About Abdelhay Ali Abdelhay Ali Postdoctoral Research Fellow, Electrical and Computer Engineering body area networks Deep learning Digital Systems Design ASIC Design FPGA Projects Related Projects 2025 AgriLink: Internet of Plants for Smart Agriculture Thu, Sep 25 2025 Research body area networks Dense vegetation severely attenuates 2.4–5 GHz RF links (additional foliage loss of order 1–3 dB/m, and often >30 dB across canopies), forcing higher transmit power or more relays in plant/greenhouse settings. In contrast, Plant-Body Communication (PBC) utilizes signals within the stem, where ionic pathways and distributed capacitances form a guided, low-radiation medium, thereby reducing path loss and environmental variability. We target two practical outcomes: 1. quantify and model the intra-plant channel across coupling modes (galvanic vs. capacitive), species (herbaceous Dieffenbachia vs BioLink: Internet of Body via HBC for Healthcare Thu, Sep 25 2025 Research body area networks Human Body Communication (HBC) is an innovative technology that uses the human body itself as a transmission medium for data exchange. This approach enables secure, energy-efficient, and reliable communication between smart health sensors, wearable devices, and medical implants in and around the body. Unlike traditional wireless methods that rely on radio frequency signals prone to interference and privacy risks, HBC offers low power consumption, reduced signal loss, and enhanced data privacy by transmitting signals through direct skin or body tissue contact. This project focuses on advancing Ultra-Low-Power HBC Transceiver ICs Thu, Sep 25 2025 Research body area networks The CCSL advances next-generation Human Body Communication (HBC) through innovative ASIC implementations that enable ultra-low power, secure, and efficient data transmission using the human body as a communication medium. Our research focuses on silicon-proven solutions that bridge the gap between theoretical HBC concepts and practical wearable/implantable applications. We develop mixed-signal transceivers that support multiple communication modes, adaptive signal processing, and AI-enhanced performance optimization. The lab's ASIC implementations demonstrate >100x energy efficiency
AgriLink: Internet of Plants for Smart Agriculture Thu, Sep 25 2025 Research body area networks Dense vegetation severely attenuates 2.4–5 GHz RF links (additional foliage loss of order 1–3 dB/m, and often >30 dB across canopies), forcing higher transmit power or more relays in plant/greenhouse settings. In contrast, Plant-Body Communication (PBC) utilizes signals within the stem, where ionic pathways and distributed capacitances form a guided, low-radiation medium, thereby reducing path loss and environmental variability. We target two practical outcomes: 1. quantify and model the intra-plant channel across coupling modes (galvanic vs. capacitive), species (herbaceous Dieffenbachia vs
BioLink: Internet of Body via HBC for Healthcare Thu, Sep 25 2025 Research body area networks Human Body Communication (HBC) is an innovative technology that uses the human body itself as a transmission medium for data exchange. This approach enables secure, energy-efficient, and reliable communication between smart health sensors, wearable devices, and medical implants in and around the body. Unlike traditional wireless methods that rely on radio frequency signals prone to interference and privacy risks, HBC offers low power consumption, reduced signal loss, and enhanced data privacy by transmitting signals through direct skin or body tissue contact. This project focuses on advancing
Ultra-Low-Power HBC Transceiver ICs Thu, Sep 25 2025 Research body area networks The CCSL advances next-generation Human Body Communication (HBC) through innovative ASIC implementations that enable ultra-low power, secure, and efficient data transmission using the human body as a communication medium. Our research focuses on silicon-proven solutions that bridge the gap between theoretical HBC concepts and practical wearable/implantable applications. We develop mixed-signal transceivers that support multiple communication modes, adaptive signal processing, and AI-enhanced performance optimization. The lab's ASIC implementations demonstrate >100x energy efficiency
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