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This manuscript proposes a low-power and high-speed hybrid approximate multiplier using 15-4 approximate compressors in partial product stage for image processing application. Initially, the most significant bits (MSB) of approximate multiplier is encoded by approximate radix-8 Booth’s (R-8B) encoding, and also least significant bits (LSB) is encoded by approximate truncated-round approximate multiplier (TRAM) encoding both are used to rounding the LSB to the adjacent power of two. Then, approximate 15-4 compressors are subjugated in partial product lessening stage to produce MSB result. Then, the hybrid approximate multiplier under 15-4 approximate compressors is carried out in the application of image processing. The proposed approach is done in MATLAB and Vivado Design Suite 2018.1 simulator, then observes that the power consumption of proposed design attains 31.814%, 23.562% lower than existing models. Similarly, the velocity attains 42.63%, 6.263% higher than the existing models. [ABSTRACT FROM AUTHOR]

Deep learning-based sustainable subsurface for anomaly detection in different materials is an objective to improve the reliability of thermographic inspection. This article aims to describe a method that uses Barker-coded thermal wave imaging to identify subsurface anomalies in materials. The novelty of the proposed methodology is to detect smaller defects at a higher depth even on a fully corroded sample of mild steel. Experiments were carried out with different kinds of samples like mild steel and glass fiber reinforced plastic (GFRP). Various commonly used modern post-processing techniques are applied alongside the proposed techniques for detecting subsurface anomalies. Subsurface anomalies visualized using the proposed deep learning method give better visualization and results when compared to that of other approaches. In addition to it, region-based active contour segmentation-based detection is also proposed for the GFRP sample. This study results in a high signal-to-noise ratio (SNR) of value 108 dB; the least error in defect size is nearly 0.01% using full width at half maximum (FWHM), and the aspect ratio is nearly 1 for the proposed convolutional neural network (CNN)-based processing approach. [ABSTRACT FROM AUTHOR]

This paper proposes an energy-efficient hybrid Networks-on-Chip architecture called SaHNoC, which is more self adaptive with respect to selection of Wi-Fi positions in a Mesh topology. In comparison with wired on-chip interconnect NoC, wireless interconnect NoC offers a promising performance with respect to communication speed. However, the wireless switch occupies a larger area and has higher power-energy consumption than the wired switch. In this paper, we develop an optimal energy-efficient wire-wireless NoC structure called Hybrid NoC. The proposed hybrid NoC approach explores the self-adaptive technique for the selection of wireless switching positions in a suitable location against topology with respect to network diameter and average hop count. The effectiveness of the proposed SaHNoC is validated with a modified version of Booksim 2.0 in mutual with Orion 3.0. [ABSTRACT FROM AUTHOR]

In the technological era, exponential increase of unorganized text documents offers increased difficulties retrieving the most relevant data. The document clustering is a most prominent technique that transforms unorganized contents into organized contents in the form of clusters. The recognition technique always undergoes clustering of text documents with misleading or redundant information that degrades document clustering quality. In this study, a salp swarm algorithm (SSA) is used for clustering the text documents. The study is improved with a similarity and a distance-based measurements as an objective function in the clustering domain. The experimental validation is conducted to show the efficacy of SSA-based similarity distance measurement that prominently improves the quality of clustering the text documents. The comparison with existing methods shows that the proposed SSA offers better clustering of text documents in accuracy, sensitivity, specificity, and f-measure. [ABSTRACT FROM AUTHOR]

The capacity of additive manufacturing (AM) to create products with intricate characteristics has produced its rapid expansion in the manufacturing business and widespread interest from a wide range of other sectors. To ensure that parts made for functional use meet specified standards, especially in terms of quality and dependability, quality management of the AM components has drawn a lot of attention from the industries. Unnecessary porosities have a substantial impact on how well the products manufactured via AM operate mechanically. In order to continue to enhance the standard of the AM components by outlier detection, the quality control procedures are thus necessary. In order to efficiently identify outliers in AM data, this work introduces the binary-weighted sparrow search fine-tuned random forest (BSS-RF) method. The binary-weighted sparrow search optimization (BSSO) method is proposed to optimize the RF’s performance in order to increase the detection accuracy. To remove the noisy data, raw data samples are enhanced using the normalization procedure. The important features are extracted using quadratic discriminant analysis (QDA). Finally, the suggested strategy is used to identify abnormal occurrences. The efficacy of the suggested approach is supported by experimental data. The suggested approach can be used in various industrial sectors, including electrical engineering, mechanical engineering, and medical engineering. We intend to create an interactive outlier identification system that is based on the suggested technique for real-time outlier identification and fault forecasting in the future. [ABSTRACT FROM AUTHOR]