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Signal, Information and Data Processing (ICSIDP), IEEE International Conference on

作者： Chunlei Zhao Jiarong Yao Xuhui Li Jingxiao Li Juan Li Shanghai Aerospace Electronic Technology Institute China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

High accuracy and low computational complexity are always contradictory for any grid-based DOA estimators. To overcome such difficulty, we establish a novel off-gird model based on approximated linear representation of continuous steering vector space, and propose an estimator based on grid location and adaptive step-size iteration (GLASI). The model is available for arbitrary array geometry as well as real-world arrays with imperfection, and allows optimal estimation through effective but compact approximation. Either MUSIC-type or sparse-based algorithm can be applied for coarse grid location, which enables GLASI to work regardless of source coherence and the number of snapshots. Simulation results verify GLASI enjoys remarkable performance superiority over its on-grid counterparts, with low grid density and thereby high computational efficiency.

关键词： Geometry Adaptation models Direction-of-arrival estimation Array signal processing Computational modeling Estimation approximation algorithms Iterative methods Arrays Computational complexity

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OFVO: A Visual Odometry Designed for Motion Trajectory Estimation of Autonomous Vehicles

OFVO: A Visual Odometry Designed for Motion Trajectory Estim...

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Robotics, Automation and Artificial Intelligence (RAAI), IEEE International Conference on

作者： Fengchen Wei Weiji Wang Dept. Engineering and Design University of Sussex Brighton UK

ISBN: (数字)9798331520038

ISBN: (纸本)9798331520045

Camera motion estimation is crucial for measuring the trajectory of moving objects. Fortunately, there is an odometry to help us solve this problem. Unlike other research works that primarily rely on odometry for indoor robots and drones, we aim to utilize visual odometry to enhance our autonomous driving perception system. The outstanding contributions of this paper are as follows: Firstly, by utilizing the feature point method in image processing, we explore various feature extraction and matching algorithms to develop a visual odometry system based on Oriented FAST and Rotated BRIEF (ORB) and Fast Library for Approximate Nearest Neighbors (FLANN) for autonomous cars, which is named the ORB-FLANN Visual Odometry System (OFVO). Secondly, we validate our algorithm in the self-driving simulator Carla and achieve satisfactory results.

关键词： Location awareness Soft sensors Feature extraction approximation algorithms Robustness Libraries Trajectory Odometry Autonomous vehicles Visual odometry

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AI-based 6G Space-Air-Ground Integrated Network Resource Allocation Algorithm

AI-based 6G Space-Air-Ground Integrated Network Resource All...

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Radio Frequency and Antenna Technologies (iWRF&AT), IEEE International Workshop on

作者： Mingxiang Guan Zhou Wu Changwei Lv Yuxi Gan Bin Guo Xing Liu Chen Lu Le Wang Kuandong Gao Yongpan Feng Shenzhen Institute of Information Technology Shenzhen China Shenzhen Jinzhi Technology Co. Ltd China Konka Group Co. Ltd.

ISBN: (数字)9798350384765

ISBN: (纸本)9798350384772

A two-stage intelligent optimization algorithm for 6G space-air-ground integrated network resource configuration is proposed. The configuration process is divided into two stages: short-term stage and long-term stage. In the short-term stage, an approximate solution and a low-complexity solution are proposed to solve the problem of association and power allocation. In the approximate solution, a binary linear optimization problem is constructed and solved to find the optimal association, and then the power allocation is optimized using Taylor expansion approximation. In the latter solution, a low-complexity method based on frequency segmentation technology is proposed to solve the problem of association and power allocation. On the other hand, in the long-term stage, an efficient algorithm based on recursive shrinkage and reordering process is proposed to optimize the positions of airborne mobile base stations and the intelligent collaborative optimization of air and ground. Finally, simulation experiments are conducted to verify the effectiveness and superiority of the algorithm.

关键词： 6G mobile communication Simulation Collaboration Space-air-ground integrated networks approximation algorithms Taylor series Delays

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Meta-Learning-Based Classification of Melanoma Types in Dermoscopy Images

Meta-Learning-Based Classification of Melanoma Types in Derm...

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Iranian Conference of Biomedical Engineering (ICBME)

作者： Ali Norouzi Kahnamoei Ali Mahloojifar Departement of Biomedical Engineering Tarbiat Modares University Tehran Iran

ISBN: (数字)9798331529710

ISBN: (纸本)9798331529727

Melanoma is the most dangerous skin cancer that affects a large number of patients every year; however, if melanoma is diagnosed at the initial stages the probability of treatment and survival of the patient increases to a great extent. Since there is no access to a dermatologist in all places, and on the other hand, even less experienced physicians face problems in diagnosing melanoma, the existence of computer-aided diagnosis systems that can accurately diagnose skin cancers, especially melanoma is necessary. Despite extensive research in this field, most of which has been conducted on the binary classification of skin lesions into melanoma and non-melanoma, very little researches have focused on the classification of melanoma types due to the scarcity of labeled datasets for different kinds of melanoma. Deep learning algorithms require large datasets for training, making them ineffective for diagnosing rare diseases or their subtypes due to insufficient data. Therefore, the purpose of this research is to utilize new algorithms of artificial intelligence, such as meta-learning that is a sub-field of deep learning. This method is capable of classifying images even with a limited number of images, and this method is used to classify the types of melanoma. Therefore, in this research, various meta-learning algorithms, such as Prototypical Networks, Matching Networks, Relation Networks, and MAML, have been used to classify melanoma types, which are suitable for classifying images even with a limited number. Given that the meta-learning paradigm comprises two phases—meta-training and meta-testing—we utilized distinct datasets for each phase. The HAM10000 dataset was used for meta-training, while the melanoma-types dataset was employed for meta-testing. We also employed various data augmentation techniques, such as random cropping, color jittering, and random horizontal flipping, to enhance the model’s performance and achieve robust results. Out of all the algorithms, t

关键词： Metalearning Deep learning Training Technological innovation Melanoma Medical services approximation algorithms Skin Classification algorithms Lesions

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A Cost-Effective Baugh-Wooley Approximate Multiplier for FPGA-based Machine Learning Computing

A Cost-Effective Baugh-Wooley Approximate Multiplier for FPG...

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IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)

作者： Shervin Vakili Énergie Matériaux Télécommunications Research Centre Institut National de la Recherche Scientifique Montreal Canada

ISBN: (数字)9798350383638

ISBN: (纸本)9798350383645

Deep learning hardware accelerators commonly incorporate a substantial quantity of multiplier units. Yet, the considerable complexity of multiplier circuits renders them a bottleneck, contributing to increased costs and latency. Approximate computing proves to be an effective strategy for mitigating the overhead associated with multipliers. This paper introduces an original approximation technique for signed multiplication on FPGAs. The approach involves a novel segmentation method applied to the Baugh-Wooley multiplication algorithm. Each segment is optimally accommodated within look-up table resources of modern AMD-Xilinx FPGA families. The paper details the design of an INT8 multiplier using the proposed approach, presenting implementation results and accuracy assessments for the inference of benchmark deep learning models. The implementation results reveal significant savings of 53.6% in LUT utilization compared to the standard INT8 Xilinx multiplier. Accuracy measurements conducted on four popular deep learning benchmarks show an average accuracy degradation of 4.8% in post-training deployment and 0.7% after retraining. The source code for this work is available on GitHub 1 .

关键词： Deep learning Accuracy Costs Source coding Benchmark testing approximation algorithms Inference algorithms

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Boolean Matrix Factorization Algorithm Based on Error Shaping Technique and its Application on Approximate Logic Synthesis

Boolean Matrix Factorization Algorithm Based on Error Shapin...

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International Conference on Solid-State and Integrated Circuit Technology

作者： Runhua Yang Rensheng Shen School of Integrated circuits Dalian University of Technology Dalian China

ISBN: (数字)9798350361834

ISBN: (纸本)9798350361841

Boolean matrix factorization method can be used to create an approximate circuit from a given circuit. The accuracy loss of approximate circuit originates from the factorization error of each subcircuit. Hence, reducing factorization error of Boolean matrix has significant value in the approximate logic synthesis. In the case of the truth table of the logic circuit, the number of rows is much larger than the number of columns that can be utilized to further optimize the factorization method. In this brief, the error shaping technique is proposed to concentrate the factorization errors in several specific columns, which can be easily cleared by the proposed column error clear scheme. Compared with the typical factorization methods, for the truth table of an n-input, m-output logic circuit, the accuracy of proposed factorization method can be improved significantly. Finally, the proposed Boolean matrix factorization algorithm is integrated into the approximate logic synthesis tool and compared with BLASYS. The synthesis results demonstrate state-of-the-art performance.

关键词： Integrated circuit technology Accuracy Logic circuits Logic gates approximation algorithms Logic

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Hypergraph Unreliability in Quasi-Polynomial Time

arXiv

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arXiv 2024年

作者： Cen, Ruoxu Li, Jason Panigrahi, Debmalya Department of Computer Science Duke University United States Computer Science Department Carnegie Mellon University United States

The hypergraph unreliability problem asks for the probability that a hypergraph gets disconnected when every hyperedge fails independently with a given probability. For graphs, the unreliability problem has been studied over many decades, and multiple fully polynomial-time approximation schemes are known starting with the work of Karger (STOC 1995). In contrast, prior to this work, no non-trivial result was known for hypergraphs (of arbitrary rank). In this paper, we give quasi-polynomial time approximation schemes for the hypergraph unreliability problem. For any fixed Ε ∈ (0, 1), we first give a (1 + Ε)-approximation algorithm that runs in mO(log n) time on an m-hyperedge, n-vertex hypergraph. Then, we improve the running time to m · nO(log2 n) with an additional exponentially small additive term in the approximation. © 2024, CC BY.

关键词： approximation algorithms

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Study on Arc Diagnostic Device for 1000V Class Electric Vehicle Systems

Study on Arc Diagnostic Device for 1000V Class Electric Vehi...

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IEEE Conference on Electromagnetic Field Computation

作者： Jun-Kyu Park Hyun-Jae Jang Ki-Dong Song Yeon-Ho Oh Hyun-Mo Ahn Eco-Friendly Power Apparatus Research Center Korea Electrotechnology Research Institute (KERI) Changwon Republic of Korea

ISBN: (数字)9798350348958

ISBN: (纸本)9798350348965

This study analyzed the necessity of applying a DC arc fault detection device to diagnose DC arcs that may occur in the line between the battery and inverter. The arc voltage and arc power characteristics according to arc current were analyzed through constructing a 1000V, 400A battery bank and arc generator. The time required for actual arc diagnosis or blocking when using full load was figured out by using the Paukert V-I equation.

关键词： Fault detection Current measurement Electric vehicles approximation algorithms Inverters Generators Batteries

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Algorithm Improvement for Bacterial Detection in Microfluidic Environments

Algorithm Improvement for Bacterial Detection in Microfluidi...

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Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

作者： Yang Liao Jixiang Zu Chengpan Lei Hanyang Bao Siqi Dong Ying Xu Hangzhou Dianzi University Hangzhou China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

In this work, we applied a combination of microfluidic chip technology and microscopic imaging to observe bacterial growth under antibiotic influence allows for faster and more accurate assessment of antibiotic efficacy against bacteria, which is crucial for clinical diagnosis and prevention of antibiotic misuse. This study proposes an algorithm improvement method for E. coli detection on a microfluidic platform based on YOLOv8, which effectively addressed issues such as missed detections in small target detection like bacteria. Firstly, a dense connection mechanism is introduced in the C2f module. Secondly, Attention-based Intra-scale Feature Interaction is added deep within the backbone to enhance the model's capability in recognizing and localizing small targets. Additionally, an iterative attention-based multi-scale feature fusion method is incorporated into the Neck, which not only reduces computational costs but also allows the model to focus more on relevant features, minimizing interference from redundant information. In image preprocessing, we applied contrast limited adaptive histogram equalization to the captured bacterial images. According to experimental results, the model has improved mAP by approximately 10% after preprocessing the bacterial images. Compared to the Baseline, the improved model achieved a slight decrease in accuracy but increased recall and mAP@0.5 by 5.4% and 2.4%, respectively.

关键词： Adaptation models Histograms Accuracy Target recognition Antibiotics Computational modeling Adaptive equalizers Bacteria approximation algorithms Microfluidics

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Blahut-Arimoto Algorithm for Computing Capacity Region of Gaussian Vector Broadcast Channels

Blahut-Arimoto Algorithm for Computing Capacity Region of Ga...

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IEEE International Symposium on Information Theory

作者： Tian Jiao Yanlin Geng Zai Yang School of Mathematics and Statistics Xi'an Jiaotong University China State Key Laboratory of ISN Xidian University China

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

We design an algorithm from the perspective of information theory to calculate the capacity region of the Gaussian vector broadcast channel with private messages. For a continuous channel, a common method to approximately calculate its capacity is to apply the Blahut-Arimoto algorithm after discretization. In this work, we derive an equivalent form of the objective function and decouple the coupled variables in the original problem by exploiting the property that a Gaussian distribution is uniquely determined by its mean and variance. And thus develop a Gaussian Blahut-Arimoto algorithm without discretization.

关键词： Gaussian distribution approximation algorithms Linear programming Vectors Information theory

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