International Journal of Electronics, AI & Robotics
International Journal of Electronics, AI & Robotics (IJEAR) is an international peer-reviewed, open-access journal that publishes high-quality research in electronics, artificial intelligence, and robotics. The journal covers embedded systems, intelligent electronics, machine learning, computer vision, autonomous robots, Internet of Things (IoT), signal processing, control systems, and smart automation technologies. It provides a platform for innovative research that advances intelligent electronic systems and next-generation robotic applications across academia and industry.
EFFICIENT ALGORITHM DESIGN FOR FM DIGITAL SIGNAL PROCESSORS: CHALLENGES AND SOLUTIONS
Efficient algorithm design for frequency modulation (FM) digital signal processor (DSPs) is critical for meeting the increasing demands of modern communication systems characterized by high data rates, low latency requirements, and constrained hardware resources. This study investigates the computational and architectural challenges associated with FM DSP implementations, including arithmetic complexity, power consumption, memory limitations, and susceptibility to noise and interference. A comprehensive analysis of existing approaches shows that conventional FM demodulation algorithms typically exhibit computational complexities on the order of O (NlogN), with latency exceeding 10–15 ms in real-time embedded environments. To address these limitations, this work proposes an optimized hybrid algorithmic framework that integrates adaptive filtering, approximation techniques, and hardware-aware pipelining. Simulation results indicate a reduction in computational complexity by approximately 35–45%, while latency is decreased to below 5 ms under typical signal conditions. Furthermore, the proposed approach improves signal-to-noise ratio (SNR) performance by 6–8 dB in low-SNR environments compared to baseline methods. Energy consumption is also reduced by up to 30% through efficient resource utilization and dynamic scaling strategies. These findings demonstrate that the proposed solutions significantly enhance the performance, scalability, and robustness of FM DSP systems, making them suitable for next-generation wireless and embedded communication applications.
Copyright (c) 2026 International Journal of Electronics, AI & Robotics
DESIGN AND CONSTRUCTION OF RF COUPLING COIL FOR THE MICE
An optimized radio-frequency (RF) coupling coil has been developed for application in the Muon Ionization Cooling Experiment (MICE) to enhance power transfer efficiency, electromagnetic field stability, and beam-cavity interaction performance in high-gradient accelerator environments. The design framework integrated Maxwell’s equations, impedance matching theory, and S-parameter analysis to achieve efficient coupling between the RF power source and the accelerating cavity under strong solenoidal magnetic fields and cryogenic operating conditions. Electromagnetic simulation and experimental validation demonstrated a significant improvement in RF performance metrics. The reflection coefficient (S11) was reduced from −18.5 dB in conventional configurations to −32.7 dB in the proposed design, indicating superior impedance matching. Correspondingly, the transmission coefficient (S21) increased from 0.68 to 0.91, yielding an RF power transfer efficiency improvement from 68% to 91%. The coupling coefficient approached near-critical coupling at β ≈ 1.02, ensuring optimal energy delivery into the TM010 cavity mode. Thermal analysis showed reduced localized heating due to improved current distribution, while mechanical stress remained within acceptable structural limits. Enhanced electromagnetic field symmetry and improved Q-factor stability were also observed. The results confirm that optimized RF coupling coil design significantly improves operational performance in muon ionization cooling systems and provides a robust solution for next-generation accelerator applications requiring high efficiency and stability.
Copyright (c) 2025 International Journal of Electronics, AI & Robotics
ELECTRONIC GRAPHENE AND GOLD NANOPARTICLE BIOMEDICAL ELECTRODES FOR IMPROVED CLINICAL SIGNAL QUALITY
Electronic graphene and gold nanoparticle biomedical electrodes were developed to enhance clinical bioelectrical signal acquisition and diagnostic accuracy. The proposed electrodes combined graphene’s high electrical conductivity (10⁴–10⁵ S/m) with gold nanoparticles to improve surface area and electrode skin interface performance. Experimental characterization was conducted using electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG) signal acquisition under controlled clinical conditions. Results demonstrated a 38% reduction in electrode skin impedance compared to conventional Ag/AgCl electrodes, decreasing from 12 kΩ to 7.4 kΩ at 10 Hz. Signal-to-noise ratio (SNR) improved by 27%, increasing from 18.5 dB to 23.5 dB, while motion artifact interference decreased by approximately 31% during patient movement. The fabricated electrodes also exhibited enhanced biocompatibility and operational stability over 72 hours of continuous monitoring. Flexible electrode substrates further improved patient comfort and wearable integration. These findings confirm that graphene and gold nanoparticle-based electronic electrodes significantly enhance clinical signal quality and reliability for advanced biomedical monitoring applications.
ADVANCED ELECTRONIC NOISE REDUCTION FOR BIOMEDICAL SIGNALS IN EMERGENCY MEDICAL SYSTEMS
Reliable acquisition of biomedical signals in emergency medical systems is often degraded by motion artifacts, power-line interference, baseline wander, and electrode noise, leading to diagnostic uncertainty. This study presents advanced electronics-based noise reduction methods integrating analog front-end optimization with digital adaptive filtering for real-time biomedical signal enhancement. A low-noise instrumentation amplifier (input-referred noise < 1 µVrms, CMRR > 110 dB) is combined with active notch (50/60 Hz attenuation > 40 dB) and high-pass filtering (cutoff 0.5 Hz) to suppress baseline drift. The conditioned signals are further processed using adaptive least mean squares (LMS) and wavelet denoising techniques. Experimental validation was performed on electrocardiogram (ECG) datasets with signal-to-noise ratios (SNR) ranging from −5 dB to 10 dB under simulated emergency conditions. Results demonstrate an average SNR improvement of 18.7 dB, noise reduction of 72%, and QRS detection accuracy increase from 85.3% to 97.6%. The proposed system achieves processing latency below 20 ms, making it suitable for real-time deployment. These findings confirm that integrating advanced electronic design with adaptive algorithms significantly enhances biomedical signal reliability in time-critical emergency medical environments.
RELIABILITY ANALYSIS AND PREVENTIVE MAINTENANCE STRATEGIES FOR FM AND TV BROADCAST TRANSMITTERS TO REDUCE OPERATIONAL FAILURES
Reliable operation of FM and television transmitters is a critical requirement for maintaining uninterrupted broadcast services and ensuring optimal signal coverage, audio-visual quality, and operational efficiency. However, recurring technical faults in transmitter systems continue to affect broadcast performance globally, particularly in environments characterized by aging infrastructure, harsh climatic conditions, and increasing demand for continuous broadcasting. This study investigates common faults in FM and TV transmitters and offers proven prevention methods for reducing recurring failures and improving operational broadcast performance. The research identifies major technical faults including RF power amplifier degradation, exciter instability, frequency drift, cooling system failure, power supply fluctuations, transmission line mismatch, antenna system faults, modulation distortion, grounding deficiencies, and software control errors. Scientific diagnostic techniques such as Voltage Standing Wave Ratio (VSWR) measurement, spectrum analysis, thermal imaging, signal-to-noise ratio (SNR) evaluation, Total Harmonic Distortion (THD) analysis, and fault tree analysis were employed to evaluate transmitter performance and identify root causes of failures. The study further examines environmental factors such as humidity, temperature variations, lightning surges, and dust accumulation, which significantly contribute to transmitter component degradation and system instability. Preventive measures including predictive maintenance, redundancy architecture, intelligent monitoring systems, surge protection devices, automatic gain control optimization, and improved grounding systems are analyzed for their effectiveness in minimizing operational downtime. Additionally, modern solutions such as Internet of Things (IoT)-based monitoring, artificial intelligence-based fault prediction, and modular transmitter architecture are evaluated to enhance reliability and maintain continuous broadcasting operations. Results indicate that implementing integrated preventive maintenance strategies can reduce transmitter failure rates by up to 35–45%, improve signal stability, and enhance operational lifespan of broadcast equipment. The findings demonstrate that systematic fault identification combined with advanced monitoring techniques significantly improves broadcast continuity, reduces maintenance costs, and enhances overall operational broadcast performance. This study contributes to the development of reliable FM and TV broadcasting systems by providing a comprehensive framework for fault prevention and performance optimization in modern broadcast transmitter operations.
DETECTING A VALVE SPRING FAILURE OF A PISTON COMPRESSOR WITH THE HELP OF THE VIBRATION MONITORING
The article presents problems related to vibration diagnostics in reciprocating compressors. This paper presents the evaluation of several techniques of the digital signal processing, such as the spectrum calculation with the Discrete Fourier Transform (DFT), Continuous Wavelet Transform (CWT), Segmented Analysis for detection the spring failure in reciprocating compressor valve with the help of the vibration monitoring. The experimental investigation to collect the data from the compressor with both the faultless valve and the valve with spring failure was conducted. Three 112DV1 vibration acceleration probes manufactured by TIK were mounted on the cylinder of the compressor. The keyphasor probe was mounted on the compressor’s flywheel. The signal of the vibration acceleration probe mounted on the top of the cylinder was used for the Condition Monitoring and Fault Detection of the valve. The TIK-RVM system of monitoring and data acquisition was used for gathering the signal samples from the probes. The sampling frequency was 30193.5 Hz, signal length was 65535 samples. To imitate the spring fault, the exhaust valve spring was replaced by the shortened one with the same stiffness. As it can be seen from the signal processing results in the article, the techniques used are showing quite different results for the cases of the normal valve spring and the short one. It seems what for this type of the compressor and valve, the valve spring failure can be quite reliably detected with the help of the vibration monitoring. To see if this is a case for other compressor types and other valve types, the additional experiments are needed.
Copyright (c) 2025 International Journal of Electronics, AI & Robotics https://creativecommons.org/licenses/by/4.0/deed.en