Summary: Low power consumption has become one of the most important features of modern electronic devices, due to several factors, ranging from the increased market share of mobile and portable systems for telecom and computing to the increased cost of implementing, packaging and manufacturing circuits working at high speed and temperature. In the beginning, the power problem has been faced manually, thanks to the development of ad-hoc design techniques, mainly applied at the low levels of abstraction (i.e., from physical design up to the gate-level). The increased complexity of modern electronic systems, facilitated by the advent of aggressively scaled technologies, and the augmented pressure of time-to-market constraints, called for automated design support. As a consequence, in the last few years, novel CAD algorithms, and methods that enable tight power consumption control during design have been the subject of extensive research, then originating powerful tools and design frameworks that can deal with power optimization at different levels of the design hierarchy.

Power dissipation has become a critical design metric for an increasingly large number of VLSI circuits. The exploding market of portable electronic appliances fuels the demand for complex integrated systems that can be powered by lightweight batteries with long times between re-charges. Additionally, system cost must be extremely low to achieve high market penetration. Both battery lifetime and system cost are heavily impacted by power dissipation. The last decade has thus witnessed a growing interest in low-power design, resulting in a tremendous research effort for the development of new design techniques, algorithms, methods and tools for controlling power during the various stages of the design process.

The scientific literature covering various aspects of low power design technologies is vast; as such, the non-expert reader may find difficulties in retrieving the appropriate contributions that may help him/her in getting quickly in control of the subject. This survey will provide the reader with a terse, yet complete and easy to navigate, reference to the leading-edge technologies for low power design of digital electronic circuits and systems.

Summary: In living organisms, deoxyribonucleic acid (DNA) is the carrier of genetic information from one generation to the next and encodes proteins, which are the actual building blocks of cells and participate in most processes within cells. DNA molecules are composed of several different regions, and the DNA regions that encode proteins are called genes. A DNA microarray is a collection of microscopic spots, each of which can detect a specific type of gene. Using DNA microarrays, researchers now can monitor the expression of thousand of genes simultaneously, thus making numerous discoveries previously unappreciated. With the explosion of genomic data by high-throughput biology such as DNA microarrays, many people observe that biology is becoming information science.

The process of detecting gene expression through DNA microarrays provides ample opportunities for electrical engineers and computer scientists as well. For instance, the photolithography techniques, which has been widely used in the semiconductor industry, are now being used to manufacture certain types of DNA microarrays. Biological signals can be converted to electrical signals and then be processed by analog-to-digital conversion (ADC) circuits. Biological signals also tend to be very noisy, and signal processing techniques developed in engineering have utmost value in biological signal analysis. DNA molecules are sequences of four alphabets, and numerous discrete mathematics methodologies borrowed from the computer science community have found frequent usage in modeling biological systems and analyzing signals empirically obtained.

In this survey, fundamentals of DNA microarray technologies and analysis methods are reviewed. In the first section, a molecular biology primer for engineers and computer scientists is presented, along with a brief introduction to two widely used DNA microarray technologies. The second section provides foundations for DNA microarray data analysis covering several important issues in DNA microarray data preprocessing and statistical hypothesis testing. In the third section, various data analysis methods are described with special emphasis on unsupervised clustering and supervised classification. The last section introduces a few applications of DNA microarrays in biomedicine.