Reza Ghaderi

In this study, the design and optimization of a dipole electromagnet pole tip are presented, with the aim of compacting the structure and enhancing the magnetic field quality across the Tanabe optimal width. The optimization process was conducted by introducing adjustable control points at the pole edges of this beam-bending precision instrument, using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) techniques, coupled with a 2D FEMM simulation code. The simulation results demonstrated a 15.9 % improvement in the horizontal Good Field Region (GFR) compared to the existing dipole in the ES150 ion accelerator’s mass spectrometer, achieving the field uniformity (1 × 10^-3). Additionally, the proposed approach led to a 12.3 % reduction in the dipole electromagnet’s overall dimensions, while also reducing magnetic saturation at the edges by 60 % and enhancing manufacturability. Subsequent mass spectrometry of hydrogen ion beams confirmed the presence of H⁺, H₂⁺, H₃⁺ ions with relative abundances of 72.2 %, 8.9 %, and 18.9 %, respectively. The results validate the accuracy, effectiveness, and potential of the proposed design for future development in advanced diagnostic instrumentation.

This work investigates dimensionality reduction for small-sample music classification using the NAVA dataset of Iranian Classical music (1,786 recordings). Three techniques are evaluated: Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and a lightweight Convolutional Neural Network (CNN) employed solely as a non-linear feature compressor. The reduced features are classified using a Multilayer Perceptron with Error-Correcting Output Codes (MLP-ECOC). The CNN encoder with 64-dimensional embeddings achieves the best performance, reaching 98.28% accuracy (98.28% accuracy (95% CI [97.10, 98.90]). Ablation tests on threshold tuning, class weighting, and scaling show variations below 0.1%, confirming model stability. CNN embeddings coupled with an ECOC ensemble provide a strong and efficient solution in low-resource settings, while PCA and LDA remain useful when interpretability and minimal computation are priorities. Although evaluated on music, the framework is applicable to other small-sample classification problems with high-dimensional features, highlighting its potential for broader signal-processing and pattern-recognition applications.

EEG is the most powerful tool for epilepsy discharge detection in brain. Visual evaluation is hard in long term monitoring EEG data as huge amount of data needs to be inspected. Considering the fast and efficient results from deep learning networks especially convolutional networks, and its capability for detection of complex epileptic wave forms, inspired us to evaluate YOLO network for spike detection solution.The most used versions of YOLO (V3, V4 and V7) were evaluated for various epileptic signals. The epileptic discharge wave-forms were first labeled to 9 different signal types, but classified to four group combinations based on their features. EEG data from 20 patients were used under guidance of expert epileptologist. The YOLO networks were all trained for four various class-grouping strategies. The most suitable network to recommend was found to be YOLO-V4, for all four classifying methods giving average sensitivity, specificity, and accuracy of 96.7, 94.3, and 92.8, respectively. YOLO networks have shown promising results in detection of epileptic signals, which by adding some extra measurements this can become a great assistant tool for epileptologists. In addition, besides YOLO’s High speed and accuracy in detection of epileptic signals in EEG, it can classify these signals to different morphologies.

Introduction: Motor imagination is a dynamic mental state that simulates a similar brain mechanism to actual physical movement. This brain mechanism could be traced using Electroencephalography (EEG) recording during motor imagination. Nevertheless, it is still unclear how development changes this mechanism. As the brain mechanism of motor imagination must gain expertise during ...

Signed multiplication is crucial for performing arithmetic operations with positive and negative numbers. It has applications in signal processing, digital image processing, communication, cryptography, neural network hardware accelerators, and more. Until today, to multiply signed numbers, the data are often converted to unsigned format, and the sign is added to the final product. This method imposes high hardware requirements for data conversion and presents challenges in designing approximate multipliers. This paper proposes a novel method for designing approximate signed multipliers that eliminate the need for data conversion, thereby reducing the overall hardware requirement while maintaining accuracy. Comprehensive evaluations at the system level using MATLAB and circuit-level performance analysis using HSPICE demonstrate that the proposed approach offers a successful trade-off between area overhead, power consumption, and accuracy. The achieved trade-off makes the proposed method a promising solution for optimizing digital circuits in various applications such as artificial intelligence and image processing.

JPEG compression is a widely used technique for reducing the file size of digital images, but it often compromises visual quality. The purpose of this research is to explore a novel approach that combines machine learning, discrete cosine transform (DCT) feature clustering, and genetic algorithms to customize image compression methods. The goal is to enhance visual quality while maintaining an appropriate bit-rate. In this study, an auto-encoder neural network is utilized to extract DCT features from images. These features are then clustered, and optimized quantization tables for each cluster center are generated using a genetic algorithm. The resulting tables are assigned to their respective clusters, enabling the preservation of visual quality during compression. Experimental evaluations were conducted on 1800 random images using this machine learning-based approach. The results demonstrate superior visual quality compared to traditional JPEG compression, while maintaining comparable bit-rates. The research shows significant improvements in peak signal-to-noise ratio (PSNR) by 2.34 dB and structural similarity index (SSIM) by 1.26%, indicating enhanced image quality. The findings of this research highlight the potential of combining machine learning, DCT feature clustering, and genetic algorithms to customize image compression techniques. The proposed approach enables effective image compression with improved visual quality preservation and maintained bit-rates. This research contributes to the advancement of image-based methods in achieving optimized image compression.

Introduction: During early adolescence, peer influences play a crucial role in shaping learning and decision preferences. When teens observe what their peers are doing, they can learn and change their behavior, especially when they are taking risks. Our study incorporated an economical behavioral task and computational modeling framework to examine whether and how early male adolescents’ risk attitudes change when they see information about their peers’ choices. Methods: We recruited 38 middle school male students aged 12-15 years. The experiment consisted of three sessions: The first and third sessions were designed to evaluate the risk attitude of the participants. In the second session, participants were asked to guess the choices made by their peers, and then, the computer gave them feedback on the correctness of their predictions. Each participant was randomly assigned to risk-taking or risk-averse peers. Results: Our results revealed that teenagers who predicted risk-averse peers exhibited significant declines in their risk attitudes during the last session. On the other hand, participants with risk-seeking peers exhibited a significantly higher level of risk attitudes after predicting their peers. The data showed that these peer-biased changes in risk attitudes are proportional to the gap between teens and their peers’ risk perspectives. Results showed that their perspectives aligned closer after receiving the information, and approximately a third of the gap was eliminated. Conclusion: Here, by combining choice data and computational modeling, we demonstrate that risky behavior is contagious among male adolescents. According to our data, peer-biased risk contagion, a socially motivated and deliberate process, is associated with social distance in teens. There's no causal directionality here, but we could speculate that peer influence goes hand-in-hand with social integration as an adaptive process.