Current Issue : July-September Volume : 2025 Issue Number : 3 Articles : 5 Articles
An electrocardiogram (ECG) measures the electric signals from the heartbeat to diagnose various heart issues; nevertheless, it is susceptible to noise. ECG signal noise must be removed because it significantly affects ECG signal characteristics. In addition, speed and occupied area play a fundamental role in ECG structures. The Vedic multiplier is an essential part of signal processing and is necessary for various applications, such as ECG, clusters, and finite impulse response filter architectures. All ECGs have a Vedic multiplier circuit unit that is necessary for signal processing. The Vedic multiplier circuit always performs multiplication and accumulation steps to execute continuous and complex operations in signal processing programs. Conversely, in the Vedic multiplier framework, the circuit speed and occupied area are the main limitations. Fixing these significant defects can drastically improve the performance of this crucial circuit. The use of quantum technologies is one of the most popular solutions to overcome all previous shortcomings, such as the high occupied area and speed. In other words, a unique quantum technology like quantum dot cellular automata (QCA) can easily overcome all previous shortcomings. Thus, based on quantum technology, this paper proposes a multiplier for ECG using carry skip adder, half-adder, and XOR circuits. All suggested frameworks utilized a single-layer design without rotated cells to increase their operability in complex architectures. All designs have been proposed with a coplanar configuration in view, having an impact on the circuits’ durability and stability. All proposed architectures have been designed and validated with the tool QCADesigner 2.0.3. All designed circuits showed a simple structure with minimum quantum cells, minimum area, and minimum delay with respect to state-of-the-art structures....
With the continuous expansion of communication bandwidth, accurately modeling the non-linear characteristics of power amplifiers has become increasingly challenging, directly affecting the performance of digital pre-distortion (DPD) technology. The high peak-to-average power ratio and complex modulation schemes of wideband signals further exacerbate the difficulty of DPD implementation, necessitating more efficient algorithms. To address these challenges, this paper proposes a wideband DPD algorithm based on edge signal correction. By acquiring signals near the center frequency and comparing them with equally band-limited feedback signals, the algorithm effectively reduces the required processing bandwidth. The incorporation of cross-terms for model calibration enhances the model fitting accuracy, leading to significant improvement in pre-distortion performance. Simulation results demonstrate that compared with traditional DPD algorithms, the proposed method reduces the error vector magnitude (EVM) from 1.112% to 0.512%. Experimental validation shows an average improvement of 11.75 dBm in adjacent channel power at a 2 MHz frequency offset compared to conventional memory polynomial DPD. These improvements provide a novel solution for power amplifier linearization in wideband communication systems....
Brain–machine interfaces are an emerging way that enables communication by using brain power, affecting the sensitive nerves and muscles. Over the last 20 years, this advancement in technology has motivated a lot of disabled patients. This continues to grow, helping more and more patients suffering from paralysis or any other disabilities. At present, extensive research is being conducted in this complex emerging field. Significant efforts have been directed towards implementing Brain-Computer Interface (BCI) systems in laboratory settings to assist individuals with disabilities, enabling them to perform tasks akin to those of able-bodied individuals. This manuscript will examine the current landscape and future potential of BCI technology, along with its correlation to various signal processing techniques. This review seeks to bridge the gap in understanding the influence of diverse signal processing methodologies on the efficacy of BCI systems. Consequently, the paper will cover advancements in the domains of signal acquisition and processing. In addition, the study is focused on analyzing all the previous studies done on improving signal quality. Moreover, the paper discusses how creating advanced algorithms significantly improves the interpretation of user intentions and commands....
The object of this study is the vibration signals received from engines with existing defects. The problem that was solved within the framework of this work arises from the need to construct an accurate and reliable system of prognostic diagnostics, capable of automatically recognizing malfunctions in electric motors, despite the influence of external noises, complex operating conditions, and the similarity of characteristics of signals of various types of defects. The essence of the results is the devised methodology, which includes several stages of vibration signal processing and the use of a convolutional neural network (CNN) for the identification and classification of engine states. At the first stage, the signal is processed in the time domain, in which its main characteristics are analyzed. The signal is then transformed into the frequency domain using a Fast Fourier Transform (FFT) to extract its spectral components. To obtain a more informative representation of the signal, the shorttime Fourier transform (STFT) is applied, which makes it possible to obtain a time-frequency characteristic in the form of a spectrogram. The resulting spectrograms represent a vibration signal in a form suitable for processing by a convolutional neural network, which performs their further analysis. The use of CNN as the main analysis tool allowed us to achieve high results in the classification of engine states. According to the results of experiments, the model showed 100 % accuracy in detecting various types of engine malfunctions, including the most difficult to diagnose conditions. This high level of accuracy is due to the neural network’s ability to efficiently process spectrograms and detect hidden patterns in the data. In addition, the application of STFT ensured the preservation of critical time-frequency information that is not available for use with only conventional FFT. The main advantage of the proposed approach is its versatility and adaptability to different types of engines and malfunctions. The methodology can be used under industrial conditions for automated monitoring of equipment condition. This makes it possible to accidentally detect malfunctions, prevent emergencies, reduce maintenance costs, and increase the overall reliability of the equipment. The proposed approach is particularly useful in applications in which high diagnostic accuracy and fast response to engine state changes are required Keywords: predictive maintenance, machine learning, vibration analysis, frequency analysis, neural networks...
Software radio technology has become one of the core technologies of modern communication systems due to its high flexibility and reconfigurability. The rapid development of artificial intelligence technology has brought new solutions to the signal processing and transmission quality of software radio systems, especially in the face of complex electromagnetic environments, intelligent algorithms can improve the performance of the system. Aiming at the interference and fading problems of wireless signals in complex electromagnetic environments, this paper designs a deep learning-based intelligent algorithm to assist in solving the bottlenecks in traditional signal processing. The study employs a variety of machine learning models, including convolutional neural networks and reinforcement learning, for signal classification, noise suppression, and channel estimation experiments. The superior performance of AI algorithms in terms of signal decoding, BER reduction, and anti-jamming capability can be verified through simulation experiments. The results show that the signal processing scheme using AI makes significant progress in improving the stability of data transmission and signal accuracy compared to traditional methods, especially with better robustness in dynamic environments. It is demonstrated that the AI-assisted software radio system has enhanced processing capability and has the potential to improve transmission quality in complex environments....
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