Asymmetric 4.77 Three-Way Unequal Filtering Power Divider/Combiner for Communication Systems Application

This study presents a novel three-way unequal filtering power divider/combiner, addressing challenges in unequal power distribution while incorporating filtering functions in communication systems. Wilkinson power divider (WPD) is the traditional power division approach using quarter-wavelength transmission lines [1]. This type of power divider is popularly used in communication systems due to its good electrical isolation and simple structure. The problem with WPD is that its operation requires the use of an externally connected bandpass filter (BPF) to achieve filtering functionality. This leads to increased footprint and increased loss coefficients in a system. In contrast to the traditional design approach involving a BPF, a matching transmission line, and a Wilkinson power divider as separate components, the proposed integrated filtering power divider (FPD) consolidates all three components into a single device, leading to lower footprint and lower loss coefficient in a system. Circuit modelling and electromagnetic (EM) simulations were conducted to ensure alignment between theoretical and practical results. The design demonstrates effective unequal power division at the three output ports while maintaining very good filtering performance. Results show a return loss better than 15 dB and a minimum insertion loss of 1.2 dB. The overall size of the device is 32.2 x 50.0 mm. This paper contributes to advancements in power divider design by addressing unequal power division challenges and integrating filtering functions. The findings offer a foundation for future developments in advanced power divider/combiner systems, with insights into potential challenges and areas for further improvements.

Detecting and Preventing Data Poisoning Attacks on AI Models

This paper investigates the critical issue of data poisoning attacks on AI models, a growing concern in the ever-evolving landscape of artificial intelligence and cybersecurity. As advanced technology systems become increasingly prevalent across various sectors, the need for robust defence mechanisms against adversarial attacks becomes paramount. The study aims to develop and evaluate novel techniques for detecting and preventing data poisoning attacks, focusing on both theoretical frameworks and practical applications. Through a comprehensive literature review, experimental validation using the CIFAR-10 and Insurance Claims datasets, and the development of innovative algorithms, this paper seeks to enhance the resilience of AI models against malicious data manipulation. The study explores various methods, including anomaly detection, robust optimization strategies, and ensemble learning, to identify and mitigate the effects of poisoned data during model training. Experimental results indicate that data poisoning significantly degrades model performance, reducing classification accuracy by up to 27% in image recognition tasks (CIFAR-10) and 22% in fraud detection models (Insurance Claims dataset). The proposed defence mechanisms, including statistical anomaly detection and adversarial training, successfully mitigated poisoning effects, improving model robustness and restoring accuracy levels by an average of 15-20%. The findings further demonstrate that ensemble learning techniques provide an additional layer of resilience, reducing false positives and false negatives caused by adversarial data injections.

Energy Consumption of Robotic Arm with the Local Reduction Method

Energy consumption in robotic arms is a significant concern in industrial automation due to rising operational costs and environmental impact. This study investigates the use of a local reduction method to optimize energy efficiency in robotic systems without compromising performance. The approach refines movement parameters, minimizing energy use while maintaining precision and operational reliability. A three-joint robotic arm model was tested using simulation over a 30-second period for various tasks, including pick-and-place and trajectory-following operations. The results revealed that the local reduction method reduced energy consumption by up to 25% compared to traditional techniques such as Model Predictive Control (MPC) and Genetic Algorithms (GA). Unlike MPC, which requires significant computational resources, and GA, which has slow convergence rates, the local reduction method demonstrated superior adaptability and computational efficiency in real-time applications. The study highlights the scalability and simplicity of the local reduction approach, making it an attractive option for industries seeking sustainable and cost-effective solutions. Additionally, this method can integrate seamlessly with emerging technologies like Artificial Intelligence (AI), further enhancing its application in dynamic and complex environments. This research underscores the potential of the local reduction method as a practical tool for optimizing robotic arm operations, reducing energy demands, and contributing to sustainability in industrial automation. Future work will focus on extending the approach to real-world scenarios and incorporating AI-driven adjustments for more dynamic adaptability.

Design of a Rectangular Linear Microstrip Patch Antenna Array for 5G Communication

This paper presents the design and characterization of a rectangular microstrip patch antenna array optimized for operation within the Ku-band frequency range. The antenna array is impedance-matched to 50 Ohms and utilizes a microstrip line feeding mechanism for excitation. The design maintains compact dimensions, with the overall antenna occupying an area of 29.5x7 mm. The antenna structure is modelled on an R03003 substrate material, featuring a dielectric constant of 3, a low-loss tangent of 0.0009, and a thickness of 1.574 mm. The substrate is backed by a conducting ground plane, and the array consists of six radiating patch elements positioned on top. Evaluation of the designed antenna array reveals a resonant frequency of 18GHz, with a -10 dB impedance bandwidth extending over 700MHz. The antenna demonstrates a high gain of 7.51dBi, making it well-suited for applications in 5G and future communication systems. Its compact form factor, cost-effectiveness, and broad impedance and radiation coverage further underscore its potential in these domains.