Spectral imaging is achieved effectively with the fast and readily portable Spectral Filter Array cameras. Camera-captured image texture classification, typically dependent on a preceding demosaicking process, is highly susceptible to the quality of the demosaicking stage. This study scrutinizes the texture categorization methods when implemented directly on the raw image. A Convolutional Neural Network was trained, and its classification outcomes were benchmarked against the performance of the Local Binary Pattern method. The HyTexiLa database's real SFA images of the objects form the foundation of this experiment, contrasting with the frequently employed simulated data. The role of integration time and light conditions is also studied to assess the performance of the classification approaches. Compared to other texture classification techniques, the Convolutional Neural Network excels in accuracy, even with a small amount of training data. Moreover, we exhibited the model's capacity to adjust and expand its functionality in response to environmental variables like illumination and exposure, outperforming other methodologies. To elucidate these outcomes, we scrutinize the extracted attributes of our methodology and demonstrate the model's capacity to discern diverse shapes, patterns, and markings across varying textures.
By adopting smart technologies within different industrial components, the economic and environmental consequences of industrial processes can be reduced. This work showcases tube smartening through the direct creation of a copper (Cu)-based resistive temperature detector (RTD) on their external surfaces. The investigation focused on copper depositions at temperatures ranging from room temperature to 250°C. The investigation employed mid-frequency (MF) and high-power impulse magnetron sputtering (HiPIMS). After a shot blasting process, the stainless steel tubes were subsequently coated with an inert ceramic layer on the exterior. Improving the sensor's adhesion and electrical properties was the aim of the Cu deposition process, executed at approximately 425 degrees Celsius. To formulate the Cu RTD's pattern, a photolithography procedure was undertaken. A silicon oxide film, deposited via sol-gel dipping or reactive magnetron sputtering, shielded the RTD from external degradation. For evaluating the sensor's electrical behaviour, a custom test setup was established. This setup combined internal heating with external temperature readings from a thermographic camera. The copper RTD's electrical properties demonstrate a high degree of linearity (R-squared value exceeding 0.999) and remarkable repeatability (confidence interval less than 0.00005), as per the results.
A micro/nano satellite remote sensing camera's primary mirror design must account for the need for lightweight materials, high stability, and resilience to high temperatures. This paper investigates and validates, through experimentation, the optimized design of the space camera's 610mm-diameter primary mirror. The coaxial tri-reflective optical imaging system's requirements were used to determine the design performance index for the primary mirror. In view of its exceptional and thorough performance characteristics, silicon carbide, or SiC, was designated as the primary mirror material. The initial structural parameters of the primary mirror were resultant of the traditional empirical design method's application. Improvements in SiC material casting and complex structure reflector technology resulted in an improved initial primary mirror structure, achieved by integrating the flange directly into the primary mirror body design. By acting directly upon the flange, the support force modifies the transmission path from the traditional back plate. This design feature guarantees the primary mirror's surface accuracy endures for extended periods under conditions of shock, vibration, and temperature variations. To optimize the initial structural parameters of the improved primary mirror and its flexible hinge, a parametric optimization algorithm derived from compromise programming was applied. Finite element simulation was then used to analyze the optimized primary mirror assembly. Under simulated conditions of gravity, a 4°C temperature increase, and an assembly error of 0.01mm, the root mean square (RMS) surface error was determined to be below the threshold of 50, equivalent to 6328 nm. The primary mirror's weight is precisely 866 kilograms. The primary mirror assembly's utmost displacement is capped at a value less than 10 meters, coupled with a maximum inclination angle less than 5 degrees. A fundamental frequency of 20374 Hz is present. check details Precision manufacture and assembly of the primary mirror assembly culminated in a ZYGO interferometer test, which indicated a surface shape accuracy of 002. The primary mirror assembly's vibration test was carried out with a fundamental frequency of 20825 Hz. The optimized primary mirror assembly's design, corroborated by simulation and experimental results, successfully meets the space camera's design requirements.
This research details a hybrid frequency shift keying and frequency division multiplexing (FSK-FDM) technique for incorporating information into dual-function radar and communication (DFRC) designs, enabling a superior communication data rate. Because existing works primarily concentrate on the transmission of just two bits per pulse repetition interval (PRI) utilizing amplitude modulation (AM) and phase modulation (PM), this paper advances a new method that effectively doubles the data rate by incorporating a hybrid frequency-shift keying and frequency-division multiplexing technique. The radar's sidelobe region necessitates the application of AM-based methods for appropriate communication reception. Unlike other approaches, prime-method techniques exhibit improved performance when the communication receiver is situated within the main beam. While a different design was proposed, it facilitates the delivery of information bits to receivers with superior bit rate (BR) and bit error rate (BER), irrespective of their location within the radar's main lobe or side lobe areas. The proposed scheme incorporates FSK modulation for encoding information, structured according to the transmitted waveforms and frequencies. Finally, the modulated symbols are integrated through FDM to obtain a double data rate. In the final analysis, a single transmitted composite symbol encompasses multiple FSK-modulated symbols, resulting in a faster data rate for the communication receiving unit. The effectiveness of the proposed technique is demonstrated through a compilation of simulation results.
A surge in renewable energy deployment usually results in a reorientation of the power systems community's perspective, from conventional grid models to the more comprehensive smart grid approach. This transitional phase demands comprehensive load forecasting across diverse time spans, a crucial element in electric grid network planning, operation, and maintenance. Employing a novel technique, this paper presents a mixed power-load forecasting system for multiple future periods, from 15 minutes ahead to 24 hours. The proposed approach is built upon a pool of models, trained with varied machine learning techniques including, but not limited to, neural networks, linear regression, support vector regression, random forests, and sparse regression. The final prediction values emerge from an online decision system that assigns weights to individual models based on their past performance records. The proposed scheme's performance was assessed against real-world electrical load data from a high-voltage/medium-voltage substation. The results show high effectiveness, with R2 coefficients varying from 0.99 to 0.79 for different prediction horizons, ranging from 15 minutes to 24 hours, respectively. Against a backdrop of advanced machine learning approaches and a unique ensemble method, the proposed method demonstrates highly competitive predictive accuracy.
The increasing appeal of wearable technology is driving a significant surge in consumer purchases of these devices. This technology is advantageous because it streamlines a variety of daily activities, making them simpler. In spite of this, the data they collect, being sensitive in nature, exposes them to the machinations of cybercriminals. Due to the large number of attacks on wearable devices, manufacturers are under pressure to bolster the protection of these devices. access to oncological services Bluetooth communication protocols are now riddled with a substantial number of vulnerabilities. In our examination of the Bluetooth protocol, we prioritize comprehending the security countermeasures adopted in its updated versions to address the most frequent security vulnerabilities. Six smartwatches were the targets of our passive attack, designed to detect vulnerabilities in their pairing procedures. Furthermore, our proposed requirements for maximum wearable device security include specifications for a minimum secure pairing process facilitated by Bluetooth connections.
The reconfiguration abilities of an underwater robot, enabling alterations during a mission, are crucial for confined space exploration and precise docking, showcasing the robot's versatility. Selecting appropriate robot configurations for a mission is possible, but this reconfigurability might incur higher energy costs. The key to extending the reach of underwater robots across vast distances lies in their energy-saving capabilities. faecal microbiome transplantation For a redundant system, the constraints on input must be factored into the control allocation procedure. This paper proposes an approach for optimizing energy consumption in a dynamically reconfigurable underwater robot, dedicated to karst exploration, through configuration and control allocation. The proposed method hinges on sequential quadratic programming, which optimizes an energy-equivalent metric. This optimization is subject to robotic constraints, specifically mechanical limitations, actuator saturation, and a dead zone. The optimization problem's resolution happens in each sampling instant. The simulation of underwater robots, specifically focused on path-following and station-keeping (observation), yielded results that attest to the efficiency of the method.