The proceedings contain 76 papers. The topics discussed include: a new vehicle detection approach in traffic jam conditions;non-pixel robot stereo;a novel method to recognize complex dynamic gesture by combining HMM a...
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ISBN:
(纸本)1424407079
The proceedings contain 76 papers. The topics discussed include: a new vehicle detection approach in traffic jam conditions;non-pixel robot stereo;a novel method to recognize complex dynamic gesture by combining HMM and FNN models;boundary refined texture segmentation based on K-views and datagram methods;single-row superposition-type spherical compound-like eye for pan-tilt motion recovery;bare bones strategy for human detection and tracking;Daubechies complex wavelet transform based moving object tracking;identification of dynamic nonlinear systems using computationalintelligence techniques;a new invariant descriptor for shape representation and recognition;a wavelet-fuzzy logic based system to detect and identify electric disturbs;evolution strategies based particle filters for fault detection;and a multi-window stereo vision algorithm with improved performance at object borders.
Generative Artificial intelligence (AI) has gained significant attention in recent years, revolutionizing various applications across industries. Among these, advanced vision models for image super-resolution are in h...
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ISBN:
(数字)9798331509422
ISBN:
(纸本)9798331509439
Generative Artificial intelligence (AI) has gained significant attention in recent years, revolutionizing various applications across industries. Among these, advanced vision models for image super-resolution are in high demand, particularly for deployment on edge devices where real-time processing is crucial. However, deploying such models on edge devices is challenging due to limited computing power and memory. In this paper, we present MambaLiteSR, a novel lightweight image Super-Resolution (SR) model that utilizes the architecture of Vision Mamba. It integrates State Space Blocks and a reconstruction module for efficient feature extraction. To optimize efficiency without affecting performance, MambaLiteSR employs knowledge distillation, transferring essential information from a larger Mamba-based teacher model to a smaller student model through hyperparameter tuning. Through a mathematical analysis of model parameters and their impact on the Peak signal-to-Noise Ratio (PSNR), we identify key factors and adjust them accordingly. Our comprehensive evaluation shows that MambaLiteSR outperforms state of the art edge SR methods by reducing power consumption while maintaining competitive PSNR and SSIM scores across benchmark datasets such as Set5, Set14, and BSD100. It also reduces the power usage during training by adopting low-rank approximation. Moreover, MambaLiteSR reduces the total number of parameters without degrading performance, enabling the efficient deployment of generative AI models on resource-constrained devices. Deployment on the embedded NVIDIA Jetson Orin Nano confirms the superior balance of MambaLiteSR size, latency, and resource efficiency. The experimental results show that MambaLiteSR achieves performance comparable to both the baseline and other edge models while using 15% fewer parameters than the baseline. It also improves the power consumption by up to 58% compared to state-of-the-art SR edge models, all while maintaining low energy consumpt
This paper outlines a solution to the multi-channel synthetic aperture radar (SAR) moving target indication and detection (MTI/MTD) by means of inverse systems approach. A novel model of the problem is presented and a...
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ISBN:
(纸本)9781424407071
This paper outlines a solution to the multi-channel synthetic aperture radar (SAR) moving target indication and detection (MTI/MTD) by means of inverse systems approach. A novel model of the problem is presented and an approximate analytic solution to it will be given. It will be demonstrated how a moving target indicator can benefit from a multichannel SAR system as opposed to a traditional approach that separates MTI and SAR systems. It will be shown that the problem of separation of moving targets from stationary ones can be solved completely by using multi-channel approach and in such a way that a spatial distribution of the stationary targets does not play a role.
This paper presents a neural network based approach for vehicle classification. The proposed vehicle classification approach extracts various features from a vehicle image, normalises and classifies them into one of t...
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ISBN:
(纸本)9781424407071
This paper presents a neural network based approach for vehicle classification. The proposed vehicle classification approach extracts various features from a vehicle image, normalises and classifies them into one of the known classes. It is based on structural features and a direct solution training method. The preliminary experiments on training and testing of 4 types of vehicles patterns were conducted. The experimental results are very promising and demonstrate the effectiveness and usefulness of the proposed approach.
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