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IEEE international conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Oluwadunsin Emmanuel Fajemila Ayodeji Olalekan Salau Pipelolu Samuel Olayiwola Department of Electronics and Electrical Engineering Obafemi Awolowo University Osun State Nigeria Department of Electrical/Electronics and Computer Engineering Afe Babalola University Ado-Ekiti Nigeria Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Language is a vital aspect of human culture and communication, with over 7,000 languages spoken worldwide. Despite this diversity, many languages, particularly those spoken in Africa, lack extensive research and support for speech processing technologies. this study focuses on identifying five Nigerian languages, which are Yoruba, Nigerian Pidgin, Nigerian English, Hausa, and Igbo using machine learning techniques. We utilized Mel-Frequency Cepstral Coefficients (MFCCs) to extract acoustic features from audio clips and trained several models, including 1-Dimensional Convolutional Neural Network (1D CNN), 2-Dimensional Convolutional Neural Network (2D CNN), Deep Neural Network (DNN), Long Short-Term Memory (LSTM), Random Forest, and Support Vector Machine (SVM), to evaluate their effectiveness in language identification. the dataset comprised audio from OpenSLR, Common Voice, local recordings, and NWU, totaling 37.8 hours across all languages. Results indicated that the LSTM model achieved the highest accuracy (0.830) and precision (0.856), while the DNN model showed the best F1 score (0.797) and recall (0.852). Neural network-based models consistently outperformed traditional machine learning approaches. We observed varying levels of classification difficulty among the languages, with some presenting more challenges than others. the dataset's uneven distribution across languages posed additional complexities in model training and evaluation. this research underscores the importance of diverse data sources and advanced neural network architectures in improving language classification for underrepresented African languages.

关键词： Support vector machines Training Accuracy Confusion matrices Transfer learning Feature extraction Multilingual Convolutional neural networks Speech processing Long short term memory

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Research on OpenMP Model of the parallel Programming Technology for Homogeneous Multicore DSP

Research on OpenMP Model of the Parallel Programming Technol...

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2014 IEEE 5th international conference on Software Engineering and Service Science

作者： Minjie Wu Weiwei Wu Ning Tai Hongyu Zhao Jiawu Fan Naichang Yuan School of Electronic Science and Engineering National University of Defense Technology

As application complexity continues to grow,using multicore processors has been proved to be an effective methodology to meet the ever-increasing processing demand across the industry *** Master/Slave model,the Data Flow model and the OpenMP model are the three dominant models for parallel *** this paper,the first two models are briefly discussed while the OpenMP model is *** factors(*** number of threads,the scheduling strategy,the load balance,etc.) that affecting the execution performance of OpenMP programs were also studied in this *** paper presents a method of taking advantage of the OpenMP model to realize the image edge detection within the platform of TMS320C6678 *** experimental results show that the OpenMP model has a better advantage on scalability and flexibility compared to the Master/Slave model and the Data Flow *** best performance can be obtained when the number of threads is equal to the number of cores which are available within the *** the circumstance of using the eight cores of TMS320C6678 DSP simultaneously,an image of 1024x768 pixels just needs 6.192 ms to complete the edge *** result is impressive compared to the Master/Slave model's which saves 32.10% in *** more,if we use 1 to 8 cores,the respective execution time reduces resulting in the speedup approximately conforms to the Gustafson's *** the case of 8 cores,the speedup reaches 7.233.

关键词： OpenMP model TMS320C6678 DSP Number of threads Scheduling Strategy Image Edge Detection

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A Retina-LGN-V1 Structure-like Spiking Neuron Network for Image Feature Extraction 21

A Retina-LGN-V1 Structure-like Spiking Neuron Network for Im...

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Proceedings of the 2021 5th international conference on Video and Image processing

作者： Sheng Ji Qijun Gu Ye Yuan Peng Zhao Tao Fang Hong Huo Xin Niu Department of Automation_School of Electronic Information and Electrical Engineering_Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University China College of Computer Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology China

ISBN: (纸本)9781450385893

SNNs have achieved great attention in recent years as they contain neurons more like those in the brain and use spikes to encode and transmit information efficiently among neurons with lower energy consumption. A Retina-LGN-V1 structure-like spiking neuron network (RLVSL-SNN) is proposed in this paper. It is inspired by the structure of mammalian primary visual pathway, and simulates different biological structures of Retina, LGN and V1. Noise reduction circuit and light adaptation circuit are also simulated for enhancing the robustness of its extracted features. RLVSL-SNN is a bio-plausible neuron network as it has firing rates of neurons in each layer that are similar to those of biological experiments. Besides, a full-connected SNN (FC SNN) is implemented following RLVSL-SNN for classification to evaluate the extracted features. the additive spiking timing dependent plasticity (STDP) learning rules and the ANN-to-SNN conversion method are utilized to train RLVSL-SNN and FC SNN, respectively. the experiments on MNIST dataset have verified that RLVSL-SNN is comparable to AlexNet for classification by features from spikes.

关键词： Additive Spiking Timing Dependent Plasticity Learning Primary Visual Pathway Image Classification Spiking Neural Network

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Performance Prediction for parallel Applications Running on HPC architectures through Petri Net Modelling and Simulation

Performance Prediction for Parallel Applications Running on ...

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IEEE international conference on Intelligent Computer Communication and processing

作者： Ion Dan MIRONESCU Lucian VINTAN Computer Science Department Technical University of Cluj-Napoca

ISBN: (纸本)9781479914920

the paper presents the development of a model and a simulation technique for the prediction of the performance of a concurrent application running on a HPC architecture. Models for the hardware and software were developed using the Coloured Petri Net formalism and then coupled for the simulation. Timed simulations were performed in the Coloured Petri Net tools environment and in the Charm++ respectively the BigSim simulator. the results of both simulations for the same optimization problem show similar trends.

关键词： Modelling and Simulation Performance prediction Petri net parallel Application HPC

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Enhancing Plant Leaf Disease Identification with a CNN and DenseNet Hybrid Model 23

Enhancing Plant Leaf Disease Identification with a CNN and D...

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Proceedings of the 5th international conference on Information Management & Machine Intelligence

作者： Sukhvinder Singh Dari Shweta Tiwaskar Jambi Ratna Raja Kumar Vaishali Langote Gopal Madhukar Mohadikar Rashmi Ashtagi Symbiosis Law School Nagpur Campus Symbiosis International (Deemed University) Pune Symbiosis Law School Nagpur Campus Symbiosis International (Deemed University) Pune India Assistant Professor Computer Engg. VIIT Pune Maharashtra Assistant Professor Computer Engg. VIIT Pune Maharashtra India Department of Computer Engineering Genba Sopanrao Moze College of Engineering Balewadi Pune Maharashtra Department of Computer Engineering Genba Sopanrao Moze College of Engineering Balewadi Pune Maharashtra India Department of Polytechnic Dr. Vishwanath karad World Peace University Pune Maharashtra Department of Polytechnic Dr. Vishwanath karad World Peace University Pune Maharashtra India Tolani Maritime Institute Induri Talegaon Dabhade Maharashtra Tolani Maritime Institute Induri Talegaon Dabhade Maharashtra India Department of Computer Engineering Vishwakarma Institute of Technology Bibwewadi Pune Department of Computer Engineering Vishwakarma Institute of Technology Bibwewadi Pune India

ISBN: (纸本)9798400709418

the identification of plant leaf diseases is a essential section of agriculture, serving as a critical measure in mitigating the reduction in crop productivity. this study presents a novel methodology for improving the precision of plant leaf disease detection. this is achieved by employing a hybrid architecture that combines Convolutional Neural Networks (CNNs) and DenseNet models. the main aim of our study is to enhance the accuracy and effectiveness of disease detection by leveraging the combined capabilities of Convolutional Neural Networks (CNNs) and DenseNet. these two deep learning architectures are recognized for their proficiency in extracting and reusing features. In this study, we conduct a comparative analysis of our proposed hybrid model by evaluating its performance against traditional Convolutional Neural Networks (CNNs) as well as other advanced models such as Recurrent Neural Networks (RNNs) and Capsule Networks (CapsNets). the findings of our study demonstrate that the hybrid model, combining CNN and DenseNet architectures, showed an accuracy rate of 98.79%. this performance surpassed that of other models under comparison. the hybrid model demonstrated higher levels of precision, recall, F1 score, and AUC-ROC values, shows its effectiveness in the identification of plant leaf diseases. this study highlights the potential of hybrid deep learning models in the domain of precision agriculture and emphasizes the importance of integrating diverse architectural paradigms for accurate disease identification. the research conducted in this study makes a significant contribution to the progress of agricultural technology by providing a dependable mechanism for the early detection of diseases. As a result, this research contributes to the enhancement of crop health and the promotion of food security.

关键词： Image processing

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RNC: Efficient RRAM-aware NAS and Compilation for DNNs on Resource-Constrained Edge Devices

RNC: Efficient RRAM-aware NAS and Compilation for DNNs on Re...

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IEEE international conference on Computer Design: VLSI in Computers and Processors, (ICCD)

作者： Kam Chi Loong Shihao Han Sishuo Liu Ning Lin Zhongrui Wang Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong China ACCESS - AI Chip Center for Emerging Smart Systems Hong Kong Science Park Hong Kong China School of Astronautics Harbin Institute of Technology Harbin China School of Microelectronics Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350380408

ISBN: (纸本)9798350380415

Computing-in-memory (CIM) is an emerging computing paradigm, offering noteworthy potential for accelerating neural networks with high parallelism, low latency, and energy efficiency compared to conventional von Neumann architectures. However, existing research has primarily focused on hardware architecture and network co-design for large-scale neural networks, without considering resource constraints. In this study, we aim to develop edge-friendly deep neural networks (DNNs) for accelerators based on resistive random-access memory (RRAM). To achieve this, we propose an edge compilation and resource-constrained RRAM-aware neural architecture search (NAS) framework to search for optimized neural networks meeting specific hardware constraints. Our compilation approach integrates layer partitioning, duplication, and network packing to maximize the utilization of computation units. the resulting network architecture can be optimized for either high accuracy or low latency using a one-shot neural network approach with Pareto optimality achieved through the Non-dominated Sorted Genetic Algorithm II (NSGA-II). the compilation of mobile-friendly networks, like Squeezenet and MobilenetV3 small can achieve over 80% of utilization and over 6x speedup compared to ISAAC-like framework with different crossbar resources. the resulting model from NAS optimized for speed achieved 5x-30x speedup. the code for this paper is available at https://***/ArChiiii/rram_nas_comp_pack.

关键词： Computer architecture parallel processing Network architecture In-memory computing Solids Hardware Common Information Model (computing) Neural architecture search Low latency communication Genetic algorithms

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Mitigating Data Hotspots in Distributed Cloud Storage Architecture

Mitigating Data Hotspots in Distributed Cloud Storage Archit...

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Computer, Big Data and Artificial Intelligence (ICCBD+AI), international conference on

作者： Junjie Li Haodong Zou Wangdong Wu Lei Wang Lichen Wang Wenpan Liu Mei Yan State Grid Electric Power Research Institute Co. Ltd Nanjing China Information & Telecommunication Branch State Grid Jiangsu Electric Power Co. Ltd. Nanjing China State Grid Information & Telecommunication Branch

ISBN: (数字)9798331533991

ISBN: (纸本)9798331534004

Cloud storage is a vital component of cloud architecture, often utilizing distributed key-value stores like Amazon S3 and Google Cloud Storage for managing da-ta and metadata. these systems distribute data across nodes using key range or consistent hashing, but they face challenges such as load imbalance and limited parallelism due to uneven data distribution and varying node performance. Cur-rent implementations, such as MongoDB, address these imbalances by migrating data between nodes but often neglect the characteristics of the underlying data structures, leading to increased overhead from costly delete and insert operations. To address these issues, this design leverages the properties of the LSM tree, a commonly used storage engine, to optimize data migration. the approach intro-duces hot zone prediction using nonlinear regression to accurately identify data hotspots based on key characteristics, insertion time, and TTL. A storage engine-aware migration system is developed to migrate grouped SSTable files rather than individual key-value pairs, significantly reducing migration overhead. Additionally, the data migration I/O process is offloaded using the NVMe-oF protocol, minimizing CPU involvement and preserving node performance. Implemented on mongo-rocks, this solution improves load balancing by directly moving SSTable files across nodes, enhancing efficiency and reducing performance degradation in distributed key-value stores.

关键词： Degradation Cloud computing Protocols Distributed databases parallel processing Metadata Load management Optimization Faces Engines

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PN-AMLTrans: A Semantic Classification Method Based on parallel Network of Transformer and Attention Perception Mechanism

PN-AMLTrans: A Semantic Classification Method Based on Paral...

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Computer Vision, Image and Deep Learning (CVIDL), international conference on

作者： Yuanjun Tan Zengzhao Chen Quanling Liu Gaoyang Ji Tingting Liu Hai Liu Faculty of Artificial Intelligence in Education Central China Normal University Wuhan P.R China National Engineering Research Center for E-Learning China Central China Normal University Wuhan P.R China School of Education Hubei University Wuhan P.R China National Engineering Research Center for E-Learning Central China Normal University Wuhan P.R China

ISBN: (数字)9798350373820

ISBN: (纸本)9798350373837

In recent years, pre-trained language models based on Transformer have brought significant breakthroughs to natural language processing (NLP) tasks. their outstanding performance in general text understanding enables such models to handle a variety of tasks. However, large language models exhibit problems such as the inability to provide a definitive label for topic identification tasks after interactive prompts and difficulties in controlling the label range. In specific application scenarios, the performance of large models cannot accurately perceive the demands of the tasks due to insufficient data. To address this issue, we propose a text semantic classification method called PNAMLTrans. PN-AMLTrans mainly consists of three modules: Non-Adjacent Sequence Learning Module, AMLNet, and AvgSoftmax Classification Module. In model computation, we first use the BERT pre-trained model to learn the initial features of the text, extracting complete semantic representations of the text as much as possible. Next, we utilize the deep residual connection mechanism to serially connect the Transformer Encoder for learning word embedding semantic matrices. Simultaneously, the attention scores from each layer of the Transformer Encoder are input into AMLNet to extract the mapping relationship between attention scores and classification clues. Finally, effective classification with key weight amplification is achieved through joint weighted calculation using average pooling and Softmax. Experimental results show that the model achieves $98.1 \%$ generalization performance on the Chinese topic recognition dataset thUCNews and $83.5 \%$ state-of-the-art performance on the English topic recognition dataset arXiv-10, validating the effectiveness of our method across different languages and demonstrating the effectiveness of the PN-AMLTrans model in specific applications.

关键词： Image recognition Computational modeling Large language models Semantics Bidirectional control Transformers Feature extraction

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Preface

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2003年 2542卷 v-vi页

作者： Dimov, Ivan Lirkov, Ivan Margenov, Svetozar Zlatev, Zahari Bulgarian Academy of Sciences Central Laboratory for Parallel Processing Acad. G. Bonchev bl. 25A Sofia Bulgaria National Environmental Research Institute Frederiksborgvej 399 P.O. Box 358 Roskilde Denmark

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international conference on Chemical and Bioprocess Engineering

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IOP conference Series: Earth and Environmental Science 2016年第1期36卷

作者： Rosalam Sarbatly Awang Bono Chu Chi Ming tham Heng Jin Noor Maizura Ismail Zykamilia Kamin Universiti Malaysia Sabah (UMS

the 5th international conference on Chemical and Bioprocess Engineering (ICCBPE) was organized by the Chemical Engineering Programme of the Faculty of Engineering, Universiti Malaysia Sabah. the primary purpose of ICCBPE 2015 was to discuss bridging technologies from laboratory and pilot scales to commercial scales for manufacturing advanced products by involving innovative processing of biomass and feedstock suitable for SME's. this event serves as a platform for researchers, engineers, academicians as well as industrial professionals from countries such as the United State of America, Taiwan, Korea, Iran, thailand and Malaysia to present their research and development activities in chemical engineering sciences and applications. During the conference, a total of 81 manuscripts under the Oil and Gas, Waste, Biomass and Biofuels, and Environment categories were presented and discussed in a series of parallel presentations. We are honoured to have distinguished keynote speakers participating in this event. they were Prof. Dr. Ahmad Fauzi Ismail, the founding Director of Advanced Membrane Technology Research Center (AMTEC) (Universiti Teknologi Malaysia, Malaysia), Prof. Dr. Ghasem D. Najafpour (Chairman of the Biotechnology Research Center, Babol Noshirvani University of Technology, Iran) and Prof. Dr. Shizhong Li (Executive Director of MOST-USDA Joint Research Center for Biofuels, China). Last but not least, we are grateful to all of the reviewers for maintaining the standards and quality of the manuscripts throughout the reviewing process. Kota Kinabalu, Sabah April 2016 Chairman : Professor Ir. Dr. Rosalam Sarbatly Chief Editor : Professor Dr. Awang Bono Editorial Team : Associate Professor Dr. Chu Chi Ming Dr. tham Heng Jin Dr. Noor Maizura Ismail Ms. Zykamilia Kamin Universiti Malaysia Sabah (UMS)

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