Miracle-dz/UniFAR-FTUs huggingface dataset:
Partial parallel interference cancellation for multi-carrier DS-CDMA

This paper proposes a partial parallel interference cancellation for multi-carrier DS-CDMA (which is termed FDC-PPIC) by integrating frequency diversity combining. The analysis and simulations are made over FDC-PPIC, and we conclude that FDC-PPIC acquires superior performance improvement over match-filtered (MF) receiver of multi-carrier DS-CDMA. Meanwhile, compared to parallel interference cancellation with frequency diversity combining (abbreviated as FDC-PIC) discussed previously for multi-carrier DS-CDMA, FDC-PPIC exhibits a better performance with implementation complexity no higher than FDC-PIC. At signal to noise rate (SNR) of 16 dB, for example, with the given simulation parameters, FDC-PPIC achieves a bit error rate (BER) of about 2/spl times/10/sup -4/, which is far below the BER of MF receiver and FDC-PIC with the same interference cancellation stage. Due to these advantages over MF receiver and FDC-PIC, FDC-PPIC is more potential for future application.
A method of extracting word tendencies to understand popular subjects

In recent years, there has been a tremendous growth of online text information related to digital libraries, medical diagnostic systems, remote education, news sources and electronic commerce. There is a great need to search and organize huge amount of information in text documents. This paper focuses on word tendencies in documents and suggests an automatic extraction method for specific subject. Field judgment is conducted by using field association words, and similarity among word tendencies and other word tendencies is computed with information. Then word tendencies which have the same subject are grouped as one group and the important word tendencies are chosen from that group. Finally, a system suggests word tendencies from specific subjects, and fields are implemented.
How you look depends on where you are: individual and situational factors in body image

ii Dedication. iv Acknowledgments. v Vita. vi List of Tables. ix Introduction. 1 Review of the Literature. 9 Methodology. 25 Results. 34 Discussion. 75 List of References. 92 APPENDIX: Script for Guided Imagery. 102
Unmasked tuberculosis and tuberculosis immune reconstitution inflammatory disease: a disease spectrum after initiation of antiretroviral therapy.

Antiretroviral therapy (ART) has beneficial effects on mortality and lowers the incidence of diseases caused by opportunistic infections, such as tuberculosis (TB). Although ART has sustained long-term benefits, the risk of TB is high during the first 3 months after ART initiation. Among cases of ART-associated TB, we define "unmasked TB" as that which occurs in patients with reactivation disease who develop clinically recognizable TB after ART with the restoration of previously acquired TB antigen-specific functional immune responses. TB cases with clinical evidence of an inflammatory syndrome are a subset of these unmasked cases, which we define as "unmasked TB-immune reconstitution inflammatory syndrome. " With more widespread use of ART in areas with a high prevalence of TB, unmasked TB will likely become more common. TB diagnostics with improved sensitivity and specificity are urgently needed to detect subclinical TB before it is unmasked.
The Effect of Bariatric Surgery on the Spectrum of Fatty Liver Disease

Nonalcoholic fatty liver disease is becoming one of the most common causes of liver disease in the western world. The most significant risk factors are obesity and the metabolic syndrome for which bariatric surgery has been shown to be an effective treatment. However, the effects of bariatric surgery on nonalcoholic fatty liver disease, specifically liver fibrosis and cirrhosis, are not well established. We review published bariatric surgery outcomes with respect to nonalcoholic liver disease. On the basis of this review we suggest that bariatric surgery may provide a viable treatment option for the treatment of nonalcoholic fatty liver disease, including patients with fibrosis and compensated cirrhosis, and that this topic should be a target of future investigation.
A novel technique for investigating collisions and backoff strategies in three access control protocols

This paper presents a new technique for studying backoff strategies in wireless access control protocols. The technique is applied to develop discrete-time Markov chain models for three wireless MAC protocols: pure ALOHA, slotted ALOHA and CSMA/CA. Network throughput is investigated for the protocols. It was found that optimum performance of the MAC protocol critically depends on the choice of backoff window. However, the optimum backoff window size depends on the MAC protocol used and the number of users in the system. The effect of backoff window size on protocol performance was investigated. It was found that the optimum backoff window size is w = 5N for pure ALOHA and CSMA/CA. On the other hand, the optimum backoff window size for slotted ALOHA is w = N
Stability criteria of uncertain networked control systems

This paper is concerned with stability criteria of networked control systems (NCSs) with both the time-varying network-induced delay and data packet dropout taken into account. A continuous time model of NCSs with time-varying and uncertainties is proposed. Based on the dividing of the time-varying delay, a new Lyapunov functional is constructed, and a delay-dependent stability criterion is derived to guarantee the globally asymptotic stability of the networked control systems (NCSs). Expressed in terms of linear matrix inequalities (LMIs), the stability condition can be checked using the numerically efficient Matlab LMI Control Toolbox. Compared with some report methods, no slack matrix variable is introduced and the proposed methods gave a much less conservative maximum allowable delay bound (MADB). A numerical example is provided to demonstrate the effectiveness of the proposed method.
Elliptic Localization: Performance Study and Optimum Receiver Placement

Range based localization often relies on TOA that creates a circular contour or TDOA that yields hyperbolic surface to determine the position of an emitting object. Another approach that has gained attention recently is elliptic localization. Elliptic localization uses an active transmitter to send out a signal and several receivers to acquire the reflected or relayed signal from an object to obtain its location. This paper performs a fundamental investigation on the performance of elliptic localization, for both the synchronous and asynchronous cases between the transmitter and the receivers. Their performance is also characterized with respect to the hyperbolic localization. The study is based on the Cramér-Rao lower bound (CRLB) for the object location under Gaussian measurement noise as well as Gaussian errors in the transmitter and receiver positions. When the receiver positions are controllable, we derive the optimum receiver placement for elliptic positioning to improve the localization accuracy. We conclude the study with simulation results to demonstrate the elliptic localization performance using the CRLBs and the Maximum Likelihood estimators.
Steerable pyramid for contrast enhancement and directional structures detection

The object of this work is the application of steerable filters for the diagnosis of breast cancer. Because of the very specific geometry of malignant densities, we intend to apply a multiscale and orientation shiftable method for the detection of breast cancer in mammograms. Stellate masses have an irregular appearance and are frequently surrounded by a radiating pattern of linear spicules. Multiscale approaches using oriented filters have proved to be efficient to detect such stellate patterns. We first use the fact that tumors are always brighter than the surrounding tissues to locate suspicious regions. The detection is then based on steerable filters, which can be steered to any orientation fixed by the user, and are synthesized using a limited number of basis filters. These filters are used in a recursive multi-scale transform: the steerable pyramid. Using the steerable pyramid, oriented contrast enhancement is performed. The resulting enhanced image is then analyzed to estimate whether a stellate mass is present or not.
THE USE OF ACTIVE CONTOURS FOR THE DETECTION OF COASTLINES IN SAR IMAGES: A MODULAR KNOWLEDGE-BASED FRAMEWORK

Over the last years, active contour methods have become a basic tool in computer vision. They have proven to be efficient for various image processing applications, like reconstruction of the edges inside images or the tracing of image features. However, when applying the basic snake technique to synthetic aperture radar (SAR) remote sensing images, the detection of edges may not be satisfactory. This is caused by the special imaging technique of SAR that may tend to produce varying-contrast edges and the commonly known speckle noise. In (Seppke et al., 2010) we proposed the use of asymmetric external energy terms to cope with these problems. In this paper we extend our approach and present a modular framework for the application of snake algorithms to SAR imagery. The main emphasis of the framework is the use of higher knowledge about the scene depicted, e.g to initialize the snake with suitable parameters. Another objective is to establish a modular designed and thus highly ﬂexible testbed for the comparison of different active contour approaches. We present the framework’s design and preliminary results for the detection of coastlines in SAR images. The proposed framework has already proven to be a valuable tool for both, the interpretation and understanding of the results. For future projects, the framework will be used to investigate and compare the results of snakes when applied to hi-resolution SAR imagery, e.g. TerraSAR-X HR Spotlight images.
Efficacy of adding clonidine to intrathecal morphine in acute postoperative pain: meta-analysis.

BACKGROUND Clonidine may be used along with intrathecal morphine for single-dose postoperative analgesia in adults. The efficacy of this is not clear. METHODS A meta-analysis was performed for two endpoints of efficacy: the time to first postoperative analgesia request and the amount of systemic morphine used during the first 24 h after operation. A Bayesian inference supporting direct statements about the probability of the magnitude of an effect was also used. The frequency of the five adverse events (postoperative nausea or vomiting, sedation, respiratory depression, pruritus, and hypotension) was analysed. RESULTS Clonidine increased the duration of analgesia by 1.63 h [95% confidence interval (CI): 0.93-2.33]. There is a 90% probability that clonidine increases the duration of postoperative analgesia by more than 75 min compared with morphine alone. Clonidine reduced the amount of postoperative morphine by a mean of 4.45 mg (95% CI: 1.40-7.49 mg). There is a probability of 90% to obtain a decrease >2.3 mg but only 35% to obtain a decrease >5 mg. The incidence of hypotension was the only adverse event increased by clonidine (odds ratio 1.78; 95% CI: 1.02-3.12). CONCLUSIONS The addition of clonidine to intrathecal morphine extends the time to first analgesia and decreases the amount of morphine used. However, as the effects are small, and the results heavily influenced by a study in which intrathecal fentanyl was also given, this must be balanced with the increased frequency of hypotension.
PIC Detector for Joint Distributed STBC under Imperfect Synchronization

In this paper, a new signal detection scheme using both parallel interference cancellation (PIC) and equalization for the efficient joint distributed space-time coding is proposed to suppress the impact of imperfect synchronisation in distributed cellular systems. Simulation results show that the proposed scheme outperforms the existing joint DSTBC decoded scheme in terms of BER. And also it achieves the same system capacity as the existing scheme. Meanwhile, a low structure and computational complexity has been retained.
Naturalness in a high-level synthetic speech system

Naturalness in synthetic speech is to a large extent determined by how well the system models the variability found in human speech. Good models of variability are now emerging, and this paper describes how variability of several different types is incorporated into SPRUCE — a high level text-to-speech synthesis system. The synthesiser is carefully engineered according to the requirements of a recent computational model of speech production. The resulting voice output illustrates the usefulness of well motivated theory in speech synthesiser design.
A Unifying Organ Model of Pancreatic Insulin Secretion

The secretion of insulin by the pancreas has been the object of much attention over the past several decades. Insulin is known to be secreted by pancreatic β-cells in response to hyperglycemia: its blood concentrations however exhibit both high-frequency (period approx. 10 minutes) and low-frequency oscillations (period approx. 1.5 hours). Furthermore, characteristic insulin secretory response to challenge maneuvers have been described, such as frequency entrainment upon sinusoidal glycemic stimulation; substantial insulin peaks following minimal glucose administration; progressively strengthened insulin secretion response after repeated administration of the same amount of glucose; insulin and glucose characteristic curves after Intra-Venous administration of glucose boli in healthy and pre-diabetic subjects as well as in Type 2 Diabetes Mellitus. Previous modeling of β-cell physiology has been mainly directed to the intracellular chain of events giving rise to single-cell or cell-cluster hormone release oscillations, but the large size, long period and complex morphology of the diverse responses to whole-body glucose stimuli has not yet been coherently explained. Starting with the seminal work of Grodsky it was hypothesized that the population of pancreatic β-cells, possibly functionally aggregated in islets of Langerhans, could be viewed as a set of independent, similar, but not identical controllers (firing units) with distributed functional parameters. The present work shows how a single model based on a population of independent islet controllers can reproduce very closely a diverse array of actually observed experimental results, with the same set of working parameters. The model’s success in reproducing a diverse array of experiments implies that, in order to understand the macroscopic behaviour of the endocrine pancreas in regulating glycemia, there is no need to hypothesize intrapancreatic pacemakers, influences between different islets of Langerhans, glycolitic-induced oscillations or β-cell sensitivity to the rate of change of glycemia.
Solving Multi-Objective Flexible Job Shop Scheduling Problems Using Immune Algorithm

Scheduling for the flexible job-shops has great importance in both fields of production management and combinatorial optimization. However, it is quite difficult to achieve an optimal solution to this problem with traditional optimization methods because of the high computational complexity. Considering several optimization criteria in this problem will bring on additional complexity and new problems. So it makes traditional methods not practical, and urges new ways of optimization like meta-heuristic algorithms. Immune algorithm is an evolutionary computation technique imitating the behavior of biological immune systems in body. We developed an easily implemented approach for the multiobjective flexible job-shop scheduling problems (FJSP). The results obtained from the computational study have shown that the proposed algorithm is a viable and effective approach for the multi-objective FJSP, especially for problems on a large scale.
Research of Coastal Area Ecological Economic System Based on Stochastic Gradient Regression

Coastal area was defined as the conjunction of land and sea and it had very important ecological, economic and social value. Because the economy developed fast and demand of resource increased heavily, there were serious pressures on the ecological economic system of coastal area, such as large amount land-sourced pollutant and unreasonable fishing. The gross output value of fishery at Tianjin, which is a seaside city of Pohai, was studied in order to analyze the influence on the ecological economic system of coastal area from those stress factors. Prediction model of the gross output value of fishery at Tianjin was established by stochastic gradient regression algorithm. The influence on the system from those stress factors was analyzed by the model. Comparing to the prediction results of models using support vector machine method and adaptive spline regression model, it was concluded that stochastic gradient regression method performed better.
The fun.tast.tisch. project: a novel approach to neuro-rehabilitation using an interactive multiuser multitouch tabletop

Acquired brain injury, mostly caused by stroke, is one main cause for adult disability, often involving cognitive impairment. Neuro-rehabilitation aims at treating these impairments by maximizing the effect of brain plasticity and functional reorganization. Specific exercises help patients to regain skills that have temporarily been lost. Yet, conventional training can involve disadvantages, e.g., the setup of an individual training environment causes a lot of effort, the computation of statistics is time-consuming and must be done by therapists manually, and it is usually not possible to discreetly adapt the level of difficulty of an exercise. Further, software solutions for desktop PCs often do not lead to the desirable results because they are too distinct from the conventional therapy setting. The fun.tast. tisch. ~project introduces a tabletop-based training system for the application in neuro-rehabilitation. This system should not only come close to the conventional setting but also overcome problems involved in existing solutions. The paper introduces the project, describes its first module Tangram, and summarizes the results of a small-scale study conducted to evaluate the module with the help of therapists and patients at an early stage of development.
Lable-free biosensor based on localized surface plasmon resonance in a multi-channel microfluidic chip

A label-free biosensor based on localized surface plasmon resonance (LSPR) was developed. A multi-channel microfluidic chip was integrated with the sensor, providing referencing channel to minimize noise level and improving the throughput. With a CCD as a detector, a sensitivity of 10™4 RIU was demonstrated. The adsorption of S-layer protein was detected successfully by the sensor system.
Effect of complex wavelet transform filter on thyroid tumor classification in three-dimensional ultrasound

Ultrasonography has great potential in differentiating malignant thyroid nodules from the benign ones. However, visual interpretation is limited by interobserver variability, and further, the speckle distribution poses a challenge during the classification process. This article thus presents an automated system for tumor classification in three-dimensional contrast-enhanced ultrasonography data sets. The system first processes the contrast-enhanced ultrasonography images using complex wavelet transform–based filter to mitigate the effect of speckle noise. The higher order spectra features are then extracted and used as input for training and testing a fuzzy classifier. In the off-line training system, higher order spectra features are extracted from a set of images known as the training images. These higher order spectra features along with the clinically assigned ground truth are used to train the classifier and obtain an estimate of the classifier or training parameters. The ground truth tells the class label of the image (i.e. whether the image belongs to a benign or malignant nodule). During the online testing phase, the estimated classifier parameters are applied on the higher order spectra features that are extracted from the testing images to predict their class labels. The predicted class labels are compared with their corresponding original ground truth to evaluate the performance of the classifier. Without utilizing the complex wavelet transform filter, the fuzzy classifier demonstrated an accuracy of 91.6%, while utilizing the complex wavelet transform filter, the accuracy significantly boosted to 99.1%.
Benchmarking Multiprocessing Parameters in a Virtualized Multi-Core Environment

A virtualized multi-core environment is a multi-core machine running virtual machines with multiple virtual cpus. Previous research has demonstrated that the performance of multi-threaded, disk intensive, and network intensive applications in such an environment can be greatly improved by correctly tuning certain simple multiprocessing related system parameters like cpu affinity and interrupt affinity. However, there is no clarity on the implications of tuning these parameters together, and the implications of tuning these parameters in an environment where application behavior is dynamic. We present preliminary results of experiments designed towards filling up these gaps, and developing a unified framework to tune this set of parameters.
Optimized discrete wavelet transforms in the cubed sphere with the lifting scheme—implications for global finite-frequency tomography

SUMMARY Wavelets are extremely powerful to compress the information contained in finite-frequency sensitivity kernels and tomographic models. This interesting property opens the perspective of reducing the size of global tomographic inverse problems by one to two orders of magnitude. However, introducing wavelets into global tomographic problems raises the problem of computing fast wavelet transforms in spherical geometry. Using a Cartesian cubed sphere mapping, which grids the surface of the sphere with six blocks or ‘chunks’, we define a new algorithm to implement fast wavelet transforms with the lifting scheme. This algorithm is simple and flexible, and can handle any family of discrete orthogonal or bi-orthogonal wavelets. Since wavelet coefficients are local in space and scale, aliasing effects resulting from a parametrization with global functions such as spherical harmonics are avoided. The sparsity of tomographic models expanded in wavelet bases implies that it is possible to exploit the power of compressed sensing to retrieve Earth’s internal structures optimally. This approach involves minimizing a combination of a l2 norm for data residuals and a l1 norm for model wavelet coefficients, which can be achieved through relatively minor modifications of the algorithms that are currently used to solve the tomographic inverse problem.
A reduced protein diet induces stearoyl-CoA desaturase protein expression in pig muscle but not in subcutaneous adipose tissue: relationship with intramuscular lipid formation

A reduced protein diet (RPD) is known to increase the level of intramuscular lipid in pig meat with a smaller effect on the amount of subcutaneous adipose tissue. This might be due to tissue-specific activation of the expression of lipogenic enzymes by the RPD. The present study investigated the effect of a RPD, containing palm kernel oil, soyabean oil or palm oil on the activity and expression of one of the major lipogenic enzymes, stearoyl-CoA desaturase (SCD) and on the level of total lipids and the fatty acid composition of muscle and subcutaneous adipose tissue in pigs. The RPD significantly increased SCD protein expression and activity in muscle but not in subcutaneous adipose tissue. The level of MUFA and total fatty acids in muscle was also elevated when the RPD was fed, with only small changes in subcutaneous adipose tissue. A positive significant correlation between SCD protein expression and total fatty acids in muscle was found. The results suggest that an increase in intramuscular but not subcutaneous adipose tissue fatty acids under the influence of a RPD is related to tissue-specific activation of SCD expression. It is suggested that the SCD isoform spectra in pig subcutaneous adipose tissue and muscle might be different.