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FPGA Implementation of Real-Time Soldier Activity Detection based on Neural Network Classifier in Smart Military Suit

FPGA Implementation of Real-Time Soldier Activity Detection ...

引用

IEEE Bombay Section Signature Conference (IBSSC)

作者： Nikhil B. Gaikwad Avinash G. Keskar Varun Tiwari NC Shivaprakash Electronics and Communication Department Visvesvaraya National Institute of Technology (VNIT) Nagpur India Department of Instrumentation and Applied Physics Indian Institute of Science (IISc) C V Raman Ave Bengaluru India

ISBN: (纸本)9781538674765

The wearable technology carries sufficient potential to incorporate smartness into working of the military workforces like the Military Control Unit, Medical Responders, Backup Unit and War Strategist. The proposed work focuses on real-time soldier activity detection, which is essential for the operation of the smart military suit. The customized Artificial Neural Network (ANN) IP core is developed for the soldier activity classification, which is an integral component of suit gateway design. The multilayer perceptron (7-5-4) classification algorithm is implemented on the low-cost (99$) FPGA evaluation platform by using Xilinx vivado and system generator development tools. The training (70%) and testing (30%) of this ANN design is performed on the UCI human activity dataset. The LabVIEW GUI and IP test design completed the hardware testing of this IP. The presented ANN IP is able to achieve 98.5% classification accuracy by utilizing minimal FPGA (Artix-7 xc7a35t) resources. The implemented ANN design requires only 285 nanoseconds for a classification and consumes 103 milliwatts of dynamic power. The system's accuracy at different development levels is also studied in this work.

关键词： Smart military suit Soldier activity detection Artificial neural network Smart sensor gateway Network centric warfare Field programmable gate array Field programmable gate arrays Artificial neural networks Soldiers Activity Detection Military Personnel Hardware Test brightness EIS IMPLEMENTATION PLANS Bayesian classifier sorting algorithm

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A Cost-effective Method for Epileptic Seizure Classification

A Cost-effective Method for Epileptic Seizure Classification

引用

IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)

作者： Md Mursalin Syed Mohammed Shamsul Islam Adel Al-Jumaily Computing and Security Edith Cowan University Perth Australia Biomedical Engineering University of Technology Sydney Sydney Australia

ISBN: (纸本)9781728117096

The ongoing development of various lightweight and portable EEG signal acquisition devices provides the opportunity to implement home-based epilepsy monitoring. However, it is essential to apply a highly effective method to handle the limited computational power of such devices. In this paper, we propose a cost-effective method to classify epileptic seizure using stratified sampling technique. Additionally, to reduce the required computational power, this paper proposes a novel correlation and threshold-based feature selection algorithm. For evaluating the performance of our proposed method, five different classification algorithms are applied to classify the epileptic seizure from the reduced feature set. In our experiment, the random forest classifier shows the highest accuracy compared to other classifiers.

关键词： Epilepsy EEG signal Wavelet analysis Optimum allocation Classification optimal allocation epileptic seizure Wavelet Analysis Taxonomy Epilepsy computational power sorting algorithm

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HYDROMETEOR CLASSIFICATION FOR X-BAND DUAL POLARIZATION RADAR ON-BOARD CIVIL AIRCRAFTS

HYDROMETEOR CLASSIFICATION FOR X-BAND DUAL POLARIZATION RADA...

引用

IEEE International Geoscience and Remote Sensing Symposium

作者： N. Roberto A. Adirosi L. Baldini L. Facheris F. Cuccoli A. Lupidi A. Garzelli CNIT - RASS

ISBN: (纸本)9781479979301

Polarimetric techniques applied to radars on-board civil aircraft can improve the estimation of risk zones due to dangerous weather during flight. Usually, X-band weather radars are installed at the nose of civil aircrafts. This band is affected by strong attenuation in case of intense precipitation (liquid or mixed phase). The current systems do not compensate backscattered power measurements for attenuation caused by propagation through precipitation, while dual-polarization radars are able to compensate effectively this source of error and to discriminate hydrometeors. In this work, a classification algorithm based on support vector machines (SVM) is proposed. Training is driven by the output of a Fuzzy Logic (FL) classification algorithm (typical classification approach used for ground-based weather radar). SVM high performance in terms of time processing and its flexibility of configuration using all type of inputs variables are important characteristics to be included in some avionic specific equipment, such as the Electronic Flight Bag (EFB). Two datasets have been used to test the SVM classification algorithm. The first dataset is composed of simulated radar polarimetric observations at X-band and the second one is composed of actual dual-polarization radar measurements collected during the Special Observation Period (SOP) 1.1 of HYdrological cycle in MEditerranean EXperiment (Hymex) campaign by the C-band Doppler dual-polarization weather radar (Polar 55C) installed at ISAC-CNR in Rome. Good performance are obtained for SVM classificator. The comparison with FL output shows a good agreement (up to 90%) both in qualitative comparison maps by maps and using a quantitative approach which metric is based on the confusion matrix.

关键词： Support Vector Machine Dual polarization radar Hydrometeor classification Avionic radar Fuzzy logic Support Vector Network sorting algorithm radar Fuzzy logic Superhigh frequency confusion matrix water circulation Radar measurement Power measurement Hydrometeors HIGH-PERFORMANCE meteorological radar mechanical heart valve

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A SURVEY: CURSIVE HANDWRITING RECOGNITION TECHNIQUES

A SURVEY: CURSIVE HANDWRITING RECOGNITION TECHNIQUES

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IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology

作者： Umesh Choudhary Sampada Bhosale Sonali Bhise Purushottam Chilveri Department of Electronics and Telecommunication Smt. Kashibai Navale College Of Engineering Department of Electronics and Telecommunication Smt. Kashibai Navale College Of

ISBN: (纸本)9781509037056

In computer vision, recognition of cursive alphabets is a difficult and challenging task. Cursive alphabet recognition has its own importance within the field of bank cheques, American postcard, medical prescription letter, etc. The complexness of recognizing cursive alphabets lies within the stroke, inclination, size and the different handwriting styles. Here we have presented a survey on the recognition of cursive handwriting. Numerous methodologies are presented with their accuracy of recognition or recognition rate.

关键词： Feature extraction Image segmentation Handwriting recognition Artificial neural networks Classification algorithms Communications technology handwriting recognition Communication technology sorting algorithm image segmentation Feature extraction Artificial neural networks Recognition (Psychology) Alphabets

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Automatic Detection of Laser Marks in Retinal Digital Fundus Images

Automatic Detection of Laser Marks in Retinal Digital Fundus...

引用

European Signal Processing Conference

作者： Joao G. R. Almeida e Sousa Carlos Manta Oliveira Luis A. da Silva Cruz Department of Physics University of Coimbra Retmarker S.A. Instituto de Telecomunicacoes Department of Electrical and Computer Engineering University of Coimbra

ISBN: (纸本)9781509018918

Diabetic retinopathy (DR) is the most frequent complication of diabetes mellitus that affects vision to the point of causing blindness. In advanced stages its progress can be delayed with laser photocoagulation which leaves behind marks on the retina. Modern screening programs rely on automatic diagnostic algorithms to detect signs of DR in patients. These systems performance may be impaired when patient retina presents marks from previous laser photocoagulation treatments. Since these patients are already being treated, it is desirable to detect and remove them from the screening program. An algorithm that automatically detects the presence of laser marks in retinal images using tree-based classifiers is proposed and the results on its performance are obtained and described. Two new public accessible datasets containing retinal images with laser marks are provided in this paper.

关键词： Diabetes Biomedical image processing Feature extraction Classification algorithms Medical image processing sorting algorithm Feature extraction Diabetes Mellitus automatic detection Laser Coagulation System performance Retinaldehyde DR Complications of Diabetes Mellitus Retinal Image images

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Operability Improvement for Myoelectric Prosthetic Hand Using Brain Machine Interface Identification of Eight Forearm Motions Based on Myoekectric Potential

Operability Improvement for Myoelectric Prosthetic Hand Usin...

引用

IEEE International Conference on Systems, Man, and Cybernetics

作者： Kana Kuniyasu Jun Ishikawa Department of Robotics and Mechatronics Tokyo Denki University Tokyo Japan

ISBN: (纸本)9781479906505

This paper proposes a myoelectric prosthetic hand control system using a brain machine interface that uses electromyogram signals and other biosignals such electro-encephalographic signals. The proposed system consists of a feature vector extractor, a neural network estimator, a reference trajectory generator, and an adaptive corrector. As a preparation, an experimental system has been prototyped without adaptive correction using other biosignals. The system is to classify eight forearm motions by using two electromyogram signals and to generate reference trajectories for servo controllers of a prosthetic hand. The experimental results showed that the proposed classification algorithm has basically worked well and that the reference trajectory generator has a potential to make appropriate smooth trajectories. Furthermore, electroencephalogram signals during forearm motion identification were measured and analyzed to check whether reaction to indicate any malfunctions was observed.

关键词： myoelectric prosthetic hand electromyogram (EMG) signals brain machine interface (BMI) neural network Neural network signals Forearm trajectory malfunctions control systems sorting algorithm Electromyogram machine servo controller

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Agnostic Selective Classification 11

Agnostic Selective Classification

引用

Annual Conference on Neural Information Processing Systems

作者： RanEl-Yaniv Yair Wiener Department Technion - Israel Institute of Technology

ISBN: (纸本)9781618395993

For a learning problem whose associated excess loss class is (β, B)-Bernstein, we show that it is theoretically possible to track the same classification performance of the best (unknown) hypothesis in our class, provided that we are free to abstain from prediction in some region of our choice. The (probabilistic) volume of this rejected region of the domain is shown to be diminishing at rate O(Bθ(1/m~1/2β), where 6 is Hanneke's disagreement coefficient. The strategy achieving this performance has computational barriers because it requires empirical error minimization in an agnostic setting. Nevertheless, we heuristically approximate this strategy and develop a novel selective classification algorithm using constrained SVMs. We show empirically that the resulting algorithm consistently outperforms the traditional rejection mechanism based on distance from decision boundary.

关键词： Decision Boundary sorting algorithm diminish excess loss Learning Problems

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LARGE MARGIN FILTERING FOR SIGNAL SEQUENCE LABELING

LARGE MARGIN FILTERING FOR SIGNAL SEQUENCE LABELING

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Remi Flamary Benjamin Labbe Alain Rakotomamonjy LITIS EA 4108 INSA-Universite de Rouen 76801 Saint-Etienne-du-Rouvray France

ISBN: (纸本)9781424442959

Signal Sequence Labeling consists in predicting a sequence of labels given an observed sequence of samples. A naive way is to filter the signal in order to reduce the noise and to apply a classification algorithm on the filtered samples. We propose in this paper to jointly learn the filter with the classifier leading to a large margin filtering for classification. This method allows to learn the optimal cutoff frequency and phase of the filter that may be different from zero. Two methods are proposed and tested on a toy dataset and on a real life BCI dataset from BCI Competition III.

关键词： Filtering SVM BCI Sequence Labeling Filtration Support Vector Network Filters sorting algorithm signals Cutoff frequency LARGE gene Classifiers

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Whole-House Non-Intrusive Appliance Load Monitoring via Multi-Label Classification

Whole-House Non-Intrusive Appliance Load Monitoring via Mult...

引用

International Joint Conference on Neural Networks

作者： Ding Li Scott Dick Department of Electrical and Computer Engineering University of Alberta

ISBN: (纸本)9781509006212

Non-intrusive appliance load monitoring is a technique to help power companies monitor and analyze residential energy usage. Aggregated power load measurements are disaggregated into individual appliance loads by examining the appliance-specific power consumption characteristics. This data can then be used to modify consumer behaviors via detailed billing data and/or demand-pricing tariffs. A number of advances in the field have been reported in the past two decades, many of which apply machine learning algorithms. However, these algorithms only assign one label to an example, which is a poor match to the NIALM problem, meaning elaborate encodings or classifier ensembles are needed for NIALM. A more elegant solution would be to use algorithms that assign multiple labels to a single example. These multi-label classification algorithms have received very little attention in this field to date. We conduct an experimental investigation of four multi-label classification algorithms for non-intrusive monitoring, and find that they are superior to all prior reported results on a real-world household dataset.

关键词： Non-intrusive monitoring Load disaggregation Feature extraction Multi-label classification load monitoring Feature extraction sorting algorithm Classifier Ensembles Power consumption

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CLASSIFYING THE CANADIAN BOREAL FOREST'S STRUCTURE USING MULTI-MODAL REMOTE SENSING

CLASSIFYING THE CANADIAN BOREAL FOREST'S STRUCTURE USING MUL...

引用

IEEE International Geoscience and Remote Sensing Symposium

作者： Michael L. Benson Leland E. Pierce Kathleen M. Bergen Kamal Sarabandi The University of Michigan The Radiation Laboratory EECS Dept. The University of Michigan School of Natural Resources and the Environment

ISBN: (纸本)9781467311601

One of the most fundamental new technical challenges of a DESDynI-R space-borne mission is the fusion of the several sensor modalities, including the onboard SAR and external LiDAR and Optical sensors in order to accurately estimate desired 3D vegetation structures and biomass parameters in areas where the sensors overlap. The objective of this paper is to use measured datasets in conjunction with our sensor models to develop a classification algorithm that fuses multi-modal remote sensing technologies with a minimal amount of ground information and yields an accurate estimate of forest structure including dry biomass and canopy height.

关键词： SAR LiDAR Landsat Forest structure Classification Fractal tree VIR Fusion BOREAS DESDynI sorting algorithm Sensors remote sensing optical sensors Forest forest structure Fusion reactions FUSION fusion (melting) Remote Sensing Technologies

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