Current Trends in Signal Processing

Current Trends in Signal Processing Current Trends in Signal Processing is a peer-reviewed hybrid open-access journal launched in 2011, focused on the rapid publication of fundamental research papers on areas of Signal Processing. The journal emphasizes original research articles that explore theoretical advances, innovative applications, and interdisciplinary approaches in signal processing technologies.

• Digital Signal Processing: Discrete-time signals and systems, digital filters, multirate signal processing, sampling, reconstruction, spectral analysis, fast Fourier transform, digital transformations, and real-time DSP implementations.
• Analog and Mixed-Signal Processing: Analog filters, signal conditioning, continuous-time processing, analog front ends, mixed-signal systems, data conversion, and hybrid analog-digital signal-processing architectures.
• Statistical Signal Processing: Random processes, stochastic modelling, parameter estimation, detection theory, Bayesian signal processing, statistical inference, covariance analysis, and uncertainty-aware signal analysis.
• Adaptive Signal Processing: Adaptive filters, LMS and RLS algorithms, adaptive noise cancellation, system identification, channel equalization, echo cancellation, online learning, and self-adjusting signal-processing systems.
• Time-Frequency and Time-Scale Analysis: Short-time Fourier transform, wavelet transforms, Hilbert-Huang transform, spectrogram analysis, empirical mode decomposition, and non-stationary signal analysis.
• Image Processing and Computer Vision: Image enhancement, restoration, segmentation, compression, feature extraction, object detection, image reconstruction, pattern recognition, and computational imaging.
• Audio and Speech Signal Processing: Speech recognition, speech enhancement, speaker recognition, audio compression, acoustic modelling, source separation, noise suppression, music information retrieval, and audio analytics.
• Biomedical Signal Processing: ECG, EEG, EMG, PPG, respiratory signals, biosignal filtering, physiological monitoring, diagnostic signal analysis, wearable-health signals, and biomedical feature extraction.
• Multimedia Signal Processing: Image, audio and video processing, multimedia compression, multimedia analytics, content retrieval, digital watermarking, deepfake detection, multimedia forensics, and multimodal signal fusion.
• Communication Signal Processing: Modulation and demodulation, channel estimation, equalization, synchronization, coding, MIMO processing, OFDM, spectrum sensing, interference mitigation, and communication-receiver algorithms.
• Wireless and Mobile Signal Processing: 5G, 6G, cognitive radio, massive MIMO, beamforming, mmWave signal processing, wireless channel modelling, mobility-aware processing, and intelligent wireless networks.
• Radar and Sonar Signal Processing: Target detection, tracking, radar imaging, beamforming, Doppler processing, synthetic-aperture radar, sonar analysis, underwater acoustics, and remote-sensing applications.
• Optical Signal Processing: Fiber-optic signals, optical filtering, wavelength conversion, photonic signal processing, optical communication, optical computing, integrated photonics, and nonlinear optical signal processing.
• Sensor Signal Processing and Data Fusion: Sensor arrays, smart sensors, wireless sensor networks, sensor calibration, sensor fusion, multisensor integration, distributed sensing, wearable sensors, and edge-based signal analytics.
• Control-System Signal Processing: Signal analysis for control systems, state estimation, observer design, sensor feedback, control-oriented filtering, cyber-physical systems, reinforcement-learning-assisted control signals, and MIMO control applications.
• Machine Learning for Signal Processing: Supervised and unsupervised learning, deep learning, convolutional neural networks, transformers, representation learning, feature learning, signal classification, regression, and data-driven signal models.
• Generative AI and Signal Synthesis: Generative adversarial networks, diffusion models, generative signal restoration, synthetic data generation, speech synthesis, image synthesis, and learned signal reconstruction.
• AI-Assisted Signal Enhancement: Denoising, deblurring, super-resolution, signal restoration, artifact removal, speech enhancement, image reconstruction, and intelligent filtering.
• Feature Extraction and Pattern Recognition: Feature selection, dimensionality reduction, signal descriptors, statistical pattern recognition, machine-learning-based classification, anomaly detection, and dynamic feature-stream analysis.
• Natural Language and Text Signal Processing: Text representation, automatic summarization, transformer-based text processing, semantic analysis, information extraction, sequence modelling, and language-related signal-processing methods.
• Signal Processing for Cybersecurity: Intrusion detection, anomaly detection, network-traffic analysis, encrypted signal analysis, physical-layer security, multimedia forensics, and detection of manipulated signals.
• Signal Integrity and Transmission: Transmission lines, impedance matching, signal distortion, waveform analysis, high-speed digital signals, interconnects, crosstalk, jitter, and signal-quality assessment.
• Environmental and Geophysical Signal Processing: Seismic signals, atmospheric signals, oceanographic measurements, climate time series, remote-sensing data, wave measurements, and environmental monitoring.
• Plant and Biological Electrical Signaling: Plant electrophysiology, bioelectrical responses, long-distance electrical signaling, plant stress monitoring, biosignal sensing in agriculture, and biological electrical communication.
• IoT and Edge Signal Processing: IoT sensor signals, edge analytics, embedded signal processing, low-power signal processing, real-time monitoring, distributed intelligence, and intelligent connected devices.
• Neuromorphic Signal Processing: Event-driven processing, spiking neural networks, neuromorphic sensors, brain-inspired computation, low-power intelligent signal systems, and hardware-efficient signal processing.
• Quantum Signal Processing: Quantum algorithms for signal transformation, quantum sensing, quantum signal reconstruction, quantum communication signals, quantum-enhanced estimation, and emerging quantum signal-processing methods.
• Digital Twins and Cyber-Physical Signal Systems: Real-time signal integration, sensor-driven digital twins, predictive modelling, simulation-linked measurements, and intelligent monitoring of cyber-physical systems.
• Signal Processing Hardware and Embedded Systems: DSP processors, FPGAs, GPUs, ASICs, embedded signal processors, hardware accelerators, real-time architectures, and energy-efficient signal-processing implementations.
• Signal Processing for Autonomous Systems: Autonomous vehicles, robotics, UAVs, navigation, localization, sensor fusion, perception, obstacle detection, and real-time decision-support signals.
• Emerging Trends in Signal Processing: Federated signal learning, edge AI, explainable AI for signals, privacy-preserving signal processing, multimodal AI, digital twins, quantum methods, neuromorphic computing, and next-generation signal-processing architectures.