In this work, a hybrid radio frequency (RF)- and acoustic-based activity recognition system was developed to demonstrate the advantage of combining two non-invasive sensors in Human Activity Recognition (HAR) systems and smart assisted living. We used a hybrid approach, employing RF and acoustic signals to recognize falling, walking, sitting on a chair, and standing up from a chair. To our knowledge, this is the first work that attempts to use a mixture of RF and passive acoustic signals for Human Activity Recognition purposes. We conducted experiments in the lab environment using a Vector Network Analyzer measuring the 2.4 GHz frequency band and a microphone array. After recording data, we extracted the Mel-spectrogram feature of the audio data and the Doppler shift feature of the RF measurements. We fed these features to six classification algorithms. Our result shows that using a hybrid acoustic- and radio-based method increases the accuracy of recognition compared to just using only one kind of sensory data and shows the possibility of expanding for a variety of other different activities that can be recognized. We demonstrate that by using a hybrid method, the recognition accuracy increases in all classification algorithms. Among these classifiers, five of them achieve over 98% recognition accuracy.

A novel low-cost microwave sensor system is proposed for accurate sensing of the real relative permittivity of materials under test (MUT). The proposed solution eliminates the need for using advanced measurement devices such as the vector network analyzer (VNA) for sensor characterization. The proposed sensor system is built on a software-defined radio platform. A suitable two-stage frequency estimation approach was developed for estimating the frequency shift of the resonator sensor associated with the real relative permittivity of the MUT. First, the neighborhood of the resonance frequency is obtained utilizing a low-resolution coarse search, followed by a fine search method to accurately estimate the resonance frequency. For the fine search, we modified the AM frequency estimation algorithm and the Golden Section search algorithm to suite the proposed sensor system. The performance of the proposed sensor system is validated through simulations and experiments. To demonstrate the feasibility of the concept, experiments were conducted by implementing the solution on a Universal Software Radio Peripheral transceiver using a resonator sensor for detecting the binary mixture of water and methanol. The results show that the proposed sensor system achieves measurement accuracy comparable to advanced equipment such as the VNA. Thus, the proposed sensor system could be a low-cost alternative for sensor characterization purposes with accuracy comparable to standard equipment.

We investigate the problem of reconfigurable intelligent surface (RIS)-aided monostatic sensing of multiple mobile targets under LoS blockage using a dual-functional radar-communication base station. For the purpose of target detection and delay/Doppler/angle estimation, we derive a detector based on the generalized likelihood ratio test (GLRT), which entails a high-dimensional parameter search with high complexity due to angle-Doppler coupling. To circumvent the coupling effect, we introduce a repetitive RIS phase profile design accompanied by a multi-target detection/estimation algorithm based on orthogonal matching pursuit where each iteration solves the single-target GLRT problem and outputs the corresponding parameter estimates. Moreover, as a low-complexity alternative, we introduce a novel subspace-based algorithm based on two-dimensional unitary ESPRIT to retrieve target parameters in a non-iterative manner. The simulation results verify the effectiveness of the proposed algorithms, demonstrating their convergence to theoretical bounds and revealing significant improvements over the state-of-the-art benchmarks in complexity and performance.

Vertical cavity surface-emitting laser (VCSEL)-based optical interconnects (OI) are crucial for high-speed data transmission in data centers, supercomputers, and vehicles, yet their performance is challenged by harsh and fluctuating thermal conditions. This paper addresses these challenges by integrating an ordinary differential equation (ODE) solver within the VCSEL communication chain, leveraging the adjoint method to enable effective gradient-based optimization of pre-equalizer weights. We propose a machine learning (ML) approach to optimize feed-forward equalizer (FFE) weights for VCSEL transceivers, which significantly enhances signal integrity by managing inter-symbol interference (ISI) and reducing the symbol error rate (SER).

We introduce FLEX, a family of fast Fourier transform(FFT)-based low complexity beamspace channel estimation algorithms for mmWave MIMO-OFDM systems, enabling five-dimensional parameter estimation; delay, two-dimensional angles of arrival (AoAs), and two-dimensional angles of departure (AoDs). FLEX operates without prior knowledge of the number of propagation paths and automatically pairs multidimensional parameters. The proposed methods first estimate path delays using an FFT-based approach, then compensate for these delays to decouple the spatial dimensions. Angle estimation is performed using either a correlation-based or an ESPRIT-based method, accommodating different precoder structures. A modified approach resolves paths that share a delay bin but are separable in angular domain. Simulation results demonstrate that FLEX significantly reduces computational complexity while achieving accuracy comparable to state-of-the-art methods, converging to theoretical bounds at lower transmit power. Its efficiency and adaptability make FLEX well-suited for real-time channel estimation in wideband communication systems.