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2022 IEEE Global Conference on Computing, Power and Communication Technologies, GlobConPT 2022

作者： Baidya, Dayarnab Krishnan, Athul Bhattacharjee, Mitradip Indian Institute of Science Education and Research Bhopal I-Lab Department of Electrical Engineering and Computer Science Bhopal India

ISBN: (数字)9781665493659

ISBN: (纸本)9781665493659

Wireless systems and Internet of Things (IoT) sensing applications need Low Noise Amplifiers (LNA). However, a simple design is desirable for wearable IoT applications. In this direction, a single-stage LNA having considerable gain, stability, and noise figure (NF) for wearable IoT applications have been designed. An analysis of gain, noise factor, and stability for various values of gate voltage is performed to determine the utility and performance. The transistor used here is a Low Noise Gallium Arsenide FET and lumped components are used for matching at input and output side. It is found that the proposed LNA has 15.689 dB forward gain, a bandwidth of 1600 MHz and 0.672 dB Noise Figure at frequency 2.4 GHz that can be used for sensors operating in LS bands of frequencies. The LNA has been designed with fewer components in order to make it suitable for wearable IoT. © 2022 IEEE.

关键词： Low noise amplifiers

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Real-Time Myanmar Hand Gestures Recognition Using Deep Learning Models 8th

Real-Time Myanmar Hand Gestures Recognition Using Deep Learn...

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8th International Conference on Intelligent Technologies, ICIT 2023

作者： Kyaw, Nwe Ni Mitra, Pabitra Sinha, G.R. Department of Computer Science and Engineering Indian Institute of Technology Kharagpur West Bengal721302 India Indian Knowledge System Lab International Institute of Information Technology Bangalore Karnataka Bengaluru560100 India

ISBN: (纸本)9789819738588

The proposed system for recognizing Myanmar sign language between individuals who are deaf. The aim of this study is to develop deep learning models for the purpose of accurately identifying dynamic hand gesture images depicting 14 classes of Myanmar words and numbers. The present system utilizes a new dataset comprising 7834 images for 14 classes obtained through Webcam. The objective of this study is to enhance the robustness of the new model. Next, we proceed to evaluate the proposed model by comparing them to those of existing convolutional neural network models. These models are generally recognized as effective deep-learning approaches for recognition tasks. The proposed model exhibits a performance level of 96% for both validation and testing accuracies. Furthermore, the experimental finding demonstrates that the model, convolutional layers, achieved an accuracy of 100% in recognizing Myanmar Sign Language (MSL). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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General and Scalable Optimization for Robust AI 37

General and Scalable Optimization for Robust AI

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Liu, Sijia Department of Computer Science & Engineering Michigan State University MI United States MIT-IBM Watson AI Lab IBM Research United States

ISBN: (纸本)9781577358800

Deep neural networks (DNNs) can easily be manipulated (by an adversary) to output drastically different predictions and can be done so in a controlled and directed way. This process is known as adversarial attack and is considered one of the major hurdles in using DNNs in high-stakes and real-world applications. Although developing methods to secure DNNs against adversaries is now a primary research focus, it suffers from limitations such as lack of optimization generality and lack of optimization scalability. My research highlights will offer a holistic understanding of optimization foundations for robust AI, peer into their emerging challenges, and present recent solutions developed by my research group. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep neural networks

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Image Fusion Using Wavelet Transformation and XGboost Algorithm

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computers, Materials & Continua 2024年第4期79卷 801-817页

作者： Shahid Naseem Tariq Mahmood Amjad Rehman Khan Umer Farooq Samra Nawazish Faten S.Alamri Tanzila Saba Department of Information Sciences Division of S&TUniversity of EducationLahore54770Pakistan Artificial Intelligence and Data Analytics(AIDA)Lab CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Information Sciences University of EducationVehari CampusVehari61100Pakistan Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan Department of Mathematical Sciences College of SciencePrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Recently,there have been several uses for digital image *** fusion has become a prominent application in the domain of imaging *** create one final image that provesmore informative and helpful compared to the original input images,image fusion merges two or more initial images of the same *** fusion aims to produce,enhance,and transform significant elements of the source images into combined images for the sake of human visual *** fusion is commonly employed for feature extraction in smart robots,clinical imaging,audiovisual camera integration,manufacturing process monitoring,electronic circuit design,advanced device diagnostics,and intelligent assembly line robots,with image quality varying depending on *** research paper presents various methods for merging images in spatial and frequency domains,including a blend of stable and curvelet transformations,everageMax-Min,weighted principal component analysis(PCA),HIS(Hue,Intensity,Saturation),wavelet transform,discrete cosine transform(DCT),dual-tree Complex Wavelet Transform(CWT),and multiple wavelet *** fusion methods integrate data from several source images of an identical target,thereby enhancing information in an extremely efficient *** precisely,in imaging techniques,the depth of field constraint precludes images from focusing on every object,leading to the exclusion of certain *** tackle thess challanges,a very efficient multi-focus wavelet decomposition and recompositionmethod is *** use of these wavelet decomposition and recomposition techniques enables this method to make use of existing optimized wavelet code and filter *** simulated outcomes provide evidence that the suggested approach initially extracts particular characteristics from images in order to accurately reflect the level of clarity portrayed in the original *** study enhances the performance of the eXtreme Gradient Boosting(XGBoos

关键词： Image fusion max-min average CWT XGBoost DCT inclusive innovations spatial and frequency domain

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Physically Aware Synthesis Revisited: Guiding Technology Mapping with Primitive Logic Gate Placement 24

Physically Aware Synthesis Revisited: Guiding Technology Map...

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43rd International Conference on computer-Aided Design, ICCAD 2024

作者： Pan, Hongyang Lan, Cunqing Liu, Yiting Wang, Zhiang Shang, Li Zeng, Xuan Yang, Fan Zhu, Keren State Key Lab of Integrated Circuits and Systems School of Microelectronics Fudan University China School of Computer Science Fudan University China Department of Electrical and Computer Engineering University of California San Diego United States

ISBN: (纸本)9798400710773

A typical VLSI design flow is divided into separated front-end logic synthesis and back-end physical design (PD) stages, which often require costly iterations between these stages to achieve design closure. Existing approaches face significant challenges, notably in utilizing feedback from physical metrics to better adapt and refine synthesis operations, and in establishing a unified and comprehensive metric. This paper introduces a new Primitive logic gate placement guided technology MAPping (PigMAP) framework to address these challenges. With approximating technology-independent spatial information, we develop a novel wirelength (WL) driven mapping algorithm to produce PD-friendly netlists. PigMAP is equipped with two schemes: a performance mode that focuses on optimizing the critical path WL to achieve high performance, and a power mode that aims to minimize the total WL, resulting in balanced power and performance outcomes. We evaluate our framework using the EPFL benchmark suites with ASAP7 technology, using the OpenROAD tool for place-and-route. Compared with OpenROAD flow scripts, performance mode reduces delay by 14% while increasing power consumption by only 6%. Meanwhile, power mode achieves a 3% improvement in delay and a 9% reduction in power consumption. © 2024 Copyright is held by the owner/author(s).

关键词： Logic design

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When LoRa meets distributed machine learning to optimize the network connectivity for green and intelligent transportation system

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Green Energy and Intelligent Transportation 2024年第3期3卷 24-41页

作者： Malak Abid Ali Khan Hongbin Ma Arshad Farhad Asad Mujeeb Imran Khan Mirani Muhammad Hamza National Key Lab of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Computer Science Namal UniversityPakistan Department of Electrical Engineering Tsinghua UniversityBeijing 100084China Faculty of Information Technology Beijing University of TechnologyBeijing 100124China School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China

LoRa technology contributes to green energy by enabling efficient,long-range communication for the Internet of Things(IoT).This paper addresses the challenges related to coverage range in outdoor monitoring systems utilizing LoRa,where the network performance is affected by the density of gateways(GWs)and end devices(EDs),as well as environmental *** mitigate interference,data throughput losses,and high-power consumption,the proposed spreading factor(SF)and hybrid(data rate|SF)models dynamically adjust the transmission *** orchestration of concurrent data modifications within the network server(NS)is crucial for uninterrupted communication between GWs and EDs,especially in monitoring electric vehicle(EV)stations to reduce traffic congestion and *** K-means and density-based spatial clustering of applications with noise(DBSCAN)algorithms optimizes ED allocation,averts data congestion,and improves the signal-tointerference noise ratio(SINR).These methods ensure seamless information reception by meticulously allocated EDs across various GW *** estimate the free-space losses(FSL),a log-distance path loss model(log-PL)is *** various bandwidths(BWs),bidirectional communications,and duty cycles(DCs)helps to prevent saturation,thus prolonging the operational lifespan of *** findings reveal a notable packet rejection rate(PRR)of 0%for the DBSCAN(hybrid model).In contrast,the K-means exhibits a PRR ranging from 5%(hybrid model)to 35.29%(SF model)for the ten GWs ***,the network saturation is reduced to 10.185%and 9.503%,respectively,highlighting an improvement in the average efficiency of slotted ALOHA(91.1%)and pure ALOHA(90.7%).These enhancements increase the lifespan of EDs to 15,465.27 days.

关键词： DBSCAN Green transportation Hybrid model Intelligent transportation K-means

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Fire and Smoke Detection using an Enhanced YOLOv8 Model

Fire and Smoke Detection using an Enhanced YOLOv8 Model

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computer Vision and Machine Intelligence (CVMI), International Conference on

作者： Bhavesh Kumar Bohra Bulla Rajesh Mohammed Javed David Doermann Department of IT Computer Vision and Biometrics Lab IIIT Allahabad India Department of Computer Science and Engineering IIIT Sricity India Department of Computer Science and Engineering University at Buffalo Buffalo NY USA

ISBN: (数字)9798350376876

ISBN: (纸本)9798350376883

Due in part to global warming, the frequency of wildfires has increased in recent years, significantly impacting humans, wildlife, and forest ecosystems. To address this challenge, using technology for early fire detection through smoke can help firefighters and other stakeholders limit their spread and reduce the impact. In this research, we adopt the latest object detection model - YOLOv8 and use a fine-tuned version for detecting smoke and fire using the latest datasets. YOLOv8 backbone begins with two convolutional (Conv) layers, followed by a series of alternating Conv layers and C2F modules, and ends with a Spatial Pyramid Pooling - Fast (SPPF) module. Our improvement involves adding two extra Conv layers at the beginning of the backbone, between the alternating sequence of Conv and C2F layers. As the smoke and fire detection problem involves searching for smaller objects in the image, increasing the feature extraction layers improves the performance of smoke and fire detection. The fine-tuned model is tested with the FASDD v4 dataset, which depicts fire, smoke, non-fire, and non-smoke scenarios.

关键词： Climate change Fire safety Smoke detectors YOLO Global warming Wildfires Fires

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An Efficient Method For Selecting and Prioritizing Business Processes In Robotic Process Automation 7

An Efficient Method For Selecting and Prioritizing Business ...

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7th Iranian Conference on Advances in Enterprise Architecture, ICAEA 2023

作者： Alimoradi, Reyhaneh Aliee, Fereidoon Shams Farahani, Bahar Shahid Beheshti University Soea Lab Computer Science and Engineering Department Tehran Iran Shahid Beheshti University Cyberspace Research Institute Tehran Iran

ISBN: (纸本)9798350308068

The use of robotic process automation (RPA) has been on the rise lately. However, more research needs to be done on the selection of the process and the factors affecting this choice. The key to the effectiveness of the outcome and the success of RPA hinge on selecting the appropriate processes and prioritizing them for automation. This study presents a novel method for selecting and prioritizing processes to enhance their efficiency within the organization. This approach enables us to prioritize processes for maximum automation advantage. It also identifies processes where RPA does not yield substantial advantages. The proposed approach was implemented in a real-world environment by conducting a case study at a logistics firm, demonstrating its benefits and increased efficiency. In addition, to validate the results, interviews with domain experts were conducted. During these interviews, the experts affirmed that the recommended approach successfully identified the most suitable processes and tasks for automation. © 2023 IEEE.

关键词： Robotics

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An Enhanced Deep Super-Resolution Generative Adversarial Network Approach for Skin Disease Image Regeneration and Assessment 3

An Enhanced Deep Super-Resolution Generative Adversarial Net...

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3rd IEEE International Conference on Electronic engineering, ICEEM 2023

作者： El-Fattah, Ibrahim Abd Taha, Taha E. El-Shafai, Walid Menoufia University Faculty of Electronic Engineering Department of Electronics and Electrical Communications Engineering Menouf32952 Egypt Department Electronics and Electrical Communications Engineering Alexandria Egypt Prince Sultan University Security Engineering Lab Computer Science Department Riyadh11586 Saudi Arabia

ISBN: (纸本)9798350323511

Skin cancer is a critical medical concern, posing significant challenges in accurate diagnosis. Algorithmic approaches have seen remarkable advancements across various occupations, including skin disease assessment. This study introduces a distinctive Improved Deep Super-Resolution Generative Adversarial Network (I_DSUR-GAN) methodology for regenerating low-resolution (LOR) skin disease images into super-resolution (SUR) format. Additionally, a modified SUR-dataset inspired by HAM10000 is presented for skin disease applications. The proposed approach incorporates a novel loss function design to provide supplementary information, facilitating the creation of high-quality SUR images. Experimental results demonstrate that our method outperforms existing approaches on the HAM10000 dataset. A comprehensive evaluation and employing sensitive metrics is conducted to assess the effectiveness, training periods, and memory requirements of the proposed framework. The outcomes reveal that the suggested model excels in restoring and identifying hue and texture compared to conventional and earlier models. © 2023 IEEE.

关键词： Generative adversarial networks

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SoftPatch+: Fully Unsupervised Anomaly Classification and Segmentation

arXiv

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arXiv 2024年

作者： Wang, Chengjie Jiang, Xi Gao, Bin-Bin Gan, Zhenye Liu, Yong Zheng, Feng Ma, Lizhuang Department of Computer Science and Engineering Shanghai Jiao Tong University China Youtu Lab Tencent China Department of Computer Science and Engineering Southern University of Science and Technology China

Although mainstream unsupervised anomaly detection (AD) (including image-level classification and pixel-level segmentation) algorithms perform well in academic datasets, their performance is limited in practical application due to the ideal experimental setting of clean training data. Training with noisy data is an inevitable problem in real-world anomaly detection but is seldom discussed. This paper is the first to consider fully unsupervised industrial anomaly detection (i.e., unsupervised AD with noisy data). To solve this problem, we proposed memory-based unsupervised AD methods, SoftPatch and SoftPatch+, which efficiently denoise the data at the patch level. Noise discriminators are utilized to generate outlier scores for patch-level noise elimination before coreset construction. The scores are then stored in the memory bank to soften the anomaly detection boundary. Compared with existing methods, SoftPatch maintains a strong modeling ability of normal data and alleviates the overconfidence problem in coreset, and SoftPatch+ has more robust performance which is particularly useful in real-world industrial inspection scenarios with high levels of noise (from 10% to 40%). Comprehensive experiments conducted in diverse noise scenarios demonstrate that both SoftPatch and SoftPatch+ outperform the state-of-the-art AD methods on the MVTecAD, ViSA, and BTAD benchmarks. Furthermore, the performance of SoftPatch and SoftPatch+ is comparable to that of the noise-free methods in conventional unsupervised AD setting. The code of the proposed methods can be found at https://***/TencentYoutuResearch/AnomalyDetection-SoftPatch. © 2024, CC BY.

关键词： Image segmentation

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