Notes

Video-Assisted Thoracic Surgical treatment Lobectomy.
The precise localization of an underground mine environment is key to achieving unmanned and intelligent underground mining. However, in an underground environment, GPS is unavailable, there are variable and often poor lighting conditions, there is visual aliasing in long tunnels, and the occurrence of airborne dust and water, presenting great difficulty for localization. We demonstrate a high-precision, real-time, without-infrastructure underground localization method based on 3D LIDAR. The underground mine environment map was constructed based on GICP-SLAM, and inverse distance weighting (IDW) was first proposed to implement error correction based on point cloud mapping called a distance-weight map (DWM). The map was used for the localization of the underground mine environment for the first time. The approach combines point cloud frames matching and DWM matching in an unscented Kalman filter fusion process. Finally, the localization method was tested in four underground scenes, where a spatial localization error of 4 cm and 60 ms processing time per frame were obtained. We also analyze the impact of the initial pose and point cloud segmentation with respect to localization accuracy. The results showed that this new algorithm can realize low-drift, real-time localization in an underground mine environment.In this paper, we propose a global navigation function applied to model predictive control (MPC) for autonomous mobile robots, with application to warehouse automation. The approach considers static and dynamic obstacles and generates smooth, collision-free trajectories. The navigation function is based on a potential field derived from an E* graph search algorithm on a discrete occupancy grid and by bicubic interpolation. It has convergent behavior from anywhere to the target and is computed in advance to increase computational efficiency. The novel optimization strategy used in MPC combines a discrete set of velocity candidates with randomly perturbed candidates from particle swarm optimization. Adaptive horizon length is used to improve performance. The efficiency of the proposed approaches is validated using simulations and experimental results.Smart devices have become an integral part of people's lives. The most common activities for users of such smart devices that are energy sources are voice calls, text messages (SMS) or email, browsing the World Wide Web, streaming audio/video, and using sensor devices such as cameras, GPS, Wifi, 4G/5G, and Bluetooth either for entertainment or for the convenience of everyday life. In addition, other power sources are the device screen, RAM, and CPU. The need for communication, entertainment, and computing makes the optimal management of the power consumption of these devices crucial and necessary. In this paper, we employ a computationally efficient linear mapping algorithm known as Concurrent Brightness & Contrast Scaling (CBCS), which transforms the initial intensity value of the pixels in the YCbCr color system. We introduce a methodology that gives the user the opportunity to select a picture and modify it using the suggested algorithm in order to make it more energy-friendly with or without the application of a histogram equalization (HE). The experimental results verify the efficacy of the presented methodology through various metrics from the field of digital image processing that contribute to the choice of the optimal values for the parameters a,b that meet the user's preferences (low or high-contrast images) and green power. For both low-contrast and low-power images, the histogram equalization should be omitted, and the appropriate a should be much lower than one. To create high-contrast and low-power images, the application of HE is essential. Finally, quantitative and qualitative evaluations have shown that the proposed approach can achieve remarkable performance.This paper presents the performance improvement of a co-planar waveguide rectangularly notched UWB-MIMO antenna. The isolation and gain of the antenna are enhanced by using a parasitic isolator. The antenna consists of four microstrip patch antennas and an isolator. The UWB characteristic of the antenna is achieved by truncating the lower ends of the radiating patch by a semicircle. The rectangular notch characteristic is obtained by adding two electromagnetic bandgap structures on the backside of the antenna, which is attached to the radiator via shorting pin. The performance, especially the decoupling of the MIMO antenna is improved by using a novel parasitic decoupler, which is placed between the antennas to receive uncorrelated signals. The decoupling structure consists of a square shape metallic element with a circular slot inside and a half-semicircle slot edged at each corner. Four rectangular metallic stubs are extended from opposite parallel sides to improve further isolation. The simulated and measured results show that the antenna has a rectangular notch band (5.25-5.85 GHz) across the working band of 3-12.8 GHz. In addition, the antenna has a planar structure with an overall size of 60 × 60 × 1.52 mm3 and offers stable gain, reduced mutual coupling ( less then -21 dB), and lower envelop correlation ( less then 0.001).In this work, the design, building, and testing of the most portable, easy-to-build, robust, handheld, and cost-effective Fourier Lightfield Microscope (FLMic) to date is reported. The FLMic is built by means of a surveillance camera lens and additional off-the-shelf optical elements, resulting in a cost-effective FLMic exhibiting all the regular sought features in lightfield microscopy, such as refocusing and gathering 3D information of samples by means of a single-shot approach. The proposed FLMic features reduced dimensions and light weight, which, combined with its low cost, turn the presented FLMic into a strong candidate for in-field application where 3D imaging capabilities are pursued. The use of cost-effective optical elements has a relatively low impact on the optical performance, regarding the figures dictated by the theory, while its price can be at least 100 times lower than that of a regular FLMic. The system operability is tested in both bright-field and fluorescent modes by imaging a resolution target, a honeybee wing, and a knot of dyed cotton fibers.Transportation infrastructure is an integral part of the world's overall functionality; however, current transportation infrastructure has aged since it was first developed and implemented. Consequently, given its condition, preservation has become a main priority for transportation agencies. check details Billions of dollars annually are required to maintain the United States' transportation system; however, with limited budgets the prioritization of maintenance and repairs is key. Structural Health Monitoring (SHM) methods can efficiently inform the prioritization of preservation efforts. This paper presents an acoustic monitoring SHM method, deemed tap testing, which is used to detect signs of deterioration in structural/mechanical surfaces through nondestructive means. This method is proposed as a tool to assist bridge inspectors, who already utilize a costly form of SHM methodology when conducting inspections in the field. Challenges arise when it comes to this method of testing, especially when SHM device deployment is done by hand, and when the results are based solely upon a given inspector's abilities. This type of monitoring solution is also, in general, only available to experts, and is associated with special cases that justify their cost. With the creation of a low-cost, cyber-physical system that interrogates and classifies the mechanical health of given surfaces, we lower the cost of SHM, decrease the challenges faced when conducting such tests, and enable communities with a revolutionary solution that is adaptable to their needs. The authors of this paper created and tested a low-cost, interrogating robot that informs users of structural/mechanical defects. This research describes the further development, validation of, and experimentation with, a tap testing device that utilizes remote technology.This paper comprehensively investigates the performance of the D2D underlaying cellular networks where D2D communications are operated concurrently with cellular networks provided that the aggregate interference measured on licensed users is strictly guaranteed. In particular, we derive the outage probability (OP), the average rate, and the amount of fading (AoF) of the D2D networks in closed-form expressions under three distinct power allocation schemes, i.e., the path-loss-based, equal, and random allocation schemes. It is noted that the considered networks take into consideration the impact of the intra-D2D networks, the inter-interference from the cellular networks and background noise, thus involving many random variables and leading to a complicated mathematical framework. Moreover, we also reveal the behavior of the OP with respect to the transmit power based on the rigorous mathematical frameworks rather than the computer-based simulation results. The derived framework shows that increasing the transmit power is beneficial for the OP of the D2D users. Regarding the cellular networks, the coverage probability (Pcov) of the cellular users is computed in closed-form expression too. Monte Carlo simulations are given to verify the accuracy of the proposed mathematical frameworks. Our findings illustrate that the power allocation method based on prior path-loss information outperforms the other methods in the average sum rate.Nowadays, wearables-based Human Activity Recognition (HAR) systems represent a modern, robust, and lightweight solution to monitor athlete performance. However, user data variability is a problem that may hinder the performance of HAR systems, especially the cross-subject HAR models. Such a problem may have a lesser effect on the subject-specific model because it is a tailored model that serves a specific user; hence, data variability is usually low, and performance is often high. However, such a performance comes with a high cost in data collection and processing per user. Therefore, in this work, we present a personalized model that achieves higher performance than the cross-subject model while maintaining a lower data cost than the subject-specific model. Our personalization approach sources data from the crowd based on similarity scores computed between the test subject and the individuals in the crowd. Our dataset consists of 3750 concentration curl repetitions from 25 volunteers with ages and BMI ranging between 20-46 and 24-46, respectively. We compute 11 hand-crafted features and train 2 personalized AdaBoost models, Decision Tree (AdaBoost-DT) and Artificial Neural Networks (AdaBoost-ANN), using data from whom the test subject shares similar physical and single traits. Our findings show that the AdaBoost-DT model outperforms the cross-subject-DT model by 5.89%, while the AdaBoost-ANN model outperforms the cross-subject-ANN model by 3.38%. On the other hand, at 50.0% less of the test subject's data consumption, our AdaBoost-DT model outperforms the subject-specific-DT model by 16%, while the AdaBoost-ANN model outperforms the subject-specific-ANN model by 10.33%. Yet, the subject-specific models achieve the best performances at 100% of the test subjects' data consumption.
Read More: https://www.selleckchem.com/products/gossypol.html