Vector approximate message passing (VAMP) has emerged as an effective and robust solution for sparse signal recovery (SSR). However, it could face a substantial computational burden when the dictionary matrix undergoe...
Vector approximate message passing (VAMP) has emerged as an effective and robust solution for sparse signal recovery (SSR). However, it could face a substantial computational burden when the dictionary matrix undergoes frequent variations in practical implementations. In this paper, we will illustrate that the challenges encountered by VAMP mainly arise from a matrix inversion operation. To circumvent this matrix inversion, we propose an elemental AMP-based algorithm by introducing additional auxiliary variables. This enables the processing of measurements element-by-element, thereby efficiently transforming any matrix operations into vector multiplications. Moreover, the proposed elemental AMP-based algorithm allows for adopting much more flexible approximation strategies (e.g., diagonal approximation) rather than resorting to the essential and overly simplistic coarse averaging operation as in VAMP. These innovations potentially contribute to both the reduction in computational complexity and improvement in recovery performance.
This paper presents a preliminary study on the use of machine learning-based methods to select the appropriate parameters of cascade filters in the analysis of brain signals recorded using functional infrared spectros...
This paper presents a preliminary study on the use of machine learning-based methods to select the appropriate parameters of cascade filters in the analysis of brain signals recorded using functional infrared spectroscopy (fNIRS), which shows the level of oxygenation in the brain and, unlike EEG signals (showing electrical brain activity), are less prone to potential interference, disturbances or artifacts occurrence.
Blind people face difficulty in practicing their daily life safely because they cannot know the objects surrounding them and this exposes them to many dangers. Given that the number of blind people around the world is...
Blind people face difficulty in practicing their daily life safely because they cannot know the objects surrounding them and this exposes them to many dangers. Given that the number of blind people around the world is a significant number, this research deals to build an intelligent system that helps the blind people to know the objects surrounding them in an internal environment where it is formed. The system consists of two parts, the first part is the software, which depends on the use of one of the deep learning algorithms and its name is YOLO (You Only Look Once). This algorithm was chosen because of its high speed and accuracy, and this is what this group of people needs, which the research aims to help them as much as possible. The algorithm was trained on a ready-made dataset called COCO (Common Objects in COntex) Dataset which It is used for the purposes of discovering objects and detecting faces … etc. The other part of the system is the part of the hardware, which mainly depends on a fast and lightweight microprocessor, which is Raspberry Pi B3, in addition to the headphone and Raspberry Pi camera. After the images is taken by the Raspberry Pi Camera, these images are send to the Raspberry pi and the YOLO algorithm detect the objects in each image, and after the object is detected, it sends the output that is converted into sound via the head Phone to the person and thus can avoid the things surrounding them.
In this paper the research on optimisation of visual object tracking using a Siamese neural network for embedded vision systems is presented. It was assumed that the solution shall operate in real-time, preferably for...
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— Model order reduction plays a crucial role in simplifying complex systems while preserving their essential dynamic characteristics, making it an invaluable tool in a wide range of applications, including robotic sy...
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This paper presents the design of three types of optimal sliding mode controllers (SMCs); the classical sliding mode controller (CSMC), the modified sliding mode controller (MSMC), and the integral sliding mode contro...
This paper presents the design of three types of optimal sliding mode controllers (SMCs); the classical sliding mode controller (CSMC), the modified sliding mode controller (MSMC), and the integral sliding mode controller (ISMC), for position control of the electrical servo drive system under disturbance. The sliding mode control methodology is considered one of the best approaches for the robust controller design and the chattering phenomenon in the control effort of the CSMC is attenuated by using the boundary layer method. The Fruit Fly optimization (FFO) algorithm has been utilized to get and tune the gain variable of the proposed sliding mode controllers and the thickness of the boundary layer function in order to find the best current action for the system. The numerical simulation results obtained by using MATLAB package reveal that all controllers can give excellent performance; however, in terms of minimizing the settling time, hitting time, and the amplitude of chattering phenomenon in the control effort, the performance of the optimal ISMC is better than those of the optimal CSMC and optimal MSMC. Moreover, the fitness evaluation value is reduced.
This paper presents a novel nonparametric backpropagation Bayesian compressive sensing (BBCS) classification approach. While the state-of-the-art parametric classifiers such as logistic regression require model traini...
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Convergent sequences of real numbers play a fundamental role in many different problems in system theory, e.g., in Lyapunov stability analysis, as well as in optimization theory and computational game theory. In this ...
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This article describes the study on simulation model of exoskeleton robot based on real dataset acquired through human gait dynamic identification. Analysis of kinematic chain with emphasis on proper mapping of joints...
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ISBN:
(数字)9781728169569
ISBN:
(纸本)9781728169576
This article describes the study on simulation model of exoskeleton robot based on real dataset acquired through human gait dynamic identification. Analysis of kinematic chain with emphasis on proper mapping of joints movement regarding to swing phase of gait has been conducted. Simulation model of proposed conceptual mechanism was developed using MATLAB SimScape MultiBody. The results of simulation studies were compared with the experimental one. Developed model is the starting point for further research on automatic exoskeleton and bipedal robot.
The paper is dedicated to the area of feature selection, in particular a notion of attribute rankings that allow to estimate importance of variables. In the research presented for ranking construction a new weighting ...
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