Nonlinear phononic crystals based on chains of disks alternating with toroidal structures

We study experimentally the acoustic response of a load-bearing, phononic crystal composed of alternating steel disks, and polytetrafluoroethylene o-rings under precompression. The crystal allows for axial, rocking, and shear-polarized wavemodes when excited by a broad-band signal applied off-axis. Finite element analysis is employed to determine the system’s wave modes. The nonlinear interaction between disks and o-rings supports a dynamic response that is tunable with variations in static precompression, leading to controllable frequency shifts in a large band gap. A modal analysis reveals that four of the six principal wave modes are susceptible to external precompression while two modes are not

Guest Editorial Special Issue on: Big Data Analytics in Intelligent Systems

The amount of information that is being created, every day, is quickly growing. As such, it is now more common than ever to deal with extremely large datasets. As systems develop and become more intelligent and adaptive, analysing their behaviour is a challenge. The heterogeneity, volume and speed of data generation are increasing rapidly. This is further exacerbated by the use of wireless networks, sensors, smartphones and the Internet. Such systems are capable of generating a phenomenal amount of information and the need to analyse their behaviour, to detect security anomalies or predict future demands for example, is becoming harder. Furthermore, securing such systems is a challenge. As threats evolve, so should security measures develop and adopt increasingly intelligent security techniques. Adaptive systems must be employed and existing methods built upon to provide well-structured defence in depth. Despite the clear need to develop effective protection methods, the task is a difficult one, as there are significant weaknesses in the existing security currently in place. Consequently, this special issue of the Journal of Computer Sciences and Applications discusses big data analytics in intelligent systems. The specific topics of discussion include the Internet of Things, Web Services, Cloud Computing, Security and Interconnected Systems

The thyroid secretion rate in the mouse and its relation to various physiological processes

Publication authorized may 12, 1948Digitized 2007 AES.Includes bibliographical references (pages 52-62)

Profiling Users in the Smart Grid

The implementation of the smart grid brings with it many new components that are fundamentally different to traditional power grid infrastructures. The most important addition brought by the smart grid is the application of the Advanced Metering Infrastructure (AMI). As part of the AMI, the smart meter device provides real time energy usage about the consumer to all of the smart grids stakeholders. Detailed statistics about a consumer’s energy usage can be accessed by the end user, utility companies and other parties. The problem, however, is in how to analyse, present and make best use of the data. This paper focuses on the data collected from the smart grid and how it can be used to detect abnormal user behaviour for energy monitoring applications. The proposed system employs a data classification technique to identify irregular energy usage in patterns generated by smart meters. The results show that it is possible to detect abnormal behaviour with an overall accuracy of 99.45% with 0.100 for sensitivity, 0.989 for specificity and an error of 0.006 using the LDC classifier

Digital Memories Based Mobile User Authentication for IoT

The increasing number of devices within the IoT is raising concerns over the efficiency and exploitability of existing authentication methods. The weaknesses of such methods, in particular passwords, are well documented. Although alternative methods have been proposed, they often rely on users being able to accurately recall complex and often unmemorable information. With the profusion of separate online accounts, this can often be a difficult task. The emerging digital memories concept involves the creation of a repository of memories specific to individuals. We believe this abundance of personal data can be utilised as a form of authentication. In this paper, we propose our digital memories based two-factor authentication mechanism, and also present our promising evaluation results. Keywords—Digital memories, authentication, IoT, securit

Don't break a leg: Running birds from quail to ostrich prioritise leg safety and economy in uneven terrain

Cursorial ground birds are paragons of bipedal running that span a 500-fold mass range from quail to ostrich. Here we investigate the task-level control priorities of cursorial birds by analysing how they negotiate single-step obstacles that create a conflict between body stability (attenuating deviations in body motion) and consistent leg force–length dynamics (for economy and leg safety). We also test the hypothesis that control priorities shift between body stability and leg safety with increasing body size, reflecting use of active control to overcome size-related challenges. Weight-support demands lead to a shift towards straighter legs and stiffer steady gait with increasing body size, but it remains unknown whether non-steady locomotor priorities diverge with size. We found that all measured species used a consistent obstacle negotiation strategy, involving unsteady body dynamics to minimise fluctuations in leg posture and loading across multiple steps, not directly prioritising body stability. Peak leg forces remained remarkably consistent across obstacle terrain, within 0.35 body weights of level running for obstacle heights from 0.1 to 0.5 times leg length. All species used similar stance leg actuation patterns, involving asymmetric force–length trajectories and posture-dependent actuation to add or remove energy depending on landing conditions. We present a simple stance leg model that explains key features of avian bipedal locomotion, and suggests economy as a key priority on both level and uneven terrain. We suggest that running ground birds target the closely coupled priorities of economy and leg safety as the direct imperatives of control, with adequate stability achieved through appropriately tuned intrinsic dynamics

Identifying Behavioural Changes for Health Monitoring Applications using the Advanced Metering Infrastructure

The rising demand for health and social care, and around the clock monitoring services, is increasing and are unsustainable under current care provisions and legislation. Consequently, a safe and independent living environment is hard to achieve; yet the detection of sudden or worsening changes in a patient’s condition is vital for early intervention. The use of smart technologies in primary care delivery is increasing significantly. However, substantial research gaps remain in non-invasive and cost effective monitoring technologies. Where such technologies are used, they are considered too intrusive and often incapable of being personalised to the individual needs of patients. The inability to learn the unique characteristics of patients and their conditions seriously limits the effectiveness of most current solutions. The smart metering infrastructure provides new possibilities for a variety of emerging applications that are unachievable using the traditional energy grid. Between now and 2020, UK energy suppliers will install and configure of 50 million smart meters therefore providing access to a highly accurate sensing network. Each smart meter records accurately the electrical load for a given property at 30 minute intervals, 24 hours a day. This granular data captures detailed habits and routines through the occupant’s interactions with electrical devices, enabling the detection and identification of alterations in behaviour. The research presented in this paper explores how this data could be used to achieve a safe living environment for people living with progressive neurodegenerative disorders, such as Dementia

A Smart Health Monitoring Technology

With the implementation of the Advanced Metering Infrastructure (AMI), comes the opportunity to gain valuable insights into an individual’s daily habits, patterns and routines. A vital part of the AMI is the smart meter. It enables the monitoring of a consumer’s electricity usage with a high degree of accuracy. Each device reports and records a consumer’s energy usage readings at regular intervals. This facilitates the identification of emerging abnormal behaviours and trends, which can provide operative monitoring for people living alone with various health conditions. Through profiling, the detection of sudden changes in behaviour is made possible, based on the daily activities a patient is expected to undertake during a 24-hour period. As such, this paper presents the development of a system which detects accurately the granular differences in energy usage which are the result of a change in an individual’s health state. Such a process provides accurate monitoring for people living with self-limiting conditions and enables an early intervention practice (EIP) when a patient’s condition is deteriorating. The results in this paper focus on one particular behavioural trend, the detection of sleep disturbances; which is related to various illnesses, such as depression and Alzheimer’s. The results demonstrate that it is possible to detect sleep pattern changes to an accuracy of 95.96% with 0.943 for sensitivity, 0.975 for specificity and an overall error of 0.040 when using the VPC Neural Network classifier. This type of behavioral detection can be used to provide a partial assessment of a patient’s wellbeing