Electrical Engineering And Systems Science

Fetal motion discernment utilizing spectral images extracted from accelerometric data incident on pregnant mothers abdomen has gained substantial attention in the state-of-the-art research. It is an essential practice to avoid adverse scenarios such as stillbirths and intrauterine growth restrictions. However, this endeavor of ensuring fetus safety has been arduous due to the existence of random noise in medical images. This novel research is an in depth approach to analyze how the interference of different noise variations affect the retrieval of information in those images. For that, an algorithm employing auto-encoder-based deep learning was modeled and the accuracy of reconstruction of the STFT images mitigating the noise has been measured examining the loss. From the results, it is manifested that even a substantial addition of the Super-Gaussian noises which have a higher correlation of the frequencies possessed by the Fetal movement images can be restored successfully with the slightest error.

Wireless networks operating at terahertz (THz) frequencies have been proposed as a promising candidate to support the ever-increasing capacity demand, which cannot be satisfied with existing radio-frequency (RF) technology. On the other hand, it likely will serve as backbone infrastructure and could therefore be an attractive target for eavesdropping attacks. Compared with regular RF spectrum, wireless channels in the THz range could be less vulnerable to interceptions because of their high beam directionality and small signal coverage. However, a risk for eavesdropping can still exist due to the multipath effects caused by unintended scattering. In this work, an eavesdropping risk for THz channel passing atmospheric turbulences and producing compromising emissions is investigated from a physical layer perspective. A model combining signal attenuation due to turbulence, gaseous absorption and beam divergence, is developed for prediction of deterministic and probabilistic signal leakages. The secrecy capacity and outage probability of the THz channel are derived and analyzed with respect to variations of the turbulence strength and other channels characteristics. The dependence of the channel performance on the eavesdropper's position is investigated with respect to the maximum safe data transmission rate (MSR) and the signal leakage region. Design results for THz channels are provided to minimize an eavesdropping risk at physical layer.

We consider the problem of finding the Discrete Fourier Transform (DFT) of N??length signals with known frequency support of size k . When N is a power of 2 and the frequency support is a spectral set, we provide an O(klogk) algorithm to compute the DFT. Our algorithm uses some recent characterizations of spectral sets and is a generalization of the standard radix-2 algorithm.

We all know that Wi-Fi is presently the most commonly used technology for data transmission and connecting devices to the Internet, at the same time due to much reasonable concern, (such as Wi-Fi can be vulnerable when it comes to hacking, health concern, and low latency, etc.) the concept of Li-Fi is becoming very popular as a new way of data transmission that use light waves to transmit data rather than radio waves. Light-emitting diodes LED are used when transmitting the data in the visible light spectrum. Li-fi uses visible light communication and it has a promising future. Unlike Wi-fi, Li-Fi has low latency, high efficiency, accessible spectrum, and high data can be achieved. It is highly secured so the data cannot be hacked. In this paper, we design a concept of Li-fi audio signal transmission by reusing and repurposing pre-programmed modules to simplify and discuss visible light communication (VLC) in other to give a new researcher the idea on how the concept of LiFi and VLC. In addition to designing the concept we experiment to test the concept and we illustrated the result within this paper.

In this paper, we propose multi-input multi-output (MIMO) beamforming designs towards joint radar sensing and multi-user communications. We employ the Cramér-Rao bound (CRB) as a performance metric of target estimation, under both point and extended target scenarios. We then propose minimizing the CRB of radar sensing while guaranteeing a pre-defined level of signal-to-interference-plus-noise ratio (SINR) for each communication user. For the single-user scenario, we derive a closed form for the optimal solution for both cases of point and extended targets. For the multi-user scenario, we show that both problems can be relaxed into semidefinite programming by using the semidefinite relaxation approach, and prove that the global optimum can always be obtained. Finally, we demonstrate numerically that the globally optimal solutions are reachable via the proposed methods, which provide significant gains in target estimation performance over state-of-the-art benchmarks.

It is expensive and time-consuming to collect sufficient labeled data for human activity recognition (HAR). Domain adaptation is a promising approach for cross-domain activity recognition. Existing methods mainly focus on adapting cross-domain representations via domain-level, class-level, or sample-level distribution matching. However, they might fail to capture the fine-grained locality information in activity data. The domain- and class-level matching are too coarse that may result in under-adaptation, while sample-level matching may be affected by the noise seriously and eventually cause over-adaptation. In this paper, we propose substructure-level matching for domain adaptation (SSDA) to better utilize the locality information of activity data for accurate and efficient knowledge transfer. Based on SSDA, we propose an optimal transport-based implementation, Substructural Optimal Transport (SOT), for cross-domain HAR. We obtain the substructures of activities via clustering methods and seeks the coupling of the weighted substructures between different domains. We conduct comprehensive experiments on four public activity recognition datasets (i.e. UCI-DSADS, UCI-HAR, USC-HAD, PAMAP2), which demonstrates that SOT significantly outperforms other state-of-the-art methods w.r.t classification accuracy (9%+ improvement). In addition, our mehtod is 5x faster than traditional OT-based DA methods with the same hyper-parameters.

The inverse problem of inferring electrocardiogram (ECG) from photoplethysmogram (PPG) is an emerging research direction that combines the easy measurability of PPG and the rich clinical knowledge of ECG for long-term continuous cardiac monitoring. The prior art for reconstruction using a universal basis has limited fidelity for uncommon ECG waveform shapes due to the lack of rich representative power. In this paper, we design two dictionary learning frameworks, the cross-domain joint dictionary learning (XDJDL) and the label-consistent XDJDL (LC-XDJDL), to further improve the ECG inference quality and enrich the PPG-based diagnosis knowledge. Building on the K-SVD technique, our proposed joint dictionary learning frameworks aim to maximize the expressive power by optimizing simultaneously a pair of signal dictionaries for PPG and ECG with the transforms to relate their sparse codes and disease information. The proposed models are evaluated with 34,000+ ECG/PPG cycle pairs containing a variety of ECG morphologies and cardiovascular diseases. We demonstrate both visually and quantitatively that our proposed frameworks can achieve better inference performance than previous methods, suggesting an encouraging potential for ECG screening using PPG based on the proactive learned PPG-ECG relationship.

An optical gateway to convert four-level pulse amplitude modulation to quadrature phase shift keying modulation format having shaping gain was proposed for flexible intensity to phase mapping which exploits non-uniform phase noise. The power consumption of the optical modulation format conversion can save by making a DNN-based decision on the receiver side for the generated QPSK signal with non-uniform phase noise. A proof-of-principle experiment has shown that an optically geometric constellation shaped QPSK modulated signals generated from regular PAM4 signals with Gaussian-distributed noise. The shaped QPSK signal shows BER and generalized mutual information improvement by 1dB gain through the use of digital neural network signal recovery.

Phase wrapping is a major problem in direction-of-arrival (DOA) estimation using phase-difference observations. For a sensor pair with an inter-sensor spacing greater than half of the wavelength ( λ/2 ) of the signal, phase wrapping occurs at certain DOA angles leading to phase-difference ambiguities. Existing phase unwrapping methods exploit either frequency or spatial diversity. These techniques work by imposing restrictions on the utilized frequencies or the receiver array geometry. In addition to sensitivity to noise and calibration errors, these methods may also have high computational complexity. We propose a grid-less \emph{phase-difference projection} (PDP) DOA algorithm to overcome these issues. The concept of \emph{wrapped phased-difference pattern} (WPDP) is introduced, which allows the proposed algorithm to compute most of the parameters required for DOA estimation in an offline manner, hence resulting in a superior computational speed in realtime. Simulation results demonstrate the excellent performance of the proposed algorithm, both in terms of accuracy and speed.

Electrocardiogram (ECG) is the most crucial monitoring modality to diagnose cardiovascular events. Precise and automatic detection of abnormal ECG patterns is beneficial to both physicians and patients. In the automatic detection of abnormal ECG patterns, deep neural networks (DNNs) have shown significant achievements. However, DNNs require large amount of labeled data, which are often expensive to obtain. On the other hand, recent research have shown by randomly combining data augmentations can improve image classification accuracy. Thus, in this work we explore data augmentation suitable for ECG data and propose ECG Augment. We show by introducing ECG Augment, we can improve classification of atrial fibrillation with single lead ECG data, without changing an architecture of DNN.