Employing this approach offers greater command over potentially adverse conditions, enabling a balanced compromise between well-being and energy efficiency targets.
Using the reflected light intensity modulation method and the concept of total reflection, a novel fiber-optic ice sensor is proposed in this paper to accurately identify and measure the characteristics of ice types and thickness, thereby addressing the inaccuracies inherent in current sensors. Employing ray tracing, the performance of the fiber-optic ice sensor was simulated. Icing tests conducted at low temperatures verified the functionality of the fiber-optic ice sensor. Studies demonstrate the ice sensor's ability to differentiate various ice types and measure their thickness ranging from 0.5 to 5 mm, under temperatures of -5°C, -20°C, and -40°C. The maximum observed error in measurement is 0.283 mm. Promising applications of the proposed ice sensor are evident in its ability to detect icing on both aircraft and wind turbines.
Within Advanced Driver Assist Systems (ADAS) and Autonomous Driving (AD), the detection of target objects relies on the use of advanced Deep Neural Network (DNN) technologies, which are essential for numerous automotive functions. Although effective, a critical problem with current DNN-based object detection is the high computational expense. Real-time inference of a DNN-based system on a vehicle is impeded by this particular requirement. The system's real-time deployment relies heavily on the combination of low response time and high accuracy within automotive applications. The computer-vision-based object detection system is implemented in real-time for automotive applications, as presented in this paper. Transfer learning, utilizing pre-trained DNN models, is employed to develop five separate vehicle detection systems. The DNN model that performed the best displayed a 71% increase in Precision, a 108% upswing in Recall, and an astounding 893% improvement in F1 score, surpassing the YOLOv3 model. Layers of the developed DNN model were fused horizontally and vertically to optimize it for deployment in the in-vehicle computing device. The optimized deep neural network model's deployment targets the embedded in-vehicle computing device, allowing real-time program execution. Optimized DNN model performance on the NVIDIA Jetson AGA showcases a remarkable 35082 fps rate, an astounding 19385 times faster than its unoptimized counterpart. The experimental findings corroborate that the optimized transferred DNN model achieves higher accuracy and a faster processing time for vehicle detection, which is imperative for ADAS system deployment.
The Smart Grid, bolstered by IoT, employs smart devices to gather consumer electricity data, transmitting it to service providers via the public network, thereby introducing novel security concerns. Ensuring the secure operation of smart grid communication networks hinges upon extensive research into authentication and key agreement protocols for enhanced protection from cyber threats. see more Unfortunately, most of them are exposed to a broad range of assaults. This paper examines the security of a prevailing protocol by considering the impact of an internal attacker, and concludes that the protocol's security claims cannot be validated under the given adversary model. We then offer an enhanced lightweight authentication and key agreement protocol for improving the security of smart grid systems that use IoT technology. Furthermore, we validated the scheme's security using the real-or-random oracle model's assumptions. The improved scheme's security was demonstrated against both internal and external attackers. In terms of both computational efficiency and security, the new protocol outperforms the original protocol, however the security aspect has been elevated. Both subjects had a reaction time of 00552 milliseconds, respectively. The new protocol's communication, at 236 bytes, is an acceptable measure for use within the smart grid environment. To put it differently, while preserving comparable communication and computation resources, we developed a more secure protocol specifically for smart grid applications.
5G-NR vehicle-to-everything (V2X) technology is critical for enhancing safety and enabling effective management of traffic data in the process of autonomous vehicle development. By exchanging traffic and safety data, 5G-NR V2X roadside units (RSUs) connect nearby vehicles, including future autonomous ones, bolstering traffic safety and efficiency. This paper presents a vehicular communication system, leveraging a 5G cellular network. The system utilizes roadside units (RSUs), comprised of base stations (BSs) and user equipment (UEs), to provide validated performance across diverse RSU deployments. biomimetic transformation This approach aims for optimal network usage and assures strong V2I/V2N connections between vehicles and every individual RSU. The 5G-NR V2X environment benefits from reduced shadowing, thanks to the collaborative access of base station and user equipment (BS/UE) RSUs, thus maximizing average vehicle throughput. The paper leverages diverse resource management techniques, including dynamic inter-cell interference coordination (ICIC), coordinated scheduling and coordinated multi-point (CS-CoMP), cell range extension (CRE), and three-dimensional beamforming, to satisfy stringent reliability demands. Simultaneous utilization of BS- and UE-type RSUs, as evidenced by simulation results, produces better outage probability, a smaller shadowing area, and enhanced reliability through reduced interference and elevated average throughput.
Images underwent continuous analysis to locate any cracks with persistent scrutiny. CNN models, with diverse architectures, were created and tested with the goal of precisely detecting or segmenting crack regions. However, the preponderant number of datasets investigated in prior works comprised demonstrably distinct crack imagery. No validation of previous methods encompassed blurry cracks in low-definition images. For this reason, a framework for locating obscured, vague areas of concrete cracks was presented in this paper. Small, square-shaped sections of the image, according to the framework, are sorted into categories of crack or non-crack. Well-known CNN models were used for classification tasks, and experimental comparisons were made. Furthermore, this paper delved into key factors, encompassing patch size and labeling procedures, which exerted considerable sway over training performance. Additionally, a succession of post-treatment procedures for assessing crack extents were introduced. Utilizing bridge deck images exhibiting blurred thin cracks, the performance of the proposed framework was assessed, yielding results comparable to those of expert practitioners.
A time-of-flight image sensor, specifically designed for hybrid short-pulse (SP) ToF measurements under strong ambient light conditions, is introduced using 8-tap P-N junction demodulator (PND) pixels. An 8-tap demodulator, incorporating multiple p-n junctions, shows high-speed demodulation in large photosensitive areas. This design efficiently modulates electric potential to transfer photoelectrons to eight charge-sensing nodes and charge drains. A 0.11 m CIS-based ToF image sensor, configured with a 120 (horizontal) x 60 (vertical) array of 8-tap PND pixels, effectively employs eight consecutive 10 ns time-gating windows. This demonstration marks the first successful implementation of long-range (>10 meters) ToF measurements under high ambient light utilizing only single frames, critical for eliminating motion artifacts from the ToF measurements. The presented paper also introduces an improved method for depth-adaptive time-gating-number assignment (DATA), capable of widening depth range, reducing ambient light influence, and rectifying nonlinearity errors. By implementing these techniques within the image sensor chip, hybrid single-frame time-of-flight (ToF) measurements were achieved. Depth precision reached a maximum of 164 cm (14% of the maximum range), while non-linearity error for the full 10-115 m depth range was limited to 0.6% under direct sunlight ambient light conditions of 80 klux. The linearity of depth in this study demonstrates a 25-fold improvement over the cutting-edge 4-tap hybrid ToF image sensor.
To enhance indoor robot path planning, a refined whale optimization algorithm is introduced, overcoming the shortcomings of the original approach, namely, slow convergence rate, limited pathfinding ability, low efficiency, and the tendency to get trapped in local shortest paths. To enhance the initial whale population and bolster the algorithm's global search proficiency, an enhanced logistic chaotic mapping is initially applied. Next, a nonlinear convergence factor is presented, and the equilibrium parameter A is modified to achieve a harmonious interplay between global and local search techniques within the algorithm, hence improving search effectiveness. The culmination of the Corsi variance and weighting strategy, fused together, modifies the whales' locations for improved path quality. Experimental comparisons of the enhanced logical whale optimization algorithm (ILWOA) with the WOA and four other enhanced whale optimization algorithms are performed, utilizing eight test functions and three raster map settings. The observed results indicate that, in the context of the test function, ILWOA demonstrates superior convergence and a strong propensity for merit-seeking. Experiments in path planning reveal that ILWOA's performance surpasses other algorithms when assessed across three evaluation factors: path quality, merit-seeking ability, and robustness.
Age is associated with a decline in both cortical activity and walking speed, which can put elderly people at greater risk for falling. Though age is acknowledged as a contributing factor to this deterioration, individual aging rates vary considerably. The present study sought to explore the impact of walking speed on the modulation of cortical activity within both the left and right hemispheres in the elderly population. Data on cortical activation and gait were gathered from fifty healthy senior citizens. Digital Biomarkers A cluster assignment was made for each participant, contingent upon whether their preferred walking speed was slow or fast.