Drone Controller Localization Based on RSSI Ratio.

We proposed two methods for the localization of drone controllers based on received signal strength indicator (RSSI) ratios: the RSSI ratio fingerprint method and the model-based RSSI ratio algorithm. To evaluate the performance of our proposed algorithms, we conducted both simulations and field trials. The simulation results show that our two proposed RSSI-ratio-based localization methods outperformed the distance mapping algorithm proposed in literature when tested in a WLAN channel. Moreover, increasing the number of sensors improved the localization performance. Averaging a number of RSSI ratio samples also improved the performance in propagation channels that did not exhibit location-dependent fading effects. However, in channels with location-dependent fading effects, averaging a number of RSSI ratio samples did not significantly improve the localization performance. Additionally, reducing the grid size improved the performance in channels with small shadowing factor values, but this only resulted in marginal gains in channels with larger shadowing factors. Our field trial results align with the simulation results in a two-ray ground reflection (TRGR) channel. Our methods provide a robust and effective solution for the localization of drone controllers using RSSI ratios.

SenseFi: A library and benchmark on deep-learning-empowered WiFi human sensing.

Over the recent years, WiFi sensing has been rapidly developed for privacy-preserving, ubiquitous human-sensing applications, enabled by signal processing and deep-learning methods. However, a comprehensive public benchmark for deep learning in WiFi sensing, similar to that available for visual recognition, does not yet exist. In this article, we review recent progress in topics ranging from WiFi hardware platforms to sensing algorithms and propose a new library with a comprehensive benchmark, SenseFi. On this basis, we evaluate various deep-learning models in terms of distinct sensing tasks, WiFi platforms, recognition accuracy, model size, computational complexity, and feature transferability. Extensive experiments are performed whose results provide valuable insights into model design, learning strategy, and training techniques for real-world applications. In summary, SenseFi is a comprehensive benchmark with an open-source library for deep learning in WiFi sensing research that offers researchers a convenient tool to validate learning-based WiFi-sensing methods on multiple datasets and platforms.

Actor-critic learning-based energy optimization for UAV access and backhaul networks.

In unmanned aerial vehicle (UAV)-assisted networks, UAV acts as an aerial base station which acquires the requested data via backhaul link and then serves ground users (GUs) through an access network. In this paper, we investigate an energy minimization problem with a limited power supply for both backhaul and access links. The difficulties for solving such a non-convex and combinatorial problem lie at the high computational complexity/time. In solution development, we consider the approaches from both actor-critic deep reinforcement learning (AC-DRL) and optimization perspectives. First, two offline non-learning algorithms, i.e., an optimal and a heuristic algorithms, based on piecewise linear approximation and relaxation are developed as benchmarks. Second, toward real-time decision-making, we improve the conventional AC-DRL and propose two learning schemes: AC-based user group scheduling and backhaul power allocation (ACGP), and joint AC-based user group scheduling and optimization-based backhaul power allocation (ACGOP). Numerical results show that the computation time of both ACGP and ACGOP is reduced tenfold to hundredfold compared to the offline approaches, and ACGOP is better than ACGP in energy savings. The results also verify the superiority of proposed learning solutions in terms of guaranteeing the feasibility and minimizing the system energy compared to the conventional AC-DRL.

[Relationship between blood lead level and motor nerve conduction velocity among 317 lead-exposed workers].

OBJECTIVE: To find a sensitive index of early injury of the nervous system in lead-exposed workers and to provide a scientific basis for establishing an efficient occupational health surveillance route. METHODS: A total of 317 lead-exposed workers (blood lead levels: 26.90∼ 912.80 µg/L, determined with the atomic absorption spectrum) were divided into four groups according to the normal blood lead level (201 µg/L), acceptable upper limit of blood lead (400 µg/L), and diagnostic value (600 µg/L). The motor nerve conduction function was examined and analyzed by one-way ANOVA. RESULTS: The distal latency and amplitude of the median nerve were significantly different between groups. The median distal latency of the highest blood lead group (>600 µg/L) was 3.63 ms, which was significantly longer than the average level (3.30 ms), and the median nerve amplitude of the highest blood lead group was 5.63 µV, significantly lower than the average level (7.27 µV). No significant difference was found between different groups in motor conduction velocity. Significant difference was found in ulnar nerve amplitude between groups. The ulnar nerve amplitude of the highest blood lead group was 4.31 µV, significantly lower than the average level (4.87 µV). No significant differences were observed in other parameters between groups. CONCLUSION: The distal latency and amplitude of the median nerve can be used as a sensitive index for the diagnosis of early subclinical motor nerve injury in lead?exposed workers.

Mind your manners: quality of manner of death certification among medical examiners and coroners in Taiwan.

To assess the quality of manner of death (MOD) certification among medical examiners/coroners (ME/Cs) in Taiwan, death certificates issued in 2002 for which the final MOD was suicide or undetermined were extracted for analysis. Indicators of the quality of MOD certification included (1) MOD not given by the ME/Cs; (2) MOD assigned by the ME/Cs was changed by the coder; (3) ratio between undetermined and suicide deaths (U/S ratio). There were 450 death certificates for which the ME/Cs did not assign the MOD in the original certificate. Three fifths (285/450) of them were issued by 4 ME/Cs. The same 4 ME/Cs also had extremely high U/S ratios (1.25-1.84) compared with the average (0.31). The overall quality of MOD certification among ME/Cs in Taiwan was fair; only a small number of ME/Cs had poor quality in MOD certification. The high U/S ratio among the 4 ME/Cs would certainly affect the suicide mortality rates of the counties the 4 ME/Cs were in charge of. Actions should be taken to improve the certification quality of these 4 ME/Cs.