The utilization of sensing signals from multiple anchors for three-dimensional (3D) spatial localization represents one of the commonly employed wireless localization techniques for unmanned aerial vehicles (UAVs) in global navigation satellite system (GNSS)-denied environments, which has been extensively investigated. However, this methodology typically necessitates more than three anchors with distinct spatial distribution characteristics, coupled with precise alignment between the UAV local coordinate system and the global reference frame. These stringent requirements are often challenging to meet in practical operational scenarios. In this work, we explore an observable UAV 3D self positioning and orientation alignment of local coordinate system supported by only one angle of arrival (AoA) anchor, with significantly reduced implementation cost and complexity. We first proved the observability of designed positioning system with a static anchor which is the new theoretical limit supporting observable UAV positioning on minimal anchor number, and can significantly reduce the requirement for anchor number in practical positioning applications. Then, we develop an efficient two-layer iterative algorithm for the estimation problem which provides real-time positioning estimation with extremely low computing cost. Finally, numerical results confirm that the proposed scheme has high positioning accuracy and strong robustness to measurement noise.

In this article, we consider an Industrial Internet of Things (IIoT) network operating under a carrier sense multiple access with collision avoidance (CSMA/CA) protocol. Latency-sensitive packets generated at multiple stations randomly are transmitted to a destination for decision making. To meet the strict timeliness constraint, the transmissions are carried by finite blocklength (FBL) codes, while the truncated hybrid automatic repeat request (HARQ) scheme is exploited to improve the reliability. For such unsaturated CSMA/CA networks, for the first time, we characterize the timeliness of packets utilized for decision-making via the age upon decisions (AuD) metric which emphasizes the information freshness at decision moments in comparison to age of information (AoI). To explicitly quantify the AuD performance, we develop an equivalent and tractable unsaturated Markov transfer model for the considered network and investigate the transmission probability and collision probability, respectively. Subsequently, the probability density functions of interarrival time and service time of the successfully transmitted packets are derived. We further derive a closed-form expression for the average AuD under a geometric decision process accordingly. Based on these characterizations, we aim at improving average AuD by jointly allocating the blocklength and transmit power. The formulated nonconvex problem is decomposed into subproblems, and we prove its joint convexity across all feasible intervals. Via simulations, we evaluate the performance of the considered network and conclude a series of design guidelines.

In this paper, we consider an CSMA/CA network supporting multi-node transmissions. To meet the timeliness and reliability, the communications are operated with finite blocklength (FBL) codes and truncated hybrid automatic repeat request (HARQ) scheme. We characterize the timeliness of packets utilized for decision-making via Age upon Decisions (AuD) in such unsaturated CSMA/CA wireless networks under truncated HARQ protocol. In particular, we develop an equivalent unsaturated Markov transfer model according to the considered network and calculate the value of transmission probability and collision probability, respectively. Then, we introduce a method to calculate the average AuD with Bernoulli decision process and obtain a closed-form expression following these characterizations. Via simulations, the performance of the considered network is evaluated and we conclude a series of design guidelines.

We consider an update-and-decide IoT-based wireless network, where information packets generated from dual sources are co-stored in the transmitter's buffer, while decisions are made at the destination. Two practical assumptions about the communications between the transmitter and destination are taken into account: the communications are operating with finite blocklength (FBL) codes, and truncated hybrid automatic repeat request (HARQ) schemes are exploited to improve the FBL reliability, i.e., the number of allowed rounds of (re)transmissions is finite. For the first time, this paper characterizes the timeliness of status updates, namely age upon decisions (AuD) (which highlights the timeliness of the information at decisions in comparison to the concept of age of information), for such truncated HARQ-assisted wireless network. First, we characterize the inter-arrival time between two adjacent successfully transmitted packets, while taking into consideration the preemption policy and the randomness of the number of preempted packets from the same source. In particular, the probability density function, statistical performance of such inter-arrival time are derived. Following these characterizations, we propose a new approach to determine the average AuD and obtain a closed-form expression accordingly. Via simulations, we evaluate the performance and conclude a set of guidelines for designs on the considered network.

Complex biomedical data generated during clinical, omics and mechanism-based experiments have increasingly been exploited through cloud- and visualization-based data mining techniques. However, the scientific community still lacks an easy-to-use web service for the comprehensive visualization of biomedical data, particularly high-quality and publication-ready graphics that allow easy scaling and updatability according to user demands. Therefore, we propose a community-driven modern web service, Hiplot (https://hiplot.org), with concise and top-quality data visualization applications for the life sciences and biomedical fields. This web service permits users to conveniently and interactively complete a few specialized visualization tasks that previously could only be conducted by senior bioinformatics or biostatistics researchers. It covers most of the daily demands of biomedical researchers with its equipped 240+ biomedical data visualization functions, involving basic statistics, multi-omics, regression, clustering, dimensional reduction, meta-analysis, survival analysis, risk modelling, etc. Moreover, to improve the efficiency in use and development of plugins, we introduced some core advantages on the client-/server-side of the website, such as spreadsheet-based data importing, cross-platform command-line controller (Hctl), multi-user plumber workers, JavaScript Object Notation-based plugin system, easy data/parameters, results and errors reproduction and real-time updates mode. Meanwhile, using demo/real data sets and benchmark tests, we explored statistical parameters, cancer genomic landscapes, disease risk factors and the performance of website based on selected native plugins. The statistics of visits and user numbers could further reflect the potential impact of this web service on relevant fields. Thus, researchers devoted to life and data sciences would benefit from this emerging and free web service.

We consider an Internet-of-Things (IoT) system in which a sensor observes a phenomenon of interest with exponentially distributed intervals and delivers updates to a monitor with random service times. At the monitor, the received updates are used to make decisions with deterministic or random intervals. For this system, we investigate the freshness of the received updates at decision epochs using the age upon decisions (AuDs) metric. With the first-come-first-served (FCFS) policy, theoretical results show that: 1) when the decisions are made with exponentially distributed intervals, the average AuD of the system is smaller when the service time (e.g., transmission time) is uniformly distributed than when it is exponentially distributed and would be the smallest if it is deterministic; 2) when the decisions are made periodically, the average AuD of the system is larger than, and decreases with decision rate to, the average AuD of the corresponding system with Poisson decision intervals; and 3) the probability of missing to use a received update for any decisions is decreasing with the decision rate and is the smallest if the service time is deterministic. When the last-come-first-served (LCFS) policy with preemption is used, we observe that systems with Poisson service processes perform the best while systems with periodic service processes perform the worst. For IoT-based monitoring systems, therefore, it is suggested to use deterministic monitoring schemes, deterministic transmitting schemes, and Poisson decision schemes so that the received updates are as fresh as possible at the time they are used to make decisions.