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Construction Progress Monitoring In Smart Cities Using Deep Alexnet

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Construction Progress Monitoring In Smart Cities Using Deep ...

Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： A. Rajavel S. Karkuzhali Sarvdaman Sharma Sitesh Kumar Singh Senthilkumar P S. Jhansi Ida Electronics and Instrumentation Engineering Kamaraj College of Engineering and Technology Virudhnagar Department of Electrical and Electronics Engineering R.M.D. Engineering College Chennai Department of Civil Engineering University Institute of Engineering and Technology University of Jammu Jammu & Kashmir India Department of Civil Engineering Wollega University Nekemte Oromia Ethiopia KL School of Architecture Koneru Lakshmaiah Education Foundation Guntur AP India Department of Computer Science and Business Systems R.M.K Engineering College India

Construction progress monitoring plays a crucial role in ensuring the timely and efficient completion of infrastructure projects within smart cities. This paper proposes a novel approach utilizing the Deep AlexNet architecture for construction progress assessment in smart cities. The Deep AlexNet model is trained on a diverse dataset of images captured from construction sites, enabling it to accurately analyze the visual cues indicative of construction advancement. By leveraging deep learning techniques, our system extracts and processes features from images, enabling automated and real-time progress evaluation. The proposed method contributes to enhancing construction management practices by providing stakeholders with a reliable tool to monitor and manage construction projects efficiently. Experimental results demonstrate the effectiveness and robustness of the Deep AlexNet-based approach in accurately assessing construction progress, thereby facilitating informed decision-making throughout the construction lifecycle.

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Robust Semantic Transmission of Images with Generative Adversarial Networks

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Robust Semantic Transmission of Images with Generative Adver...

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Qi He Haohan Yuan Daquan Feng Bo Che Zhi Chen Xiang-Gen Xia National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu China College of Electronics and Information Engineering Shenzhen University Shenzhen China Department of Electrical and Computer Engineering University of Delaware Newark DE USA

ISBN: (纸本)9781665435413

Image compression and bit transmission are con-ducted separately in most existing methods for image trans-mission, leading to possible transmission failure or a waste of communication resource for a time-varying channel condition. This paper proposes a neural network-based image transmission system trained by generative adversarial networks (GANs) aiming to achieve robust transmission. Specifically, the deep semantic of an input image is extracted and represented as bit streams at the transmitter, and the receiver reconstructs the original image based on possible bit error and the same background knowledge as the transmitter. Experimental results show that the proposed robust transmission system trained by GAN can adapt to the current communication condition, and achieve a high-quality reconstruction even with a high transmission error rate and a smaller transmission data size than engineered codecs such as JPEG.

关键词： Image coding Transmitters Image communication Semantics Transform coding Receivers Time-varying channels

Integration of Droplet Microfluidic Tools for Single-cell Functional Metagenomics:An engineering Head Start

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Genomics, Proteomics & Bioinformatics 2021年第3期19卷 504-518页

作者： David Conchouso Amani Al-Ma’abadi Hayedeh Behzad Mohammed Alarawi Masahito Hosokawa Yohei Nishikawa Haruko Takeyama Katsuhiko Mineta Takashi Gojobori Department of Industrial and Mechanical Engineering Universidad de las Américas PueblaPuebla 72810Mexico Computational Bioscience Research Center King Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Biological and Environmental Sciences and Engineering Division King Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Research Organization for Nano&Life Innovation Waseda UniversityTokyo 162-0041Japan Department of Life Science and Medical Bioscience Waseda UniversityTokyo 162-8480Japan Institute for Advanced Research of Biosystem Dynamics Waseda Research Institute for Science and EngineeringWaseda UniversityTokyo 169-8555Japan Computational Bio Big-Data Open Innovation Laboratory AIST-Waseda UniversityTokyo 169–0072Japan Computer Electricaland Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia

Droplet microfluidic techniques have shown promising outcome to study single cells at high ***,their adoption in laboratories studying“-omics”sciences is still irrelevant due to the complex and multidisciplinary nature of the *** facilitate their use,here we provide engineering details and organized protocols for integrating three droplet-based microfluidic technologies into the metagenomic pipeline to enable functional screening of bioproducts at high ***,a device encapsulating single cells in droplets at a rate of~250 Hz is described considering droplet size and cell ***,we expand on previously reported fluorescence-activated droplet sorting systems to integrate the use of 4 independent fluorescence-exciting lasers(i.e.,405,488,561,and 637 nm)in a single platform to make it compatible with different fluorescence-emitting *** this sorter,both hardware and software are provided and optimized for effortlessly sorting droplets at 60 ***,a passive droplet merger is also integrated into our pipeline to enable adding new reagents to already-made droplets at a rate of 200 ***,we provide an optimized recipe for manufacturing these chips using silicon dry-etching *** of the overall integration and the technical details presented here,our approach allows biologists to quickly use microfluidic technologies and achieve both single-cell resolution and high-throughput capability(>50,000 cells/day)for mining and bioprospecting metagenomic data.

关键词： Droplet microfluidics Single cell Metagenomics Droplet sorter Biotechnology

Semi-Self Representation Learning for Crowdsourced WiFi Trajectories

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arXiv 2025年

作者： Kuo, Yu-Lin Tseng, Yu-Chee Chiang, Ting-Hui Chen, Yan-Ann Department of Computer Science National Yang Ming Chiao Tung University Taiwan Advanced Technology Laboratory Chunghwa Telecom Laboratories Taiwan Department of Computer Science and Engineering Yuan Ze University Taiwan

WiFi fingerprint-based localization has been studied intensively. Point-based solutions rely on position annotations of WiFi fingerprints. Trajectory-based solutions, however, require end-position annotations of WiFi trajectories, where a WiFi trajectory is a multivariate time series of signal features. A trajectory dataset is much larger than a pointwise dataset as the number of potential trajectories in a field may grow exponentially with respect to the size of the field. This work presents a semi-self representation learning solution, where a large dataset C of crowdsourced unlabeled WiFi trajectories can be automatically labeled by a much smaller dataset C˜ of labeled WiFi trajectories. The size of C˜ only needs to be proportional to the size of the physical field, while the unlabeled C could be much larger. This is made possible through a novel "cut-and-flip" augmentation scheme based on the meet-in-the-middle paradigm. A two-stage learning consisting of trajectory embedding followed by endpoint embedding is proposed for the unlabeled C. Then the learned representations are labeled by C˜ and connected to a neural-based localization network. The result, while delivering promising accuracy, significantly relieves the burden of human annotations for trajectory-based localization. © 2025, CC BY-NC-ND.

关键词： Deep learning

NTIRE 2023 Challenge on Efficient Super-Resolution: Methods and Results

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NTIRE 2023 Challenge on Efficient Super-Resolution: Methods ...

2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Li, Yawei Zhang, Yulun Timofte, Radu Van Gool, Luc Yu, Lei Li, Youwei Li, Xinpeng Jiang, Ting Wu, Qi Han, Mingyan Lin, Wenjie Jiang, Chengzhi Luo, Jinting Fan, Haoqiang Liu, Shuaicheng Wang, Yucong Cai, Minjie Li, Mingxi Zhang, Yuhang Fan, Xian-Jun Sheng, Yankai Mao, Yanyu Zhang, Nihao Wang, Qian Zheng, Mingjun Sun, Long Pan, Jinshan Dong, Jiangxin Tang, Jinhui Yang, Zhongbao Wang, Yan Pan, Erlin Cai, Qixuan Dai, Xinan Zhussip, Magauiya Kalyazin, Nikolay Vyal, Dmitry Zou, Xueyi Yan, Youliang Chung, Heaseo Zhang, Jin Yu, Gaocheng Zhang, Feng Wang, Hongbin Liao, Bohao Du, Zhibo Wu, Yu-Liang Shi, Gege Peng, Long Wang, Yang Cao, Yang Zha, Zhengjun Huang, Zhi-Kai Chen, Yi-Chung Chiang, Yuan-Chun Yang, Hao-Hsiang Chen, Wei-Ting Chang, Hua-En Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Liu, Xin Pan, Jiahao Yu, Hongyuan Yu, Weichen Ge, Lin Dong, Jiahua Zou, Yajun Wu, Zhuoyuan Han, Binnan Zhang, Xiaolin Zhang, Heng Yin, Xuanwu Zuo, Kunlong Deng, Weijian Yuan, Hongjie Lu, Zengtong Ouyang, Mingyu Ma, Wenzhuo Liu, Nian Zheng, Hanyou Zhang, Yuantong Zhang, Junxi Chen, Zhenzhong Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Zhu, Yurui Wang, Xi Fu, Xueyang Zha, Zheng-Jun Yin, Daheng Liu, Mengyang Chen, Baijun Li, Ao Luo, Lei Jin, Kangjun Zhu, Ce Zhang, Xiaoming Xie, Chengxing Li, Linze Meng, Haiteng Zhang, Tianlin Li, Tianrui Zhao, Xiaole Zhang, Zhao Li, Baiang Zheng, Huan Zhao, Suiyi Gao, Yangcheng Ren, Jiahuan Hu, Kang Shi, Jingpeng Wu, Zhijian Huang, Dingjiang Zhu, Jinchen Li, Hui Xv, Qianru Liu, Tianle Weng, Shizhuang Wu, Gang Jiang, Junpeng Liu, Xianming Jiang, Junjun Zhang, Mingjian Hu, Jing Wu, Chengxu Fan, Qinrui Feng, Chengming Luo, Ziwei Hu, Shu Lyu, Siwei Wu, Xi Wang, Xin Computer Vision Lab ETH Zurich Switzerland Computer Vision Lab University of Würzburg Germany Megvii Technology China MicroBT China College of Computer Science and Electronic Engineering Hunan University China Attrsense Xian University of Posts and Telecommunications Xi'an China National Engineering Laboratory for Cyber Event Warning and Control Technologies China Nanjing University of Science and Technology China Nankai University China University of Electronic Science and Technology of China China Tianjin University China Noah's Ark Lab Huawei Technologies McMaster University Canada Espresomedia Korea Republic of AntGroup China University of Science and Technology of China China Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan ServiceNow United States China Mobile Research Institute China Chongqing University of Technology China Multimedia Department Xiaomi Inc. China Institute of Automation Chinese Academy of Sciences China Shenyang Institute of Automation Chinese Academy of Sciences China School of Information and Communication Engineering Communication University of China China Smart Classroom Division Ruijie Networks Co. Ltd. China School of Remote Sensing and Information Engineering Wuhan University China Micro-Nano Electronics Department Shanghai Jiao Tong University Shanghai200240 China Electrical Engineering Department Faculty of Engineering Assiut University Assiut71516 Egypt Southeast University China University of Electronic Science and Technology of China Chengdu China Chongqing University of Posts and Telecommunications Chongqing China Information Technology Co. Ltd Hangzhou China Southwest Jiaotong University China National Space Science Center Chinese Academy of Science China Hefei University of Technology Hefei China Anhui University China Fried Ric

ISBN: (纸本)9798350302493

This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several aspects such as runtime, parameters, FLOPs, activations, memory footprint, and depth of RFDN while at least maintaining the PSNR of 29.00dB on DIV2K validation datasets. The challenge had 272 registered participants, and 35 teams made valid submissions. They gauge the state-of-the-art for efficient single-image super-resolution. © 2023 IEEE.

关键词： Optical resolving power

Multimode amplitude squeezing through cascaded nonlinear optical processes

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arXiv 2024年

作者： Pontula, Sahil Salamin, Yannick Roques-Carmes, Charles Soljačić, Marin Department of Physics MIT CambridgeMA02139 United States Department of Electrical Engineering and Computer Science MIT CambridgeMA02139 United States Research Laboratory of Electronics MIT CambridgeMA02139 United States E. L. Ginzton Laboratories Stanford University 348 Via Pueblo StanfordCA United States

Multimode squeezed light is enticing for several applications, from squeezed frequency combs for spectroscopy to signal multiplexing in optical computing. To generate squeezing in multiple frequency modes, optical parametric oscillators have been vital in realizing multimode squeezed vacuum states through second-order nonlinear processes. However, most work has focused on generating multimode squeezed vacua and squeezing in mode superpositions (supermodes). Bright squeezing in multiple discrete frequency modes, if realized, could unlock novel applications in quantum-enhanced spectroscopy and optical quantum computing. Here, we show how Q factor engineering of a multimode nonlinear cavity with cascaded three wave mixing processes creates strong, spectrally tunable single mode output amplitude noise squeezing over 10 dB below the shot noise limit. In addition, we demonstrate squeezing for multiple discrete frequency modes above threshold. This bright squeezing arises from enhancement of the (noiseless) nonlinear rate relative to decay rates in the system due to the cascaded generation of photons in a single idler "bath" mode. A natural consequence of the strong nonlinear coupling in our system is the creation of an effective cavity in the synthetic frequency dimension that sustains Bloch oscillations in the modal energy distribution. Bloch mode engineering could provide an opportunity to better control nonlinear energy flow in the synthetic frequency dimension, with exciting applications in quantum random walks and topological photonics. Lastly, we show evidence of long-range correlations in amplitude noise between discrete frequency modes, pointing towards the potential of long-range entanglement in a synthetic frequency dimension. © 2024, CC BY.

关键词： Q factor measurement

Simon's algorithm in the NISQ cloud

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arXiv 2024年

作者： Robertson, Reece Doucet, Emery Spicer, Ernest Deffner, Sebastian Department of Computer Science and Electrical Engineering University of Maryland Baltimore County BaltimoreMD21250 United States Department of Physics University of Maryland Baltimore County BaltimoreMD21250 United States sagax.ai PullmanWA99163 United States National Quantum Laboratory College ParkMD20740 United States

Simon's algorithm was one of the first problems to demonstrate a genuine quantum advantage. The algorithm, however, assumes access to noise-free qubits. In our work we use Simon's algorithm to benchmark the error rates of devices currently available in the "quantum cloud." As a main result we obtain an objective comparison between the different physical platforms made available by IBM and IonQ. Our study highlights the importance of understanding the device architectures and chip topologies when transpiling quantum algorithms onto hardware. For instance, we demonstrate that two-qubit operations on spatially separated qubits on superconducting chips should be avoided. © 2024, CC BY.

关键词： Qubits

Quantum Neural Network Compression

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arXiv 2022年

作者： Hu, Zhirui Dong, Peiyan Wang, Zhepeng Lin, Youzuo Wang, Yanzhi Jiang, Weiwen Electrical and Computer Engineering Department George Mason University FairfaxVA22030 United States Quantum Science and Engineering Center George Mason University FairfaxVA22030 United States Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States Earth and Environmental Sciences Division Los Alamos National Laboratory NM87545 United States

Model compression, such as pruning and quantization, has been widely applied to optimize neural networks on resource-limited classical devices. Recently, there are growing interest in variational quantum circuits (VQC), that is, a type of neural network on quantum computers (a.k.a., quantum neural networks). It is well known that the near-term quantum devices have high noise and limited resources (i.e., quantum bits, qubits);yet, how to compress quantum neural networks has not been thoroughly studied. One might think it is straightforward to apply the classical compression techniques to quantum scenarios. However, this paper reveals that there exist differences between the compression of quantum and classical neural networks. Based on our observations, we claim that the compilation/traspilation has to be involved in the compression process. On top of this, we propose the very first systematical framework, namely CompVQC, to compress quantum neural networks (QNNs). In CompVQC, the key component is a novel compression algorithm, which is based on the alternating direction method of multipliers (ADMM) approach. Experiments demonstrate the advantage of the CompVQC, reducing the circuit depth (almost over 2.5×) with a negligible accuracy drop ( Copyright © 2022, The Authors. All rights reserved.

关键词： Qubits

Stochastic-Robust Planning of Networked Hydrogen-electrical Microgrids: A Study on Induced Refueling Demand

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arXiv 2024年

作者： Sun, Xunhang Cao, Xiaoyu Zeng, Bo Zhai, Qiaozhu Başar, Tamer Guan, Xiaohong School of Automation Science and Engineering The Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China Coordinated Science Laboratory University of Illinois Urbana-Champaign UrbanaIL61801 United States Smart Integrated Energy Department Sichuan Digital Economy Industry Development Research Institute Sichuan Chengdu610037 China Department of Industrial Engineering The Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15106 United States Center for Intelligent and Networked Systems Department of Automation Tsinghua University Beijing100084 China

Hydrogen-electrical microgrids are increasingly assuming an important role on the pathway toward decarbonization of energy and transportation systems. This paper studies networked hydrogen-electrical microgrids planning (NHEMP), considering a critical but often-overlooked issue, i.e., the demand-inducing effect (DIE) associated with infrastructure development decisions. Specifically, higher refueling capacities will attract more refueling demand of hydrogen-powered vehicles (HVs). To capture such interactions between investment decisions and induced refueling demand, we introduce a decision-dependent uncertainty (DDU) set and build a trilevel stochastic-robust formulation. The upper-level determines optimal investment strategies for hydrogen-electrical microgrids, the lower-level optimizes the risk-aware operation schedules across a series of stochastic scenarios, and, for each scenario, the middle-level identifies the "worst" situation of refueling demand within an individual DDU set to ensure economic feasibility. Then, an adaptive and exact decomposition algorithm, based on Parametric Column-and-Constraint Generation (PC&CG), is customized and developed to address the computational challenge and to quantitatively analyze the impact of DIE. Case studies on an IEEE exemplary system validate the effectiveness of the proposed NHEMP model and the PC&CG algorithm. It is worth highlighting that DIE can make an important contribution to the economic benefits of NHEMP, yet its significance will gradually decrease when the main bottleneck transits to other system restrictions. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems