Indoor positioning has become a very promising research topic due to the growing demand for accurate node location information for indoor environments. Nonetheless, current positioning algorithms typically present the issue of inaccurate positioning due to communication noise and interferences. In addition, most of the indoor positioning techniques require additional hardware equipment and complex algorithms to achieve high positioning accuracy. This leads to higher energy consumption and communication cost. Therefore, this paper proposes an enhanced indoor positioning technique based on a novel received signal strength indication (RSSI) distance prediction and correction model to improve the positioning accuracy of target nodes in indoor environments, with contributions including a new distance correction formula based on RSSI log-distance model, a correction factor (Beta) with a correction exponent (Sigma) for each distance between unknown node and beacon (anchor nodes) which are driven from the correction formula, and by utilizing the previous factors in the unknown node, enhanced centroid positioning algorithm is applied to calculate the final node positioning coordinates. Moreover, in this study, we used Bluetooth Low Energy (BLE) beacons to meet the principle of low energy consumption. The experimental results of the proposed enhanced centroid positioning algorithm have a significantly lower average localization error (ALE) than the currently existing algorithms. Also, the proposed technique achieves higher positioning stability than conventional methods. The proposed technique was experimentally tested for different received RSSI samples’ number to verify its feasibility in real-time. The proposed technique’s positioning accuracy is promoted by 80.97% and 67.51% at the office room and the corridor, respectively, compared with the conventional RSSI trilateration positioning technique. The proposed technique also improves localization stability by 1.64 and 2.3-fold at the office room and the corridor, respectively, compared to the traditional RSSI localization method. Finally, the proposed correction model is totally possible in real-time when the RSSI sample number is 50 or more.

Skin cancer is becoming increasingly common. Fortunately, early discovery can greatly improve the odds of a patient being healed. Many Artificial Intelligence based approaches to classify skin lesions have recently been proposed. but these approaches suffer from limited classification accuracy. Deep convolutional neural networks show potential for better classification of cancer lesions. This paper presents a fine-tuning on Xception pretrained model for classification of skin lesions by adding a group of layers after the basic ones of the Xception model and all model weights are set to be trained. The model is fine-tuned over HAM10,000 dataset seven classes by augmentation approach to mitigate the data imbalance effect and conducted a comparative study with the most up to date approaches. In comparison to prior models, the results indicate that the proposed model is both efficient and reliable.

The heart is one of the most vital organs in the human body therefore; its wellbeing and health are monitored continuously to determine the medical conditions of patients. The current paper presents a heart rate measuring device, which can also be used to measure oxygen saturation levels, and body temperature. All measured results have been analyzed by an Arduino platform to decide the patient’s health status. The obtained measurements were also monitored using an in-house mobile application with an integrated alarm system acquainted by both the therapist and patient. The design of this device is dependent on many types of platforms such as Arduino, Android and cloud server. This device can help the patients to check their health status and provide suggested course of action.

To design a multiband microstrip patch antenna, the Defected Ground Structure (DGS) technique is applied to add a disturbance effect in the surface current distribution and create a multi-resonance frequency. Furthermore, and in the aim to achieve a high gain, a high superstrate is added above the basic antenna design. The developed antenna is dedicated to the 5G sub 6GHz band application. The proposed antenna is based on RO5880 dielectric substrate of ɛ=2.2 and has an overall dimension of 77×70.11×1.6 mm3. The antenna operates at a sub 6GHz frequency range (at 4.53 to 4.97 GHz) and fits in 5G band application standard. Using CST Studio Suite Electromagnetics (EM) Solver, antenna’s performances are investigated; an average gain of 5 dB with acceptable radiation efficiency is obtained at the operating frequencies, suitable for sub-6GHz 5G application. The proposed antenna is fabricated, and experimental analysis is conducted using ROHDE & SCHWARZ ZVB20 network analyser, which shows a good agreement with the simulation analysis.

The simultaneous imaging and tracking of drones and small UAVs is one of the most challenging problems in inverse synthetic aperture radar (ISAR) signal processing and has received significant attention recently. Wide-angle ISAR imaging may offer better resolution and richer geometrical features. Aiming at this issue, we propose a new algorithm for multiple extended drone targets (EDTs) detection and tracking from wide-angle ISAR images. Since a conventional tracking algorithm using a random matrices model (RMM) is not accurate enough to reconstruct a drone's extension by one ellipse, an appropriate technique needs to be developed for ISAR imaging and tracking of EDTs. This paper models the EDT's extension as multiple ellipses with a time-varying orientation angle and solves the resulting interference problem involving data association between the nonlinear measurements and sub-ellipses. Recently, the multi-Bernoulli (MB) filter for extended targets has been proposed with the RMM approach for linear ellipsoidal target tracking by additional state variables. However, the implementation of the MB-RMM filter for nonlinear non-Gaussian models and the Doppler effect has not been presented, especially for ISAR images. We consider linearizing the nonlinear observations to solve the nonlinearity due to the Doppler effect, target motion, and wide-angle ISAR images. We presented a new robust Sub-RMM-MB-TBD filter to obtain the estimated orientation of EDT's shape after using the range-only motion compensation that uses both range and Doppler information. Moreover, the authors applied the k-space algorithm to generate ISAR image. Simulation results demonstrate this remarkable performance.