Nonalcoholic fatty liver disease (NAFLD) is responsible for a wide range of pathological disorders. It is characterized by the prevalence of steatosis, which results in excessive accumulation of triglyceride in the liver tissue. At high rates, it can lead to a partial or total occlusion of the organ. In contrast, nonalcoholic steatohepatitis (NASH) is a progressive form of NAFLD, with the inclusion of hepatocellular injury and inflammation histological diseases. Since there is no approved pharmacotherapeutic solution for both conditions, physicians and engineers are constantly in search for fast and accurate diagnostic methods. The proposed work introduces a fully automated classification approach, taking into consideration the high discrimination capability of four histological tissue alterations. The proposed work utilizes a deep supervised learning method, with a convolutional neural network (CNN) architecture achieving a classification accuracy of 95%. The classification capability of the new CNN model is compared with a pre-trained AlexNet model, a visual geometry group (VGG)-16 deep architecture and a conventional multilayer perceptron (MLP) artificial neural network. The results show that the constructed model can achieve better classification accuracy than VGG-16 (94%) and MLP (90.3%), while AlexNet emerges as the most efficient classifier (97%).

Unmanned Aerial Vehicles (UAVs) or drones presently are enhanced with miniature sensors that can provide information relative to their environment. As such, they can detect changes in temperature, orientation, altitude, geographical location, electromagnetic fluctuations, lighting conditions, and more. Combining this information properly can help produce advanced environmental awareness; thus, the drone can navigate its environment autonomously. Wireless communications can also aid in the creation of drone swarms that, combined with the proper algorithm, can be coordinated towards area coverage for various missions, such as search and rescue. Coverage Path Planning (CPP) is the field that studies how drones, independently or in swarms, can cover an area of interest efficiently. In the current work, a CPP algorithm is proposed for a swarm of drones to detect points of interest and collect information from them. The algorithm’s effectiveness is evaluated under simulation results. A set of characteristics is defined to describe the coverage radius of each drone, the speed of the swarm, and the coverage path followed by it. The results show that, for larger swarm sizes, the missions require less time while more points of interest can be detected within the area. Two coverage paths are examined here—parallel lines and spiral coverage. The results depict that the parallel lines coverage is more time-efficient since the spiral increases the required time by an average of 5% in all cases for the same number of detected points of interest.

5G is the latest mobile communications standard that is spreading fast across the world. Recently defined requirements for 5G systems have led to higher applications’ requirements regarding data rates, lower requirements for latency, and higher efficiency regarding the spectrum usage. Universal Filtered Multi-Carrier (UFMC) is one new candidate modulation scheme for emergent Fifth Generation (5G) communication systems. This paper focuses on Universal Filtered Multi-Carrier (UFMC) design aspects in terms of Bit Error Rate (BER) performance in relation to the filter length used in subband filtering. Simulation results show that BER and CCDF performance varies for different filter lengths and modulation schemes. The main achievement of this work is that the results show that different Dolph–Chebyshev FIR filter lengths do not affect the BER performance both for the 64 and 256 QAM.

It has been shown in the literature that the lifetime of a wireless sensor network is heavily connected to the number of transmissions that network nodes have to undertake. Considering this finding, along with the effects of the energy hole problem where nodes closer to the sink node transmit more than the more distant ones, a node close to the sink node will be the one that transmits the most, while it will also be the node that will deplete its battery first. Taking into consideration that the failure of a single network node to operate, due to its battery being discharged, can lead to a network stopping its operation, the most energy-consuming node in the network will also be the one that will be responsible for the network’s termination. In this sense, the most energy-consuming node’s energy consumption optimization is the main case in this paper. More specifically, in this work, it is firstly shown that the energy consumption of a wireless sensor network is closely related to each network node’s traffic load, that is the transmissions of the packets that are created or forwarded by a node. The minimization of the most energy-consuming node’s energy consumption was studied here, while the implementation of a traffic-load-based algorithm is also proposed. Under the proposed algorithm, given a simple shortest path approach that assigns a parent (i.e., the next hop towards the sink node) in each network node and the knowledge it provides regarding the distance (in hops in this paper’s case) of network nodes from the sink node, the proposed algorithm exploits the shortest path’s results in order to discover, for all network nodes, neighbors that are of the same distance (from the sink node) with the initially assigned parent. Then, if such neighbors exist, all these neighbors are equally burdened with the parenting role. As a result, the traffic load is shared by all of them. To evaluate the proposed algorithm, simulation results are provided, showing that the goals set were achieved; thus, the network lifetime was prolonged. In addition, it is shown that under the algorithm, a fairer distribution of the traffic load takes place.

The emerging new paradigm under the fifth generation of wireless communications technologies (5G) and high expectations for massively expanding today’s Internet of Things (IoT) under 5G, are expected to support a large plurality of low-cost devices for an all-increasing number of new IoT applications. Many emerging IoT applications are going to take advantage of techniques and technologies that have high demands from low-cost devices in terms of processing large amounts of data and communication. For example, in systems based on fog computing technology, low-cost devices have to assign some of their limited resources for processing purposes. Considering the drawbacks emerging from using low-cost devices and the fact that many applications are in need for time-constrained approaches, TDMA-based Medium Access Control (MAC) policies need to be revisited and implemented in low-cost devices of today. In this sense, a policy independent of the underlying topology, TiMAC policy, is considered here and is implemented in low-cost devices using 433 MHz RF modules. Even though the implementation is limited by synchronization issues and a small number of nodes, the obtained experimental results demonstrate the potential for employing TDMA-based MAC policies on IoT systems consisting of low-cost devices.

In recent years, the scientific community has increasingly focused on state-of-the-art techniques, such as MIMO and mmWave transmission, aimed at enhancing the performance of telecommunication channels both quantitatively and qualitatively through various approaches. These efforts often rely on channel models designed to more accurately represent real-world conditions, thereby ensuring that the results are objective and practically applicable. In the present study, we employ one of the most scientifically reliable system- level simulators, Vienna SLS Simulator, to evaluate the performance of a wireless channel that we configure based on the latest standards (3GPP TR 36.873). We take into account the well-known non-symmetrical behavior of mMIMOs, where m stands for microwave MIMOs, in wireless communication systems and analyze the resulting changes in key performance metrics including average cell throughput, average user spectral efficiency and signal-to-interference-plus-noise ratio (SINR). We vary specific parameters such as transmission power, antenna polarization, ratio of indoor to outdoor users, and others with the aim of validating or challenging existing scientific assumptions. Particular attention is given to studying how variations in the aforementioned factors affect channel geometry and spatial uniformity, emphasizing the role of antenna geometry, polarization and user distribution in shaping channel asymmetries in mmWave MU-MIMO systems. Overall, this study provides insights into designing more balanced and efficient wireless systems in realistic urban environments.