Distributed and adaptive location identification system for mobile devices

Indoor location identification and navigation need to be as simple, seamless, and ubiquitous as its outdoor GPS-based counterpart is. It would be of great convenience to the mobile user to be able to continue navigating seamlessly as he or she moves from a GPS-clear outdoor environment into an indoor environment or a GPS-obstructed outdoor environment such as a tunnel or forest. Existing infrastructure-based indoor localization systems lack such capability, on top of potentially facing several critical technical challenges such as increased cost of installation, centralization, lack of reliability, poor localization accuracy, poor adaptation to the dynamics of the surrounding environment, latency, system-level and computational complexities, repetitive labor-intensive parameter tuning, and user privacy. To this end, this paper presents a novel mechanism with the potential to overcome most (if not all) of the abovementioned challenges. The proposed mechanism is simple, distributed, adaptive, collaborative, and cost-effective. Based on the proposed algorithm, a mobile blind device can potentially utilize, as GPS-like reference nodes, either in-range location-aware compatible mobile devices or preinstalled low-cost infrastructure-less location-aware beacon nodes. The proposed approach is model-based and calibration-free that uses the received signal strength to periodically and collaboratively measure and update the radio frequency characteristics of the operating environment to estimate the distances to the reference nodes. Trilateration is then used by the blind device to identify its own location, similar to that used in the GPS-based system. Simulation and empirical testing ascertained that the proposed approach can potentially be the core of future indoor and GPS-obstructed environments

PANC Study (Pancreatitis: A National Cohort Study): national cohort study examining the first 30 days from presentation of acute pancreatitis in the UK

Abstract Background Acute pancreatitis is a common, yet complex, emergency surgical presentation. Multiple guidelines exist and management can vary significantly. The aim of this first UK, multicentre, prospective cohort study was to assess the variation in management of acute pancreatitis to guide resource planning and optimize treatment. Methods All patients aged greater than or equal to 18 years presenting with acute pancreatitis, as per the Atlanta criteria, from March to April 2021 were eligible for inclusion and followed up for 30 days. Anonymized data were uploaded to a secure electronic database in line with local governance approvals. Results A total of 113 hospitals contributed data on 2580 patients, with an equal sex distribution and a mean age of 57 years. The aetiology was gallstones in 50.6 per cent, with idiopathic the next most common (22.4 per cent). In addition to the 7.6 per cent with a diagnosis of chronic pancreatitis, 20.1 per cent of patients had a previous episode of acute pancreatitis. One in 20 patients were classed as having severe pancreatitis, as per the Atlanta criteria. The overall mortality rate was 2.3 per cent at 30 days, but rose to one in three in the severe group. Predictors of death included male sex, increased age, and frailty; previous acute pancreatitis and gallstones as aetiologies were protective. Smoking status and body mass index did not affect death. Conclusion Most patients presenting with acute pancreatitis have a mild, self-limiting disease. Rates of patients with idiopathic pancreatitis are high. Recurrent attacks of pancreatitis are common, but are likely to have reduced risk of death on subsequent admissions. </jats:sec

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Capacity Analysis Based on Using Adaptive Antenna Arrays Jointly Optimized for Trunking and Spatial Efficiency

Cellular systems using adaptive antennas for spatial processing have been shown to provide an increase in capacity. When employing adaptive antennas, the standard approach to achieve capacity gain has been to maximize the link quality between the mobile and base station via optimum combining. This paper presents an alternative optimization approach based on jointly optimizing the adaptive antenna array to maximize the spatial and trunking efficiencies. An upper bound on the joint optimization was formulated to evaluate the capacity. Based on this upper bound, the effect of using adaptive antenna arrays to maximize the trunking efficiency was subsequently an outstanding issue. Monte Carlo simulations, substantiated with theoretical analysis, were used to evaluate this issue. Based on the analysis, utilizing the adaptive antennas to optimize trunking efficiency could increase the capacity by 2 to 4 times

Distributed Visual Crowdsensing Framework for Area Coverage in Resource Constrained Environments

Visual crowdsensing applications using built-in cameras in smartphones have recently attracted researchers’ interest. Making the most out of the limited resources to acquire the most helpful images from the public is a challenge in disaster recovery applications. Proposed solutions should adequately address several constraints, including limited bandwidth, limited energy resources, and interrupted communication links with the command center or server. Furthermore, data redundancy is considered one of the main challenges in visual crowdsensing. In distributed visual crowdsensing systems, photo sharing duplicates and expands the amount of data stored on each sensor node. As a result, if any node can communicate with the server, then more photos of the target region would be available to the server. Methods for recognizing and removing redundant data provide a range of benefits, including decreased transmission costs and energy consumption overall. To handle the interrupted communication with the server and the restricted resources of the sensor nodes, this paper proposes a distributed visual crowdsensing system for full-view area coverage. The target area is divided into virtual sub-regions, each of which is represented by a set of boundary points of interest. Then, based on the criteria for full-view area coverage, a specific data structure theme is developed to represent each photo with a set of features. The geometric context parameters of each photo are utilized to extract the features of each photo based on the full-view area coverage criteria. Finally, data redundancy removal algorithms are implemented based on the proposed clustering scheme to eliminate duplicate photos. As a result, each sensor node may filter redundant photographs in dispersed contexts without requiring high computational complexity, resources, or global awareness of all photos from all sensor nodes inside the target area. Compared to the most recent state-of-the-art, the improvement ratio of the added values of the photos provided by the proposed method is more than 38%. In terms of traffic transfer, the proposed method requires fewer data to be transferred between sensor nodes and between sensor nodes and the command center. The overall reduction in traffic exceeds 20% and the overall savings in energy consumption is more than 25%. It was evident that in the proposed system, sending photos between sensor nodes, as well as between sensor nodes and the command center, consumes less energy than existing approaches due to the considerable amount of photo exchange required. Thus, the proposed technique effectively transfers only the most valuable photos needed

Collaborative Indoor Access Point Localization Using Autonomous Mobile Robot Swarm

Localization of access points has become an important research problem due to the wide range of applications it addresses such as dismantling critical security threats caused by rogue access points or optimizing wireless coverage of access points within a service area. Existing proposed solutions have mostly relied on theoretical hypotheses or computer simulation to demonstrate the efficiency of their methods. The techniques that rely on estimating the distance using samples of the received signal strength usually assume prior knowledge of the signal propagation characteristics of the indoor environment in hand and tend to take a relatively large number of uniformly distributed random samples. This paper presents an efficient and practical collaborative approach to detect the location of an access point in an indoor environment without any prior knowledge of the environment. The proposed approach comprises a swarm of wirelessly connected mobile robots that collaboratively and autonomously collect a relatively small number of non-uniformly distributed random samples of the access point’s received signal strength. These samples are used to efficiently and accurately estimate the location of the access point. The experimental testing verified that the proposed approach can identify the location of the access point in an accurate and efficient manner