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In this paper we introduce a new methodology that integrates system resilience engineering and hazard analysis into complex system design. We then demonstrate its performance by applying it to the design of a Prognosis and Health Monitoring (PHM) system for an ocean current power generator. Three common methodologies for system hazard analysis were tested by applying them to the PHM system's network topology architecture; STAMP-based Process Analysis (STPA), Hazard and Operability Analysis (HAZOP), and a Resilience Engineering, Heuristic-based approach. While all three approaches adequately revealed most PHM system hazards, which assisted in identifying the means with which to mitigate them, none of the approaches fully addressed the multi-state dimensionality of the sub-components of the system, missing risky and hazardous scenarios. We developed the System Hazard Indication and Extraction Learning Diagnosis (SHIELD) methodology for system hazard analysis and resilient design. SHIELD integrates state space analysis into the hazard analysis process in order to facilitate the location of undiscovered hazard scenarios. Our approach uses recursive, top-down system decomposition with subsystem, interface, and process cycle identification. Then, a bottom-up recursive evaluation is completed where we analyze the subsystem state space and state transitions with regard to hazards/failures in process cycles. This yields a comprehensive list of failure states and scenarios. Finally, a top-down prioritized application of resilient engineering heuristics which address hazard scenarios is prescribed. This final phase results in a comprehensive, complete analysis of complex system architectures forcing resilience into the final system design.

Various implementations of wireless sensor networks (i.e. personal area-, wireless body area-networks) are prone to node and network failures by such characteristics as limited node energy resources and hardware damage incurred from their surrounding environment (i.e. flooding, forest fires, a patient falling). This may jeopardize their reliability to act as early warning systems, monitoring systems for patients and athletes, and industrial and environmental observation networks. Following the current trend and widespread use of hand held, mobile communication devices, we outline an application architecture designed to detect and predict faulty nodes in wireless sensor networks. Furthermore, we implement our design as a proof of concept prototype for Android-based smartphones, which may be extended to develop other applications used for monitoring networked wireless personal area and body sensors used in other capacities. We have conducted several preliminary experiments to demonstrate the use of our design, which is capable of monitoring networks of wireless sensor devices and predicting node faults based on several localized metrics. As attributes of such networks may change over time, any models generated when the application is initialized must be updated periodically such that the applied machine learning algorithm maintains high levels of both accuracy and precision. The application is designed to discover node faults and, once identified, alert the user so that appropriate action may be taken.

This paper describes a pattern for the architecture of web-based wireless sensor network monitoring. Sensor nodes are used to measure characteristics of the physical environment and sensed data is stored on the Internet using web-based technologies. Users can access data remotely as long as they have Internet connectivity. Many wireless sensor network applications developed today use smartphones as a gateway between the sensor network or the user, and the Internet. This allows the sensor network and/or the users to be mobile. Implementation of a web-based wireless sensor network architecture, that uses smartphones, provides a scalable solution with applicability in many areas such as healthcare, environmental monitoring, border security, structural health monitoring, and many more.

We describe a wireless link management framework for an ocean platform-to-shore communication system that uses time series forecasting to predict the available link capacity using ocean platform sensor data metrics to boost link robustness and to efficiently manage quality of service. Based on the predicted link capacity the OCTT Wireless Link (OWL) manager coordinates transmission scheduling of XML/HTTP sensor data at the web service layer and controls queue management for IP packet routing. To validate our framework, we developed a link management tool, the OCTT Wireless Link (OWL) manager to ensure optimal throughput and quality of service (QoS) for the wireless communication system linking ocean-based instrumented platforms with users on the shore. OWL applies sensor fusion to the platform attribute data which is then used to forecast the throughput of the wireless link in the harsh and rapidly fluctuating oceanic environment. OWL continually sends this forecast to the Queue Manager (QM) and when the signal power is forecast to drop outside the ideal range, using Linux networking tools, OWL provides bandwidth provisioning at the IP layer over each of the wireless radios in the network. This article describes our work on this project and experimentation with the OWL manager applied to sensory data collected from early stage OCTT platform testing.

This paper presents a REST-compliant service oriented architecture of a web-based heterogeneous wireless sensor network monitoring system that has applicability in remote patient monitoring in healthcare. As smartphone and webpage technologies are ubiquitous nowadays, our architecture uses smartphone as a gateway between the data collected and the Internet. The system uses a heterogeneous WSN consisting of environmental sensors, medical sensors, and smartphone's internal sensors. To validate the system, a prototype experiment of the system is implemented using Android smartphone platform, Crossbow Micaz motes, and Alive Technology hart and activity monitor sensor.

This paper details the architecture and describes the preliminary experimentation with the proposed framework for anomaly detection in medical wireless body area networks for ubiquitous patient and healthcare monitoring. The architecture integrates novel data mining and machine learning algorithms with modern sensor fusion techniques. Knowing wireless sensor networks are prone to failures resulting from their limitations (i.e. limited energy resources and computational power), using this framework, the authors can distinguish between irregular variations in the physiological parameters of the monitored patient and faulty sensor data, to ensure reliable operations and real time global monitoring from smart devices. Sensor nodes are used to measure characteristics of the patient and the sensed data is stored on the local processing unit. Authorized users may access this patient data remotely as long as they maintain connectivity with their application enabled smart device. Anomalous o...

Wireless Sensor Networks are vulnerable to a plethora of different fault types and external attacks after their deployment. We focus on sensor networks used in healthcare applications for vital sign collection from remotely monitored patients. These types of personal area networks must be robust and resilient to sensor failures as their capabilities encompass highly critical systems. Our objective is to propose an anomaly detection algorithm for medical wireless sensor networks. Our proposed approach firstly classifies instances of sensed patient attributes as normal and abnormal. Once we detect an abnormal instance, we use regression prediction to discern between a faulty sensor reading and a patient entering into a critical state. Our experimental results on real patient datasets show that our proposed approach is able to quickly detect patient anomalies and sensor faults with high detection accuracy while maintaining a low false alarm ratio.

The System Hazard Indication and Extraction Learning Diagnosis (SHIELD) methodology was developed as a novel method to perform system hazard analysis and resilient design. In an earlier paper we described SHIELD conceptually and outlined the details necessary to conduct the analysis manually. This approach integrates state space examination into the analysis process in order to facilitate efficient and comprehensive identification of undiscovered risks and hazard scenarios. SHIELD requires that three phases be performed serially to achieve a system hazard evaluation: decomposition, evaluation and prescription. The first phase of SHIELD, decomposition, breaks the system down hierarchically and recursively into smaller components so that the state space associated with each component is more manageable for the user. In the evaluation phase experts analyze the associated state space and transitions for each component, recursively, bottom-up. The prescription phase applies a set of heuristics to the results from the preceding phase to reduce system hazard. The main contribution of this paper is the automation of the methodology to reduce the effort used for analysis without sacrificing accuracy or overlooking hazardous state combinations. We describe in detail our automation concept and preliminary tests with the prototype.

To ascertain specific features in bio-/medical-images, a new avenue of using the so-called Needleman-Wunsch (NW) and Smith-Waterman (SW) algorithms (of bioinformatics) is indicated. In general, NW/SW algorithms are adopted in genomic science to obtain optimal (global and local) alignment of two linear sequences (like DNA nucleotide bases) to determine the similarity features between them and such 1D-sequence algorithms are presently extended to compare 2D-images via binary correlation. The efficacy of the proposed method is tested with synthetic images and a brain scan image. Thus, the way of finding the location of a distinct part in a synthetic image and that of a tumour in the brain scan image is demonstrated.

Time-evolution and hence, forecasting the growth profiles of business-centric technoeconomics are ascertained. As an example, the vast telecommunication (telco)-specific business is considered as a complex enterprise depicting a cyberspace of digital ecology (DE) with a backbone of network that supports a host of information sources and destinations facilitating a variety of triple (voice, data and video) services. To specify the temporal trend of evolution of telco economics in a series format, the approach pursued here (and differs from traditional series analyses) takes into account only a selective (and justifiable) set of autoregressive integrated moving average (ARIMA) parameters consistent with the test data. However, this simplified approach yields sufficiently accurate time-series (depicting the business growth) extendable to forecasting regimes. The efficacy of the proposed method is determined via goodness-fit evaluations both in time-and frequency-domains. The data adopted in the computations conform to typical telco service industry.

Objective: The present study is concerned with the need that exists in bioinformatics to identify and delineate overlapping codon and noncodon structures in a deoxyribonucleic acid (DNA) complex so as to ascertain the boundary of separation between them. Codons refer to those parts in a DNA complex encoded towards forming a desired set of proteins. Also coexist in the DNA structure noncodons (or ''junk'' codons), whose functions are not so well defined. Such codon and noncodon parts (at least over some sections of a DNA chain) may conform to diffused (overlapping) states exhibiting sharpless boundaries with indistinctive statistics of occurrence of their constituents. Such overlapping mix of codon and noncodon entities constitutes a (fuzzy) universe with information constituent having a fuzzy structure, which can only be identified in descriptive norms with characteristic membership of belongingness to certain attributes. Hence, this work is directed to develop a fuzzy inference engine (FIE), which delineates the fuzzy codon-noncodon parts. Methods and material: Relevant algorithms developed for the fuzzy inference in question are based on information-theoretic (IT) considerations applied to symbolic as well as binary sequence data representing the DNA. Pseudocodes, as needed are furnished. Results: Simulated studies using human and other bacterial codon statistics are presented to illustrate the efficacy of the approach pursued. The outcome of the study is illustrated via tabulated results and graphs depicting the delineation sought. Conclusion: The results signify the success of IT-approach pursued in delineating imprecise codon/noncodon boundaries. The FIE applies both for human and bacterial codon statistics.

In neural networks the activation process controls the output as a nonlinear function of the input; and, this output remains bounded between limits as decided by a logistic function known as the sigmoid (S-shaped). Presently, by applying the considerations of Maxwell-Boltzmann statistics, the Langevin function is shown as the appropriate and justifiable sigmoid (instead of the conventional hyperbolic tangent function) to depict the bipolar nonlinear logic-operation enunciated by the collective stochastical response of artificial neurons under activation. That is, the graded response of a large network of 'neurons' such as Hopfield's can be stochastically justified via the proposed model. The model is consistent with the established link between the Hopfield model and the statistical mechanics. The possible outcomes and implications of using the Langevin function (in lieu of conventional hyperbolic tangent and/or exponential sigmoids) in determining nonlinear decision boundaries, in characterizing the neural networks by the Langevin machine versus the Boltzmann machine, in sharpening and annealing schedules and in the optimization of nonlinear detector performance are discussed.

This article describes a passive, economical strategy towards enhancing the security feature of conventional plastic cards by embedding a set of electromagnetic (EM) material that emulates an invisible "watermarking". This is an overlay strategy to prevailing security measures and consists of incorporating (invisibly embedding, say by ink-jet printing or otherwise) a set of foil/film-like grids of electromagnetic (EM) material (such as high-μ material or high-conductivity metal) within the cross-section of the card. The test-card when exposed to a suitable excitation of microwave (ISM band) excitation, the embedment of EM material in the card is rendered to yield distinct path-loss to the traversing EM energy. That is, by making each element of embedment a grid-frame made of vertical or set of horizontal strips, (relative to the plane of polarization of EM excitation), each grid-frame will offer high (logic 1) or low (logic 0) transmissions when the card is swiped across the EM field. By sensing appropriately, this differentiable EM attenuation across the card would depict an output signal annunciating the presence of a binary-logic encoding in the embedded "watermarking". The proposed effort augments the existing security features of a plastic card design and robustly reduces the chances of malpractices, such as plastic card counterfeiting and misuse. The concept-design as proposed is positively verified through experimental test cards and also justified with theoretical considerations.

An "active" method of controlling acoustical beamforming with a linear array of electrodes configured on a composite piezoelectric rubber sheet is demonstrated. The active control is achieved by applying a dc bias on the electrode elements. The electroded regions of the piezorubber sheet exhibit piezoelectric activity proportional to the magnitude of the bias. By selectively biasing the electrodes, the corresponding locales of the array are forced to be active or inactive (piezoelectrically) for electroacoustic synergism. The ability to control the piezoelectric response of individually electroded regions provides a method to change the wave vector response of the electroacoustic transducer array. Tailored beamforming is therefore, made plausible with appropriate dc bias applied to selective electrodes. This "active" method could be useful in "smart" beamforming strategies and "active" control of vibrations. Test results on a prototype sample are presented as the proof of the concept.

This paper addresses a study on simulated space radiation effects on charge-coupled devices (CCD). An implicit method of assessing the performance degradation and field reliability of CCDs operated in natural ionizing-radiation ambients is devised via test studies using an electrical overstress parameter to represent the ionizing radiation dosage equivalently. Relevant cumulative degradation due to accelerated aging performed on a set of commercially available CCD is analyzed to establish the endurance coefficient of device(s). The proposed method is devoid of handling hazardous ionizing radiation sources and also enables fast reliability predictions. The test results are discussed.

Information-theoretic notion of entropy (in Shannon sense) is a versatile avenue in analyzing bioinformatic details as well as in mining data pertinent to molecular biology. Also, the informatics of bio-and medical-imaging can be comprehended via entropy considerations. Presented in this paper is a comprehensive review on salient methods, newer techniques and open-questions on unexplored efforts vis-à-vis the entropy principles cast on bioinformatic computations and bio-/medicalimage informatics. Supplementing energetics algorithms are indicated

Review Focus: General considerations on ablation procedure advocated in clinical contexts using electromagnetic (EM) energy are comprehensively reviewed. Relevant radiofrequency (RF) and/or microwave ablation techniques that have been in vogue and in traditional use across clinical procedures are revisited. Traditionally, RF/microwave ablations have been applied to a variety of pathological states, (in lieu of surgical methods and/or electrocautry procedures) so as to remove unwanted/cancerous tissue layers. Relevantly, new avenues of adopting unexplored electromagnetic (EM) energy falling in the spectral range of mm-wave/THz frequencies for such medical ablation purposes are studied. These higher frequencies for ablation can be considered either to supplant or used in parallel with the existing RF/microwave bands (at 915 MHz and 2.45 GHz). The motivation thereof is to identify certain improved ablation feasibility and derivable merits in clinical sense. Hence, the efficacy of the proposed scheme is identified for two exemplar ablation therapy procedures pertinent to Barrett's esophagus and menorrhagia. Pertinent pros and cons are discussed and practical ablator designs