) (n = 8). Body weight was measured daily and the pigs were euthanized after 1 month. Crypt depths, villus heights and muscle layers thicknesses were measured. For the evaluation of microvilli of the brush border of the epithelium and cell proliferation...... was found in the SBS group and increase in the thickness of the circular and longitudinal muscle layers in the SBS-RS group. In the distal ileal segment the longitudinal muscle layer thicknesses were increased in the SBS group. Otherwise, no significant changes were found. Conclusion: Reversal of a 20-cm...

  19. Fractures of the distal tibia treated with polyaxial locking plating.

    Science.gov (United States)

    Gao, Hong; Zhang, Chang-Qing; Luo, Cong-Feng; Zhou, Zu-Bin; Zeng, Bing-Fang

    2009-03-01

    We evaluated the healing rate, complications, and functional outcomes in 32 adult patients with very short metaphyseal fragments in fractures of the distal tibia treated with a polyaxial locking system. The average distance from the distal extent of the fracture to the tibial plafond was 11 mm. All fractures healed and the average time to union was 14 weeks. Six patients (19%) reported occasional local disturbance over the medial malleolus. There were two cases of postoperative superficial infections and evidence of delayed wound healing. Using the American Orthopaedic Foot and Ankle Society ankle score, the average functional score was 87.3 points (of 100 total possible points). Our results show the polyaxial locking plates, which offer more fixation versatility, may be a reasonable treatment option for distal tibia fractures with very short metaphyseal segments.

  20. Estimating the number of sources in a noisy convolutive mixture using BIC

    DEFF Research Database (Denmark)

    Olsson, Rasmus Kongsgaard; Hansen, Lars Kai

    2004-01-01

    The number of source signals in a noisy convolutive mixture is determined based on the exact log-likelihoods of the candidate models. In (Olsson and Hansen, 2004), a novel probabilistic blind source separator was introduced that is based solely on the time-varying second-order statistics of the s......The number of source signals in a noisy convolutive mixture is determined based on the exact log-likelihoods of the candidate models. In (Olsson and Hansen, 2004), a novel probabilistic blind source separator was introduced that is based solely on the time-varying second-order statistics...

  1. Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber

    International Nuclear Information System (INIS)

    Acciarri, R.; Adams, C.; An, R.; Asaadi, J.; Auger, M.

    2017-01-01

    Here, we present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. Lastly, we also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level.

  2. Alternate symbol inversion for improved symbol synchronization in convolutionally coded systems

    Science.gov (United States)

    Simon, M. K.; Smith, J. G.

    1980-01-01

    Inverting alternate symbols of the encoder output of a convolutionally coded system provides sufficient density of symbol transitions to guarantee adequate symbol synchronizer performance, a guarantee otherwise lacking. Although alternate symbol inversion may increase or decrease the average transition density, depending on the data source model, it produces a maximum number of contiguous symbols without transition for a particular class of convolutional codes, independent of the data source model. Further, this maximum is sufficiently small to guarantee acceptable symbol synchronizer performance for typical applications. Subsequent inversion of alternate detected symbols permits proper decoding.

  3. Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber

    Energy Technology Data Exchange (ETDEWEB)

    Acciarri, R.; Adams, C.; An, R.; Asaadi, J.; Auger, M.; Bagby, L.; Baller, B.; Barr, G.; Bass, M.; Bay, F.; Bishai, M.; Blake, A.; Bolton, T.; Bugel, L.; Camilleri, L.; Caratelli, D.; Carls, B.; Fernandez, R. Castillo; Cavanna, F.; Chen, H.; Church, E.; Cianci, D.; Collin, G. H.; Conrad, J. M.; Convery, M.; Crespo-Anad?n, J. I.; Del Tutto, M.; Devitt, D.; Dytman, S.; Eberly, B.; Ereditato, A.; Sanchez, L. Escudero; Esquivel, J.; Fleming, B. T.; Foreman, W.; Furmanski, A. P.; Garvey, G. T.; Genty, V.; Goeldi, D.; Gollapinni, S.; Graf, N.; Gramellini, E.; Greenlee, H.; Grosso, R.; Guenette, R.; Hackenburg, A.; Hamilton, P.; Hen, O.; Hewes, J.; Hill, C.; Ho, J.; Horton-Smith, G.; James, C.; de Vries, J. Jan; Jen, C. -M.; Jiang, L.; Johnson, R. A.; Jones, B. J. P.; Joshi, J.; Jostlein, H.; Kaleko, D.; Karagiorgi, G.; Ketchum, W.; Kirby, B.; Kirby, M.; Kobilarcik, T.; Kreslo, I.; Laube, A.; Li, Y.; Lister, A.; Littlejohn, B. R.; Lockwitz, S.; Lorca, D.; Louis, W. C.; Luethi, M.; Lundberg, B.; Luo, X.; Marchionni, A.; Mariani, C.; Marshall, J.; Caicedo, D. A. Martinez; Meddage, V.; Miceli, T.; Mills, G. B.; Moon, J.; Mooney, M.; Moore, C. D.; Mousseau, J.; Murrells, R.; Naples, D.; Nienaber, P.; Nowak, J.; Palamara, O.; Paolone, V.; Papavassiliou, V.; Pate, S. F.; Pavlovic, Z.; Porzio, D.; Pulliam, G.; Qian, X.; Raaf, J. L.; Rafique, A.; Rochester, L.; von Rohr, C. Rudolf; Russell, B.; Schmitz, D. W.; Schukraft, A.; Seligman, W.; Shaevitz, M. H.; Sinclair, J.; Snider, E. L.; Soderberg, M.; S?ldner-Rembold, S.; Soleti, S. R.; Spentzouris, P.; Spitz, J.; St. John, J.; Strauss, T.; Szelc, A. M.; Tagg, N.; Terao, K.; Thomson, M.; Toups, M.; Tsai, Y. -T.; Tufanli, S.; Usher, T.; Van de Water, R. G.; Viren, B.; Weber, M.; Weston, J.; Wickremasinghe, D. A.; Wolbers, S.; Wongjirad, T.; Woodruff, K.; Yang, T.; Zeller, G. P.; Zennamo, J.; Zhang, C.

    2017-03-01

    We present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. We also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level.

  4. Convolutional Encoder and Viterbi Decoder Using SOPC For Variable Constraint Length

    DEFF Research Database (Denmark)

    Kulkarni, Anuradha; Dnyaneshwar, Mantri; Prasad, Neeli R.

    2013-01-01

    Convolution encoder and Viterbi decoder are the basic and important blocks in any Code Division Multiple Accesses (CDMA). They are widely used in communication system due to their error correcting capability But the performance degrades with variable constraint length. In this context to have...... detailed analysis, this paper deals with the implementation of convolution encoder and Viterbi decoder using system on programming chip (SOPC). It uses variable constraint length of 7, 8 and 9 bits for 1/2 and 1/3 code rates. By analyzing the Viterbi algorithm it is seen that our algorithm has a better...

  5. Convolute laminations — a theoretical analysis: example of a Pennsylvanian sandstone

    Science.gov (United States)

    Visher, Glenn S.; Cunningham, Russ D.

    1981-03-01

    Data from an outcropping laminated interval were collected and analyzed to test the applicability of a theoretical model describing instability of layered systems. Rayleigh—Taylor wave perturbations result at the interface between fluids of contrasting density, viscosity, and thickness. In the special case where reverse density and viscosity interlaminations are developed, the deformation response produces a single wave with predictable amplitudes, wavelengths, and amplification rates. Physical measurements from both the outcropping section and modern sediments suggest the usefulness of the model for the interpretation of convolute laminations. Internal characteristics of the stratigraphic interval, and the developmental sequence of convoluted beds, are used to document the developmental history of these structures.

  6. Minimally Invasive Distal Pancreatectomy: Review of the English Literature.

    Science.gov (United States)

    Wang, Kai; Fan, Ying

    2017-02-01

    Recently, the superiority of the minimally invasive approach, which results in a better cosmetic result, faster recovery, and shorter length of hospital stay, is a technique that has been progressively recognized as it has developed. And the minimally invasive approach has been applied to distal pancreatectomy (DP), which is a standard method for the treatment of benign, borderline, and part of malignant lesions of the pancreatic body and tail. This article aims to analyze the types, postoperative recovery, and outcomes of laparoscopic distal pancreatectomy (LDP). A systematic search of the scientific literature was performed using PubMed, EMBASE, online journals, and the Internet for all publications on LDP. Articles were selected if the abstract contained patients who underwent LDP for pancreatic diseases. All selected articles were reviewed and analyzed. If there were no contraindications for LDP, this operation is suitable for benign, borderline, or malignant tumors of the pancreatic body and tail, which should try to be performed with preservation of the spleen. LDP is safe and feasible under some conditions to experienced surgeon. Single-incision laparoscopic distal pancreatectomy (S-LDP) and robotic laparoscopic distal pancreatectomy (R-LDP) perioperative outcomes are similar with conventional multi-incision laparoscopic distal pancreatectomy (C-LDP). And the advantages of S-LDP and R-LDP require further exploration. With the application of enhanced recovery program (ERP), length of hospital stay and costs are reduced. LDP is safe and feasible under some conditions. Compared with open distal pancreatectomy, LDP has a lot of advantages; a trend was observed for LDP to replace traditional open surgery. LDP combined with ERP is expected to become standard in the treatment of pancreatic body and tail lesions.

  7. Laparoscopic distal pancreatectomy for adenocarcinoma: safe and reasonable?

    Science.gov (United States)

    Postlewait, Lauren M.

    2015-01-01

    As a result of technological advances during the past two decades, surgeons now use minimally invasive surgery (MIS) approaches to pancreatic resection more frequently, yet the role of these approaches for pancreatic ductal adenocarcinoma resections remains uncertain, given the aggressive nature of this malignancy. Although there are no controlled trials comparing MIS technique to open surgical technique, laparoscopic distal pancreatectomy for pancreatic adenocarcinoma is performed with increasing frequency. Data from retrospective studies suggest that perioperative complication profiles between open and laparoscopic distal pancreatectomy are similar, with perhaps lower blood loss and fewer wound infections in the MIS group. Concerning oncologic outcomes, there appear to be no differences in the rate of achieving negative margins or in the number of lymph nodes (LNs) resected when compared to open surgery. There are limited recurrence and survival data on laparoscopic compared to open distal pancreatectomy for pancreatic adenocarcinoma, but in the few studies that assess long term outcomes, recurrence rates and survival outcomes appear similar. Recent studies show that though laparoscopic distal pancreatectomy entails a greater operative cost, the associated shorter length of hospital stay leads to decreased overall cost compared to open procedures. Multiple new technologies are emerging to improve resection of pancreatic cancer. Robotic pancreatectomy is feasible, but there are limited data on robotic resection of pancreatic adenocarcinoma, and outcomes appear similar to laparoscopic approaches. Additionally fluorescence-guided surgery represents a new technology on the horizon that could improve oncologic outcomes after resection of pancreatic adenocarcinoma, though published data thus far are limited to animal models. Overall, MIS distal pancreatectomy appears to be a safe and reasonable approach to treating selected patients with pancreatic ductal

  8. Neglected Distal Humeral Epiphyseal Injury - Two Case Reports

    Directory of Open Access Journals (Sweden)

    Dr. Pankaj Kumar

    2008-07-01

    Full Text Available Distal humeral epiphyseal separation is an uncommon injury in children, which can be missed or misdiagnosed at initial presentation. Awareness of this injury and appropriate radiological assessment helps in proper management. Neglected cases because of inappropriate diagnosis can result in cubitus varus deformity. Full range of movements of elbow can be achieved if properly diagnosed and managed. We present two cases of neglected distal humeral epiphyseal injury in children that resulted in cubitus varus deformity in one case. Full range of movements was achieved in both cases after proper management.

  9. Distal vertebral artery reconstruction when managing vertebrobasilar insufficiency

    Directory of Open Access Journals (Sweden)

    D. M. Galaktionov

    2017-11-01

    Full Text Available This article presents a literature review devoted to the reconstruction of the distal vertebral artery and a clinical case of successful surgical treatment of a patient suffering from vertebrobasilar insufficiency caused by occlusion of the vertebral artery in a proximal segment. The external carotid artery-distal vertebral artery bypass was performed by using the radial artery.Received 27 February 2017. Revised 25 July 2017. Accepted 3 August 2017.Funding: The study did not have sponsorship.Conflict of interest: The authors declare no conflict of interest. 

  10. Robotic spleen-preserving distal pancreatectomy. A case report.

    Science.gov (United States)

    Vasilescu, C; Sgarbura, O; Tudor, S; Herlea, V; Popescu, I

    2009-01-01

    Distal pancreatectomy (DP) is the removal of the pancreatic tissue at the left side of the superior mesenteric vein and it is traditionally approached by an open or laparoscopic exposure. Preservation of the spleen is optional but appears to have a better immunological outcome. We present the case of a 53-year old patient with a 2.4/2.2 tumor located in the tail of the pancreas, with high tumour marker values for whom we decided to perform a robotic spleen-preserving distal pancreatectomy (RSPDP). The postoperative outcome was satisfactory. In conclusion, we recommend this type of approach for small pancreatic tail lesions.

  11. Locking plates in distal humerus fractures: study of 43 patients

    Directory of Open Access Journals (Sweden)

    Gupta Rakesh Kumar

    2013-08-01

    Full Text Available 【Abstract】Objective: The treatment of multi-fragmentary, intraarticular fractures of the distal humerus is difficult, even in young patients with bone of good quality. Small distal fragment, diminished bone mineral quality and increased trauma-associated joint destruction make stable joint reconstruction more problematic. The anatomically preshaped locking plates allow angular stable fixation for these complex fractures. We evaluated functional results of patients treated with open reduction and internal fixation with distal humerus locking plates for complex distal hu-merus fractures. Methods: Forty-three consecutive patients with ar-ticular fractures of the distal humerus were treated by open reduction and internal fixation with AO distal humerus plate system and locking reconstruction plates. Forty patients were available for the final outcome analysis. According to AO/ASIF classification, there were 2 cases of type A2, 4 cases of type A3, 1 case of type B1, 1 case of type B2, 14 cases of type C1, 7 cases of type C2 and 11 cases of type C3. Open reduction with triceps splitting technique was used in all patients. The clinical and radiographic follow-up was performed and outcome measures included pain assessment, range of motion, and Mayo elbow performance score. Results: Forty patients were available for the final outcome analysis. There were 29 males and 11 females with an average age of 38.4 years (18-73 years. Clinical and ra-diological consolidation of the fracture was observed in all cases at an average of 11.6 weeks (9-14 weeks. The average follow-up was 12 months (10-18 months. Using the Mayo elbow performance score the results obtained were graded as excellent or good results in 33 patients (82.5%. One pa-tient had superficial infection, and 4 had myositis ossificans. There were no cases of primary malposition or secondary displacement, implant failure or ulnar neuropathy. Conclusion: Anatomically preshaped distal humerus locking

  12. Traumatisk distal humerus-epifysiolyse hos nyfødt

    DEFF Research Database (Denmark)

    Al-Aubaidi, Zaid; Nielsen, Keld Daubjerg

    2010-01-01

    Traumatic distal humerus epiphysiolysis (TDHE) is a rare injury in infants with an incidence of about 1:35,000 births. It is primarily a birth injury, but it is also seen in cases of battered child syndrome. Because of its rare occurrence and the diagnostic difficulties, the lesion may be overloo......Traumatic distal humerus epiphysiolysis (TDHE) is a rare injury in infants with an incidence of about 1:35,000 births. It is primarily a birth injury, but it is also seen in cases of battered child syndrome. Because of its rare occurrence and the diagnostic difficulties, the lesion may...

  13. Primary bone lymphoma of the distal tibia mimicking brodie's abscess

    International Nuclear Information System (INIS)

    Park, Jina; Lee, Seung Hun; Joo, Kyung Bin; Park, Chan Kum

    2014-01-01

    The 'penumbra sign' on an unenhanced T1-weighted image is a well-known characteristic of Brodie's abscess, and this sign is extremely helpful for discriminating subacute osteomyelitis from other bone lesions. We present a case of primary bone lymphoma of the distal tibia mimicking subacute osteomyelitis with Brodie's abscess in a 50-year-old woman. Initial radiographs and MRI showed a lesion in the distal tibia consistent with Brodie's abscess with the penumbra sign. Histopathological examination of the surgical biopsy specimen confirmed the presence of a diffuse large B-cell lymphoma involving the bone.

  14. Nontraumatic osteonecrosis of the distal pole of the scaphoid

    Directory of Open Access Journals (Sweden)

    Bhavuk Garg

    2011-01-01

    Full Text Available Post traumatic osteonecrosis of distal pole of scaphoid is very rare. We present a case of 34 years old male, drill operator by occupation with nontraumatic osteonecrosis of distal pole of the scaphoid. The patient was managed conservatively and was kept under regular follow-up every three months. The patient was also asked to change his profession. Two years later, the patient had no pain and had mild restriction of wrist movements (less than 15 degrees in either direction. The radiographs revealed normal density of the scaphoid suggesting revascularization.

  15. Osteoblastoma-like osteosarcoma of the distal tibia

    Energy Technology Data Exchange (ETDEWEB)

    Abramovici, Luigia; Steiner, German C. [Department of Pathology and Laboratory Medicine, Hospital for Joint Diseases, New York, NY (United States); Kenan, Samuel [Department of Orthopaedic Oncology Surgery, Hospital for Joint Diseases, New York, NY (United States); Hytiroglou, Prodromos [Aristotle University, Thessaloniki (Greece); Rafii, Mahvash [Department of Radiology, Hospital for Joint Diseases, New York, NY (United States)

    2002-03-01

    We report a case of a 14-year-old boy with an intracompartmental lytic lesion with poorly defined margins in the right distal tibia that was originally treated with curettage and bone grafting. Histologic examination showed an osteoblastic tumor with unusual features, which was found on consultation to be an osteoblastoma-like osteosarcoma, a rare, low-grade variant of osteosarcoma. Subsequently, the patient underwent en bloc resection of the distal tibia, which was replaced with vascularized bone graft and followed by chemotherapy. Two years later, he is alive with lung metastases. (orig.)

  16. Osteoblastoma-like osteosarcoma of the distal tibia

    International Nuclear Information System (INIS)

    Abramovici, Luigia; Steiner, German C.; Kenan, Samuel; Hytiroglou, Prodromos; Rafii, Mahvash

    2002-01-01

    We report a case of a 14-year-old boy with an intracompartmental lytic lesion with poorly defined margins in the right distal tibia that was originally treated with curettage and bone grafting. Histologic examination showed an osteoblastic tumor with unusual features, which was found on consultation to be an osteoblastoma-like osteosarcoma, a rare, low-grade variant of osteosarcoma. Subsequently, the patient underwent en bloc resection of the distal tibia, which was replaced with vascularized bone graft and followed by chemotherapy. Two years later, he is alive with lung metastases. (orig.)

  17. Distal renal tubular acidosis and amelogenesis imperfecta: A rare association

    Directory of Open Access Journals (Sweden)

    P Ravi

    2013-01-01

    Full Text Available Renal tubular acidosis (RTA is characterized by a normal anion gap with hyperchloremic metabolic acidosis. Primary distal RTA (type I is the most common RTA in children. Childhood presentation of distal RTA includes vomiting, failure to thrive, metabolic acidosis, and hypokalemia. Amelogenesis imperfecta (AI represents a condition where the dental enamel and oral tissues are affected in an equal manner resulting in the hypoplastic or hypopigmented teeth. We report a 10-year-old girl, previously asymptomatic presented with the hypokalemic paralysis and on work-up found out to have type I RTA. The discoloration of teeth and enamel was diagnosed as AI.

  18. Modified distal shoe appliance--fabrication and clinical performance.

    Science.gov (United States)

    Gujjar, Kumar Raghav; Indushekar, K R; Amith, H V; Sharma, Shefali Li

    2012-01-01

    When the primary second molar is prematurely lost, mesial movement and migration of the permanent first molar often occurs. This is one of the most difficult problems of the developing dentition confronted by pediatric dentists. Use of a space maintainer that will guide the permanent first molar into its normal position is indicated. In cases with bilateral premature loss of primary molars, the conventional design of distal shoe poses a variety of problems and, therefore, necessitates a customized design for the eruption guidance of permanent first molars. The purpose of this case report is to discuss an innovative design of a distal shoe appliance, which was used with good clinical results.

  19. Screening renal stone formers for distal renal tubular acidosis

    DEFF Research Database (Denmark)

    Osther, P J; Hansen, A B; Røhl, H F

    1989-01-01

    A group of 110 consecutive renal stone formers were screened for distal renal tubular acidosis (RTA) using morning fasting urinary pH (mfUpH) levels followed by a short ammonium chloride loading test in patients with levels above 6.0. In 14 patients (12.7%) a renal acidification defect was noted...... RTA in renal stone formers. Regardless of whether the acidification defect is primary or secondary to stone formation, however, all renal stone formers with distal RTA can expect to benefit from prophylactic alkaline therapy and it is recommended that the screening procedure, which is easy to use...

  20. Development and Characterization of a Human and Mouse Intestinal Epithelial Cell Monolayer Platform

    Directory of Open Access Journals (Sweden)

    Kenji Kozuka

    2017-12-01

    Full Text Available Summary: We describe the development and characterization of a mouse and human epithelial cell monolayer platform of the small and large intestines, with a broad range of potential applications including the discovery and development of minimally systemic drug candidates. Culture conditions for each intestinal segment were optimized by correlating monolayer global gene expression with the corresponding tissue segment. The monolayers polarized, formed tight junctions, and contained a diversity of intestinal epithelial cell lineages. Ion transport phenotypes of monolayers from the proximal and distal colon and small intestine matched the known and unique physiology of these intestinal segments. The cultures secreted serotonin, GLP-1, and FGF19 and upregulated the epithelial sodium channel in response to known biologically active agents, suggesting intact secretory and absorptive functions. A screen of over 2,000 pharmacologically active compounds for inhibition of potassium ion transport in the mouse distal colon cultures led to the identification of a tool compound. : Siegel and colleagues describe their development of a human and mouse intestinal epithelial cell monolayer platform that maintains the cellular, molecular, and functional characteristics of tissue for each intestinal segment. They demonstrate the platform's application to drug discovery by screening a library of over 2,000 compounds to identify an inhibitor of potassium ion transport in the mouse distal colon. Keywords: intestinal epithelium, organoids, monolayer, colon, small intestine, phenotype screening assays, enteroid, colonoid

  1. Distally based superficial sural artery flap for soft tissue coverage in the distal 2/3 of leg and foot

    Directory of Open Access Journals (Sweden)

    Kamath B

    2005-01-01

    Full Text Available Background: Skin coverage for defects in the lower 2/3 of leg, ankle region and posterior heel has always been a difficult challenge for reconstructive surgeon. Methods: We describe our experience with the distally based superficial sural artery flap coverage in 48 patients with moderate sized defects in these difficult areas. Results: One out of 48 flaps (in 48 patients was lost totally and 3 suffered marginal necrosis which did not require any secondary procedure. These complications could have been avoided by proper selection of cases and refining technical skills. Conclusion: This simple procedure could be an important and versatile tool for any reconstructive surgeon in providing skin coverage in the distal leg and proximal foot. Preservation of major arteries of the lower limb, minimal donor defect, relatively uninjured donor area in compound fracture or poly trauma involving distal leg are some of the advantages of the flap.

  2. Gaze beats mouse

    DEFF Research Database (Denmark)

    Mateo, Julio C.; San Agustin, Javier; Hansen, John Paulin

    2008-01-01

    Facial EMG for selection is fast, easy and, combined with gaze pointing, it can provide completely hands-free interaction. In this pilot study, 5 participants performed a simple point-and-select task using mouse or gaze for pointing and a mouse button or a facial-EMG switch for selection. Gaze...

  3. Identification of distal silencing elements in the murine interferon-A11 gene promoter.

    Science.gov (United States)

    Roffet, P; Lopez, S; Navarro, S; Bandu, M T; Coulombel, C; Vignal, M; Doly, J; Vodjdani, G

    1996-08-01

    The murine interferon-A11 (Mu IFN-A11) gene is a member of the IFN-A multigenic family. In mouse L929 cells, the weak response of the gene's promoter to viral induction is due to a combination of both a point mutation in the virus responsive element (VRE) and the presence of negatively regulating sequences surrounding the VRE. In the distal part of the promoter, the negatively acting E1E2 sequence was delimited. This sequence displays an inhibitory effect in either orientation or position on the inducibility of a virus-responsive heterologous promoter. It selectively represses VRE-dependent transcription but is not able to reduce the transcriptional activity of a VRE-lacking promoter. In a transient transfection assay, an E1E2-containing DNA competitor was able to derepress the native Mu IFN-A11 promoter. Specific nuclear factors bind to this sequence; thus the binding of trans-regulators participates in the repression of the Mu IFN-A11 gene. The E1E2 sequence contains an IFN regulatory factor (IRF)-binding site. Recombinant IRF2 binds this sequence and anti-IRF2 antibodies supershift a major complex formed with nuclear extracts. The protein composing the complex is 50 kDa in size, indicating the presence of IRF2 or antigenically related proteins in the complex. The Mu IFN-A11 gene is the first example within the murine IFN-A family, in which a distal promoter element has been identified that can negatively modulate the transcriptional response to viral induction.

  4. Identification of quiescent, stem-like cells in the distal female reproductive tract.

    Directory of Open Access Journals (Sweden)

    Yongyi Wang

    Full Text Available In fertile women, the endometrium undergoes regular cycles of tissue build-up and regression. It is likely that uterine stem cells are involved in this remarkable turn over. The main goal of our current investigations was to identify slow-cycling (quiescent endometrial stem cells by means of a pulse-chase approach to selectively earmark, prospectively isolate, and characterize label-retaining cells (LRCs. To this aim, transgenic mice expressing histone2B-GFP (H2B-GFP in a Tet-inducible fashion were administered doxycycline (pulse which was thereafter withdrawn from the drinking water (chase. Over time, dividing cells progressively loose GFP signal whereas infrequently dividing cells retain H2B-GFP expression. We evaluated H2B-GFP retaining cells at different chase time points and identified long-term (LT; >12 weeks LRCs. The LT-LRCs are negative for estrogen receptor-α and express low levels of progesterone receptors. LRCs sorted by FACS are able to form spheroids capable of self-renewal and differentiation. Upon serum stimulation spheroid cells are induced to differentiate and form glandular structures which express markers of mature műllerian epithelial cells. Overall, the results indicate that quiescent cells located in the distal oviduct have stem-like properties and can differentiate into distinct cell lineages specific of endometrium, proximal and distal oviduct. Future lineage-tracing studies will elucidate the role played by these cells in homeostasis, tissue injury and cancer of the female reproductive tract in the mouse and eventually in man.

  5. A Wnt/beta-catenin pathway antagonist Chibby binds Cenexin at the distal end of mother centrioles and functions in primary cilia formation.

    Directory of Open Access Journals (Sweden)

    Nathan Steere

    Full Text Available The mother centriole of the centrosome is distinguished from immature daughter centrioles by the presence of accessory structures (distal and subdistal appendages, which play an important role in the organization of the primary cilium in quiescent cells. Primary cilia serve as sensory organelles, thus have been implicated in mediating intracellular signal transduction pathways. Here we report that Chibby (Cby, a highly conserved antagonist of the Wnt/β-catenin pathway, is a centriolar component specifically located at the distal end of the mother centriole and essential for assembly of the primary cilium. Cby appeared as a discrete dot in the middle of a ring-like structure revealed by staining with a distal appendage component of Cep164. Cby interacted with one of the appendage components, Cenexin (Cnx, which thereby abrogated the inhibitory effect of Cby on β-catenin-mediated transcriptional activation in a dose-dependent manner. Cby and Cnx did not precisely align, as Cby was detected at a more distal position than Cnx. Cnx emerged earlier than Cby during the cell cycle and was required for recruitment of Cby to the mother centriole. However, Cby was dispensable for Cnx localization to the centriole. During massive centriogenesis in in vitro cultured mouse tracheal epithelial cells, Cby and Cnx were expressed in a similar pattern, which was coincident with the expression of Foxj1. Our results suggest that Cby plays an important role in organization of both primary and motile cilia in collaboration with Cnx.

  6. A Wnt/beta-catenin pathway antagonist Chibby binds Cenexin at the distal end of mother centrioles and functions in primary cilia formation.

    Science.gov (United States)

    Steere, Nathan; Chae, Vanessa; Burke, Michael; Li, Feng-Qian; Takemaru, Ken-ichi; Kuriyama, Ryoko

    2012-01-01

    The mother centriole of the centrosome is distinguished from immature daughter centrioles by the presence of accessory structures (distal and subdistal appendages), which play an important role in the organization of the primary cilium in quiescent cells. Primary cilia serve as sensory organelles, thus have been implicated in mediating intracellular signal transduction pathways. Here we report that Chibby (Cby), a highly conserved antagonist of the Wnt/β-catenin pathway, is a centriolar component specifically located at the distal end of the mother centriole and essential for assembly of the primary cilium. Cby appeared as a discrete dot in the middle of a ring-like structure revealed by staining with a distal appendage component of Cep164. Cby interacted with one of the appendage components, Cenexin (Cnx), which thereby abrogated the inhibitory effect of Cby on β-catenin-mediated transcriptional activation in a dose-dependent manner. Cby and Cnx did not precisely align, as Cby was detected at a more distal position than Cnx. Cnx emerged earlier than Cby during the cell cycle and was required for recruitment of Cby to the mother centriole. However, Cby was dispensable for Cnx localization to the centriole. During massive centriogenesis in in vitro cultured mouse tracheal epithelial cells, Cby and Cnx were expressed in a similar pattern, which was coincident with the expression of Foxj1. Our results suggest that Cby plays an important role in organization of both primary and motile cilia in collaboration with Cnx.

  7. Automated mammographic breast density estimation using a fully convolutional network.

    Science.gov (United States)

    Lee, Juhun; Nishikawa, Robert M

    2018-03-01

    The purpose of this study was to develop a fully automated algorithm for mammographic breast density estimation using deep learning. Our algorithm used a fully convolutional network, which is a deep learning framework for image segmentation, to segment both the breast and the dense fibroglandular areas on mammographic images. Using the segmented breast and dense areas, our algorithm computed the breast percent density (PD), which is the faction of dense area in a breast. Our dataset included full-field digital screening mammograms of 604 women, which included 1208 mediolateral oblique (MLO) and 1208 craniocaudal (CC) views. We allocated 455, 58, and 91 of 604 women and their exams into training, testing, and validation datasets, respectively. We established ground truth for the breast and the dense fibroglandular areas via manual segmentation and segmentation using a simple thresholding based on BI-RADS density assessments by radiologists, respectively. Using the mammograms and ground truth, we fine-tuned a pretrained deep learning network to train the network to segment both the breast and the fibroglandular areas. Using the validation dataset, we evaluated the performance of the proposed algorithm against radiologists' BI-RADS density assessments. Specifically, we conducted a correlation analysis between a BI-RADS density assessment of a given breast and its corresponding PD estimate by the proposed algorithm. In addition, we evaluated our algorithm in terms of its ability to classify the BI-RADS density using PD estimates, and its ability to provide consistent PD estimates for the left and the right breast and the MLO and CC views of the same women. To show the effectiveness of our algorithm, we compared the performance of our algorithm against a state of the art algorithm, laboratory for individualized breast radiodensity assessment (LIBRA). The PD estimated by our algorithm correlated well with BI-RADS density ratings by radiologists. Pearson's rho values of

  8. Genetics Home Reference: CAV3-related distal myopathy

    Science.gov (United States)

    ... gene causes a peculiar form of distal myopathy. Neurology. 2002 Jan 22;58(2):323-5. Erratum in: Neurology 2002 Mar 12;58(5):839. Itoyoma Y [ ... 3 cause four distinct autosomal dominant muscle diseases. Neurology. 2004 Feb 24;62(4):538-43. Review. ...

  9. Urethral advancement procedure in the treatment of primary distal ...

    African Journals Online (AJOL)

    advancement in the repair of primary distal penile hypospadias with regard to feasibility, complication rates .... stones and growth, and any associated congenital anomaly. Meticulous local examination was performed ... with a tourniquet placed on the root of the penis. A submeatal crescent-like incision was performed a few.

  10. Neglected distal humeral epiphyseal injury - Two Case Reports ...

    African Journals Online (AJOL)

    We present two cases of neglected distal humeral epiphyseal injury in children that resulted in cubitus varus deformity in one case. Full range of movements was achieved in both cases after proper management. Keywords: Neglected epiphyseal injury; Cubitus varus; Diagnosis; Treatment Internet Journal of Medical Update ...

  11. Evaluation of Various Filling Techniques in Distal Canals of ...

    African Journals Online (AJOL)

    2017-03-06

    Mar 6, 2017 ... How to cite this article: Dumani A, Yilmaz S, Yoldas O, Kuden C. Evaluation ... Niger J. Clin Pract 2017;20:307-12. This is an open access article distributed ... in oval-shaped distal canals of mandibular molars was inadequate.

  12. Social Support Contributes to Outcomes following Distal Radius Fractures

    Directory of Open Access Journals (Sweden)

    Caitlin J. Symonette

    2013-01-01

    Full Text Available Background. Distal radius fractures are the most common fracture of the upper extremity and cause variable disability. This study examined the role of social support in patient-reported pain and disability at one year following distal radius fracture. Methods. The Medical Outcomes Study Social Support Survey was administered to a prospective cohort of 291 subjects with distal radius fractures at their baseline visit. Pearson correlations and stepwise linear regression models (F-to-remove 0.10 were used to identify whether social support contributes to wrist fracture outcomes. The primary outcome of pain and disability at one year was measured using the Patient Rated Wrist Evaluation. Results. Most injuries were low energy (67.5% and were treated nonoperatively (71.9%. Pearson correlation analysis revealed that higher reported social support correlated with improved Patient Rated Wrist Evaluation scores at 1 year, r(n=181=-0.22, P<0.05. Of the subscales within the Social Support Survey, emotional/informational support explained a significant proportion of the variance in 1-year Patient Rated Wrist Evaluation scores, R2=4.7%, F (1, 181 = 9.98, P<0.05. Conclusion. Lower emotional/informational social support at the time of distal radius fracture contributes a small but significant percentage to patient-reported pain and disability outcomes.

  13. Simultaneous bilateral distal biceps tendon repair: case report

    Directory of Open Access Journals (Sweden)

    Thiago Medeiros Storti

    Full Text Available ABSTRACT Simultaneous bilateral rupture of the distal biceps tendon is a rare clinical entity, seldom reported in the literature and with unclear therapeutic setting. The authors report the case of a 39-year-old white man who suffered a simultaneous bilateral rupture while working out. When weightlifting with elbows at 90° of flexion, he suddenly felt pain on the anterior aspect of the arms, coming for evaluation after two days. He presented bulging contour of the biceps muscle belly and ecchymosis in the antecubital fossa, extending distally to the medial aspect of the forearm, as well as a marked decrease of supination strength and pain in active elbow flexion. MRI confirmed the rupture with retraction of the distal biceps bilaterally. The authors opted for performing the tendon repairs simultaneously through the double incision technique and fixation to the bicipital tuberosity with anchors. The patient progressed quite well, with full return to labor and sports activities, being satisfied with the result after two years of surgery. In the literature search, few reports of simultaneous bilateral rupture of the distal biceps were retrieved, with only one treated in the acute phase of injury. Therefore, the authors consider this procedure to be a good option to solve this complex condition.

  14. Simultaneous bilateral distal biceps tendon repair: case report.

    Science.gov (United States)

    Storti, Thiago Medeiros; Paniago, Alexandre Firmino; Faria, Rafael Salomon Silva

    2017-01-01

    Simultaneous bilateral rupture of the distal biceps tendon is a rare clinical entity, seldom reported in the literature and with unclear therapeutic setting. The authors report the case of a 39-year-old white man who suffered a simultaneous bilateral rupture while working out. When weightlifting with elbows at 90° of flexion, he suddenly felt pain on the anterior aspect of the arms, coming for evaluation after two days. He presented bulging contour of the biceps muscle belly and ecchymosis in the antecubital fossa, extending distally to the medial aspect of the forearm, as well as a marked decrease of supination strength and pain in active elbow flexion. MRI confirmed the rupture with retraction of the distal biceps bilaterally. The authors opted for performing the tendon repairs simultaneously through the double incision technique and fixation to the bicipital tuberosity with anchors. The patient progressed quite well, with full return to labor and sports activities, being satisfied with the result after two years of surgery. In the literature search, few reports of simultaneous bilateral rupture of the distal biceps were retrieved, with only one treated in the acute phase of injury. Therefore, the authors consider this procedure to be a good option to solve this complex condition.

  15. Evaluation of various filling techniques in distal canals of mandibular ...

    African Journals Online (AJOL)

    Evaluation of various filling techniques in distal canals of mandibular molars instrumented with different single-file nickel-titanium systems. ... Comparisons between groups were applied using Student's t-test or one-way ANOVA for normally distributed data. The Mann-Whitney U-test or Kruskal-Wallis test was used when ...

  16. A unique physeal injury of the distal phalanx.

    Science.gov (United States)

    Berber, Onur; Singh, Bijayendra

    2015-01-01

    An unusual Salter-Harris Type 1 fracture variant of the distal phalanx of the index finger is described. The epiphysis was dislocated, sitting dorsally over the middle phalanx head with the articular surface facing dorsal. Reduction could only be achieved through an open procedure. The reduction was stable without supplemental fixation.

  17. Unusual migration of ventriculo peritoneal distal catheter into vagina

    Directory of Open Access Journals (Sweden)

    Sghavamedin Tavallaee

    2015-04-01

    Full Text Available VP shunt is one of the most popular methods for ICP reduction and treatment of hydrocephalus. Various complications of this method are not uncommon such as shunt malfunction, infection and unusual migration of distal catheter. I present a case of migration of the peritoneal catheter out of the vagina.

  18. Laparoscopic versus open distal pancreatectomy for pancreatic cancer

    NARCIS (Netherlands)

    Riviere, D.M.; Gurusamy, K.S.; Kooby, D.A.; Vollmer, C.M.; Besselink, M.G.; Davidson, B.R.; Laarhoven, C.J.H.M. van

    2016-01-01

    BACKGROUND: Surgical resection is currently the only treatment with the potential for long-term survival and cure of pancreatic cancer. Surgical resection is provided as distal pancreatectomy for cancers of the body and tail of the pancreas. It can be performed by laparoscopic or open surgery. In

  19. Laparoscopic versus open distal pancreatectomy for pancreatic cancer

    NARCIS (Netherlands)

    Riviere, Deniece; Gurusamy, Kurinchi Selvan; Kooby, David A.; Vollmer, Charles M.; Besselink, Marc G. H.; Davidson, Brian R.; van Laarhoven, Cornelis J. H. M.

    2016-01-01

    Surgical resection is currently the only treatment with the potential for long-term survival and cure of pancreatic cancer. Surgical resection is provided as distal pancreatectomy for cancers of the body and tail of the pancreas. It can be performed by laparoscopic or open surgery. In operations on

  20. Autosomal dominant distal myopathy: Linkage to chromosome 14

    Energy Technology Data Exchange (ETDEWEB)

    Laing, N.G.; Laing, B.A.; Wilton, S.D.; Dorosz, S.; Mastaglia, F.L.; Kakulas, B.A. [Australian Neuromuscular Research Institute, Perth (Australia); Robbins, P.; Meredith, C.; Honeyman, K.; Kozman, H.

    1995-02-01

    We have studied a family segregating a form of autosomal dominant distal myopathy (MIM 160500) and containing nine living affected individuals. The myopathy in this family is closest in clinical phenotype to that first described by Gowers in 1902. A search for linkage was conducted using microsatellite, VNTR, and RFLP markers. In total, 92 markers on all 22 autosomes were run. Positive linkage was obtained with 14 of 15 markers tested on chromosome 14, with little indication of linkage elsewhere in the genome. Maximum two-point LOD scores of 2.60 at recombination fraction .00 were obtained for the markers MYH7 and D14S64 - the family structure precludes a two-point LOD score {ge} 3. Recombinations with D14S72 and D14S49 indicate that this distal myopathy locus, MPD1, should lie between these markers. A multipoint analysis assuming 100% penetrance and using the markers D14S72, D14S50, MYH7, D14S64, D14S54, and D14S49 gave a LOD score of exactly 3 at MYH7. Analysis at a penetrance of 80% gave a LOD score of 2.8 at this marker. This probable localization of a gene for distal myopathy, MPD1, on chromosome 14 should allow other investigators studying distal myopathy families to test this region for linkage in other types of the disease, to confirm linkage or to demonstrate the likely genetic heterogeneity. 24 refs., 3 figs., 1 tab.

  1. Detection of Lateglacial distal tephra layers in the Netherlands

    NARCIS (Netherlands)

    Davies, S.M.; Hoek, W.Z.; Bohncke, S.J.P.; Lowe, J.; Pyne O'Donnle, S.; Turney, C.S.M.

    2005-01-01

    Three distal tephra layers or cryptotephras have been detected within a sedimentary sequence from the Netherlands that spans the last glacial-interglacial transition. Geochemical analyses identify one as the Vedde Ash, which represents the southernmost discovery of this mid-Younger Dryas tephra so

  2. Outcome of management of distal radius fractures in ...

    African Journals Online (AJOL)

    Background and Purpose: Distal radial fractures are common fractures of postmenopausal age group patients. They are often called fractures of osteoporosis. These fractures are considered to be one of the commonest minor injuries to cause major morbidity in the community. A lot of patient who need surgery, fail to afford ...

  3. Snodgrass repair for distal hypospadias: a review of 75 cases

    African Journals Online (AJOL)

    urologists for distal hypospadias correction. We review our cases to find out whether there was any difference in the rate of urethrocutaneous fistula after the use of single- versus double-layer tubularization, the use of thick versus thin dorsal prepuce subcutaneous flap (DPF), the use of. DPF versus the ventral dartos flap for ...

  4. Spontaneous resolution of splenic infarcts after distal splenorenal ...

    African Journals Online (AJOL)

    Background: In cases of portal hypertension with splenic infarcts, splenectomy with proximal splenorenal shunt has been recommended. We are sharing our experience with distal splenorenal shunt in these cases contrary to the popular belief. Materials and Methods: Splenic infarcts were graded as mild, moderate and ...

  5. Class II correction prior to orthodontics with the carriere distalizer.

    Science.gov (United States)

    McFarlane, Bruce

    2013-01-01

    Class II correction is a challenge in orthodontics with many existing devices being complex, too compliance-driven, or too prone to breakage. The Carriere Distalizer allows for straightforward Class II correction prior to orthodontics (fixed or clear aligners) at a time when no other mechanics interfere, and compliance is at its best.

  6. The distal shoe space maintainer chairside fabrication and clinical performance.

    Science.gov (United States)

    Brill, Warren A

    2002-01-01

    The chairside-fabricated distal shoe appliance, with a stainless steel crown as the retainer, is an efficacious and cost-effective appliance for guiding the unerupted permanent first molar into position after premature loss or extraction of the second primary molar. The fabrication technique is illustrated in this case report and data is presented on the success rate of the appliance.

  7. Multimodal Classification of Violent Online Political Extremism Content with Graph Convolutional Networks

    NARCIS (Netherlands)

    Rudinac, S.; Gornishka, I.; Worring, M.

    2017-01-01

    In this paper we present a multimodal approach to categorizing user posts based on their discussion topic. To integrate heterogeneous information extracted from the posts, i.e. text, visual content and the information about user interactions with the online platform, we deploy graph convolutional

  8. Convolution quotients in the production of heat in an infinite cylinder

    Energy Technology Data Exchange (ETDEWEB)

    Battig, A; Kalla, S L [Universidad Nacional de Tucuman (Argentina). Facultad de Ciencias Exactas y Tecnologia

    1974-12-01

    A solution of the problem of heat production in an infinite cylinder is considered by an appeal to the concept of convolution quotients and finite Hankel transforms. The result given by Erdelyi follows as a particular case of the result established here.

  9. Cascaded K-means convolutional feature learner and its application to face recognition

    Science.gov (United States)

    Zhou, Daoxiang; Yang, Dan; Zhang, Xiaohong; Huang, Sheng; Feng, Shu

    2017-09-01

    Currently, considerable efforts have been devoted to devise image representation. However, handcrafted methods need strong domain knowledge and show low generalization ability, and conventional feature learning methods require enormous training data and rich parameters tuning experience. A lightened feature learner is presented to solve these problems with application to face recognition, which shares similar topology architecture as a convolutional neural network. Our model is divided into three components: cascaded convolution filters bank learning layer, nonlinear processing layer, and feature pooling layer. Specifically, in the filters learning layer, we use K-means to learn convolution filters. Features are extracted via convoluting images with the learned filters. Afterward, in the nonlinear processing layer, hyperbolic tangent is employed to capture the nonlinear feature. In the feature pooling layer, to remove the redundancy information and incorporate the spatial layout, we exploit multilevel spatial pyramid second-order pooling technique to pool the features in subregions and concatenate them together as the final representation. Extensive experiments on four representative datasets demonstrate the effectiveness and robustness of our model to various variations, yielding competitive recognition results on extended Yale B and FERET. In addition, our method achieves the best identification performance on AR and labeled faces in the wild datasets among the comparative methods.

  10. A MacWilliams Identity for Convolutional Codes : The General Case

    NARCIS (Netherlands)

    Gluesing-Luerssen, Heide; Schneider, Gert

    A MacWilliams Identity for convolutional codes will be established. It makes use of the weight adjacency matrices of the code and its dual, based on state space realizations (the controller canonical form) of the codes in question. The MacWilliams Identity applies to various notions of duality

  11. Convolution Theorem of Fractional Fourier Transformation Derived by Representation Transformation in Quantum Mechancis

    International Nuclear Information System (INIS)

    Fan Hongyi; Hao Ren; Lu Hailiang

    2008-01-01

    Based on our previous paper (Commun. Theor. Phys. 39 (2003) 417) we derive the convolution theorem of fractional Fourier transformation in the context of quantum mechanics, which seems a convenient and neat way. Generalization of this method to the complex fractional Fourier transformation case is also possible

  12. Inverse Problems for a Parabolic Integrodifferential Equation in a Convolutional Weak Form

    Directory of Open Access Journals (Sweden)

    Kairi Kasemets

    2013-01-01

    Full Text Available We deduce formulas for the Fréchet derivatives of cost functionals of several inverse problems for a parabolic integrodifferential equation in a weak formulation. The method consists in the application of an integrated convolutional form of the weak problem and all computations are implemented in regular Sobolev spaces.

  13. Improving the Separability of Deep Features with Discriminative Convolution Filters for RSI Classification

    Directory of Open Access Journals (Sweden)

    Na Liu

    2018-03-01

    Full Text Available The extraction of activation vectors (or deep features from the fully connected layers of a convolutional neural network (CNN model is widely used for remote sensing image (RSI representation. In this study, we propose to learn discriminative convolution filter (DCF based on class-specific separability criteria for linear transformation of deep features. In particular, two types of pretrained CNN called CaffeNet and VGG-VD16 are introduced to illustrate the generality of the proposed DCF. The activation vectors extracted from the fully connected layers of a CNN are rearranged into the form of an image matrix, from which a spatial arrangement of local patches is extracted using sliding window strategy. DCF learning is then performed on each local patch individually to obtain the corresponding discriminative convolution kernel through generalized eigenvalue decomposition. The proposed DCF learning characterizes that a convolutional kernel with small size (e.g., 3 × 3 pixels can be effectively learned on a small-size local patch (e.g., 8 × 8 pixels, thereby ensuring that the linear transformation of deep features can maintain low computational complexity. Experiments on two RSI datasets demonstrate the effectiveness of DCF in improving the classification performances of deep features without increasing dimensionality.

  14. Paediatric frontal chest radiograph screening with fine-tuned convolutional neural networks

    CSIR Research Space (South Africa)

    Gerrand, Jonathan D

    2017-07-01

    Full Text Available of fine-tuned convolutional neural networks (CNN). We use two popular CNN models that are pre-trained on a large natural image dataset and two distinct datasets containing paediatric and adult radiographs respectively. Evaluation is performed using a 5...

  15. Digital Tomosynthesis System Geometry Analysis Using Convolution-Based Blur-and-Add (BAA) Model.

    Science.gov (United States)

    Wu, Meng; Yoon, Sungwon; Solomon, Edward G; Star-Lack, Josh; Pelc, Norbert; Fahrig, Rebecca

    2016-01-01

    Digital tomosynthesis is a three-dimensional imaging technique with a lower radiation dose than computed tomography (CT). Due to the missing data in tomosynthesis systems, out-of-plane structures in the depth direction cannot be completely removed by the reconstruction algorithms. In this work, we analyzed the impulse responses of common tomosynthesis systems on a plane-to-plane basis and proposed a fast and accurate convolution-based blur-and-add (BAA) model to simulate the backprojected images. In addition, the analysis formalism describing the impulse response of out-of-plane structures can be generalized to both rotating and parallel gantries. We implemented a ray tracing forward projection and backprojection (ray-based model) algorithm and the convolution-based BAA model to simulate the shift-and-add (backproject) tomosynthesis reconstructions. The convolution-based BAA model with proper geometry distortion correction provides reasonably accurate estimates of the tomosynthesis reconstruction. A numerical comparison indicates that the simulated images using the two models differ by less than 6% in terms of the root-mean-squared error. This convolution-based BAA model can be used in efficient system geometry analysis, reconstruction algorithm design, out-of-plane artifacts suppression, and CT-tomosynthesis registration.

  16. Object recognition using deep convolutional neural networks with complete transfer and partial frozen layers

    NARCIS (Netherlands)

    Kruithof, M.C.; Bouma, H.; Fischer, N.M.; Schutte, K.

    2016-01-01

    Object recognition is important to understand the content of video and allow flexible querying in a large number of cameras, especially for security applications. Recent benchmarks show that deep convolutional neural networks are excellent approaches for object recognition. This paper describes an

  17. Evolutionary image simplification for lung nodule classification with convolutional neural networks.

    Science.gov (United States)

    Lückehe, Daniel; von Voigt, Gabriele

    2018-05-29

    Understanding decisions of deep learning techniques is important. Especially in the medical field, the reasons for a decision in a classification task are as crucial as the pure classification results. In this article, we propose a new approach to compute relevant parts of a medical image. Knowing the relevant parts makes it easier to understand decisions. In our approach, a convolutional neural network is employed to learn structures of images of lung nodules. Then, an evolutionary algorithm is applied to compute a simplified version of an unknown image based on the learned structures by the convolutional neural network. In the simplified version, irrelevant parts are removed from the original image. In the results, we show simplified images which allow the observer to focus on the relevant parts. In these images, more than 50% of the pixels are simplified. The simplified pixels do not change the meaning of the images based on the learned structures by the convolutional neural network. An experimental analysis shows the potential of the approach. Besides the examples of simplified images, we analyze the run time development. Simplified images make it easier to focus on relevant parts and to find reasons for a decision. The combination of an evolutionary algorithm employing a learned convolutional neural network is well suited for the simplification task. From a research perspective, it is interesting which areas of the images are simplified and which parts are taken as relevant.

  18. Texture synthesis using convolutional neural networks with long-range consistency and spectral constraints

    NARCIS (Netherlands)

    Schreiber, Shaun; Geldenhuys, Jaco; Villiers, De Hendrik

    2017-01-01

    Procedural texture generation enables the creation of more rich and detailed virtual environments without the help of an artist. However, finding a flexible generative model of real world textures remains an open problem. We present a novel Convolutional Neural Network based texture model

  19. Acral melanoma detection using a convolutional neural network for dermoscopy images.

    Science.gov (United States)

    Yu, Chanki; Yang, Sejung; Kim, Wonoh; Jung, Jinwoong; Chung, Kee-Yang; Lee, Sang Wook; Oh, Byungho

    2018-01-01

    Acral melanoma is the most common type of melanoma in Asians, and usually results in a poor prognosis due to late diagnosis. We applied a convolutional neural network to dermoscopy images of acral melanoma and benign nevi on the hands and feet and evaluated its usefulness for the early diagnosis of these conditions. A total of 724 dermoscopy images comprising acral melanoma (350 images from 81 patients) and benign nevi (374 images from 194 patients), and confirmed by histopathological examination, were analyzed in this study. To perform the 2-fold cross validation, we split them into two mutually exclusive subsets: half of the total image dataset was selected for training and the rest for testing, and we calculated the accuracy of diagnosis comparing it with the dermatologist's and non-expert's evaluation. The accuracy (percentage of true positive and true negative from all images) of the convolutional neural network was 83.51% and 80.23%, which was higher than the non-expert's evaluation (67.84%, 62.71%) and close to that of the expert (81.08%, 81.64%). Moreover, the convolutional neural network showed area-under-the-curve values like 0.8, 0.84 and Youden's index like 0.6795, 0.6073, which were similar score with the expert. Although further data analysis is necessary to improve their accuracy, convolutional neural networks would be helpful to detect acral melanoma from dermoscopy images of the hands and feet.

  20. End-to-end unsupervised deformable image registration with a convolutional neural network

    NARCIS (Netherlands)

    de Vos, Bob D.; Berendsen, Floris; Viergever, Max A.; Staring, Marius; Išgum, Ivana

    2017-01-01

    In this work we propose a deep learning network for deformable image registration (DIRNet). The DIRNet consists of a convolutional neural network (ConvNet) regressor, a spatial transformer, and a resampler. The ConvNet analyzes a pair of fixed and moving images and outputs parameters for the spatial