The rise in Electric Vehicle (EV) usage has significantly increased the need for high-power fast charging systems that must meet rigorous performance standards. This paper examines the performance of 50 kW grid-connected EV charger. The design of the charger utilizes a dual stage Vienna rectifier at the front end, combined with a bi-directional DC-DC stage, to achieve a high-power factor, minimize total harmonic distortion (THD), and maintain stable operation of the DC bus. Vienna rectifiers are commonly utilized in high-power electric vehicle chargers because of their excellent efficiency greater than 94% and nearly unity power factor. An analysis of 50 kW charger that employs a Vienna rectifier focuses on its output ripple, input signal distortion, and power input factor. Under standard operating conditions, the performance remains equable. However, once the battery charge status surpasses 80%, the notable decline in performance occurs. In this scenario, both ripple and THD increase, and the power factor strays from unity, potentially harming the battery State of Health (SOH) during constant-voltage charging. Based on research results, this paper quantified the implication of current ripple on conversion efficiency in Electric vehicle charger through experimental verification and the results communicate that the current ripples have important influence on EV chargers.
Signal generators are essential instruments for testing, measurement, and embedded system validation. Commercial function generators, however, are often expensive and non-customizable for educational or prototyping environments. This study presents the design and realization of a low-cost, microcontroller-based multi-waveform generator capable of producing sine, triangular, sawtooth, and square waveforms with adjustable frequency, phase, and duty cycle. The system integrates an Arduino Mega 2560 controller with an AD9833 Direct Digital Synthesis (DDS) module for high-precision sine and triangular outputs, while hardware-timed PWM channels generate sawtooth and square waveforms. Three potentiometers provide real-time user control of frequency (50 Hz-1 kHz), phase (0°-360°), and duty ratio (0-100%), and a 16×2 I²C LCD displays the selected waveform parameters. Experimental characterization demonstrates frequency accuracy of ±0.05% and phase error within ±2° for AD9833-based signals, and total harmonic distortion (THD) below 0.8% for sine output up to 1 kHz. PWM-derived waveforms exhibit amplitude linearity of 96-98% and negligible drift across 8 h continuous operation. Compared with conventional analog Wien-bridge or XR2206-based function generators, the proposed system offers higher frequency stability, lower power consumption (≈310 mW), and greater flexibility for digital control at less than 15 USD total cost. The developed prototype successfully reproduces clean, noise-free waveforms observable on an oscilloscope and matches reference laboratory generators with an RMS amplitude deviation under 0.03 V (5 V scale). The compact and modular design enables rapid educational deployment and portable instrumentation. Future enhancements may include amplitude modulation through DAC expansion, frequency sweep automation, and PC-linked waveform visualization. The proposed design thus bridges the gap between low-cost educational tools and professional waveform generation, demonstrating the potential of open-source microcontroller architectures for accurate, user-interactive signal synthesis.
This paper proposes an efficient image reconstruction for compressive sensing (CS) that combines the Lifting Wavelet Transform (LWT) using Biorthogonal 5.5 (bior5.5) wavelets with three reconstruction algorithms: Subspace Pursuit (SP), Compressive Sampling Matching Pursuit (CoSaMP), and the Analytic Learned Iterative Shrinkage Thresholding Algorithm (ALISTA). Unlike the conventional Discrete Wavelet Transform (DWT) which relies on computationally intensive convolution operations the LWT provides a faster sparse representation while preserving the sparsity crucial for CS. The proposed approach leverages a key insight: among the four subbands produced by the LWT namely the approximation (CA) and the detail coefficients (LH, HL, HH) only the latter three are inherently sparse. Therefore, compressive sensing is applied exclusively to these detail subbands, while the CA subband is left uncompressed to retain essential low-frequency information. Experiments were conducted on both a natural test image (Lena) and a medical MRI scan, across image resolutions ranging from 200×200 to 512×512 pixels and sampling rates from 10% to 80%. Performance was assessed using the Structural Similarity Index (SSIM) and reconstruction time. Results consistently demonstrate that ALISTA significantly outperforms SP and CoSaMP in both reconstruction fidelity and computational efficiency. At an 80% sampling rate, ALISTA achieves SSIM values of 0.99409 for Lena and 0.9775 for the MRI image, compared to approximately 0.96 and 0.95644, respectively, for the other two methods. Furthermore, ALISTA maintains remarkably low reconstruction times under 4 seconds even for 512×512-pixel images. These findings confirm that the ALISTA + LWT/bior5.5 combination offers the best trade-off between image quality and speed, exhibiting robustness across different image types and scales.
Wire bonding attaches a fine wire from one connection pad to another, completing an electrical connection. Reduction-assisted immersion gold (RAIG) has recently gained popularity due to its ability to eliminate corrosion and plate thicker gold deposits. Implementing a RAIG process improves quality and reduces wire bonding product reliability risk. The purpose of this research was two-fold: 1) evaluate the effects of crossing two RAIG thicknesses against three wire bonding gram-forces on wire bond pull strength, and 2) evaluate two independent RAIG thickness effects on wire bond pull strength, all on an electroless nickel electroless palladium immersion gold (ENEPIG) final finished printed circuit board. A quantitative, experimental research methodology was used to manipulate independent variables to observe the effect on the dependent variable, establishing cause-and-effect relationships for wire bonding. This method was selected because of its ability to identify and quantify statistically significant factors for gold plating and wire bonding. Data was generated and collected in a controlled laboratory setting. Multi-variate charts, analysis of variance (ANOVA), lognormal distributions, and descriptive statistics were used for data analysis. As the RAIG deposit thickness increases, the bond gram-force is not vital for wire pull strength. Thicker RAIG deposits statistically outperform thinner RAIG deposits for wire pull strength. A mixed reaction RAIG electrolyte enables robust designs and achieves world-class quality "on target with minimal variation."
This paper presents an efficient image reconstruction method based on Compressive Sensing (CS) theory, leveraging the level-3 Reverse Biorthogonal 4.4 (rbio4.4) discrete wavelet transform in combination with three reconstruction algorithms: Subspace Pursuit (SP), Compressive Sampling Matched Pursuit (CoSaMP), and the Analytic Learned Iterative Shrinkage Thresholding Algorithm (ALISTA). The approach exploits the sparsity of images in a suitable wavelet basis, enabling compressed acquisition from a reduced number of random linear measurements. The process consists of four stages: (1) decomposition of the original image using the rbio4.4 wavelet transform to obtain sparse coefficients, (2) compressed sampling via a random measurement matrix, (3) reconstruction of the sparse signal using SP, CoSaMP, or ALISTA, and (4) final image reconstruction through the inverse wavelet transform. Experimental evaluation was conducted on the classic Lena image (200 × 200 pixels), comparing the three algorithms in terms of reconstruction quality measured by the Structural Similarity Index (SSIM) and computational cost (reconstruction time in minutes) across sampling rates ranging from 10% to 60%. Results show that all three algorithms achieve nearly identical reconstruction quality (virtually indistinguishable SSIM values at each sampling rate), confirming their effectiveness within the CS. However, ALISTA stands out significantly due to its exceptional speed, exhibiting substantially lower reconstruction times thanks to its learned nature, which replaces iterative procedures with fixed, optimized operations. In contrast, CoSaMP demonstrates higher and sometimes unpredictable computational times depending on the sampling rate. These findings highlight ALISTA’s strong potential for real-time or embedded applications.
