Within the vast expanse of computerized language processing, a revolutionary entity known as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to comprehend intricate linguistic patterns and conjure coherent and contextually fitting responses. Large language models (LLMs) are a type of artificial intelligence (AI) that have emerged as powerful tools for a wide range of tasks, including natural language processing (NLP), machine translation, and question-answering. This survey paper provides a comprehensive overview of LLMs, including their history, architecture, training methods, applications, and challenges. The paper begins by discussing the fundamental concepts of generative AI and the architecture of generative pre- trained transformers (GPT). It then provides an overview of the history of LLMs, their evolution over time, and the different training methods that have been used to train them. The paper then discusses the wide range of applications of LLMs, including medical, education, finance, and engineering. It also discusses how LLMs are shaping the future of AI and how they can be used to solve real-world problems. The paper then discusses the challenges associated with deploying LLMs in real-world scenarios, including ethical considerations, model biases, interpretability, and computational resource requirements. It also highlights techniques for enhancing the robustness and controllability of LLMs, and addressing bias, fairness, and generation quality issues. Finally, the paper concludes by highlighting the future of LLM research and the challenges that need to be addressed in order to make LLMs more reliable and useful. This survey paper is intended to provide researchers, practitioners, and enthusiasts with a comprehensive understanding of LLMs, their evolution, applications, and challenges. By consolidating the state-of-the-art knowledge in the field, this survey serves as a valuable resource for further advancements in the development and utilization of LLMs for a wide range of real-world applications. The GitHub repo for this project is available at https://github.com/anas-zafar/LLM-Survey

Radio over Fiber (RoF) is pivotal for extending reliable 5G connectivity to enhanced remote area communications (ERAC) use cases, that can be used for transporting analog signals from the central office to a simplified remote base station, composed only of an optical detector and radio-frequency front-end. However, the RoF link introduces undesired nonlinear effects that can severely degrade overall system performance and prohibitively increase the out-of-band emissions. We investigate and propose the use of reinforcement learning (RL) algorithms based digital predistortion (DPD) called as RLDPD method for linearizing next-generation Analog Radio over Fiber (A-RoF) links within the 5G landscape. We experimentally compare the proposed RLDPD with the conventional methods including generalized memory polynomial (GMP), canonical piecewise linearization (CPWL) and deep learning based convolutional neural networks (CNN). The experiment evaluation involves multiband 5G new radio (NR) flexible-waveform signals at 3 GHz and 10 GHz carrier signal transmitted over a 10 km single mode fiber length. The performance is compared in terms of error vector magnitude (EVM), adjacent channel leakage ratio (ACLR) and computation complexities. The RLDPD achieves a EVM of 2.85% for 5G NR waveform, surpassing GMP's 4.8%, CPWL's 3.5%, CNN's 3.08% and 11.25% without linearization, while also reducing ACPR by 19 dBc when compared to absence of linearization.

Heavy winds hinder the execution of essential tasks for Rotary Unmanned Aerial Vehicles (RUAVs) such as mountain rescue operations or civil engineering assessments during typical Northern Irish winters. This study uses Reinforcement Learning (RL) methods to select controller gains, enhancing RUAV stability under challenging wind conditions, employing a Deep Deterministic Policy Gradient (DDPG) agent over conventional and optimal controllers. The proposed DDPG agent enables the controller to be built as a “Black Box” approach, where the agent can adapt to slight changes or model uncertainty in a real system enabling a more robust controller. Simulations carried out on Full State Feedback, Full State Compensator and Linear Quadratic Gaussian controllers tuned by a variety of techniques revealed that RL out-performed conventional manual tuning by 26% and Particle Swarm Optimization by 19% in performance measured in settling time.

Data generated from sources such as wearable sensors, medical imaging, personal health records, pathology records, and public health organizations have resulted in a  massive information increase in the medical sciences over the last decade. Advances in computational hardware, such as cloud computing, Graphical Processing Units (GPUs), and Tensor  Processing Units (TPUs), provide the means to utilize these data.  Consequently, many Artificial Intelligence (AI)-based methods have been developed to infer from large healthcare data. Here,  we present an overview of recent progress in artificial intelligence  and biosensors in medical and life sciences. We discuss the role  of machine learning in medical imaging, precision medicine,  and biosensors for the Internet of Things (IoT). We review the  most recent advancements in wearable biosensing technologies  that use AI to assist in monitoring bodily electro-physiological  and electro-chemical signals and disease diagnosis, demonstrating  the trend towards personalized medicine with highly effective, inexpensive, and precise point-of-care treatment. Furthermore,  an overview of the advances in computing technologies, such as  accelerated artificial intelligence, edge computing, and federated  learning for medical data, are also documented. Finally, we investigate challenges in data-driven AI approaches, the potential  issues that biosensors and IoT-based healthcare generate, and the distribution shifts that occur among different data modalities,  concluding with an overview of future prospects

Mobile edge computing (MEC) seems to be highly efficient to process the generated data from IoT devices by providing computational resources locating in close range to network edge. MEC can be promising in reduction of latency and consumption of energy from data transmissions from offloading computational tasks from IoT devices to nearby edge servers. In this paper, a computation offloading optimization algorithm is proposed which is based on deep deterministic policy gradient for realistic Aurelia X6 Pro unmanned aerial vehicle (UAV)-assisted MEC systems. The proposed algorithm optimizes the offloading decision for UAVs by taking task characteristics and the communication environment into consideration. The simulation yields outcomes indicating that the suggested algorithm can considerably enhance the competency of MEC systems.