Implementation of VLSI on Signal Processing-Based Digital Architecture Using AES Algorithm

Continuous improvements in very-large-scale integration (VLSI) technology and design software have significantly broadened the scope of digital signal processing (DSP) applications. The use of application-specific integrated circuits (ASICs) and programmable digital signal processors for many DSP applications have changed, even though new system implementations based on reconfigurable computing are becoming more complex. Adaptable platforms that combine hardware and software programmability efficiency are rapidly maturing with discrete wavelet transformation (DWT) and sophisticated computerized design techniques, which are much needed in today’s modern world. New research and commercial efforts to sustain power optimization, cost savings, and improved runtime effectiveness have been initiated as initial reconfigurable technologies have emerged. Hence, in this paper, it is proposed that the DWT method can be implemented on a field-programmable gate array in a digital architecture (FPGA-DA). We examined the effects of quantization on DWT performance in classification problems to demonstrate its reliability concerning fixed-point math implementations. The Advanced Encryption Standard (AES) algorithm for DWT learning used in this architecture is less responsive to resampling errors than the previously proposed solution in the literature using the artificial neural networks (ANN) method. By reducing hardware area by 57%, the proposed system has a higher throughput rate of 88.72%, reliability analysis of 95.5% compared to the other standard methods.     

Visual perception and mixed-initiative interaction for assisted visualization design

This paper describes the integration of perceptual guidelines from human vision with an AI-based mixed-initiative search strategy. The result is a visualization assistant called ViA, a system that collaborates with its users to identify perceptually salient visualizations for large, multidimensional datasets. ViA applies knowledge of low-level human vision to: (1) evaluate the effectiveness of a particular visualization for a given dataset and analysis tasks; and (2) rapidly direct its search towards new visualizations that are most likely to offer improvements over those seen to date. Context, domain expertise, and a high-level understanding of a dataset are critical to identifying effective visualizations. We apply a mixed-initiative strategy that allows ViA and its users to share their different strengths and continually improve ViA's understanding of a user's preferences. We visualize historical weather conditions to compare ViA's search strategy to exhaustive analysis, simulated annealing, and reactive tabu search, and to measure the improvement provided by mixed-initiative interaction. We also visualize intelligent agents competing in a simulated online auction to evaluate ViA's perceptual guidelines. Results from each study are positive, suggesting that ViA can construct high-quality visualizations for a range of real-world datasets.     

Antifungal activity of five different essential oils in vapour phase for the control of Colletotrichum gloeosporioides and Lasiodiplodia theobromae in vitro and on mango

Mango fruit has high commercial value; however, major postharvest losses are encountered throughout the supply chain due to postharvest diseases. These results lead to the search for natural fungicide for postharvest diseases control. The antifungal effects of five essential oils (thyme, clove, cinnamon, anise and vitex) were assessed by disc volatilisation method. Thyme oil vapours at 5 μL per Petriplate, and clove and cinnamon oil at 8 μL per Petriplate showed 100% growth inhibition of mango pathogens in vitro. GC/MS analysis of essential oil showed thymol (23.88), o‐cymol (23.88) and terpinolene (23.88) as the major constituents of thyme oil. Clove and cinnamon oils contain 3‐allyl‐2‐methoxyphenol (37.42%) and benzofuran 3‐methyl (17.97%), respectively. Thyme oil as a fumigant at 66.7 μL L^?1 showed a significant (P < 0.05) inhibition on postharvest pathogens of mango fruits stored at 25 °C for 6 days. Results of our study suggest the possibility of using thyme oil as an alternate natural fungicide to manage postharvest diseases in mango.     

Imperative role of electron microscopy in toxicity assessment: A review

Electron microscope (EM) was developed in 1931 and since then microscopical examination of both the biological and non-biological samples has been revolutionized. Modifications in electron microscopy techniques, such as scanning EM and transmission EM, have widened their applicability in the various sectors such as understanding of drug toxicity, development of mechanism, criminal site investigation, and characterization of the nano-molecule. The present review summarizes its role in important aspects such as toxicity assessment and disease diagnosis in special reference to SARS-COV2. In the biological system, EM studies have elucidated the impact of toxicants at the ultra-structural level in various tissue in conformity to physiological alterations. Thus, EM can be concluded as an important tool in toxicity assessment and disease prognosis.     

The effects of different methods of cooking on proximate, mineral and heavy metal composition of fish and shrimps consumed in the Arabian Gulf

This study analyzed eight cooked species of fish and one species of shrimps (grilled, curried, fried and cooked in rice) commonly consumed in Bahrain for their proximate, mineral and heavy metal content. The results reveled that the protein content was in the range of 22.8-29.2 g/100 g, while the fat content was between 2.9-11.9 g/100 g. The energy content was the highest in the fried Scomberomorus commerson being 894.2 KJ/100 g, followed by Scomberomorus commerson cooked in rice (867.3 KJ/100 g). The samples also had a considerable content of sodium ranging from 120-600 mg/100 g, potassium (310-560 mg/100 g) phosphorous (200-330 mg/100 g), magnesium (26-54 mg/100 g) and zinc (0.4-2.0 mg/100 g), while the other minerals were present to a lower extent. Lead was present to an extent of 0.30 microg/g in the grilled Plectorhinchus sordidus while Lethrinus nebulosus cooked in rice contained 0.35 microg/g of mercury. Cadmium levels were constant at <0.02 microg/g. It can be concluded the traditional methods of cooking fish and shrimps have an effect on their nutrient composition and heavy metal content hence, it is advisable to avoid excessive frying and use minimal salt. In addition, consuming of a wide variety of species of fish and alternating between the various modes of cooking is the best approach to achieve improved dietary habits, minimizing mercury exposure and increasing omega-3 fatty acid intake.     

Fuzzy least squares twin support vector clustering

In this paper, we have formulated a fuzzy least squares version of recently proposed clustering method, namely twin support vector clustering (TWSVC). Here, a fuzzy membership value of each data pattern to different cluster is optimized and is further used for assigning each data pattern to one or other cluster. The formulation leads to finding k cluster center planes by solving modified primal problem of TWSVC, instead of the dual problem usually solved. We show that the solution of the proposed algorithm reduces to solving a series of system of linear equations as opposed to solving series of quadratic programming problems along with system of linear equations as in TWSVC. The experimental results on several publicly available datasets show that the proposed fuzzy least squares twin support vector clustering (F-LS-TWSVC) achieves comparable clustering accuracy to that of TWSVC with comparatively lesser computational time. Further, we have given an application of F-LS-TWSVC for segmentation of color images.

     

Robust Parametric Twin Support Vector Machine for Pattern Classification

In this paper, we propose a robust parametric twin support vector machine (RPTWSVM) classifier based on Parametric-\(\nu \)-Support Vector Machine (Par-\(\nu \)-SVM) and twin support vector machine. In order to capture heteroscedastic noise present in the training data, RPTWSVM finds a pair of parametric margin hyperplanes that automatically adjusts the parametric insensitive margin to incorporate the structural information of data. The proposed model of RPTWSVM is not only useful in controlling the heteroscedastic noise but also has much faster training speed when compared to Par-\(\nu \)-SVM. Experimental results on several machine learning benchmark datasets show the advantages of RPTWSVM both in terms of generalization ability and training speed over other related models.