An intrusion detection system serves as the backbone for providing high-level network security. Different forms of network attacks have been discovered and they continue to become gradually more sophisticated and complicated. With the wide use of internet-based applications, cyber security has become an important research area. Despite the availability of many existing intrusion detection systems, intuitive cybersecurity systems are needed due to alarmingly increasing intrusion attacks. Furthermore, with new intrusion attacks, the efficacy of existing systems depletes unless they evolve. The lack of real datasets adds further difficulties to properly investigating this problem. This study proposes an intrusion detection approach for the modern network environment by considering the data from satellite and terrestrial networks. Incorporating machine learning models, the study proposes an ensemble model RFMLP that integrates random forest (RF) and multilayer perceptron (MLP) for increasing intrusion detection performance. For analyzing the efficiency of the proposed framework, three different datasets are used for experiments and validation, namely KDD-CUP 99, NSL-KDD, and STIN. In addition, performance comparison with state-of-the-art models is performed which suggests that the RFMLP can detect intrusion attacks with high accuracy than the existing approaches.

Patients with cognitive difficulties and impairments must be given innovative wheelchair systems to ease navigation and safety in today’s technologically evolving environment. This study presents a novel system developed to convert a manual wheelchair into an electric wheelchair. A portable kit has been designed so that it may install on any manual wheelchair with minor structural changes to convert it into an electric wheelchair. The multiple modes include the Joystick module, android app control, and voice control to provide multiple features to multiple disabled people. The proposed system includes a cloud-based data conversion model for health sensor data to display on an android application for easy access for the caretaker. A novel arrangement of sensors has been applied according to the accurate human body weight distribution in a sitting position that has greatly enhanced the accuracy of the applied model. Furthermore, seven different machine learning algorithms are applied to compare the accuracy, i.e., KNN, SVM, logistic regression, decision tree, random forest, XG Boost, and NN. The proposed system uses force-sensitive resistance (FSR) sensors with prescribed algorithms incorporated into wheelchair seats to detect users’ real-time sitting positions to avoid diseases, such as pressure ulcers and bed sores. Individuals who use wheelchairs are more likely to develop pressure ulcers if they remain in an inappropriate posture for an extended period because the blood supply to specific parts of their skin is cut off owing to increased pressure. Two FSR configurations are tested using seven algorithms of machine learning techniques to discover the optimal fit for a high-efficiency and high-accuracy posture detection system. Additionally, an obstacle detection facility enables one to drive safely in unknown and dynamic environments. An android application is also designed to provide users of wheelchairs with the ease of selecting the mode of operation of the wheelchair and displaying real-time posture and health status to the user or caretaker.

Three-stub waveguides are common impedance matching devices which are essential for a wide range of high-power microwave applications. Based on equivalent circuit analysis method, this article presents a novel matching algorithm which can be applied for three-stub tuners to implement continuous, automatic and real-time impedance matching. Equivalent model of a stub waveguide is firstly established by using transmission lines in series and capacitors in parallel. Then, according to the proposed model, the mathematical expression of reflection coefficient at different stub depth is derived by using currently detected reflection coefficient and stub depth. Later, based on the derived expression, an algorithm is developed to determine the optimum insertion depths of the stubs when VSWR is minimum. The S ₁₁ parameters of the stub waveguide which are calculated by the method agree well with those measured by vector network analyser. Meanwhile, the algorithm is able to limit reflection coefficient under –10 dB and can easily be redesigned for waveguides of different sizes. Thus, it shows a promising future in industrial impedance matching applications.

This paper presents the design of a simple dual-band circularly polarized monopole antenna using characteristic mode analysis. First, a dual-band elliptically polarized “L”-shaped monopole antenna with a partial ground is designed; then, a rectangular stub and a parasitic structure on the ground plane are implemented to achieve dual-band CP operation. To enhance impedance bandwidth and generate circular polarization in the upper band, the rectangular stub is attached to the “L”-shaped strip. The parasitic structure is employed for simultaneous dual-band CP radiation. Characteristic mode analysis is undertaken to predict the performance of the antenna before excitation. The modal analysis which is undertaken before excitation shows the natural modes that can be excited by the antenna structure to generate a dual-band CP response. The analysis gives approximate bandwidths that can be achieved by the antenna even before excitation. The overall dimension of the antenna , where is the corresponding free-space wavelength at 5.7 GHz. The measured -10 dB impedance bandwidth (ZBW) is realized to be 75.9% (4.5 GHz–10 GHz). The measured 3 dB axial ratio bandwidths (ARBW) at the lower and upper bands are 6% (5.6 GHz–5.95 GHz) and 28% (6.65 GHz–8.82 GHz), respectively. The proposed antenna features a simple and compact structure for Wi-Fi, WLAN, WiMAX, and C band applications.