With the increasing demand for high-speed data, rapid deployments of the 5G terrestrial heterogeneous wireless networks are expected worldwide in the next decade. In such networks, sub-6 GHz macro-cells overlapped by mmWave small-cells are being used to cater to densely populated regions.As a result, several challenges arise with the antenna design technologies used at the mobile terminal, access points, or backhaul/front haul levels. These challenges are being addressed using multiple-input multiple-output (MIMO), massive-MIMO, and phased array antenna technologies. In order to implement these antenna technologies, considering the expanding 5G scenario, substrate integrated waveguide (SIW) offers a viable solution due to its low-cost, low-profile, high-power handling, low transmission loss, and ease of integration with the radio circuits; therefore, the SIW plays a vital role in developing the modern radio systems. The proposed study aimed to provide a comprehensive overview of SIW MIMO and phased array antennas operating in the 5G sub-6 GHz and mmWave bands. It deliberates the specific issues related to the band of operations and challenges in designing different antenna structures. After careful investigation and detailed analysis, this paper identified existing research gaps and suggested possible antenna design solutions for prospective researchers who intend to explore further the aforementioned promising area and present future research directions.

The integration of Micro Electronic Mechanical Systems (MEMS) sensor technology in smartphones has greatly improved the capability for Human Activity Recognition (HAR). By utilizing Machine Learning (ML) techniques and data from these sensors, various human motion activities can be classified. This study performed experiments and compiled a large dataset of nine daily activities, including Laying Down, Stationary, Walking, Brisk Walking, Running, Stairs-Up, Stairs-Down, Squatting, and Cycling. Several ML models, such as Decision Tree Classifier, Random Forest Classifier, K Neighbors Classifier, Multinomial Logistic Regression, Gaussian Naive Bayes, and Support Vector Machine, were trained on sensor data collected from accelerometer, gyroscope, and magnetometer embedded in smartphones and wearable devices. The highest test accuracy of 95% was achieved using the random forest algorithm. Additionally, a custom-built Bidirectional Long-Short-Term Memory (Bi-LSTM) model, a type of Recurrent Neural Network (RNN), was proposed and yielded an improved test accuracy of 98.1%. This approach differs from traditional algorithmic-based human activity detection used in current wearable technologies, resulting in improved accuracy.

The use of speech as a biomedical signal for diagnosing COVID-19 is investigated using statistical analysis of speech spectral features and classification algorithms based on machine learning. It is established that spectral features of speech, obtained by computing the short-time Fourier Transform (STFT), get altered in a statistical sense as a result of physiological changes. These spectral features are then used as input features to machine learning-based classification algorithms to classify them as coming from a COVID-19 positive individual or not. Speech samples from healthy as well as “asymptomatic” COVID-19 positive individuals have been used in this study. It is shown that the RMS error of statistical distribution fitting is higher in the case of speech samples of COVID-19 positive speech samples as compared to the speech samples of healthy individuals. Five state-of-the-art machine learning classification algorithms have also been analyzed, and the performance evaluation metrics of these algorithms are also presented. The tuning of machine learning model parameters is done so as to minimize the misclassification of COVID-19 positive individuals as being COVID-19 negative since the cost associated with this misclassification is higher than the opposite misclassification. The best performance in terms of the “recall” metric is observed for the Decision Forest algorithm which gives a recall value of 0.7892.

Identifying correct human postures is crucial in areas like patient care in hospitals. However, the traditional vision-based methods widely used for this purpose raise privacy concerns for the subject, and the other wearable sensor-based approaches are impractical for real-world scenarios. In this paper, we propose a contactless, privacy-conscious, and memory-efficient posture classification system based on millimeter wave (mmWave) radar. This system utilizes three-dimension(3D) point-cloud data captured using Texas Instrument’s IWR1843BOOST Frequency Modulated Continuous Wave (FMCW) radar module to classify the posture of the subject. Two types of datasets are extracted from this radar data: ( i ) image dataset derived from the isometric view of the point-cloud data, and ( ii ) spatial coordinates dataset also extracted from the point-cloud data. A low-computational Tiny Machine Learning (TinyML) model is employed on the datasets for efficient implementation on embedded hardware, Raspberry Pi 3 B+. The proposed model’s parameters were quantized to 8 bits (int8), which accurately classify four postures, i.e., standing, sitting, lying, and bending, with an accuracy of 98.97% for the image data. However, to make it more computationally efficient, the int8 quantized TinyML model was trained on the spatial coordinates dataset, giving an accuracy of 96.12%. This highlights the efficiency and effectiveness of our proposed lightweight model that can be deployed on edge devices for real-world applications.

This letter presents the development of a real-time object detection system using frequency modulated continuous wave millimeter-wave (mmWave) radar signals and the python productivity for zynq ultrascale + mpsocs (PYNQ-ZU) field-programmable gate array (FPGA) board, which is widely used in advanced driving assistance system and robotic applications. A hardware FPGA platform serves as a valid embedded architecture for the purpose of validating object detection and recognition applications. We used our experiment's point cloud images to apply different machine learning models to detect these objects. Using a top-view (TV) filter to convert 3-D point cloud images into 2-D representations made object detection more accurate. Following the use of filtration techniques, we extracted features from the filtered 2-D image using the visual geometry group (VGG) 16 model. We then assessed four machine learning models for object detection and found that the support vector machine (SVM) model and logistic regression (LR) had better results, obtaining an accuracy of 97%. Our proposed work uses mmWave radar, TV filter, VGG 16 Model, and LR to highly increase object detection accuracy over existing methods.

This paper proposes the direction of arrival (DOA) estimation of an acoustic source based on a recurrent neural network (RNN) with a uniform linear array (ULA). The RNN has been trained using a large number of independent realizations of microphones signal of the ULA. The authors have compared the DOA estimation accuracy of RNN model against the standard algorithm i.e. delay and sum (DAS) beamformer and it has been observed that RNN based DOA estimation outperforms the DAS beamformer.