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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jian Zhang Jing Ma Xiaochen Guo Lin Li Liang He School of Computer Science and Technology Xinjiang University China Xinjiang Key Laboratory of Signal Detection and Processing China School of Electronic Science and Engineering Xiamen University China Department of Electronic Engineering Tsinghua University China

With the development of deep learning, speaker recognition systems have shown increasingly better performance. The generalization ability of the models is also an important aspect of performance evaluation. Typically, a baseline system is used to compare against the improved models to demonstrate performance enhancements. However, we cannot determine the differences in learned voiceprint features between the improved models and the baseline system. This paper introduces an improved speaker recognition system based on the ECAPA-TDNN model. It utilizes stable learning to eliminate sample correlation and employs attribution analysis to compare the differences in voiceprint feature learning between the improved and baseline systems. Experimental results demonstrate that stable learning improves the model’s generalization performance and helps it learn better voiceprint features. The effectiveness and generalization capability of the proposed method are verified through experiments on the VoxCeleb, CNCeleb, and LibriSpeech datasets. This work is important for enhancing speaker recognition performance, analyzing differences in voiceprint feature learning, and promoting advancements in the field.

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A Radiation Performance Improvement Method of Extremely Low Frequency Acoustics Promoted Antenna

A Radiation Performance Improvement Method of Extremely Low ...

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2023 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition, iWEM 2023

作者： Qi, Junwei Zhang, Chenwei Li, Yingsong Huang, Zhixiang Loh, Tian Hong Chung, Kwok L. Harbin Engineering University College of Information and Communications Engineering Harbin150001 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China National Physical Laboratory Electromagnetic and Eletrochemical Technologies Department Middlesex United Kingdom Huizhou University School of Computer Science and Engineering 516007 China

ISBN: (纸本)9798350336740

In this paper, Q-value theory of conventional antennas is used to analyze reasons of low radiation performance of acoustics promoted antennas, where we modify the radiating structure of existing acoustics promoting antenna using the simulations to get the required improvement on radiation performance. The simulation experiments are carried out in the finite element simulation software Comsol Multiphysics to analyze the radiating performance. © 2023 IEEE.

关键词： Antennas

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Remarks on Gibbs Permutation Matrices for Ternary Bent Functions

Remarks on Gibbs Permutation Matrices for Ternary Bent Funct...

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International Symposium on Multiple-Valued Logic

作者： Radomir S. Stanković Milena Stanković Claudio Moraga Jaakko T. Astola Mathematical Institute of SASA Belgrade Serbia Department of Computer Science Faculty of Electronic Engineering Niš Serbia Faculty of Computer Science Technical University of Dortmund Dortmund Germany Department of Signal Processing Tampere University of Technology Tampere Finland

As in the binary case, ternary bent functions are a very small portion of the set of all ternary functions for a given number of variables. For example, for n = 2, there are 486 ternary bent functions out of 19683 ternary functions, which is 2, 47%, and this number reduces exponentially with the increase of n. However, finding, or alternatively, constructing them is a challenging task. A possible approach is based upon the manipulation of known ternary bent functions to construct other ternary bent functions. In this paper, we define Gibbs permutation matrices derived from the Gibbs derivative with respect to the Vilenkin-Chrestenson transform and propose their usage in constructing bent functions. The method can be extended to p-valued bent functions, where p is a prime larger than 3.

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Personalized Multimedia Recommendation Systems Using Higher-Order Tensor Singular-Value-Decomposition

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IEEE Transactions on Broadcasting 2024年第1期70卷 148-160页

作者： Chang, Shih Yu Wu, Hsiao-Chun Yan, Kun Chen, Xinjia Huang, Scott Chih-Hao Wu, Yiyan San Jose State University Department of Applied Data Science San JoseCA95192 United States Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center for AI Applications Chungli32003 Taiwan Guilin University of Electronic Technology Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Department of Information and Telecommunication Guilin541004 China Northwestern State University Department of Engineering Technology NatchitochesLA71497 United States National Tsing Hua University Department of Electrical Engineering Hsinchu300 Taiwan Communications Research Centre Canada OttawaK2H 8S2 Canada

Because of rapid growth of multimedia data over the Internet, the infobesity has been emerging in recent years. Many recommender systems (RSs) have been proposed using a variety of techniques, including artificial intelligence, machine learning, statistical analysis, and data mining. Nowadays, E-commerce companies such as Amazon, eBay, Netflix, etc. have applied RSs conveniently to provide better customer services than before by enabling customized multimedia-content recommendations. It is well known that classical RSs are almost all based on the user-item attribute matrices. However, this two-fold user-item relationship should be extended to the emerging many-fold relationship among an arbitrary number of attribute groups so that the corresponding new RSs may lead to better performances. Therefore, in this paper, we introduce a novel personalized multimedia recommender system (PMRS) according to our proposed new parallel higher-order tensor singular-value-decomposition (PHOTSVD) algorithm. This PHOTSVD algorithm is capable of accommodating an arbitrary number of entities such as social-media channels, users, queries, webpages, etc. in a robust RS. Furthermore, we also investigate the cold start and data bias problems critical to RSs. Finally, we compare the performances of our proposed new PMRS and three other existing tensor-based RSs over realworld data with respect to the well-adopted metric, namely normalized root-mean-square error (NRMSE). Our proposed new PMRS greatly outperforms the other three existing tensor-based RSs in terms of NRMSE. © 1963-12012 IEEE.

关键词： Singular value decomposition

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A Multi-input Deep Neural Network Framework for Non-invasive Detection of Anemia using Finger Nail Images

A Multi-input Deep Neural Network Framework for Non-invasive...

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International Conference on Biosignals, Images and Instrumentation (ICBSII)

作者： Krithika S Ajay Kumar Reddy Poreddy Thunakala Balakrishna Priyanka Kokil Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India Department of Electronics and Communication Engineering Advanced Signal and Image Processing (ASIP) Lab Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai

ISBN: (数字)9798350350951

ISBN: (纸本)9798350350968

Anemia, characterized by a deficiency in red blood corpuscles or hemoglobin, poses a significant global health challenge, particularly affecting vulnerable populations. Traditional diagnostic methods often involve invasive procedures, posing challenges in resource-limited settings. This study aims to explore non-invasive anemia detection using fingernail images and convolutional neural networks (CNNs) as a promising alternative to conventional diagnostic approaches. The study utilizes a dataset of fingernail images collected from hospitals in Ghana, comprising both anemic and non-anemic cases. The dataset undergoes preprocessing, including selective enhancement of red components, conversion to the CIELAB color space, and feature extraction. A multi-input Deep Neural Network (DNN) framework employing pre-trained CNNs is proposed for anemia classification. The pre-trained CNN architectures include EfficientNet B1, EfficientNet B4, and MobileNet V3. The framework’s performance was assessed using two methodologies: The first involved random shuffling of the dataset, followed by division into training, testing, and validation sets, with evaluation metrics including Accuracy, Precision, F1 scores, and a Confusion Matrix. The second employed five-fold cross-validation, measured using accuracy. The evaluation of the proposed DNN framework using both of the methodologies indicates that EfficientNet B4 achieved the highest testing accuracy (97.87%), precision (97.88%), recall (97.87%), and F1 score (97.88%) and a cross-validation accuracy of 97.37% for the first and second methodologies respectively making it best fit for the proposed DNN framework. The findings demonstrate that the proposed framework yields promising results, especially under the second approach, and opens avenues for further exploration in transfer learning, fine-tuning of deep neural networks for multi-input feature integration, and cross-validation.

关键词： Training Accuracy Transfer learning Sociology Nails Quality assessment Convolutional neural networks

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An Optimal Sequence Reconstruction Algorithm for Reed-Solomon Codes

An Optimal Sequence Reconstruction Algorithm for Reed-Solomo...

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IEEE International Symposium on Information Theory

作者： Shubhransh Singhvi Roni Con Han Mao Kiah Eitan Yaakobi Signal Processing & Communications Research Center IIIT Hyderabad India Department of Computer Science Technion-Israel Institute of Technology Haifa Israel School of Physical and Mathematical Sciences Nanyang Technological University Singapore

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

The sequence reconstruction problem, introduced by Levenshtein in 2001, considers a scenario where the sender transmits a codeword from some codebook, and the receiver obtains $N$ noisy outputs of the codeword. We study the problem of efficient reconstruction using $N$ outputs that are corrupted by substitutions. Specifically, for the ubiquitous Reed-Solomon codes, we adapt the Koetter-Vardy soft-decoding algorithm, presenting a reconstruction algorithm capable of correcting beyond Johnson radius. Furthermore, the algorithm uses $\mathrm{O}(nN)$ field operations, where $n$ is the codeword length.

关键词： Reed-Solomon codes Receivers Reconstruction algorithms Noise measurement Information theory

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MoD-ART: Modal Decomposition of Acoustic Radiance Transfer

arXiv

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arXiv 2024年

作者： Scerbo, Matteo Schlecht, Sebastian J. Ali, Randall Savioja, Lauri De Sena, Enzo Institute of Sound Recording University of Surrey Guildford United Kingdom Multimedia Communications and Signal Processing University of Erlangen-Nuremberg Germany Department of Computer Science Acoustics Lab Aalto University Espoo Finland

Modeling late reverberation at interactive speeds is a challenging task when multiple sound sources and listeners are present in the same environment. This is especially problematic when the environment is geometrically complex and/or features uneven energy absorption (e.g. coupled volumes), because in such cases the late reverberation is dependent on the sound sources’ and listeners’ positions, and therefore must be adapted to their movements in real time. We present a novel approach to the task, named modal decomposition of Acoustic Radiance Transfer (MoD-ART), which can handle highly complex scenarios with efficiency. The approach is based on the geometrical acoustics method of Acoustic Radiance Transfer, from which we extract a set of energy decay modes and their positional relationships with sources and listeners. In this paper, we describe the physical and mathematical meaningfulness of MoD-ART, highlighting its advantages and applicability to different scenarios. Through an analysis of the method’s computational complexity, we show that it compares very favourably with ray-tracing. We also present simulation results showing that MoD-ART can capture multiple decay slopes and flutter echoes. Copyright © 2024, The Authors. All rights reserved.

关键词： Reverberation

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Blockchain-Based Resilient Pairing and Bonding of BLE Devices Using Deep Reinforcement Learning

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IEEE Transactions on Consumer Electronics 2024年

作者： Devi, Aguru Aswani Babu, Erukala Suresh Rathore, Rajkumar Singh Jhaveri, Rutvij H. Benedetto, Francesco SRM University AP Dept. of Computer Science and Engineering India National Institute of Technology Warangal Dept. of Computer Science and Engineering Telangana India Cardiff Metropolitan University Dept. of Computer Science Cardiff School of Technologies United Kingdom Pandit Deendayal Energy University Computer Science and Engineering School of Technology Gandhinagar India Roma Tre University Signal Processing for Telecommunications and Economics Department of Economics Rome Italy

In the day-to-day deployment of the Internet of Things (IoT), IPv6-over-Bluetooth Low Energy (BLE) devices became significant enablers due to their low power, low range, and effortless connectivity. To overcome the latency during BLE device migration, we have proposed a Blockchain-based resilient pairing and bonding of BLE devices using a lightweight authenticated encryption scheme. The bonding information is stored on the local ledger of the intermediate IPv6 over Low power Wireless Personal Area Networks (6LoWPAN) for BLE (6LoWPAN for BLE or 6LoWBLE) over gateway. Whenever the BLE device migrates from one gateway to another, the bonding information is transferred to the local ledger of the current gateway through the global ledger with the Proof-of-Voting consensus algorithm. The resilience of the proposed authentication scheme is analyzed using BAN logic. In addition, we proposed a Man-in-the-Middle (MITM) detection framework using Deep Reinforcement Learning (DRL) due to the escalation of MITM attacks on BLE devices. The DRL-based model is implemented in Python, and its performance is evaluated using the Kitsune Network Attack Dataset. The performance of the overall framework is tested on the Hyperledger Fabric, which results in low latency and a low average pairing time in comparison with existing frameworks. © 1975-2011 IEEE.

关键词： Deep reinforcement learning

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Securing Data in an Internet of Things Network Using Blockchain Technology: Smart Home Case

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SN computer science 2022年第2期3卷 167页

作者： Tchagna Kouanou, Aurelle Tchito Tchapga, Christian Sone Ekonde, Michael Monthe, Valery Mezatio, Brice Anicet Manga, Josépha Simo, Gael R. Muhozam, Yves Department of Computer Engineering College of Technology University of Buea Buea Cameroon Department of Training Research Development and Innovation InchTech’s Solutions Yaoundé Cameroon Department of Electrical and Electronic Engineering College of Technology University of Buea Buea Cameroon Department of Computer Science Faculty of Sciences University of Yaoundé 1 Yaoundé Cameroon Research Unit Condensed Matter Electronics and Signal Processing Department of Physics Faculty of Science University of Dschang PO Box 67 Dschang Cameroon

Every day, innovative technologies emerge (5G networks, quantum computing) or change to existing technologies (big data, deep learning, blockchain, etc.). These technologies are expected to play an essential role on the Internet of Things (IoT) systems and applications. The IoT architecture generates substantial amounts of diverse types of data at extremely high speeds. All these generated data need to be secure. Scalable and secure data sharing is essential for all technological domains. Taking into consideration of blockchain technology today in data security, we can also think about a method to secure IoT data based on blockchain. In this paper, we proposed a blockchain method to secure data in an IoT architecture. We used EOS Blockchain for its scalability, flexibility and native enhanced throughput. Architecture in a concept of a smart home using blockchain is proposed. We combine Arduino, Raspberry Pi and sensors to create an IoT ecosystem for a smart home. Data are collected and stored in EOS blockchain for better security and integrity. We use Raspberry Pi as a client to get data in our developed blockchain. The real implementation is performed and the obtained results show us that we can send more than 500 data per second in our EOS blockchain. We discuss our results and saw that our blockchain method outperformed those in related works in terms of the number of data stored per millisecond in blockchain and the scalability. The proposed EOS blockchain pipeline for securing Smart Home data is provided at the end of this paper. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2022.

关键词： Architecture Blockchain Data security IoT Smart home

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Instruction Tuning-free Visual Token Complement for Multimodal LLMs

arXiv

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arXiv 2024年

作者： Wang, Dongsheng Cui, Jiequan Li, Miaoge Lin, Wang Chen, Bo Zhang, Hanwang College of Computer Science and Software Engineering Shenzhen University China School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computing Hong Kong Polytechnic University Hong Kong College of Computer Science and Technology Zhejiang University China National Key Lab of Radar Signal Processing Xidian University China

As the open community of large language models (LLMs) matures, multimodal LLMs (MLLMs) have promised an elegant bridge between vision and language. However, current research is inherently constrained by challenges such as the need for high-quality instruction pairs and the loss of visual information in image-to-text training objectives. To this end, we propose a Visual Token Complement framework (VTC) that helps MLLMs regain the missing visual features and thus improve response accuracy. Specifically, our VTC integrates text-to-image generation as a guide to identifying the text-irrelevant features, and a visual selector is then developed to generate complementary visual tokens to enrich the original visual input. Moreover, an iterative strategy is further designed to extract more visual information by iteratively using the visual selector without any additional training. Notably, the training pipeline requires no additional image-text pairs, resulting in a desired instruction tuning-free property. Both qualitative and quantitative experiments demonstrate the superiority and efficiency of our VTC. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual BASIC

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