Recent Patents on Electrical & Electronic Engineering (Formerly Recent Patents on Electrical Engineering) - Volume 6, Issue 1, 2013

At present, electroencephalogram (EEG) and electrocardiogram (EKG) recording requires contact electrodes that are inconvenient and time-consuming to apply. Emerging technology based on non-contact electrodes may alter the way such data are gathered in clinical, field and research settings. A number of investigators have demonstrated that clinical- grade EKG and EEG tracings can be obtained using electrodes that make no direct contact with the body. Using noncontact electrodes, an EKG can be taken from outside a person’s clothing. The data can be transmitted wirelessly and stored in a cell phone. This technology is the basis of a number of patents and patents pending. It may also lead to new devices such as a whole body electromagnetic scanner, with potential applications in medicine, anthropology, agriculture, geophysics and nutrition.

Electrical Discharge Machining (EDM) is a well-established machining process in which material is gradually removed during an electrical discharge process to form a desired shape. There are a number of factors that impact the efficiency and surface finish quality of the EDM process. In this paper, background of the EDM and such factors are discussed, followed by a review of different control methods that have been proposed in the literature for the EDM process. The control methods are categorised into four classes, namely the methods that are based on electrode movement control, on-time power control, pulse number control, and cracks prevention. The most commonly used EDM control methods belong to the family of methods that work based on directly controlling the movements of electrode. In such methods, the control is mainly based on direct comparison of some measured signal(s) such as voltage or current with some references. More recent designs include pulse discrimination methods and fuzzy logic controllers. The article presents some promising patents on a survey of control methods for electrical discharge machining process

This technical article presents the concepts for improving the electricity price forecasting which were patented (application no. IN236/MUM/2011). A novel system and method for electricity price forecasting are also disclosed to set the context for explaining the technicalities involved in the invention. The invention relates to forecasting, and particularly to price forecasting by employing error correction techniques external to the algorithm chosen to solve the forecasting problem. Existing mechanisms do not offer effective error correction mechanisms external to the forecasting engine. The proposed technique uses the stochastic formulations and golden search methods to improve the final forecast. The method initially employs a forecasting module which consists of SVM, robust regressions, and neural network based mechanisms to determine the initial forecasts. In case there is an error, it determines if the error is within the permissible values through various probability checks. Further, if the error is outside the permissible values then a correction factor is applied to the computed values and prediction is updated. The correction factor is determined using the golden search technique. The framework for the proposed approach has been tested on the Indian and Australian electricity market data to prove its efficacy.

Recent progress and patents on channel estimation algorithms in optical orthogonal frequency division multiplexing (OFDM) systems are reviewed. The channel estimation algorithms can be divided into two categories of pilotaided and blind approaches. For the pilot-aided schemes, the two pilot patterns including the comb-type pilots and blocktype pilots are evaluated by experiments. For the blind schemes, the phase noise caused by the laser linewidth and chromatic dispersion (CD) in the fiber channel can be effectively compensated by the proposed data-aided channel estimation algorithms, which is experimentally demonstrated by the significant improvement in the performances of transmitted OFDM signals.

All-optical logic gate and switch are the key elements for ultrafast (>100 Gbit/s) signal processing in highspeed optical networks. Recent progress and related patents on all-optical logic gates based on semiconductor optical amplifiers (SOAs) are reviewed and optical switches with various configurations are discussed. The frequency-domain analysis of the turbo-switch (TS) incorporating two cascading SOAs is presented in detail to explore its ultimate operation bandwidth. The results show that the 3dB bandwidth of the TS is ~300 GHz, which can be a good candidate for ultrafast all-optical switches.

Recent patents and progress on all-optical binary pattern recognition using the passive correlator and all-optical logic gates have been reviewed. The correlator is relative simpler and easy to be implemented, however the path number of the correlator is dependent on the length of target pattern. In contrast, the pattern recognition system based on alloptical SOA logic gates can be a promising alternative even for high bit-rate optical signals. It is shown that, the gate count can be reduced dramatically when employing serial logical processing, which allows also reconfiguration capability of the target patterns.

Concentration prediction of dissolved gases in power transformer oil is very significant to detect incipient failures of transformer early. Concentration prediction of dissolved gases in power transformer oil is complicated problem due to its nonlinearity and little training data. Relevance vector regression algorithm (RVR) is applied to concentration prediction of dissolved gases in transformer oil in this paper. Compared with traditional support vector machine, relevance vector regression algorithm has the higher prediction accuracy because it has less support vectors than support vector regression algorithm. The concentration prediction model of dissolved gases in power transformer oil is established based on regression algorithm of RVR. The experimental results indicate that the RVR method can achieve greater prediction accuracy than support vector regression algorithm. Consequently, the RVR model is a proper alternative for predicting the concentration of dissolved gases in power transformer oil. The article presents some promising patents on concentration prediction of dissolved gases in transformer oil by relevance vector regression algorithm.

Most of the equipment found in the modern electrical installations can be considered as harmonic sources. Harmonic pollution causes serious problems to all electrical users. Many solutions are used to limit the problems related to harmonics and other power quality disturbances. The most used approach is to use shunt passive filters due to their simplicity, economical cost and their various frequency response characteristics. This article presents an application of FORTRAN Feasible Sequential Quadratic Programming package (FFSQP), in which it is implemented to deal with the constrained optimization of conflicting goals which allow the appropriate calculation of optimal shunt passive filter parameters. The article includes different optimization criteria depending on the designer concerns. Furtherer more, a mathematical model of a single tuned shunt filter on economical basis is demonstrated to select the size of its parameters with minimum total cost, providing improvement of the load power factor and complying with the IEEE standard 18-2002 limits. This article presents some promising patents for assessment of the optimal design for harmonic passive filter of multi-constraint and multi-objective functions.