Numerical Simulation and Assessment of Meta Heuristic Optimization Based Multi Objective Dynamic Job Shop Scheduling System

In today's world of manufacturing, cost reduction becomes one of the most important issues. A successful business needs to reduce its cost to be competitive. The programming of the machine is playing an important role in production planning and control as a tool to help manufacturers reduce their costs maximizing   the   use   of   their   resources.   The   programming problem is not only limited to the programming of the machine, but also covers many other areas such such as computer and information technology and communication. From the definition, programming is an art that involves allocating, planning the allocation and utilization of resources to achieve a goal. The aim of the program is complete tasks in a reasonable amount of time. This reasonableness is a performance measure of how well the resources   are   allocated   to   tasks.   Time   or   time-dependent functions are always it used as performance measures. The objectives of this research are to develop Intelligent Search Heuristic algorithms (ISHA) for equal and variable size sub lot for  m  machine  flow  shop  problems,  to  Implement  Particle Swarm Optimization algorithm (PSO) in matlab, to develop PSO based Optimization program for efficient job shop scheduling problem. The work also address solution to observe and verify results of PSO based Job Shop Scheduling with help of graft chart

Dual-Tox 7T SRAM Cell Design for Leakage Power Reduction on 45nm Technology

This paper presents techniques based on dual oxide thickness assignment to reduce the leakage power of SRAM but maintaining their performance. The proposed a new seven transistors (7T) dual oxide thickness SRAM cell is proposed in this paper for simultaneously reducing the active and standby mode power consumption while enhancing the data stability and the read speed. With the new 7T SRAM cell, the storage nodes are isolated from the bit lines during a read operation, thereby enhancing the data stability as compared to the standard six transistors (6T) SRAM circuits. The transistors of the cross­coupled inverters are not on the critical read delay path with the new technique. Minimum sized dual-oxide thickness transistors are therefore conveniently used in the cross-coupled inverters for significantly reducing the leakage power consumption without causing degradation in the read speed. With the proposed 7T SRAM circuit, the static noise margin and the read speed are enhanced by up to 83% and 15%, respectively, as compared to the conventional 6T SRAM circuits. Furthermore, the leakage and the write power consumptions of the proposed dual-Tox SRAM circuit are reduced by up to76% as compared to the conventional6T SRAM circuits in a 45nm CMOS technology

Design Simulation and Analysis of Deep Convolutional Neural Network Based Complex Image Classification System

There are 350 families and over 250,000 known varieties of flowering plants. Furthermore, effective flower classification, including content-based image recovery, is essential for the order, plant inspections of buildings, the gardening sector, live plantations, and scientific flower classification guidelines. The representation of flowers has a broad variety of uses. However, manual categorization is time-consuming and exhausting, particularly when the image basis is confusing, has a large number of images, and is perhaps erroneous for several flower groupings. Therefore, effective flower division, discovery, and categorization processes are of great significance. To ensure robust, trustworthy, and ongoing characterization during the preparation stage, new approaches are proposed in this work. On three datasets of flowers that are undeniably known, our technique is tested. Results that are better than the best in this aim for all data sets with accuracy over 98 percent. The categorization of flowers from a wide variety of animal groups is attempted in this research using a unique two-way deep learning method. In order for the foundation box to be placed around the floral area, it was first separated into sections. In a system that uses just convolutional networks, the suggested method for floral distribution is shown to be a parallel classifier. Make a powerful classification using convolutional neural networks in order to recognize the various flower types

Asymmetric Synthesis of Nodulones C & D by Chemoenzymatic Approach gives Insight into their Biosynthesis

The first asymmetric total synthesis of fungal secondary metabolites, (R)-nodulone C (4) and trans-nodulone D (5) has been reported through the chemoenzymatic approach. The strategy utilizes NADPH-dependent naphthol reductases of Magnaporthe grisea for the reduction of putative biosynthetic substrates, synthesized non-enzymatically in multiple steps. A dihydronaphthalenone 32 and cis-nodulone D (30) has also been synthesized chemoenzymatically. The work implies for similar steps during the biosynthesis of nodulones and their analogs with the involvement of tetrahydroxynaphthalene reductase related enzyme(s)