Real-time electrochemical monitoring of covalent bond formation in solution via nanoparticle-electrode collisions

We describe an alternative electrochemical technique to monitor covalent bond formation in real-time using nanoparticle-electrode collisions. The method is based on recognising the redox current when MP-11 functionalised chemical reduced graphene oxide (rGO) nanosheets collide with Lomant's reagent modified gold microelectrode. This facile and highly sensitive monitoring method can be useful for investigating the fundamental of single-molecule reactions.

Real-time traffic flow statistical analysis based on network-constrained moving object trajectories

In this paper, we propose a novel traffic flow analysis method, Network-constrained Moving Objects Database based Traffic Flow Statistical Analysis (NMOD-TFSA) model. By sampling and analyzing the spatial-temporal trajectories of network constrained moving objects, NMOD-TFSA can get the real-time traffic conditions of the transportation network. The experimental results show that, compared with the floating-car methods which are widely used in current traffic flow analyzing systems, NMOD-TFSA provides an improved performance in terms of communication costs and statistical accuracy.

A mutation-based evolving neural network model and its application to condition monitoring

Data analysis using intelligent systems is a key solution to many industrial problems. In this paper, a mutation-based evolving artificial neural network, which is based on an integration of the Fuzzy ARTMAP (FAM) neural network and evolutionary programming (EP), is proposed. The proposed FAMEP model is applied to detect and classify possible faults from a number of sensory signals of a circulating water system in a power generation plant. The efficiency of FAM-EP is assessed and compared with that of the original FAM network in terms of classification accuracy as well as network complexity. In addition, the bootstrap method is used to quantify the performance statistically. The results positively demonstrate the usefulness of FAM-EP in tackling data classification problems.

Maximizing real-time streaming services based on a multi-servers networking framework

In recent years, we have witnessed substantial exploitation of real-time streaming applications, such as video surveillance system on road crosses of a city. So far, real world applications mainly rely on the traditional well-known client-server and peer-to-peer schemes as the fundamental mechanism for communication. However, due to the limited resources on each terminal device in the applications, these two schemes cannot well leverage the processing capability between the source and destination of the video traffic, which leads to limited streaming services. For this reason, many QoS sensitive application cannot be supported in the real world. In this paper, we are motivated to address this problem by proposing a novel multi-server based framework. In this framework, multiple servers collaborate with each other to form a virtual server (also called cloud-server), and provide high-quality services such as real-time streams delivery and storage. Based on this framework, we further introduce a (1-?) approximation algorithm to solve the NP-complete "maximum services"(MS) problem with the intention of handling large number of streaming flows originated by networks and maximizing the total number of services. Moreover, in order to backup the streaming data for later retrieval, based on the framework, an algorithm is proposed to implement backups and maximize streaming flows simultaneously. We conduct a series of experiments based on simulations to evaluate the performance of the newly proposed framework. We also compare our scheme to several traditional solutions. The results suggest that our proposed scheme significantly outperforms the traditional solutions.

Measurement and data transmission validity of a multi-biosensor system for real-time remote exercise monitoring among cardiac patients

Background: Remote telemonitoring holds great potential to augment management of patients with coronary heart disease (CHD) and atrial fibrillation (AF) by enabling regular physiological monitoring during physical activity. Remote physiological monitoring may improve home and community exercise-based cardiac rehabilitation (exCR) programs and could improve assessment of the impact and management of pharmacological interventions for heart rate control in individuals with AF.

Objective: Our aim was to evaluate the measurement validity and data transmission reliability of a remote telemonitoring system comprising a wireless multi-parameter physiological sensor, custom mobile app, and middleware platform, among individuals in sinus rhythm and AF.

Methods: Participants in sinus rhythm and with AF undertook simulated daily activities, low, moderate, and/or high intensity exercise. Remote monitoring system heart rate and respiratory rate were compared to reference measures (12-lead ECG and indirect calorimeter). Wireless data transmission loss was calculated between the sensor, mobile app, and remote Internet server.

Results: Median heart rate (-0.30 to 1.10 b∙min-1) and respiratory rate (-1.25 to 0.39 br∙min-1) measurement biases were small, yet statistically significant (all P≤.003) due to the large number of observations. Measurement reliability was generally excellent (rho=.87-.97, all P<.001; intraclass correlation coefficient [ICC]=.94-.98, all P<.001; coefficient of variation [CV]=2.24-7.94%), although respiratory rate measurement reliability was poor among AF participants (rho=.43, P<.001; ICC=.55, P<.001; CV=16.61%). Data loss was minimal (<5%) when all system components were active; however, instability of the network hosting the remote data capture server resulted in data loss at the remote Internet server during some trials.

Conclusions: System validity was sufficient for remote monitoring of heart and respiratory rates across a range of exercise intensities. Remote exercise monitoring has potential to augment current exCR and heart rate control management approaches by enabling the provision of individually tailored care to individuals outside traditional clinical environments.

Emission-based condition monitoring technique for ZnO surge arrester

In this paper, electromagnetic emission at the frequency range of 30MHz to 300MHz is used to detect physical defects on the 22kV outdoor zinc-oxide (ZnO) surge arresters. Different weather conditions combining with artificially created pollution were produced in a laboratory environment and measurements were recorded over a fixed period of time. Pollution due to fine dust particles has been created according to IEC standard under both wet and dry conditions. The aim is to detect the defects (bushing damage) when the surge arrester is subjected to various weather and surface condition. The collected electromagnetic signals were sampled and analyzed using analysis tools such as the autocorrelation coefficient and Wigner-Ville distribution. The results from the present paper indicate that electromagnetic radiation from the defects on surge arrester combining with the adequate analysis tools can be used as a valuable diagnostic tool for power system operator.

A brief review on offshore wind turbine fault detection and recent development in condition monitoring based maintenance system

Offshore wind turbine requires more systematized operation and maintenance strategies to ensure systems are harmless, profitable and cost-effective. Condition monitoring and fault diagnostic systems ominously plays an important role in offshore wind turbine in order to cut down maintenance and operational costs. Condition monitoring techniques which describing complex faults and failure mode types and their generated traceable signs to provide cost-effective condition monitoring and predictive maintenance and their diagnostic schemes. Continuously monitor the condition of critical parts are the most efficient way to improve reliability of wind turbine. Implementation of Condition Based Maintenance (CBM) strategy provides right time maintenance decisions and Predictive Health Monitoring (PHM) data to overcome breakdown and machine downtime. Fault detection and CBM implementation is challenging for off shore wind farm due to the complexity of remote sensing, components health and predictive assessment, data collection, data analysis, data handling, state recognition, and advisory decision. The rapid expansion of wind farms, advanced technological development and harsh installation sites needs a successful CM approach. This paper aims to review brief status of recent development of CM techniques and focusing with major faults takes place in gear box and bearing, rotor and blade, pitch, yaw and tower system and generator and control system.

A video-based real-time vehicle detection method by classified background learning

A new two-level real-time vehicle detection method is proposed in order to meet the robustness and efficiency requirements of real world applications. At the high level, pixels of the background image are classified into three categories according to the characteristics of Red, Green, Blue (RGB) curves. The robustness of the classification is further enhanced by using
line detection and pattern connectivity. At the lower level, an exponential forgetting algorithm with adaptive parameters for different categories is utilised to calculate the background and reduce the distortion by the small motion of video cameras. Scene tests show that the proposed method is more robust and faster than previous methods, which is very suitable for real-time vehicle detection in outdoor environments, especially concerning locations where the level of illumination changes frequently and speed detection is important.

RFID-based real-time smart waste management system

In an environmental context, the use of RFID (radio frequency identification) and load cell sensor technology can be employed for not only bringing down waste management costs, but also to facilitate automating and streamlining waste (e.g., garbage, recycling, and green) identification and weight measurement processes for designing smart waste management systems. In this paper, we outline a RFID and sensor model for designing a system in real-time waste management. An application of the architecture is described in the area of RFID and sensor based automatic waste identity, weight, and stolen bins identification system (WIWSBIS).

Is internet-based CBT for panic disorder and agoraphobia as effective as face-to-face CBT

This study compared Panic Online (PO), an internet-based CBT intervention, to best-practice face-to-face CBT for people with panic disorder with or without agoraphobia. Eighty-six people with a primary diagnosis of panic disorder were recruited from Victoria, Australia. Participants were randomly assigned to either PO (n = 46) or best practice face-to-face CBT (n = 40). Effects of the internet-based CBT program were found to be comparable to those of face-to-face CBT. Both interventions produced significant reductions in panic disorder and agoraphobia clinician severity ratings, self reported panic disorder severity and panic attack frequency, measures of depression, anxiety, stress and panic related cognitions, and displayed improvements in quality of life. Participants rated both treatment conditions as equally credible and satisfying. Participants in the face-to-face CBT treatment group cited higher enjoyment with communicating with their therapist. Consistent with this, therapists’ ratings for compliance to treatment and understanding of the CBT material was higher in the face-to-face CBT treatment group. PO required significantly less therapist time than the face-to-face CBT condition.

Internet-based treatment for panic disorder : does frequency of therapist contact make a difference?

Internet-based interventions with therapist support have proven effective for treating a range of mental health conditions. This study examined whether frequency of therapist contact affected treatment outcomes. Fifty-seven people with panic disorder (including 32 with agoraphobia) were randomly allocated to an 8-week Internet-based cognitive behavioural treatment intervention (Panic Online) with either frequent (three e-mails per week) or infrequent (one e-mail per week) support from a psychologist. Posttreatment, intention-to-treat analyses revealed that both treatments were effective at improving panic disorder and agoraphobia severity ratings, panicrelated cognitions, negative affect, and psychological and physical quality of life domains, with no differences between conditions. High end-state functioning was achieved by 28.6% of the frequent and infrequent participants, respectively. Therapist alliance, treatment credibility, and satisfaction also did not differ between groups, despite significantly greater therapist time invested in the frequent contact condition. The results provide evidence that the effectiveness of Internet-based mental health interventions may be independent of the frequency of therapist support and may, therefore, be more cost-effective than previously reported.

A cache-based mobility-aware scheme for real-time continuous media delivery in wireless networks

Providing real-time or continuous media (CM) application services in wireless networks poses a significant challenge, as it requires timely delivery of data in a best-effort network. In this paper, we propose a cache-based scheme for mobility-aware, CM applications. The proposed scheme exploits a previously proposed caching strategy to complement Mobile-IP by placing services closer to migrated mobile nodes. The central idea of this work is based on the migration of sessions in order to facilitate uninterrupted delivery of CM in mobile environments. The performance of the proposed scheme is investigated by simulation studies. In particular, the effect of the proposed scheme on several QoS parameters under varying conditions of mobility and CM data is measured.