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IEEE Asian Conference on Solid-State Circuits (ASSCC)

作者： Zehao Li Wenhao Lu Yuncheng Lu Junying Li Yucen Shi Yuanjin Zheng Tony Tae-Hyoung Kim Nanyang Technology University Singapore

ISBN: (数字)9798350376326

ISBN: (纸本)9798350376333

Neuromorphic vision sensors (NVSs) [1], which generate an event only when a pixel’s log intensity changes, feature low data redundancy and transmission latency, making them increasingly popular in various edge applications, such as traffic monitoring and object detection. Compared to event-by-event processing paradigms that require the processor to be always-on [2], frame-based counterparts are more power-efficient, as they keep the processor in an idle state during frame construction, resulting in a reduced signal toggling rate. However, as NVSs continue to evolve, the increasing resolution necessitates a substantial amount of on-chip memory to hold the reconstructed event-based binary image (EBBI), as depicted in Fig. 1 (top). Furthermore, most existing systems are suited only for stationary scenes, as moving NVSs introduce background noise [3]. In addition, prior object detection systems based on deep neural networks (DNN) or connected component labeling (CCL) suffer from significant memory overhead and long processing latency due to large model sizes and high computational intensity.

关键词： Neuromorphics Redundancy Object detection artificial neural networks Vision sensors Real-time systems System-on-chip Solid state circuits Signal resolution Monitoring

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Crop Disease Detection using CNN Architecture VGG16

Crop Disease Detection using CNN Architecture VGG16

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Sandeep Raj Surinder Kaur Neetu Singh Monica Gupta Department of Artificial Intelligence & Machine Learning Dronacharya College of Engineering Farrukh Nagar Haryana Department of Information Technology Bharati Vidyapeeth’s College of Engineering New Delhi Department of Electronics and Communication Engineering Bharati Vidyapeeth’s College of Engineering New Delhi

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

The Agriculture plays a major part in any nation for economy through the production of various crops and it’s an essential source of income in India. The plant disease is one of the leading task of agriculture. When the infections occur in crops result in reduced quantity and quality of production of crops which will result in economic loss. Therefore, plague needs to be identified as quickly as possible for the healthy production of crops and plants. Various methods and solutions were proposed for crop disease detection by the researchers using image processing, machine learning, and deep learning. The deep learning plays an essential part in the field of image processing using convolution neural networks and their architecture. In this experiment, we have examined the CNN, Transfer learning VGG16 model on the plant-village dataset which was available on the Kaggle. The dataset(records) includes images of various potato, tomato and pepper crops of 15 different classes. The detection was done using the leaf samples. Our goal is to detect the disease in plants with accurate without taking so much time so that people can take care of their plants and apply the appropriate treatment. In future this VGG16 can be used to identify specific objects, animals, plants, and more in a picture. to identify, recognition and classification of image, that will use in many applications. Medical, Banking, Transportations well as Agriculture.

关键词： Deep learning Plant diseases Transfer learning neural networks Crops Transportation Production Computer architecture Agriculture Object recognition

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Engineering of A-site cations in APbI3 (A = Cs, Rb, K) perovskites for resistive switching control and self-rectifying memristors for next-generation computing applications

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NANO ENERGY 2025年 138卷

作者： Jain, Muskan Fasnick, C. K. Bismiya Khemnani, Manish Kortstee, Lotte Andola, Bhawana Patel, Mayur Jagdishbhai Guerrero, Antonio Srivastava, Yogesh Kumar Castelli, Ivano E. Solanki, Ankur Pandit Deendayal Energy Univ Flextron Lab Gandhinagar 382426 Gujarat India Pandit Deendayal Energy Univ Sch Energy Technol Dept Phys Gandhinagar 382426 India Tech Univ Denmark Dept Energy Convers & Storage Anker Engelundsvej 411 DK-2800 Lyngby Denmark Indian Inst Technol Hyderabad Dept Phys Ultrafast Terahertz Photon Lab Sangareddy 502284 Telangana India Indian Inst Technol Guwahati Dept Chem Gauhati 781039 Assam India Univ Jaume 1 Inst Adv Mat INAM Castellon de La Plana 12006 Spain

The resistive switching in memristor devices is highly influenced by structural and electronic properties, which can be tailored through material composition. Unlike optoelectronic devices, lattice distortion driven by A-site cation variation (A = Cs, Rb, K) is a key mechanism for enhancing the switching properties of APbI(3)-based flexible memristor devices. This distortion, particularly noticeable in KPbI3's orthorhombic phase, alters crystal structure, electronic as well as charge transport properties. Density functional theory (DFT) calculations are performed to calculate ionic migration barriers in the orthorhombic structures, while X-ray photoelectron spectroscopy (XPS) analysis provides insights into the chemical environment and bonding states. Leveraging this effect, KPbI3 exhibits exceptional non-volatile data storage performance, with a high ON/OFF ratio (similar to 10(3)) and self-rectification (similar to 10(3)). Conversely, CsPbI3 with subtle lattice distortion, displays superior synaptic behavior and efficient spike-timing-dependent plasticity (STDP), ideal for neuromorphic computing. CsPbI3 demonstrates remarkable potential for artificial neural networks (ANNs), achieving similar to 97 % accuracy in Modified National Institute of Standards and Technology (MNIST) dataset, image classification with minimal training, and 85 % accuracy in convolutional neural networks (CNNs) on the Canadian Institute for Advanced Research (CIFAR-10) dataset. Our findings highlight the critical role of small cation size in modulating Schottky barriers, morphology, and ionic migration, and their direct impact on resistive switching properties. These results underscore the potential of cation size modulation as an effective strategy for designing memristors for a wide range of applications, including neuromorphic computing, signal processing, logic gates, and quantum computing.

关键词： Memristor KPbI3 Self-rectifying Perovskites artificial Synapse Neuromorphic Computation Resistive Switching

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FeSHI: Feature Map-Based Stealthy Hardware Intrinsic Attack

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IEEE ACCESS 2021年 9卷 115370-115387页

作者： Odetola, Tolulope A. Khalid, Faiq Mohammed, Hawzhin Sandefur, Travis C. Hasan, Syed Rafay Tennessee Technol Univ Dept Elect & Comp Engn Cookeville TN 38505 USA Tech Univ Wien TU Wien Dept Comp Engn A-1040 Vienna Austria

Convolutional neural networks (CNN) have shown impressive performance in computer vision, natural language processing, and many other applications, but they exhibit high computations and substantial memory requirements. To address these limitations, especially in resource-constrained devices, the use of cloud computing for CNNs is becoming more popular. This comes with privacy and latency concerns that have motivated the designers to develop embedded hardware accelerators for CNNs. However, designing a specialized accelerator increases the time-to-market and cost of production. Therefore, to reduce the time-to-market and access to state-of-the-art techniques, CNN hardware mapping and deployment on embedded accelerators are often outsourced to untrusted third parties, which is going to be more prevalent in futuristic artificial intelligence of things (AIoT) systems. These AIoT systems anticipates horizontal collaboration among different resource constrained AIoT node devices, where CNN layers are partitioned and these devices collaboratively compute complex CNN tasks. This horizontal collaboration opens another attack surface to the CNN-based application, like inserting the hardware Trojans (HT) into the embedded accelerators designed for the CNN. Therefore, there is a dire need to explore this attack surface for designing the secure embedded hardware accelerators for CNNs. Towards this goal, in this paper, we exploited this attack surface to propose an HT-based attack called FeSHI. Since in horizontal collaboration of RC AIoT devices different sections of CNN architectures are outsourced to different untrusted third parties, the attacker may not know the input image, but it has access to the layer-by-layer output feature maps information for the assigned sections of the CNN architecture. This attack exploits the statistical distribution, i.e., Gaussian distribution, of the layer-by-layer feature maps of the CNN to design two triggers for stealthy HT with a very low

关键词： Convolutional neural networks IP networks Field programmable gate arrays Trojan horses Statistical analysis Gaussian distribution Collaboration Convolutional neural Network CNN hardware security edge intelligence AIoT FPGA hardware Trojan hardware intrinsic attack

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Graph neural Network (GNN) in image and Video Understanding Using Deep Learning for Computer Vision applications 2

Graph Neural Network (GNN) in Image and Video Understanding ...

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2nd International Conference on Electronics and Sustainable Communication Systems, ICESC 2021

作者： Pradhyumna, P. Shreya, G.P. Mohana RV College of Engineering® Electronics and Telecommunication Engineering Karnataka Bengaluru India

ISBN: (纸本)9781665428675

Graph neural networks (GNNs) is an information - processing system that uses message passing among graph nodes. In recent years, GNN variants including graph attention network (GAT), graph convolutional network (GCN), and graph recurrent network (GRN) have shown revolutionary performance in computer vision applications using deep learning and artificial intelligence. These neural network model extensions, collect information in the form of graphs. GNN may be divided into three groups based on the challenges it solves: link prediction, node classification, graph classification. Machines can differentiate and recognise objects in image and video using standard CNNs. Extensive amount of research work needs to be done before robots can have same visual intuition as humans. GNN architectures, on the other hand, may be used to solve various image categorization and video challenges. The number of GNN applications in computer vision not limited, continues to expand. Human-object interaction, actin understanding, image categorization from a few shots and many more. In this paper use of GNN in image and video understanding, design aspects, architecture, applications and implementation challenges towards computer vision is described. GNN is a strong tool for analysing graph data and is still a relatively active area that needs further researches attention to solve many computer vision applications. © 2021 IEEE.

关键词： Convolutional neural networks

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AI Based Automated image Caption Tool Implementation for Visually Impaired 1

AI Based Automated Image Caption Tool Implementation for Vis...

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1st International Conference on Industrial Electronics Research and applications, ICIERA 2021

作者： Wadhwa, Vanshika Gupta, Bhoomi Gupta, Sachin Maharaja Agrasen Institute of Technology Department of It Delhi India School of Engineering and Technology Mvn University Haryana India

ISBN: (纸本)9781665435420

image captioning is a rapidly emerging area in the artificial Intelligence applications for natural language definitions. It works at the confluence of image data obtained through datasets, and the sentence definitions towards capturing meaningful interpretations of the interaction that exists between them. It uses CNN's (Convolutional neural networks) reading techniques in image and LSTM (Long Short Term Memory) type RNN (Recurrent neural Network) over sentences together so that the computer can see the context of the image and display it in a natural language like English. This paper combines the application of computer vision and natural language processing towards building assistive technology that supplements visual data like images by providing braille readable captions for the visually impaired to get a better sense of what is happening around them and understand their surroundings. © 2021 IEEE.

关键词： Computer vision

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Accelerating Weed Detection for Smart Agricultural Sprayers Using a neural processing Unit

SSRN

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SSRN 2024年

作者： Herterich, Nils Liu, Kai Stein, Anthony Robert Bosch GmbH Heilbronner Straße 358 Stuttgart70469 Germany University of Hohenheim Department of Artificial Intelligence in Agricultural Engineering Garbenstraße 9 Stuttgart70599 Germany

Smart spraying technology represents a breakthrough in precision agriculture, enabling innovative weed control methods and increasing crop protection effectiveness through the use of deep neural networks (DNNs). This advancement brings its own set of challenges, particularly in terms of the time neural networks need to reach conclusions. In large sprayers, weed detection is performed by multiple sensors, each focused on a distinct region of interest. While existing research has mainly concentrated on powerful GPUs, the shift to practical applications requires the use of energy-efficient embedded systems to enable intelligent spraying in the field. In this study, the effects of different backbone and neck architectures on an object detection model are analyzed in terms of inference time and compatibility with a neural processing Unit (NPU), focusing on efficiency and accuracy. Considering real-time operating conditions, the neural network architectures were optimized for a maximum inference time of 50 ms and a model size of less than 10 MB. To achieve this, the models were validated for operational compatibility during the deployment process and quantized to an 8-bit integer data format. A dataset of 12 weed species was used to test a variety of novel lightweight CNN and hybrid transformer backbones and necks of an object detection model at different image resolutions. The results show that these core components of the model have different effects on inference time and accuracy, with certain configurations meeting the required specifications. The speedup factor, which describes the comparison of CPU and NPU inference time on the target hardware, significantly influences the efficiency. Experimental tests with different backbones showed distinct advantages in weed detection. Building on this success, further efficiency improvements could be achieved by replacing the neck. While the required quantization led to a reduction in accuracy, the proposed combination of compo

关键词： Object detection

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Comparative Analysis of Machine Learning and Deep Learning Algorithms for Plant Disease Classification and Detection

Comparative Analysis of Machine Learning and Deep Learning A...

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Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Harminder Kaur Neeraj Raheja Amit Kumar Bindal Department of Computer Science and Engineering Maharishi Markandeshwar Engineering College Maharishi Markandeshwar (Deemed to be University) Mullana Ambala Haryana India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

Agriculture, a crucial foundation for human existence, exists at the intersection of conventional methods and modern innovations. Any country's primary issue in agriculture science is effective plant disease control, driven by the growing need for food due to population growth. Moreover, developments in modern technology have greatly improved the precision and effectiveness of plant detection. A critical challenge in the agricultural sector is leaf diseases, significantly impacting crop production and economic profit. Integrating digitalization and technology in agriculture is imperative for augmenting productivity and ensuring food security. This research emphasizes the principal concerns and obstacles associated with classifying leaf diseases. By experimenting with different approaches, researchers have already achieved great strides in the identification and categorization of illnesses. But due to assessments, new information, and discussions, changes are required. The resilience of deep learning (DL) and machine learning (ML) techniques, such as feed-forward neural networks (FFNN), k-means clustering (KMC), fuzzy logic (FL), artificial neural networks (ANN), and genetic algorithms (GA),so forth, has been demonstrated by earlier research. Due to their inherent ability to automatically obtain relevant visual information and understand spatial hierarchies, CNNs are usually the preferred option for image identification and classification among the ML and DL methods addressed in this study. The selection between machine learning and deep learning is dependent upon numerous issues, the data that is available, and the amount of processing power that is available. Because of this, DL—primarily using CNNs are advised for many complicated image detection and classification applications. They also show outstanding results for classification and detection on their datasets but not on others. Lastly, by using a variety of methods for processing images in the field of artific

关键词： Deep learning Fuzzy logic Support vector machines Plant diseases Visualization Technological innovation Feature extraction Agriculture Data mining Convolutional neural networks

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Research on Fault Diagnosis and Parameter Identification of Bidirectional LLC Resonant Converter Based on Data Driven

Research on Fault Diagnosis and Parameter Identification of ...

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image processing, Electronics and Computers (IPEC), Asia-Pacific Conference on

作者： Xiaorui Zhang Shuo Jiang Pingxin Ma Dongyang Luo Tian Zhao Fengping He China Academy of Space Technology(CAST) Institute of Telecommunication and Navigation Satellites Beijing China

ISBN: (数字)9798350374407

ISBN: (纸本)9798350374414

At present, few people have studied the fault diagnosis of bidirectional LLC resonant converters. In order to promote the development of digital and intelligent fault diagnosis of power converters, this article analyzes the structural and parametric fault characteristics of bidirectional LLC resonant converters, designs structural fault diagnosis algorithms based on Support Vector Machine (SVM) and Random Forest (RF), and compares the diagnostic results of the two algorithms. Among them, the accuracy of random forest fault diagnosis is $100 \%$, the accuracy of SVM fault diagnosis is $98.58 \%$, the accuracy of RF fault diagnosis is higher, and the fault diagnosis speed is faster; At the same time, a bidirectional LLC resonant converter resonant capacitor parameter identification algorithm based on Extreme Learning Machines (ELM) and Deep neural Network (DNN) was studied. As a deep learning algorithm, DNN has an identification error of less than $5 \%$ and significantly better performance than ELM. The data-driven bidirectional LLC resonant converter fault diagnosis and parameter identification carried out in this article have achieved good results, laying a foundation for subsequent engineering applications.

关键词： Fault diagnosis Support vector machines Radio frequency Parameter estimation Accuracy Resonant converters artificial neural networks Prediction algorithms Circuit faults Random forests

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AdaBM: On-the-Fly Adaptive Bit Mapping for image Super-Resolution

AdaBM: On-the-Fly Adaptive Bit Mapping for Image Super-Resol...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Cheeun Hong Kyoung Mu Lee Dept. of ECE & ASRI IPAI Seoul National University Seoul Korea

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Although image super-resolution (SR) problem has ex-perienced unprecedented restoration accuracy with deep neural networks, it has yet limited versatile applications due to the substantial computational costs. Since differ-ent input images for SR face different restoration difficul-ties, adapting computational costs based on the input image, referred to as adaptive inference, has emerged as a promising solution to compress SR networks. Specifically, adapting the quantization bit-widths has successfully re-duced the inference and memory cost without sacrificing the accuracy. However, despite the benefits of the resul-tant adaptive network, existing works rely on time-intensive quantization-aware training with full access to the origi-nal training pairs to learn the appropriate bit allocation policies, which limits its ubiquitous usage. To this end, we introduce the first on-the-fly adaptive quantization frame-work that accelerates the processing time from hours to sec-onds. We formulate the bit allocation problem with only two bit mapping modules: one to map the input image to the image-wise bit adaptation factor and one to obtain the layer-wise adaptation factors. These bit mappings are cali-brated and fine-tuned using only a small number of calibration images. We achieve competitive performance with the previous adaptive quantization methods, while the processing time is accelerated by × 2000. Codes are available at https://***/Cheeun/AdaBM.

关键词： Training Quantization (signal) Accuracy Adaptive systems Superresolution Bit rate Pipelines

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