Recent advances in the field of biomedical engineering has prompted modern research to focus on challenges of human machine interface. This paper provides an improvement in unsupervised learning methods already availa...
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ISBN:
(纸本)9781728167947
Recent advances in the field of biomedical engineering has prompted modern research to focus on challenges of human machine interface. This paper provides an improvement in unsupervised learning methods already available for estimating myoelectric intention of individual fingers using the kernel technique. The unsupervised methods which have been improved upon for simultaneous and proportional intention estimation are NMF and NMF-HP. These methods are called semi unsupervised algorithms as models are evaluated blindly using only the target finger. The algorithms implemented with kernels are named as kNMF and kNMF-HP. The kernel technique increases the feature matrix for the NMF and NMF-HP models and improves the performance of these algorithms. The algorithms were analyzed in terms of signal to noise ratio using the strength of the signal of the activated finger and the levels of other fingers not activated. Significant improvements were seen through the implementation of the kernel matrix on the parameters analyzed. An in-house eight channel signal instrumentation scheme was used to acquire the EMG signals using dry electrodes. In addition, a comprehensive signal filtering scheme was designed in order to remove the acquired EMG signal of noise. Finally, we used the algorithms to successfully drive a robotic hand.
This paper presents a newly developed in-band full-duplex (IBFD) communication and sensing testbed utilizing digital beamforming on the advanced Xilinx ZCU216 Radio Frequency System-on-Chip (RFSoC) platform, featuring...
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ISBN:
(数字)9788831299107
ISBN:
(纸本)9798350366327
This paper presents a newly developed in-band full-duplex (IBFD) communication and sensing testbed utilizing digital beamforming on the advanced Xilinx ZCU216 Radio Frequency System-on-Chip (RFSoC) platform, featuring 16 DACs and 16 ADCs with 14-bit resolution divided into four tiles. Key design and deployment challenges include achieving precise phase synchronization across multiple RFSoC tiles (due to independent clocks causing random phase shifts) and mitigating self-interference (SI) caused by strong mutual coupling effects between closely spaced transmitting (Tx) and receiving (Rx) antenna arrays. To address these challenges, we employed a Tx beamforming algorithm for maximum antenna gain and an Rx beamforming method to maximize the signal-to-self-interference ratio (SSIR) at the receiver output. Specifications of two 1 x 5 Vivaldi antenna arrays (3–6 GHz), integrated into our RFSoC-based testbed, and the multi-tile synchronization (MTS) framework were established to meet IBFD performance requirements. The deployment involved implementing the beam-forming algorithms, configuring the Tx and Rx arrays, and ensuring calibration and synchronization across RFSoC tiles. Experimental results demonstrate over 80 dB SI suppression with half-wavelength separation (@6 GHz) between the Tx and Rx arrays, highlighting the testbed's potential for applications in 6G integrated communication and radar sensing.
The proceedings contain 28 papers. The special focus in this conference is on Artificial Intelligence and Knowledge processing. The topics include: How Do Senior Secondary Level Students and Their Teachers Perceive Ar...
ISBN:
(纸本)9783031686160
The proceedings contain 28 papers. The special focus in this conference is on Artificial Intelligence and Knowledge processing. The topics include: How Do Senior Secondary Level Students and Their Teachers Perceive Artificial Intelligence and Its Implementation? An Exploratory Study;anomalous Sound Pattern Detection for Machine Health Monitoring;performance Evaluation of Various Machine Learning algorithms for Lung Cancer Prediction Using Demographic Data;enhancing Stock Portfolio Optimization Based on a Hybrid Approach Using Artificial Bee Colony Optimization and Firefly Optimization;Enhancing Yarn Quality in the Cotton Industry: AI- Based Nep Detection for Improved Manufacturing Processes;breast Cancer Diagnosis from Ultrasonic Image and Histopathology Image Using Deep Learning Approach;Advancing Time Series Forecasting: LSTM Networks with Multiple Attention Mechanisms;trajectory Tracking and Navigation Model for Autonomous Vehicles Using Reinforcement Learning;quantum Graph Neural Networks Based Protein-Ligand Classification;comparative Analysis on Speech Driven Gesture Generation;enhancing Deep Learning: Leveraging Skip Connections and Memory Efficiency;quality-Based Decision-Making Using Image processing for Supply Chain Management;enhancing Endometrial Tumor Detection: Early Diagnosis with advanced Vision Transformer Architecture;sweetSight: A Deep Convolutional Neural Network Approach for Automatic Categorization of Bengal Sweets;a Systematic Review: How Computer Vision is Transforming Agriculture in Economic Growth;automatic Conversion of Broadcasted Football Match Recordings to Its 2D Top View;measuring the Vehicle-in-Motion, Density and Allocation of Traffic signal Using Transfer Learning;Ensemble Model of VGG16, ResNet50, and DenseNet121 for Human Identification Through Gait Features;Performance of Sentiment Analysis APIs on Political Opinion Polling;summarization of Telugu Text Discourses;crowd-Sourced Supervisors for the Automatic Invigilation of Online
With the rapid development of robotics and sensor technology, indoor mobile robots have become important in the field of robotics, so it is especially important to study the positioning methods of mobile robots. In th...
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ISBN:
(纸本)9781665479691
With the rapid development of robotics and sensor technology, indoor mobile robots have become important in the field of robotics, so it is especially important to study the positioning methods of mobile robots. In this paper, we first analyze the bottlenecks of indoor mobile robots and the localization problems that need to be solved; then, we design an online low-complexity algorithm for super-resolution localization using one frame of IF signal to address the shortcomings of localization technology, and verify the performance of the algorithm through numerical experiments. The relative motion trajectory of the object is calculated for one frame of data provided. The orthogonal matching pursuit algorithm requires a pseudo-inverse of the matrix, and the dictionary needs to be very large to ensure the reconstruction accuracy, resulting in a high complexity of matrix operations. To reduce the complexity, the most direct way is to reduce the size of the dictionary matrix, which can use the information of the previous frame to estimate the distance and angle range of the possible target in the next frame. Finally, the discrete data points are then linked together in a data association manner. Simulation results show that the proposed method has significant improvement in the probability of target detection as well as localization accuracy.
This paper presents a new adaptive decision directed equalizer (DDE) for the receivers of advanced Wireless communication systems. The proposed approach is based on the use of a recursive non quadratic (RNQ) adaptive ...
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This paper presents a new adaptive decision directed equalizer (DDE) for the receivers of advanced Wireless communication systems. The proposed approach is based on the use of a recursive non quadratic (RNQ) adaptive algorithm in a DDE system with a training sequence, the new algorithm is denoted DDE-RNQ. The proposed DDE-RNQ equalizer achieves better performances in different situations even when strong noise components are present in the channel. In the proposed DDERNQ, the non quadratic distance criterion between the equalizer output and the decision circuit is used rather than the squared mean square error (MSE) distance for the computation of the branch metrics. Compared to the conventional DDE based normalized least mean square (DDE-NLMS) equalizer, the proposed DDE-RNQ shows superior performances of speed convergence and residual error versus the original DDE-NLMS equalizer in terms of MSE.
To ensure reliability and safety, embedded prognostics are essential for ultrasonic motor, which is a key component of spacecraft. Although many big-data algorithms have been proposed for prognostics and health manage...
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With the continuous development of drone-related technologies, the application of UAV (Unmanned Aerial Vehicle) in urban low-altitude scenes is gradually increasing. Autonomous localization and perception of the surro...
ISBN:
(纸本)9798400708831
With the continuous development of drone-related technologies, the application of UAV (Unmanned Aerial Vehicle) in urban low-altitude scenes is gradually increasing. Autonomous localization and perception of the surrounding environment are prerequisites for autonomous control. As a result, our team apply the VDO-SLAM algorithm to the scene of coexistence of dynamic and static obstacles in the complex background of urban low altitude. Effective segmentation of static background and moving objects is achieved by combining semantic segmentation and optical flow estimation algorithms. Since there is a lack of relevant public data for urban low-altitude scenarios, a binocular image dataset containing both static and dynamic objects is created by Airsim simulator, which is utilized for training a semantic segmentation model. In addition, a novel numbering maintenance algorithm is introduced for the semantic segmentation of continuous image sequences. The experimental results show that the algorithm can realize the autonomous perception of UAV posture and the motion state of dynamic objects in low altitude complex urban environment. This method provides the necessary self-pose information and reliable environmental data for autonomous obstacle avoidance of UAVs, and provides important support for the application of UAVs in urban low-altitude environment.
Fundus imaging is a valuable diagnostic tool in ophthalmology, providing clinicians with detailed visualizations of the retina and aiding in the detection and monitoring of various eye diseases, including age-related ...
Fundus imaging is a valuable diagnostic tool in ophthalmology, providing clinicians with detailed visualizations of the retina and aiding in the detection and monitoring of various eye diseases, including age-related macular degeneration (AMD), glaucoma, diabetic retinopathy (DR), and cataract. However, the quality of fundus images can be significantly affected by noise, mainly additive white Gaussian noise (AWGN), which is inherent in many imaging systems. The presence of noise in real-world data poses significant challenges for computer vision tasks. In the field of medical image classification, a wrong diagnoisis has heavy consequences. Understanding the impact of AWGN on fundus images is crucial for developing practical denoising algorithms and improving diagnostic accuracy. This work presents an analysis of AWGN noise in fundus images aims to characterize its effects on image quality and assess its impact on diagnostic tasks. The work also analyzes the performance of six models (3 each) of two popular deep learning architectures, Convolutional Neural Networks (CNN) and Vision Transformers (ViT) in the presence of AWGN. AWGN is first introduced to the clean image datasets to conduct the analysis. The CNN and ViT models are trained on the noisy datasets to evaluate the performance of the image classification task. The work also involves six denoising algorithms and a popular image enhancement algorithm- Contrast Limited Adaptive Histogram Equalization (CLAHE).
The identification of traffic signs is a major challenge for intelligent automobiles. Recognition of traffic signs gives useful data, such as alerts and directions, for cooperative intelligent transport systems (CITS)...
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ISBN:
(数字)9798350371406
ISBN:
(纸本)9798350371413
The identification of traffic signs is a major challenge for intelligent automobiles. Recognition of traffic signs gives useful data, such as alerts and directions, for cooperative intelligent transport systems (CITS) and advanced driver assistance systems (ADAS). In realistic autonomous driving scenarios, traffic signs can be challenging to identify even with a very precise real-time method. As a result, drivers are calling for more secure and trustworthy traffic sign recognition technology, and current automakers are also working on developing one. All images captured by the actual driving car are blurry and distorted. The study of traffic sign recognition systems faces several challenges due to external, unmanageable components, such as the impact of unfavorable conditions, and the practical use is still in its infancy. This study provides the YOLO model for traffic sign identification as a solution for the issues mentioned above. The first step is to generally arrange the traffic signs into various categories. Next, the prepossessing process then follows the characteristics of the different kinds of signs. The improved convolution neural network receives the altered images and breaks down the categories into more specific subcategories. The YOLOv3-tiny and YOLOv4-tiny algorithms are examined by using the CCTSDB dataset and the Tsinghua-Tecent 100k(TTK100) dataset and YOLOv2,YOLOv3 andYOLOv5 are examined using the CCTSDB and HRRSD(High Resolution Remote Sensing Detection) datasets depending on the various categories. The findings indicate that YOLOv4-tiny algorithm achieves good recognition performance on both TTK100 and CCTSDB datasets and YOLOv5 on both CCTSDB and HRRSD dataset are most suited for use in traffic sign recognition systems and has a high classification accuracy.
We proposed an improved retiming algorithm for optical PAM-4 system by introducing a moving average filter into the conventional Gardner loop. It exhibits an enhanced stability especially when system bandwidth is limi...
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ISBN:
(纸本)9781665486996
We proposed an improved retiming algorithm for optical PAM-4 system by introducing a moving average filter into the conventional Gardner loop. It exhibits an enhanced stability especially when system bandwidth is limited.
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