Edges in a digital image provide important information about the objects contained within the image since they constitute boundaries between objects in the image. This paper proposes a new approach based on independent component analysis (ICA) for edge-detection in noisy images. The proposed approach works in two phases—the training phase and the edge-detection phase. The training phase is carried out only once to determine parameters for the ICA. Once calculated, these ICA parameters can be employed for edge-detection in any number of noisy images. The edge-detection phase deals with transitioning in and out of ICA domain and recovering the original image from a noisy image. Both gray scale as well as colored images corrupted with Gaussian noise are studied using the proposed approach, and remarkably improved results, compared to the existing edge-detection techniques, are achieved. Performance evaluation of the proposed approach using both subjective as well as objective methods is presented.

A microelectromechanical system (MEMS) device might function perfectly well in the controlled environment in which it has been created. However, the device can be a real viable product only after it has been fabricated with proven performance in a package. As such, the assembly yield of a MEMS package is often a challenging target to meet. The design and fabrication of a free-floating membrane on a flexible substrate to enable easy and cost-effective packaging of MEMS devices is examined. Since standard MEMS fabrication processes are designed for rigid substrates, several process modifications were required to handle flexible substrates. The adaptation of each fabrication process has been documented. Furthermore, detailed information regarding the selection of compatible materials, as well as incompatibilities that were encountered, has been presented to aid future researchers in developing processes for flexible substrates.

There has been a constant endeavor towards improving the available circuit design automation tools to match technological advancements in the electronic industry. However, inadequate research efforts in the analog domain are holding back the exploitation of advanced technologies. A dearth of design expertise in the analog domain is the principal driving force for the growth of Design Automation (DA) tools. Transistor sizing is one of the most crucial steps in the analog IC design. In this paper, we put forward a new computer aided design framework for the sizing of transistors in MOS Integrated Circuit (IC) amplifiers by incorporating powerful modeling capabilities of Artificial Neural Networks (ANN). ANNs have proven to be efficient and accurate modeling tools in several applications. The proposed tool is capable of directly computing transistor related design parameters, of the MOS IC amplifier and associated peripheral circuitry. The proposed tool thus avoids several time-consuming simulations and/or tuning runs at the very bottom level of analog IC amplifier implementation, using a given CMOS process. It also reduces manual intervention in the design process, thus enhancing the automation of the design process. This paper presents design examples of several analog IC functional modules that are developed and verified successfully.