Voice activity detection (VAD) is a vital process in voice communication systems to avoid unnecessary coding and transmission of noise. Most of the existing VAD algorithms continue to suffer high false alarm rates and low sensitivity when the signal-to-noise ratio (SNR) is low, at 0 dB and below. Others are developed to operate in offline mode or are impractical for implementation in actual devices due to high computational complexity. This paper proposes the upper envelope weighted entropy (UEWE) measure as a means to enable high separation of speech and non-speech segments in voice communication. The asymmetric nonlinear filter (ANF) is employed in UEWE to extract the adaptive weight factor that is subsequently used to compensate the noise effect. In addition, this paper also introduces a dual-rate adaptive nonlinear filter (DANF) with high adaptivity to rapid time-varying noise for computation of the decision threshold. Performance comparison with standard and recent VADs shows that the proposed algorithm is superior especially in real-time practical applications.

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The ongoing Russia-Ukraine war has brought the SWIFT payment system into the spotlight, with the US and European countries having taken steps to remove Russia from the financial system. This could end up being a lethal catastrophe for Russian banks, particularly the smaller ones. International money transfer using SWIFT banking system was established many years ago. SWIFT has developed to play a critical part in the progression of global business since its beginning in 1973. It facilitates the exchange of funds between monetary institutions that are members of SWIFT. Streamlining and accelerating bank correspondence is at the core of SWIFT's underlying plan objective. Transferring money across borders specifically. This system was evolved over the years and maintained its reputation. Almost all international transactions became reliable on SWIFT systems. Due to the Russian ban on SWIFT system, it can be disruptive on global economy. This study aims to determine the impact of the sanction of SWIFT system that has caused in the supply chain and provides a better insight about the current situations and its impact in the global economy as well as Russian economy .

Electrocardiogram is a method of recording the heartbeat of a patient electronically. Storing or transmitting enormous amount of ECG signals to another device or via online is an unendurable process without compressing the signals. The purpose of this paper is to develop an efficient Electrocardiogram (ECG) compression technique using non-linear predictor and ASCII character encoding. The digitized ECG signal values are applied to Multi-layer Perceptron (MLP) neural network algorithm for non-liner prediction and the residues of the signal are passed through ASCII character encoding for further compression. It is shown that a compression ratio of 2.3726 can be achieved through this technique without any loss of information for MIT-BIH arrhythmia database records.

Background: A major player in industry is the induction motor. The constant motion and mechanical nature of motors causes much wear and tear, creating a need for frequent maintenance such as changing contact brushes. Unmannered and infrequent monitoring of motors, as is common in the industry, can lead to overexertion and cause major faults. If a motor fault is detected earlier through the use of automated fault monitoring, it could prevent minor faults from developing into major faults, reducing the cost and down-time of production due the motor repairs. There are few available methods to detect three-phase motor faults. One method is to analyze average vibration signals values of V, I, pf, P, Q, S, THD and frequency. Others are to analyze instantaneous signal signatures of V and I frequencies, or V and I trajectory plotting a Lissajous curve. These methods need at least three sensors for current and three for voltage for a three-phase motor detection. Methods: Our proposed method of monitoring faults in three-phase industrial motors uses Hilbert Transform (HT) instantaneous current signature curve only, reducing the number of sensors required. Our system detects fault signatures accurately at any voltage or current levels, whether it is delta or star connected motors. This is due to our system design, which incorporates normalized curves of HT in the fault analysis database. We have conducted this experiment in our campus laboratory for two different three-phase motors with four different fault experiments. Results: The results shown in this paper are a comparison of two methods, the V and I Lissajous trajectory curve and our HT instantaneous current signature curve. Conclusion: We have chosen them as our benchmark as their fault results closely resemble our system results, but our system benefits such as universality and a cost reduction in sensors of 50%.