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A Fully-integrated Analog Front-end for Carbon-based Short-wave Infrared image Sensor

A Fully-integrated Analog Front-end for Carbon-based Short-w...

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international conference on ASIC

作者： Weirong Xi Jianhua Jiang Chengying Chen School of Opto-Electronic and Communication Engineering Xiamen University of Technology Xiamen China Research Center for Carbon-Based Electronics School of Electronics Peking University Beijing China

Carbon Nanotube Field Effect Transistor (CNT-FET) have many advantages in electronics materials such as high mobility and ultra-thin body, which are promising for the fabrication of high performance, low power consumption and low cost shortwave infrared image sensors. In this paper, a column-level Analog Front-end (AFE) circuit is designed as a processing circuit for a carbon-based short-wave infrared image sensor, which includes Capacitor TransImpedance Amplifier (CTIA) integrator, Correlated Double Sampling (CDS), and 10bit/125KHz Successive AppRoximation Analog-to-Digital Converter (SAR ADC). The proposed AFE is designed using SMIC 0.18µm CMOS 1P6M process with area of 1.9mm 2 and an power supply voltage of 1.8V. The post-simulation results show that the signal-to-Noise Ratio (SNR) is 56.84dB and signal-to-Noise and Distortion Ratio (SNDR) is 55.885dB. Meanwhile the Effective Number of Bits (ENOB) achieves 8.99bit. The range of detection current is 500p-100nA, and the power consumption is 7.01mW.

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Covid-19 Classification using Fine-tuned EfficientNet Architecture

Covid-19 Classification using Fine-tuned EfficientNet Archit...

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international conference for Convergence of technology (I2CT)

作者： Srigiri Sruthi Siddharth Emadaboina Pradyumna Machavarapu Rimjhim Padam Singh Sneha Kanchan Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India Department of Internet Engineering and Computer Science Universiti Tunku Abdul Rahman Malaysia

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

This work utilizes an Efficient Net-based deep learning architecture, leveraging its efficiency and scalability for image classification tasks. We employ a Dataset of chest X-ray images, including COVID-19 cases as well as other relevant Pathology, to train and evaluate the model. The training process involves data augmentation, model optimization, and early stopping to ensure robust and generalizable performance. It specifies the image size, channels, and class count, and then proceeds to create a pre-trained model using the EfficientNetB0 architecture. The model is built upon the pre-trained EfficientNetB0 model, with additional layers including Layer Normalization, Dense, and Dropout layers for fine-tuning on the Covid-19 data. Subsequently, the model is rigorously evaluated using standard performance metrics, including precision, recall, F1-score, and accuracy, and has proven to outperform other state-of-art models by a 3% increase in F1-score value.

关键词： COVID-19 Training Pathology Scalability Data models Robustness Task analysis

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A Deep Residual-based Model on Multi-Branch Aggregation for Stress and Emotion Recognition through Biosignals 19

A Deep Residual-based Model on Multi-Branch Aggregation for ...

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19th international conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information technology, ECTI-CON 2022

作者： Mekruksavanich, Sakorn Hnoohom, Narit Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakhon Pathom Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9781665485845

Stress and emotion recognition (SER) is a rapidly growing field of study that has applications in various areas, including psychological wellbeing, rehabilitative services, athletic training, and human-computer interaction. Biological information such as the electrocardiogram (ECG), electromyography (EMG), and electrodermal activity (EDA) has been frequently utilized for the SER for learning-based approaches. This study introduces a convolutional neural network motivated by ResNeXt to facilitate multimodal awareness. The proposed model, named StressNeXt, can extract high-level insights from raw bio-signal signals and classify emotional expressions effectively. We undertake a series of investigations using a publicly released standard dataset (WESAD) to determine the optimal implementation of the proposed solution for recognizing stress and emotion. After incorporating preliminary fusion events, we examined deep learning models using 5-fold cross-validation. Our study demonstrates that the suggested technique can comprehend robust multimodal representations with an accuracy of 87.73% utilizing EDA. Additionally, the identification was designed to provide better to 99.92% by fusing with accelerometer sensor data. © 2022 ieee.

关键词： Emotion Recognition

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TensorFlow vs Theano: Issues and Challenges for Natural Language Processing

TensorFlow vs Theano: Issues and Challenges for Natural Lang...

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Applied Intelligence and Computing (AIC), ieee World conference on

作者： Gulshan Dhasmana Praveen Gujjar J Guru Prasad M S Raghavendra M Devadas Vani Hiremani Divya Shree N Department of Computer Science and Engg. Graphic Era (Deemed to be University) Dehradun India Faculty of Management Studies JAIN (Deemed-to-be University) Bengaluru India Department of Information Technology Manipal Institue of Technology Bengaluru India Symbiosis Institute of Technology Symbiosis International (Deemed) University Pune India School of Computing & Information Technology Reva University Bengaluru India

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

ensorFlow and Theano were popular deep learning frameworks for building and training neural networks, including image classification and NLP models. TensorFlow's evolution from a static graph-based approach to incorporating Eager Execution is examined, shedding light on its impact on ease of use and dynamic operations. Deep learning frameworks play a pivotal role in shaping the landscape of machine learning research and applications. Natural Language Processing (NLP) has witnessed remarkable advancements in recent years, owing much of its progress to deep learning frameworks. This paper presents an in-depth comparative analysis of two influential deep learning frameworks, TensorFlow and Theano, with a specific focus on their issues and challenges in the domain of NLP. Through a comprehensive exploration, this study aims to provide NLP practitioners and researchers with insights to navigate the complexities of framework selection for their linguistic tasks. By distilling the nuances of TensorFlow and Theano in the context of NLP challenges, this research equips NLP practitioners with a well-rounded understanding of the trade-offs and considerations in framework selection. Findings contribute to the informed decision-making process for choosing the appropriate framework based on the specific requirements of NLP tasks.

关键词： Deep learning Training Navigation Neural networks Syntactics Linguistics Natural language processing internet Optimization image classification

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Narrow-Band internet of Things for Smart Metering Infrastructure in Urban Area: Medan City Case 4

Narrow-Band Internet of Things for Smart Metering Infrastruc...

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4th international conference on Information and Communications technology, ICOIACT 2021

作者： Nugraha, Muhammad Adam Nashiruddin, Muhammad Imam Hutagalung, Gunawan Perum Percetakan Uang Republik Indonesia [PERURI Karawang Indonesia Telkom University Bandung Indonesia School of Electrical Engineering Telkom University Bandung Indonesia Ministry of Communication and Information Technology Jakarta Indonesia

ISBN: (纸本)9781665433945

Narrow Band internet of Things (NB-IoT) is a 3GPP standards-based Low Power Wide Area Network (LPWAN) technology that provides a broad coverage range of connection for many devices, low and efficient power consumption, and easy deployment. However, NB-IoT deployments in an area are varied due to the coverage and capacity size affected by the different geographic, demographic, and economic trends. This research aims to study NB-IoT’s deployment for Smart Metering Infrastructure (SMI) in an urban area in Indonesia, namely Medan city, hoping that an adequate and optimal coverage and capacity calculation are accomplished. After conducting the simulations and calculations, this research obtained that NB-IoT deployment for SMI in Medan city is feasible by requiring a minimum gateway of 17 sites. based on the simulations that have been completed. The average acceptable signal level and signal to Noise Ratio (SNR) in Medan city are -57.79 dBm and -4.37 dB, respectively. Lastly, the average received signal strength (RSSI) is -79 dBm. These results have fulfilled the minimum requirement of implementing NB-IoT in the urban area, Medan city. © 2021 ieee

关键词： internet of things

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EALaneNet: Enhancing Lane Detection with Edge-Aware Unit and Curvature-Optimized Loss

EALaneNet: Enhancing Lane Detection with Edge-Aware Unit and...

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ieee international conference on Information Communication and signal Processing (ICICSP)

作者： Wangjie Cong Junjie Zhang Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai University Shanghai China School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

Lane detection plays a crucial role in intelligent driving and smart transportation systems. However, existing methods often overlook edge information in images, leading to insufficient detection accuracy in complex scenarios. To address this issue, we propose a novel lane detection method that incorporates an Edge Aware Unit (EAU). By integrating the EAU, we effectively utilize edge information in images and embed prior knowledge into the model, thereby enhancing lane detection accuracy. Additionally, to tackle the challenge of low regression accuracy for points in curve sections, we design a weighted smooth L1 loss function. This method applies an exponential decay function to weight the smooth L1 loss, assigning weights based on the difficulty of the regression points. By increasing the weights for points in complex curve sections, we improve the model's adaptability to challenging curvatures. We conducted experiments on the CULane dataset and our private lane detecion dataset LM-Lane, and the results demonstrate that the proposed method not only excels in standard lane detection tasks but also shows significant advantages in handling curve scene.

关键词： Adaptation models Accuracy Lane detection image edge detection signal processing Smart transportation Standards

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DHCN: DEEP HIERARCHICAL CONTEXT NETWORKS FOR image ANNOTATION

DHCN: DEEP HIERARCHICAL CONTEXT NETWORKS FOR IMAGE ANNOTATIO...

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ieee international conference on Acoustics, Speech and signal Processing (ICASSP)

作者： Jiu, Mingyuan Sahbi, Hichem Zhengzhou Univ Sch Informat Engn Zhengzhou Peoples R China Zhengzhou Univ Res Inst Ind Technol Co Ltd Zhengzhou Peoples R China Sorbonne Univ LIP6 CNRS UPMC F-75005 Paris France

ISBN: (纸本)9781728176055

Context modeling is one of the most fertile sub-fields of visual recognition which aims at designing discriminant image representations while incorporating their intrinsic and extrinsic relationships. However, the potential of context modeling is currently under-explored and most of the existing solutions are either context-free or restricted to simple handcrafted geometric relationships. We introduce in this paper DHCN: a novel Deep Hierarchical Context Network that leverages different sources of contexts including geometric and semantic relationships. The proposed method is based on the minimization of an objective function mixing a fidelity term, a context criterion and a regularizer. The solution of this objective function defines the architecture of a bi-level hierarchical context network;the first level of this network captures scene geometry while the second one corresponds to semantic relationships. We solve this representation learning problem by training its underlying deep network whose parameters correspond to the most influencing bi-level contextual relationships and we evaluate its performances on image annotation using the challenging imageCLEF benchmark.

关键词： Hierarchical context learning deep context-aware networks image annotation

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Lightweight Multilevel Feature Fusion Network for Hyperspectral image Classification 5

Lightweight Multilevel Feature Fusion Network for Hyperspect...

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5th international conference on Information Communication and signal Processing, ICICSP 2022

作者： Huang, Quanyu Tang, Haojin Xie, Weixin Li, Yanshan Shenzhen University Atr National Key Laboratory of Defense Technology Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (纸本)9781665485890

Hyperspectral image (HSI) classification is the key technology of remote sensing image processing. In recent years, convolutional neural network (CNN), which is a powerful feature extractor, has been introduced into the field of HSI classification. Since the features of HSI are the basis of HSI classification, how to effectively extract the spectral-spatial features from HSI with CNN has become a research hotspot. The HSI feature extraction network, based on two-dimensional (2D) and three-dimensional (3D) CNN which can extract both spectral and spatial information, may lead to the increase of parameters and computational cost. Compared with 2D CNN and 3D CNN, the number of parameters and computational cost of one-dimensional (1D) CNN will be greatly reduced. However, 1D CNN based algorithms can only extract the spectral information without considering the spatial information. Therefore, in this paper, a lightweight multilevel feature fusion network (LMFFN) is proposed for HSI classification, which aims to achieve efficient extraction of spectral-spatial features and to minimize the number of parameters. The main contributions of this paper are divided into the following two points: First, we design a hybrid spectral-spatial feature extraction network (HSSFEN) to combine the advantages of 1D, 2D and 3D CNN. It introduces the idea of depthwise separable convolution method, which effectively reduces the complexity of the proposed HSSFEN. Then, a multilevel spectral-spatial feature fusion network (MSSFFN) is proposed to further obtain more effective spectral-spatial features, which effectively fuses the bottom spectral-spatial features and the top spectral-spatial features. To demonstrate the performance of our proposed method, a series of experiments are conducted on three HSI datasets, including Indian Pine, University of Pavia, and Salinas Scene datasets. The experimental results indicate that our proposed LMFFN is able to achieve better performance than the manual f

关键词： Convolution

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Depth Completion Network Guided by RGB Attention 3

Depth Completion Network Guided by RGB Attention

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3rd ieee international conference on Frontiers technology of Information and Computer, ICFTIC 2021

作者： Liu, Kaixiang Hohai University College of Internet of Things Engineering Changzhou China

ISBN: (纸本)9781665406055

The goal of the depth completion task is to generate a dense depth map from a sparse depth map. The RGB image is usually introduced as a guide to help the network obtain the missing depth information. Due to the rich semantic features of RGB images, the method based on RGB guidance could obtain better performance. However, RGB guidance will bring wrong information in feature fusion, because the segmentation boundary of RGB features does not match the real depth distribution. A depth completion network based on the attention mechanism is proposed to correct the wrong fusion information. Bidirectional attention is introduced to correct wrong segmentation boundaries in RGB images. Compared with the SOTA methods, the proposed method still maintains excellent performance under different sparse input conditions. And the proposed method has shorter inference time and smaller model size, while obtaining competitive results. © 2021 ieee.

关键词： Semantics

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Physics-aware TomoSAR Reconstruction with Deep-and-Shallow Hybrid Regularizations

Physics-aware TomoSAR Reconstruction with Deep-and-Shallow H...

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signal, Information and Data Processing (ICSIDP), ieee international conference on

作者： Liang Liu Xiaoling Zhang Jun Shi Shunjun Wei Tianjiao Zeng Xiangdong Ma Xu Zhan University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Tomographic Synthetic Aperture Radar (TomoSAR) can produce three-dimensional images. Currently, imaging methods based on deep learning achieve high accuracy and efficiency but typically rely on supervised learning, which requires paired data. However, obtaining true scene values and highly reliable simulation data is challenging. To address this issue, this paper aims to achieve TomoSAR reconstruction using physics-aware unsupervised learning with deep-and-shallow hybrid regularizations. The proposed method generally employs the first-order gradient descent optimization algorithm for reconstruction. Here, the embedded proximal mapping steps are fused with both shallow and deep regularizations through the techniques of deep image prior and the plug-and-play denoiser, respectively. The deep regularizations are used in an unsupervised learning manner, with parameters gradually refined to optimal values during reconstruction without supervised learning. The proposed method is validated through both simulation and measured data imaging experiments. Results confirm that it is robust to noise interference while retrieving more scatterers in the scene, providing an alternative way to harness the strengths of deep learning while maintaining a physics-aware process.

关键词： Deep learning Three-dimensional displays Accuracy Supervised learning Refining Training data Reliability image reconstruction Unsupervised learning Synthetic aperture radar

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