Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

A major part of organismal complexity and versatility of prokaryotes resides in their ability to fine-tune gene expression to adequately respond to internal and external stimuli. Evolution has been very innovative in creating intricate mechanisms by which different regulatory signals operate and interact at promoters to drive gene expression. The regulation of target gene expression by transcription factors (TFs) is governed by control logic brought about by the interaction of regulators with TF binding sites (TFBSs) in cis-regulatory regions. A factor that in large part determines the strength of the response of a target to a given TF is motif stringency, the extent to which the TFBS fits the optimal TFBS sequence for a given TF. Advances in high-throughput technologies and computational genomics allow reconstruction of transcriptional regulatory networks in silico. To optimize the prediction of transcriptional regulatory networks, i.e., to separate direct regulation from indirect regulation, a thorough understanding of the control logic underlying the regulation of gene expression is required. This review summarizes the state of the art of the elements that determine the functionality of TFBSs by focusing on the molecular biological mechanisms and evolutionary origins of cis-regulatory regions.

/pdf/mechanisms-and-evolution-of-control-logic-in-prokaryotic-8j9vwe8ov3.pdf

Mechanisms and Evolution of Control Logic in Prokaryotic Transcriptional Regulation

This paper presents the implementation of single-bit SRAM cell architecture along with its peripherals in standard gpdk 90nm technology library using Cadence tool. Different sense_amplifiers such as current mode and charge transfer sense_amplifiers have been implemented along with the low power reduction techniques in single-bit SRAM cell architecture. We have mainly focused on read, write delay as well as average power. It has been observed that single-bit SRAM cell architecture consists of charge transfer sense_amplifier results in a reduction of 30-35% in average power dissipation as well as 1.5% reduction in read delay and 15% reduction in write delay as compared to single-bit SRAM cell architecture implemented with a current sense amplifier. Furthermore, the single bit SRAM cell architecture along with CMSA implemented with MTCMOS techniques results in 1–5% reduction in average power dissipation. However, single bit SRAM cell architecture along with CTSA implemented with MTCMOS and footer stack techniques results in about 1% reduction in average power dissipation.

Analysis of Cache(SRAM) Memory for Core i™ 7 Processor

The semiconductor electronic industry is advancing at a very fast pace. The size of portable and handheld devices are shrinking day by day and the demand for longer battery backup is also increasing. With these requirements, the leakage power in stand-by mode becomes a critical concern for researchers. In most of these devices, memory is an integral part and its size also scales down as the device size is reduced. So, low power and high speed memory design is a prime concern. Another crucial factor is the stability of static random-access memory (SRAM) cells. This paper combines multi threshold and fingering techniques to propose a modified 6T SRAM cell which has high speed, improved stability and low leakage current in stand-by mode of the memory cell. The simulations are done using the Cadence Virtuoso tool on UMC 55 nm technology.

https://www.mdpi.com/2079-9268/8/4/41/pdf

A Novel Approach to Design SRAM Cells for Low Leakage and Improved Stability

Electronics industry in present-day scenario is facing the major problem of standby leakage current in most of the electronic devices As the speed of processor is increasing, the demand for high-speed cache memory is ever increasing SRAM being mainly used for cache memory design, several low-power techniques are being used to reduce its leakage current Full CMOS 6T SRAM cell is the most preferred choice for most of the digital circuits This paper implements 6T CMOS SRAM cell using MTCMOS technique and simulation results show significant reduction in leakage during standby mode The simulations are done on Cadence Virtuoso Tool using 45 nm technology

Implementation of Low-Power 6T SRAM Cell Using MTCMOS Technique

Methods for the identification of transcription factor binding sites have proved to be useful for deciphering genetic regulatory networks. The strengths and weaknesses for a number of available web tools are not fully understood. Here, we designed a comprehensive set of performance measures and benchmarked sequence-based motif discovery tools using large scale datasets (derived from Escherichia coli genome and RegulonDB database). The benchmark study showed that nucleotide based and binding site based prediction accuracy is often low and activator binding site based prediction accuracy is high.

/pdf/performance-evaluation-of-dna-motif-discovery-programs-u0p0i526xw.pdf

Performance evaluation of DNA motif discovery programs.

Trade-off for Leakage Power Reduction in Deep Sub Micron SRAM Design

Leakage power is becoming a major concern in battery operated and hand held devices. With the ever reducing size of electronic devices and the use of memory in most of them, the need for low power devices is vastly increasing. These devices are either in active or standby mode of operation. Leakage power in standby mode of operation is of major concern and various methods to minimize it have been proposed at various stages of design cycle. This paper proposes fingering technique that can be used in 6T SRAM cell to reduce leakage power. Leakage power is calculated for 6T SRAM cell designed using two fingers in access transistors and on comparison with conventional 6T SRAM cell, significant reduction in leakage current is obtained. The layout has been designed in UMC 55nm technology using Cadence Virtuoso tool and it has been shown that the leakage power and delay can be reduced.  

/pdf/low-leakage-sram-cell-for-ulp-applications-5bv6or2elm.pdf

D. S. Chauhan

Papers

Implementation of Low-Power 6T SRAM Cell Using MTCMOS Technique

A Novel Approach to Design SRAM Cells for Low Leakage and Improved Stability

Performance evaluation of DNA motif discovery programs.

Trade-off for Leakage Power Reduction in Deep Sub Micron SRAM Design

Low leakage SRAM cell for ULP applications