Design of H-/H∞ based fault detection filter for linear uncertain systems using linear matrix inequalities

One of the significant challenges in model-based fault detection is achieving robustness against disturbances and model uncertainties while ensuring sensitivity to faults. This study proposes an optimized approach for designing fault detection filters for discrete-time linear systems with norm-bounded model uncertainties. The design leverages the H-/H∞ optimization framework and is expressed through linear matrix inequality constraints. The filter is designed to produce a residual signal that balances two opposing objectives: minimizing the impact of disturbances and model uncertainties while maximizing fault sensitivity. The effectiveness of the proposed method is demonstrated through simulations involving sensor and actuator fault detection in the well-known three-tank system. Simulation results illustrate the method's ability to maintain robustness against disturbances and uncertainties while effectively detecting faults in the system

Position and orientation analysis of Jupiter robot arm for navigation stability

Jupiter robot has made a great impact in the educational field with its support for autonomous navigation, visual perception, and many other features from its artificial intelligence platform's learning box. This study undertakes a kinematic model design of Jupiter's arm to aid the robot's motion stability. This process involved the determination of a homogeneous transformation matrix, followed by the determination of orientation, position, and Euler angles. Ultimately, the homogeneous transformation matrix was successfully derived, and the simplification of direct kinematic matrices was achieved. Consequently, the kinematic analysis for Jupiter's arm was established using the position Denavit–Hartenberg method, orientation, and Euler angles, proving to be valuable in the context of this research

Optimizing robot anomaly detection through stochastic differential approximation and Brownian motion

This paper presents an adaptive approximation method for detecting anomalous patterns in extensive data streams gathered by mobile robots operating in rough terrain. Detecting anomalies in such dynamic environments poses a significant challenge, as it requires continuous monitoring and adjustment of robot movement, which can be resource intensive. To address this, a cost-effective solution is proposed that incorporates a threshold mechanism to track transitions between different regions of the data stream. The approach utilizes stochastic differential approximation (SDA) and optimistic optimization of Brownian motion to determine optimal parameter values and thresholds, ensuring efficient anomaly detection. This method focuses on minimizing the movement cost of the robots while maintaining accuracy in anomaly identification. By applying this technique, robots can dynamically adjust their movements in response to changes in the data stream, reducing operational expenses. Moreover, the temporal performance of the data stream is prioritized, a key factor often overlooked by conventional search engines. This paper demonstrates how the approach enhances the precision of anomaly detection in resource-constrained environments, making it particularly beneficial for real-time applications in rugged terrains

IoT-based cricket environment system to maximize egg production and reduce mortality rate

The deployment of Internet of things (IoT) technologies presents an opportunity to improve efficiency in cricket farming. This study investigates the implementation of an IoT-based system utilizing an ESP32 microcontroller, a suite of environmental sensors, and actuators. The system is supported by a ThingsBoard dashboard for data visualization and a Telegram bot for notifications. The setup was tested on a single cricket cage over a 28-day period and compared against a control group. Each cage contained 20 male and 100 female Cliring crickets. Key parameters analyzed included temperature, humidity, soil moisture, egg yield, food conversion ratio (FCR), and mortality rate. Findings show that the IoT-enabled cage consistently maintained optimal environmental conditions—temperature (20 to 32 °C), humidity (65% to 85%), and soil moisture (60% to 80%)—unlike the control, which experienced greater variability. The IoT cage yielded 87.28 grams of eggs, a 33.33% improvement over the control's 65.46 grams. Additionally, FCR improved from 2.53 to 2.01 grams per egg, and mortality rate dropped from 0.816 to 0.708. These results underscore the effectiveness of IoT systems in enhancing environmental stability, productivity, and survival rates in small- to medium-scale cricket farming operations

LoRa-enabled remote-controlled surveillance robot for monitoring and navigation in disaster response missions

Rescue missions must be conducted within a strict timeframe, and the safety of all rescuers and civilians is prioritized. The proposed system aims to design a remote-operated aerial surveillance robot for disaster-affected areas for search and rescue missions. Real-time video transmission and RS-232 long-range communication enable operators to navigate rough environments and monitor data collected in real-time. This powerful tool ensures the protection of human life while collecting accurate and meaningful data. Cloud storage for data and surveillance strengthens the system, preventing part failure and fostering collaboration among users. This is a significant step towards using Internet of Things systems alongside remote-controlled robots in disaster response. The robot's key contribution to disaster management is identifying the environment, addressing issues of no visibility, complicated terrains, and speed. Its modification and expansion capabilities make it useful in armed surveillance, industrial monitoring, and environmental studies, making it an important innovation for many other fields

VotTomNet: Voting-based tomato disease diagnosis with transfer learning

The research presents an advanced automation system, termed VotTomNet, designed for diagnosing tomato leaf diseases using transfer learning, and soft and hard voting ensemble techniques. By leveraging six pre-trained deep learning convolutional neural networks—VGG16, InceptionNet, ResNet, MobileNet, EfficientNet, and DenseNet—the system achieved an impressive accuracy of 99.2%. These models were meticulously fine-tuned to diagnose multiple types of tomato diseases with heightened precision. The integration of a soft and hard voting mechanism further enhanced the overall diagnostic accuracy by combining the strengths of these diverse models into a powerful ensemble. The findings underscore the robustness, reliability, and effectiveness of this ensemble technique, marking a significant advancement in precision agriculture and crop health assessment. By outperforming traditional methods, this approach offers a more practical and efficient solution for large-scale agricultural applications, enabling comprehensive crop management and improved yield. In conclusion, this research lays a strong foundation for future innovations in automated plant disease diagnosis and agricultural technology. Its contributions have the potential to revolutionize disease management, reduce crop losses, and ultimately enhance food security on a global scale

Evaluating the development and cutting capacity of a one-square computer numeric controlled milling machine

Traditional subtractive technology is rapidly losing significance with the advent of digital manufacturing technologies, which offer affordable machining with high accuracy and repeatability. Computer numeric controlled (CNC) machining has been around for a while; however, it has been costly to own one. Since the concept of CNC machining is now broadly understood and open-source software is available for control, designers can make use of available local materials to develop cheaper CNC machines. Hence, this presents the evaluation of the design and development of a one-square-meter CNC milling machine. The control was implemented on Arduino Uno, while open-source Universal G-code Sender (UGS) and G-code reference block library (GRBL) were used for the G-code generation and machine control, respectively. The built CNC was calibrated and tested on wood and plastic materials, and the resulting products were acceptable in accuracy up to ±0.02 mm in the first trial, but attained perfect accuracy by the third trial. Multiple tests repeatedly showed that accuracy was maintained. Since the machine is reconfigurable, future work entails automation and incorporating laser cutting capabilities into the machine

NAPLAM: a novel ledger-based algorithm for detection and mitigation of sinkhole attacks in routing protocol for low power and lossy networks-based Internet of things

The Internet of Things (IoT) is a network of connected physical objects that collect and share data over the Internet. However, routing attacks can disrupt data exchange, especially multi-node sinkhole attacks in low power and lossy IoT networks (LLNs). To support communication in LLN IoT, the IPv6-based routing protocol for LLNs (RPL) is used. Despite having several advantages, RPL also faces challenges like being vulnerable to attacks, having limited resources, compatibility, and scalability issues. Additionally, traditional security methods often do not work well for LLN-IoT devices because they lack the necessary computing power. To overcome these challenges, we have proposed a novel ledger-based framework called network and packet ledger to ascertain malicious devices using routing protocol for LLN (NAPLAM-RPL). This framework can effectively detect and mitigate multi-node sinkhole attacks in IoT networks. This paper also compares NAPLAM-RPL with similar protocols using the NetSim Simulator. The experimental analysis shows that NAPLAM-RPL improves network performance and outperforms existing methods like RF-trust, SoS-RPL, INTI, C-TRUST, and heartbeat algorithm in crucial areas, including packet delivery rate (PDR), throughput, End-to-End (E2E) delay, energy consumed, and detection accuracy

Performance comparison of optical flow and background subtraction and discrete wavelet transform methods for moving objects

Self-driving cars and other autonomous vehicles rely on systems that can recognize and follow objects. The ways help people make safe decisions and navigate by showing things like people, cars, obstacles, and traffic lights. Computer vision algorithms encompass both object detection and tracking. Different methods are specifically developed for picture or video analysis not only to identify items within the visual content but also to accurately determine their precise locations. This can operate independently as an algorithm or as a constituent of an item-tracking system. Object tracking algorithms can be used to follow objects over video frames, providing a contrasting approach. The research article focuses on the mathematical model simulation of optical flow, background subtraction, and discrete wavelet transform (DWT) methods for moving objects. The performance evaluation of the methods is done based on simulation response time, accuracy, sensitivity, and specificity doe several images in different environments. The DWT has shown optimal behavior in terms of the response time of 0.27 seconds, accuracy of 95.34 %, selectivity of 95.96 %, and specificity of 94.68 %

Optimization model for endurance performance of electric rotorcraft transport drones and its application prospects

The operational parameter configuration and performance optimization of electric rotorcraft transport unmanned aerial vehicles (UAVs) currently lack comprehensive guiding theory, impacting UAV endurance and efficiency, thereby limiting industry growth. This paper analyzes factors affecting UAV endurance and establishes a hover endurance model for electric rotorcraft transport UAVs through theoretical derivation and testing. Based on this model, we introduce the concepts of thrust redundancy coefficient and load cut-off line, proposing an optimal endurance configuration theory. This theory categorizes the parameter configuration range into light load, ideal configuration, load cut-off, and endurance saturation zones. Using current operational parameters, we evaluate and optimize UAV performance. Verification results demonstrate high model accuracy, with error rates ranging from 1.89% to 5.69%. After optimization, the payload capacities of two transport UAVs increased by 6.25%, and their endurance improved by 6.97% and 9.5%, respectively, enhancing overall efficiency. This model provides a solid framework for assessing endurance capabilities and offers targeted optimization suggestions, making it crucial for improving UAV performance