Ring Cross-Over Based Ga For Dfmb Chip Design And Medical Image Compression

G. Brindha, G. Rohini
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Abstract

Currently, the clinical data stored in the cloud is easily accessible, and the patient’s data can be shared among treatment centers. In such a case, to handle additional data, the cloud data must be of a lesser scale. A process of compression was introduced to minimize the data with no losing data in order to achieve this size reduction. This paper conducts the experiment in two approaches: fast routing operations and compression from the chip in the DMFB approach. To apply this process of compression, the collected data from the chip was transformed into an image, and then compression of the image was performed utilizing a genetic algorithm (GA) based on a ring crossover. Consequently, the biochip of the 8x8 array is integrated into the power and area with the ring cross-module for an effective energy consumption operation. The technique of the process is utilized by the Microfluidic (MF) feature to handle and maintain the droplets. Also, the optimization process is performed by combining related pin actuation segments in parallel and the control pin to prevent pin-actuation conflicts. Through the optimization process, it synchronizes the length. This proposed approach decreases the consumption of the power and area. The outcome of the simulation indicates an increase in dynamic power, static power, and delay. Image compression is performed with the aid of this algorithm. In addition, for better outcomes, this GA compression application was contrasted with wavelet compressions.
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基于环交叉的Ga-For-Dfmb芯片设计与医学图像压缩
目前,存储在云中的临床数据很容易访问,患者的数据可以在治疗中心之间共享。在这种情况下,为了处理额外的数据,云数据的规模必须较小。引入了一种压缩过程,以在不丢失数据的情况下最小化数据,从而实现这种大小缩减。本文采用两种方法进行了实验:快速路由操作和DMFB方法中的芯片压缩。为了应用这种压缩过程,将从芯片收集的数据转换为图像,然后利用基于环交叉的遗传算法(GA)对图像进行压缩。因此,8x8阵列的生物芯片通过环形交叉模块集成到电源和区域中,以实现有效的能耗操作。微流体(MF)功能利用该工艺技术来处理和保持液滴。此外,通过将相关的销致动段平行地与控制销组合来执行优化过程,以防止销致动冲突。通过优化过程,它可以同步长度。这种提出的方法减少了功率和面积的消耗。仿真结果表明,动态功率、静态功率和延迟都有所增加。图像压缩是在该算法的帮助下进行的。此外,为了获得更好的结果,将这种GA压缩应用与小波压缩进行了对比。
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来源期刊
CiteScore
1.70
自引率
0.00%
发文量
18
审稿时长
>12 weeks
期刊介绍: In recent years a breakthrough has occurred in our understanding of the molecular pathomechanisms of human diseases whereby most of our diseases are related to intra and intercellular communication disorders. The concept of signal transduction therapy has got into the front line of modern drug research, and a multidisciplinary approach is being used to identify and treat signaling disorders. The journal publishes timely in-depth reviews, research article and drug clinical trial studies in the field of signal transduction therapy. Thematic issues are also published to cover selected areas of signal transduction therapy. Coverage of the field includes genomics, proteomics, medicinal chemistry and the relevant diseases involved in signaling e.g. cancer, neurodegenerative and inflammatory diseases. Current Signal Transduction Therapy is an essential journal for all involved in drug design and discovery.
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