Efficient measurement matrix for speech compressive sampling

Ahmed, Irfan; Khan, Aftab; Khan, Akhtar Naeem; Mujahid, Kamran; Khan, Naveed Iqbal

doi:10.1007/s11042-021-10657-x

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…Data-driven techniques have been used in many areas because of the inherent benefits they provide in comparison to conventional methods [18] [19]. Some of the prominent advantages offered by the data-driven methods are their structural learning capability, offline training mechanism, and flexibility to adapt according to the available datasets.…”

Section: Literature Reviewmentioning

confidence: 99%

Improved Commodity Supply Chain Performance Through AI and Computer Vision Techniques

Ahmed,

Alkahtani,

Khalid

et al. 2024

IEEE Access

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In the realm of supply chain management, the impact of Artificial Intelligence (AI) tools on optimizing commodity distribution is undeniable. This study presents the transformative potential of AI and computer vision in the field of commodity supply chain management. The capability of AI to reduce yield loss and enhance supply chain efficiency is a growing trend and vision-based commodity defect monitoring can be useful in this regard. We explored the employment of real-time computer vision techniques in supply chain flaw management, which include Detection Transformer (DETR), a type of Vision Transformer (ViT), and compared its performance with the You Only Look Once (YOLO) and other AI models. Computational feasibility is assessed, encompassing various computer vision and AI models, by using a dataset comprising images of commodity items used to substantiate our findings. The obtained results have shown the improved performance of DETR with a detection and classification accuracy of 96%, directly correlating with improved supply chain management. On the other hand, the higher computational burden imposed by DETR makes it less feasible for the higher constrained embedded applications. The practicality of AI algorithms for real-time defect identification reveals promising prospects for integration into supply chain systems. This research underscores AI's potential to revolutionize commodity supply chain management, extending its benefits to various commodity distribution networks.

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Section: Literature Reviewmentioning

confidence: 99%

Improved Commodity Supply Chain Performance Through AI and Computer Vision Techniques

Ahmed,

Alkahtani,

Khalid

et al. 2024

IEEE Access

Self Cite

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“…In this quest, work done in [30] explored a diverse range of random and deterministic sensing matrices to propose an efficient matrix. In this work, the experimented matrices were compared on the basis of reconstruction accuracy and reconstruction time, while DCT is applied as a sparse transformation basis owing to its efficiency in sparsity of speech signals [31].…”

Section: B Random and Deterministic Samplingmentioning

confidence: 99%

“…In CS framework, the prudently designed sampling matrix is able to assist in effective signal recovery task [30]. The proposed work in [77], have used Autoencoder wherein encoder and decoder are employed as sampling and reconstruction network, respectively.…”

Section: ) Machine Learning and Deep Learningmentioning

confidence: 99%

Sparse Signal Representation, Sampling, and Recovery in Compressive Sensing Frameworks

et al. 2022

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Compressive sensing allows the reconstruction of original signals from a much smaller number of samples as compared to the Nyquist sampling rate. The effectiveness of compressive sensing motivated the researchers for its deployment in a variety of application areas. The use of an efficient sampling matrix for high-performance recovery algorithms improves the performance of the compressive sensing framework significantly. This paper presents the underlying concepts of compressive sensing as well as previous work done in targeted domains in accordance with the various application areas. To develop prospects within the available functional blocks of compressive sensing frameworks, a diverse range of application areas are investigated. The three fundamental elements of a compressive sensing framework (signal sparsity, subsampling, and reconstruction) are thoroughly reviewed in this work by becoming acquainted with the key research gaps previously identified by the research community. Similarly, the basic mathematical formulation is used to outline some primary performance evaluation metrics for 1D and 2D compressive sensing.

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WITH: Weighted Truncated Hadamard-Matrix-Based Deterministic Compressive Sampling for Sparse Multiband Signals

Zhang

Qiao

2022

Circuits Syst Signal Process

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Efficient measurement matrix for speech compressive sampling

Cited by 6 publications

References 22 publications

Improved Commodity Supply Chain Performance Through AI and Computer Vision Techniques

Improved Commodity Supply Chain Performance Through AI and Computer Vision Techniques

Sparse Signal Representation, Sampling, and Recovery in Compressive Sensing Frameworks

WITH: Weighted Truncated Hadamard-Matrix-Based Deterministic Compressive Sampling for Sparse Multiband Signals

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