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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Satish Mulleti Kumar Appaiah Sibi Raj B. Pillai Department of Electrical Engineering Indian Institute of Technology Bombay Mumbai India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Modulo-folding ADCs (MF-ADCs) offer a potential alternative to conventional ADCs by requiring fewer bits. However, the algorithms that follow an MF-ADC typically require an unfolding method, which demands significant oversampling. In this paper, we explore the problem of symbol detection in digital communication at a receiver using an MF-ADC. We demonstrate that, in certain noisy conditions, unfolding is not necessary for detection, allowing the MF-ADC to operate at a lower rate. Additionally, we show that any unfolding process may negate the benefits of fewer bits or reduced quantization error associated with MF-ADCs. We derive theoretical bounds and discuss optimal symbol design to achieve the best performance. The proposed approach, which eliminates the need for unfolding, can facilitate the development of low-rate MF-ADCs for various other applications.

关键词： Quantization (signal) Bit rate Signal processing algorithms Receivers Digital communication Noise measurement Speech processing detection algorithms Communication symbols Signal to noise ratio

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Research on Big Data Distributed Computing Model and Algorithm Optimization for Complex Network Analysis

Research on Big Data Distributed Computing Model and Algorit...

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Digital Analysis and Processing, Intelligent Computation (DAPIC), International Conference on

作者： Aiting Sun Liaoning Vocational College of light industry Liaoning China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

With the advent of the era of big data, the traditional stand-alone computing model can no longer meet the needs of complex network analysis, and the distributed computing model provides a new solution. This paper first introduces the importance of complex network analysis and the challenges brought by big data, then discusses the application of distributed computing model in complex network analysis in detail, and focuses on the distributed computing algorithm based on MapReduce. Then, this paper puts forward an optimization strategy of distributed community detection algorithm based on modularity optimization. This strategy performs community detection tasks in parallel through MapReduce framework, and optimizes community division through iterative updating to maximize modularity. The experimental results show that the optimized algorithm has a significant improvement in computing time, accuracy and resource consumption. The distributed computing model based on MapReduce framework and its optimization strategy have obvious advantages in dealing with large-scale complex networks. It can not only improve the calculation efficiency and accuracy, but also reduce the resource consumption, which provides an efficient and feasible solution for complex network analysis.

关键词： Analytical models Accuracy Computational modeling Memory management Complex networks Big Data Data models Distributed computing detection algorithms Optimization

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Efficient Partial Order Based Transaction Processing for Permissioned Blockchains

Efficient Partial Order Based Transaction Processing for Per...

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International Conference on Data Engineering

作者： Shuai Zhao Zhiwei Zhang Junkai Wang Ye Yuan Meihui Zhang Guoren Wang Jiang Xiao Beijing Institute of Technology Beijing China Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

With the development of permissioned blockchains, transaction processing plays an increasingly crucial role in improving performance. The execution and consensus phases in existing transaction processing methods are based on total order. The consensus phase constructs a total order representing the execution order and submission order of different transactions. Then, in the execution phase, transactions are executed or validated sequentially based on this total order. However, while the total order guarantees consistency across nodes, it also restricts the execution order of any two transactions, even if there is no conflict between them. Additionally, existing methods process transactions based on block snapshots before the consensus phase, but these snapshots are only updated after reaching consensus. The stale data between these phases results in high transaction abort rates due to delays in updated visibility. Therefore, we propose a novel blockchain called Partial Order-Based Ledger (POBL). POBL constructs a partial order of transaction executions in the execution phase and then, in the consensus phase, builds a consistent submission order based on this execution partial order. Notably, POBL allows the visibility of transaction processing results in the execution phase even before committing its block. To ensure the correct execution, the consensus and execution phases need to consider the consistency of data and the dependencies between transactions. Therefore, we use a graph, PGraph, to capture the concurrent partial order in the execution phase. In the consensus phase, we propose a consensus algorithm to conduct the maximal common subgraph, CPGraph, based on the PGraphs of different nodes. We propose to validate blocks and transactions in parallel based on CPGraph, without being restricted by the order between blocks. We perform extensive experiments compared to state-of-the-art architectural systems, and our method significantly outperforms existing work.

关键词： Schedules Consensus algorithm Throughput Data engineering Blockchains Delays detection algorithms

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Digital Twin Enabled Collision Early Warning for Smart Site Safety System (SSSS)

Digital Twin Enabled Collision Early Warning for Smart Site ...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Shuxuan Zhao Chenglin Yu Xinye Qu Zhengxu Zhu JunJie Lei Ray Y. Zhong Department of Industrial and Manufacturing Systems Engineering The University of Hong Kong Hong Kong China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Construction industry is one of the most dangerous industry sectors as its high accident rates and fatality rates. Being struck by working construction machines is one major reason for accidents in construction sites. In this research, a digital twin-enabled collision warning system is designed to avoid collision and improve safety in construction industry. The system is composed of physical entity layer, virtual entity layer, digital data layer, service layer and connection. The digital data layer collects and analyzes virtual information collected from the physical layer. The virtual entity layer is responsible for constructing digital models with the same constraints, behaviors, and rules as the physical models. The service layer can provide on-site services such as on-site detection, collision warning, and motion status monitoring for site managers and workers. Besides, deep learning-based algorithms are also designed to achieve the classification, location, and key points detection of construction machines. The experiment results show that the proposed deep learning detection algorithms can achieve more than 90% detection performance, and the proposed framework can provide precise on-site collision early warning.

关键词： Deep learning Alarm systems Activity recognition Safety Digital twins Synchronization detection algorithms Monitoring Accidents Construction industry

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Deep Models for Peak Finding with FMCW Radar Depth Estimation Applications

Deep Models for Peak Finding with FMCW Radar Depth Estimatio...

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IEEE Southeastcon

作者： Chris Beam Andrew Willis Kevin Brink Adrienne Dorr Electrical and Computer Engineering University of North Carolina at Charlotte Charlotte NC USA Munitions Directorate Air Force Research Laboratory FL USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

This article describes deep learning models for peak detection for FMCW radar depth estimation. Peak detection is a common problem for many fields, where in radar it is used for distance and speed estimation by analyzing the in-phase and quadrature components of the signal. Traditional methods for peak detection focus on local maxima and local derivative information to determine the location of a peak, however, because of this it is fine-tuned for specific peak shapes. We seek to approach this problem using a custom deep neural network to find peaks of various shapes and sizes that would not be found using traditional methods. Using this approach, it is demonstrated that it can outperform commonly used peak detection algorithms such as Scipy's find_peaks. For this article, we have: (1) a new AI algorithm for peak detection for FMCW radar depth estimation, (2) viewed the problem as a regression vs classification problem, and (3) Considered the pros and con of multiple loss functions for both problems.

关键词： Deep learning Shape Depth measurement Radar detection Estimation Radar Artificial neural networks Classification algorithms detection algorithms Artificial intelligence

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ERASER: Efficient RTL FAult Simulation Framework with Trimmed Execution Redundancy

ERASER: Efficient RTL FAult Simulation Framework with Trimme...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Jiaping Tang Jianan Mu Silin Liu Zizhen Liu Feng Gu Xinyu Zhang Leyan Wang Shengwen Liang Jing Ye Huawei Li Xiaowei Li State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China CASTEST Co. Ltd. Beijing China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

As intelligent computing devices increasingly integrate into human life, ensuring the functional safety of the corresponding electronic chips becomes more critical. A key metric for functional safety is achieving a sufficient fault coverage. To meet this requirement, extensive time-consuming fault simulation of the RTL code is necessary during the chip design phase. The main overhead in RTL fault simulation comes from simulating behavioral nodes (always blocks). Due to the limited fault propagation capacity, fault simulation results often match the good simulation results for many behavioral nodes. A key strategy for accelerating RTL fault simulation is the identification and elimination of redundant simulations. Existing methods detect redundant executions by examining whether the fault inputs to each RTL node are consistent with the good inputs. However, we observe that this input comparison mechanism overlooks a significant amount of implicit redundant execution: although the fault inputs differ from the good inputs, the node's execution results remain unchanged. Our experiments reveal that this overlooked redundant execution constitutes nearly half of the total execution overhead of behavioral nodes, becoming a significant bottleneck in current RTL fault simulation. The underlying reason for this overlooked redundancy is that, in these cases, the true execution paths within the behavioral nodes are not affected by the changes in input values. In this work, we propose a behavior-level redundancy detection algorithm that focuses on the true execution paths. Building on the elimination of redundant executions, we further developed an efficient RTL fault simulation framework, Eraser. Experimental results show that compared to commercial tools, under the same fault coverage, our framework achieves a 3.9 × improvement in simulation performance on average.

关键词： Fault diagnosis Measurement Runtime Codes Simulation Redundancy Europe Safety detection algorithms Chip scale packaging

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Deep Learning-Based Object Recognition in AR-VR Environment

Deep Learning-Based Object Recognition in AR-VR Environment

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Suchi Sharma Manoj Diwakar Priyansh Kumar Aditya Joshi Prabhishek Singh Gunjan Mehra Department of CSE Graphic Era Deemed to be University Dehradun India Department of Computer Applications Graphic Era Deemed to be University Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Artificial intelligence (AI), AR-VR (Augmented Reality and Virtual Reality) have recently made significant strides, paving the way for innovation and change in a wide range of fields and industries. The quick advancements in AR-VR and computer vision (CV) made it easier to comprehend and analyze the surroundings. In this study, works that combined augmented, virtual reality (AR-VR) and deep learning for object recognition throughout the recent years are thoroughly reviewed and presented. The three primary focuses of this paper are: 1) List out the object identification techniques used in AR-VR in detail, 2) Describe the techniques, tools, and frameworks that are being utilized to create various types of AR- VR object detection, 3) Discussing object detection in AR-VR in detail and 4) Concluding the problems and identifying possible study fields in the AR-VR environment.

关键词： Deep learning Energy consumption Technological innovation Solid modeling Object detection Object recognition detection algorithms Artificial intelligence Wearable devices Sun

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IEEE Winter Applications and Computer Vision Workshops (WACVW)

作者： Dennis Menn Feng Liang Hung-Yueh Chiang Diana Marculescu Chandra Family Department of Electrical and Computer Engineering The University of Texas at Austin

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

Artifact detection algorithms are crucial to correcting the output generated by diffusion models. However, because of the variety of artifact forms, existing methods require substantial annotated data for training. This requirement limits their scalability and efficiency, which restricts their wide application. This paper shows that the similarity of denoised images between consecutive time steps during the sampling process is related to the severity of artifacts in images generated by diffusion models. Building on this observation, we introduce the concept of Similarity Trajectory to characterize the sampling process and its correlation with the image artifacts presented. Using an annotated data set of 680 images, which is only 0.1% of the amount of data used in the prior work, we trained a classifier on these trajectories to predict the presence of artifacts in images. By performing 10-fold validation testing on the balanced annotated data set, the classifier can achieve an accuracy of 72.35%, highlighting the connection between the Similarity Trajectory and the occurrence of artifacts. This approach enables differentiation between artifact-exhibiting and natural-looking images using limited training data. Project page: https://***/DennisMenn/Similarity_***

关键词： Training Correlation Image synthesis Scalability Conferences Training data Diffusion models Trajectory detection algorithms Testing

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Identifying Incoherent Search Sessions: Search Click Fraud Remediation Under Real-World Constraints

Identifying Incoherent Search Sessions: Search Click Fraud R...

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IEEE Symposium on Security and Privacy

作者： Runze Zhang Ranjita Pai Sridhar Mingxuan Yao Zheng Yang David Oygenblik Haichuan Xu Vacha Dave Cormac Herley Paul England Brendan Saltaformaggio Georgia Institute of Technology Microsoft Corporation

ISBN: (数字)9798331522360

ISBN: (纸本)9798331522377

Search engines and advertisers continuously suffer substantial financial losses from click fraud, which poses challenges to existing detection algorithms. Even more concerning, despite ongoing advancements, our understanding of click fraud remains limited, leaving room for sophisticated fraudulent techniques to bypass existing detection measures. In this study, we pivot from examining individual search requests to analyzing search sessions, defined as sequences of consecutive search queries made by the same user. We found that benign users exhibit coherent behavior patterns within these sessions, which contrast clearly with those of fraudulent actors. Specifically, legitimate users tend to conduct searches focused on a single topic at a time. In contrast, fraudsters or automated bots often exhibit diverse, illogical, and incoherent search behaviors within a session. To address this behavioral distinction, we propose CoSeC, a system designed to quantify the “incoherence index” of search sessions. CoSeC integrates literal semantic, temporal, and ad-click behavioral features to evaluate sessions' coherence quantitatively. Our evaluation of CoSeC demonstrates high efficacy, achieving a precision of 95.79% and a recall of 92.40% in identifying incoherent sessions, highlighting CoSeC's substantial potential to enhance real-world click fraud detection.

关键词： Data privacy Semantics Prototypes Coherence Search engines Feature extraction Fraud Security detection algorithms

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A Correlation detection Method of Low SNR Based on Multi-Channelization

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IEEE SIGNAL PROCESSING LETTERS 2020年 27卷 1375-1379页

作者： Nie, Donghu Xie, Kai Zhou, Feng Qiao, Gang Harbin Engn Univ Coll Underwater Acoust Engn Acoust Sci & Technol Lab Harbin 150001 Peoples R China Harbin Engn Univ Key Lab Marine Informat Acquisit & Secur Minist Ind & Informat Technol Harbin 150001 Peoples R China

This letter proposes a correlation detection algorithm based on multi-channelization. The existing method has poor performance for detecting a time-varying signal with a low signal-to-noise ratio(SNR). To overcome this problem, we use the method of correlation summation after multi-channelization. According to the difference between the correlation between signals and noise in the received data, the single-channel data is converted into multi-channel data by a multi-channelization method without changing the signals. Then the noise is filtered by correlation calculation of multi-channel data, which can improve the detection probability of signals under low SNR. Based on the theory of multi-channelization, we propose two different methods to detect signals. To better reflect the performance of the algorithm, we compared several classical signal detection methods, such as energy detection (ED), correlation detection (CD), and cyclic spectrum detection (CSD). Simulation analysis and experimental results show that the multi-channelization correlation detection algorithm has better detection performance when the SNR is low.

关键词： Correlation Signal to noise ratio Signal processing algorithms Signal detection detection algorithms Analytical models Underwater acoustics Cross-correlation multi-channelization low SNR signal detection time-varying signal

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