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computer Vision and Machine Intelligence (CVMI), International Conference on

作者： Kaisei Matsumoto Takao Toma Shiho Oshiro Tomohisa Wada Computer Science and Intelligent Systems Program University of the Ryukyus Okinawa Japan Magna Design Net Inc Okinawa Japan Information Technology Center University of the Ryukyus Okinawa Japan Dept. of Engineering Area of Computer Science and Intelligent Systems University of the Ryukyus Okinawa Japan

This paper discusses intelligent constellation generation based on autoencoder communication system. In previous studies, the amplitude was set to fluctuate between r=0.0 and 1.0. However, when checking the generated constellation, distortion was confirmed instead of the conventional symbol arrangement. Therefore, in this paper, it compares the case where the amplitude is constant, the case where the average amplitude within a Minibatch is 1, and the case where the average amplitude is 1 for Interval time. The communication standard used in this research is IEEE 802.11a, assuming wireless Local Area Network (LAN) specifications. The IEEE 802.11a standard has an Fast Fourier Transform (FFT) length of 64, a subcarrier number of 52, and Quadrature Phase Shift Keying (QPSK) and 16 Quadrature Amplitude Modulation (QAM), modulation methods. A guard interval of 800 ns is added and the symbol length is 4000 ns. First, a simulation was performed under the condition that the amplitude was kept constant. QPSK with 4 symbols, constant amplitude model is rounded more than previous research result. 16QAM with 16 symbols is arranged regularly like lined up on a line. Second, the simulation was performed under the condition that the average amplitude within the minibatch was set to 1. QPSK with 4 symbols, appears to rotate clockwise. 16QAM with 16 symbols has a more uniform symbol placement than previous research result. Third, a simulation was performed under the condition that the average amplitude within Interval time was set to 1. QPSK with 4 symbols, is the closest to square among QPSK output results so far. The direction is slightly tilted, but if it can be rotated a little more, it may be possible to reproduce the same symbol arrangement as before. 16QAM with 16 symbols, the symbol arrangement is biased as a whole. However, it can be seen that are arranged in line on the line, perhaps due to regularity. As future work, in addition to the conditions set this time, it will exa

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Evaluating the Solutions to Predict the Impact of Lung Cancer with an Advanced Intelligent Computing Method

Evaluating the Solutions to Predict the Impact of Lung Cance...

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International Conference on Smart systems and Inventive Technology (ICSSIT)

作者： R. Sundar Sudhir Ramadass D. Meeha Balambigai Subramanian S Siva Shankar Gayatri Parasa Dept. of Computer Science and Engineering Madanapalle Institute of Technology & Science Sterck Systems Chennai Dept. of Information Technology Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engg CollegeAvadi Chennai Dept. of ECE Kongu Engineering College Perundurai Dept. of CSE -AI&ML KG Reddy college of Engineering and Technology Chilukuru Village Telangana Dept. of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Andhra Pradesh

Using symptoms as a basis for diagnosing lung cancer, Lung cancer detection was accomplished using several different machine-learning regression strategies. By comparing the efficacy of several regression algorithms for predicting lung cancer, considering factors including age, gender, chest discomfort, shortness of breath, alcohol intake, chronic illness, trouble swallowing, anxiety, and peer pressure. Lung cancer predictions and evaluations are made using regression methods such as the linear algorithm, polynomial regression, logistic regression, logarithmic regression, and multiple regression. With a predictive accuracy of 96%, multiple regression is superior to other regression techniques when identifying future lung cancer cases. The r-squared value, which can be calculated using several regression machine learning approaches, may also be used to evaluate the association between the various symptoms and lung cancer. Lung cancer is diagnosed using the r-squared value, which is calculated using several algorithms and takes into symptoms, including chronic illness.

关键词： Machine learning algorithms Correlation Linear regression Anxiety disorders Lung cancer Machine learning Prediction algorithms

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Self-Supervised Motion Segmentation with Confidence-Aware Loss Functions for Handling Occluded Pixels and Uncertain Optical Flow Predictions

Self-Supervised Motion Segmentation with Confidence-Aware Lo...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Chung-Yu Chen Bo-Yun Lai Ying-Shiuan Huang Wen-Chieh Lin Chieh-Chih Wang Dept. of Computer Science National Yang Ming Chiao Tung Univ. College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Mechanical and Mechatronics Systems Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In driving scenarios, motion segmentation is a crucial and fundamental component that is needed for many tasks. Recently, a self-supervised multitasking framework was proposed for driving scenarios. It simultaneously trains motion segmentation, optical flow, dept., and ego-motion models without annotated data. The self-supervised architecture derives training signals from training data via loss functions. If these loss functions lack robustness, they may result in model inaccuracies. To reduce the bad influences of occlusion and optical flow estimation errors on motion segmentation, we propose two loss functions: (1) Soft-Per-Pixel-Minimum (Soft-PPM) loss that excludes occluded pixels while balancing the contribution of each frame on the loss function temporally; (2) Flow difference loss that excludes pixels with unclear motion states to diminish the effect of optical flow estimation errors. Our loss function design is based on the key insight that information such as dept. and optical flow can be used to train motion segmentation models and act as a reliable measure for pixels during training. Our approach can improve segmentation accuracy for both moving and static objects and has achieved IoU scores on moving and static classes comparable to the state-of-the-art methods on the KITTI dataset.

关键词： Optical losses Training computer vision Estimation error Motion segmentation Training data Loss measurement Data models Robustness Optical flow

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Statistical Learning Theory for control: A FINITE SAMPLE PERSPECTIVE

arXiv

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arXiv 2022年

作者： Tsiamis, Anastasios Ziemann, Ingvar Matni, Nikolai Pappas, George J. The Dept. of Information Technology and Electrical Engineering ETH Zürich Zürich Switzerland The Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden The Dept. of Electrical and Systems Engineering University of Pennsylvania Philadelphia United States

This vances tutorial in statistical survey provides learning an theory overview relevant of recent to control ad- and system identification featuring non-asymptotics. While there has been substantial progress across all areas of control, the theory is most well-developed when it comes to linear system identification and learning for the linear quadratic regulator, which are the focus of this manuscript. From a theoretical perspective, much of the labor underlying these advances has been in adapting tools from modern high-dimensional statistics and learning theory. While highly relevant to control theorists interested in integrating tools from machine learning, the foundational material has not always been easily accessible. To remedy this, we provide a self-contained presentation of the relevant material, outlining all the key ideas and provide an overview of the technical machinery that underpin recent results. We also present a number of open problems and future directions. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear systems

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Formal control Synthesis for Stochastic Neural Network Dynamic Models

arXiv

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arXiv 2022年

作者： Adams, Steven Lahijanian, Morteza Laurenti, Luca Delft Center for Systems and Control TU Delft Netherlands Dept. of Aerospace Eng. Sciences and Computer Science CU Boulder United States

Neural networks (NNs) are emerging as powerful tools to represent the dynamics of control systems with complicated physics or black-box components. Due to complexity of NNs, however, existing methods are unable to synthesize complex behaviors with guarantees for NN dynamic models (NNDMs). This work introduces a control synthesis framework for stochastic NNDMs with performance guarantees. The focus is on specifications expressed in linear temporal logic interpreted over finite traces (LTLf), and the approach is based on finite abstraction. Specifically, we leverage recent techniques for convex relaxation of NNs to formally abstract a NNDM into an interval Markov decision process (IMDP). Then, a strategy that maximizes the probability of satisfying a given specification is synthesized over the IMDP and mapped back to the underlying NNDM. We show that the process of abstracting NNDMs to IMDPs reduces to a set of convex optimization problems, hence guaranteeing efficiency. We also present an adaptive refinement procedure that makes the framework scalable. On several case studies, we illustrate the our framework is able to provide nontrivial guarantees of correctness for NNDMs with architectures of up to 5 hidden layers and hundreds of neurons per layer. © 2022, CC BY.

关键词： Dynamic models

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A comparative study of different radiometer antennas for soil moisture 27

A comparative study of different radiometer antennas for soi...

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27th International Conference on Telecommunications, ICT 2020

作者： Bilawal, Farah Babaeian, Fatemeli Karmakar, Nemai Chandra Dept. of Electrical and Computer Systems Engineering Monash University Melbourne Australia

ISBN: (纸本)9781728165875

This paper consists of a brief survey along with an elaborative comparison of different multi-beam antenna types which have been used in radiometers for Soil moisture measurements in the past few decades. For multi-beam phased array antennas, power distribution methods are compared with their strengths and drawbacks. Furthermore, to get more agile and controllable beams, some beamforming techniques have also been incorporated and studied for smart antenna designs. As time evolved, the need for smart, compact, and miniaturized radiometer systems evolved for remote sensing. So, this need led towards different techniques of miniaturization which are also reviewed in this paper. Furthermore, instead of single-band antennas, tri-band antennas gained more attention due to then-high resolution and large spatial coverage. © 2020 IEEE.

关键词： Beamforming

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DynaPAT: A Dynamic Pattern-Aware Encoding Technique for Robust MLC PCM-Based Deep Neural Networks

DynaPAT: A Dynamic Pattern-Aware Encoding Technique for Robu...

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IEEE International Conference on computer-Aided Design

作者： Thai-Hoang Nguyen Muhammad Imran Joon-Sung Yang Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea School of Electrical Engineering and Computer Science National University of Sciences and Technology Islamabad Pakistan School of Electrical and Electronic Engineering and Dept. of Semiconductor Systems Engineering Yonsei University Seoul South Korea

As the effectiveness of Deep Neural Networks (DNNs) is rising over time, so is the need for highly scalable and efficient hardware architectures to capitalize this effectiveness in many practical applications. Emerging non-volatile Phase Change Memory (PCM) technology has been found to be a promising candidate for future memory systems due to its better scalability, non-volatility and low leakage/dynamic power consumption, compared to conventional charged-based memories. Additionally, with its cell's wide resistance span, PCM also has the Flash-like Multi-Level Cell (MLC) capability, which has enhanced storage density, providing an opportunity for the deployment of data-intensive applications such as DNNs on resource-constrained edge devices. However, the practical deployment of MLC PCM is hampered by certain reliability challenges, among which, the resistance drift is considered to be a critical concern. In a DNN application, the presence of resistance drift in MLC PCM can cause a severe impact to DNN's accuracy if no drift-error-tolerance technique is utilized. This paper proposes DynaPAT, a low-cost and effective pattern-aware encoding technique to enhance the drift-error-tolerance of MLC PCM-based Deep Neural Networks. DynaPAT has been constructed on the insight into DNN's vulnerability against different data pattern switching. Based on this insight, DynaPAT efficiently maps the most-frequent data pattern in DNN's parameters to the least-drift-prone level of the MLC PCM, thus significantly enhancing the robustness of the system against drift errors. Various experiments on different DNN models and configurations demonstrate the effectiveness of DynaPAT. The experimental results indicate that DynaPAT can achieve up to 500× enhancement in the drift-errors-tolerance capability over the baseline MLC PCM based DNN while requiring only a negligible hardware overhead (below 1% storage overhead). Being orthogonal, DynaPAT can be integrated with existing drifttolerance sc

关键词： Phase change materials Resistance Deep learning Microprocessors Neural networks Memory management Switches

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On the Ergodic Rate of Uplink Rate-Splitting Multiple Access

On the Ergodic Rate of Uplink Rate-Splitting Multiple Access

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IEEE Conference on Wireless Communications and Networking

作者： Athanasios P. Chrysologou Sotiris A. Tegos Panagiotis D. Diamantoulakis Nestor D. Chatzidiamantis Paschalis C. Sofotasios George K. Karagiannidis Dept. of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Department of Computer and Communication Engineering C2PS and 6G Center Khalifa University Abu Dhabi UAE Cyber Security Systems and Applied AI Research Center Lebanese American University (LAU) Lebanon

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

One of the principal challenges anticipated for the forthcoming sixth-generation (6G) wireless networks is the imper-ative need to design advanced multiple access techniques capable of enabling massive connectivity. In this direction, rate splitting multiple access (RSMA) has been reported as a promising approach. In this work, we investigate the ergodic rate (ER) performance of an uplink RSMA network which consists of two sources. Specifically, analytical closed-form expressions for the sources' ERs and the system ergodic sum rate (ESR) are derived under the case of perfect successive interference cancellation $(\text{pSIC})$ and perfect channel state information (pCSI) as well as under the more realistic scenario of imperfect SIC (ipSIC) and imperfect CSI (ipCSI). Furthermore, an asymptotic analysis for the high signal-to-noise ratio regime is presented, which provides useful insights into the sources' behavior in all considered cases. The accuracy of the provided analytical expressions is validated by simulation results, which not only explore how different system parameters affect the extracted expressions, but also reveal the detrimental impact of ipSIC and ipCSI on system performance.

关键词： 6G mobile communication Interference cancellation Closed-form solutions Accuracy Wireless networks System performance Simulation

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Detecting Pneumonia from X-Ray Images using Transfer Learning

Detecting Pneumonia from X-Ray Images using Transfer Learnin...

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2020 IEEE International Conference on Advent Trends in Multidisciplinary Research and Innovation, ICATMRI 2020

作者： Ali, Syed Arshad Sampath Kumar, M. Andhra University Dept. of Computer Science and Systems Engineering Visakhapatnam India

ISBN: (纸本)9781728177342

Pneumonia is one such deadly infectious disease that causes inflammation the air sacs and could be life threatening if not diagnosed and treated early. Diagnosis based on symptoms and examination of Chest X-ray by a doctor to find the infection in lungs and severity of the Pneumonia, which is tedious and prone to errors. The main objective of this paper is to propose a Deep Convolution Neural Network (CNN) model based on Inception-V3 architecture and perform various hyper parameter tuning to get better accuracy than original Inception-V3 to classify the X-Ray images and find whether the person has pneumonia or not. This paper presents a model which reduces the abruptness in the Inception-V3 model while converting to a binary classification model. The proposed network consists of 130,514 trainable parameters which is significantly less compared to models that are not pre-Trained and do not use transfer learning. Data augmentation is performed to improve the veracity of the data and have evaluated the results in varying environments by applying batch normalization and dropout separately and evaluated the accuracy. © 2020 IEEE.

关键词： Diagnosis

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Exploring the potential of collaborative UAV 3D mapping in Kenyan savanna for wildlife research

arXiv

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arXiv 2024年

作者： Shukla, Vandita Morelli, Luca Trybala, Pawel Remondino, Fabio Gan, Wentian Yu, Yifei Wang, Xin Trento Italy Computer Vision and Machine Learning Systems Group Institute for Geoinformatics University of Muenster Germany Dept. of Civil Environmental and Mechanical Engineering University of Trento Italy School of Geodesy and Geomatics Wuhan University China

UAV-based biodiversity conservation applications have exhibited many data acquisition advantages for researchers. UAV platforms with embedded data processing hardware can support conservation challenges through 3D habitat mapping, surveillance and monitoring solutions. High-quality real-time scene reconstruction as well as real-time UAV localization can optimize the exploration vs exploitation balance of single or collaborative mission. In this work, we explore the potential of two collaborative frameworks - Visual Simultaneous Localization and Mapping (V-SLAM) and Structure-from-Motion (SfM) for 3D mapping purposes and compare results with standard offline approaches. © 2024, CC BY-NC-SA.

关键词： Unmanned aerial vehicles (UAV)

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