In this survey we are focusing on utilizing drone-based systems for the detection of individuals, particularly by identifying human screams and other distress signals. This study has significant relevance in post-disaster scenarios, including events such as earthquakes, hurricanes, military conflicts, wildfires, and more. These drones are capable of hovering over disaster-stricken areas that may be challenging for rescue teams to access directly, enabling them to pinpoint potential locations where people might be trapped. Drones can cover larger areas in shorter timeframes compared to ground-based rescue efforts or even specially trained search dogs. Unmanned aerial vehicles (UAVs), commonly referred to as drones, are frequently deployed for search-and-rescue missions during disaster situations. Typically, drones capture aerial images to assess structural damage and identify the extent of the disaster. They also employ thermal imaging technology to detect body heat signatures, which can help locate individuals. In some cases, larger drones are used to deliver essential supplies to people stranded in isolated disaster-stricken areas. In our discussions, we delve into the unique challenges associated with locating humans through aerial acoustics. The auditory system must distinguish between human cries and sounds that occur naturally, such as animal calls and wind. Additionally, it should be capable of recognizing distinct patterns related to signals like shouting, clapping, or other ways in which people attempt to signal rescue teams. To tackle this challenge, one solution involves harnessing artificial intelligence (AI) to analyze sound frequencies and identify common audio “signatures.” Deep learning-based networks, such as convolutional neural networks (CNNs), can be trained using these signatures to filter out noise generated by drone motors and other environmental factors. Furthermore, employing signal processing techniques like the direction of arrival (DOA) based on microphone array signals can enhance the precision of tracking the source of human noises.

We consider the problem of extracting blindly data embedded over a wide band in a spectrum (transform) domain of a digital medium (image, audio, video). We develop a novel multicarrier/signature iterative generalized least-squares (M-IGLS) core procedure to seek unknown data hidden in hosts via multicarrier spread-spectrum embedding. Neither the original host nor the embedding carriers are assumed available. Experimental studies on images show that the developed algorithm can achieve recovery probability of error close to what may be attained with known embedding carriers and host autocorrelation matrix.

In this paper, we study American sign language (ASL) hand gesture recognition using Doppler radar. A set of ASL hand gesture motions are captured as micro- Doppler signals using a microwave X-band Doppler radar transceiver. We apply joint time-frequency analysis and observe the presence of the micro- Doppler signatures in the spectrogram. The micro- Doppler signatures of different hand gestures are analyzed using Matlab. Each hand gesture is observed to contain unique spectral characteristics. Based on unique spectral characteristics, we investigate the classification of ASL essential short phrases including emergency signals. For recognizing and characterizing the presence of micro-Doppler signatures in spectrogram we explore deep convolution neural network (DCNN) algorithm. We show that the DCNN algorithm can classify different sign language gestures based on the presence of micro- Doppler signatures in the spectrogram with fairly high accuracy. Experimental results reveal that utilizing 80% of data for training, and the remaining 20% for validation purposes in DCNN algorithm a validation accuracy of 87.5% is achieved. To further improve the recognition system, we apply a very deep learning algorithm VGG-16 using transfer learning, which improves the validation accuracy to 95%.

In this letter, we investigate the sequence estimation problem of faster-than-Nyquist (FTN) signaling as a promising approach for increasing spectral efficiency (SE) in future communication systems. In doing so, we exploit the concept of Gaussian separability and propose two probabilistic data association (PDA) algorithms with polynomial time complexity to detect binary phase-shift keying (BPSK) FTN signaling. Simulation results show that the proposed PDA algorithm outperforms the recently proposed SSSSE and SSSgbKSE algorithms for all SE values with a modest increase in complexity. The PDA algorithm approaches the performance of the semidefinite relaxation (SDRSE) algorithm for SE values of 0.96 bits/sec/Hz, and it is within the 0.5 dB signal-to-noise ratio (SNR) penalty at SE values of 1.10 bits/sec/Hz for the fixed values of β = 0.3.

We consider the problem of designing binary antipodal uniquely decodable (errorless) code sets for overloaded code-division multiplexing applications where the number of signals K is larger than the code length L. Our proposed errorless code set design aims at identifying the maximum number of columns that can be potentially appended to a Sylvester-Hadamard matrix of order L, while maintaining the errorless code property. In particular, we derive formally the maximum number of columns that may be appended to the Sylvester-Hadamard matrix of order L = 8 and use this result as a seed to produce an infinite sequence of designs in increasing L. In the noiseless transmission case, a simple algorithm is developed to uniquely decode all signals. In additive white Gaussian noise (AWGN), a slab-sphere decoding scheme can be utilized for efficient and effective decoding.

Recognizing and categorizing items in weather-adverse environments poses significant challenges for autonomous vehicles. To improve the robustness of object-detection systems, this paper introduces an innovative approach for detecting objects at different levels by leveraging sensors and deep learning-based solutions within a traffic circle. The suggested approach improves the effectiveness of single-stage object detectors, aiming to advance the performance in perceiving autonomous racing environments and minimizing instances of false detection and low recognition rates. The improved framework is based on the one-stage object-detection model, incorporating multiple lightweight backbones. Additionally, attention mechanisms are integrated to refine the object-detection process further. Our proposed model demonstrates superior performance compared to the state-of-the-art method on the DAWN dataset, achieving a mean average precision (mAP) of 99.1%, surpassing the previous result of 84.7%.