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.     

Machining ferrous materials with carbides coated by chemical vapour deposition II: Wear mechanisms

A broad variety of carbides coated by chemical vapour deposition (CVD) have been used to cut four types of steel and two types of cast iron. In general, the application of hard CVD layers to cemented carbides is a very effective method for enhancing wear resistance, but the extent of the wear enhancement depends upon the coating material selected and the workpiece being machined. No single coating material is always superior to another. Hence, multiphased coatings are a necessity. Examination of the worn tool surfaces, in conjunction with earlier observations on the interfacial conditions (Part I), show that a variety of wear mechanisms can pertain when cutting ferrous materials, including dissolution/diffusion, discrete plastic deformation/fracture, cracking and spalling, localized oxidation and gross plastic deformation of the cutting edge.     

Effect of overload on fatigue crack retardation of aerospace Al-alloy laser welds using crack-tip plasticity analysis

Investigations on fatigue crack growth retardation due to single tensile and periodic multiple over load in strength undermatched laser beam welded 3.2 mm thick aerospace grade aluminium alloy 2139-T8 sheets are conducted. The effect of overload on the fatigue crack propagation behaviours of the homogenous base metal and welded panels (200 mm wide, centre cracked) was compared using experimental and FE analysis methods. The effective crack tip plasticity has been determined in homogeneous M(T) specimens using Irwin’s method and in both homogeneous and laser welded specimen by calculating crack tip plastic strain using FE analysis for single tensile overload. The crack retardation due to the overload in welded specimens is described by the Wheeler Model. The crack tip plastic zone size in the welded specimen was determined by FE analysis using maximum plastic zone extension at the mid sheet thickness. The results show that the Wheeler Model can be implemented to the highly heterogeneous undermatched weld to describe the crack retardation in fatigue following single tensile overload. Fatigue crack growth retardation due to single overload is found to be larger than the base metal. However, after periodic multiple overload, shorter crack retardation has occurred for undermatched welds than the base metal.     

Grasshopper optimization algorithm for optimal load frequency control considering Predictive Functional Modified PID controller in restructured multi-resource multi-area power system with Redox Flow Battery units

Load frequency control (LFC) is a well-established issue in design and operation of power systems considering to the extension, restructuring, and complexity of the interconnected power systems and also the emergence utilization of renewable energy resources. This paper studies the frequency control of multi-area multi-source power system based on the importance of the LFC in the stability of the power system which includes various generation units of thermal, hydroelectric, wind, natural gas and diesel under the restructured environment. In this system, non-linear physical constraints, governor dead band (GDB) and generation rate constraint (GRC) are considered. In this paper, a new Predictive Functional Modified PID (PFMPID) controller is proposed that the effectiveness of this controller is verified compared to the traditional one. In order to optimize and demonstrate the superiority of the proposed control method, Grasshopper Optimization Algorithm (GOA) is proposed as a suitable solution. To further improve the performance of the under study system, the use of the Redox Flow Battery (RFB) energy storage unit has also been proposed. Since the operation evaluation of the proposed process is necessary in different system conditions, the performance of the proposed method is studied under various disturbances and simulation results are presented.     

Granularity-based workflow scheduling algorithm for cloud computing

The workflow scheduling problem has drawn a lot of attention in the research community. This paper presents a workflow scheduling algorithm, called granularity score scheduling (GSS), which is based on the granularity of the tasks in a given workflow. The main objectives of GSS are to minimize the makespan and maximize the average virtual machine utilization. The algorithm consists of three phases, namely B-level calculation, score adjustment and task ranking and scheduling. We simulate the proposed algorithm using various benchmark scientific workflow applications, i.e., Cybershake, Epigenomic, Inspiral and Montage. The simulation results are compared with two well-known existing workflow scheduling algorithms, namely heterogeneous earliest finish time and performance effective task scheduling, which are also applied in cloud computing environment. Based on the simulation results, the proposed algorithm remarkably demonstrates its performance in terms of makespan and average virtual machine utilization.     

Optimal breast cancer classification using Gauss–Newton representation based algorithm

Breast cancer is a decisive disease worldwide. It is one of the most widely spread cancer among women. As per the survey, one out of eight women in the world are at risk of breast cancer at some point of time in her life. One of the methods to reduce breast cancer mortality rate is timely detection and effective treatment. That is why, more accurate classification of a breast cancer tumor has become a challenging problem in the medical field. Many classification techniques are proposed in the literature. Today, expert systems and machine learning techniques are being extensively used in the breast cancer classification problem. They provide high classification accuracy and effective diagnostic capabilities. In this paper, we have proposed a novel Gauss-Newton representation based algorithm (GNRBA) for breast cancer classification. It uses the sparse representation with training sample selection. Until now, sparse representation has been successfully applied in pattern recognition only. The proposed method introduces a novel Gauss-Newton based approach to find the optimal weights for the training samples for classification. In addition, it evaluates the sparsity in a computationally efficient way as compared to the conventional l₁-norm method. The effectiveness of the GNRBA is examined on the Wisconsin Breast Cancer Database (WBCD) and the Wisconsin Diagnosis Breast Cancer (WDBC) database from the UCI Machine Learning repository. Various performance measures like classification accuracy, sensitivity, specificity, confusion matrices, a statistical test and the area under the receiver operating characteristic (AUC) are reported to show the superiority of the proposed method as compared to classical models. The experimental results show that the proposed GNRBA could be a good alternative for breast cancer classification for clinical experts.     

An investigation on enhancing ceramic shell properties using naturally available additives

The ceramic shell properties are very crucial in investment casting (IC) process. Colloidal silica based ceramic shells usually lack sufficient shell properties which causes shell cracking. Sometimes minor repairs can make up the shells. However, every time shell repairing may not be feasible which causes loss to the manufacturer. In the present work, an investigation has been made to improve the properties of colloidal silica based IC ceramic shell by addition of naturally available products such as saw dust and coconut fibres into the ceramic slurries. The important shell properties tested were shell thickness, green and fired strengths, porosity and permeability. The properties of saw dust and coconut fibre modified shells were compared with that of conventional ceramic shell using statistical analysis and it was found that the new shells were better than the conventional shell in terms of shell properties. Further, it was found that the aluminium alloy casting obtained from new shells showed comparatively increase in tensile strength and reduced porosity with that obtained from the conventional ceramic shell. The adopted method proved effective in reducing both production lead time and cost of manufacturing the qualitative IC ceramic shells.     

Cache-based high-level simulation of microthreaded many-core architectures

The accuracy of simulated cycles in high-level simulators is generally less than the accuracy in detailed simulators for a single-core systems, because high-level simulators simulate the behaviour of components rather than the components themselves as in detailed simulators. The simulation problem becomes more challenging when simulating many-core systems, where many cores are executing instructions concurrently. In these systems data may be accessed from multiple caches and the abstraction of the instruction execution has to consider the dynamic resource sharing on the whole chip. The problem becomes even more challenging in microthreaded many-core systems, because there may exist concurrent hardware threads. Which means that the latency of long latency operations can be tolerated from many cycles to just few cycles. We have previously presented a simulation technique to improve the accuracy in high-level simulation of microthreaded many-core systems, known as Signature-based high- level simulator, which adapts the throughput of the program based on the type of instructions, number of instructions and number of active threads in the pipeline. However, it disregards the access to different levels of the caches on the many-core system. Accessing L1-cache has far less latency than accessing off-chip memory and if the core is not able to tolerate latency, different levels of caches can not be treated equally. The distributed cache network along with the synchronization-aware coherency protocol in the Microgrid is a complicated memory architecture and it is difficult to simulate its behaviour at a high-level. In this article we present a high-level cache model, which aims to improve the accuracy in high-level simulators for general-purpose many-core systems by adding little complexity to the simulator and without affecting the simulation speed.