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Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), IEEE International Conference on

作者： Mohammed A. Alqodah Mahdia Tahsin Mohammad H. Omari Mustafa M. Matalgah David Harrison Dep. of Electrical & Computer Engineering University of Mississippi University MS USA Dept. of Computer and Information Science University of Mississippi University MS USA

ISBN: (数字)9798331529529

ISBN: (纸本)9798331529536

The rapid rise of commercially available drones, driven by miniaturization and affordability, has introduced potential security risks alongside numerous societal benefits. While drones serve various industries, their unregulated use necessitates robust detection and classification methods. This paper proposes a novel, efficient model for classifying unwanted drone activity. We leverage statistical and time-domain features extracted from radio frequency (RF) signals and machine learning (ML) classifiers. This approach aims to not only detect drones but also distinguish their models and operational modes. Utilizing the DroneRF dataset, our feature selection strategy prioritizes informative signals in the low-band spectrum, achieving ${100\%}$ accuracy in both drone detection and drone model identification tasks. Furthermore, our hybrid model, combining time-domain and frequency-domain features, achieves an accuracy of ${97.66\%}$ in classifying different flight modes. This demonstrates the effectiveness of this comprehensive feature extraction approach for drone activity classification.

关键词： Radio frequency Accuracy RF signals Neural networks Feature extraction Real-time systems Security Time-domain analysis Random forests Drones

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CLIP-VAD: Exploiting Vision-Language Models for Voice Activity Detection

arXiv

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arXiv 2024年

作者： Appiani, Andrea Beyan, Cigdem Dep. of Management Information and Production Engineering University of Bergamo Bergamo Italy Department of Computer Science University of Verona Verona Italy

Voice Activity Detection (VAD) is the process of automatically determining whether a person is speaking and identifying the timing of their speech in an audiovisual data. Traditionally, this task has been tackled by processing either audio signals or visual data, or by combining both modalities through fusion or joint learning. In our study, drawing inspiration from recent advancements in visual-language models, we introduce a novel approach leveraging Contrastive Language-Image Pretraining (CLIP) models. The CLIP visual encoder analyzes video segments composed of the upper body of an individual, while the text encoder handles textual descriptions automatically generated through prompt engineering. Subsequently, embeddings from these encoders are fused through a deep neural network to perform VAD. Our experimental analysis across three VAD benchmarks showcases the superior performance of our method compared to existing visual VAD approaches. Notably, our approach outperforms several audio-visual methods despite its simplicity, and without requiring pre-training on extensive audio-visual datasets. © 2024, CC BY-NC-ND.

关键词： Visual languages

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Investigating 3D reconstruction of non-collaborative surfaces through photogrammetry and photometric stereo 24

Investigating 3D reconstruction of non-collaborative surface...

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2021 24th ISPRS Congress Commission II: Imaging Today, Foreseeing Tomorrow

作者： Karami, A. Menna, F. Remondino, F. Trento Italy Dep. of Information Engineering and Computer Science University of Trento Trento Italy

3D digital reconstruction techniques are extensively used for quality control purposes. Among them, photogrammetry and photometric stereo methods have been for a long time used with success in several application fields. However, generating highly-detailed and reliable micro-measurements of non-collaborative surfaces is still an open issue. In these cases, photogrammetry can provide accurate low-frequency 3D information, whereas it struggles to extract reliable high-frequency details. Conversely, photometric stereo can recover a very detailed surface topography, although global surface deformation is often present. In this paper, we present the preliminary results of an ongoing project aiming to combine photogrammetry and photometric stereo in a synergetic fusion of the two techniques. Particularly, hereafter, we introduce the main concept design behind an image acquisition system we developed to capture images from different positions and under different lighting conditions as required by photogrammetry and photometric stereo techniques. We show the benefit of such a combination through some experimental tests. The experiments showed that the proposed method recovers the surface topography at the same high-resolution achievable with photometric stereo while preserving the photogrammetric accuracy. Furthermore, we exploit light directionality and multiple light sources to improve the quality of dense image matching in poorly textured surfaces. © 2021 International Archives of the Photogrammetry, Remote Sensing and Spatial Information sciences - ISPRS Archives. All rights reserved.

关键词： Topography

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Fine-Tuning Pre-Trained Models for Automated Analysis of Ophthalmic Imaging in Diagnosing Eye Diseases

Fine-Tuning Pre-Trained Models for Automated Analysis of Oph...

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Information Technology (ACIT)

作者： Rabia Emhamed Al Mamlook Ahmad Nasayreh Hasan Gharaibeh Mohammad Aljaidi Qais Al-Na'amneh Dalia Alzu'bi Hanin Bzizi Abdelrahman Abdeltawab Dep. of Business Administration Trine University Indiana USA Department of Industrial Engineering University Zawia Tripoli Libia Dep. Computer Science Yarmouk University Irbid Jordan Dep. of Computer Science Zarqa University Zarqa Jordan Dep. of Cyber Security Applied Science Private University Amman Jordan Dep. Information and Systems Engineering Concordia University Montreal Canada Dep of Biomedical Science Western Michigan University Kalamazoo MI USA Faculty of pharmacy Bani Sweif university Bani Suwayf Egypt

This paper searches into the convergence of such ad-vanced techniques with architectures like DenseNet201, VGG16, InceptionResNetV2, and NasNetMobile. Our focus centers on harnessing deep learning capabilities for the precise detection of eye ailments. Preliminary findings spotlight a notable uplift in diagnostic precision. Among the tested algorithms, the Nas-NetMobile architecture emerged superior, boasting an impressive accuracy rate of 92% on the chosen dataset. Through this work, we extend a significant advancement in the realm of automated eye disease diagnosis, potentially equipping medical professionals with sharper, swifter diagnostic tools. The outcomes presented herein could play a seminal role in sculpting more sophisticated and precise diagnostic approaches within ophthalmology.

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IoT-Enabled Low-Cost Fog Computing System with Online Machine Learning for Accurate and Low-Latency Heart Monitoring in Rural Healthcare Settings

arXiv

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arXiv 2023年

作者： Maneshti, Hamidreza Dadashi, Morteza Rostami, Kamyar Dept. Computer Eng. & Information Tech Engineering Kermanshah Iran Dep. Computer Science Montreal Canada

Healthcare services in rural areas face numerous challenges due to the high cost of treatment and a lack of appropriate services. The application of Internet of Things (IoT) technology has shown potential in mitigating these issues. This article discusses the potential of Internet of Things (IoT) and fog computing to reduce healthcare costs and improve patient outcomes. The use of these technologies in cardiovascular health informatics is explored, along with the economic thought process of hospital decision-makers and end-of-life practices in intensive care units. Remote monitoring using IoT devices is highlighted as a promising way to detect health issues before they become serious, leading to earlier interventions and improved health outcomes. The use of fog computing in healthcare is also discussed, with a focus on its ability to provide real-time data processing, analysis, and decision-making capabilities. The article presents a novel architecture for Device-as-a-Service, utilizing both fog and cloud computing to improve the efficiency and accuracy of ECG device processing, and concludes that it has the potential to reduce costs by up to 80% in the Iranian market. The adoption of fog computing in healthcare is acknowledged to present significant challenges, such as security and privacy concerns, but its potential benefits make it a promising technology for the future of healthcare services. © 2023, CC BY.

关键词： Electrocardiograms

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Improved Distance (Sensitivity) Oracles with Subquadratic Space

Improved Distance (Sensitivity) Oracles with Subquadratic Sp...

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Annual IEEE Symposium on Foundations of computer science

作者： Davide Bilò Shiri Chechik Keerti Choudhary Sarel Cohen Tobias Friedrich Martin Schirneck Dep. of Inf. Eng. CS and Mathematics University of L'Aquila Department of Computer Science Tel Aviv University Dep. of CS and Engineering Indian Institute of Technology Delhi School of Computer Science The Academic College of Tel Aviv-Yaffo Hasso Plattner Institute University of Potsdam Faculty of Computer Science University of Vienna

ISBN: (数字)9798331516741

ISBN: (纸本)9798331516758

A distance oracle (DO) for a graph $G$ is a data structure that, when queried with vertices $s,t$ , returns an estimate $\widehat{d}(s,t)$ of their distance in $G$ . The oracle has stretch $(\alpha, \beta)$ if the estimate satisfies $d(s,t)\leqslant \widehat{d}(s,t)\leqslant \alpha\cdot d(s,t)+\beta$ . An $f-\mathbf{edge}$ fault-tolerant distance sensitivity oracle $(f-\mathbf{DSO})$ additionally receives a set $F$ of up to $f$ edges and estimates the distance in $G-F$ . Our first contribution is the design of new distance oracles with subquadratic space for undirected graphs. We show that introducing a small additive stretch $\beta > 0$ allows one to make the multiplicative stretch $\alpha$ arbitrarily small. This sidesteps a known lower bound of $\alpha\geqslant 3$ (for $\beta=0$ and subquadratic space) [Thorup & Zwick, JACM 2005]. We present a DO for graphs with edge weights in $[0, W]$ that, for any positive integer $\ell$ and any $c\in(0,\ell/2]$ , has stretch $(1+\frac{1}{\ell},2W)$ , space $\widetilde{O}(n^{2-\frac{c}{\ell}})$ , and query time $O(n^{c})$ , generalizing results by Agarwal and Godfrey [SODA 2013] to arbitrarily dense graphs. Our second contribution is a framework that turns an $(\alpha,\beta)- \mathbf{stretch}$ DO for unweighted graphs into an $(\alpha(1+\varepsilon),\beta)-\mathbf{stretch}. f-\mathbf{DSO}$ with sensitivity $f=o(\log(n)/\log\log n)$ retaining sub-quadratic space. This generalizes a result by Bilò, Chechik, Choudhary, Cohen, Friedrich, Krogmann, and Schirneck [TheoretiCS 2024]. Combining the framework with our new DO gives an $f-\mathbf{DSO}$ that, for any $\gamma\in(0, (\ell+1)/2]$ , has stretch $((1+\frac{1}{\ell})(1+\varepsilon), 2)$ , space $n^{2-\frac{\gamma}{(t+1)(f+1)}+o(1)}/\varepsilon^{f+2}$ , and query time $\widetilde{O}(n^{\gamma}/\varepsilon^{2})$ . This is the first $f-\mathbf{DSO}$ with subquadratic space, n

关键词： computer science Fault tolerance Sensitivity Additives Fault tolerant systems Data structures

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ROME: Robustifying Memory-Efficient NAS via Topology Disentanglement and Gradient Accumulation

ROME: Robustifying Memory-Efficient NAS via Topology Disenta...

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International Conference on computer Vision (ICCV)

作者： Xiaoxing Wang Xiangxiang Chu Yuda Fan Zhexi Zhang Bo Zhang Xiaokang Yang Junchi Yan Dep. of Computer Science and Engineering & MoE Key Lab of AI Shanghai Jiao Tong University Meituan

Albeit being a prevalent architecture searching approach, differentiable architecture search (DARTS) is largely hindered by its substantial memory cost since the entire supernet resides in the memory. This is where the single-path DARTS comes in, which only chooses a single-path submodel at each step. While being memory-friendly, it also comes with low computational costs. Nonetheless, we discover a critical issue of single-path DARTS that has not been primarily noticed. Namely, it also suffers from severe performance collapse since too many parameter-free operations like skip connections are derived, just like DARTS does. In this paper, we propose a new algorithm called RObustifying Memory-Efficient NAS (ROME) to give a cure. First, we disentangle the topology search from the operation search to make searching and evaluation consistent. We then adopt Gumbel-Top2 reparameterization and gradient accumulation to robustify the unwieldy bi-level optimization. We verify ROME extensively across 15 benchmarks to demonstrate its effectiveness and robustness.

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Performance Analysis of Intelligent Reflecting Surfaces for 5G/6G-Enabled Future Smart Industries with a Focus on Millimeter-Wave Band Communications

Performance Analysis of Intelligent Reflecting Surfaces for ...

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IEEE International Conference on Communications Workshops, ICC

作者： Muhammad Farhan Khan Ali Raza Adeel Iqbal Adnan Rashid Muhammad Ali Jamshed Dirk Pesch School of Computer Science and Information Technology University College Cork Cork Ireland Dep. of Electrical and Computer Engineering COMSATS University Islamabad Pakistan Dep. of Info. & Com. Engineering Yeungnam University Gyeongsan-si Republic of Korea Dep. of Electrical & Information Engineering Politecnico di Bari Bari Italy James Watt School of Engineering University of Glasgow Glasgow U.K

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

Industry 4.0's emergence necessitates reliable communication systems. Millimeter-wave (mmWave) communication is a promising solution, but challenges like limited coverage and atmospheric absorption persist. The Intelligent Reflecting Surfaces (IRS) offers transformative potential by dynamically manipulating wave propagation to improve millimeter-wave (mmWave) communication. This research investigates the impact of the distance of Industrial User Equipment (iUE) from the IRS and Base Station (BS), and the height of the IRS from the ground, along with the impact of sum throughput by increasing the number of IRS elements. The IRS-assisted system exhibits enhanced performance within the mmWave frequency band compared to the sub-6 GHz band. The observed achievable rate gains, exceeding 200% on average, underscore the significant performance enhancement enabled by IRS dep.oyments. Moreover, increasing the number of IRS elements significantly enhances the overall throughput. The decrease in required transmit power with an increasing number of Intelligent Reflecting Surfaces IRS elements can be attributed to the enhanced ability of IRS to efficiently reflect and manipulate electromagnetic waves. As more IRS elements are dep.oyed, they collectively contribute to optimizing signal propagation, allowing for better control and redirection of the transmitted signals. This phenomenon enables the system to achieve the desired communication objectives with less overall transmit power. Additionally, the attainable capacity of the examined IRS-assisted wireless communication system exhibits growth alongside an increase in the elevation of the IRS installation. Results showcase the potential of IRSs to enhance coverage and communication reliability, providing valuable insights for designing efficient wireless systems in smart factories and Industry 4.0 applications.

关键词： Wireless communication Industries Surface waves Conferences Throughput Millimeter wave propagation Reliability engineering

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Packet Too Big Detection and its Integration into QUIC

Packet Too Big Detection and its Integration into QUIC

引用

International Conference on Signal Processing and Communication Systems, ICSPCS

作者： Timo Völker Michael Tüxen Dep. of Electrical Engineering and Computer Science FH Münster University of Applied Sciences Steinfurt Germany

A communication over an Internet Protocol (IP) based network fails if an endpoint sends packets that are too big to reach their destination and if the sender is unable to detect that. The node on the path that drops these packets should respond with a Packet Too Big (PTB) message. However, multiple scenarios exist in which the sender will not receive a PTB message. Even if it does, it refrains from using the information in case it suspects that a potential attacker forged the message. In particular, we are not aware of any implementation of the secure transport protocol QUIC (e.g., used by HTTP/3) that processes PTB messages. In this paper, we present a novel parameterizable PTB detection algorithm for reliable transport protocols that does not dep.nd on PTB messages. We further describe how to integrate our algorithm into QUIC, present results from an evaluation using the algorithm within a QUIC simulation model and, based on these results, suggest concrete parameter values.

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Planing It by Ear: Convolutional Neural Networks for Acoustic Anomaly Detection in Industrial Wood Planers

Planing It by Ear: Convolutional Neural Networks for Acousti...

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

作者： Anthony Deschênes Rémi Georges Cem Subakan Bruna Ugulino Antoine Henry Michael Morin Dep. of Wood and Forest Sciences Université Laval Canada Dep. of Computer Science and Software Engineering Université Laval Canada FORAC Research Consortium Université Laval Canada Mila-Quebec AI Institute Canada Concordia University Canada FPInnovations Canada Dep. of Operations and Decision Systems Université Laval Canada

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In recent years, the wood product industry has been facing a skilled labor shortage. The result is more frequent sudden failures, resulting in additional costs for these companies already operating in a very competitive market. Moreover, sawmills are challenging environments for machinery and sensors. Given that experienced machine operators may be able to diagnose defects or malfunctions, one possible way of assisting novice operators is through acoustic monitoring. As a step towards the automation of wood-processing equipment and decision support systems for machine operators, in this paper, we explore using a deep convolutional autoencoder for acoustic anomaly detection of wood planers on a new real-life dataset. Specifically, our convolutional autoencoder with skip connections (Skip-CAE) and our Skip-CAE transformer outperform the DCASE autoencoder baseline, one-class SVM, isolation forest and a published convolutional autoencoder architecture, respectively obtaining an area under the ROC curve of 0.846 and 0.875 on a dataset of real-factory planer sounds. Moreover, we show that adding skip connections and attention mechanism under the form of a transformer encoder-decoder helps to further improve the anomaly detection capabilities.

关键词： Support vector machines Convolution Autoencoders Forestry Transformers Acoustics Sensors Convolutional neural networks Speech processing Anomaly detection

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