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A Dynamic Reconfiguration Scheme for embedded System Based on Multi-core DSP 7

A Dynamic Reconfiguration Scheme for Embedded System Based o...

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2020 7th international conference on computer-Aided Design, Manufacturing, modeling and simulation, CDMMS 2020

作者： Liu, Xiaojian Liu, Shuo Lv, Hao Computing Technique Research Institute Aviation Industry Corporation Xi'an China

High-performance embedded system is needed in high-speed and accurate avionics control systems, but its hardware resources are limited. It is of great significance to reuse hardware resource through static or dynamic reconfiguration. A dynamic local reconfiguration scheme is designed based on c6678 isomorphic multi-core DSP to realize the dynamic reconfiguration of software on a single core under the premise of ensuring the normal operation of the whole system. This reconfiguration scheme can realize the complete switching of software on the whole core and realize the time division multiplexing of multiple algorithms in the same memory space. Experimental results show that the scheme can fully meet the requirements of real-time. © Published under licence by IOP Publishing Ltd.

关键词： embedded systems

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Reinforcement Learning for Control of Human Locomotion in simulation

Reinforcement Learning for Control of Human Locomotion in Si...

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IEEE/RAS-EMBS international conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Andrii Dashkovets Brokoslaw Laschowski Department of Computer Science Ukrainian Catholic University Lviv Ukraine Department of Mechanical and Industrial Engineering University of Toronto Toronto Canada KITE Research Institute Toronto Rehabilitation Institute Toronto Canada

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Control of robotic leg prostheses and exoskeletons is an open challenge. computer modeling and simulation can be used to study the dynamics and control of human walking and extract principles that can be programmed into robotic legs to behave similar to biological legs. In this study, we present the development of an efficient two-layer Q-learning algorithm, with k-d trees, that operates over continuous action spaces and a reward model that estimates the degree of muscle activation similarity between the agent and human state-to-action pairs and state-to-action sequences. We used a human musculoskeletal model acting in a high-dimensional physics-based simulation environment to train and evaluate our algorithm to simulate biomimetic walking. We used imitation learning and artificial biomechanics data to accelerate training via “expert” demonstrations and used experimental human data to compare and validate our predictive simulations, achieving 79% accuracy. Moreover, when compared to the previous state-of-the-art that used deep deterministic policy gradient, our algorithm was significantly more efficient with lower computational and memory storage requirements (i.e., requiring 7 times less RAM and 87 times less CPU compute), which can benefit real-time embedded computing for robot control. Overall, our new two-layer Qlearning algorithm using sequential data for continuous imitation of human locomotion serves as a first step towards the development of bioinspired controllers for robotic prosthetic legs and exoskeletons. Future work will focus on improving the prediction accuracy compared to experimental data and expanding our simulations to other locomotor activities.

关键词： Legged locomotion Training Accuracy Q-learning Biological system modeling Exoskeletons Robot control Random access memory Prediction algorithms Real-time systems

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simulation of Multi-Agent architectures for Fruit and Berry Picking Robot in Active-HDL

Simulation of Multi-Agent Architectures for Fruit and Berry ...

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2020 IEEE international conference on Problems of Infocommunications Science and Technology, PIC S and T 2020

作者： Kiktev, Nikolay Didyk, Anna Antonevych, Maryna National University of Life and Environmental Sciences of Ukraine Department of Automation and Robotic Systems Kyiv Ukraine University of Kyiv Faculty of Information Technology Taras Shevchenko National Kyiv Ukraine

ISBN: (纸本)9781728191775

The paper analyzes approaches to the engineering of multi-agent robotic systems for agricultural production processes, including harvesting. The system is presented in the form of typical state machines for simulating the states of robots with implicitly defined scenarios that allow automatic planning and monitoring of tasks in real time. The structure and the HDL-model of a group control system for autonomous robots and related devices for picking fruits and berries operating as part of a multi-agent group have been formed. In experiments on modeling a group of robots for tasks with implicitly defined scenarios, examples related to the collection of fruits and berries are considered. The simulation results fully confirmed the effectiveness of the proposed algorithms for the automated generation and execution of scenarios. © 2020 IEEE.

关键词： computer aided design

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IoT Enabled Early Detection of Lung Cancer Using CT Scan Images and Machine Learning Techniques

IoT Enabled Early Detection of Lung Cancer Using CT Scan Ima...

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international conference on Signal Processing, Communication, Power and embedded System (SCOPES)

作者： Satya Brata Rath A.V.S Pavan Kumar Vishal Kumar Swain Neelamadhab Padhy Rasmita Panigrahi Tirupati Sahu Department of CSE Gandhi Institute of Engineering and Technology University Gunupur India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Context: The IoT sensors can play a crucial role in enhancing CT scan lung detection, which will help monitor patient vital signs and improve patient comfort. In this disease the lung grows out of control, in recent years, many systems have been developed for the diagnosis of various diseases, among which the early detection of lung cancer is critical for which early detection is crucial for improving treatment outcomes. Objective: The objective is to develop and evaluate deep learning models for lung cancer detection using CT scans (Computed tomography scans). Method and Materials: We trained and tested different architectures of convolutional neural network (CNN) models, including VGG16, ResNet50, Inception-v3, DenseN et121, and an ensemble model combining DenseNet121 + Inception-v3, using a dataset of CT scans. Dataset Descriptions: We have taken an existing dataset of CT scans from the Kaggle repository which contains the different types of chest cancer. The images are in the form of JPGs to fit in the model. The dataset includes 3 chest cancer types large cell carcinoma, Adenocarcinoma, Squamous cell carcinoma, and normal cell. Results: In this experiment research the ensemble (DenseNet121 + Inception - v3) model achieved the highest accuracy 98.25% training and 99.4% testing.

关键词： Deep learning Computed tomography Lungs Computational modeling Microprocessors Lung cancer computer architecture Feature extraction Convolutional neural networks Monitoring

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Mutation Analysis and Model Checking Guided Test Generation for SoC Run-Time Monitors

Mutation Analysis and Model Checking Guided Test Generation ...

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international conference on VLSI Design

作者： Suriya Srinivasan Ranga Vemuri Electrical and Computer Engineering Department University of Cincinnati Cincinnati OH USA

Security verification and validation both before and after fabrication emerged as an important concern in the design of systems-on-chip (SoC) architectures. Assertion-based formal methods are widely for pre-fabrication verification of SoC designs. Assertions denoting the SoC's security policies are formally verified to check if the design satisfies these specifications. In addition, several researchers have proposed to migrate these assertions into silicon as run-time monitors to facilitate continuous run-time security monitoring. However, due to limited observability, post-silicon validation of these run-time monitors requires input sequences that are specifically targeted to testing these monitors. In this paper, we present a methodology to produce a variety of tests for assertion-based run-time monitors. Given temporal logic assertions which denote security properties of an SoC design, the methodology consists of the following steps: (1) introduce mutations into the SoC design model to cause assertion failures, (2) use model checking to produce counterexample traces, (3) generate alternative and longer counterexamples for a mutant, with the help of novel algorithms to enhance the test suite, (4) extract test programs from the counterexamples and winnow repetitive test cases, (5) investigate the code coverage of the design with the help of these multiple tests. This methodology can be used to generate a large number of tests for each security monitor, enhancing the design's code coverage. We demonstrate the methodology for a large OpenRISC-1200 based SoC design. We show the effectiveness of the generated test programs via simulation and FPGA-based emulation.

关键词： Fabrication Codes Model checking Very large scale integration Silicon Security Test pattern generators

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Digital Learning Ecosystem: Online Concept for Engineering Education

Digital Learning Ecosystem: Online Concept for Engineering E...

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international Scientific conference on Power and Electrical Engineering of Riga Technical University (RTUCON)

作者： Anatolijs Zabasta Volodymyr Kazymyr Nadezhda Kunicina Oleksandr Velihorskyi Faculty of Computer Science Information Technology and Energy Riga Technical University Riga Latvia Information and Computer Systems Department Chernihiv Polytechnic National University Chernihiv Ukraine Radio Engineering and Embedded Systems Department Chernihiv Polytechnic National University Chernihiv Ukraine

ISBN: (数字)9798350365771

ISBN: (纸本)9798350365788

This article examines the development of a Digital Learning Ecosystem (DLE) for engineering education, supported by online learning, within the framework of Erasmus+ international educational projects. We hypothesize that a DLE can address the challenges posed by rapid technological changes associated with Industry 4.0 and digital transformation, thus transforming engineering education to meet modern industry needs. Building on the earlier Erasmus+ CybPhys project, which established a Sharing Modelling and simulation Environment (SMSE) for digitalizing online technical courses, the DIGITRANS project aims to enhance this architecture to include remote laboratories through the Sharing Remote Experiment Environment (SREE) platform. The project involves acquiring hardware and open-source software for SREE and establishing two physical remote laboratories. A study conducted in spring 2023, using the Technology Acceptance Model framework, indicated positive student perceptions of SMSE, with students believing it would enhance learning outcomes and expressing intentions to use the platform frequently. The efficacy of the DLE in addressing technological advancement challenges will be further evaluated through post-ante assessments and feedback from prospective employers and stakeholders, focusing on the implementation of industry recommendations and the correlation between pre- and post-implementation survey responses from industry and academic experts.

关键词： Training Surveys Remote laboratories Biological system modeling Ecosystems Hardware Stakeholders Engineering education Kernel Programming profession

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Truth Table Based Intelligent Computing

Truth Table Based Intelligent Computing

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international conference on Mixed Design of Integrated Circuits and systems (MIXDES)

作者： Vladimir Hahanov Eugenia Litvinova Zaza Davitadze Svetlana Chumachenko David Devadze Abdullayev Vugar Hacimahmud Design Automation Department Kharkov National University of Radioelectronics Kharkov Ukraine Department of Computer Sciences Batumi Shota Rustaveli State University Batumi Georgia Computer Engineering Department Azerbaijan State Oil and Industry University Baku Azerbaijan

ISBN: (数字)9788363578268

ISBN: (纸本)9798350375701

Computing architectures for solving design and test problems and managing social processes based on monitoring and vector logical processing of big data as addresses are considered. A model of data structures in the form of a truth table is considered for solving combinatorial problems associated with technical diagnostics, monitoring, and control of cyber-physical and cyber-social space. The problem of transferring the von Neumann architecture into memory and replacing a powerful processor by read-write transactions on logical vectors to reduce energy and latency when simulation logical functionality of any dimension is considered. A truth table is proposed to describe the tested combinations of faults on the input lines. Representing functionality in the form of a structure of smaller elements reduces the memory for storing logical vectors by introducing connections between elements but complicates the simulation algorithm and increases the processing time of the logical circuit. Logic vector functionality from n-inputs has the advantage of simulating more input variables in parallel in a single clock cycle. If there are no memory restrictions, then representing the functionality as a single logical vector is more time efficient and simpler for the algorithm for in-memory simulation, as well as for executing functions in production. Therefore, the use of a logical vector instead of the structure of typical technological elements is a definite step towards smart data structures in-memory computing. If the speed of read-write transactions on memory approaches the performance of logic circuits in the processor, then the next step in the electronic technology market will be the transition from processor logic to vector logic in in-memory computing. Smart data structures for fault-free simulation are represented by a superposition of logical vectors of functionality included in the digital circuit. Examples are given of processing big data as truth table addresses for

关键词： Computational modeling Big Data Data structures In-memory computing Vectors Data models Logic

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Poster Abstract: Text2Net: Transforming Plain Text into Dynamic, Interactive Network simulations

Poster Abstract: Text2Net: Transforming Plain Text into Dyna...

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international Symposium on Information Processing in Sensor Networks (IPSN)

作者： Alireza Marefat Abbaas Alif Mohamed Nishar Ashwin Ashok Georgia State Univerisity Atlanta Georgia USA

ISBN: (数字)9798350362015

ISBN: (纸本)9798350362022

This paper introduces Text2Net, an innovative system designed to transform plain English descriptions into dynamic, interactive network simulations within the Emulator Virtual Engine–Next Generation (Eve-NG) environment. By integrating SOTA technologies from Natural Language Processing (NLP), Large Language Models (LLMs), and proposed adaptor software, Text2Net bridges the technical knowledge gap, enabling both technical and non-technical users to effortlessly create and interact with complex network topologies within a simulation environment. The system architecture combines an intuitive chat interface, GPT4 LLM to interpret user inputs, NLP key-value extraction, a simulation adaptor, and the EVE-NG engine. TextNet democratizes network emulation, empowering educators to efficiently construct simulations for interactive learning. It also benefits industrial prototyping and testing *** CONCEPTS•Applied computing → Interactive learning environments; computer-assisted instruction; IT architectures;•Networks → Network design principles; Programming interfaces; Topology analysis and generation; Logical / virtual topologies; Network manageability; Programmable networks; Network management; Network monitoring.

关键词： Adaptation models Automation Computational modeling systems architecture Transforms Complex networks Natural language processing

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Profiling Dilithium Digital Signature Traces for Correlation Differential Side Channel Attacks 20th

Profiling Dilithium Digital Signature Traces for Correlation...

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20th international conference on embedded computer systems - architectures, modeling, and simulation (SAMOS)

作者： Fournaris, Apostolos P. Dimopoulos, Charis Koufopavlou, Odysseas Res Ctr ATHENA Ind Syst Inst Patras Sci Pk Patras Greece Univ Patras Elect & Comp Engn Dept Rion Campus Patras Greece

ISBN: (纸本)9783030609399;9783030609382

A significant concern for the candidate schemes of the NIST postquantum cryptography standardization project is the protection they support against side-channel attacks. One of these candidate schemes currently in the NIST standardization race is the Dilithium signature scheme. This postquantum signature solution has been analyzed for side channel attack resistance especially against timing attacks. Expanding our attention on other types of side-channel analysis, this work is focused on correlation based differential side channel attacks on the polynomial multiplication operation of Dilithium digital signature generation. In this paper, we describe how a Correlation Power Attack should be adapted for the Dilithium signature generation and describe the attack process to be followed. We determine the conditions to be followed in order for such an attack to be feasible, (isolation of polynomial coefficient multiplication inpower traces) and we create a power trace profiling paradigm for the Dilithium signature scheme executed in embedded systems to showcase that the conditions can be met in practice. Expanding the methodology of recent works that mainly use simulations for power trace collection, in this paper, power trace capturing and profiling analysis of the signature generation process was succesfully done on a, noisy, Commercial off-the-shelf ARM Cortex-M4 embedded system.

关键词： Postquantum cryptography Side channel attack Secure embedded systems

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Enhancing Traffic Management with Deep Learning Based Vehicle Detection and Scheduling systems

Enhancing Traffic Management with Deep Learning Based Vehicl...

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modeling, simulation & Intelligent Computing (MoSICom), international conference on

作者： Archita Patil Ashwini Raorane Jyoti Kundale Department of Electronics Engineering Ramrao Adik Institute of Technology Navi Mumbai India Department of Information Technology Ramrao Adik Institute of Technology Navi Mumbai India

Traffic congestion is an inescapable issue in contemporary urban life, leading to time wastage and increased accident risks. This paper presents an innovative approach that harnesses machine learning and computer vision to monitor vehicle density on roads and optimize traffic signal timings at intersections. With the escalating number of vehicles on the roads, traffic congestion has become a critical problem. Queue lengths at intersections surge with rising traffic flows, rendering traditional traffic system inefficient. In response, this paper introduces a real-time traffic light control algorithm that adapts to traffic flow. utilizing computer vision and machine learning to discern traffic flow characteristics at signalized road intersections, employing state-of-the-art real-time object detection using a deep Convolutional Neural Network known as YOLO. YOLOcan be implemented on embedded controllers through Transfer Learning, enabling deep neural network capabilities even on limited hardware resources. The proposed system comprises a camera system capturing real-time video feeds of roads, which are then processed by a machine learning model to detect vehicle density. The scheduling system dynamically adjusts traffic signal green times based on observed vehicle density. Furthermore, we conduct an in-depth analysis using various machine learning models to predict traffic patterns. This analysis encompasses a performance comparison of models like K-Nearest Neighbor, random forests, and decision trees to forecast traffic volume with respective accuary of 53.43%, 98.88%, 99.09%. The study also investigates the influence of parameters such as time of day, weather and temperature conditions. The primary objective of our system is to optimize traffic flow by minimizing waiting times at traffic signals, thereby reducing congestion and enhancing the overall commuting experience for drivers.

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