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2023 IEEE Biomedical Circuits and Systems Conference, BioCAS 2023

作者： Beyene, Abel Wu, ZhongPan Hammad, Karim Ghafar-Zadeh, Ebrahim Magierowski, Sebastian York University Electrical Engineering and Computer Science Toronto Canada Technology and Maritime Transport Arab Academy for Science Cairo Egypt

ISBN: (纸本)9798350300260

Hand-sized DNA sequencers have demonstrated excellent scientific utility. They also present exciting opportunities for the expansion of DNA analysis to a diverse range of uses and users. But this potential is currently blunted by mini-sequencers' lack of on-board computing. In this paper we explore the potential of embedded processors to address this gap. We outline the general hardware and software organization of existing systems and identify a deep learning bioinformatics task as an exemplary workload for processors embedded in miniature sequencers. We then describe a candidate multicore RISC-V arrangement and experimentally study the relative performance, power, and cost trade-offs for alternate configurations of this arrangement. The experiments show no saturation in performance scaling up to at least 6 cores with maximum throughput and efficiency achieved with 512KB and 128KB L2 cache sizes respectively. © 2023 IEEE.

关键词： DNA sequencing embedded computing machine learning multicore RISC-V

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DWT-SVD Based Image Steganography Using Threshold Value Encryption Method

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computers, Materials & Continua 2022年第8期72卷 3299-3312页

作者： Jyoti Khandelwal Vijay Kumar Sharma Dilbag Singh Atef Zaguia School of Computing and Information Technology(SCIT) Manipal University JaipurJaipur302004India School of Electrical Engineering and Computer Science Gwangju Institute of Science and TechnologyGwangju61005Korea Department of Computer Science College of Computers and Information Technology Taif University11099Saudi Arabia

Digital image steganography technique based on hiding the secret data behind of cover image in such a way that it is not detected by the human visual *** paper presents an image scrambling method that is very useful for grayscale secret *** this method,the secret image decomposes in three parts based on the pixel’s threshold *** division of the color image into three parts is very easy based on the color channel but in the grayscale image,it is difficult to *** proposed image scrambling method is implemented in image steganography using discrete wavelet transform(DWT),singular value decomposition(SVD),and sorting *** is no visual difference between the stego image and the cover *** extracted secret image is also similar to the original secret *** proposed algorithm outcome is compared with the existed image steganography *** comparative results show the strength of the proposed technique.

关键词： Image steganography threshold value sorting discrete wave transformation singular value decomposition color band division permutation

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The hydrogen peroxide sensors based on localized surface plasmon resonance in Ag-PVA nanocomposite 3

The hydrogen peroxide sensors based on localized surface pla...

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3rd International Conference on Laser, Optics, and Optoelectronic technology, LOPET 2023

作者： Yang, Zihan Liu, Zhiyue Hu, Haifeng School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (纸本)9781510667624

In this work, a chemical sensor for concentration measurement of hydrogen peroxide is proposed based on localized surface plasmon resonance (LSPR). The silver nanoparticles coated by polyvinyl alcohol (PVA) have been prepared by the reduction process of silver nitrate (AgNO3) in aqueous PVA matrix. The LSPR wavelength of the Ag-PVA nanocomposites is 420nm, as measured by ultraviolet -visible spectrometer. When hydrogen peroxide is added into the solution of Ag-PVA nanocomposites, the silver nanoparticles become degradation during the decomposition of hydrogen peroxide. In this process, the light absorption caused by LSPR effect is reduced obviously. Based on this mechanism, the concentration of hydrogen peroxide can be determined by measuring the change of transmission power through the Ag-PVA nanocomposites. In our experiments, the threshold detection level of the sensor is less than 10-3M. Besides the chemical sensing applications, the proposed system can be also used to analyze the generation process of silver nanostructures under UV light illumination. © 2023 SPIE.

关键词： Hydrogen peroxide

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Dynamic Planning for LLM-based Graphical User Interface Automation

Dynamic Planning for LLM-based Graphical User Interface Auto...

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2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Zhang, Shaoqing Zhang, Zhuosheng Chen, Kehai Ma, Xinbei Yang, Muyun Zhao, Tiejun Zhang, Min School of Computer Science and Technology Harbin Institute of Technology China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China

ISBN: (纸本)9798891761681

The advent of large language models (LLMs) has spurred considerable interest in advancing autonomous LLMs-based agents, particularly in intriguing applications within smartphone graphical user interfaces (GUIs). When presented with a task goal, these agents typically emulate human actions within a GUI environment until the task is completed. However, a key challenge lies in devising effective plans to guide action prediction in GUI tasks, though planning have been widely recognized as effective for decomposing complex tasks into a series of steps. Specifically, given the dynamic nature of environmental GUIs following action execution, it is crucial to dynamically adapt plans based on environmental feedback and action history. We show that the widely-used ReAct approach fails due to the excessively long historical dialogues. To address this challenge, we propose a novel approach called Dynamic Planning of Thoughts (D-PoT) for LLM-based GUI agents. D-PoT involves the dynamic adjustment of planning based on the environmental feedback and execution history. Experimental results reveal that the proposed D-PoT significantly surpassed the strong GPT-4V baseline by +12.7% (34.66% → 47.36%) in accuracy. The analysis highlights the generality of dynamic planning in different backbone LLMs, as well as the benefits in mitigating hallucinations and adapting to unseen tasks. Code is available at https://***/sqzhang-lazy/D-PoT. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Hybrid Edge-Cloud Approach for Renewable Energy Management Using Deep Learning with Predictive Analytics

Hybrid Edge-Cloud Approach for Renewable Energy Management U...

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2024 IEEE International Conference on Recent Advances in science and engineering technology, ICRASET 2024

作者： Rekha, V. Sharmila, N. Komala, M. Pavithra, G.S. Mallikarjunaswamy, S. Naveen, K.B. Shilpa, M. Sheela, S. Department of Computer Science and Engineering Bangalore India Jss Science and Technology University Dept of Electronics and Electrical Engineering Mysuru India Sjb Institute of Technology Dept of Electronics and Communication Engineering Bengaluru India Jss Academy of Technical Eduction Dept of Computer Science and Engineering Bengaluru India Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India Bgs Institute of Technology Adichunchanagiri University Dept of Electronics and Electrical Engineering BG Nagara India Bengaluru Institute of Technology Dept of Information Science and Engineering Bengaluru India Global Academy of Technology Dept of Computer Sceinec and Engineering Bengaluru India

ISBN: (纸本)9798350388602

The integration of renewable energy sources into smart grids requires advanced management systems to optimize energy production, storage, and distribution. Conventional methods like Static Energy Allocation (SEA) and Rule-Based Control (RBC) struggle with real-time adaptability, leading to inefficiencies in handling the variable nature of renewable energy. SEA and RBC lack the predictive capabilities needed to effectively manage dynamic energy generation, resulting in increased energy losses and operational *** paper proposes a Hybrid Edge-Cloud Renewable Energy Management System (HECREMS) using Deep Learning with Predictive Analytics (DLPA). HECREMS integrates edge computing for real-time data processing with cloud computing for enhanced predictive analytics, providing a robust framework for efficient energy management. The proposed system leverages the low latency of edge devices and the computational power of the cloud to improve energy forecasting and decision-making. HECREMS outperforms traditional SEA and RBC methods, achieving a 0.25% improvement in energy utilization efficiency, a 0.20% reduction in operational costs, and a 0.30% increase in system responsiveness. By addressing the drawbacks of conventional approaches, HECREMS demonstrates significant advancements in the efficient and sustainable management of renewable energy within smart grids. © 2024 IEEE.

关键词： Cloud analytics

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On Robust Cross-view Consistency in Self-supervised Monocular Depth Estimation

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Machine Intelligence Research 2024年第3期21卷 495-513页

作者： Haimei Zhao Jing Zhang Zhuo Chen Bo Yuan Dacheng Tao School of Computer Science University of SydneySydney2008Australia Shenzhen International Graduate School Tsinghua UniversityShenzhen518055China School of Information Technology&Electrical Engineering University of QueenslandBrisbane4072Australia

Remarkable progress has been made in self-supervised monocular depth estimation (SS-MDE) by exploring cross-view consistency, e.g., photometric consistency and 3D point cloud consistency. However, they are very vulnerable to illumination variance, occlusions, texture-less regions, as well as moving objects, making them not robust enough to deal with various scenes. To address this challenge, we study two kinds of robust cross-view consistency in this paper. Firstly, the spatial offset field between adjacent frames is obtained by reconstructing the reference frame from its neighbors via deformable alignment, which is used to align the temporal depth features via a depth feature alignment (DFA) loss. Secondly, the 3D point clouds of each reference frame and its nearby frames are calculated and transformed into voxel space, where the point density in each voxel is calculated and aligned via a voxel density alignment (VDA) loss. In this way, we exploit the temporal coherence in both depth feature space and 3D voxel space for SS-MDE, shifting the “point-to-point” alignment paradigm to the “region-to-region” one. Compared with the photometric consistency loss as well as the rigid point cloud alignment loss, the proposed DFA and VDA losses are more robust owing to the strong representation power of deep features as well as the high tolerance of voxel density to the aforementioned challenges. Experimental results on several outdoor benchmarks show that our method outperforms current state-of-the-art techniques. Extensive ablation study and analysis validate the effectiveness of the proposed losses, especially in challenging scenes. The code and models are available at https://***/sunnyHelen/RCVC-depth.

关键词： 3D vision depth estimation cross-view consistency self-supervised learning monocular perception

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Vulnerabilities and Challenges in the Development of PUF-Based Authentication Protocols on FPGAs: A Brief Review 7

Vulnerabilities and Challenges in the Development of PUF-Bas...

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7th IEEE Conference on Dependable and Secure Computing, DSC 2024

作者： Yavuz, Sinan Naroska, Edwin Daniel, Kai University of Siegen Department of Electrical Engineering and Computer Science Siegen Germany Niederrhein University of Applied Sciences Department of Electrical Engineering and Computer Science Krefeld Germany Institute of Measurement and Sensor Technology University of Applied Sciences Ruhr West Mülheim Germany

ISBN: (纸本)9798331540289

The security of IoT (Internet of Things) devices and the protection of sensitive information processed by these devices such as personal data, sensor values, process-related information is an important and difficult challenge. A major task in IoT communication is secure identification of devices. Unfortunately, traditional cryptographic methods are often not suitable for IoT devices due to their limited hardware resources. However, typical methods are computationally intensive, require a large amount of memory, and have a high-power consumption. On the other hand, Physical Unclonable Functions (PUFs) are low-cost and lightweight hardware-based primitives that can be used as a security component to protect data against third parties and thus increase security of a device. In this paper, the challenges and vulnerabilities in the development of PUF-based authentication protocols are presented. To this end, a security analysis of different approaches known from literature are discussed. Furthermore, possible attack vectors and prevention techniques are also considered. © 2024 IEEE.

关键词： Authentication Protocol

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Joint Optimization of Positioning and Computation Offloading in Multi-UAV MEC Networks for Low Latency Applications

Joint Optimization of Positioning and Computation Offloading...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Wang, Yuhui Farooq, Junaid Ghazzai, Hakim Setti, Gianluca University of Michigan-Dearborn Department of Electrical and Computer Engineering DearbornMI48126 United States King Abdullah University of Science & Technology Computer Electrical and Mathematical Sciences Division Saudi Arabia

ISBN: (纸本)9798350368369

The advent of multi-unmanned aerial vehicle (multi-UAV) networks in mobile edge computing (MEC) introduces dynamic computational topologies where UAVs, acting as mobile edge servers, are tasked with processing data from ground-based user equipment (UE). This paper addresses the dual challenges of optimizing both UAV deployment and task offloading within such networks to minimize communication latency and efficiently utilize UAV resources, which are limited by battery life and processing capabilities. We propose a bi-level optimization framework that simultaneously tackles the placement of UAVs and the distribution of computational tasks among them. At the higher level, UAV deployment is optimized to ensure minimal distance to the UEs, thereby reducing latency and energy consumption during data transmission. At the lower level, task offloading is optimized to balance the computational load across the UAV network, considering each UAV's capacity and battery constraints. We demonstrate through extensive simulations the significant improvements in system efficiency, latency, and resilience. This approach not only enhances the performance of UAV-assisted MEC networks but also provides scalable solutions adaptable to various operational scenarios. © 2025 IEEE.

关键词： Drones

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Neural Network-based Genetic Algorithm for Autonomous Boat Pathfinding 6

Neural Network-based Genetic Algorithm for Autonomous Boat P...

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6th IEEE International Conference on Knowledge Innovation and Invention, ICKII 2023

作者： Hamid, Nur Dharmawan, Willy Nambo, Hidetaka Kanazawa University Graduate School of Natural Science and Technology Department of Electrical Engineering and Computer Science Kanazawa920-1192 Japan

ISBN: (纸本)9798350323535

Genetic algorithms become widely used in various optimization as a nature-inspired algorithm. This biological-based algorithm includes three genetic operators: selection, crossover or recombination, and mutation. In this study, the genetic operator processed neural network parameters, including the weight and bias. Three-ray sensors with different directions input data into the neural network. We applied the method to autonomous boats for pathfinding responding to dynamic environment challenges. Generally, autonomous boats face difficulty in path-finding in dynamic environments. To evaluate the method, we experimented with the scenario of the static and dynamic environment (with wave surface) in a three-dimensional simulation platform. The hyperparameter of layer number and mutation rate variation were used. The results showed that the method was useful in pathfinding in static and dynamic environments. © 2023 IEEE.

关键词： Genetic algorithms

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Detecting Unauthorized Drones with Cell-Free Integrated Sensing and Communication

Detecting Unauthorized Drones with Cell-Free Integrated Sens...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Li, Xinyue Behdad, Zinat Topal, Ozan Alp Demir, Özlem Tuǧfe Cavdar, Cicek Kth Royal Institute of Technology Department of Computer Science Stockholm Sweden Tobb Etu Department of Electrical-Electronics Engineering Ankara Turkey

ISBN: (纸本)9798350368369

Integrated sensing and communication (ISAC) boosts network efficiency by using existing resources for diverse sensing applications. In this work, we propose a cell-free massive MIMO (multiple-input multiple-output)-ISAC framework to detect unauthorized drones while simultaneously ensuring communication requirements. We develop a detector to identify passive aerial targets by analyzing signals from distributed access points (APs). In addition to the precision of the sensing, timeliness of the sensing information is also crucial due to the risk of drones leaving the area before the sensing procedure is finished. We introduce the age of sensing (AoS) and sensing coverage as our sensing performance metrics and propose a joint sensing blocklength and power optimization algorithm to minimize AoS and maximize sensing coverage while meeting communication requirements. Moreover, we propose an adaptive weight selection algorithm based on concave-convex procedure to balance the inherent tradeoff between AoS and sensing coverage. Our numerical results show that increasing the communication requirements would significantly reduce both the sensing coverage and the timeliness of the sensing. Furthermore, the proposed adaptive weight selection algorithm can provide high sensing coverage and reduce the AoS by 45% compared to the fixed weights, demonstrating efficient utilization of both power and sensing blocklength © 2025 IEEE.

关键词： Signaling

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