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Floating to Fixed-Point Translation with Its Application to Speech-Based Emotion Recognition

Floating to Fixed-Point Translation with Its Application to ...

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International conference on Emerging Applications of Information Technology (EAIT)

作者： Bibek Kabi Subhasmita Sahoo Amiya Kumar Samantaray Aurobinda Routray Advanced Technology Development Centre Indian Institute of Technology Kharagpur Department of Electrical Engineering Indian Institute of Technology Kharagpur National Institute of Technology Rourkela India

Speech-based emotion recognition is one of the latest challenges in speech processing. The algorithms are developed using floating-point arithmetic because of its wide dynamic range and constant relative accuracy. However, they are finally implemented in hand held devices which are required to consume less power, time and have a lower market price. Fixed-point arithmetic with proper determination of integer and fractional bitwidths can help in satisfying these requirements. Therefore we have made an attempt to develop a fixed-point speech-based emotion recognition system using Mel frequency cepstral coefficients (MFCCs) and hidden Markov model (HMM). Accurate range and precision analysis has been carried out to compute optimum integer and fractional word lengths. The speech emotion engine has been evaluated using Berlin emotional speech database, EMO-DB. A speaker independent emotion recognition accuracy of 71.02% and 67.42% for floating-point and fixed-point formats with optimized wordlenghs respectively was achieved. Finite wordlength effect like quantization with range of relative errors and its effect on emotion recognition task has been analyzed.

关键词： Hidden Markov models Emotion recognition Speech Speech recognition Accuracy Mel frequency cepstral coefficient Quantization (signal)

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Ontology-based human behavior indexing with multimodal video data

Ontology-based human behavior indexing with multimodal video...

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International conference on Semantic Computing

作者： Julio Vizcarra Satoshi Nishimura Ken Fukuda National Institute of Advanced Industrial Science and Technology (AIST) Tokyo Japan

ISBN: (数字)9781728188997

ISBN: (纸本)9781728189000

Observing and analyzing human behavior is a labor-intensive task. Video data is a crucially important resource for ethnographical study, but the accessibility is limited because describing the semantics of video content is difficult. Moreover, the user typically must watch the entire video archive to identify important findings. This study aims to make human behavior computable and utilize it in various domains such as user-centric manufacturing and safety management. To this end, the work proposes an ontology and a semantic video indexing methodology that integrates human annotation and DNN detector-based annotations of video content and converts them into a knowledge graph. This knowledge graph of ontology-based human actions enables us to apply various computational algorithms to human behaviors. The reported proof of concept system retrieves multimodal data, represents every human behavior annotations as an RDF knowledge graph (KG), and exploits the KG to analyze behavior patterns. As a case study, the work was evaluated in the elderly care domain. A formal notation was used to retrieve complex action sequences with specific conditions. The system was validated to retrieve complex behavior patterns and display the location of such behavioral events in the video.

关键词： Annotations Semantics Senior citizens Safety management Task analysis Video signal processing Indexing

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Accelerating Algebraic Reconstruction Using CUDA-Enabled GPU

Accelerating Algebraic Reconstruction Using CUDA-Enabled GPU

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International conference on Computer Graphics, Imaging and Visualization, CGIV

作者： Yuqiang Lu Weiming Wang Shifu Chen Yongming Xie Jing Qin Wai-Man Pang Pheng-Ann Heng Shenzhen Institute of Advanced Integration Technology Chinese Academy and Sciences Shenzhen China Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong China

In this paper, we apply the compute unified device architecture (CUDA) to the 3D cone-beam CT reconstruction using simultaneous algebraic reconstruction technique (SART). With the hardware acceleration, the computationally complex SART can run at speed comparable to the commonly used filtered back-projection, and provide even better quality volume with less samples. The main contributions include two novel techniques to accelerate the reconstruction. We introduce a ray-driven projection along with hardware built-in trilinear interpolation, as well as a voxel-driven back-projection that can avoid redundant computation by combining CUDA shared memory. Significant performance boost is reported from experiments using our techniques. A real-time reconstruction is achieved within 3 seconds for a 128 3 volume from 80 128 2 projections, without compromising image quality. Our proposed method realizes the instantaneous presentation of CT volume to the physician once projection images are acquired.

关键词： Acceleration Image reconstruction Hardware Computed tomography Computer architecture Graphics Interpolation signal processing algorithms Field programmable gate arrays Concurrent computing

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Implementation of Genetic Algorithm Optimization based Artificial Neural Network on Raspberry Pi for Fault Diagnosis

Implementation of Genetic Algorithm Optimization based Artif...

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International conference on Control, Decision and Information Technologies (CoDIT)

作者： Amal Hichri Wajdi Saadaoui Mansour Hajji Majdi Mansouri Mohamed Nounou Kais Bouzrara Research Unit Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Qatar Laboratory of Automatic Signal and Image Processing National Engineering School of Monastir University of Monastir Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This study presents an embedded system (ES) designed for fault detection and diagnosis in grid-connected photovoltaic (GCPV) systems using transient regime analysis. The primary aim of transient regime analysis is to facilitate real-time decision-making, especially during critical faults. A neural network classifier, incorporating a Genetic Algorithm for automated hyperparameter optimization, is developed for GCPV fault classification. These classifiers are seamlessly integrated into a Raspberry Pi 4 platform for fault diagnosis in GCPV systems. Both simulation and experimental results substantiate the ES's viability for fault diagnosis in the examined GCPV system, achieving high accuracy and enabling prompt decision-making to enhance the reliability and safety of GCPV systems.

关键词： Fault diagnosis Photovoltaic systems Embedded systems Accuracy Fault detection Decision making Artificial neural networks Hyperparameter optimization Transient analysis Genetic algorithms

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Implementation of Area Efficient Pipelined R22SDF FFT Architecture

Implementation of Area Efficient Pipelined R22SDF FFT Archit...

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International conference on Smart Structures and Systems (ICSSS)

作者： C.Ramesh Kumar M.P. Chitra Sathyabama Institute of Science and Technology Chennai India Panimalar Institute of Technology Chennai India

ISBN: (数字)9781728172231

ISBN: (纸本)9781728172286

This paper presents the analysis of various architectures of Low power FFT Processors. FFT is a major building block in DSP and communication system. FFT is an efficient way to implement DFT in a faster manner. Using FFT the number of multiplication and addition order O(N/2log 2 N) and O(Nlog 2 N) are reduced. Conventional FFT has more chip area and delay. Hence pipelined FFT is preferred to overcome the demerits of the conventional processor. This paper reports a detailed study of various FFT architectures and mainly about R2 2 SDF SDF FFT. In this paper, to reduce delay an advanced design of 64-point Pipelined Radix 2 2 Single Path Delay Feedback (R2 2 SDF) FFT architecture has been proposed by using Verilog Hardware Description Language (Verilog HDL). The simulation results has been evaluated by using Modelsim 6.3C and synthesis results are estimated by using Xilinx Planahead 12.4i design tool.

关键词： Delays Program processors Commutators Memory architecture signal processing algorithms Discrete Fourier transforms

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Adaptive Video Subsampling For Energy-Efficient Object Detection

Adaptive Video Subsampling For Energy-Efficient Object Detec...

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Asilomar conference on signals, Systems & Computers

作者： Divya Mohan Sameeksha Katoch Suren Jayasuriya Pavan Turaga Andreas Spanias Sensor Signal and Information Processing (SenSIP) Center School of Electrical Computer and Energy Engineering Arizona State University

ISBN: (数字)9781728143002

ISBN: (纸本)9781728143019

Energy-efficient computer vision is vitally important for embedded and mobile platforms where a longer battery life can allow increased deployment in the field. In image sensors, one of the primary causes of energy expenditure is the sampling and digitization process. Smart subsampling of the image-array in a manner that is task-specific, can result in significant savings of energy. We present an adaptive algorithm for video subsampling, which is aimed at enabling accurate object detection, while saving sampling energy. The approach utilizes objectness measures, which we show can be accurately estimated even from sub-sampled frames, and then uses that information to determine the adaptive sampling for the subsequent frame. We show energy savings of 18 - 67% with only a slight degradation in object detection accuracy in experiments. These results motivated us to further explore energy-efficient subsampling using advanced techniques such as, reinforcement learning and Kalman filtering. The experiments using these techniques are underway and provide ample support for adaptive subsampling as a promising avenue for embedded computer vision in the future.

关键词： Object detection Streaming media Computer vision Image sensors Optical imaging Task analysis Adaptive algorithms

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A restricted Boltzmann machine based two-lead electrocardiography classification

A restricted Boltzmann machine based two-lead electrocardiog...

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International Workshop on Wearable and Implantable Body Sensor Networks (BSN)

作者： Yan Yan Xinbing Qin Yige Wu Nannan Zhang Jianping Fan Lei Wang Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences

ISBN: (纸本)9781467372022

An restricted Boltzmann machine learning algorithm were proposed in the two-lead heart beat classification problem. ECG classification is a complex pattern recognition problem. The unsupervised learning algorithm of restricted Boltzmann machine is ideal in mining the massive unlabelled ECG wave beats collected in the heart healthcare monitoring applications. A restricted Boltzmann machine (RBM) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs. In this paper a deep belief network was constructed and the RBM based algorithm was used in the classification problem. Under the recommended twelve classes by the ANSI/AAMI EC57: 1998/(R)2008 standard as the waveform labels, the algorithm was evaluated on the two-lead ECG dataset of MIT-BIH and gets the performance with accuracy of 98.829%. The proposed algorithm performed well in the two-lead ECG classification problem, which could be generalized to multi-lead unsupervised ECG classification or detection problems.

关键词： Electrocardiography Heart beat Feature extraction Accuracy Training Data models signal processing algorithms

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Multiagent Multi-Armed Bandit Techniques for Millimeter Wave Concurrent Beamforming

Multiagent Multi-Armed Bandit Techniques for Millimeter Wave...

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Japan-Egypt International conference on Electronics, Communications and Computers (JEC-ECC)

作者： Sherief Hashima Kohei Hatano Hani Kasban Mohamed Rihan Ehab Mahmoud Mohamed RIKEN-Advanced Intelligent Project 819-0395 Japan Nuclear Research Center Egyptian Atomic Energy Authority Cairo Inshas 13759 Egypt Kyushu University Fukuoka 819-0395 Japan Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt College of Engineering Prince Sattam ABBin Abdulaziz University Wadi Addwasir 11991 Saudi Arabia Faculty of Engineering Aswan University Aswan 81542 Egypt

ISBN: (数字)9781665404365

ISBN: (纸本)9781665404372

This paper leverages multiagent multi-armed bandit (MA-MAB) schemes to handle efficiently the millimeter wave (mmWave) concurrent transmission problem. MA-MAB selects the beams with the maximum long-term reward, mostly data rate, from the concurrent links. The mmWave access points (APs) are the agents of the bandit game, while the arms are the existing beam directions. Towards that, MA-KLUCB and MA-EXP3 are proposed and tested via applying them within each AP in a selfish concurrent beamforming scenario. It turns out that the proposed algorithms provide near-optimal performances as compared to the exhaustive search of all concurrent beams' combinations. Also, they demonstrate reasonable fast convergence rates too.

关键词： Couplings Array signal processing Millimeter wave technology Interference Games IEEE 802.11 Standard Convergence

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Two-Stage Hybrid A* Path-Planning In Large Petrochemical Complexes

Two-Stage Hybrid A* Path-Planning In Large Petrochemical Com...

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IEEE International conference on advanced Intelligent Mechatronics (AIM)

作者： Shamsudin, A. U. Ohno, K. Hamada, R. Kojima, S. Mizuno, N. Westfechtel, T. Suzuki, T. Tadokoro, S. Fujita, J. Amano, H. Tohoku Univ Grad Sch Informat Sci Aoba Ku 6-6-01 Aramaki Aza Aoba Sendai Miyagi 9808579 Japan Tohoku Univ New Ind Creat Hatchery Ctr Aoba Ku 6-6-10 Aramaki Aza Aoba Sendai Miyagi 9808579 Japan Mitsubishi Heavy Ind Co Ltd Equipment Designing Sect Nucl Plant Component Designing Dept Hyogo Ku 1-1Wadasaki Cho 1 Chome Kobe Hyogo 6528585 Japan Fire & Disaster Management Agcy JAPAN Natl Res Inst Fire & Disaster 4-35-3 Jindaiji Higashimachi Chofu Tokyo 1828508 Japan

ISBN: (纸本)9781509060009

In this study, we aim to achieve path-planning for firefighter robots in large petrochemical complexes. In large environments, path-planning (e.g., Hybrid A*) requires a large computation memory and a long execution time. These constrains are not feasible for firefighter robots. In order to overcome these two challenges, we propose a two-stage hybrid A* path-planning. For the first stage we use a global path-planner that makes a path using a low-resolution grid map of 2 m. The global path-planner generates a path for an area of approx. 500 m x 1000 m in 10 seconds. In the second stage, we refine the path by using a local-planner that uses a local-map of 100 m x 100 m size around the robot with a high resolution grid of 1 m. The local planner receives its sub-goal from the global planner and recalculates a local path at a high speed of a few hundred milliseconds. Therefore, the local-planner can react to changes of the map due to obstacles in real-time. We evaluated our proposed method by comparing with conventional hybrid A* in simulated as well as real experimental data of petrochemical complexes. By employing the local-planner our method could drastically reduce the used memory and execution time for the re-planning. For a trajectory of 600 m, our method reduces the execution time by 99.2% for real data and by 94.34% for simulated data. The memory usage was likewise drastically reduced by 97.45% for real data and by 97.91% for simulated data.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks signal processing algorithms

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ATCA control system hardware for the plasma vertical stabilization in the JET tokamak

ATCA control system hardware for the plasma vertical stabili...

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IEEE NPSS conference on Real Time

作者： A. J. N. Batista A. Neto M. Correia A. M. Fernandes B. B. Carvalho J. C. Fortunato J. Sousa C. A. F. Varandas F. Sartori M. Jennison Instituto de Plasmas e Fusão Nuclear Associacão Euratom/IST Lisboa Portugal Culham Science Centre Association UKAEA/Euratom Abingdon Oxfordshire UK

A multi-input-multi-output controller for the plasma vertical stabilization was implemented and installed on the Joint European Torus Tokamak. The system can attain a control-cycle time of approximately 30 mus using times86 multi-core processors but targets 10 mus via field programmable gate array (FPGA) based processing. The hardware, complying with the advanced telecommunications computing architecture (ATCA) standard, was in-house designed and implemented to achieve the required performance and consists of: a total of 6 synchronized ATCA control boards, each one with 32 analog input channels which provide up to 192 galvanically-isolated channels, used mainly for magnetic measurements. Each board contains an FPGA, which performs digital signal processing and includes a PCI express communications interface; an ATCA rear transition module, which comprises up to 8 galvanically-isolated analog output channels to control the fast radial field amplifier (plusmn10 kV, plusmn2.5 kA). An optical link to digitally control the enhanced radial field amplifier (plusmn12 kV, plusmn5 kA). Up to 8 EIA-485 digital inputs for timing and monitoring information; An ATCA processor blade with a quad-core processor, where the control algorithm is presently running, connected to the 6 ATCA control boards through the PCI express interface. All FPGAs are interconnected by low-latency links via the ATCA full-mesh backplane, allowing all channel data to be available on each FPGA running an upcoming distributed control algorithm.

关键词： Control systems Hardware Plasmas Tokamaks Field programmable gate arrays Telecommunication control Communication system control Galvanizing Optical amplifiers signal processing algorithms

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