Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications.

Controlled actuation of a floating liquid marble, a liquid droplet coated with hydrophobic particles floating on another liquid surface, is a potential digital microfluidics platform for the transport of aqueous solution with minimal volume loss. This paper reports our recent investigation on the magnetic actuation of floating liquid marbles filled with magnetic particles. The magnetic force and frictional force acting on the floating liquid marble determine the horizontal movement of the marble. We varied the magnetic flux density, flux density gradient, concentration of magnetic particles and speed of the marble to elucidate the relationship between the acting forces. We subsequently determined the suitable operating conditions for the actuation and derived the scaling laws for the actuation parameters.

We summarize the recipes and describe the role of sputtering parameters in producing highly c-axis Aluminum Nitride (AlN) films for piezoelectric applications. The information is collated from the analysis of around 80 journal articles that sputtered this film on variety of substrate materials, processes and equipment. This review will be a good starting point to catch up with the state-of-the-arts research on the reactive sputtering of AlN (002) thin film, as well as its evolving list of piezoelectric applications such as energy harvesters.

Electromyography gives an electrical representation of neuromuscular activation associated with a contracting muscle. The electromyography signal acquires noise while travelling though different media. The wavelet transform is employed for removing noise from surface electromyography (SEMG) and higher order statistics are applied for analysing the signal. With the appropriate choice of wavelet, it is possible to remove interference noise (denoise) effectively in order to analyse the SEMG. Daubechies wavelets (db2, db4, db5, db6, db8), symmlet (sym4, sym5) and the orthogonal Meyer (dmey) wavelet can efficiently remove noise from the recorded SEMG signals. However, the most effective wavelet for SEMG denoising is chosen by calculating the root mean square difference and signal-to-noise ratio values. Results for both root mean square difference and signal-to-noise ratio show that wavelet db2 performs denoising best out of the wavelets. Furthermore, the higher order statistics method is applied for SEMG signal analysis because of its unique properties when applied to random time series, such as parameter estimation, testing of Gaussianity and linearity, deterministic and nondeterministic signal detection etc. Gaussianity and linearity tests as part of higher order statistics are conducted to understand changes in muscle contraction and to quantify the effectiveness of the noise removal process. According to the results, the SEMG signal becomes less Gaussian and more linear with increased force.

This review provides a comprehensive recap of noise research in MEMS. Some background on noise and on MEMS is provided. We review noise production mechanisms, and highlight work on the theory and modeling of noise in MEMS. Then noise measurements in the specific types of MEMS are reviewed. Inertial MEMS (accelerometers and angular rate sensors), pressure and acoustic sensors, optical MEMS, RF MEMS, surface acoustic wave devices, flow sensors, and chemical and biological MEMS, as well as data storage devices and magnetic MEMS, are reviewed. We indicate opportunities for additional experimental and computational research on noise in MEMS.