Breast cancer is a medical condition characterized by the uncontrolled proliferation of cells in the breast tissue. Breast cancer can originate from various parts of the breast, and methods such as Breast ultrasound, Diagnostic mammogram, Breast magnetic resonance imaging, and Biopsy are currently used for its diagnosis. However, these methods have certain limitations, and their size can be a hindrance. To overcome this, low-power, flexible antennas can be designed for bio-communication between medical equipment and external instrumentation. Flexible and wearable antennas have advantages such as affordability, ease of fabrication, and high gain. In this article, a microstrip patch antenna operating at 2.45 GHz and made of polyamide material is designed using High Frequency Structure Simulator software. The simulation results show the patch antenna has a gain of 1, −14.81 dB return loss at 2.45 GHz based on |S11| ≤ −10 dB. The directive radiation pattern with axial ratio of 63.39 dB and voltage standing wave ratio ≤3. Furthermore, the hardware development of proposed antenna with polyamide substrate provides the resonance frequency nearing to simulation results as 2.318 GHz with return loss of −28.19 dB. Based on mathematical analysis, simulation and hardware results, the proposed antenna is a superior option for breast cancer detection.

The robot path planning and trajectory planning were studied and explored for many generations using algorithms. These studies explain to find most efficient path throughout the selected environment is really complicated. In this paper, different environments were created and the modified parameters in Dijkstra algorithm were implemented in a robot for finding an appropriate path for reaching the destination. The mobile robot model was developed using V-REP open-source simulation software and implemented the Dijkstra algorithm for identifying the sub-optimal and collision-free path. The simulated results were showing that the reduction method was effective in terms of time and velocity in the created environments for robot path planning.

Autonomous transportation is a new paradigm of an Industry 5.0 cyber-physical system that provides a lot of opportunities in smart logistics applications. The safety and reliability of deep learning-driven systems are still a question under research. The safety of an autonomous guided vehicle is dependent on the proper selection of sensors and the transmission of reflex data. Several academics worked on sensor-based difficulties by developing a sensor correction system and fine-tuning algorithms to regulate the system’s efficiency and precision. In this paper, the introduction of vision sensor and its scene terrain classification using a deep learning algorithm is performed with proposed datasets during sensor failure conditions. The proposed classification technique is to identify the mobile robot obstacle and obstacle-free path for smart logistic vehicle application. To analyze the information from the acquired image datasets, the proposed classification algorithm employs segmentation techniques. The analysis of proposed dataset is validated with U-shaped convolutional network (U-Net) architecture and region-based convolutional neural network (Mask R-CNN) architecture model. Based on the results, the selection of 1400 raw image datasets is trained and validated using semantic segmentation classifier models. For various terrain dataset clusters, the Mask R-CNN classifier model method has the highest model accuracy of 93%, that is, 23% higher than the U-Net classifier model algorithm, which has the lowest model accuracy nearly 70%. As a result, the suggested Mask R-CNN technique has a significant potential of being used in autonomous vehicle applications.

The loading and unloading operations of smart logistic application robots depend largely on their perception system. However, there is a paucity of study on the evaluation of Lidar maps and their SLAM algorithms in complex environment navigation system. In the proposed work, the Lidar information is finetuned using binary occupancy grid approach and implemented Improved Self-Adaptive Learning Particle Swarm Optimization (ISALPSO) algorithm for path prediction. The approach makes use of 2D Lidar mapping to determine the most efficient route for a mobile robot in logistical applications. The Hector SLAM method is used in the Robot Operating System (ROS) platform to implement mobile robot real-time location and map building, which is subsequently transformed into a binary occupancy grid. To show the path navigation findings of the proposed methodologies, a navigational model has been created in the MATLAB 2D virtual environment using 2D Lidar mapping point data. The ISALPSO algorithm adapts its parameters inertia weight, acceleration coefficients, learning coefficients, mutation factor, and swarm size, based on the performance of the generated path. In comparison to the other five PSO variants, the ISALPSO algorithm has a considerably shorter path, a quick convergence rate, and requires less time to compute the distance between the locations of transporting and unloading environments, based on the simulation results that was generated and its validation using a 2D Lidar environment. The efficiency and effectiveness of path planning for mobile robots in logistic applications are validated using Quanser hardware interfaced with 2D Lidar and operated in environment 3 using proposed algorithm for production of optimal path.

In this study, a heuristic algorithm is used to find an optimal route for smart logistics loading and unloading applications. Various environments, such as traditional building blocks, satellite images, terrain environments, and Google map environments are developed by converting into a binary occupancy grid and used to optimize the viable path in the smart mobile logistics application. The proposed autonomous vehicle (AV) route planning navigation approach is to forecast the AV’s path until it detects an imminent obstacle, at which point it should turn to the safest area before continuing on its route. To demonstrate the path navigation results of proposed algorithms, a navigational model is developed in the MATLAB/Simulink 2D virtual environment. The particle swarm optimization (PSO) method, the Bat search algorithm, and its proposed variants are used to identify a smooth and violation-free path for a given application environment. The proposed variants improve the algorithm’s effectiveness in finding a violation-free path while requiring less time complexity by using cubic spline curve interpolation and its improved constriction factor. Extensive simulation and benchmark validation results show the proposed standard PSO has a significantly shorter violation-free path, quick convergence rate and takes less time to compute the distance between loading and unloading environment locations than the cooperative coevolving PSO, Bat algorithm, or modified frequency Bat algorithms.