signal processing

Attention deficit hyperactivity disorder (ADHD) is one of the most common mental disorders affecting children. Delayed diagnosis and treatment of ADHD might result in poor school performance. In this paper, an intelligent system is proposed for detecting ADHD from electroencephalograms. The system first preprocesses the electroencephalograms to remove the powerline and blinking interferences. Then, it applies independent component analysis to separate the signals of the sources that generate the preprocessed electroencephalograms. Later, 3 features of Shannon entropy, kurtosis, and skewness are extracted from the independent components. A total of 57 features are obtained for 19 electroencephalogram channels. Finally, two classifiers (i.e., the k-nearest neighbors with k=3, and an artificial neural network) were used to detect ADHD from the extracted features. We used a public database containing signals from 61 children with ADHD and 60 healthy children to evaluate the performance of the proposed system. The accuracy, sensitivity, and specificity of the artificial neural network were 99.92%, 99.89%, and 99.95%, respectively, while these metrics were all 100% for the k nearest neighbors. The results imply that the features extracted from independent components of electroencephalograms are more powerful than other linear/nonlinear features directly extracted from the signals.

In this paper a novel method for realization of Gaussian function is presented. The current-mode circuits are employed for the implementation of main circuits owing to the simple circuitry and intuitive configuration. Unlike the previous works which were based on the transistors worked in weak inversion region, the proposed configuration operates in the saturation region, therefore high-accuracy as well as the high-speed performance are obtained. The proposed circuit is fully programmable in terms of mean value, standard deviation and peak gain of the Gaussian function. Simulation results of the circuit is obtained by HSPICE with TSMC level 49 (BSIM3v3) parametes in 0.35μm standard CMOS process.

This study focuses on diagnosing gear defects in the S5-380 transmission using advanced vibration analysis and validating findings through the HÖFLER Gear Testing Machine. The research highlights key faults such as backlash, eccentricity, and shaft deflection identified via vibration measurements and confirmed using detailed gear profile and lead inspections. The vibration analysis employed V-SAM Vibrometer and V-SAM-Web Soft to detect critical parameters, including Gear Mesh Frequency (GMF) and Gear Hunting Tooth Frequency (GHTF). Results demonstrated the robustness of vibration analysis as an efficient, cost-effective alternative to Coordinate Measuring Machines (CMM) for end-of-line quality control. Furthermore, integrating vibration analysis with CMM-based validation provided precise fault identification, enabling targeted maintenance and enhanced gearbox performance. The findings underscore the potential of transitioning to AI-driven diagnostic systems in Industry 5.0 for real-time, scalable, and intelligent gearbox monitoring. This approach offers significant implications for improving durability, quality, and operational efficiency in gear manufacturing.

Use of efficient signal processing tools (SPTs) to extract proper indices for fault detection in induction motors (IMs) is the essential part of any fault recognition procedure. The Par 1 of the two parts paper focuses on Fourier-based techniques including fast Fourier transform and short time Fourier transform. In this paper, all utilized SPTs which have been employed for fault fetection in IMs are analyzed in details. Then, their competency and their drawbacks for extracting indices in transient and steady state modes are criticized from different aspects. The considerable experimental results are used tocertificate demonstrated discussion. Different kinds of faults, including eccentricity, broken bar and bearing faults as major internal faults, in IMs are investigated. The use of efficient signal processing tools (SPTs) to extract proper indices for faultdetection in induction motors (IMs) is the essential part of any fault recognitionprocedure. In the first part of the present paper, we focus on Fourier-based techniques, including fast Fourier transform and short time Fourier transform. In this paper, all utilized SPTs which have been employed for fault detection in IMs are analyzed in detail. Then, their competency and their drawbacks to extract indices in transient and steady state modes are criticized from different aspects. Different kinds of faults, namely, eccentricity, broken bar, and bearing faults as the major internal faults in IMs, are investigated.