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Due to rapid growth in the integrated circuit (IC) industry, the demand for compact digital system design is high. However, the continued technology reductions made the feasibility of further scaling down transistor size more challenging. In response to the growing demand for ultra-compact IC designs, the revolutionary quantum-dot cellular automata (QCA) technology has emerged as a promising solution. In a digital era, the counters are widely adopted in the peer-to-peer process flow to establish a mechanism for generating unique values for each identifier/number. In this work, a unique synchronous and asynchronous counters architecture is proposed with a reliable D and T flip-flop design. The proposed QCA architecture is implemented and validated with the QCA designer tool. Furthermore, in QCA technology, unreliable QCA designs can lead to frequent errors and malfunctions in the implemented logic. To overcome this challenge, the proposed design prioritizes cell placement (the relative positions of QCA cells) to make the circuit more robust. As a result, the circuit can still produce the expected functionality even if some QCA cells malfunction. Hence, to ensure the reliability of the proposed QCA architecture, the missing cell defect analysis is carried out in comparison with existing state-of-the-art designs. Based on comparison results, the unique designs like the proposed multiplexer, D flip-flop and T flip-flop design exhibit success rates of 67.28, 77.04 and 85.15%, respectively. The experimental results demonstrate that the proposed counter-architecture outperforms existing architectures. [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]

Networks-on-Chip (NoC) is an impending research area for designing future System-on-Chip (SoC). NoC futures are allied with intellectual property (IP) reusability, parallel communication within IPs and scalability against standard interconnection NoC topology. In this paper, we introduce a new hybrid topology namely Four Regular Dense Spidergon (FRDS) based deterministic NoC architecture with 64 cores. We propose cluster and generic heuristic based approach For mapping of applications onto FRDS topology. Genetic algorithm (GA) ＋ simulated annealing (SA) have been considered under generic heuristic approach for successful mapping of core into topology and fair comparison of the performance of the proposed FRDS. Under normalized condition with respect to Mesh, experimental findings of proposed FRDS against real benchmark traces provide an average improvement on execution time with 1.20% and 4.81% over DMesh and ZMesh. Further, an average improvement on energy consumption was obtained 8.13% and 5.35% over DMesh and ZMesh. We adopt constant geometric traffic and the synthetic traffic pattern to measure the performance of proposed FRDS. The experimental results clearly indicates an improvement in power latency product under geometric traffic by 16.27% and 12.23% over DMesh and ZMesh. Similarly under synthetic traffic pattern, the proposed FRDS offers an improvement up to 22.51% and 45.53% over DMesh and ZMesh respectively. To realize the effectiveness of the link usage, brief evaluation of link utilization has been carried out with balanced load. • Four Regular Dense Spidergon (FRDS) – irregular NoC architecture • Unicast-multicast routing adopt for promising deadlock-free high-performance routing within the network • Promising improvements over mean packet latency; the total power of the network; and power latency product • Analysis of link utilization to understand the usage of directional and diagonal links of network topology • Analyzed with standard and real-time application benchmarks [ABSTRACT FROM AUTHOR]

Purpose. Networks-on-Chip (NoC) is a network-based communication between operating cores and intellectual property (IP) cores integrated on the same chip. An efficient design of NoC ensures high-speed data transfer and minimum essential connections in large-scale multicore, low power applications. Design/methodology/approach. A unique technique called Multicriteria Deming Regressive African Buffalo Optimized Mapping Weighted Directive Graph Theory (MDRABOMWDGT) is introduced for an efficient Network-on-Chip architecture design. The main aim of the proposed technique is to find efficient operating cores integrated on the chip with minimum time. Initially, a set of IP cores are listed with their connections from the benchmark dataset. Then Multicriteria Deming Regressive African Buffalo Optimization is applied to the topology for mapping strategy on 3D NoC, with communication metrics such as throughput, latency, and computation time. The optimization technique initializes the population of cores in search space. For each core in the network, communication metrics are measured; then Deming regression is applied to analyze multicriteria functions for minimizing computation time. Further, fitness is measured to get an optimal IP core for improving the performance of mapping in 3D NoC. Findings. Comprehensive experimental evaluation is conducted using a benchmark dataset, communication metrics are measured, and results show significant improvement in performance with respect to energy parameters compared to state-of-the-art works. Originality/value. The mapping strategy for 3D NOC is developed and the results are compared with the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Due to increased demands for communication at low power, an efficient application mapping has become vital in the area of network on chip (NoC). Optimisation of architectural structure in on‐chip design is essential to maximise the performance of the network and minimise the cost functions. To address this issue, a knowledge‐based memetic algorithm (KBMA) is proposed for 3D NoC for successful mapping with standard network topologies. The proposed KBMA adopts power, area and delay as a cost function for an effective mapping. The competence of the proposed method is verified through comparison with other natural inspired algorithms like particle swarm optimisation and genetic algorithm. The presented work is validated through four case studies which include real application benchmarks of NoC and random generated benchmarks using test graph for free. [ABSTRACT FROM AUTHOR]

This paper proposes a constructive partitioning approach as an effective mapping approach for 2D Networks-on-Chip. Cluster-based mapping approach offers a promising improvement on the fastest initial mapping against the standard Networks-on-Chip (NoC) architecture. In this paper, the spectral partitioning algorithm is adopted for the fastest mapping with NoC, named SMA. The proposed spectral partitioning-based mapping approach (SMA) explores the efficacious NoC mapping concerning communication bandwidth, average hop counts, and total power consumption NoC architecture. The experimental analysis indicates that the proposed SMA outperforms existing partitioning approaches. [ABSTRACT FROM AUTHOR]

The antibacterial performance of biocomposite films prepared from lignocellulosic waste (hazelnut husk or hazelnut leafy green cover) modified with silver nanoparticles and polylactic acid (PLA) was determined. The amount of hazelnut husk in the PLA matrix ranged from 10 to 40% by weight in 10% increments. The composite pellets were produced using a twin-screw extruder. Biocomposite films of 0.6 mm x 40 mm x 200 mm were produced from the pellets in a laboratory hydraulic hot press. The surfaces of the modified hazelnut husk and biocomposite specimens were analyzed by scanning electron microscopy (SEM) and inductively coupled plasma optical emission spectrometry (ICP-OES). The antibacterial activity of the biocomposite films against Staphylococcus aureus bacteria was determined using the ASTM E 2149 (2020) method. The antibacterial activity of the biocomposite films increased noticeably with the addition of hazelnut husk modified with the silver nanoparticles. Compared to the pure PLA film, the biocomposite films with 10 wt% modified husk flour showed the lowest antibacterial activity (31.3%) against S. aureus over 24-h while the films with 40 wt% showed the highest antibacterial activity (99.9%). The biocomposite films made of hazelnut husk flour with silver nanoparticles and PLA matrix could be considered for food packaging applications. [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]

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]