Spatio-temporal object relationships in image sequences using adjacency matrices

In this work, we bring together object tracking and digital watermarking to solve the spatio-temporal object adjacency problem in image sequences. Spatio-temporal relationships are established by embedding objects with unique digital watermarks and then by propagating the watermark frame by frame. Watermark propagation is accomplished by an existing object tracking module so that a tracked object acquires its watermark from the correspondences established by the object tracker. The spatio-temporally marked image sequences can then be searched to establish spatial and temporal adjacency among objects without using traditional spatio-temporal graphs. Borrowing from graph theory, we construct binary adjacency matrices among tracked objects and develop interpretation rules to establish a track history for each object. Track history can be used to determine the arrival of new objects in frames or the changing of spatial and temporal positions of objects with respect to each other as they move through time and space.     

COMPARE: classification of morphological patterns using adaptive regional elements

This paper presents a method for classification of structural brain magnetic resonance (MR) images, by using a combination of deformation-based morphometry and machine learning methods. A morphological representation of the anatomy of interest is first obtained using a high-dimensional mass-preserving template warping method, which results in tissue density maps that constitute local tissue volumetric measurements. Regions that display strong correlations between tissue volume and classification (clinical) variables are extracted using a watershed segmentation algorithm, taking into account the regional smoothness of the correlation map which is estimated by a cross-validation strategy to achieve robustness to outliers. A volume increment algorithm is then applied to these regions to extract regional volumetric features, from which a feature selection technique using support vector machine (SVM)-based criteria is used to select the most discriminative features, according to their effect on the upper bound of the leave-one-out generalization error. Finally, SVM-based classification is applied using the best set of features, and it is tested using a leave-one-out cross-validation strategy. The results on MR brain images of healthy controls and schizophrenia patients demonstrate not only high classification accuracy (91.8% for female subjects and 90.8% for male subjects), but also good stability with respect to the number of features selected and the size of SVM kernel used.     

Phenomenological model of diffuse global and regional atrophy using finite-element methods

The main goal of this work is the generation of ground-truth data for the validation of atrophy measurement techniques, commonly used in the study of neurodegenerative diseases such as dementia. Several techniques have been used to measure atrophy in cross-sectional and longitudinal studies, but it is extremely difficult to compare their performance since they have been applied to different patient populations. Furthermore, assessment of performance based on phantom measurements or simple scaled images overestimates these techniques' ability to capture the complexity of neurodegeneration of the human brain. We propose a method for atrophy simulation in structural magnetic resonance (MR) images based on finite-element methods. The method produces cohorts of brain images with known change that is physically and clinically plausible, providing data for objective evaluation of atrophy measurement techniques. Atrophy is simulated in different tissue compartments or in different neuroanatomical structures with a phenomenological model. This model of diffuse global and regional atrophy is based on volumetric measurements such as the brain or the hippocampus, from patients with known disease and guided by clinical knowledge of the relative pathological involvement of regions and tissues. The consequent biomechanical readjustment of structures is modelled using conventional physics-based techniques based on biomechanical tissue properties and simulating plausible tissue deformations with finite-element methods. A thermoelastic model of tissue deformation is employed, controlling the rate of progression of atrophy by means of a set of thermal coefficients, each one corresponding to a different type of tissue. Tissue characterization is performed by means of the meshing of a labelled brain atlas, creating a reference volumetric mesh that will be introduced to a finite-element solver to create the simulated deformations. Preliminary work on the simulation of acquisition artefacts is also presented. Cross-sectional and longitudinal sets of simulated data are shown and a visual classification protocol has been used by experts to rate real and simulated scans according to their degree of atrophy. Results confirm the potential of the proposed methodology.     

Impedance comparator using digitally generated sinewaves with accurate phase relationships

An impedance comparator has been constructed from two digital sinewave generators with provision for accurate phase shifts. Stability of the instrument is such that impedances may be compared with a resolution of 5 parts in 105.     

Learning contextual relationships in mammograms using a hierarchical pyramid neural network

This paper describes a pattern recognition architecture, which we term hierarchical pyramid/neural network (HPNN), that learns to exploit image structure at multiple resolutions for detecting clinically significant features in digital/digitized mammograms. The HPNN architecture consists of a hierarchy of neural networks, each network receiving feature inputs at a given scale as well as features constructed by networks lower in the hierarchy. Networks are trained using a novel error function for the supervised learning of image search/detection tasks when the position of the objects to be found is uncertain or ill defined. We have evaluated the HPNN's ability to eliminate false positive (FP) regions of interest generated by the University of Chicago's (UofC) Computer-aided diagnosis (CAD) systems for microcalcification and mass detection. Results show that the HPNN architecture, trained using the uncertain object position (UOP) error function, reduces the FP rate of a mammographic CAD system by approximately 50% without significant loss in sensitivity. Investigation into the types of FPs that the HPNN eliminates suggests that the pattern recognizer is automatically learning and exploiting contextual information. Clinical utility is demonstrated through the evaluation of an integrated system in a clinical reader study. We conclude that the HPNN architecture learns contextual relationships between features at multiple scales and integrates these features for detecting microcalcifications and breast masses.     

Real-time monitoring of cardiac regional function using fastHARP MRI and region-of-interest reconstruction

Cardiovascular stress test imaging assists in the diagnosis and monitoring of cardiovascular disease. The procedure can be carried out in a magnetic resonance (MR) scanner using pharmacological agents that mimic the effects of natural exercise. In order to provide real time indication of ischemia, thereby assisting in diagnosis and helping to assure patient safety, it is desirable to have real time monitoring of the myocardial regional function. This paper presents an algorithm for the real time myocardium region-of-interest reconstruction and myocardial strain computation using data acquired from a real time pulse sequence that has been previously reported. The chirp Fourier transform is used for efficient computation, enabling a real-time continuous strain map at a rate of 25 frames/s. Coupled with a real time data path from the scanner to a laptop computer, this algorithm enables real time continuous monitoring of cardiac strain and is targeted for use in the early detection and quantification of ischemia during MR stress tests.     

采用模糊控制的网络视频质量评估方法

随着宽带通信技术的快速发展，网络视频业务迅猛普及。尽管人们可以方便收看网络视频，但是由于网络视频在传输过程中，会受到各种因素干扰，降低用户感受。本文针对网络视频质量评估问题，提出一种采用模糊控制的评估方法。首先分析了影响视频质量的网络、应用、图像等主要指标；通过仿真实验提取了不同情况下的影响指标，构造了三层模糊控制的评估模型；为了进一步提高方法的准确性，对每个模糊层赋予了不同权值，同时给出了两种去模糊化方法。该方法综合考虑了不同影响因素，适用性广，且具有较好的实用性。实验结果表明，该算法能够有效地提高视频质量评估的主客观相似度。      

Effects of regional stimulation using a miniature stimulatorimplanted in feline posterior biceps femoris

The effects of placement of a miniature implantable stimulator on motor unit recruitment were examined in the posterior head of cat biceps femoris. The implantable stimulator (13-mm long×2-mm diameter) was injected either proximally near the main nerve branch, or distally near the muscle insertion, through a 12-gauge hypodermic needle. Glycogen-depletion methods were used to map the distribution of fibers activated by electrical stimulation. Muscle fibers were found to be depleted at most or all proximodistal levels of the muscle, but the density of depleted fibers varied transversely according to the stimulus strength and proximity of the device to the nerve-entry site. Thus, muscle cross sections often had a “patchy” appearance produced because different proportions of depleted fibers intermingled with undepleted fibers in different parts of the cross section. In other preparations, the force of muscle contraction was measured when stimuli of varying strengths were delivered by the stimulator positioned at the same proximal or distal sites within the muscle. Devices placed close to the nerve-entry site produced the greatest forces. Those placed more distally produced less force. As stimulus current and/or pulse width increased, muscle force increased, often in steps, until a maximum was reached, which was usually limited by the compliance voltage of the device to less than the force produced by whole nerve stimulation     

Precision and accuracy of regional radioactivity quantitation using the maximum likelihood EM reconstruction algorithm

The imaging characteristics of maximum likelihood (ML) reconstruction using the EM algorithm for emission tomography have been extensively evaluated. There has been less study of the precision and accuracy of ML estimates of regional radioactivity concentration. The authors developed a realistic brain slice simulation by segmenting a normal subject's MRI scan into gray matter, white matter, and CSF and produced PET sinogram data with a model that included detector resolution and efficiencies, attenuation, scatter, and randoms. Noisy realizations at different count levels were created, and ML and filtered backprojection (FBP) reconstructions were performed. The bias and variability of ROI values were determined. In addition, the effects of ML pixel size, image smoothing and region size reduction were assessed. Hit estimates at 3,000 iterations (0.6 sec per iteration on a parallel computer) for 1-cm(2) gray matter ROIs showed negative biases of 6%+/-2% which can be reduced to 0%+/-3% by removing the outer 1-mm rim of each ROI. FBP applied to the full-size ROIs had 15%+/-4% negative bias with 50% less noise than hit. Shrinking the FBP regions provided partial bias compensation with noise increases to levels similar to ML. Smoothing of ML images produced biases comparable to FBP with slightly less noise. Because of its heavy computational requirements, the ML algorithm will be most useful for applications in which achieving minimum bias is important.     

Assessment of repairable-system reliability using proportional intensity models: a review

This paper provides an overview of methods, and surveys the literature on engineering applications of proportional intensity (PI) models with explanatory variables (covariates), for repairable systems reliability assessment. The semi-parametric PI method relaxes the assumption of an underlying distribution, and is potentially useful in engineering practice, where the underlying information for a failure process is usually not available. PI semi-parametric models initially proposed for clinical studies in medical applications include PWP (Prentice, Williams, and Peterson), AG (Andersen, and Gill), and WLW (Wei, Lin, and Weissfeld). Abundant funding received in medical research has advanced PI models to become well developed, and widely referenced in the biostatistics field. This paper reviews both the available methods for repairable-system reliability assessment, and the published engineering application case studies. An engineering application example that applies PI model to a maintainability process used in US Army M1A2 Arams Main Battle Tank is presented.     

Assessment of Microfluidic System Testability using Fault Simulation and Test Metrics

In this paper we introduce a Microfluidic Fault Simulator, MFS, which uses a novel method of fault modeling and injection, the Fault Block, a generic and low abstraction fault modeling technique. This technique has been utilized over a wide range of fault conditions, in this paper we present a trapped bubble condition. In conjunction with injecting fault conditions, we can apply test methods. Two methods proving sensitive to microfluidic faults are; impedance spectroscopy and Levich electro-chemical sensors, illustrated here by a diffusional “Y” channel mixing system case study. Data from the MFS is analyzed using a Neyman-Pearson probabilistic approach, providing information on each sensor’s test capability. Overall fault coverage for a given test is determined. This approach allows the analysis of fault coverage offered by functional-test orientated sensors to be compared to alternative approaches, which potentially offer increased coverage at lower cost.     

Supplier-supplier relationships and their implications forbuyer-supplier relationships

Many researchers have investigated the dynamics of buyer-supplier relationships and have, in general, posited the importance of long-term, cooperative relationships. However, the relationship between suppliers (i.e., supplier-supplier relationship) and its potential impact on the buyer-supplier relationship have not yet been considered. This research addresses a void in the literature, especially given that many buyers now work with a smaller supplier base and deliberately try to foster certain types of supplier-supplier relationships. Building on the existing buyer-supplier and strategic alliance literature, we propose three archetypes of supplier-supplier relationships. This research further illustrates the strategic role of the buying firm in structuring these relationships and explores the managerial implications of different types of supplier-supplier relationships from the perspectives of both the buying firm and its suppliers     

Assessment of the security of operating electric power systems using probability methods

A review is given of probability methods which have been proposed for the assessment of the security of operating electric power systems. The goal of all the methods presented is to provide the system operator with a consistent, on-line, indication of system security in the near-term future as a guide for operating decisions. Mathematical models used in the various methods are described and data requirements are discussed. Finally, areas of desirable future investigation are outlined.     

Differential-incremental-sensitivity relationships

The exact relationships between differential and incremental sensitivity, which provide interesting results regarding the practical significance of differential sensitivity, zero sensitivity and the sensitivity of the network-function denominator, are investigated.     

Impedance cardiography using band and regional electrodes insupine, sitting, and during exercise

The electrical impedance and its first derivative (dZ/dt) were measured at 100 kHz on 10 normal males in supine, sitting, and during upright bicycle exercise in order to compare the contribution of regional electrodes to the standard band electrode signal and to evaluate the possible use of spot electrodes for stroke volume (SV) measurements. Simultaneous measurements were made from band electrodes placed around the neck and lower thorax and from spot electrodes which recorded signals from the neck, upper thorax, and lower thorax. The results showed that approximately equal parts of the dZ/dt waveform came from the neck and upper thorax with the lower thorax contribution small but providing important features of the band signal. Changing from supine to sitting showed percentage decreases of 35% and 46% for the band and neck signals, respectively, with an increase of 19% for the upper thorax signal. The percentage increases in SV with upright exercise were 34%, 52%, and 24% for the bands, neck, and upper thorax signals, respectively. Band signal is made up of different signals from various regions of the thorax. Its ability to predict correct changes in SV may result from some "lucky" coincidences. The use of regional electrodes will probably not give the same SV information but may be important in measuring regional activities of the central circulation.     

A data correction method in radio-occultation experiments using regional atmospheric models by the example of Arctic

A method for correction of the data obtained in atmospheric studies using radio-occultation experiments is proposed. The method is based on obtaining the a priori data from regional atmospheric models, estimating the distortions inherent in radio-occultation experiments, and constructing the correction algorithms. As a result, the accuracy of reconstruction of the refractive index in real atmosphere is improved in the range of heights from 2 km to 12 km. By the example of the Arctic region, a technique for testing the suggested correction methods with the use of independent sonde measurements of atmospheric profiles is developed. Using the data obtained from the CHAMP satellite in the years 2001–2004, the suggested correction methods are tested and the statistical analysis of their efficiency is carried out. The validity limits of the suggested method are determined for different geographic conditions.     

通过增加回风静压改善在用通信机房局部热点问题

中国移动上海公司某数据机房存在明显的局部热点,通过普通手段无法消除.经过对该机房的深入研究,通过在机房回风口安装风机提高热点区域回风管回风静压的方式提高回风量,有效降低机房热点区域温度并达到机房要求.通过本改造方案为在用老机房局部热点改造提供了一些思路.     

Identifying regional cardiac abnormalities from myocardial strains using nontracking-based strain estimation and spatio-temporal tensor analysis

Myocardial strain is a critical indicator of many cardiac diseases and dysfunctions. The goal of this paper is to extract and use the myocardial strain pattern from tagged magnetic resonance imaging (MRI) to identify and localize regional abnormal cardiac function in human subjects. In order to extract the myocardial strains from the tagged images, we developed a novel nontracking-based strain estimation method for tagged MRI. This method is based on the direct extraction of tag deformation, and therefore avoids some limitations of conventional displacement or tracking-based strain estimators. Based on the extracted spatio-temporal strain patterns, we have also developed a novel tensor-based classification framework that better conserves the spatio-temporal structure of the myocardial strain pattern than conventional vector-based classification algorithms. In addition, the tensor-based projection function keeps more of the information of the original feature space, so that abnormal tensors in the subspace can be back-projected to reveal the regional cardiac abnormality in a more physically meaningful way. We have tested our novel methods on 41 human image sequences, and achieved a classification rate of 87.80%. The regional abnormalities recovered from our algorithm agree well with the patient's pathology and clinical image interpretation, and provide a promising avenue for regional cardiac function analysis.     

Assessment of model-based scintillation variance prediction on long-term basis using Italsat satellite measurements

The objective of this paper is to assess the accuracy of model-based statistical methods for predicting clear-air scintillation amplitude variance from ground-based meteorological measurements on long term basis. Six model-based estimation methods are considered and discussed, two of them including also the use of vertically integrated water vapour content as a predictor. They are derived from synthetic data, obtained by applying an electromagnetic model to a large historical radiosounding dataset in order to simulate the received scintillation power at microwave and millimeter-wave. The empirical methods of ITU-R, Karasawa, and Ortgies are also considered for comparison. The long-term predictions derived from each method are compared with measurements from the Italsat satellite beacons at 18·7, 39·6, and 49·5 GHz, acquired during 1995 at Spino d'Adda (Milan, Italy) site. The method intercomparison is carried out by checking the assumed best-fitting probability density function for the log-amplitude fluctuation variance and by applying the considered methods to the available ground-based meteorological measurements. Statistical results in terms of bias, root mean square value and skewness of the percentage error are discussed in order to understand the potential and the limits of each model-based prediction method within this case study. Copyright © 1999 John Wiley & Sons, Ltd.     

Assessment of tremor activity in the Parkinson's disease using a set of wearable sensors

Tremor is the most common motor disorder of Parkinson's disease (PD) and consequently its detection plays a crucial role in the management and treatment of PD patients. The current diagnosis procedure is based on subject-dependent clinical assessment, which has a difficulty in capturing subtle tremor features. In this paper, an automated method for both resting and action/postural tremor assessment is proposed using a set of accelerometers mounted on different patient's body segments. The estimation of tremor type (resting/action postural) and severity is based on features extracted from the acquired signals and hidden Markov models. The method is evaluated using data collected from 23 subjects (18 PD patients and 5 control subjects). The obtained results verified that the proposed method successfully: 1) quantifies tremor severity with 87 % accuracy, 2) discriminates resting from postural tremor, and 3) discriminates tremor from other Parkinsonian motor symptoms during daily activities.