Search results for: forward error correction

Authors: Shih-Hao Chen, Chi-Wai Chow

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Multiple-input and multiple-output (MIMO) scheme can extend the transmission capacity for the light-emitting-diode (LED) visible light communication (VLC) system. The MIMO VLC system using the popular mobile-phone camera as the optical receiver (Rx) to receive MIMO signal from n x n Red-Green-Blue (RGB) LED array is desirable. The key step of decoding the received RGB LED array signals is detecting the direction of received array signals. If the LED transmitter (Tx) is rotated, the signal may not be received correctly and cause an error in the received signal. In this work, we propose and demonstrate a novel hierarchical transmission scheme which can reduce the computation complexity of rotation detection in LED array VLC system. We use the n x n RGB LED array as the MIMO Tx. A novel two dimension Hadamard coding scheme is proposed and demonstrated. The detection correction rate is above 95% in the indoor usage distance. Experimental results confirm the feasibility of the proposed scheme.

Keywords: Visible Light Communication (VLC), Multiple-input and multiple-output (MIMO), Red-Green-Blue (RGB), Hadamard coding scheme

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Authors: Sidra A. Shaikh, M. A. Ahmed, M. W. Akhtar

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Global and diffuse solar radiation studies have been carried out for Islamabad (Lat: 330 43’ N, Long: 370 71’) to access the solar potential of the area using sunshine hour data. A detailed analysis of global solar radiation values measured using several methods is presented. These values are then compared with the NASA SSE model. The variation in direct and diffuse components of solar radiation is observed in summer and winter months for Islamabad along with the clearness index KT. The diffuse solar radiation is found maximum in the month of July. Direct and beam radiation is found to be high in the month of April to June. From the results it appears that with the exception of monsoon months, July and August, solar radiation for electricity generation can be utilized very efficiently throughout the year. Finally, the mean bias error (MBE), root mean square error (RMSE) and mean percent error (MPE) for global solar radiation are also presented.

Keywords: solar potential, global and diffuse solar radiation, Islamabad, errors

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Authors: Wararit Panichkitkosolkul

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The unit root tests based on the robust estimator for the first-order autoregressive process are proposed and compared with the unit root tests based on the ordinary least squares (OLS) estimator. The percentiles of the null distributions of the unit root test are also reported. The empirical probabilities of Type I error and powers of the unit root tests are estimated via Monte Carlo simulation. Simulation results show that all unit root tests can control the probability of Type I error for all situations. The empirical power of the unit root tests based on the robust estimator are higher than the unit root tests based on the OLS estimator.

Keywords: autoregressive, ordinary least squares, type i error, power of the test, Monte Carlo simulation

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Authors: Atheer Al-Nuaimi, Harry Evdorides

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Every day many people die or get disabled or injured on roads around the world, which necessitates more specific treatments for transportation safety issues. International road assessment program (iRAP) model is one of the comprehensive road safety models which accounting for many factors that affect road safety in a cost-effective way in low and middle income countries. In iRAP model road safety has been divided into five star ratings from 1 star (the lowest level) to 5 star (the highest level). These star ratings are based on star rating score which is calculated by iRAP methodology depending on road attributes, traffic volumes and operating speeds. The outcome of iRAP methodology are the treatments that can be used to improve road safety and reduce fatalities and serious injuries (FSI) numbers. These countermeasures can be used separately as a single countermeasure or mix as multiple countermeasures for a location. There is general agreement that the adequacy of a countermeasure is liable to consistent losses when it is utilized as a part of mix with different countermeasures. That is, accident diminishment appraisals of individual countermeasures cannot be easily added together. The iRAP model philosophy makes utilization of a multiple countermeasure adjustment factors to predict diminishments in the effectiveness of road safety countermeasures when more than one countermeasure is chosen. A multiple countermeasure correction factors are figured for every 100-meter segment and for every accident type. However, restrictions of this methodology incorporate a presumable over-estimation in the predicted crash reduction. This study aims to adjust this correction factor by developing new models to calculate the effect of using multiple countermeasures on the number of fatalities for a location or an entire road. Regression models have been used to establish relationships between crash frequencies and the factors that affect their rates. Multiple linear regression, negative binomial regression, and Poisson regression techniques were used to develop models that can address the effectiveness of using multiple countermeasures. Analyses are conducted using The R Project for Statistical Computing showed that a model developed by negative binomial regression technique could give more reliable results of the predicted number of fatalities after the implementation of road safety multiple countermeasures than the results from iRAP model. The results also showed that the negative binomial regression approach gives more precise results in comparison with multiple linear and Poisson regression techniques because of the overdispersion and standard error issues.

Keywords: international road assessment program, negative binomial, road multiple countermeasures, road safety

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Authors: Olumayede Emmanuel Gbenga, Adeniyi Azeez Adebayo

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Carbonyls are the first-generation products from tropospheric degradation reactions of volatile organic compounds (VOCs). This computational study examined the mechanism of removal of carbonyls from the atmosphere via hydroxyl radical. The kinetics of the reactions were computed from the activation energy (using enthalpy (ΔH**) and Gibbs free energy (ΔG**). The minimum energy path (MEP) analysis reveals that in all the molecules, the products have more stable energy than the reactants, which implies that the forward reaction is more thermodynamically favorable. The hydrogen abstraction of the aromatic aldehyde, especially without methyl substituents, is more kinetically favorable compared with the other aldehydes in the order of aromatic (without methyl or meta methyl) > alkene (short chain) > diene > long-chain aldehydes. The activation energy is much lower for the forward reaction than the backward, indicating that the forward reactions are more kinetically stable than their backward reaction. In terms of thermodynamic stability, the aromatic compounds are found to be less favorable in comparison to the aliphatic. The study concludes that the chemistry of the carbonyl bond of the aldehyde changed significantly from the reactants to the products.

Keywords: atmospheric carbonyls, oxidation, mechanism, kinetic, thermodynamic

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Authors: Rutej R. Mehta, Michael A. Chappell

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Introduction: Arterial spin labelling (ASL) is an increasingly popular perfusion MRI technique, in which arterial blood water is magnetically labelled in the neck before flowing into the brain, providing a non-invasive measure of cerebral blood flow (CBF). The accuracy of ASL CBF measurements, however, is hampered by dispersion effects; the distortion of the ASL labelled bolus during its transit through the vasculature. In spite of this, the current recommended implementation of ASL – the white paper (Alsop et al., MRM, 73.1 (2015): 102-116) – does not account for dispersion, which leads to the introduction of errors in CBF. Given that the transport time from the labelling region to the tissue – the arterial transit time (ATT) – depends on the region of the brain and the condition of the patient, it is likely that these errors will also vary with the ATT. In this study, various dispersion models are assessed in comparison with the white paper (WP) formula for CBF quantification, enabling the errors introduced by the WP to be quantified. Additionally, this study examines the relationship between the errors associated with the WP and the ATT – and how this is influenced by dispersion. Methods: Data were simulated using the standard model for pseudo-continuous ASL, along with various dispersion models, and then quantified using the formula in the WP. The ATT was varied from 0.5s-1.3s, and the errors associated with noise artefacts were computed in order to define the concept of significant error. The instantaneous slope of the error was also computed as an indicator of the sensitivity of the error with fluctuations in ATT. Finally, a regression analysis was performed to obtain the mean error against ATT. Results: An error of 20.9% was found to be comparable to that introduced by typical measurement noise. The WP formula was shown to introduce errors exceeding 20.9% for ATTs beyond 1.25s even when dispersion effects were ignored. Using a Gaussian dispersion model, a mean error of 16% was introduced by using the WP, and a dispersion threshold of σ=0.6 was determined, beyond which the error was found to increase considerably with ATT. The mean error ranged from 44.5% to 73.5% when other physiologically plausible dispersion models were implemented, and the instantaneous slope varied from 35 to 75 as dispersion levels were varied. Conclusion: It has been shown that the WP quantification formula holds only within an ATT window of 0.5 to 1.25s, and that this window gets narrower as dispersion occurs. Provided that the dispersion levels fall below the threshold evaluated in this study, however, the WP can measure CBF with reasonable accuracy if dispersion is correctly modelled by the Gaussian model. However, substantial errors were observed with other common models for dispersion with dispersion levels similar to those that have been observed in literature.

Keywords: arterial spin labelling, dispersion, MRI, perfusion

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Authors: Watcharin Sangma, Onsiri Chanmuang, Pitsanu Tongkhow

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A forecasting model for steel demand uncertainty in Thailand is proposed. It consists of trend, autocorrelation, and outliers in a hierarchical Bayesian frame work. The proposed model uses a cumulative Weibull distribution function, latent first-order autocorrelation, and binary selection, to account for trend, time-varying autocorrelation, and outliers, respectively. The Gibbs sampling Markov Chain Monte Carlo (MCMC) is used for parameter estimation. The proposed model is applied to steel demand index data in Thailand. The root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) criteria are used for model comparison. The study reveals that the proposed model is more appropriate than the exponential smoothing method.

Keywords: forecasting model, steel demand uncertainty, hierarchical Bayesian framework, exponential smoothing method

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Authors: Ahmed Sakr Ashour, Hoda Saad AboRemila

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An attempt was made to fill the gap in the macro analysis of corruption by suggesting a conceptual framework that differentiates four types of macro corruption: state capture, political, bureaucratic and financial/corporate. The economic consequences or forward linkages (growth, inclusiveness and sustainability of development) and macro institutional determinants constituting the backward linkages of each type were delineated. The research implications of the macro perspective and proposed framework were discussed. Implications of the findings for theory, research and reform policies addressing macro corruption issues were discussed.

Keywords: economic growth, inclusive growth, macro corruption, sustainable development

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Authors: Norjan Jumaa, David Graham

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We present a Lattice Boltzmann Method (LBM) for multiphase flows with high viscosity and density ratios. The motion of the interface between fluids is modelled by solving the Cahn-Hilliard (CH) equation with LBM. Incompressibility of the velocity fields in each phase is imposed by using a pressure correction scheme. We use a unified LBM approach with separate formulations for the phase field, the pressure less Naiver-Stokes (NS) equations and the pressure Poisson equation required for correction of the velocity field. The implementation has been verified for various test case. Here, we present results for some complex flow problems including two dimensional single and multiple mode Rayleigh-Taylor instability and we obtain good results when comparing with those in the literature. The main focus of our work is related to interactions between aerated or non-aerated waves and structures so we also present results for both high viscosity and low viscosity waves.

Keywords: lattice Boltzmann method, multiphase flows, Rayleigh-Taylor instability, waves

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Authors: Abdel-Razzaq Mugdadi, Ruqayyah Sani

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The Kernel Distribution Function Estimator (KDFE) method is the most popular method for nonparametric estimation of the cumulative distribution function. The kernel and the bandwidth are the most important components of this estimator. In this investigation, we replace the kernel in the KDFE with a linear combination of kernels to obtain a new estimator based on the linear combination of kernels, the mean integrated squared error (MISE), asymptotic mean integrated squared error (AMISE) and the asymptotically optimal bandwidth for the new estimator are derived. We propose a new data-based method to select the bandwidth for the new estimator. The new technique is based on the Plug-in technique in density estimation. We evaluate the new estimator and the new technique using simulations and real-life data.

Keywords: estimation, bandwidth, mean square error, cumulative distribution function

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Authors: Wenjiang Deng, Tian Mou, Yudi Pawitan, Trung Nghia Vu

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Estimation of isoform-level gene expression from RNA-seq data depends on simplifying assumptions, such as uniform reads distribution, that are easily violated in real data. Such violations typically lead to biased estimates. Most existing methods provide a bias correction step(s), which is based on biological considerations, such as GC content–and applied in single samples separately. The main problem is that not all biases are known. For example, new technologies such as single-cell RNA-seq (scRNA-seq) may introduce new sources of bias not seen in bulk-cell data. This study introduces a method called XAEM based on a more flexible and robust statistical model. Existing methods are essentially based on a linear model Xβ, where the design matrix X is known and derived based on the simplifying assumptions. In contrast, XAEM considers Xβ as a bilinear model with both X and β unknown. Joint estimation of X and β is made possible by simultaneous analysis of multi-sample RNA-seq data. Compared to existing methods, XAEM automatically performs empirical correction of potentially unknown biases. XAEM implements an alternating expectation-maximization (AEM) algorithm, alternating between estimation of X and β. For speed XAEM utilizes quasi-mapping for read alignment, thus leading to a fast algorithm. Overall XAEM performs favorably compared to other recent advanced methods. For simulated datasets, XAEM obtains higher accuracy for multiple-isoform genes, particularly for paralogs. In a differential-expression analysis of a real scRNA-seq dataset, XAEM achieves substantially greater rediscovery rates in an independent validation set.

Keywords: alternating EM algorithm, bias correction, bilinear model, gene expression, RNA-seq

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Authors: Iliya Musa Adamu

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This paper seeks to develop an expert system that would troubleshoot computer network and evaluate the network system performance so as to reduce the workload on technicians and increase the efficiency and effectiveness of solutions proffered to computer network problems. The platform of the system was developed using ASP.NET, whereas the codes are implemented in Visual Basic and integrated with SQL Server 2005. The knowledge base was represented using production rule, whereas the searching method that was used in developing the network troubleshooting expert system is the forward-chaining-rule-based-system. This software tool offers the advantage of providing an immediate solution to most computer network problems encountered by computer users.

Keywords: expert system, forward chaining rule based system, network, troubleshooting

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Authors: Mohammed Affanuddin H. Siddique, Jayesh S. Shukla, Chetan B. Meshram

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The neural networks are one of the power tools of machine learning. After the invention of perceptron in early 1980's, the neural networks and its application have grown rapidly. Neural networks are a technique originally developed for pattern investigation. The structure of a neural network consists of neurons connected through synapse. Here, we have investigated the different algorithms and cost function reduction techniques for optimization of vertical axis wind turbine (VAWT) rotor blades. The aerodynamic force coefficients corresponding to the airfoils are stored in a database along with the airfoil coordinates. A forward propagation neural network is created with the input as aerodynamic coefficients and output as the airfoil co-ordinates. In the proposed algorithm, the hidden layer is incorporated into cost function having linear and non-linear error terms. In this article, it is observed that the ANNs (Artificial Neural Network) can be used for the VAWT’s optimization.

Keywords: VAWT, ANN, optimization, inverse design

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Authors: Y. T. Lee, J. H. Na, S. H. Kim, S. U. Hong

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In this study, an experimental research for estimation of slab depth, column size and location of rebar of concrete specimen is conducted using the Impact Echo Method (IE) based on stress wave among non-destructive test methods. Estimation of slab depth had total length of 1800×300 and 6 different depths including 150 mm, 180 mm, 210 mm, 240 mm, 270 mm and 300 mm. The concrete column specimen was manufactured by differentiating the size into 300×300×300 mm, 400×400×400 mm and 500×500×500 mm. In case of the specimen for estimation of rebar, rebar of ∅22 mm was used in a specimen of 300×370×200 and arranged at 130 mm and 150 mm from the top to the rebar top. As a result of error rate of slab depth was overall mean of 3.1%. Error rate of column size was overall mean of 1.7%. Mean error rate of rebar location was 1.72% for top, 1.19% for bottom and 1.5% for overall mean showing relative accuracy.

Keywords: impact echo method, estimation, slab depth, column size, rebar location, concrete

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Authors: Said Yousif Khairi

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Being a developing economy, imports of capital, raw materials and manufactories goods are vital for sustainable economic growth. In 2006, Libya imported LD 8 billion (US$ 6.25 billion) which composed of mainly machinery and transport equipment (49.3%), raw material (18%), and food products and live animals (13%). This represented about 10% of GDP. Thus, it is pertinent to investigate factors affecting the amount of Libyan imports. An econometric model representing the aggregate import demand for Libya was developed and estimated using the bounds test procedure, which based on an unrestricted error correction model (UECM). The data employed for the estimation was from 1970–2010. The results of the bounds test revealed that the volume of imports and its determinants namely real income, consumer price index and exchange rate are co-integrated. The findings indicate that the demand for imports is inelastic with respect to income, index price level and The exchange rate variable in the short run is statistically significant. In the long run, the income elasticity is elastic while the price elasticity and the exchange rate remains inelastic. This indicates that imports are important elements for Libyan economic growth in the long run.

Keywords: import demand, UECM, bounds test, Libya

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Authors: Rufus Temidayo Akinnubi, M. O. Adeniyi

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Existing diurnal air temperature models simulate night time air temperature over Nigeria with high biases. An improved parameterization is presented for modeling the diurnal pattern of air temperature (Ta) which is applicable in the calculation of turbulent heat fluxes in Global climate models, based on Nigeria Micrometeorological Experimental site (NIMEX) surface layer observations. Five diurnal Ta models for estimating hourly Ta from daily maximum, daily minimum, and daily mean air temperature were validated using root-mean-square error (RMSE), Mean Error Bias (MBE) and scatter graphs. The original Fourier series model showed better performance for unstable air temperature parameterizations while the stable Ta was strongly overestimated with a large error. The model was improved with the inclusion of the atmospheric cooling rate that accounts for the temperature inversion that occurs during the nocturnal boundary layer condition. The MBE and RMSE estimated by the modified Fourier series model reduced by 4.45 oC and 3.12 oC during the transitional period from dry to wet stable atmospheric conditions. The modified Fourier series model gave good estimation of the diurnal weather patterns of Ta when compared with other existing models for a tropical environment.

Keywords: air temperature, mean bias error, Fourier series analysis, surface energy balance,

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Authors: Matteo Bonaventura, Giancarlo Giudici

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This paper analyses the valuation process of companies listed on the Italian Exchange in the period 2000-2009 at their Initial Public Offering (IPO). One the most common valuation techniques declared in the IPO prospectus to determine the offer price is the Discounted Cash Flow (DCF) method. We develop a ‘reverse engineering’ model to discover the short term profitability implied in the offer prices. We show that there is a significant optimistic bias in the estimation of future profitability compared to ex-post actual realization and the mean forecast error is substantially large. Yet we show that such error characterizes also the estimations carried out by analysts evaluating non-IPO companies. The forecast error is larger the faster has been the recent growth of the company, the higher is the leverage of the IPO firm, the more companies issued equity on the market. IPO companies generally exhibit better operating performance before the listing, with respect to comparable listed companies, while after the flotation they do not perform significantly different in term of return on invested capital. Pre-IPO book building activity plays a significant role in partially reducing the forecast error and revising expectations, while the market price of the first day of trading does not contain information for further reducing forecast errors.

Keywords: initial public offerings, DCF, book building, post-IPO profitability drop

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Authors: Elham Alaee, Mousa Shamsi, Hossein Ahmadi, Soroosh Nazem, Mohammad Hossein Sedaaghi

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Human face has a fundamental role in the appearance of individuals. So the importance of facial surgeries is undeniable. Thus, there is a need for the appropriate and accurate facial skin segmentation in order to extract different features. Since Fuzzy C-Means (FCM) clustering algorithm doesn’t work appropriately for noisy images and outliers, in this paper we exploit Possibilistic C-Means (PCM) algorithm in order to segment the facial skin. For this purpose, first, we convert facial images from RGB to YCbCr color space. To evaluate performance of the proposed algorithm, the database of Sahand University of Technology, Tabriz, Iran was used. In order to have a better understanding from the proposed algorithm; FCM and Expectation-Maximization (EM) algorithms are also used for facial skin segmentation. The proposed method shows better results than the other segmentation methods. Results include misclassification error (0.032) and the region’s area error (0.045) for the proposed algorithm.

Keywords: facial image, segmentation, PCM, FCM, skin error, facial surgery

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Authors: Liew Hui Fang, Syed Idris Syed Hassan, Mohd Fareq Abd. Malek, Yufridin Wahab, Norshafinash Saudin

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The paper implementation new approach method applied into transforming lumped element circuit into microstrip line form for Butterworth low pass filter which is operating at LC band. The filter’s lumped element circuits and microstrip line form were first designed and simulated using Advanced Design Software (ADS) to obtain the best filter characteristic based on S-parameter and implemented on FR4 substrate for order N=3,4,5,6,7,8 and 9. The importance of a new approach of transforming method as a correction factor has been considered into designed microstrip line. From ADS simulation results proved that the response of microstrip line circuit of Butterworth low pass filter with fringing correction factor has an excellent agreement with its lumped circuit. This shows that the new approach of transforming lumped element circuit into microstrip line is able to solve the conventional design of complexity size of circuit of Butterworth low pass filter (LPF) into microstrip line.

Keywords: Butterworth low pass filter, number of order, microstrip line, microwave filter, maximally flat

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Authors: Mojtaba Valinataj

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Nowadays reversible logic has received many attentions as one of the new fields for reducing the power consumption. On the other hand, the processing systems have weaknesses against different external effects. In this paper, some error detecting reversible logic serial multipliers are proposed by incorporating the parity-preserving gates. This way, the new designs are presented for signed parity-preserving serial multipliers based on the Booth's algorithm by exploiting the new arrangements of existing gates. The experimental results show that the proposed 4×4 multipliers in this paper reach up to 20%, 35%, and 41% enhancements in the number of constant inputs, quantum cost, and gate count, respectively, as the reversible logic criteria, compared to previous designs. Furthermore, all the proposed designs have been generalized for n×n multipliers with general formulations to estimate the main reversible logic criteria as the functions of the multiplier size.

Keywords: Booth’s algorithm, error detection, multiplication, parity-preserving gates, quantum computers, reversible logic

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Authors: Chandra Mukherjee

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The present paper is a representation of the work done in the field of ECG signal analysis using MATLAB 7.1 Platform. An accurate and simple ECG feature extraction algorithm is presented in this paper and developed algorithm is validated using BIOPAC software. To detect the QRS peak, ECG signal is processed by following mentioned stages- First Derivative, Second Derivative and then squaring of that second derivative. Efficiency of developed algorithm is tested on ECG samples from different database and real time ECG signals acquired using BIOPAC system. Firstly we have lead wise specified threshold value the samples above that value is marked and in the original signal, where these marked samples face change of slope are spotted as R-peak. On the left and right side of the R-peak, faces change of slope identified as Q and S peak, respectively. Now the inbuilt Detection algorithm of BIOPAC software is performed on same output sample and both outputs are compared. ECG baseline modulation correction is done after detecting characteristics points. The efficiency of the algorithm is tested using some validation parameters like Sensitivity, Positive Predictivity and we got satisfied value of these parameters.

Keywords: first derivative, variable threshold, slope reversal, baseline modulation correction

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Authors: Aye Pa Pa Myo

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Writing in English is accounted as a complex process for English as a foreign language learners. Besides, committing errors in writing can be found as an inevitable part of language learners’ writing. Generally, academic writing is quite difficult for most of the students to manage for getting better scores. Students can commit common errors in their writings when they try to write academic writing. Error analysis deals with identifying and detecting the errors and also explains the reason for the occurrence of these errors. In this paper, the researcher has an attempt to examine the common errors of undergraduate students in their academic writings at Pathein University. The purpose of doing this research is to investigate the errors which students usually commit in academic writing and to find out the better ways for correcting these errors in EFL classrooms. In this research, fifty-third-year non-English specialization students attending Pathein University were selected as participants. This research took one month. It was conducted with a mixed methodology method. Two mini-tests were used as research tools. Data were collected with a quantitative research method. Findings from this research pointed that most of the students noticed their common errors after getting the necessary input, and they became more decreased committing these errors after taking mini-test; hence, all findings will be supportive for further researches related to error analysis in academic writing.

Keywords: academic writing, error analysis, EFL learners, mini-tests, mixed methodology

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Authors: Brahim Boukabcha, Amar Rebbouh

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The bootstrap method based on the idea of exploiting all the information provided by the initial sample, allows us to study the properties of estimators. In this article we will present a theoretical study on the different methods of bootstrapping and using the technique of re-sampling in statistics inference to calculate the standard error of means of an estimator and determining a confidence interval for an estimated parameter. We apply these methods tested in the regression models and Pareto model, giving the best approximations.

Keywords: bootstrap, error standard, bias, jackknife, mean, median, variance, confidence interval, regression models

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Authors: Josip Vasilj, Petar Sarajcev, Damir Jakus

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One of the major difficulties introduced with wind power penetration is the inherent uncertainty in production originating from uncertain wind conditions. This uncertainty impacts many different aspects of power system operation, especially the balancing power requirements. For this reason, in power system development planing, it is necessary to evaluate the potential uncertainty in future wind power generation. For this purpose, simulation models are required, reproducing the performance of wind power forecasts. This paper presents a wind power forecast error simulation models which are based on the stochastic process simulation. Proposed models capture the most important statistical parameters recognized in wind power forecast error time series. Furthermore, two distinct models are presented based on data availability. First model uses wind speed measurements on potential or existing wind power plant locations, while the seconds model uses statistical distribution of wind speeds.

Keywords: wind power, uncertainty, stochastic process, Monte Carlo simulation

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Authors: Ho-Hsin Lee, Yue-Min Jiang, Shu-Hui Tsai, Jian-Ren Chen, Mei-Yu XU, Wen-Tien Wu

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This project presents an AI-based method and system for wound status analysis. The system uses a three-in-one sensor device to analyze wound status, including color, temperature, and a 3D sensor to provide wound information up to 2mm below the surface, such as redness, heat, and blood circulation information. The system has a 90% accuracy rate, requiring only one manual correction in 70% of cases, with a one-second delay. The system also provides an offline application that allows for manual correction of the wound bed range using color-based guidance to estimate wound bed size with 96% accuracy and a maximum of one manual correction in 96% of cases, with a one-second delay. Additionally, AI-assisted wound bed range selection achieves 100% of cases without manual intervention, with an accuracy rate of 76%, while AI-based wound tissue type classification achieves an 85.3% accuracy rate for five categories. The AI system also includes similar case search and expert recommendation capabilities. For AI-assisted wound range selection, the system uses WIFI6 technology, increasing data transmission speeds by 22 times. The project aims to save up to 64% of the time required for human wound record keeping and reduce the estimated time to assess wound status by 96%, with an 80% accuracy rate. Overall, the proposed AI method and system integrate multiple sensors to provide accurate wound information and offer offline and online AI-assisted wound bed size estimation and wound tissue type classification. The system decreases delay time to one second, reduces the number of manual corrections required, saves time on wound record keeping, and increases data transmission speed, all of which have the potential to significantly improve wound care and management efficiency and accuracy.

Keywords: wound status analysis, AI-based system, multi-sensor integration, color-based guidance

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Authors: Jiangbo Shi, Zeyu Gao, Chen Li

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Nuclei segmentation is a fundamental task in digital pathology analysis and can be automated by deep learning-based methods. However, the development of such an automated method requires a large amount of data with precisely annotated masks which is hard to obtain. Training with weakly labeled data is a popular solution for reducing the workload of annotation. In this paper, we propose a novel meta-learning-based nuclei segmentation method which follows the label correction paradigm to leverage data with noisy masks. Specifically, we design a fully conventional meta-model that can correct noisy masks by using a small amount of clean meta-data. Then the corrected masks are used to supervise the training of the segmentation model. Meanwhile, a bi-level optimization method is adopted to alternately update the parameters of the main segmentation model and the meta-model. Extensive experimental results on two nuclear segmentation datasets show that our method achieves the state-of-the-art result. In particular, in some noise scenarios, it even exceeds the performance of training on supervised data.

Keywords: deep learning, histopathological image, meta-learning, nuclei segmentation, weak annotations

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Authors: Salako Rotimi, Oshungade Stephen, Ojewoye Opeyemi

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The maintenance of price stability is one of the macroeconomic challenges facing Nigeria as a nation. This paper attempts to build a co-integration multivariate time series model for inflation movement in Nigeria using data extracted from the abstract of statistics of the Central Bank of Nigeria (CBN) from 2008 to 2017. The Johansen cointegration test suggests at least one co-integration vector describing the long run relationship between Consumer Price Index (CPI), Food Price Index (FPI) and Non-Food Price Index (NFPI). All three series show increasing pattern, which indicates a sign of non-stationary in each of the series. Furthermore, model predictability was established with root-mean-square-error, mean absolute error, mean average percentage error, and Theil’s unbiased statistics for n-step forecasting. The result depicts that the long run coefficient of a consumer price index (CPI) has a positive long-run relationship with the food price index (FPI) and non-food price index (NFPI).

Keywords: economic, inflation, model, series

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Authors: Morsy Ahmed Morsy Ismail

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In this paper, the structure of a coherent homodyne receiver for the Binary Phase Shift Keying (BPSK) Optical Code Division Multiple Access (OCDMA) network is introduced based on the Multi-Length Weighted Modified Prime Code (ML-WMPC) for multimedia applications. The Bit Error Rate (BER) of this homodyne detection is evaluated as a function of the number of active users and the signal to noise ratio for different code lengths according to the multimedia application such as audio, voice, and video. Besides, the Mach-Zehnder interferometer is used as an external phase modulator in homodyne detection. Furthermore, the Multiple Access Interference (MAI) and the receiver noise in a shot-noise limited regime are taken into consideration in the BER calculations.

Keywords: OCDMA networks, bit error rate, multiple access interference, binary phase-shift keying, multimedia

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Authors: S. O. Oyamakin, A. U. Chukwu

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Richard's growth equation being a generalized logistic growth equation was improved upon by introducing an allometric parameter using the hyperbolic sine function. The integral solution to this was called hyperbolic Richard's growth model having transformed the solution from deterministic to a stochastic growth model. Its ability in model prediction was compared with the classical Richard's growth model an approach which mimicked the natural variability of heights/diameter increment with respect to age and therefore provides a more realistic height/diameter predictions using the coefficient of determination (R2), Mean Absolute Error (MAE) and Mean Square Error (MSE) results. The Kolmogorov-Smirnov test and Shapiro-Wilk test was also used to test the behavior of the error term for possible violations. The mean function of top height/Dbh over age using the two models under study predicted closely the observed values of top height/Dbh in the hyperbolic Richard's nonlinear growth models better than the classical Richard's growth model.

Keywords: height, Dbh, forest, Pinus caribaea, hyperbolic, Richard's, stochastic

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Authors: Johan Loeckx, Joeri Bultheel

Abstract:

Recently, the application of deep learning to music has gained popularity. Its true potential, however, has been largely unexplored. In this paper, a new idea for representing the dynamic behavior of music is proposed. A ”forward” conditional RBM takes into account not only preceding but also future samples during training. Though this may sound controversial at first sight, it will be shown that it makes sense from a musical and neuro-cognitive perspective. The model is applied to reconstruct music based upon the first notes and to improvise in the musical style of a composer. Different to expectations, reconstruction accuracy with respect to a regular CRBM with the same order, was not significantly improved. More research is needed to test the performance on unseen data.

Keywords: deep learning, restricted boltzmann machine, music generation, conditional restricted boltzmann machine (CRBM)

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