Area-Optimized BCD-4221 VSLI Adder Architecture for High-Performance Computing

Decimal arithmetic circuits are promising to provide a solution for accurate decimal arithmetic operations which are not possible with binary arithmetic circuits.They can be used in banking,commercial and financial transactions,scientific measurements,etc.This article presents the Very Large Scale Integration(VLSI)design of Binary Coded Decimal(BCD)-4221 area-optimized adder architecture using unconventional BCD-4221 representation.Unconventional BCD number representations such as BCD4221 also possess the additional advantage of more effectively representing the 10's complement representation which can be used to accelerate the decimal arithmetic operations.The design uses a binary Carry Lookahead Adder(CLA)along with some other logic blocks which are required to perform internal calculations with BCD-4221 numbers.The design is verified by using Xilinx Vivado 2016.1.Synthesis results have been obtained by Cadence Genus16.1 synthesis tool using 90 nm technology.The performance parameters such as area,power,delay,and area-delay Product(ADP)are compared with earlier reported circuits.Our proposed circuit shows significant area and ADP improvement over existing designs.

A New Method for Diagnosis of Leukemia Utilizing a Hybrid DL-ML Approach for Binary and Multi-Class Classification on a Limited-Sized Database

Infection of leukemia in humans causes many complications in its later stages.It impairs bone marrow’s ability to produce blood.Morphological diagnosis of human blood cells is a well-known and well-proven technique for diagnosis in this case.The binary classification is employed to distinguish between normal and leukemiainfected cells.In addition,various subtypes of leukemia require different treatments.These sub-classes must also be detected to obtain an accurate diagnosis of the type of leukemia.This entails using multi-class classification to determine the leukemia subtype.This is usually done using a microscopic examination of these blood cells.Due to the requirement of a trained pathologist,the decision process is critical,which leads to the development of an automated software framework for diagnosis.Researchers utilized state-of-the-art machine learning approaches,such as Support Vector Machine(SVM),Random Forest(RF),Na飗e Bayes,K-Nearest Neighbor(KNN),and others,to provide limited accuracies of classification.More advanced deep-learning methods are also utilized.Due to constrained dataset sizes,these approaches result in over-fitting,reducing their outstanding performances.This study introduces a deep learning-machine learning combined approach for leukemia diagnosis.It uses deep transfer learning frameworks to extract and classify features using state-of-the-artmachine learning classifiers.The transfer learning frameworks such as VGGNet,Xception,InceptionResV2,Densenet,and ResNet are employed as feature extractors.The extracted features are given to RF and XGBoost classifiers for the binary and multi-class classification of leukemia cells.For the experimentation,a very popular ALL-IDB dataset is used,approaching a maximum accuracy of 100%.A private real images dataset with three subclasses of leukemia images,including Acute Myloid Leukemia(AML),Chronic Lymphocytic Leukemia(CLL),and Chronic Myloid Leukemia(CML),is also employed to generalize the system.This dataset achieves an impressive multi-class classification accuracy of 97.08%.The proposed approach is robust and generalized by a standardized dataset and the real image dataset with a limited sample size(520 images).Hence,this method can be explored further for leukemia diagnosis having a limited number of dataset samples.

Dual-tree complex wavelet transform and super-resolution based video inpainting application to object removal and error concealment

Video inpainting is a technique that fills in the missing regions or gaps in a video by using its known pixels.The existing video inpainting algorithms are computationally expensive and introduce seam in the target region that arises due to variation in brightness or contrast of the patches.To overcome these drawbacks,the authors propose a novel two-stage framework.In the first step,sub-bands of wavelets of a low-resolution image are obtained using the dualtree complex wavelet transform.Criminisi algorithm and auto-regression technique are then applied to these subbands to inpaint the missing regions.The fuzzy logic-based histogram equalisation is used to further enhance the image by preserving the image brightness and improve the local contrast.In the second step,the image is enhanced using super-resolution technique.The process of down-sampling,inpainting and subsequently enhancing the video using the super-resolution technique reduces the video inpainting time.The framework is tested on video sequences by comparing and analysing the structural similarity index matrix,peak-signal-to-noise ratio,visual information fidelity in pixel domain and execution time with the state-of-the-art algorithms.The experimental analysis gives visually pleasing results for object removal and error concealment.

Explainable Artificial Intelligence-A New Step towards the Trust in Medical Diagnosis with AI Frameworks:A Review

Machine learning(ML)has emerged as a critical enabling tool in the sciences and industry in recent years.Today’s machine learning algorithms can achieve outstanding performance on an expanding variety of complex tasks-thanks to advancements in technique,the availability of enormous databases,and improved computing power.Deep learning models are at the forefront of this advancement.However,because of their nested nonlinear structure,these strong models are termed as“black boxes,”as they provide no information about how they arrive at their conclusions.Such a lack of transparencies may be unacceptable in many applications,such as the medical domain.A lot of emphasis has recently been paid to the development of methods for visualizing,explaining,and interpreting deep learningmodels.The situation is substantially different in safety-critical applications.The lack of transparency of machine learning techniques may be limiting or even disqualifying issue in this case.Significantly,when single bad decisions can endanger human life and health(e.g.,autonomous driving,medical domain)or result in significant monetary losses(e.g.,algorithmic trading),depending on an unintelligible data-driven system may not be an option.This lack of transparency is one reason why machine learning in sectors like health is more cautious than in the consumer,e-commerce,or entertainment industries.Explainability is the term introduced in the preceding years.The AImodel’s black box nature will become explainable with these frameworks.Especially in the medical domain,diagnosing a particular disease through AI techniques would be less adapted for commercial use.These models’explainable natures will help them commercially in diagnosis decisions in the medical field.This paper explores the different frameworks for the explainability of AI models in the medical field.The available frameworks are compared with other parameters,and their suitability for medical fields is also discussed.

Robust backstepping global integral terminal sliding mode controller to enhance dynamic stability of hybrid AC/DC microgrids

In this paper,a Backstepping Global Integral Terminal Sliding Mode Controller(BGITSMC)with the view to enhancing the dynamic stability of a hybrid AC/DC microgrid has been presented.The proposed approach controls the switch-ing signals of the inverter,interlinking the DC-bus with the AC-bus in an AC/DC microgrid for a seamless interface and regulation of the output power of renewable energy sources(Solar Photovoltaic unit,PMSG-based wind farm),and Battery Energy Storage System.The proposed control approach guarantees the dynamic stability of a hybrid AC/DC microgrid by regulating the associated states of the microgrid system to their intended values.The dynamic stabil-ity of the microgrid system with the proposed control law has been proved using the Control Lyapunov Function.A simulation analysis was performed on a test hybrid AC/DC microgrid system to demonstrate the performance of the proposed control strategy in terms of maintaining power balance while the system’s operating point changed.Furthermore,the superiority of the proposed approach has been demonstrated by comparing its performance with the existing Sliding Mode Control(SMC)approach for a hybrid AC/DC microgrid.

Multiuser Detection for MIMO-OFDM system in Underwater Communication Using a Hybrid Bionic Binary Spotted Hyena Optimizer

Multi Access Interference (MAI) is the main source limiting the capacity and quality of the Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system which fulfills the demand of high-speed transmission rate and high quality of service for future underwater acoustic (UWA) communication. Multi User Detection (MUD) is needed to overcome the performance degradation caused by MAI. In this research, both local and global optimal solutions are obtained in Bionic Binary Spotted Hyena Optimizer (BBSHO) algorithm using the Position Coordinate Vectors (PCVs) of the social behavior of spotted hyenas to achieve MUD. Further, Extremal Optimization (EO) is introduced in BBSHO algorithm to improve the local search ability within the search space. Hence, a hybrid BBSHO algorithm is proposed for achieving MUD at the receiver of the MIMO-OFDM system whose transceiver model in underwater is implemented using BELLHOP simulation system. By MATLAB simulation, it is shown that the Bit Error Rate (BER) performance of the proposed hybrid algorithm outperforms with best optimal solution within the search space towards MUD for Interference to Noise Ratio (INR) at 10 dB, 20 dB, and 40 dB over conventional detectors and metaheuristic approaches such as Binary Spotted Hyena Optimizer (BSHO), Binary Particle Swarm Optimization (BPSO) in the UWA network.

A two-dimensional analytical-model-based comparative threshold performance analysis of SOI-SON MOSFETs

A generalized threshold voltage model based on two-dimensional Poisson analysis has been developed for SOI/SON MOSFETs.Different short channel field effects,such as fringing fields,junction-induced lateral fields and substrate fields,are carefully investigated,and the related drain-induced barrier-lowering effects are incorporated in the analytical threshold voltage model.Through analytical model-based simulation,the threshold voltage roll-off and subthreshold slope for both structures are compared for different operational and structural parameter variations.Results of analytical simulation are compared with the results of the ATLAS 2D physics-based simulator for verification of the analytical model.The performance of an SON MOSFET is found to be significantly different from a conventional SOI MOSFET.The short channel effects are found to be reduced in an SON,thereby resulting in a lower threshold voltage roll-off and a smaller subthreshold slope.This type of analysis is quite useful to figure out the performance improvement of SON over SOI structures for next generation short channel MOS devices.