Robustness, straightforwardness, and readily available data converge to make it an outstanding option for both smart healthcare and telehealth.
The authors of this paper report on measurements performed to assess the transmission performance of LoRaWAN in saltwater channels, specifically for underwater-to-above-water scenarios. In order to model the radio channel's link budget and to assess the electrical permittivity of saltwater, a theoretical analysis of operational conditions was performed. In order to define the applicable conditions for the technology, initial trials were performed in a laboratory setting with differing salinity levels, subsequently followed by field tests in the Venice Lagoon. Although these tests do not concentrate on illustrating LoRaWAN's usability for gathering data submerged, the obtained outcomes confirm that LoRaWAN transmitters can operate effectively in environments partially or completely immersed beneath a shallow layer of marine water, aligning with the predicted outcomes of the proposed theoretical model. This achievement has laid the groundwork for deploying superficial marine sensor networks in the Internet of Underwater Things (IoUT) framework, allowing the monitoring of bridges, harbor structures, water quality parameters, water sport participants, and the activation of high-water or fill-level alarm systems.
This study presents and validates a bi-directional free-space visible light communication (VLC) system, which accommodates multiple mobile receivers (Rx units) facilitated by a light-diffusing optical fiber (LDOF). From a head-end or central office (CO) positioned at a considerable distance, the downlink (DL) signal is conveyed to the client's LDOF using free-space transmission. A dispatched DL signal, targeting the LDOF, an optical antenna for retransmission, ultimately reaches various mobile receiving units (Rxs). The CO receives the uplink (UL) signal, relayed via the LDOF. During the proof-of-concept demonstration, the length of the LDOF was determined to be 100 cm, correlating with the 100 cm free space VLC transmission distance between the CO and the LDOF. The data transfer rate in the downlink (210 Mbit/s) and the uplink (850 Mbit/s) exceeds the pre-forward-error-correction bit error rate (BER) limit of 38 x 10^-3.
Contemporary smartphones, equipped with cutting-edge CMOS imaging sensor (CIS) capabilities, have facilitated the ascendancy of user-generated content, overshadowing the historical impact of traditional DSLRs. Furthermore, the minuscule sensor dimensions and the fixed focal lengths of the lenses can often create images with grainy detail, notably prominent in zoomed-in photographic compositions. Moreover, the sequential processes of multi-frame stacking and post-sharpening frequently introduce zigzag textures and over-sharpening effects, which conventional image quality metrics might overestimate. To address this problem, the current paper first creates a real-world zoom photo database, incorporating 900 telephotos from 20 distinct mobile sensors and ISP designs. A novel no-reference zoom quality metric incorporating the established principles of sharpness and the characteristic of natural image quality is put forth. For determining image sharpness, we uniquely combine the total energy inherent in the predicted gradient image with the entropy of the residual term, situated within the context of free energy theory. A set of mean-subtracted contrast-normalized (MSCN) model parameters are used to offset the influence of over-sharpening and other artifacts, acting as a measure of natural image statistics. In conclusion, these two procedures are linearly integrated. Salubrinal The zoom photo database's experimental results spotlight our quality metric's superior performance, exceeding 0.91 in SROCC and PLCC, contrasting with single sharpness or naturalness indexes remaining approximately 0.85. Moreover, the performance of our zoom metric, when measured against the most effective general-purpose and sharpness models, is superior in SROCC, outperforming them by 0.0072 and 0.0064, respectively.
The fundamental basis for ground-based assessment of satellite orbital status is telemetry data, and the use of these data for detecting anomalies significantly contributes to the reliability and security of spacecraft. Deep learning methods are currently employed in recent anomaly detection research to create a normal profile from telemetry data. Despite their implementation, these methodologies are insufficient in effectively capturing the complex interdependencies among the diverse dimensions of telemetry data, and thus fail to produce an accurate representation of the normal telemetry profile, which negatively impacts anomaly detection effectiveness. Correlation anomaly detection is addressed in this paper by means of CLPNM-AD, a contrastive learning method incorporating prototype-based negative mixing. To commence, the CLPNM-AD framework employs a random feature corruption augmentation method for the creation of augmented samples. To conclude the initial procedure, a consistency-oriented strategy is applied to pinpoint the prototype samples, and then prototype-based negative mixing contrastive learning is employed to form a standard profile. Lastly, a prototype-based approach to anomaly scoring is introduced for anomaly evaluation. Results from experiments conducted on public and mission datasets conclusively show that CLPNM-AD surpasses baseline methods, yielding a gain of up to 115% in the standard F1 score and demonstrating improved resilience against noise.
The application of spiral antenna sensors for detecting partial discharges (PD) at ultra-high frequencies (UHF) is common practice within gas-insulated switchgears (GISs). Current UHF spiral antenna sensors, however, are largely structured around a rigid base, incorporating a balun frequently composed of FR-4. For the safe, built-in integration of antenna sensors, the GIS structures must undergo a complicated structural transformation process. A flexible polyimide (PI) base is used to construct a low-profile spiral antenna sensor, aimed at resolving this problem, and its performance is improved through optimization of the clearance ratio. The profile height and diameter of the new antenna sensor, as determined through simulations and measurements, are 03 mm and 137 mm, resulting in a 997% and 254% decrease from the dimensions of the traditional spiral antenna. At varying bending radii, the antenna sensor demonstrates consistent VSWR of 5 within the frequency range of 650 MHz to 3 GHz, and exhibits a maximum gain of up to 61 dB. immediate postoperative Ultimately, the performance of the antenna sensor in detecting PD is evaluated on a real 220 kV GIS installation. immune-epithelial interactions The integrated antenna sensor, according to the results, successfully identifies partial discharges (PD) with a discharge magnitude of 45 picocoulombs (pC), demonstrating the sensor's ability to quantify the severity of the PD event. Furthermore, the simulated environment suggests the antenna sensor possesses the capability to identify minuscule water quantities within GIS systems.
Beyond-line-of-sight maritime broadband communications can be enabled or severely obstructed by atmospheric ducts, affecting signal transmission. Atmospheric ducts' inherent spatial heterogeneity and sudden changes are directly attributable to the strong spatial-temporal variability of atmospheric conditions in near-shore areas. This study examines the effect of horizontally heterogeneous ducts on radio waves in maritime environments, combining theoretical calculations and experimental verification. For a more effective use of meteorological reanalysis data, we have built a range-dependent atmospheric duct model. The prediction accuracy of path loss is enhanced using a newly proposed sliced parabolic equation algorithm. The numerical solution is derived, and the proposed algorithm's viability is examined under the specified range-dependent duct conditions. To validate the algorithm, a 35 GHz long-distance radio propagation measurement was employed. Analyzing the measurements reveals the characteristics of atmospheric duct distribution in space. Given the prevailing duct conditions, the simulated path loss aligns with the measured values. The proposed algorithm exhibits superior performance during periods characterized by multiple ducts, outperforming the existing method. We proceed with a further analysis of how differing horizontal duct configurations influence the strength of the received signal.
With advancing age, there is a gradual decline in muscle mass and strength, accompanied by joint complications and a decrease in overall mobility, which significantly raises the chance of falls or similar incidents. The integration of gait assistance exoskeletons can contribute significantly to the active aging strategy for this specific segment. Given the unique specifications of the machinery and control systems in these devices, a facility for evaluating varied design parameters is indispensable. This research project addresses the creation and design of a modular testing apparatus and prototype exosuit to evaluate varied mounting and control protocols for a cable-driven exoskeletal system. Using a single actuator, the test bench facilitates the experimental implementation of postural or kinematic synergies across multiple joints, while optimizing the control scheme for personalized adaptation to the patient's specifics. The research community's access to the design is intended to result in improvements to the design of cable-driven exosuits.
In various applications, including autonomous driving and human-robot collaboration, Light Detection and Ranging (LiDAR) technology is now the prevailing method. Due to its proficiency with cameras in challenging settings, point-cloud-based 3D object detection is seeing increased use and acceptance within the industry and in common applications. In this paper, a modular approach to detect, track, and categorize individuals is demonstrated, employing a 3D LiDAR sensor. Object segmentation, a robust implementation, is coupled with a classifier employing local geometric descriptors, and a tracking mechanism, all in one. Subsequently, a real-time solution is executed within a low-performance computing environment, accomplished by reducing the number of data points needing evaluation. Identification and anticipation of pertinent regions is accomplished through motion observation and predictive motion modeling without pre-existing environmental context.