Current Issue : July-September Volume : 2026 Issue Number : 3 Articles : 5 Articles
The Galileo Search and Rescue (SAR) service is the contribution from the European constellation to the international Cospas–Sarsat system. This system uses a variety of space and ground infrastructure to detect and localize distress signals from beacons on the 406 MHz frequency. Satellites in different orbits detect the signals coming from the Earth and transmit them back to Earth stations that route them to the appropriate government authorities. On top of the standard detection and relay service, the Galileo constellation is the first to offer a Return Link Service (RLS) that acknowledges the processing of the distress signal with a Return Link Message (RLM) back to the originating beacon. This RLM is transmitted in the SAR field of the E1 signal I/NAV message, which allocates 20 bits every 2 s page. Therefore, transmitting a short RLM (80 bits) takes four consecutive pages or eight seconds. Moreover, each RLM is transmitted in parallel from two Galileo satellites. The RLS has been active since 2020, avoiding the spotlight of the GNSS community. This paper presents an analysis of the SAR Return Link Messages extracted from more than 3 months of signalin- space data to investigate the current bandwidth use, monitor the type of SAR usage, and detect anomalies in the service. To extract and parse the Return Link Messages, we have developed and published an open-source Python library called GalileoSARlib on GitHub, which is also detailed in the paper....
Electroencephalography (EEG), as a typical non-invasive biosensing signal, reflects individual emotional changes by recording the brain’s neural activity in response to various external stimuli. However, the significant differences in brain activity among individuals and the complex interrelationships between EEG channels notably hinder the accuracy of emotion decoding in non-invasive biosensing scenarios. To address this challenge, this paper proposes a two-discriminator domain adversarial neural network method (TD-DANN). The proposed method aims to obtain more generalized and individualized emotion feature representations through adversarial learning. Specifically, graph convolution is utilized to extract features from EEG signals. By modeling the EEG channels as graph nodes, the adjacency matrix can be dynamically learned to capture the complex relationships between different channels during emotion generation. Moreover, we design a domain discriminator and an individual discriminator. The domain discriminator is used to minimize the difference in feature distribution between the source and target domains. It is able to obtain discriminative features with universality. The individual discriminator is used to learn discriminative features consistent with the individual’s brain activity. It can enhance the adaptability to the individual’s emotion. The experimental results show that the TDDANN achieves promising recognition accuracies of (98.45 ± 2.38)% and (89.45 ± 5.87)% for subject-dependent and subject-independent experiments on the SEED dataset, respectively. The proposed method attains recognition accuracies of (84.40 ± 8.70)% and (77.13 ± 7.97)% for subject-dependent and subject-independent experiments on the SEED-IV dataset, respectively. These results validate the effectiveness of the TD-DANN in the emotion decoding problem....
Digital Signal Processing (DSP) is a significant area of electronics and telecommunication engineering that employs a variety of methods to improve the accuracy and reliability of digital communications. The mathematical manipulation of digital signals from the real world, such as speech, audio, video, temperature, pressure, or position, is known as DSP. To display, analyze, or convert signals into other proper forms, their underlying data must be processed. By gathering and analyzing the digital data, DSP assumes control. After that, it feeds the digital data back for practical application. In order to analyze these digital signals and extract critical insights or enhance specific signal characteristics, DSP uses a variety of algorithms, approaches, and procedures, often with sensors, transducers, filters (analog and switched capacitors), circuit implementations, and functional materials that allow for real-time responsiveness and high-performance signal interpretation. Exponentially expanded computations and signal processing designs proportionally escalated the need to accelerate processing velocities. Contemporary systems in real-time image and signal manipulations require rapid arithmetic procedures that satisfy the requirements of high throughput. Multiplication, a kernel operation that holds a central position in many domains of those applications, hastened the development and advancement of high-speed multiplication circuits for several decades. One of the critical circuits that can improve the performance of all DSP systems is the multiplier, which plays a vital role in this system. However, modern DSP workloads from real-time filtering and spectral analysis to image pipelines are multiply intensive and constrained by tight energy, area, and latency budgets, especially in IoT/edge nodes. Conventional complementary metal-oxide–semiconductor (CMOS) multipliers face growing interconnect delays, leakage, and power limits. We address these challenges with a quantum-dot cellular automata (QCA)-based carry save adder that leverages a coplanar nanoarchitecture, which is core to shortening the critical path, decreasing the switching activity, and increasing the density. Compared with prior QCA carry save adders (CSA), our design reduces the number of used cells by 40.31%, area by 32.79%, and latency by 42.85%. These improvements translate directly to higher Multiply-Accumulate (MAC) unit throughput and deterministic response in DSP pipelines, enabling more capable real-time processing under strict edge-device power and form-factor constraints. The design is validated in QCADesigner and can be extended to support larger numbers of bits....
Digital signal processing (DSP) methods and artificial intelligence (AI) serve as a unifying platform across diverse research areas and educational courses based on analysis of signals acquired by appropriate sensors and their time-synchronized systems. Autonomous sensor systems having their own batteries, memories, and possibilities of wireless communication form the core of modern technological systems. The interconnection of sensors for data acquisition, methods for advanced analysis of signal features, and collaborative evaluation promotes both theoretical learning and practical problem solving in professional practice. This paper emphasizes a common mathematical foundation for the processing of data acquired by different sensor systems, and it presents the integration of DSP and AI, enabling the use of similar theoretical methods in different applications, including robotics, digital twins, neurology, augmented reality, and energy optimization. Through selected case studies, it shows how a combination of sensor technology for data acquisition and the use of similar computational methods, visualization, and real-world case studies strengthens interdisciplinary collaboration. Findings of this paper demonstrate how integrating AI with DSP supports innovative research and teaching strategies, redefines the field’s educational role in the digital era, and points to the development of new digital technologies....
Assessment of respiratory volumes is crucial for the long-term management of chronic respiratory diseases. However, standard methods such as spirometry require active patient cooperation and are unsuitable for regular monitoring. This study introduces capacitive pressure sensors integrated with a signal processing algorithm for respiratory assessment/monitoring. Two sensor variants using poly(glycerol sebacate) (PGS) substrates are presented: CS1, featuring a porous structure, and CS2, incorporating a pyramidal surface pattern. Both sensors measure thoracic expansion through capacitance changes. Signals are preprocessed and statistically validated against a commercial airflow transducer in 38 healthy adult participants. Although CS1 exhibits higher sensitivity (0.09 kPa− 1 ) than CS2 (0.015 kPa− 1 ), both sensors demonstrate strong correlation (mean 𝑅2 > 0.91) with the reference device across volunteers. Measurement accuracy is confirmed by low mean absolute errors across respiratory cycles: 0.122 L (95% confidence interval (CI) ± 0.027 L) for CS1, and 0.100 L (95% CI ± 0.018 L) for CS2. These results demonstrate that the developed capacitive sensors and signal processing algorithm effectively capture thoracic volume changes, showing potential for non-invasive and continuous respiratory monitoring....
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