Multi-hop Cooperative Relaying for Energy Efficient In Vivo Communications

This paper investigates cooperative relaying to support energy efficient in vivo communications. In such a network, the in vivo source nodes transmit their sensing information to an on-body destination node either via direct communications or by employing on-body cooperative relay nodes in order to promote energy efficiency. Two relay modes are investigated, namely single-hop and multi-hop (two-hop) relaying. In this context, the paper objective is to select the optimal transmission mode (direct, single-hop, or two-hop relaying) and relay assignment (if cooperative relaying is adopted) for each source node that results in the minimum per bit average energy consumption for the in vivo network. The problem is formulated as a binary program that can be efficiently solved using commercial optimization solvers. Numerical results demonstrate the significant improvement in energy consumption and quality-of-service (QoS) support when multi-hop communication is adopted

Uplink Multiuser Scheduling Techniques for Spectrum Sharing Systems

This thesis focuses on the development of multiuser access schemes for spectrum sharing systems whereby secondary users that are randomly positioned over the coverage area are allowed to share the spectrum with primary users under the condition that the interference observed at the primary receiver is below a predetermined threshold. In particular, two scheduling schemes are proposed for selecting a user among those that satisfy the interference constraints and achieve an acceptable signal-to-noise ratio level above a predetermined signal-to-noise threshold at the secondary base station. The first scheme selects the user that reports the best channel quality. In order to alleviate the high feedback load required by the first scheme, a second scheme is proposed that is based on the concept of switched diversity where the base station scans the users in a sequential manner until an acceptable user is found. In addition, the proposed scheduling schemes operate under two power adaptive settings at the secondary users that are based on the amount of interference available at the secondary transmitter. In the On/Off power setting, users are allowed to transmit based on whether the interference constraint is met or not, while in the full power adaptive setting, users are allowed to vary their transmission power to satisfy the interference constraint. A special case of the proposed schemes is also analyzed whereby all the users are assumed to be at the same position, thus operating under the influence of independent and identically distributed Rayleigh fading channels. Finally, several numerical results are illustrated for the proposed algorithms where the trade-off between the average spectral efficiency and average feedback load of both schemes are shown

Patient-Specific Epileptic Seizure Onset Detection via Fused Eeg and Ecg Signals

Epilepsy is a neurological disorder that is associated with sudden and recurrent seizures. Epilepsy affects 65 million people world-wide and is the third most common neurological disorder, after stroke and Alzheimer disease. During an epileptic seizure, the brain endures a transient period of abnormally excessive synchronous activity, leading to a state of havoc for many epileptic patients. Seizures can range from being mild and unnoticeable to extremely violent and life threating. Many epileptic individuals are not able to control their seizures with any form of treatment or therapy. These individuals often experience serious risk of injury, limited independence and mobility, and social isolation. In an attempt to increase the quality of life of epileptic individuals, much research has been dedicated to developing seizure onset detection systems that are capable of accurately and rapidly detecting signs of seizures. This thesis presents a novel seizure onset detection system that is based on the fusion of independent electroencephalogram (EEG) and electrocardiogram (ECG) based decisions. The EEG-based detector relies on a on a common spatial pattern (CSP)-based feature enhancement stage that enables better discrimination between seizure and non-seizure features. The EEG-based detector also introduces a novel classification system that uses logical operators to pool support vector machine (SVM) seizure onset detections made independently across different relevant EEG spectral bands. In the ECG-based detector, heart rate variability (HRV) is extracted and analyzed using a Matching-Pursuit and Wigner-Ville Distribution algorithm in order to effectively extract meaningful HRV features representative of seizure and non-seizure states. Two fusion systems are adopted to fuse the EEG- and ECG-based decisions. In the first system, EEG- and ECG-based decisions are directly fused to obtain a final decision. The second fusion system adopts an over-ride option that allows for the EEG-based decision to over-ride the fusion-based decision in an event that the detector observes a string of EEG-based seizure decisions. The proposed detectors exhibit an improved performance, with respect to sensitivity and detection latency, compared with the state-of-the-art detectors. Experimental results demonstrate that the second detector achieves a sensitivity of 100%, detection latency of 2.6 seconds, and a specificity of 99.91% for the MAJORITY fusion case. In addition, a novel method to calculate the amount of neural synchrony that exists between the channels of an EEG matrix is carried out. This method is based on extracting the condition number from multi-channel EEG at a particular time instant to indicate the level of neural synchrony at that particular time instant. The proposed method of neural synchrony calculation is implemented in two detection systems. The first system uses only neural synchrony as the feature for seizure classification whereas the second system fuses energy and synchrony based decision to make a final classification decision. Both systems show promising results when tested on a set of clinical patients

Unveiling the future of breast cancer assessment: a critical review on generative adversarial networks in elastography ultrasound

Elastography Ultrasound provides elasticity information of the tissues, which is crucial for understanding the density and texture, allowing for the diagnosis of different medical conditions such as fibrosis and cancer. In the current medical imaging scenario, elastograms for B-mode Ultrasound are restricted to well-equipped hospitals, making the modality unavailable for pocket ultrasound. To highlight the recent progress in elastogram synthesis, this article performs a critical review of generative adversarial network (GAN) methodology for elastogram generation from B-mode Ultrasound images. Along with a brief overview of cutting-edge medical image synthesis, the article highlights the contribution of the GAN framework in light of its impact and thoroughly analyzes the results to validate whether the existing challenges have been effectively addressed. Specifically, This article highlights that GANs can successfully generate accurate elastograms for deep-seated breast tumors (without having artifacts) and improve diagnostic effectiveness for pocket US. Furthermore, the results of the GAN framework are thoroughly analyzed by considering the quantitative metrics, visual evaluations, and cancer diagnostic accuracy. Finally, essential unaddressed challenges that lie at the intersection of elastography and GANs are presented, and a few future directions are shared for the elastogram synthesis research

Deep Learning Based Proactive Optimization for Mobile LiFi Systems with Channel Aging

This paper investigates the channel aging problem of mobile light-fidelity (LiFi) systems. In the LiFi physical layer, the majority of the optimization problems for mobile users are non-convex and require the use of dual decomposition or heuristics techniques. Such techniques are based on iterative algorithms, and often, cause a high processing delay at the physical layer. Hence, the obtained solutions are no longer optimal since the LiFi channels are evolving. In this paper, a proactive-optimization (PO) approach that can alleviate the LiFi channel aging problem is proposed. The core idea is to design a long-short-term-memory (LSTM) network that is capable of predicting posterior positions and orientations of mobile users, which can be then used to predict their channel coefficients. Consequently, the obtained channel coefficients can be exploited to derive near-optimal transmission-schemes prior to the intended service-time, which enables real-time service. Through various simulations, the performance of the designed LSTM model is evaluated in terms of prediction error and time, as well as its application in a practical LiFi optimization problem