Tachycardia and fibrillation detection by automatic implantablecardioverter-defibrillators: sequential testing in time domain

The automatic implantable cardioverter-defibrillator (AICD), a device that delivers a high-energy shock (10-30 J) directly to the heart when life-threatening arrhythmias are detected, must accurately detect ventricular fibrillation (VF) and ventricular tachycardia (VT) rhythms. The signal-processing problem is how to accurately distinguish normal sinus rhythm or supraventricular tachycardia from malignant VT and VF signals. An approach based on discriminating probability distributions of interbeat intervals in electrogram signals recorded from atrial and ventricular leads is described. A sequential hypothesis-testing algorithm that allows desired false positive and false negative rates and the time it takes to detect the arrhythmias to be preset is presented. A tradeoff between reliability and duration of test is noted; a decision can be reached in a shorter time if higher error rates are accepted as permissible. In an analysis of 85 cases of VF and 85 cases of VT, 53% of VF cases and 66% of VT cases were classified after 1 s, 97.6% of VF cases and 100% of VT cases were classified after 5 s, and within 7 s, all the remaining VF cases were classified.     

Design of ANN Based Non-Linear Network Using Interconnection of Parallel Processor

Suspicious mass traffic constantly evolves, making network behaviour tracing and structure more complex. Neural networks yield promising results by considering a sufficient number of processing elements with strong interconnections between them. They offer efficient computational Hopfield neural networks models and optimization constraints used by undergoing a good amount of parallelism to yield optimal results. Artificial neural network (ANN) offers optimal solutions in classifying and clustering the various reels of data, and the results obtained purely depend on identifying a problem. In this research work, the design of optimized applications is presented in an organized manner. In addition, this research work examines theoretical approaches to achieving optimized results using ANN. It mainly focuses on designing rules. The optimizing design approach of neural networks analyzes the internal process of the neural networks. Practices in developing the network are based on the interconnections among the hidden nodes and their learning parameters. The methodology is proven best for nonlinear resource allocation problems with a suitable design and complex issues. The ANN proposed here considers more or less 46k nodes hidden inside 49 million connections employed on full-fledged parallel processors. The proposed ANN offered optimal results in real-world application problems, and the results were obtained using MATLAB.     

Modeling the relationship between concurrent epicardial action potentials and bipolar electrograms

A signal analysis approach to building the relationship between concurrent epicardial cell action potentials (AP's) and bipolar electrograms is presented. Wavelet network, one nonlinear black-box modeling method, is used to identify the relationship between cell AP's and bipolar electrocardiograms. The electrical signals were simultaneously measured from the epicardium of isolated Langendorff-perfused rabbit hearts during three different rhythm conditions: normal sinus rhythm (NSR), normal sinus rhythm after ischemia (NSRI), and ventricular fibrillation (VF). For NSR and NSRI, the proposed modeling method successfully captures the nonlinear input-output relationship and provides an accurate output, but the method fails in case of VF. This result suggests that a time-invariant nonlinear modeling method such as wavelet network is not appropriate for VF rhythm, which is thought to be time-varying as well as chaotic, but still useful in detection of VF. A new arrhythmia detection algorithm, with potential application in implantable devices, is proposed for identifying the time of rhythmic bifurcation.     

Monitoring of global cerebral ischemia using wavelet entropy rate of change

In this paper, the subband wavelet entropy (SWE) and its time difference are proposed as two quantitative measures for analyzing and segmenting the electroencephalographic (EEG) signals. SWE for EEG subbands, namely Delta, Theta, Alpha, Beta, and Gamma, is calculated and segmented using wavelet analysis. In addition, a time difference entropy measure was calculated because it does not require a baseline and equals to zero in all clinical bands as the initial condition. Visual and quantitative results were obtained from 11 rodents that were subjected to 3, 5, and 7 min of global ischemic brain injury by asphyxic cardiac arrest. We found that the time difference of SWE is capable of amplifying the variations between clinical bands during the various stages of the recovery process and may serve as a novel analytical approach to grade and classify brain rhythms during global ischemic brain injury and recovery.     

A Microprocessor-Based Two-Channel Thromboelastograph

This paper presents a design for a digital thromboelastograph (TEG). The instrument design is based on the principle of a couette viscometer. The device measures shear elasticity of blood during clot formation. The recording mechanism comprises a two-channel light-beam deflection system and a recording system based on a charge-coupled-device (CCD) array. An 8-bit CMOS microprocessor (NSC800) supervises data acquisition from the CCD array. The two-channel system produces a record on a thermal printer allowing one to observe the time course of coagulation and simultaneously compare two different blood specimens.     

Car noise verification and applications

This study presents audio based vehicle-verification as a new area of research. The task involves verifying the claim that an acoustic sample belongs to a vehicle. Audio based vehicle verification has the potential to impact research in the areas of vehicle forensics and in-vehicle speech systems. For this task, a new corpus (UTD-CAR-NOISE) that consists of noise from 20 vehicles under 8 distinct noise environments (～8 hours of data). Our approach towards vehicle verification hypothesizes that some specific environments are more suited for vehicle verification. Towards this goal, four diverse in-vehicle noise conditions are identified on the basis of their frequency of occurrence. Additionally, four different verification systems are proposed based on their complexity and modeling strategies. Our evaluation shows that A/C on with windows closed condition is the most conducive for vehicle verification (98 %). The proposed systems were evaluated on approximately 100,000 trials, achieving performances in the range of (75–98 %) for different vehicle environments.     

Fracture and electric-field-induced crack growth behavior in NBT-6BT relaxor ferroelectrics

The fracture properties of 0.94(Na_0.5Bi_0.5)TiO₃-0.06BaTiO₃ (NBT-6BT) relaxor ferroelectrics were investigated using the Vickers indentation method and computation of crack tip opening displacement. It was found that an unpoled sample had a fracture toughness of around 1.35 MPa m^1/2. In contrast, an electrically poled sample exhibited anisotropy with a lower fracture toughness perpendicular to the poling direction and a higher value in the parallel direction, as compared to the unpoled sample. Upon cyclic electrical loading (with applied electric field amplitudes between 0.73E_C and 1.4E_C), the indented surface crack was found to propagate. In general, the crack grew rapidly during the initial cycles followed by crack arrest, and the principal driving force for crack growth was proposed to be residual stress around the indentation, as evidenced by the limited field dependence of crack growth. There was also a contribution from the electromechanical strain, which played a role at high cycles (>100 cycles) and high fields (>1.3 E_C). Evidence of a saturation threshold of crack propagation is an advantage for the electromechanical reliability of relaxor ferroelectrics in devices.     

AspectFrameNet: a frameNet extension for analysis of sentiments around product aspects

In the real-world scenarios, customer tries to evaluate a product based on sentiments conveyed by its users or reviewers. AspectFrameNet provides a framework that helps the semantic analysis of text inputs from social feeds and news (Voice of Customer) by disambiguating the contexts in which the lexical units are used. To this end, we have used this framework in analysing sentiments around different aspects of internet of things. We have tested this framework for 31 interrelated aspects in mobile domain and three possible sentiments (positive, negative and neutral).     

Real-time management of berth allocation with stochastic arrival and handling times

In this research we study the berth allocation problem (BAP) in real time as disruptions occur. In practice, the actual arrival times and handling times of the vessels deviate from their expected or estimated values, which can disrupt the original berthing plan and possibly make it infeasible. We consider a given baseline berthing schedule, and solve the BAP on a rolling planning horizon with the objective to minimize the total realized cost of the updated berthing schedule, as the actual arrival and handling time data is revealed in real time. The uncertainty in the data is modeled by making appropriate assumptions about the probability distributions of the uncertain parameters based on past data. We present an optimization-based recovery algorithm based on set partitioning and a smart greedy algorithm to reassign vessels in the event of disruption. Our research problem derives from the real-world issues faced by the Saqr port, Ras Al Khaimah, UAE, where the berthing plans are regularly disrupted owing to a high degree of uncertainty in information. A simulation study is carried out to assess the solution performance and efficiency of the proposed algorithms, in which the baseline schedule is the solution of the deterministic BAP without accounting for any uncertainty. Results indicate that the proposed reactive approaches can significantly reduce the total realized cost of berthing the vessels as compared to the ongoing practice at the port.     

Modified Laser Speckle Imaging with Adaptive Window

One of the advantages of laser speckle is detecting micro-vascular through image processing. This paper proposes a new image processing method for laser speckle, adaptive window method that adaptively processes laser speckle images in the space. Disadvantage of conventional fixed window method is that it uses the same window size regardless of target areas. Inherently laser speckle contains undesired noise. Thus a large window is helpful for removing the noise, but it results in low resolution of image. Otherwise a small window may detect micro vascular but it has limits in noise removal. To overcome this trade-off, the concept of adaptive window method is newly introduced to conventional laser speckle image analysis. In addition, the modified adaptive window method applied to other selection images. We have compared conventional Laser Speckle Contrast Analysis(LASCA) and its variants with the proposed method in terms of image quality and processing complexity. Moreover compared the result of the accompanied changing selection images have also been compared.