Frequency signatured directly printable humidity sensing tag using organic electronics

In this paper chipless RFID tag, capable of carrying 9-bit data is presented. The tag is optimized for several flexible substrates. With growing information and communication technology, sensor integration with data transmission has gained significant attention. Therefore, the tag with the same dimension is then optimized using paper substrate. For different values of permittivity, the relative humidity is observed. Hence, besides carrying information bits, the tag is capable of monitoring and sensing the humidity. The overall dimension of the tag comprising of 9 ring slot resonators is 7 mm. Due to its optimization on the paper substrate, the tag can be an ideal choice for deploying in various low-cost sensing application

Recognizing activities of daily living from patterns and extraction of web knowledge

The ability to infer and anticipate the activities of elderly individuals with cognitive impairment has made it possible to provide timely assistance and support, which in turn allows them to lead an independent life. Traditional non-intrusive activity recognition approaches are dependent on the use of various machine learning techniques to infer activities given the collected object usage data. Current activity recognition approaches are also based on knowledge driven techniques that require extensive modelling of the activities that needs to be inferred. These models can be seen as too restrictive, prescriptive and static as they are based on a finite set of activities. In this paper, we propose a novel “top down” approach to recognising activities based on object usage data, which detects patterns associated with the activity-object relationship and utilizes web knowledge in order to build dynamic activity models based on the objects used to perform the activity. Experimental results using the Kasteren dataset shows it is comparable to existing approaches

Unmanned aerial vehicles enabled IoT platform for disaster management

© 2019 by the authors. Efficient and reliable systems are required to detect and monitor disasters such as wildfires as well as to notify the people in the disaster-affected areas. Internet of Things (IoT) is the key paradigm that can address the multitude problems related to disaster management. In addition, an unmanned aerial vehicles (UAVs)-enabled IoT platform connected via cellular network can further enhance the robustness of the disaster management system. The UAV-enabled IoT platform is based on three main research areas: (i) ground IoT network; (ii) communication technologies for ground and aerial connectivity; and (iii) data analytics. In this paper, we provide a holistic view of a UAVs-enabled IoT platform which can provide ubiquitous connectivity to both aerial and ground users in challenging environments such as wildfire management. We then highlight key challenges for the design of an efficient and reliable IoT platform. We detail a case study targeting the design of an efficient ground IoT network that can detect and monitor fire and send notifications to people using named data networking (NDN) architecture. The use of NDN architecture in a sensor network for IoT integrates pull-based communication to enable reliable and efficient message dissemination in the network and to notify the users as soon as possible in case of disastrous situations. The results of the case study show the enormous impact on the performance of IoT platform for wildfire management. Lastly, we draw the conclusion and outline future research directions in this field

White Blood Cell to Platelet Ratio as a Marker of Adverse Outcome in Organophosphate Poisoning: A Retrospective Cross-Sectional Survey

Introduction: Organophosphorus compounds are pesticides commonly used for agricultural purposes. However, by nature they are poisonous, and administration either accidental or intentional is a medical emergency requiring prompt evaluation and treatment, and can even lead to death. In addition due to the ease of their availability, they are commonly used for self-harm/suicidal purposes. Many of the patients are initially managed at primary or secondary healthcare setups before being referred to tertiary care hospitals. The purpose of our study is to find a prognostic marker in the initial blood work of these patients. Materials and Methods: A total of 46 patients were included in this retrospective cross-sectional survey conducted at the Department of Emergency Medicine, Holy Family Hospital, Rawalpindi. Data were collected from patient files using specific questionnaires. Outcomes were defined in terms of Emergency Department disposition. Data were analysed using SPSS v25. A univariate analysis, followed by Spearman’s Correlation was used. Results: Patients with a higher WBC to Platelet ratio had worse outcomes. The Spearman’s rho correlation coefficient was calculated and a moderately strong correlation (rho = .458, p < .001) was found. Conclusion: WBC to Platelet ratio is a hematological parameter determined to be most strongly correlated with adverse outcomes in Organophosphate Poisoning. It has a statistically significant stronger correlation than the WBC count alone. However, further extensive and focused studies are needed to corroborate these findings and substantiate them as a definite marker of prognostic significance. Keywords: Organophosphate Poisoning; Emergency Medicine; ED; White Blood Cells; Emergency Care; Patient Outcome Assessment

Ad hoc and Opportunistic Routing in Static Scatternet Environment

Abstract Peer-to-peer connectivity between mobile phones using technologies such as Bluetooth has given a new dimension to the mobile communication. Peers through the help of various underlying protocols can form piconets and scatternets to transparently communicate the content across the network. There however are issues like reliability in communication, delay and the cost of communication that need to be considered before resorting to this form of communication. This paper presents a study where opportunistic concept such as Bubble Rap is tested in Bluetooth ad hoc networking environment. The notion behind this research is to study the properties of these two networking environments, since opportunistic networks are derived from ad hoc networks. Thus, study of these two different environments yet related to each other may help us find new ways of message forwarding in Bluetooth communication environment. This paper is aimed at investigating the behaviour of nodes present in Bluetooth static scatternet environment by 1) studying message transfer from a source to destination using traditional ad hoc communication protocols such as AODV and 2) message transfer using opportunistic algorithms such as Bubble Rap on top of traditional ad hoc communication. This paper also proposes a concept of ranking to transfer messages to the node that has higher social centrality ranking compared to the current node. Nodes with varying social ranking are allowed to join piconets and forward messages based on Bubble Rap concept in scatternet environment. In BR algorithm, nodes forward messages to only those encountering nodes which are more popular than the current node

Activities of daily life recognition using process representation modelling to support intention analysis

Purpose – This paper aims to focus on applying a range of traditional classification- and semantic reasoning-based techniques to recognise activities of daily life (ADLs). ADL recognition plays an important role in tracking functional decline among elderly people who suffer from Alzheimer’s disease. Accurate recognition enables smart environments to support and assist the elderly to lead an independent life for as long as possible. However, the ability to represent the complex structure of an ADL in a flexible manner remains a challenge. Design/methodology/approach – This paper presents an ADL recognition approach, which uses a hierarchical structure for the representation and modelling of the activities, its associated tasks and their relationships. This study describes an approach in constructing ADLs based on a task-specific and intention-oriented plan representation language called Asbru. The proposed method is particularly flexible and adaptable for caregivers to be able to model daily schedules for Alzheimer’s patients. Findings – A proof of concept prototype evaluation has been conducted for the validation of the proposed ADL recognition engine, which has comparable recognition results with existing ADL recognition approaches. Originality/value – The work presented in this paper is novel, as the developed ADL recognition approach takes into account all relationships and dependencies within the modelled ADLs. This is very useful when conducting activity recognition with very limited features

Frequent Pattern Mining Algorithms for Finding Associated Frequent Patterns for Data Streams: A Survey

Pattern recognition is seen as a major challenge within the field of data mining and knowledge discovery. For the work in this paper, we have analyzed a range of widely used algorithms for finding frequent patterns with the purpose of discovering how these algorithms can be used to obtain frequent patterns over large transactional databases. This has been presented in the form of a comparative study of the following algorithms: Apriori algorithm, Frequent Pattern (FP) Growth algorithm, Rapid Association Rule Mining (RARM), ECLAT algorithm and Associated Sensor Pattern Mining of Data Stream (ASPMS) frequent pattern mining algorithms. This study also focuses on each of the algorithm’s strengths and weaknesses for finding patterns among large item sets in database systems

Recent advances in information-centric networking based internet of things (ICN-IoT)

Information-Centric Networking (ICN) is being realized as a promising approach to accomplish the shortcomings of current IP-address based networking. ICN models are based on naming the content to get rid of address-space scarcity, accessing the content via name-based-routing, caching the content at intermediate nodes to provide reliable, efficient data delivery and self-certifying contents to ensure better security. Obvious benefits of ICN in terms of fast and efficient data delivery and improved reliability raises ICN as highly promising networking model for Internet of Things (IoTs) like environments. IoT aims to connect anyone and/or anything at any time by any path on any place. From last decade, IoTs attracts both industry and research communities. IoTs is an emerging research field and still in its infancy. Thus, this paper presents the potential of ICN for IoTs by providing state-of-the-art literature survey. We discuss briefly the feasibility of ICN features and their models (and architectures) in the context of IoT. Subsequently, we present a comprehensive survey on ICN based caching, naming, security and mobility approaches for IoTs with appropriate classification. Furthermore, we present operating systems (OS) and simulation tools for ICN-IoT. Finally, we provide important research challenges and issues faced by ICN for IoTs