Reconfigurable Intelligent Surfaces (RISs) are emerging as a key technology for enhancing wireless communications in smart city environments, offering robust, low-latency connectivity. This paper presents the design of an adaptive control system based on high-voltage, mixed-signal integrated circuits for controlling tunable RISs, tailored for wireless cellular communications in urban areas. The proposed system enables enhanced programmability by dynamically steering beams and fine-tuning signal reflections through the precise control of varactor diodes embedded within the RIS unit cells. This flexible system architecture supports real-time software reconfiguration, allowing RIS units to adapt to varying network conditions and scale seamlessly across smart city infrastructures. Simulated results on a 0.35 μm CMOS high voltage process technology demonstrate the circuit's ability to achieve low power consumption and high reconfiguration speed across a wide temperature range. The control electronics are separated from the RIS panels, this way enabling tiling of multiple surfaces for scalable, cost-effective deployment. This approach offers a promising pathway to meeting the growing demand for seamless, high-speed, and energy-efficient connectivity in next-generation smart city applications, including vehicular networks, Internet of Things, smart traffic management and smart infrastructure.

The objective of the IcyHeart project is to investigate and demonstrate a highly integrated and power-efficient microelectronic solution for remote monitoring of a subject's electrocardiogram (ECG) signals. A complete System-on-a-Chip (SoC) is being developed that embarks on a single chip an ultra-low-power signal acquisition front-end with analogue-to-digital converter (ADC) for ECG, a low-power digital signal processor (DSP) and a low-energy radio frequency (RF) transceiver. These features, for the first time, coexist on a single die. Energy efficient signal processing algorithms targeting ECG, and expandable to other bio-signals, are embedded and run on the on-chip DSP. The final IcyHeart product will consist of a tiny PCB embarking IcyHeart SoC and all the necessary discrete components and powering circuit. The outcome of the project is expected to generate high market value for the European SMEs developing novel cardio-monitoring products in home and professional environments, and to create high societal impact for several categories of European citizens requiring miniature, comfortable and easy-to-use wireless tele-healthcare solutions.

We present the Dynamic Programming Projected Phase-Slope Algorithm (DYPSA) for automatic estimation of glottal closure instants (GCIs) in voiced speech. Accurate estimation of GCIs is an important tool that can be applied to a wide range of speech processing tasks including speech analysis, synthesis and coding. DYPSA is automatic and operates using the speech signal alone without the need for an EGG signal. The algorithm employs the phase-slope function and a novel phase-slope projection technique for estimating GCI candidates from the speech signal. The most likely candidates are then selected using a dynamic programming technique to minimize a cost function that we define. We review and evaluate three existing methods of GCI estimation and compare the new DYPSA algorithm to them. Results are presented for the APLAWD and SAM databases for which 95.7% and 93.1% of GCIs are correctly identified

We present the DYPSA algorithm for automatic and reliable estimation of glottal closure instants (GCIs) in voiced speech. Reliable GCI estimation is essential for closed-phase speech analysis, from which can be derived features of the vocal tract and, separately, the voice source. It has been shown that such features can be used with significant advantages in applications such as speaker recognition. DYPSA is automatic and operates using the speech signal alone without the need for an EGG or Laryngograph signal. It incorporates a new technique for estimating GCI candidates and employs dynamic programming to select the most likely candidates according to a defined cost function. We review and evaluate three existing methods and compare our new algorithm to them. Results for DYPSA show GCI detection accuracy to within ±0.25ms on 87% of the test database and fewer than 1% false alarms and misses.