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Design and Hardware Implementation of Bit Length Adjustable Cosine and Sine Generator with CORDIC Algorithm in FPGA

Design and Hardware Implementation of Bit Length Adjustable ...

引用

international conference on Electrical and Electronics Engineering, ELECO

作者： Bahadır Özkılbaç Tevhit Karacalı Atatürk Üniversitesi

ISBN: (数字)9786050113310

ISBN: (纸本)9781665414654

Today, the need for hardware architectures with fast calculation, high usage area efficiency and accuracy are increasing in digital systems. therefore, the algorithm used for the hardware to be designed and the number format in which this algorithm operates are quite important. In this study the hardware that calculates the cosine and sinus trigonometric functions commonly used in digital applications is designed in FPGA with Coordinate Rotation Digital Computer (CORDIC) algorithm which combines the three feature mentioned. the designed hardware operates in a two-complement signed integer format and the bit length is adjustable by the user. thus, a design with the advantages of high speed and usage area efficiency as well as flexibility has been created. Simulation and implementation of the design is done for the Xilinx Artix-7 FPGA model in Vivado software.

关键词： Field programmable gate arrays Hardware Table lookup Signal processing algorithms Silicon compounds Generators Digital computers

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mpiPython: Extensions of Collective Operations

mpiPython: Extensions of Collective Operations

引用

international conference on Information and Computer Technologies (ICICT)

作者： Judah Nava Jaden Jinu Lee Hanku Lee Computer Science and Information Systems Minnesota State University Moorhead Moorhead MN

ISBN: (数字)9798350385625

ISBN: (纸本)9798350385632

Despite performance limitations due to its interpreted nature, Python remains a dominant language among scientists and engineers. Enhancing its capabilities for parallel programming unlocks significant potential within parallel and cloud computing environments. mpiPython, a Python binding for message-passing interfaces, empowers Python for Single Program Multiple Data (SPMD) execution, enabling efficient parallel computations. Additionally, Python's inherent accessibility and versatility foster a growing demand for scaling and parallelizing it on distributed cloud environments. this paper extends mpiPython, bridging the gap in collective operations for parallel computing. the extension builds upon the original mpiPython's class-based structure, emphasizing two core principles: supporting vanilla Python with MPI and focusing on a C-based CPU-focused implementation. Unlike existing implementations like mpi4py, mpiPython directly interacts with the Python C API, offering greater control. Two new functions, MPI Gather and MPI Reduce, significantly improve efficiency and streamline collective operations between working nodes. the results demonstrate mpiPython's ability to perform at the level of other libraries while prioritizing a simple implementation accessible to a broad range of users.

关键词： Cloud computing Technological innovation Reviews parallel programming parallel processing Performance gain Libraries

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Research on Environment Perception and Path Planning Modeling of UAV Navigation Guided by Deep Learning

Research on Environment Perception and Path Planning Modelin...

引用

international conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Wenyuan Yang School of Software Henan University Kaifeng China

ISBN: (数字)9798350350760

ISBN: (纸本)9798350350777

this paper proposes a deep learning-based environment perception and path planning modeling method for UAV navigation. First, this paper uses binocular vision system to obtain environmental information, and uses deep learning algorithm for image processing and feature extraction to realize real-time perception of the surrounding environment. Secondly, aiming at the local path planning problem, this paper designs an algorithm based on deep reinforcement learning to improve the computing efficiency and achieve fast and accurate path planning through parallel computing technology. In addition, this paper also proposes an autonomous navigation strategy based on deep learning to enable drones to navigate autonomously in complex environments. Finally, simulation experiments are carried out to verify the effectiveness and superiority of the proposed algorithm. the experimental results show that the proposed method can realize efficient and accurate environment perception and path planning in various scenarios, and significantly improve the navigation performance of UAVs. the research in this paper not only enriches the application of deep learning in the field of UAV navigation, but also provides a valuable reference for the research in related fields.

关键词： Deep learning Accuracy Navigation Machine vision parallel processing Autonomous aerial vehicles Path planning Real-time systems Autonomous robots Information systems

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Detection of Unstructured Sensitive Data Based on a Pre-Trained Model and Lattice Transformer

Detection of Unstructured Sensitive Data Based on a Pre-Trai...

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international conference on Artificial Intelligence and Big Data (ICAIBD)

作者： Feng Jin Shaozhi Wu Xingang Liu Han Su Miao Tian Yangtze River Delta Research Institute of University of Electronic Science and Technology of China (Quzhou) University of Electronic Science and Technology of China Quzhou China University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350385106

ISBN: (纸本)9798350385113

Sensitive data often contains a significant amount of unstructured information, posing great challenges to its processing. When dealing with unstructured data, effectively utilizing the structure of this sensitive data without a fixed format can greatly enhance the accuracy of sensitive data detection. therefore, in this paper, we have developed and evaluated a Chinese sensitive data detection method for unstructured data using a pre-trained model and a Lattice-Transformer structure. this method leverages the lattice structure to fully utilize the structural information of sensitive data, overcomes the complexity and uncertainty of unstructured information through the powerful parallel computing capability of the transformer, and benefits from the strong language representation ability of the pre-trained model, enabling the method to possess strong detection capabilities for unstructured sensitive data. the outcomes of the experiment demonstrate that the model is capable of adeptly managing sensitive data in Chinese, regardless of its unstructured nature, type, or provenance.

关键词： Training Uncertainty Computational modeling Lattices parallel processing Big Data Transformers

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Audio signal Enhancement based on amplitude and phase deep learning model

Audio signal Enhancement based on amplitude and phase deep l...

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international conference on Advanced Technologies for Signal and Image processing (ATSIP)

作者： Mahrez Benneila Salah Hadji Line Mouhamed Anouar Ben Messouad National Engineers School of Tunis Labo Signal Images et Technologies de l’Information (LR-SITI-ENIT) Tunis Tunisia CERTn Borj-Hamm-lif Tunis labo of nano-materials and Energy(lanser) Tunis Tunisia

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

In this work, we apply an approach based on simultaneous enhancement of the amplitude and phase time-frequency representation of a composite audio signal with two datasets, namely the Voice Bank+Demand and the NTCD TIMIT datasets. the approach is based on an encoder and a decoder connected by transformers having a convolution module. the encoder uses both amplitude and phase spectra as input, while the decoder has two parts one of parallel amplitude mask and the other is a phase decoder. thus allowing direct recovery of clean amplitude spectra and also clean enveloped phase spectra, its activation function is a sigmoid learnable and it estimates the phase in parallel. Like all neural networks there are losses at several levels such as: amplitude, phase, and short-term complex, without forgetting the temporal waveforms. All these losses are used jointly in training the model implemented, the validation of this model is done on the two datasets and the experimental results gave a PESQ of 3.5 for the Voice Banktdemand and a PESQ of 3.22 for NTCD-TIMIT.

关键词： Training Deep learning Time-frequency analysis Convolution Image processing Neural networks Speech enhancement

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Inverse dynamics of speech motor control 93

Inverse dynamics of speech motor control

引用

Proceedings of the 7th international conference on Neural Information processing Systems

作者： Makoto Hirayama Eric Vatikiotis-Bateson Mitsuo Kawato ATR Human Information Processing Research Laboratories Kyoto Japan ATR Human Information Processing Research Laboratories Kyoto Japan and Laboratory of Parallel Distributed Processing Research Institute for Electronic Science Hokkaido University Sapporo Hokkaido Japan

Progress has been made in computational implementation of speech production based on physiological data. An inverse dynamics model of the speech articulator's musculo-skeletal system, which is the mapping from articulator trajectories to electromyographic (EMG) signals, was modeled using the acquired forward dynamics model and temporal (smoothness of EMG activation) and range constraints. this inverse dynamics model allows the use of a faster speech motor control scheme, which can be applied to phoneme-to-speech synthesis via musclo-skeletal system dynamics, or to future use in speech recognition. the forward acoustic model, which is the mapping from articulator trajectories to the acoustic parameters, was improved by adding velocity and voicing information inputs to distinguish acoustic parameter differences caused by changes in source characteristics.

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Deep learning with Elastic Averaging SGD 15

Deep learning with Elastic Averaging SGD

引用

Annual conference on Neural Information processing Systems

作者： Sixin Zhang Anna Choromanska Yann LeCun Courant Institute NYU Center for Data Science NYU & Facebook AI Research

ISBN: (纸本)9781510825024

We study the problem of stochastic optimization for deep learning in the parallel computing environment under communication constraints. A new algorithm is proposed in this setting where the communication and coordination of work among concurrent processes (local workers), is based on an elastic force which links the parameters they compute with a center variable stored by the parameter server (master). the algorithm enables the local workers to perform more exploration, i.e. the algorithm allows the local variables to fluctuate further from the center variable by reducing the amount of communication between local workers and the master. We empirically demonstrate that in the deep learning setting, due to the existence of many local optima, allowing more exploration can lead to the improved performance. We propose synchronous and asynchronous variants of the new algorithm. We provide the stability analysis of the asynchronous variant in the round-robin scheme and compare it with the more common parallelized method ADMM. We show that the stability of EASGD is guaranteed when a simple stability condition is satisfied, which is not the case for ADMM. We additionally propose the momentum-based version of our algorithm that can be applied in both synchronous and asynchronous settings. Asynchronous variant of the algorithm is applied to train convolutional neural networks for image classification on the CIFAR and ImageNet datasets. Experiments demonstrate that the new algorithm accelerates the training of deep architectures compared to DOWNPOUR and other common baseline approaches and furthermore is very communication efficient.

关键词： Image classification Communication Research Asynchronous algorithms Steady state Stability analysis Personnel ELASTIC parallel processing (COMPUTERS) Machine Learning

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VLSI Secure communication: Advancements in Blowfish Algorithm for Secure Data Transmission

VLSI Secure communication: Advancements in Blowfish Algorith...

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international conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： R. Anusha Padmavathi K. S. Dhanalakshmi K. Kalai Selvi Department of Electronics and Communication Engineering Government College of Engineering Tirunelveli Tamilnadu India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Virudhunagar Tamilnadu India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

In the rapidly evolving field of Very Large-Scale Integration (VLSI) systems, securing communication channels is of paramount importance due to the increasing prevalence of cyber threats. this paper presents Modified Blowfish (MBF) algorithm employed for secured communication in VLSI systems. While the traditional Blowfish algorithm delivers a superior balance between security and performance, the improved version focuses on further improving encoding speed, reducing power consumption and reducing hardware footprint, which are important factors for VLSI implementations. the novel modifications are introduced in key expansion and encryption processes to increase the throughput and efficiency of the algorithm when implemented in hardware. the MBF algorithm leverages parallel processing capabilities and optimized data paths to achieve higher performance metrics compared to the traditional Blowfish implementation. Extensive simulations and hardware synthesis results demonstrate that the improved algorithm outperforms existing encryption standards in terms of speed and energy efficiency, making it ideal for real-time secure communications in resource-constrained VLSI environments.

关键词： Power demand Very large scale integration parallel processing throughput Hardware Real-time systems Energy efficiency

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VLSI phase locking architectures for feature linking in multiple target tracking systems 93

VLSI phase locking architectures for feature linking in mult...

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Proceedings of the 7th international conference on Neural Information processing Systems

作者： Andreas G. Andreou thomas G. Edwards Department of Electrical and Computer Engineering The Johns Hopkins University Baltimore MD Department of Electrical Engineering The University of Maryland College Park MD

Recent physiological research has shown that synchronization of oscillatory responses in striate cortex may code for relationships between visual features of objects. A VLSI circuit has been designed to provide rapid phase-locking synchronization of multiple oscillators to allow for further exploration of this neural mechanism. By exploiting the intrinsic random transistor mismatch of devices operated in subthreshold, large groups of phase-locked oscillators can be readily partitioned into smaller phase-locked groups. A multiple target tracker for binary images is described utilizing this phase-locking architecture. A VLSI chip has been fabricated and tested to verify the architecture. the chip employs Pulse Amplitude Modulation (PAM) to encode the output at the periphery of the system.

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Enhancing Object Detection in Low Light Environments Using Image Enhancement Techniques and YOLO architectures

Enhancing Object Detection in Low Light Environments Using I...

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IEEE international conference on Multi Topic

作者： Syed Muhammad Ammar Ali Jaffri Munim ul Haq Muhammad Farhan Dhanani School of Science and Engineering Habib University Karachi Pakistan

ISBN: (数字)9798331507213

ISBN: (纸本)9798331507220

this study analyzes how YOLO structures and image enhancement techniques can improve object detection in low light conditions. In order to overcome the difficulties caused by glare, noise, and dim lighting, the study makes use of the Exclusively Dark (ExDark) dataset, which covers a wide variety of low light situations. the research showed significant enhancements in object detection precision through the utilization of Convolutional Neural Networks (CNN) and pre-processing techniques. A comparative analysis of YOLO versions v7, v8, and v9 is included in the paper, highlighting the superior performance of YOLO v7 when combined with Enlighten GAN, achieving a mAP 0.5 of 73.7%. the results demonstrate how useful preprocessing methods are when used with YOLO algorithms to provide reliable object detection in difficult low-light situations.

关键词： YOLO Noise Lighting Generative adversarial networks Convolutional neural networks Reliability Image enhancement

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