Centralized Control for Dynamic Channel Allocation in IEEE 802.15.4 Based Wireless Sensor Networks

Coexistence problem is one of the most important issues in the IEEE 802.15.4 based Wireless Sensor Networks (WSNs), since the system operates on the highly populated 2.4 GHz ISM band. As a result, system performance of WSNs can be greatly impaired by the interference from over powering signal from other systems such as WLAN and Bluetooth. This paper proposes an approach based on centralized control for dynamic channel allocation. The proposed method offers multi-channel utilization with intelligent controlling mechanism in order to provide system performance enhancement in order to cope with variation of interfered environment. Based on centralized control, decision making process is performed by the network coordinator allowing such system flexibility. Simulation model has been developed and it is embedded with this proposed mechanism in order to test the system performance. To observe the system performance under the proposed method, variety of simulation scenarios are performed with the variation of two major factors affecting system performance including the size of the network topology and the scale of interference. Proposed method is evaluated and the simulation results are compared against tradition system as well as system with multi-channel utilization method with channel scheduling. The flexibility of the method proposed here allows the system to have better system performance under different test scenarios both in terms of average packet end-to-end delay and system throughput

Comparative Study of Takagi-Sugeno-Kang and Madani Algorithms in Type-1 and Interval Type-2 Fuzzy Control for Self-Balancing Wheelchairs

This study examines the effectiveness of four different fuzzy logic controllers in self-balancing wheelchairs. The controllers under consideration are Type-1 Takagi-Sugeno-Kang (TSK) FLC, Interval Type-2 TSK FLC, Type-1 Mamdani FLC, and Interval Type-2 Mamdani FLC. A MATLAB-based simulation environment serves for the evaluation, focusing on key performance indicators like percentage overshoot, rise time, settling time, and displacement. Two testing methodologies were designed to simulate both ideal conditions and real-world hardware limitations. The simulations reveal distinct advantages for each controller type. For example, Type-1 TSK excels in minimizing overshoot but requires higher force. Interval Type-2 TSK shows the quickest settling times but needs the most force. Type-1 Mamdani has the fastest rise time with the lowest force requirement but experiences a higher percentage of overshoot. Interval Type-2 Mamdani offers balanced performance across all metrics. When a 2.7 N control input cap is imposed, Type-2 controllers prove notably more efficient in minimizing overshoot. These results offer valuable insights for future design and real-world application of self-balancing wheelchairs. Further studies are recommended for the empirical testing and refinement of these controllers, especially since the initial findings were limited to four-wheeled self-balancing robotic wheelchairs

Exploring ResNet-18 Estimation Design through Multiple Implementation Iterations and Techniques in Legacy Databases

In a rapidly evolving landscape where automated systems and database applications are increasingly crucial, there is a pressing need for precise and efficient object recognition methods. This study contributes to this burgeoning field by examining the ResNet-18 architecture, a proven deep learning model, in the context of fruit image classification. The research employs an elaborate experimental setup featuring a diverse fruit dataset that includes Rambutan, Mango, Santol, Mangosteen, and Guava. The efficacy of single versus multiple ResNet-18 models is compared, shedding light on their relative classification accuracy. A unique aspect of this study is the establishment of a 90% decision threshold, introduced to mitigate the risk of incorrect classification. Our statistical analysis reveals a significant performance advantage of multiple ResNet-18 models over single models, with an average improvement margin of 15%. This finding substantiates the study’s central hypothesis. The implemented 90% decision threshold is determined to play a pivotal role in augmenting the system’s overall accuracy by minimizing false positives. However, it’s worth noting that the increased computational complexity associated with deploying multiple models necessitates further scrutiny. In sum, this study provides a nuanced evaluation of single and multiple ResNet-18 models in the realm of fruit image classification, emphasizing their utility in practical, real-world applications. The research opens avenues for future exploration by refining these methodologies and investigating their applicability to broader object recognition tasks

Building the pelvic endometriosis knowledge base software

The objective of this study is to develop a software-based medical expert system supporting the diagnosis of pelvic endometriosis. This system was developed to facilitate the creation of knowledge and inference engine. The diagnostic process used the interactive backward chaining inference algorithm. The medical knowledge data base was represented as production rules which represented in tree structures. The system was designed to interact with users in question information format. The clinical data from medical records of Gynecological out-patient clinic at HRH Maha Chakri Sirindhorn Medical Center were applied to the system by physician retrospectively. In this study, 35 medical records of women diagnosed with pelvic endometriosis were reviewed. The three most common presenting symptoms were dysmenorrhea, chronic pelvic pain and infertility, respectively. All of the patients were investigated with transvaginal sonography. Twenty-one patients had no histological studies. The clinical data of 30 patients accounted for 85.7 % were recorded successfully to the medical expert system. The diagnosis of these patients from the system corresponded with the previous data from the medical records of established pelvic endometriosis. Taken together, these data suggest that this medical expert system is a good tool to facilitate the decision making process in the diagnosis of pelvic endometriosis

Development of a System for Transmitting Medical Data and Diagnostic Images via the Internet for Hospitals in Thailand

This research project aims to advance healthcare data and diagnostic imaging delivery by employing a digital Picture Archiving and Communication System, also known as PACS, alongside a web application that is internet accessible. The study explores two main areas, which are User Interface design and system functionality. The User Interface design merges the aesthetic of traditional paper documentation with the advantages of electronic displays to enhance both readability and overall user experience. The system functionality has been assessed and confirmed to offer secure and efficient transmission of medical diagnostic data as well as radiographic images. By complying with the Personal Data Protection Act of 2019 or PDPA, the system ensures the safe handling of confidential personal information. Test results show that the system meets performance standards and has been well-received by users. The project serves as a timely solution for today's medical industry, focusing on the speed and security of diagnostic data and image transmission