An area is k-covered if every point of the area is covered by at least k sensors. K-coverage is necessary for many applications, such as intrusion detection, data gathering, and object tracking. It is also desirable in situations where a stronger environmental monitoring capability is desired, such as military applications. In this paper, we study the problem of k-coverage in deterministic homogeneous deployments of sensors. We examine the three regular sensor deployments - triangular, square and hexagonal deployments - for k-coverage of the deployment area, for k ≥ 1. We compare the three regular deployments in terms of sensor density. For each deployment, we compute an upper bound and a lower bound on the optimal distance of sensors from each other that ensure k-coverage of the area. We present the results for each k from 1 to 20 and show that the required number of sensors to k-cover the area using uniform random deployment is approximately 3-10 times higher than regular deployments.
This paper presents a real-time gesture recognition technique based on
RFID technology. Inexpensive and unintrusive passive RFID tags can be easily attached
to or interweaved into user clothes. The tag readings in an RFID-enabled
environment can then be used to recognize the user gestures in order to enable
intuitive human-computer interaction. People can interact with large public displays
without the need to carry a dedicated device, which can improve interactive
advertisement in public places. In this paper, multiple hypotheses tracking is used
to track the motion patterns of passive RFID tags. Despite the reading uncertainties
inherent in passive RFID technology, the experiments show that the presented
online gesture recognition technique has an accuracy of up to 96%.
With the continual expansion of multimedia and Internet applications, the needs and requirements of advanced technologies, grew and evolved. With the increasing use of multimedia technologies, image compression techniques require higher performance as well as new features. Significant progress has recently been made in image compression techniques using discrete wavelet transforms. The overall performance of these schemes may be further improved by properly designing of efficient entropy coders. In this paper, we describe an efficient architecture for JPEG2000 entropy coder, which is a new standard to address the needs in the specific area of still image encoding. Our proposed architecture consists of two main parts, the coefficient bit modeler (CBM) and the binary arithmetic coder (BAC), which communicate through a FIFO buffer. Optimizations have been made in our proposed architecture to reduce accesses to memories. Our Proposed architecture is fast and modular and is suitable for real-time applications.
Abstract Background/Aims People with Sjögren's Syndrome (SS) experience a range of symptoms, including dryness, pain, fatigue, and poor sleep. Pharmacological management is limited, and SS patients may not have timely access to non-pharmacological support with these symptoms. Accessible evidence-based support via an app may benefit some. An evidence-based app (Sjogo) was co-developed with SS patients through a series of focus groups and workshops (n = 7). Alongside the workshops, behaviour change techniques and evidence-based intervention components were identified from the literature and known evidence-based interventions and were discussed with participants in focus groups. An app was developed containing active ingredients (e.g. features supporting behaviour change, validation of experiences, reflective activity diary, goal setting, cognitive behaviour therapy for sleep) to facilitate participation in daily activities and support symptom management. An additional control app was developed which contained “information only” content. We conducted a fully remote pilot feasibility RCT of the app. The aim of the study was to test trial procedures including recruitment rates, outcome completion, and engagement with the app. Methods The Sjogo app was released internationally for 8 weeks on Android and iOS app stores in January 2021. Potential participants were alerted to the trial through social media and patient groups. Those who downloaded the app were guided through in-app study procedures (screening, informed consent, demographic questions and baseline symptom, patient activation and quality of life outcome completion). Outcome measures included ESSPRI, Modified Fatigue Impact Scale, depression (VAS), anxiety (VAS), Sleep Condition Index, PAM-10 and ICECAP-A. Participants were randomised to an information-version (control) or full-version of Sjogo containing features supporting behaviour change. Users could engage with Sjogo as they wished and were asked to complete outcomes at baseline, 5 and 10 weeks. Results 996 participants from 33 countries downloaded Sjogo, with 617 (61.95%) completing the onboarding procedures and consenting to participate in the study. These participants were randomised to the full-version of the app (n = 318) or control-version (n = 299). Participants were mostly female (95.62%) iOS users (55.11%) from the UK (54.62%) or USA (28.92%) with a mean age of 50.97 (SD 13.75). Outcome completion rates at 5 and 10 weeks were 29.24% and 13.52% respectively for the full-version and 44.48% and 28.42% for the control-version. Conclusion Completion rates demonstrate that Sjogo can be evaluated in a real-world context in a fully powered RCT with large numbers of participants over a short timescale. However, maintaining engagement rates is challenging. App design could be optimised to maintain effective engagement with the app and support behaviour change. A process evaluation which includes further analysis of app engagement data and interviews with participants will further inform improvements to app content, features and trial procedures. Disclosure C. McCallum: None. P. Asadzadeh Birjandi: None. E. Pakpahan: None. M. Campbell: None. J. Vines: None. K. McCay: None. E. McColl: None. K. Hackett: None.
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