Continuous blood pressure estimation based on multiple parameters from eletrocardiogram and photoplethysmogram by Back-propagation neural network

The cuff-less continuous blood pressure monitoring provides reliable and invaluable information about the individuals’ health condition. Conventional sphygmomanometer with a cuff measures only the value of the blood pressure intermittently and the measurement process is sometimes inconvenient. In this work, a systematic approach with multi-parameter fusion has been proposed to estimate the non-invasive beat-to-beat systolic and diastolic blood pressure with high accuracy. The methods involve real-time monitoring of the electrocardiogram (ECG) and photoplethysmogram (PPG), and extracting the R peak from the ECG and relevant feature parameters from the synchronous PPG. Also, it covers the creation of the topological model of back-propagation neural network that has fifteen neurons in the input layer, ten neurons in the single interlayer, and two neurons in the output layer, where all the neurons are fully connected. As for the results, the proposed method was validated on the volunteers. The reference blood pressure (BP) is from Finometer (MIDI, Finapres Medical System, Netherlands). The results showed that the mean ± S.D. for the estimated systolic BP (SBP) and diastolic BP (DBP) with the proposed method against reference were −0.41 ± 2.02 mmHg and 0.46 ± 2.21 mmHg, respectively. Thus, the continuous blood pressure algorithm based on Back-Propagation neural network provides a continuous BP with a high accuracy.     

Leveraging MapReduce to efficiently extract associations between biomedical concepts from large text data

Large biomedical text data represents an important source of information that not only enables researchers to discover in-depth knowledge about biological systems, but also helps healthcare professionals do evidence-based medicine in clinical settings. However, investigating and analyzing these data is often both data-intensive and computation-intensive. In this paper, we investigate how to use MapReduce, a parallel and distributed programming paradigm, to efficiently mine the associations between biomedical concepts extracted from a large set of biomedical articles. First, biomedical concepts were obtained by matching text to Unified Medical Language System (UMLS) Metathesaurus, a biomedical vocabulary and standard database. Then we developed a MapReduce algorithm that could be used to calculate a category of interestingness measures defined on the basis of a 2 × 2 contingency table. This algorithm consists of two MapReduce jobs and takes a stripes approach to reduce the number of intermediate results. Experiments were conducted using Amazon Elastic MapReduce (EMR) with an input of 33,960 articles from TREC (Text REtrieval Conference) 2006 Genomics Track. Performance test indicated that our algorithm had approximately linear scalability and was more efficient than a ‘‘pairs’’ approach in the literature. The physician in our project team evaluated a subset of the association mining results related to drug-disease treatment and found that meaningful association rules ranked high.     

Performance analysis of classification algorithms on early detection of liver disease

The human liver is one of the major organs in the body and liver disease can cause many problems in human life. Fast and accurate prediction of liver disease allows early and effective treatments. In this regard, various data mining techniques help in better prediction of this disease. Because of the importance of liver disease and increase the number of people who suffer from this disease, we studied on liver disease through using two well-known methods in data mining area.In this paper, novel decision tree based algorithms is used which leads to considering more factors in general and predictions with high accuracy compared to other studies in liver disease. In this application, 583 UCI instances of liver disease dataset from the UCI repository are considered. This dataset consists of 416 records of liver disease and 167 records of healthy liver. This dataset is analyzed by two algorithms named Boosted C5.0 and CHAID algorithms. Until now there is no work in the literature that uses boosted C5.0 and CHAID for creating the rules in liver disease. Our results show that in both algorithms, the DB, ALB, SGPT, TB and A/G factors have a significant impact on predicting liver disease which according to the rules generated by both algorithms important ranges are DB = [10.900–1.200], ALB [4.00–4.300], SGPT = [34–37], TB = [0.600–1.200] (by boosted C5.0), A/G = [1.180–1.390], as well as in the Boosted C5.0 algorithm, Alkphos, SGOT and Age have significant impact in prediction of liver disease. By comparing the performance of these algorithms, it becomes clear that C5.0 algorithm via Boosting technique has an accuracy of 93.75% and this result reveals that it has a better performance than the CHAID algorithm which is 65.00%. Another important achievement of this paper is about the ability of both algorithms to produce rules in one class for liver disease. The results of our assessment show that Boosted C5.0 and CHAID algorithms are capable to produce rules for liver disease. Our results also show that boosted C5.0 considers the gender in liver disease, a factor which is missing in many other studies. Meanwhile, using the rules generated in boosted C5.0 algorithm, we obtained the important result about low susceptibility of female to liver disease than male. This factor is missing in other studies of liver disease. Therefore, our proposed computer-aided diagnostic methods as an expert and intelligent system have impressive impact on liver disease detection. Based on obtained results, we observed that our model had better performance compared to existing methods in the literature.     

A new Mixed Radix Conversion algorithm MRC-II

In this paper, we present an efficient and simplified algorithm for the Residue Number System (RNS) conversion to weighted number system which in turn will simplify the implementation of RNS sign detection, magnitude comparison, and overflow detection. The algorithm is based on the Mixed Radix Conversion (MRC). The new algorithm simplifies the hardware implementation and improves the speed of conversion by replacing a number of multiplication operations with small look-up tables. The algorithm requires less ROM size compared to those required by existing algorithms. For a moduli set consisting of eight moduli, the new algorithm requires seven tables to do the conversion with a total table size of 519 bits, while Szabo and Tanaka MRC algorithm [N.S. Szabo, R.I. Tanaka, Residue Arithmetic and its Application to Computer Technology, McGraw-Hill, New York, 1967; C.H. Huang, A fully parallel mixed-radix conversion algorithm for residue number applications, IEEE Transactions on Computers c-32 (4) (1983)] requires 28 tables with a total table size of 8960 bits; and Huang MRC algorithm (Huang, 1983) requires 36 tables with a total table size of 5760 bits.     

A microtranslation table with scratch-drive actuators fabricated from silicon-on-insulator wafer

From a silicon-on-insulator (SOI) wafer, a microtranslation table with scratch-drive actuator (SDA) has been fabricated. The device Si layer of SOI wafer is etched to form the plate of SDA, which is partially connected to the handle Si substrate by the SiO₂ layer. Dicing the handle Si substrate, a microtranslation table with the SDA array has been fabricated. Placing the microtranslation table upside down on the other Si substrate on which a thin conductive film is patterned for the electrical connection, the microtranslation table is moved by the SDA without carrying a metal wire. The moving velocity of 45.5 μm/s has been obtained by applying the voltage of 120 V at the operating frequency of 500 Hz.     

Prediction of flow pattern of gas–liquid flow through circular microchannel using probabilistic neural network

The present study attempts to develop a flow pattern indicator for gas–liquid flow in microchannel with the help of artificial neural network (ANN). Out of many neural networks present in literature, probabilistic neural network (PNN) has been chosen for the present study due to its speed in operation and accuracy in pattern recognition. The inbuilt code in MATLAB R2008a has been used to develop the PNN. During training, superficial velocity of gas and liquid phase, channel diameter, angle of inclination and fluid properties such as density, viscosity and surface tension have been considered as the governing parameters of the flow pattern. Data has been collected from the literature for air–water and nitrogen–water flow through different circular microchannel diameters (0.53, 0.25, 0.100 and 0.050 mm for nitrogen–water and 0.53, 0.22 mm for air–water). For the convenience of the study, the flow patterns available in literature have been classified into six categories namely; bubbly, slug, annular, churn, liquid ring and liquid lump flow. Single PNN model is unable to predict the flow pattern for the whole range (0.53 mm–0.050 mm) of microchannel diameter. That is why two separate PNN models has been developed to predict the flow patterns of gas–liquid flow through different channel diameter, one for diameter ranging from 0.53 mm to 0.22 mm and another for 0.100 mm–0.05 mm. The predicted map and their transition boundaries have been compared with the corresponding experimental data and have been found to be in good agreement. Whereas accuracy in prediction of transition boundary obtained from available analytical models used for conventional channel is less for all diameter of channel as compared to the present work. The percentage accuracy of PNN (～94% for 0.53 mm ID and ～73% for 0.100 mm ID channel) has also been found to be higher than the model based on Weber number (～86% for 0.53 mm ID and ～36% for 0.05 mm ID channel).     

A high-throughput and high-capacity IPv6 routing lookup system

With the growing number of routing entries, IP routing lookup has become the major performance bottleneck in backbone routers. In this paper, a complete hardware-based routing lookup system is proposed to achieve high-throughput and high-capacity for IPv6. The proposed system is a cache-centric, hash-based architecture that contains a routing lookup application specific integrated circuit (ASIC) and a memory set. A hash function is used to reduce lookup time for the routing table and ternary content addressable memory (TCAM) effectively resolves the collision problem. The gate count of the ASIC, excluding the binary content addressable memory (BCAM), is about 5306 gates, using an in-house 0.18 μm CMOS single-poly six-metal standard cell library. The results of post-layout simulations show that the ASIC operates in 3.6 ns so that the routing lookup system approaches 260 Mega lookups per second (Mlps), which is sufficient for 100 Gbps networks. The memory density is good, with each routing entry requiring only 64 bits. Moreover, the routing table only needs 10.24 KB on-chip BCAM, 20.04 KB off-chip TCAM and 29.29 MB DRAM for 3.6 M routing entries in the proposed system.     

Constructing a novel mortality prediction model with Bayes theorem and genetic algorithm

Intensive care is one of the most important components of the modern medical system. Healthcare professionals need to utilize intensive care resources effectively. Mortality prediction models help physicians decide which patients require intensive care the most and which do not. The Simplified Acute Physiology System 2nd version (SAPS II) is one of the most popular mortality scoring systems currently available. This study retrospectively collected data on 496 patients admitted to intensive care units from year 2000 to 2001. The average patient age was 59.96 ± 1.83 years old and 23.8% of patients died before discharge. We used these data as training data and constructed an exponential Bayesian mortality prediction model by combining BSM (Bayesian statistical model) and GA (genetic algorithm). The optimal weights and the parameters were determined with GA. Furthermore, we prospectively collected data on 142 patients for testing the new model. The average patient age for this group was 57.80 ± 3.33 years old and 21.8% patients died before discharge. The mortality prediction power of the new model was better than SAPS II (p < 0.001). The new model combining BSM and GA can manage both binary data and continuous data. The mortality rate is predicted to be high if the patient’s Glasgow coma score is less than 5.     

Coping with information in social media: The effects of network structure and knowledge on perception of information value

The explosive growth of social media has intrigued many scholars to inquire into why people willingly share information with others. However, relatively little attention has been devoted to how people determine which information they share in the networked environment. In this study, a 2 (network density – dense vs. sparse) × 2 (knowledge – expert vs. novice) × 3 (information valence – negative vs. neutral vs. positive) online experiment was performed to examine how the three factors interact and cross over in shaping individuals’ perceptions of the value of information for themselves and for others in the network. Results show that individuals’ perceptions of information value are influenced not just by their level of knowledge, but also by how the network environment is structured. Implications for the findings are discussed.     

On the influence of the number of algorithms,problems, and independent runs in the comparison of evolutionary algorithms

When conducting a comparison between multiple algorithms on multiple optimisation problems it is expected that the number of algorithms, problems and even the number of independent runs will affect the final conclusions. Our question in this research was to what extent do these three factors affect the conclusions of standard Null Hypothesis Significance Testing (NHST) and the conclusions of our novel method for comparison and ranking the Chess Rating System for Evolutionary Algorithms (CRS4EAs). An extensive experiment was conducted and the results were gathered and saved of k = 16 algorithms on N = 40 optimisation problems over n = 100 runs. These results were then analysed in a way that shows how these three values affect the final results, how they affect ranking and which values provide unreliable results. The influence of the number of algorithms was examined for values k = {4, 8, 12, 16}, number of problems for values N = {5, 10, 20, 40}, and number of independent runs for values n = {10, 30, 50, 100}. We were also interested in the comparison between both methods – NHST's Friedman test with post-hoc Nemenyi test and CRS4EAs – to see if one of them has advantages over the other. Whilst the conclusions after analysing the values of k were pretty similar, this research showed that the wrong value of N can give unreliable results when analysing with the Friedman test. The Friedman test does not detect any or detects only a small number of significant differences for small values of N and the CRS4EAs does not have a problem with that. We have also shown that CRS4EAs is an appropriate method when only a small number of independent runs n are available.     

Fiber optics sensor for sub-nanometric displacement and wide bandwidth systems

In this paper, we report fiber optics sensor with sub-nanometric resolution and wide bandwidth. It relies on an increase of the reception fibers number and on low-noise electronics. Moreover, a reference channel has been implemented using a semi-reflective plate to eliminate the source fluctuations and the fiber sensor was isolated to limit external influence of temperature and pressure. Thus we achieve both a sub-nanometric resolution on a 400 ms integration time and a long-term drift as low as 40 nm h^?1. The setup has been also adapted to high speed applications by increasing the bandwidth up to 38 kHz. It can display a 28 nm peak-to-peak limit of resolution on an aluminized piezoactuator. It has been successfully used to test the resonance frequency of a vibrating plate actuated by two high-frequency prototypes of piezoactuators. These improvements lead to low cost fibers optic sensors interesting for non-contact displacement measurements with high sensitivity.     

Feasible prediction in S-system models of genetic networks

To construct the model of gene expression using microarray techniques can reveal the regulation rules from the gene expression profiles. From S-system model, it is able to analyze the regulatory system dynamics. However, with 2N(N + 1) parameters (called a set), an S-system model of N-gene genetic networks takes lots of iterations to have convergent gene expression profiles. To mining the association between the gene expression profiles and 2N(N + 1) parameters may provide information about the probability of the convergent gene expression profiles instead of trying to obtain the convergent gene expression profiles in lots of iteration. Based on this novel approach, higher accuracy of the binary classifier can be used to analyze and prediction the convergence of the gene expression profiles from an initial set to reduce the search time of the inference problem. This paper applies popular data mining algorithms to the classification tasks and compares their accuracy rates with a dataset (250 cases, including 176 training cases and 74 test cases). According to decision rules of the chosen classifier, we can provide a convergence prediction of time-series gene expression profiles on the given set of parameters.     

Performance analysis of networks of queues under active queue management scheme

Analysis of networks of queues under repetitive service blocking mechanism has been presented in this paper. Nodes are connected according to an arbitrary configuration and each node in the networks employs an active queue management (AQM) based queueing policy to guarantee certain quality of service for multiple class external traffic. This buffer management scheme has been implemented using queue thresholds. The use of queue thresholds is a well known technique for network traffic congestion control. The analysis is based on a queue-by-queue decomposition technique where each queue is modelled as a GE/GE/1/N queue with single server, R (R ⩾ 2) distinct traffic classes and {N = N₁, N₂, … , N_R} buffer threshold values per class under first-come-first-serve (FCFS) service rule. The external traffic is modelled using the generalised exponential (GE) distribution which can capture the bursty property of network traffic. The analytical solution is obtained using the maximum entropy (ME) principle. The forms of the state and blocking probabilities are analytically established at equilibrium via appropriate mean value constraints. The initial numerical results demonstrate the credibility of the proposed analytical solution.     

Adaptive Census Transform: A novel hardware-oriented stereovision algorithm

This paper presents a new hardware-oriented approach for the extraction of disparity maps from stereo images. The proposed method is based on the herein named Adaptive Census Transform that exploits adaptive support weights during the image transformation; the adaptively weighted sum of SADs is then used as the dissimilarity metric. Quality tests show that the proposed method reaches significantly better accuracy than alternative hardware-oriented approaches. To demonstrate the practical hardware feasibility, a specific architecture has been designed and its implementation has been carried out using a single FPGA chip. Such a VLSI implementation allows a frame rate up to 68 fps to be reached for 640 × 480 stereo images, using just 80,000 slices and 32 RAM blocks of a Virtex6 chip.     

Solving online dynamic time-linkage problems under unreliable prediction

Dynamic time-linkage optimization problems (DTPs) are a special class of dynamic optimization problems (DOPs) with the feature of time-linkage. Time-linkage means that the decisions taken now could influence the problem states in future. Although DTPs are common in practice, attention from the field of evolutionary optimization is little. To date, the prediction method is the major approach to solve DTPs in the field of evolutionary optimization. However, in existing studies, the method of how to deal with the situation where the prediction is unreliable has not been studied yet for the complete Black-Box Optimization (BBO) case. In this paper, the prediction approach EA + predictor, proposed by Bosman, is improved to handle such situation. A stochastic-ranking selection scheme based on the prediction accuracy is designed to improve EA + predictor under unreliable prediction, where the prediction accuracy is based on the rank of the individuals but not the fitness. Experimental results show that, compared with the original prediction approach, the performance of the improved algorithm is competitive.     

Chromium(III)-selective sensor based on tri-o-thymotide in PVC matrix

Tri-o-thymotide (I) has been used as an electroactive material in PVC (poly(vinyl chloride)) matrix for fabrication of chromium(III)-selective sensor. The membrane containing tri-o-thymotide, sodium tetraphenyl borate (NaTPB), dibutyl phthalate (DBP) and PVC in the optimum ratio 5:1:75:100 (w/w) exhibits a working concentration range of 4.0 × 10⁻⁶ to 1.0 × 10⁻¹ M with a Nernstian slope of 20.0 ± 0.1 mV/decade of activity in the pH range of 2.8–5.1. The detection limit of this sensor is 2.0 × 10⁻⁷ M. The electrode exhibits a fast response time of 15 s, shows good selectivity towards Cr³⁺ over a number of mono-, bi- and trivalent cations and can also be used in partially non-aqueous medium (up to 15%, v/v) also. The assembly has been successfully used as an indicator electrode in the potentiometric titration of chromium(III) against EDTA and also to determine Cr(III) quantitatively in electroplating industry waste samples.     

Development of SAW based gyroscope with high shock and thermal stability

A novel surface acoustic wave (SAW)-based gyroscope with an 80 MHz central frequency was developed on a 128° YX LiNbO₃ piezoelectric substrate. The developed sensor was composed of a SAW resonator, metallic dots, and two SAW delay lines. A SAW resonator was employed to generate a stable standing wave with a large amplitude, metallic dots were used to induce a Coriolis force and to form a secondary SAW, and two delay lines were formed to extract the Coriolis effect by comparing the resonance frequencies between these two delay lines. Coupling of modes (COM) modeling was conducted to determine the optimal device parameters prior to fabrication. According to the simulation results, the device was fabricated and then measured on a rate table. When the device was subjected to an angular rotation, resonant frequency differences between the two oscillators were observed because of the secondary wave, generated by the Coriolis force, perturbed the propagation of the SAW in the sense element. Depending on the angular velocity, the difference of the resonance frequency was linearly modulated. The obtained sensitivity was approximately 172 Hz deg^?1 s^?1 at an angular rate range of 0–500 deg/s. Device performances depending on different mass weights and temperatures were also characterized. Good thermal and shock stabilities were observed during the evaluation process.     

A two-dimensional optical cross-connect with integrated waveguides and surface micromachined crossbar switches

The design, fabrication and test results of an all-optical cross-connect, which uses electrostatically actuated micromechanical digital mirrors to steer optical signals in a network of planar waveguides, are presented. The substrate consists of a network of spliced planar waveguides on silica substrates. The switches, located at the waveguide intersections, are formed with an electroplated T-structure consisting of a horizontal perforated square plate suspended by four elastic beams. When operated, the horizontal plate is pulled up making the mirror move out of the optical path thus steering the beam. An 8×8 switch array has been fabricated and tested. Actuation and relaxation switching times near 3 ms have been demonstrated with an actuation voltage of 120 V. The optical insertion loss for the array typically varied from 2.3 dB for a single trench in the optical path (shortest optical path) to 8 dB for 15 trenches in the optical path (longest optical path).     

Predictive functional control of an axis positioning system with an estimator-based internal model

This paper deals with an application of the predictive functional control with a state estimator-based internal model (PFC_ EBIM). The PFC_ EBIM has been shown to be effective in simulation. However, neither detailed experimental validation nor comparison with other controllers has been reported thus far. Here, the PFC_ EBIM is implemented in a single-axis positioning system, and a few experimental tests are conducted. Tracking performance of the PFC_ EBIM, standard PFC, and P – PI control for both smooth and non-smooth reference signals are compared. The experimental results prove the effectiveness of the PFC_ EBIM.