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26th International Conference on Information Fusion, FUSION 2023

作者： Hoffmann, Folker Schily, Hans Krestel, Markus Charlish, Alexander Ritchie, Matthew Griffiths, Hugh Fraunhofer Institute for Communication Information Processing and Ergonomics Fkie The Sensor Data & Information Fusion Department Wachtberg Germany Schönhofer Sales and Engineering GmbH Siegburg Germany University College London Department of Electrical Engineering London United Kingdom

ISBN: (纸本)9798890344854

This paper addresses the problem of localizing a stationary RF emitter with a mobile UAV, equipped with a single directional antenna. By rotating around its vertical axis, it determines a bearing towards the emitter. Our interest is in optimally selecting the measurement positions to achieve a fast localization. The majority of such systems described in the literature use greedy planning to select the next measurement position. This work experimentally tests an algorithm that performs a non-myopic planning until the final localization step. The algorithm is based on the policy rollout principle and showed good performance in previous simulative studies. It is adapted to match the needs of a real world setup and evaluated in flight trials. Adaptions include the avoidance of close range measurements to prevent inaccurate measurements at high elevation, and the filtering of poor measurements. © 2023 International Society of Information Fusion.

关键词： Unmanned aerial vehicles (UAV)

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Multiple Face Detection and Recognition for System-an-Chip FPGAs

Multiple Face Detection and Recognition for System-an-Chip F...

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International Joint Conference on Computer Science and Software engineering (JCSSE)

作者： Siraphop Santiwiwat Amir Hajian Watchara Ruangsang Supavadee Aramvith Department of Electrical Engineering Multimedia Data Analytic and Processing Research Unit Faculty of Engineering Chulalongkorn University Bangkok Thailand

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

Recognizing multiple faces within a single frame or image presents a significant challenge in facial recognition tasks. This challenge demands robust algorithms capable of handling variations in facial position, unstable brightness, and the presence of accessories such as glasses or hats. However, in current surveillance applications, multiple face recognition is vital for maintaining various security measures. In such applications, there is a need for processors that are compact and deployable in diverse environments. FPGA is popular due to its compact size, cost-effectiveness, and high performance. This paper presents the implementation of multiple face detection and recognition on an SoC FPGA utilizing the Xilinx Kria KV260 platform. We chose the MTCNN algorithm for multiple face detection and the ResNet-20 model for multiple face recognition developed by Vitis AI, and we fine-tuned this model using the CUFace dataset. Our results demonstrate the robust capability to detect and recognize faces of up to 8 individuals within a single frame in just 0.11 seconds, with a recognition rate of 93.77%.

关键词： Program processors Image recognition Face recognition Surveillance Face detection Security Task analysis

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YOLO for Small Objects in Aerial Imagery: A Performance Evaluation

YOLO for Small Objects in Aerial Imagery: A Performance Eval...

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International Joint Conference on Computer Science and Software engineering (JCSSE)

作者： Wimonthip Saenprasert Ei Ei Tun Amir Hajian Watchara Ruangsang Supavadee Aramvith Department of Electrical Engineering Multimedia Data Analytic and Processing Research Unit Faculty of Engineering Chulalongkorn University Bangkok Thailand

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

Unmanned aerial vehicles (UAVs) offer significant advantages in accessing remote or high-risk locations. Yet, they also present challenges concerning privacy and safety. Effective air traffic management is crucial for ensuring compliance with security regulations. The primary objective is to monitor the flight trajectories of target objects in the airspace, necessitating efficient object detection, especially for distant objects, to facilitate surveillance and preparedness for addressing potential threats. However, the current performance of existing models in detecting small objects is inadequate for real-time surveillance applications. This study advocates prioritizing object detection over precise prediction of object bounding box sizes. We propose a novel approach where the efficacy of object detection is measured by the ability to identify small objects. This research serves as an additional variable for evaluating and enhancing models designed for small object detection. We selected several versions of the YOLO model and assessed them on the COCO val 2017 dataset, counting the number of accurately predicted objects. Based on the most popular drone size of 100–400 pixels, YOLOv5-x is best suited for the future development of smaller objects. However, because the model is large, we can choose the YOLOv5-s, which is the smaller version. YOLOv5-s detects small objects in the same way that the newest version of the YOLO family, the YOLOv9-e. By measuring this, we may establish the model's capabilities and select a model appropriate for the target object's size.

关键词： YOLO Accuracy Computational modeling Surveillance Prototypes Real-time systems Trajectory

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An Advanced Features Extraction Module for Remote Sensing Image Super-Resolution

arXiv

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arXiv 2024年

作者： Sultan, Naveed Hajian, Amir Aramvith, Supavadee Department of Electrical Engineering Chulalongkorn University Bangkok Thailand Multimedia Data Analytics and Processing Research Unit Department of Electrical Engineering Chulalongkorn University Bangkok Thailand

In recent years, convolutional neural networks (CNNs) have achieved remarkable advancement in the field of remote sensing image super-resolution due to the complexity and variability of textures and structures in remote sensing images (RSIs), which often repeat in the same images but differ across others. Current deep learning-based super-resolution models focus less on high-frequency features, which leads to suboptimal performance in capturing contours, textures, and spatial information. State-of-the-art CNN-based methods now focus on the feature extraction of RSIs using attention mechanisms. However, these methods are still incapable of effectively identifying and utilizing key content attention signals in RSIs. To solve this problem, we proposed an advanced feature extraction module called Channel and Spatial Attention Feature Extraction (CSA-FE) for effectively extracting the features by using the channel and spatial attention incorporated with the standard vision transformer (ViT). The proposed method trained over the UCMerced dataset on scales 2, 3, and 4. The experimental results show that our proposed method helps the model focus on the specific channels and spatial locations containing high-frequency information so that the model can focus on relevant features and suppress irrelevant ones, which enhances the quality of super-resolved images. Our model achieved superior performance compared to various existing models. © 2024, CC BY-NC-ND.

关键词： Feature extraction

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An Advanced Features Extraction Module for Remote Sensing Image Super-Resolution

An Advanced Features Extraction Module for Remote Sensing Im...

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International Conference on electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON

作者： Naveed Sultan Amir Hajian Supavadee Aramvith Department of Electrical Engineering Chulalongkorn University Bangkok Thailand Department of Electrical Engineering Multimedia Data Analytics and Processing Research Unit Chulalongkorn University Bangkok Thailand

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

关键词： Computational modeling Superresolution Feature extraction Transformers Decoding Telecommunications Spatial resolution

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Stroke Home Rehabilitation Approach Using Mobile Application Based on PostNet Machine Learning Model 23

Stroke Home Rehabilitation Approach Using Mobile Application...

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7th International Conference on Medical and Health Informatics, ICMHI 2023

作者： Das, Utpal Chandra Le, Ngoc Thien Benjapolakul, Watit Vitoonpong, Timporn Pluempitiwiriyawej, Charnchai Center of Excellence in Artificial Intelligence Machine Learning and Smart Grid Technology Department of Electrical Engineering Chulalongkorn University Bangkok10330 Thailand Department of Rehabilitation Medicine Faculty of Medicine Chulalongkorn University Bangkok10330 Thailand Multimedia Data Analytics and Processing Research Unit Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand

ISBN: (纸本)9798400700712

Stroke is a significant cause of mortality and disability globally, with its occurrence in the human brain and motor function being linked to various parts of the human body. Stroke victims often experience disabilities or mobility problems in affected body parts, either on one or both sides of the body. Physiotherapy exercises are the primary treatment and medication for stroke patients, necessitating daily monitoring by a physiotherapist. However, this approach is expensive, and rehabilitation centers and physicians are scarce. Numerous research studies have been conducted to address this issue from various perspectives. This study proposes a Convolutional Neural Network (CNN) based pose net machine learning (ML) model for stroke home rehabilitation using pose detection and classification with a skeleton base model and human pose estimation drawing. We trained our ML model using different human pose images for this model. The tested accuracy of our CNN model is 100% for our exercise pose image test case. Later we build and mobile application for remote rehabilitation. Tested the application with ten different subjects, achieving a 98% for elbow extension, and in the case of elbow flexion and Normal position of balancing both sides, all the persons achieved a 100 % accuracy rate under a laboratory environment. © 2023 ACM.

关键词： Patient treatment

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A Comparative Analysis of Generalised Echo and Interference Cancelling and Extended Multichannel Wiener Filtering for Combined Noise Reduction and Acoustic Echo Cancellation

A Comparative Analysis of Generalised Echo and Interference ...

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

作者： Arnout Roebben Toon van Waterschoot Marc Moonen Department of Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Two algorithms for combined acoustic echo cancellation (AEC) and noise reduction (NR) are analysed, namely the generalised echo and interference canceller (GEIC) and the extended multichannel Wiener filter (MWF ext ). Previously, these algorithms have been examined for linear echo paths, and assuming access to voice activity detectors (VADs) that separately detect desired speech and echo activity. However, algorithms implementing VADs may introduce detection errors. Therefore, in this paper, the previous analyses are extended by 1) modelling general nonlinear echo paths by means of the generalised Bussgang decomposition, and 2) modelling VAD error effects in each specific algorithm, thereby also allowing to model specific VAD assumptions. It is found and verified with simulations that, generally, the MWF ext achieves a higher NR performance, while the GEIC achieves a more robust AEC performance.

关键词： Voice activity detection Analytical models Wiener filters Interference cancellation Echo cancellers Signal processing algorithms Detectors Signal processing Acoustics

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Towards Deploying Secure and Highly Available O-RAN Components

Towards Deploying Secure and Highly Available O-RAN Componen...

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IEEE Future Networks World Forum (FNWF)

作者： Mahshid Mehrabi Ali Khandan Boroujeni Vincent Latzko Stefan Köpsell Trustworthy Data Processing Group Barkhausen Institut Dresden Germany Electrical Engineering Department TU Dresden Dresden Germany

ISBN: (数字)9798350379495

ISBN: (纸本)9798350379501

Open Radio Access Network (O-RAN) is a new paradigm for beyond 5G networks and infrastructures as a solution for the huge increase in IoT devices’ traffic. This is possible by employing concepts such as Cloud Computing (CC), Artificial Intelligence (AI), Service Function Chains (SFCs), and Virtual Network Functions (VNFs). However, the challenges for performance metrics such as availability, reliability, as well as security of the network need further discussions. In this paper, we propose a method to maximize the availability of the O-RAN components considering the security requirements by applying different Virtual Security Functions (VSFs). Since the underlying optimization problem is NP-hard and therefore, cannot be solved in polynomial time, a Genetic Algorithm (GA) is proposed which ensures the optimal placement of the components considering the whole SFC. Experimental evaluation shows the superiority of the proposed strategy to enhance the availability level compared to the existing solutions.

关键词： Cloud computing Service function chaining NP-hard problem Simulation Open RAN Polynomials Security Reliability Optimization Genetic algorithms

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Topology-Independent GEVD-Based Distributed Adaptive Node-Specific Signal Estimation in Ad-Hoc Wireless Acoustic Sensor Networks

Topology-Independent GEVD-Based Distributed Adaptive Node-Sp...

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European Signal processing Conference (EUSIPCO)

作者： Paul Didier Toon Van Waterschoot Marc Moonen Department of Electrical Engineering (ESAT) KU Leuven STADIUS Center for Dynamical Systems Signal Processing and Data Analytics Belgium

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

A low-rank approximation-based version of the topology-independent distributed adaptive node-specific signal estimation (TI-DANSE) algorithm is introduced, using a generalized eigenvalue decomposition (GEVD) for application in ad-hoc wireless acoustic sensor networks. This TI-GEVD-DANSE algorithm as well as the original TI-DANSE algorithm exhibit a non-strict convergence, which can lead to numerical instability over time, particularly in scenarios where the estimation of accurate spatial covariance matrices is challenging. An adaptive filter coefficient normalization strategy is proposed to mitigate this issue and enable the stable performance of TI-(GEVD-)DANSE. The method is validated in numerical simulations including dynamic acoustic scenarios, demonstrating the importance of the additional normalization.

关键词： Wireless communication Wireless sensor networks Adaptive systems Signal processing algorithms Estimation Adaptive filters Acoustic sensors Approximation algorithms Ad hoc networks Acoustics

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One-Shot Distributed Node-Specific Signal Estimation with Non-Overlapping Latent Subspaces in Acoustic Sensor Networks

One-Shot Distributed Node-Specific Signal Estimation with No...

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International Workshop on Acoustic Signal Enhancement (IWAENC)

作者： Paul Didier Pourya Behmandpoor Toon van Waterschoot Marc Moonen Department of Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Belgium

ISBN: (数字)9798350361858

ISBN: (纸本)9798350361865

A one-shot algorithm called iterationless DANSE (iDANSE) is introduced to perform distributed adaptive node-specific signal estimation (DANSE) in a fully connected wireless acoustic sensor network (WASN) deployed in an environment with non-overlapping latent signal subspaces. The iDANSE algorithm matches the performance of a centralized algorithm in a single processing cycle while devices exchange fused versions of their multichannel local microphone signals. Key iterationless nature, which favors deployment in real-time applications, and the fact that devices can exchange fewer fused signals than the number of latent sources in the environment. The proposed method is validated in numerical simulations including a speech enhancement scenario.

关键词： Performance evaluation Wireless communication Wireless sensor networks Conferences Estimation Speech enhancement Acoustic sensors Numerical simulation Real-time systems Microphones

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