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共 55 条记录，以下是41-50 订阅

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Fast Cosine Transform for FCC Lattices

Fast Cosine Transform for FCC Lattices

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APCA international conference on Automatic Control and soft computing (CONTROLO)

作者： Bastian Seifert Knut Hüper Christian Uhl Faculty of Engineering Sciences Ansbach University of Applied Sciences Ansbach Germany Institute of Mathematics University of Würzburg Würzburg Germany

Voxel representation and processing is an important issue in a broad spectrum of applications. E.g., 3D imaging in biomedical engineering applications, video game development and volumetric displays are often based on data representation by voxels. By replacing the standard sampling lattice with a face-centered lattice one can obtain the same sampling density with less sampling points and reduce aliasing error, as well. We introduce an analog of the discrete cosine transform for the face-centered lattice relying on multivariate Chebyshev polynomials. A fast algorithm for this transform is deduced based on algebraic signal processing theory and the rich geometry of the special unitary Lie group of degree four.

关键词： Lattices Chebyshev approximation FCC signal processing signal processing algorithms Discrete cosine transforms

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A Framework for Codon Based Analysis to detect abnormalities responsible for Esophagus Cancer using soft computing Tool

A Framework for Codon Based Analysis to detect abnormalities...

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international conference on signal processing and Integrated Networks (SPIN)

作者： Hemashree Bordoloi Daisy Roy S.R. Nirmala Dept of ECE GU Guwahati Assam India Dept of ECE Assam Don Bosco University Guwahati Assam India Dept of ECE Gauhati University Guwahati Assam India

Deoxyribo Nucleic Acid (DNA) sequencing is a rapidly expanding biomedical research technology used for molecular diagnostics. It is basically a process of determining the precise order of nucleotide bases. The three unit nucleotide combinations within a DNA form codons which are related to amino acids and subsequently to proteins. One of the primary principles of disease detection is based on irregularities in the codon grouping. The focus of this work is to determine abnormalities in the codon sequence using soft computing tools and thereby provide a reliable computational mechanism for disease detection which have distinct genetic linkage.

关键词： Cancer DNA Esophagus Sequential analysis Tools Amino acids

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Assessing the Scar Images to Check Medical Treatment Effectiveness

Assessing the Scar Images to Check Medical Treatment Effecti...

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international conference on signal processing and Integrated Networks (SPIN)

作者： Prateechi Singh Vikas Saxena Department of CSE & IT Jaypee Institute of Information Technology Noida

In case of a disease, a burn or an allergy, it is very important to have a regular check and a correct diagnosis by the doctor during the healing process of that scar or the patch affected. This will also help the doctor in deciding the perfect medicine for the patient's scars. Various Digital Image processing techniques have been devised to solve this problem. Although use of soft computing based methods are very popular now in almost every area of work, lack of training data poses a big challenge. Thus, we have devised the method to solve the problem of identifying the scar, size of the scar, find the degree of burn or disease mapping the redness of the scar and finding the number of clusters of scars in an image purely by using basic image processing ways. This method is effective and computationally fast in the decision making process for the doctors.

关键词： Skin Image segmentation Medical services Image color analysis Histograms Databases signal processing

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Efficient Method for Isolated Marathi Digits Recognition using DWT and soft computing Techniques

Efficient Method for Isolated Marathi Digits Recognition usi...

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international conference on Internet of Things: Smart Innovation and Usages (IoT-SIU)

作者： Atul Narkhede M. U. Nemade Faculty of Engineering Pacific Academy of Higher Education and Research University Udaipur Department of Electronics Engineering K. J. Somaiya Institute of Engineering Information Technology Mumbai India

ISBN: (纸本)9781538638330

Speech processing systems are actively considered for voice recognition, control and communication systems due to tremendous improvement in the digital signal processor and digital programmable logic devices. Preprocessing, feature extraction and classification are the fundamental steps involved in speech processing systems. Mostly transform domain and soft computing techniques are employed for feature extraction and classification respectively. Low computational complexity is desired to make speech processing systems as commercially viable solution. In this paper, speech recognition system for Marathi digit recognition using discrete wavelet transform (DWT), reduced order LPC coefficient and ANN is proposed. The main objective was to estimate the performance of the speech processing system at minimum feature vector length without significantly sacrificing on recognition rate. Better representation of speech frame was obtained using LPC coefficients derived from discrete wavelet-decomposed subbands instead of modeling the frame directly. Artificial neural network was used in the implementation of speech recognition system as classifier. An experimental result on Marathi digits from one to nine has demonstrated 78 % of the recognition rate at the minimum feature vector length of 24.

关键词： Speech recognition Discrete wavelet transforms Artificial neural networks Speech processing Feature extraction

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A Comparative Analysis of Identifying Influential Users in Online Social Networks

A Comparative Analysis of Identifying Influential Users in O...

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international conference on soft-computing and Networks Security (ICSNS)

作者： Hilal Ahmad Khanday Aaquib Hussain Ganai Rana Hashmy University of Kashmir Srinagar India

It has been the one of the primary efforts of social studies for decades to find influential people in a society due to its numerous applications, such as spreading ideas and practices. In today's world, Online Social Networks (OSNs) support their users by analyzing the behaviors of their users and helping them to maintain/create social presence. Several techniques for analyzing social networks have been developed, to gauge quantitative properties or qualitative aspects. However, online social network analysis poses novel challenges both to computer and social scientists. Influential User is a unique web and social phenomenon affecting tastes and behaviors of their users and helping them to maintain/create friendships. Since the influence of good number of connections/friends needs to be evaluated for each user, it is difficult to infer precisely which among them is influential. Many models ranging from Associative Rule mining to analyzing a bunch of social network metrices have been proposed. In all of the proposed methods, the very first step is to quantify the amount of influence an individual exerts on another. There can be many different ways to calculate the power of user. We demonstrate how these models are able to find the influential users. Besides we do an analytical comparison between different methods. Eventually, we conclude that there is no single method that is better than others as the problem is hard, so the space for new algorithms is wide open.

关键词： Social network services Measurement signal processing IP networks Security Communication networks Bayes methods

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Tutorial T2A: Safe Autonomous Systems: Real-Time Error Detection and Correction in Safety-Critical signal processing and Control Algorithms

Tutorial T2A: Safe Autonomous Systems: Real-Time Error Detec...

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international conference on VLSI Design

作者： Jacob Abraham Abhijit Chatterjee University of Texas at Austin Austin TX US Georgia Institute of Technology Atlanta GA US

ISBN: (纸本)9781538636930;9781538636923

While the last two decades have seen revolutions in computing and communications systems, the next few decades will see a revolution in the use of every-day robotics and artificial intelligence in broad societal applications. Examples of such systems include sensor networks, the smart power grid, self-driven cars and autonomous drones. Such systems are driven by signal processing, control and learning algorithms that process sensor data, actuate control functions and learn about the environment in which these systems operate. The trustworthiness and safety of such systems is of paramount importance and has significant impact on the commercial viability of the underlying technology. As a consequence, anomalies in system operation due to computation errors in on-board processors, degradation and failure of embedded sensors, actuators and electro-mechanical subsystems and unforeseen changes in their operation environment need to detected with minimum latency. Such anomalies also need to be mitigated in ways that ensure the safety of such systems under all possible failure scenarios. Many future systems will be selflearning in the field. It is necessary to ensure that such learning does not compromise the safety of all human personnel involved in the operation of such systems. To enable safe operation of such systems, the underlying hardware needs to be tuned in the field to maximize performance, reliability and error-resilience while minimizing power consumption. To enable such dynamic adaptation, device operating conditions and the onset of soft errors are sensed using post-manufacture and real-time checking mechanisms. These mechanisms rely on the use of built-in sensors and/or low-overhead function encoding techniques to detect anomalies in system functions. A key capability is that of being able to deduce multiple performance parameters of the system-under-test using compact optimized stimulus using learning algorithms. The sensors and function encodings assess the

关键词： signal processing algorithms Robot sensing systems signal processing Safety Real-time systems Hardware Electrical engineering

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Bearing fault diagnosis using TQWT based Entropy features

Bearing fault diagnosis using TQWT based Entropy features

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IEEE international conference on Recent Trends in Electronics, Information & Communication Technology

作者： Tanvi Dovedi Rahul Upadhyay Electronics and Communication Engineering Department Thapar Institute of Engineering and Technology

ISBN: (数字)9781538624401

ISBN: (纸本)9781538624418

The rolling element bearing plays a significant role in any mechanical manufacturing industry. Bearing fault diagnosis using vibration signal processing has been a vital subject of study over past many years. In present work, an automated bearing fault diagnosis technique using Tunable Q-factor Wavelet Transform based time-frequency decomposition is proposed. Proposed technique is carried out in three methodological steps. Firstly, vibration signals are decomposed into time-frequency coefficients using Tunable Q-factor Wavelet Transform. Further, entropy based features are computed from decomposed time-frequency coefficients as features of vibration signals in second step. In the third step, classification of vibration signals is performed using three soft computing methods viz. Support Vector Machine, Artificial Neural Network and Random Forest Tree classifier. Classification results revealed that presented methodology has potential to design and develop a real time bearing fault diagnosis system. In present work, classification accuracy of 100% is achieved using Support Vector Machine Classifier.

关键词： Fault diagnosis Bearing Tunable q-factor wavelet transform Permutation entropy soft computing methods Bearing fault diagnosis Fault diagnosis Bearings Support Vector Network entropy Vibration Quality factor

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Electromyogram (EMG) signal detection, classification of EMG signals and diagnosis of neuropathy muscle disease 9

Electromyogram (EMG) signal detection, classification of EMG...

引用

9th international conference on Theory and Application of soft computing, computing with Words and Perception (ICSCCW)

作者： Sadikoglu, Fahreddin Kavalcioglu, Cemal Dagman, Berk Near East Univ Dept Elect & Elect Engn Near East Blvd POB 99138 Trnc 10 Mersin Turkey

Electromyography (EMG) signals is usable in order to applications of biomedical, clinical, modern human computer interaction and Evolvable Hardware Chip (EHW) improvement. Advanced methods are needed for perception, disassembly, classification and processing of EMG signals acquired from the muscles. Objective of this article is to show various methods and algorithms in order to analyze an electromyogram signal to ensure effective and efficient ways of understanding signal and its nature. Early diagnosis was indispensable and very important in medical health practice. For this reason, it is important to design accurate diagnostic methods. Today, diagnostic methods include evaluating the patient's story, blood tests, and muscle biopsies. In this article, analysis and Electromyogram signals classification and electromyography are mostly used. System has been successfully implemented utilizing MATLAB software that can distinguish EMG signals from different patients. This article also provides the researcher with a well understanding of electromyogram signalling and analysis processes. This information will auxiliary to improve stronger, more resilient and effective implementations. (c) 2018 The Authors. Published by Elsevier B.V.

关键词： Electromyography (EMG) Fast Fourier Transformation (FFT) muscles nervous system healthy neuropathy

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Cardiac arrhythmia detection using deep learning 9

Cardiac arrhythmia detection using deep learning

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9th international conference on Theory and Application of soft computing, computing with Words and Perception (ICSCCW)

作者： Isin, Ali Ozdalili, Selen Near East Univ Dept Biomed Engn POB 99138Mersin 10 Nicosia North Cyprus Turkey Near East Univ Dept Innovat & Knowledge Management POB 99138Mersin 10 Nicosia North Cyprus Turkey

An electrocardiogram (ECG) is an important diagnostic tool for the assessment of cardiac arrhythmias in clinical routine. In this study, a deep learning framework previously trained on a general image data set is transferred to carry out automatic ECG arrhythmia diagnostics by classifying patient ECG's into corresponding cardiac conditions. Transferred deep convolutional neural network (namely AlexNet) is used as a feature extractor and the extracted features are fed into a simple back propagation neural network to carry out the final classification. Three different conditions of ECG waveform are selected from MIT-BIH arrhythmia database to evaluate the proposed framework. Main focus of this study is to implement a simple, reliable and easily applicable deep learning technique for the classification of the selected three different cardiac conditions. Obtained results demonstrated that the transferred deep learning feature extractor cascaded with a conventional back propagation neural network were able to obtain very high performance rates. Highest obtained correct recognition rate is 98.51% while obtaining testing accuracy around 92%. Based on these results, transferred deep learning proved to be an efficient automatic cardiac arrhythmia detection method while eliminating the burden of training a deep convolutional neural network from scratch providing an easily applicable technique. (c) 2018 The Authors. Published by Elsevier B.V.

关键词： biomedical signal processing deep learning ECG classification convolutional neural networks cardiac arrhythmia tranfer learning

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Binary time domain reflectometry: a simpler and more efficient way of diagnosing defects in wired networks

Binary time domain reflectometry: a simpler and more efficie...

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international Automatic Testing conference, AUTOTESTCON

作者： Fabrice Auzanneau CEA CEA Sac lay France

Today, the most efficient methods for wired networks diagnosis are based on reflectometry: they can detect hard (i.e. open/short circuits) and soft defects (i.e. insulation damage, hot spots or rust), either permanent or intermittent, and provide their location. This is an important information for maintenance operators who can focus repair in a shorter time. The possibility of monitoring the wires health using specifically designed reflectometry methods offers interesting opportunities for preventive maintenance. Electronic implementations of such methods are mostly based on the use of programmable logic digital systems, such as FPGA or microcontrollers, both for probe signals generation and measured signals analysis. But the cables have a pure analog behavior, therefore these systems are connected to additional costly devices such as digital to analog and analog to digital converters. These components have a direct impact on the systems performances: a higher sampling rate means a better defects location accuracy, but implies a higher cost. A higher resolution enables to detect lower signature defects but requires more memory and computing power for data processing. This paper presents a new purely binary reflectometry method using a simpler electronic architecture and quicker signal analysis, and showing better performances than standard methods (in terms of defect location accuracy, processing speed and memory requirement) at a lower cost. Equivalent detection performances are shown, and the new simplified electronic architecture enables to take advantage of all the digital resources, such as a higher clock frequency than that of most available converters, thus naturally improving the location accuracy. As the conversion components are not required anymore, the consumption and cost of the system is drastically reduced. Performance comparison is presented and an innovative process for the reflectogram computation is introduced, which enables a drastic reduction o

关键词： Standards Power cables Time-domain analysis Field programmable gate arrays signal resolution AWGN Probes

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