Current Trends in Signal Processing

Latest articles

• Published Subscription Review Article 5 Aug 2026

  A Review on Social Distancing ID Card

  By Anand Raje, Naganath Nikam, Shubham Pawar, V.B Utpat

  Abstract: The frequency of fire accidents in homes, industries, and public spaces has increased due to electrical faults, human negligence, and flammable materials. Traditional fire-fighting systems often depend on human presence and manual intervention, which delays the response time. To overcome this limitation, this project presents an Automatic Fire Extinguisher System based on Arduino and IoT technology for real- time detection and suppression of fire. Gas, temperature, and flame sensors are …

  Published in Current Trends in Signal Processing · Vol. 16, Issue 2, 2026 Read article →

• Published Subscription Review Article 5 Aug 2026

  Neuromorphic Spiking Neural Networks and Event-Driven Hardware: Architectures, Learning Paradigms, and Experimental Realizations

  By Diksha Rajput, Lakshya Vijayvargiya, Anuradha

  Abstract: With the current computational boom the research community is seeking for more sustainable energy efficient i.e. biologically inspired models of conventional Artificial Neural Networks (ANNs). Spiking Neural Networks (SNNs) known as the third generation of neural network models, provide a revolutionary approach by mimicking the asynchronized event-driven and temporally accurate signaling of the mammalian brain. Whereas conventional deep learning models operate with real-valued activations and dense matrix multiplications, SNNs use …

  Published in Current Trends in Signal Processing · Vol. 16, Issue 2, 2026 Read article →

• Published Subscription Review Article 25 Jun 2026

  Diffusion-Based Enhancement of Low-SNR Time- Frequency Signals

  By Pradnya Kharde, Sunil Kharde, Jyoti Lokhande, Sangita Wamane

  Abstract: Traditional enhancing techniques are useless in low signal-to-noise ratio (LSNR) situations because noise drastically interferes with communication signals. Based on an enhanced DiffBIR model, this paper suggests a dual-stage signal improvement approach that combines diffusion with deep learning. By combining the Inception module for multi-scale feature extraction with the Pixel Fusion Attention (PFA) module for significant region highlighting, the model improves signal recovery in the time- frequency domain. Experiments show …

  Published in Current Trends in Signal Processing · Vol. 17, Issue 2, 2026 Read article →

• Published Subscription Review Article 17 Jun 2026

  Real-Time Edge Detection Camera Module Using Discrete Taylor Transform and Heat Equation (PDE): An Applied Mathematical Approach

  By M.Sirisha, K.Dasaradha Ramaiah

  Abstract: In modern digital signal processing, the capability for denoising and smoothing in real time is very important in scientific, engineering, and industrial applications. This paper presents an efficient hybrid framework that merges two mathematically sound methods, namely, DTT and PDE defined as the Heat Equation, to robustly denoise a signal with minimal distortion. The model addresses one of the most challenging tasks in signal restoration, which maintains the fidelity of …

  Published in Current Trends in Signal Processing · Vol. 16, Issue 2, 2026 Read article →

• Published Subscription Review Article 6 Jun 2026

  Multi-Parameter Biomedical Sensor-Based Mental State Classification Using EEG And Deep Learning Techniques

  By Asmita Sureshrao Gawande, Amita M. Tembhare, D.S.Dhote

  Abstract: With mental health concerns becoming increasingly widespread, there is a strong need for systems that can monitor conditions like stress, anxiety, and fatigue in a continuous and non- invasive manner. This research proposes a novel multi-parameter biomedical sensing framework for mental state classification by integrating electroencephalography (EEG) signals with physiological parameters, including body temperature acquired using LM35 sensors, heart rate from pulse sensors, and blood oxygen saturation (SpO₂) measurements. The …

  Published in Current Trends in Signal Processing · Vol. 16, Issue 2, 2026 Read article →

• Published Subscription Review Article 28 Apr 2026

  Spectral Mapping and Tracking Error of MBOC signal and Wavelet based GNSS receiver

  By Shubham Kumar Singh, Soumyo Nath Tripathy, Rintu Kumar Gayen, Debashish Chakravarty, Tamesh Halder

  Abstract: This research paper delves into the historical evolution and contemporary state of the art in navigation technologies, emphasizing the paramount importance of reliable positioning systems. Tracing the journey from ancient primitive methods to the present-day Global Navigation Satellite Systems (GNSS), with a focus on the Global Positioning System (GPS), the paper explores the diverse instruments and methods developed by civilizations throughout history to determine location and navigate effectively. The main …

  Published in Current Trends in Signal Processing · Vol. 16, Issue 1, 2026 Read article →

• Published Subscription Review Article 3 Apr 2026

  From Noise to Insight: An Academic Study of Electrical Signal Processing

  By Dr V. Basil Hans

  Abstract: Electrical signal processing is very important for turning raw, often noisy data into useful and actionable information. This article gives a simple and easy-to-understand summary of the basic ideas and methods used in electrical signal processing, such as filtering, signal representation, modulation, and spectrum analysis. The focus is on how to effectively eliminate noise and interference to improve the quality and dependability of signals. The conversation connects ideas from theory …

  Published in Current Trends in Signal Processing · Vol. 16, Issue 1, 2026 Read article →

• Published Subscription Review Article 3 Apr 2026

  Fractal-Entropy Guided Adaptive Signal Reconstruction for Non-Stationary Biomedical and Communication Systems

  By Bibhu Prasad Ganthia, Rosalin Pradhan

  Abstract: This paper presents a novel Fractal-Entropy Guided Adaptive Signal Reconstruction (FEG- ASR) framework designed for accurate processing of non-stationary signals in biomedical and communication systems. The proposed approach integrates fractal dimension analysis with entropy- based feature evaluation to capture the intrinsic complexity and irregularity of time-varying signals. By dynamically adapting reconstruction parameters based on fractal-entropy measures, the method effectively separates noise from meaningful signal components while preserving critical information. The …

  Published in Current Trends in Signal Processing · Vol. 16, Issue 1, 2026 Read article →

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