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Perceptual Learning Technology Based on Computer Vision and Machine Learning Algorithms

Perceptual Learning Technology Based on Computer Vision and ...

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international conference on Communication systems and Network Technologies (CSNT)

作者： Tianjian Wang School of Computer Science and Technology Wuhan Qingchuan University Wuhan China

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

To address the issue of low accuracy and poor robustness of perceptual learning in complex scenarios, a new method integrating computer vision and machine learning is adopted, that is, by applying deep neural networks, transfer learning and self-supervised learning, combined with multimodal data fusion strategy, to improve target recognition efficiency and learning ability. First, based on the preprocessing algorithm of improved Gaussian filtering and image segmentation, the quality of image features is improved. Data enhancement methods such as random rotation, flipping, and blurring are used to expand data distribution and improve model adaptability. Secondly, a convolutional neural network (CNN) is utilized in combination with an attention mechanism to extract multi-scale target features, and transfer learning is applied to transfer common features from pre-trained models to reduce dependence on large-scale labeled data. Finally, a contrastive learning framework is constructed to mine the correlation of unlabeled data. Transformer is used to realize the fusion of multimodal data of images and texts, and the model performance is optimized through multi-task learning. The mAP (Mean Average Precision) of the traditional method in dynamic occlusion scenarios is 60.5%, which is relatively weak. This may be because the traditional method cannot fully extract the effective features of the target under the occlusion of complex moving targets. The mAP of the method in this paper is 72.8%, which is higher than that of the traditional method. The perceptual learning method adopted in this paper effectively improves the accuracy and robustness in complex scenarios, and provides reliable technical support for intelligent applications. In the future, it can be combined with edge computing to further optimize the real-time processing capability and promote its application in the fields of unmanned driving and intelligent security.

关键词： Adaptation models Computer vision Image segmentation Machine learning algorithms Computational modeling Transfer learning Feature extraction Data models Robustness real-time systems

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Unguided Machine Learning-Based Computation Offloading for Near-Memory Processing

Unguided Machine Learning-Based Computation Offloading for N...

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international conference on VLSI Design

作者： Satanu Maity Manojit Ghose Avinash Kumar Anol Chakraborty Ankit Chakraborty Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati Assam India Department of Computer Science and Engineering Jorhat Engineering College Assam India

ISBN: (数字)9798331522445

ISBN: (纸本)9798331522452

Recent data-intensive applications encounter memory wall bottlenecks in the traditional processor-centric computing architecture due to the need for frequent and extensive off-chip data movement. The emerging 3D-enabled near-memory processing (NMP) is a potential solution where computation can be offloaded and performed near memory, eliminating the need for expansive off-chip data transfer. Recently, some crucial works in this context have been proposed that use a supervised machine learning approach that requires a well-labeled dataset, which necessitates creating a time-consuming simulation process. Furthermore, their major focus was on profiling and offloading the entire application to the NMP side, causing a sub-optimal performance because of the low processing power on the NMP cores. This paper presents an unguided machine-learning-based approach (UCO) for computation offloading in NMP architecture that offloads only the memory-intensive regions of an application. The approach extracts several application performance characteristics and creates two clusters using them - one for the CPU side and another for the NMP side. The proposed approach's effectiveness is demonstrated through a series of comprehensive experiments with standard simulators and dataintensive benchmark applications. The results indicate that UCO improves performance (1.31x and 1.10x), saves energy (16% and 12%), and reduces off-chip data transfer (78% and 73%) compared to conventional and state-of-the-art approaches.

关键词： Learning systems Energy consumption embedded systems Instruments Computer architecture Machine learning Very large scale integration Benchmark testing Data transfer Standards

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Next-Generation embedded systems with Synergies VLSI with Deep Learning: Design Methodologies, Optimization Techniques, and real-World applications

Next-Generation Embedded Systems with Synergies VLSI with De...

引用

ieee Students' conference on Electrical, Electronics and Computer Science (SCEECS)

作者： G. Kalarani M. Mohanapriya P. Pattunnarajam G. Sasikala J. Sivakumar M. Suresh Department of Electronics and Instrumentation Engineering Jaya Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering SRM Valliammai Engineering College Chennai Tamil Nadu India Department of ECE Sri Venkateswara College of Engineering Sriperumbudur Tamil Nadu India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering St. Joseph’s College of Engineering Chennai Tamil Nadu India Department of Mathematics R.M.D. Engineering College Thiruvallur Tamil Nadu India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

New generation of embedded systems with superior intelligence, energy efficiency, and performance have emerged as a result of the merging of deep learning with Very-Large-Scale Integration (VLSI) technology. Methodologies for design, optimisation strategies, and practical uses of next-generation embedded systems are the foci of this study, which investigates the ways in which VLSI and deep learning might work together. These systems have the potential to transform several industries, such as transportation, medicine, robotics, and the IoT, by harnessing the processing power of deep neural networks with the improvements in semiconductor fabrication. Prior to delving into the advantages of bespoke hardware design for deep learning inference and training, we trace the history of very large scale integration (VLSI) technology and its incorporation with deep learning algorithms. Investigated here are the design techniques that, when applied to very large scale integration (VLSI) architectures like FPGAs and ASICs, allow for the efficient mapping of deep learning models onto these devices. We show case studies that show how these methods work and talk about the trade-offs between performance, power consumption, and adaptability. The development of next-generation embedded systems relies heavily on optimisation approaches. Model compression, quantisation, and pruning are some of the optimisation strategies that we examine; they lessen the memory and computational demands of deep learning models without drastically altering their accuracy. For embedded devices with limited resources, these methods are crucial for implementing deep learning models. Additionally, we explore the practical uses of embedded systems augmented with VLSI and deep learning. By capitalising on the complementary strengths of VLSI and deep learning, applications like autonomous driving, medical imaging, and smart home automation are revolutionising entire industries. In this paper, we examine the desig

关键词： Deep learning Industries Technological innovation embedded systems Design methodology Very large scale integration Hardware Optimization Autonomous vehicles Next generation networking

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Vehicle Detection and Tracking Techniques Using Yolo Detection and Kalman Filter Optimization

Vehicle Detection and Tracking Techniques Using Yolo Detecti...

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Advancement in Data Science, E-learning and Information systems (ICADEIS), international conference on

作者： Ahmad Hunain Indrabayu Amil Ahmad Ilham Department of Informatics Hasanuddin University Makassar Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

This study develops a real-time vehicle detection, tracking, and counting system by integrating the You Only Look Once (YOLO) algorithm with the Kalman Filter. The system is designed to detect multiple types of vehicles, track their movements across frames, and count the number of vehicles crossing a detection line in traffic videos. YOLO is employed for vehicle detection by generating bounding boxes and class labels, while the Kalman Filter ensures robust and stable tracking, even in scenarios with interrupted detections or occlusions. The system was evaluated on real-world traffic videos and demonstrated high accuracy in vehicle detection, achieving a Mean Average Precision (mAP) of 96.7% after 200 training epochs. The system effectively counts vehicles with minimal errors, particularly for larger vehicles like cars and trucks. With an average processing speed of 25 frames per second (FPS), the system is capable of meeting real-time application demands. Additionally, dense traffic conditions with significant vehicle overlaps can impact detection and tracking accuracy. To address these limitations, future work is suggested, including retraining the model using localized datasets to improve performance under specific conditions, integrating complementary sensors such as LIDAR for better spatial awareness, and optimizing the system for deployment on embedded devices. This system has substantial potential for implementation in intelligent transportation applications, such as traffic flow analysis, violation detection, and data-driven transportation management, contributing to advancements in smart city infrastructure.

关键词： YOLO Accuracy Tracking Vehicle detection Transportation Radar tracking real-time systems Kalman filters Videos Meteorology

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Blockchain for Data Traceability: Ensuring Integrity in Digital Ecosystems

Blockchain for Data Traceability: Ensuring Integrity in Digi...

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Computational Intelligence Approaches and applications (ICCIAA), international conference on

作者： Ali Al Maqousi Enrique Arias Jose Antonio Mateo Department of Information Security University of Petra Amman Jordan Department of Information Systems University of Castilla-La Mancha Campus Universitario Albacete Spain

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

In the modern world, where digital ecosystems are shifting towards data-oriented, data must be traceable and highly integrity. This paper aims to define the possibilities of blockchain technology to improve data tracking, emphasizing the supply chain, healthcare, and finance sectors. We explore how blockchain can be embedded in current digital environments, review the literature, and assess real-life examples and potential issues and benefits of blockchain regarding scalability, energy usage, and legal concerns. The study reveals that blockchain can offer a clear, secure, and tamper-proof record of data but at the same time examines the technological and managerial challenges associated with this technology's use.

关键词： Supply chain management Reviews Data integrity Scalability Ecosystems Supply chains Medical services Proof of Work Consensus protocol Security

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TERA: A Simulation Environment for Terrain Excavation Robot Autonomy

TERA: A Simulation Environment for Terrain Excavation Robot ...

引用

ieee international conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR)

作者： Christo Aluckal Roopesh Vinodh Kumar Lal Sean Courtney Yash Turkar Yashom Dighe Youngjin Kim Jake Gemerek Karthik Dantu Computer Science and Engineering University at Buffalo Buffalo NY Moog Constructions Moog Inc. Buffalo NY

ISBN: (数字)9798331516857

ISBN: (纸本)9798331516864

Developing excavation autonomy is challenging given the environments where excavators operate, the complexity of physical interaction and the degrees of freedom of operation of the excavator itself. Simulation is a useful tool to build parts of the autonomy without the complexity of experimentation. Traditional excavator simulators are geared towards high fidelity interactions between the joints or between the terrain but do not incorporate other challenges such as perception required for end-end autonomy. A complete simulator should be capable of supporting real-time operation while providing high fidelity simulation of the excavator(s), the environment, and their interaction. In this paper we present TERA (Terrain Excavation Robot Autonomy), a simulator geared towards autonomous excavator applications based on Unity3D/AGX that provides the extensibility and scalability required to study full autonomy. It provides the ability to configure the excavator and the environment per the user requirements. We also demonstrate realistic dynamics by incorporating a time-varying model that introduces variations in the system’s responses. The simulator is then evaluated with different scenarios such as track deformation, velocities on different terrains, similarity of the system with the real excavator and the overall path error to show the capabilities of the simulation. TERA source will be available at https://***/tera on publication.

关键词： Deformation Scalability Programming Excavation real-time systems Extensibility Complexity theory time-varying systems Autonomous robots

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Reinforcement Learning-Based Optimal Control for Continuous-time Quantized systems via Adaptive Dynamic Programming

Reinforcement Learning-Based Optimal Control for Continuous-...

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ieee SSD international Multi-conference on systems, Signals and Devices

作者： Omar Qasem Weinan Gao Omar R. Daoud Electrical and Computer Engineering Department School of Engineering and Computing American International University Al-Jahra Kuwait State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang Lianning China

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Ahstract- This paper presents a comprehensive investigation into the stability analysis and design conditions for quantizers to ensure the closed-loop stability of continuous-time linear quantized systems, under the conditions derived by the small-gain theorem. The study begins by deriving explicit bounds on the quantizer parameters required for maintaining system stability. Building on this foundation, an optimal controller is designed using the linear quadratic regulator (LQR) framework, providing an efficient data-driven control strategy. To further enhance the system's performance, an adaptive dynamic programming (ADP) algorithm, referred to as the hybrid iteration (HI) method, is developed. This algorithm effectively learns the optimal control policy by leveraging the trajectories of the quantized states and inputs, thereby addressing the challenges posed by quantization constraints. The proposed HI approach combines the advantages of adaptive learning and optimization, making it well-suited for continuous-time systems with limited information. The simulation results confirm that the ADP approach with the provided conditions not only stabilizes the quantized system but also achieves optimal control performance under the specified quantization conditions. This study offers valuable insights and a robust methodological framework for addressing stability and control challenges, with insights to be expanded to continuous-time nonlinear quantized systems, with potential applications in various engineering domains, such as networked systems, robotics and autonomous systems.

关键词： Quantization (signal) Adaptive systems Regulators Simulation Heuristic algorithms Optimal control Stability analysis Dynamic programming Trajectory Thermal stability

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Evaluating the Performance of ieee 802.15.4g for Smart City applications

Evaluating the Performance of IEEE 802.15.4g for Smart City ...

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ieee international conference on Consumer Electronics (ICCE)

作者： Pavithra Arcot Dhivya G Kartheek Inti Nalla Kranthi Kumar Konisetti Murali V Krishna Karuturi Gopal Babu Dennis Joshua D Poonguzhali P IoE & Embedded Systems Group Centre for Development of Advanced Computing Chennai India Department of ECE Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

The demand for Low Power Wide Area Network (LPWAN) solutions stems from the need for low data rate, long-range, and energy-efficient connectivity. LPWAN technologies are ideally suited for Smart City applications, where small amounts of data are transmitted infrequently over extended distances. Proprietary LPWAN technologies like LoRa, Sigfox, and Ingenu often require dependence on a specific company's infrastructure and ecosystem. In contrast, open LPWAN standards offer significant benefits, including enhanced interoperability, cost savings, flexibility, and innovation. They foster market competition, reduce reliance on individual vendors, and support the development of a diverse, compatible ecosystem of IoT devices and services. ieee 802.15.4g is an open LPWAN standard that provides several advantages over proprietary LPWAN technologies. This paper provides a performance analysis of ieee 802.15.4g in outdoor environments. The study assesses the performance by measuring the packet error rate (PER) in various outdoor conditions. The results of the PER measurements are used to establish the maximum communication range between the transmitter and receiver.

关键词： ieee 802.15 Standard Performance evaluation Technological innovation Smart cities Error analysis Transmitters Ecosystems Receivers Standards Low-power wide area networks

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PosturalNet, an Advanced Architecture to Detect Shoplifting at Megastores

PosturalNet, an Advanced Architecture to Detect Shoplifting ...

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international conference on Communication systems and Network Technologies (CSNT)

作者： Mohd Aquib Ansari Shahnawaz Ahmad Arvind Mewada Sushil Kumar Maurya SCSET Bennett University Greater Noida India ITER Siksha ’O’ Anusandhan University Bhubaneswar Odisha

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Human activity recognition (HAR) requires a full understanding of postural dynamics to create sophisticated surveillance systems that specifically detect shoplifting activities. PosturalNet represents a new architectural framework that uses human postural analytics to detect shoplifting incidents in large retail spaces. The proposed method uses a 2D body joint detection algorithm named OpenPose to extract postural information. Subsequently, these postures are pre-processed, and then features such as joint velocities, normalized body joints, magnitudes and angles are extracted. A combination of feature encoding techniques together with dimension reduction strategies maintains vital information while improving system computational efficiency. Testing PosturalNet on synthetic shoplifting data yielded an accurate detection rate of 96.87%. The method demonstrates robust performance in real-time applications which handle occluded environments when applied to practical store surveillance footage.

关键词： Deep learning Accuracy Thermal sensors Feature extraction Video surveillance real-time systems Data models Human activity recognition Reliability Testing

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FAST: Fast Audio Spectrogram Transformer

FAST: Fast Audio Spectrogram Transformer

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Anugunj Naman Gaibo Zhang Department of Computer Science Purdue University West Lafayette Indiana USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In audio classification, developing efficient and robust models is critical for real-time applications. Inspired by the design principles of MobileViT, we present FAST (Fast Audio Spectrogram Transformer), a new architecture that combines convolutional neural networks (CNNs) and transformers to capitalize on the strengths of both. FAST integrates the local feature extraction efficiencies of CNNs with the global context modeling capabilities of transformers, resulting in a model that is powerful yet lightweight, well-suited to a real-time or mobile use case. Additionally, we incorporate Lipschitz continuous attention mechanisms to improve training stability and accelerate convergence. We evaluate FAST on the ADIMA dataset, a multilingual corpus towards real-time profanity and abuse detection, as well as on the more traditional AudioSet. Our results show that FAST achieves state-of-the-art performance on both the ADIMA and AudioSet classification tasks and in some cases surpasses existing benchmarks while using up to 150x fewer parameters.

关键词： Training Transfer learning Transformers real-time systems Stability analysis Safety Convolutional neural networks Speech processing Spectrogram Context modeling

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