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To eliminate tethering effects on the small animals' behavior during electrophysiology experiments, such as neural interfacing, a robust and wideband wireless data link is needed for communicating with the implanted sensing elements without blind spots. We present a software-defined radio (SDR) based scalable data acquisition system, which can be programmed to provide coverage over standard-sized or customized experimental arenas. The incoming RF signal with the highest power among SDRs is selected in real-time to prevent data loss in the presence of spatial and angular misalignments between the transmitter (Tx) and receiver (Rx) antennas. A 32-channel wireless neural recording system-on-a-chip (SoC), known as WINeRS-8, is embedded in a headstage and transmits digitalized raw neural signals, which are sampled at 25 kHz/ch, at 9 Mbps via on-off keying (OOK) of a 434 MHz RF carrier. Measurement results show that the dual-SDR Rx system reduces the packet loss down to 0.12%, on average, by eliminating the blind spots caused by the moving Tx directionality. The system operation is verified in vivo on a freely behaving rat and compared with a commercial hardwired system.
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http://dx.doi.org/10.1109/TBCAS.2019.2949233 | DOI Listing |
Natl Sci Rev
September 2025
School of Electronic Engineering, Xidian University, Xi'an 710071, China.
The increasing demand for public safety has created an urgent need for high-performance technologies capable of detecting hazardous liquids with high accuracy, efficiency, and cost-effectiveness. Conventional liquid detection methods often fall short in addressing these requirements due to limitations in precision, operational complexity, and scalability. This study introduces a wireless intelligent system for the detection of suspicious liquids, leveraging advancements in programmable metasurface and software defined radio technologies.
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July 2025
Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka, Saudi Arabia.
Automatic modulation classification (AMC) is a critical component in modern communication systems, particularly within software-defined radios, cognitive radio networks, smart grid and and distributed renewable energy systems (RESs) where adaptive and efficient signal processing is essential. This paper proposes a novel deep learning-based AMC method for identifying M-PSK and M-QAM waveform signals in single-relay cooperative MIMO 5G systems operating under partial channel state information (CSI) and spatially correlated channels. The proposed method leverages a convolutional neural network (CNN) classifier trained on a reduced set of discriminative features, including higher-order statistics and the differential nonlinear phase peak factor, which are extracted from the received signal.
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July 2025
Department of Electrical Engineering, Iran University of Science and Technology, Tehran, 16846-13114, Iran.
This paper proposes a new deep learning and machine learning model for detecting deception and suppression jamming in Ublox-M8T receivers operating under GNSS interference. This solution employs XGBoost for real-time classification of jamming signals, implemented on an STM32H743 microcontroller to ensure ultra-low latency, making it suitable for navigation in various environments. This work's key contribution is integrating a windowing mechanism for pre-saturation alerts and early activation of jamming detection which enhances system reliability by distinguishing between high-credibility and low-credibility GNSS data under static and dynamic jamming conditions.
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June 2025
Department of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China.
The identification of vital signs is becoming increasingly important in various applications, including healthcare monitoring, security, smart homes, and locating entrapped persons after disastrous events, most of which are achieved using continuous-wave radars and ultra-wideband systems. Operating frequency and transmission power are important factors to consider when conducting earthquake search and rescue (SAR) operations in urban regions. Poor communication infrastructure can also impede SAR operations.
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June 2025
Dipartimento di Ingegneria Elettrica Elettronica e Informatica (DIEEI), University of Catania & CNIT, Viale A. Doria 6, 95125 Catania, Italy.
Underwater acoustic sensor networks, crucial for marine monitoring, face significant challenges, including limited bandwidth, high delay, and severe energy constraints. Addressing these limitations requires an energy-efficient design to ensure network survivability, reliability, and reduced operational costs. This paper proposes an artificial intelligence-driven framework aimed at enhancing energy efficiency and sustainability in applications of marine wildlife monitoring in underwater sensor networks, according to the vision of implementing an underwater acoustic sensor network.
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