Optimization of Quantum Cost for Low Energy Reversible Signed/Unsigned Multiplier Using Urdhva-Tiryakbhyam Sutra

One of the elementary operations in computing systems is multiplication. Therefore, high-speed and low-power multipliers design is mandatory for efficient computing systems. In designing low-energy dissipation circuits, reversible logic is more efficient than irreversible logic circuits but at the cost of higher complexity. This paper introduces an efficient signed/unsigned 4 × 4 reversible Vedic multiplier with minimum quantum cost. The Vedic multiplier is considered fast as it generates all partial product and their sum in one step. This paper proposes two reversible Vedic multipliers with optimized quantum cost and garbage output. First, the unsigned Vedic multiplier is designed based on the Urdhava Tiryakbhyam (UT) Sutra. This multiplier consists of bitwise multiplication and adder compressors. Compared with Vedic multipliers in the literature, the proposed design has a quantum cost of 111 with a reduction of 94% compared to the previous design. It has a garbage output of 30 with optimization of the best-compared design. Second, the proposed unsigned multiplier is expanded to allow the multiplication of signed numbers as well as unsigned numbers. Two signed Vedic multipliers are presented with the aim of obtaining more optimization in performance parameters. DesignI has separate binary two’s complement (B2C) and MUX circuits, while DesignII combines binary two’s complement and MUX circuits in one circuit. DesignI shows the lowest quantum cost, 231, regarding state-of-the-art. DesignII has a quantum cost of 199, reducing to 86.14% of DesignI. The functionality of the proposed multiplier is simulated and verified using XILINX ISE 14.2.     

Political Optimizer with Deep Learning-Enabled Tongue Color Image Analysis Model

Biomedical image processing is widely utilized for disease detection and classification of biomedical images. Tongue color image analysis is an effective and non-invasive tool for carrying out secondary detection at anytime and anywhere. For removing the qualitative aspect, tongue images are quantitatively inspected, proposing a novel disease classification model in an automated way is preferable. This article introduces a novel political optimizer with deep learning enabled tongue color image analysis (PODL-TCIA) technique. The presented PODL-TCIA model purposes to detect the occurrence of the disease by examining the color of the tongue. To attain this, the PODL-TCIA model initially performs image pre-processing to enhance medical image quality. Followed by, Inception with ResNet-v2 model is employed for feature extraction. Besides, political optimizer (PO) with twin support vector machine (TSVM) model is exploited for image classification process, shows the novelty of the work. The design of PO algorithm assists in the optimal parameter selection of the TSVM model. For ensuring the enhanced outcomes of the PODL-TCIA model, a wide-ranging experimental analysis was applied and the outcomes reported the betterment of the PODL-TCIA model over the recent approaches.     

Image forgery detection using image similarity

Multimedia Tools and Applications - Ideally, sophisticated image forgery methods leave no perceptible evidence of tampering. In response to such stringent context, researchers have proposed digital...     

The Role of Microstructure Morphology on Fracture Mechanisms of Continuously Cooled Bainitic Steel Designed for Rails Application

Metallurgical and Materials Transactions A - The low-carbon bainitic steel after a continuous cooling process was subjected to fracture toughness investigations using the J-integral approach. The...     

Search and Rescue Optimization with Machine Learning Enabled Cybersecurity Model

Presently, smart cities play a vital role to enhance the quality of living among human beings in several ways such as online shopping, e-learning, e-healthcare, etc. Despite the benefits of advanced technologies, issues are also existed from the transformation of the physical word into digital word, particularly in online social networks (OSN). Cyberbullying (CB) is a major problem in OSN which needs to be addressed by the use of automated natural language processing (NLP) and machine learning (ML) approaches. This article devises a novel search and rescue optimization with machine learning enabled cybersecurity model for online social networks, named SRO-MLCOSN model. The presented SRO-MLCOSN model focuses on the identification of CB that occurred in social networking sites. The SRO-MLCOSN model initially employs Glove technique for word embedding process. Besides, a multiclass-weighted kernel extreme learning machine (M-WKELM) model is utilized for effectual identification and categorization of CB. Finally, Search and Rescue Optimization (SRO) algorithm is exploited to fine tune the parameters involved in the M-WKELM model. The experimental validation of the SRO-MLCOSN model on the benchmark dataset reported significant outcomes over the other approaches with precision, recall, and F1-score of 96.24%, 98.71%, and 97.46% respectively.     

Sedimentary facies analysis of the Upper Bahariya sandstone reservoir in East Bahariya C area,North Western Desert,Egypt

The present work aims to deduce the depositional processes of the Lower Cenomanian Bahariya Formation, that is one of the main hydrocarbon siliciclastic reservoirs in the Northern Western Desert of Egypt. This has been achieved through the analyses of the core photographs, micro-resistivity image logs, mud logs and conventional wireline logs. The Bahariya Formation comprises a complex of depositional lithofacies such as interbedded siltstone, shale, sandstones and pebbly sandstones. The current work highlights the effectiveness of the integrative approach. A variety of datasets including core photographs, micro-resistivity image logs, mud logs and conventional wireline logs are integrated to define the conceptual depositional model in the study area.Different recorded sedimentary features point to various depositional processes. Hummocky cross stratification (HCS) is the common sedimentary structure indicating the storm action. Wave ripples are recorded providing evidence for fair-weather action on shoreface. Rhythmic heterolithics, tidalites indicate the tidal processes. The upper part of the Bahariya Formation has been influenced by wave and storm processes more than the lower parts. The core and image facies are designated, being dominated by Sandstone (S), Siltstone (Z), Mudstone (M), Heterolithic (H) and Limestone (L.s). The studied core and image facies with wireline logs facies are grouped into five major facies associations (FA). They are Tidal Channel and Tidal Creeks, Tidal Flat, Storm, Shoreface, Offshore Transition to Offshore Facies Associations. The integrative approach indicates that the Upper Bahariya Member has been deposited in a storm influenced tidal coastal realm.     

Mesoporous Ti-MCM-41 materials as photodegradation catalysts of 2,4,6-trichlorophenol in water

Titanium-modified MCM-41 type mesoporous silica materials were prepared by hydrothermal [Ti-MCM-41(HT)], sol–gel [Ti-MCM-41(SG)] and post-synthesis impregnation [TiO₂/MCM-41] methods. The materials were characterized and tested as photocatalysts in the oxidative degradation reaction of 2,4,6-trichlorophenol (2,4,6-TCP) in water. The catalysts showed high initial activity. The produced acetate and chloride ions were found to inhibit the degradation reaction. The Ti-MCM-41(HT) sample showed higher overall activity than the Ti-MCM-41(SG) catalyst. One of the probable reasons for this is the difference in the distribution of the active sites that determines the rates of electron (e⁻)–hole (h⁺) recombination within the photoactive species. The HT preparation was found to contain silica-bound titania in higher dispersion, while the SG preparation contained also polymerized species with Ti–O–Ti bonds.     

Rare Earth Ions Adsorption onto Graphene Oxide Nanosheets

Graphene oxide (GO) was synthesized and used as a coagulant of rare earth elements (REEs) from aqueous solution. Stability and adsorption capacities were exhibited for target REEs such as La(III), Nd(III), Gd(III), and Y(III). The parameters influencing the adsorption capacity of the target species including contact time, pH, initial concentration, and temperature were optimized. The adsorption kinetics and thermodynamics were studied. The method showed quantitative recovery (99%) upon desorption using HNO₃ acid (0.1 M) after a short contact time (15 min).