Any MetaAnalysis of Erectile Dysfunction as well as Having a drink

Attitude update rate is one of the important indicators of star sensor performance. In order to resolve the problem of the low attitude update rate of star sensors, this paper proposes a star sensor attitude update method based on star point correction of rolling shutter exposure. Based on the characteristics of the asynchronous exposure of the rolling shutter, recursive estimation of the motion attitude and the corrected star point information were combined to realize multiple updates of the attitude in a single frame of the star map. GLX351322 Simulation and experimental results proved that the proposed method could increase the attitude update rate of a star sensor by 15 times, up to 150 Hz.The aim of the study was to develop a simple submaximal walk test protocol and equation using heart rate (HR) response variables to predict maximal oxygen consumption (VO2max). A total of 60 healthy adults were recruited to test the validity of 3 min walk tests (3MWT). VO2max and HR responses during the 3MWTs were measured. Multiple regression analysis was used to develop prediction equations. As a result, HR response variables including resting HR and HR during walking and recovery at two different cadences were significantly correlated with VO2max. The equations developed using multiple regression analyses were able to predict VO2max values (r = 0.75-0.84; r2 = 0.57-0.70; standard error of estimate (SEE) = 4.80-5.25 mL/kg/min). The equation that predicted VO2max the best was at the cadence of 120 steps per minute, which included sex; age; height; weight; body mass index; resting HR; HR at 1 min, 2 min and 3 min; HR recovery at 1 min and 2 min; and other HR variables calculated based on these measured HR variables (r = 0.84; r2 = 0.70; SEE = 4.80 mL/kg/min). In conclusion, the 3MWT developed in this study is a safe and practical submaximal exercise protocol for healthy adults to predict VO2max accurately, even compared to the well-established submaximal exercise protocols, and merits further investigation.The multi-target tracking filter under the Bayesian framework has strict requirements on the prior information of the target, such as detection probability density, clutter density, and target initial position information. This paper proposes a novel robust measurement-driven cardinality balance multi-target multi-Bernoulli filter (RMD-CBMeMBer) for solving the multiple targets tracking problem when the detection probability density is unknown, the background clutter density is unknown, and the target's prior position information is lacking. In RMD-CBMeMBer filtering, the target state is first extended, so that the extended target state includes detection probability, kernel state, and indicators of target and clutter. Secondly, the detection probability is modeled as a Beta distribution, and the clutter is modeled as a clutter generator that is independent of each other and obeys the Poisson distribution. Then, the detection probability, kernel state, and clutter density are jointly estimated through filtering. In addition, the correlation function (CF) is proposed for creating new Bernoulli component (BC) by using the measurement information at the previous moment. Numerical experiments have verified that the RMD-CBMeMBer filter can solve the multi-target tracking problem under the condition of unknown target detection probability, unknown background clutter density and inadequate prior position information of the target. It can effectively estimate the target detection probability and the clutter density.Anthropomorphic robots need to maintain effective and emotive communication with humans as automotive agents to establish and maintain effective human-robot performances and positive human experiences. Previous research has shown that the characteristics of robot communication positively affect human-robot interaction outcomes such as usability, trust, workload, and performance. In this study, we investigated the characteristics of transparency and anthropomorphism in robotic dual-channel communication, encompassing the voice channel (low or high, increasing the amount of information provided by textual information) and the visual channel (low or high, increasing the amount of information provided by expressive information). The results showed the benefits and limitations of increasing the transparency and anthropomorphism, demonstrating the significance of the careful implementation of transparency methods. The limitations and future directions are discussed.We have developed a sensor for monitoring the hemoglobin (Hb) concentration in the effluent of a continuous bladder irrigation. The Hb concentration measurement is based on light absorption within a fixed measuring distance. The light frequency used is selected so that both arterial and venous Hb are equally detected. The sensor allows the measurement of the Hb concentration up to a maximum value of 3.2 g/dL (equivalent to ≈20% blood concentration). Since bubble formation in the outflow tract cannot be avoided with current irrigation systems, a neural network is implemented that can robustly detect air bubbles within the measurement section. The network considers both optical and temporal features and is able to effectively safeguard the measurement process. The sensor supports the use of different irrigants (salt and electrolyte-free solutions) as well as measurement through glass shielding. The sensor can be used in a non-invasive way with current irrigation systems. The sensor is positively tested in a clinical study.The power saving issue and clean energy harvesting for wireless and cost-affordable electronics (e.g., IoT applications, sensor nodes or medical implants), have recently become attractive research topics. With this in mind, the paper addresses one of the most important parts of the energy conversion system chain - the power management unit. The core of such a unit will be formed by an inductorless, low-voltage DC-DC converter based on the cross-coupled dynamic-threshold charge pump topology. The charge pump utilizes a power-efficient ON/OFF regulation feedback loop, specially designed for strict low-voltage start-up conditions by a driver booster. Taken together, they serve as the masters to control the charge pump output (up to 600 mV), depending on the voltage value produced by a renewable energy source available in the environment. The low-power feature is also ensured by a careful design of the hysteresis-based bulk-driven comparator and fully integrated switched-capacitor voltage divider, omitting the static power consumption. The presented converter can also employ the on-chip RF-based energy harvester for use in a wireless power transfer system.In order to improve the energy efficiency (EE) performance of cooperative networks, this study combines non-orthogonal multiple access (NOMA) with simultaneous wireless information and power transfer (SWIPT) technologies to construct a cooperative relay network composed of one base station (BS), multiple near users, and one far user. Based on the network characteristics, a time-division resource allocation rule is proposed, and EE formulas regarding direct-link mode and cooperative mode are derived. Considering user selection and decoding performance, to obtain the optimal EE, this study utilizes a DinkelBach iterative algorithm based on the golden section (GS-DinkelBach) to solve the EE optimization problem, which is affected by power transmitted from the BS, achievable rates under three communication links, and quality of service (QoS) constraints of users. The simulation results show that the GS-DinkelBach algorithm can obtain precise EE gains with low computational complexity. Compared with the traditional NOMA-SWIPT direct-link network model and the relay network model, the optimal EE of the established network model could be increased by 0.54 dB and 1.66 dB, respectively.The emergence of various types of commercial cameras (compact, high resolution, high angle of view, high speed, and high dynamic range, etc.) has contributed significantly to the understanding of human activities. By taking advantage of the characteristic of a high angle of view, this paper demonstrates a system that recognizes micro-behaviors and a small group discussion with a single 360 degree camera towards quantified meeting analysis. We propose a method that recognizes speaking and nodding, which have often been overlooked in existing research, from a video stream of face images and a random forest classifier. The proposed approach was evaluated on our three datasets. In order to create the first and the second datasets, we asked participants to meet physically 16 sets of five minutes data from 21 unique participants and seven sets of 10 min meeting data from 12 unique participants. The experimental results showed that our approach could detect speaking and nodding with a macro average f1-score of 67.9% in a 10-fold random split cross-validation and a macro average f1-score of 62.5% in a leave-one-participant-out cross-validation. By considering the increased demand for an online meeting due to the COVID-19 pandemic, we also record faces on a screen that are captured by web cameras as the third dataset and discussed the potential and challenges of applying our ideas to virtual video conferences.The use of unmanned aerial vehicles or drones are a valuable technique in coping with issues related to life in the general public's daily routines. Given the growing number of drones in low-altitude airspace, linking drones to form the Internet of drones (IoD) is a highly desirable trend to improve the safety as well as the quality of flight. However, there remain security, privacy, and communication issues related to IoD. In this paper, we discuss the key requirements of security, privacy, and communication and we present a taxonomy of IoD based on the most relevant considerations. Furthermore, we present the most commonly used commercial case studies and address the latest advancements and solutions proposed for the IoD environments. Lastly, we discuss the challenges and future research directions of IoD.The widespread adoption of smartphones and the new-generation wireless networks have changed the way that people interact among themselves and with their environment. The use of messaging platforms, such as WhatsApp, has become deeply ingrained in peoples' lives, and many digital services have started to be delivered using these communication channels. In this work, we propose a new OAuth grant type to be used when the interaction between the resource owner and the client takes place through a messaging platform. This new grant type firstly allows the authorization server to be sure that no Man-in-the-Middle risk exists between the resource owner and the client before issuing an access token. Secondly, it allows the authorization server to interact with the resource owner through the same user-agent already being used to interact with the client, i.e., the messaging platform, which is expected to improve the overall user experience of the authorization process. To verify this assumption, we conducted a usability study in which subjects were required to perform the full authorization process using both the standard authorization code grant type (through a web-browser) and the new grant type defined in this work. They have also been required to fill in a small questionnaire including some demographic information and their impressions about both authorization flows. The results suggest that the proposed grant type eases the authorization process in most cases.