The algorithm's rapid convergence for solving the sum rate maximization is demonstrated, and the improvement in sum rate from edge caching is contrasted with the non-caching baseline.
Due to the rise of the Internet of Things (IoT), sensing devices with several integrated wireless transceiver modules are now in greater demand. These platforms frequently assist in the beneficial application of multiple radio technologies, leveraging their differing characteristics for optimal performance. By implementing intelligent radio selection techniques, these systems gain substantial adaptability, securing more robust and reliable communications in varying channel dynamics. Our focus in this paper is on the wireless communication links connecting deployed personnel's devices to the intermediary access point network. Through the adaptive manipulation of accessible transceivers, we create resilient and trustworthy links using multi-radio platforms and wireless devices equipped with various and numerous transceiver technologies. This paper uses the term 'robust' to refer to communications that remain stable in the face of environmental and radio fluctuations, encompassing situations like interference from non-cooperative actors or multipath/fading conditions. A multi-objective reinforcement learning (MORL) framework is used in this paper to resolve the multi-radio selection and power control challenge. We advocate for independent reward functions to reconcile the divergent objectives of minimizing power consumption and maximizing bit rate. Our method involves an adaptive exploration strategy for the purpose of learning a strong behavior policy, and we evaluate its real-time effectiveness relative to established methods. We propose an extension to the multi-objective state-action-reward-state-action (SARSA) algorithm, which enables the implementation of this adaptive exploration strategy. The extended multi-objective SARSA algorithm, when equipped with adaptive exploration, demonstrated a 20% superior F1 score compared to approaches relying on decayed exploration policies.
The problem of buffer-supported relay choice, with the goal of enabling secure and trustworthy communications, is explored in this paper, considering a two-hop amplify-and-forward (AF) network infiltrated by an eavesdropper. In wireless networks, broadcast signals, susceptible to signal decay, can arrive at the receiver end in a corrupted format or be intercepted by unauthorized listeners. Most schemes for buffer-aided relay selection in wireless communication tackle either the reliability or security aspects, but seldom both, which is a significant gap. The paper proposes a deep Q-learning (DQL) driven buffer-aided relay selection scheme, designed to ensure both reliability and security. Monte Carlo simulations are used to determine the connection outage probability (COP) and secrecy outage probability (SOP), which serve as metrics for the reliability and security of the proposed scheme. The simulation data underscores the reliability and security of our proposed scheme for two-hop wireless relay networks, ensuring dependable communication. Comparative experiments were also conducted between our proposed approach and two established benchmark schemes. Our proposed method, as evidenced by the comparison results, shows higher performance than the max-ratio method concerning the standard operating procedure.
Our team is developing a transmission-based probe for point-of-care assessment of vertebral strength. This probe is vital in creating the instrumentation needed to support the spinal column during spinal fusion surgical procedures. This device utilizes a transmission probe, consisting of thin coaxial probes. These probes are inserted through the pedicles into the small canals within the vertebrae, and a broad band signal is subsequently transmitted across the bone tissue between the probes. A machine vision methodology has been crafted to measure the separation distance between the probe tips as they are being inserted into the vertebrae. The latter technique entails the positioning of a small camera on one probe's handle, alongside printed fiducials on the second probe. Machine vision allows for a correlation between the fiducial-based probe tip's position and the camera-based probe tip's static coordinate system. Calculating tissue characteristics straightforwardly is possible using the two methods, provided the antenna far-field approximation is utilized. In preparation for clinical prototype development, validation tests of the two concepts are demonstrated.
Force plate testing is gaining traction in the sporting world, thanks to the availability of readily accessible, portable, and reasonably priced force plate systems—hardware and software combined. Motivated by the validation of Hawkin Dynamics Inc. (HD)'s proprietary software, as reported in recent literature, this study sought to establish the concurrent validity of HD's wireless dual force plate hardware for vertical jump performance analysis. To collect simultaneous vertical ground reaction forces from 20 participants (27.6 years, 85.14 kg, 176.5923 cm) during countermovement jump (CMJ) and drop jump (DJ) tests at 1000 Hz, HD force plates were positioned directly on top of two adjacent in-ground Advanced Mechanical Technology Inc. force plates (considered the gold standard) within a single testing session. Agreement among force plate systems was evaluated using ordinary least squares regression and 95% confidence intervals generated via bootstrapping. No bias was observed between the two force plate systems for any countermovement jump (CMJ) or depth jump (DJ) variable, except for the depth jump peak braking force (showing a proportional bias) and depth jump peak braking power (showing a fixed and proportional bias). Compared to the established industry standard, the HD system is a feasible alternative for assessing vertical jumps because no bias (fixed or proportional) was observed in any of the CMJ variables (n = 17) and only two among the eighteen DJ variables exhibited such bias.
To understand their physical state, gauge the intensity of their workouts, and evaluate their training progress, real-time sweat monitoring is essential for athletes. A patch-relay-host multi-modal sweat sensing system was devised, composed of a wireless sensor patch, a wireless data relay, and a controlling host computer. The wireless sensor patch's real-time functionality allows for the monitoring of lactate, glucose, potassium, and sodium concentrations. Wireless data transmission, achieved using Near Field Communication (NFC) and Bluetooth Low Energy (BLE), leads to the data becoming available on the host controller. In sweat-based wearable sports monitoring systems, existing enzyme sensors are characterized by limited sensitivities. For enhanced sensitivity, this paper presents a dual enzyme sensing optimization strategy, exemplified by Laser-Induced Graphene (LIG) sweat sensors integrated with Single-Walled Carbon Nanotubes (SWCNT). Within a minute, a whole LIG array can be manufactured, requiring only about 0.11 yuan worth of materials; this makes it ideal for mass production. The in vitro lactate sensing test results demonstrated sensitivities of 0.53 A/mM and glucose sensing sensitivities of 0.39 A/mM. Furthermore, potassium sensing exhibited a sensitivity of 325 mV/decade, while sodium sensing displayed a sensitivity of 332 mV/decade. To illustrate the characterization of personal physical fitness, an ex vivo sweat analysis test was additionally performed. check details In conclusion, a high-sensitivity lactate enzyme sensor employing SWCNT/LIG technology fulfills the demands of sweat-based wearable sports monitoring systems.
Due to the rising cost of healthcare and the rapid growth of remote physiological monitoring and care, there is a growing need for budget-friendly, accurate, and non-invasive continuous measurement of blood analytes. Leveraging radio frequency identification (RFID), the Bio-RFID sensor, a new electromagnetic technology, was constructed to non-invasively acquire data from distinct radio frequencies on inanimate surfaces, converting the data into physiologically relevant insights. Bio-RFID is used in our innovative proof-of-principle research to accurately assess the varying levels of analytes in deionized water. Our investigation centered on the Bio-RFID sensor's ability to precisely and non-invasively measure and identify a diverse array of analytes in vitro. For the purposes of this evaluation, randomized, double-blind trials were conducted to assess the efficacy of various solutions, including (1) water and isopropyl alcohol; (2) salt and water; and (3) commercial bleach and water, as representatives of biochemical solutions in general. ARV-associated hepatotoxicity Concentrations of 2000 parts per million (ppm) were successfully identified using Bio-RFID technology, with supporting data implying that even smaller concentration differences could be measured.
Infrared (IR) spectroscopy is a nondestructive, quick, and uncomplicated method for analysis. With the increasing demand for speed in sample analysis, IR spectroscopy, combined with chemometric methods, is becoming popular among pasta producers. Polymicrobial infection Conversely, there are fewer models which have applied deep learning for the classification of cooked wheat food products, and an even smaller number that have used deep learning for classifying Italian pasta. To address these issues, a refined CNN-LSTM neural network is presented for the identification of pasta in various physical states (frozen and thawed) via infrared spectroscopy. A 1D convolutional neural network (1D-CNN) and a long short-term memory (LSTM) network were constructed to extract, respectively, local spectral abstraction and sequential position information from the spectra. Italian pasta spectral data subjected to principal component analysis (PCA) resulted in a 100% accurate prediction by the CNN-LSTM model for thawed pasta and 99.44% accuracy for frozen pasta, signifying the method's high analytical accuracy and generalization potential. Hence, the application of CNN-LSTM neural networks with IR spectroscopy enables the recognition of distinct pasta varieties.