Search results for: image encryption algorithms

Authors: Thuvanan Borvornvitchotikarn, Werasak Kurutach

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Mutual information (MI) is widely used in medical image registration. In the different medical images analysis, it is difficult to choose an optimal bins size number for calculating the probability distributions in MI. As the result, this paper presents a new adaptive bins number selection approach that named a hybrid EMPCA-Scott approach. This work combines an expectation maximization principal component analysis (EMPCA) and the modified Scott’s rule. The proposed approach solves the binning problem from the various intensity values in medical images. Experimental results of this work show the lower registration errors compared to other adaptive binning approaches.

Keywords: mutual information, EMPCA, Scott, probability distributions

Procedia PDF Downloads 248

Authors: N. Zoghi, Laleh Maleknia , M. E. Olya

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The nano-Cu particles were produced on cotton fabric substrate by ink-jet printing technology with water-soluble ink, which was based on copper. The surface tension and viscosity of the prepared inks were evaluated. The ink-jet printing process was repeated 1, 3, and 5 times in order to evaluate variations in the optical properties by changing thickness of printed film. Following initial drying of the printed film, the samples were annealed at different temperatures (150 °C, 200 °C and 250 °C) to determine the optimum temperature for the parameters set out in this experiment. The prepared nano-Cu particles were characterized by XRD and UV spectroscopy. The appearance of printed image and the nano-Cu particles morphology were observed by SEM. The results demonstrated that the ink-jet printing technology can be used to produce nano-particles on the cotton fabrics surface.

Keywords: ink-jet printing, nano-cu, fabric ink, in situ production, cotton fabric, water-soluble ink, morphology

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Authors: Mhaned Oubounyt, Jan Baumbach

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Differences in co-expression networks between two or multiple cells (sub)types across conditions is a pressing problem in single-cell RNA sequencing (scRNA-seq). A key challenge is to define those co-variations that differ between or among cell types and/or conditions and phenotypes to examine small regulatory networks that can explain mechanistic differences. To this end, we developed SCANet, an all-in-one Python package that uses state-of-the-art algorithms to facilitate the workflow of a combined single-cell GCN (Gene Correlation Network) and GRN (Gene Regulatory Networks) pipeline, including inference of gene co-expression modules from scRNA-seq, followed by trait and cell type associations, hub gene detection, co-regulatory networks, and drug-gene interactions. In an example case, we illustrate how SCANet can be applied to identify regulatory drivers behind a cytokine storm associated with mortality in patients with acute respiratory illness. SCANet is available as a free, open-source, and user-friendly Python package that can be easily integrated into systems biology pipelines.

Keywords: single-cell, co-expression networks, drug-gene interactions, co-regulatory networks

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Authors: M. Suresh Kumar, K. Ramani

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This paper proposed the harmonic elimination of Cascaded H-Bridge Multi-Level Inverter by using Selective Harmonic Elimination-Pulse Width Modulation method programmed with Particle Swarm Optimization algorithm. PSO method determine proficiently the required switching angles to eliminate low order harmonics up to the 11th order from the inverter output voltage waveform while keeping the magnitude of the fundamental harmonics at the desired value. Results demonstrate that the proposed method does efficiently eliminate a great number of specific harmonics and the output voltage is resulted in minimum Total Harmonic Distortion. The results shown that the PSO algorithm attain successfully to the global solution faster than other algorithms.

Keywords: multi-level inverter, Selective Harmonic Elimination Pulse Width Modulation (SHEPWM), Particle Swarm Optimization (PSO), Total Harmonic Distortion (THD)

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Authors: Sagir M. Yusuf, Chris Baber

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We describe issues bedeviling the coordination of heterogeneous (different sensors carrying agents) multi-agent missions such as belief conflict, situation reasoning, etc. We applied Bayesian and agents' presumptions inferential reasoning to solve the outlined issues with the heterogeneous multi-agent belief variation and situational-base reasoning. Bayesian Belief Network (BBN) was used in modeling the agents' belief conflict due to sensor variations. Simulation experiments were designed, and cases from agents’ missions were used in training the BBN using gradient descent and expectation-maximization algorithms. The output network is a well-trained BBN for making inferences for both agents and human experts. We claim that the Bayesian learning algorithm prediction capacity improves by the number of training data and argue that it enhances multi-agents robustness and solve agents’ sensor conflicts.

Keywords: distributed constraint optimization problem, multi-agent system, multi-robot coordination, autonomous system, swarm intelligence

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Authors: Jiahe Liu, Hongyang Yu, Feng Qian, Miao Luo, Linhang Zhu

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This study proposed a 3D human body reconstruction method, which integrates multi-view joint information into a set of joints and processes it with a parametric human body template. Firstly, we obtained human body image information captured from multiple perspectives. The multi-view information can avoid self-occlusion and occlusion problems during the reconstruction process. Then, we used the MvP algorithm to integrate multi-view joint information into a set of joints. Next, we used the parametric human body template SMPL-X to obtain more accurate three-dimensional human body reconstruction results. Compared with the traditional single-view parametric human body template reconstruction, this method significantly improved the accuracy and stability of the reconstruction.

Keywords: parametric human body templates, reconstruction of the human body, multi-view, joint

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Authors: Baoshan Wei, Shuai Han, Xing Zhang

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Local obstacle avoidance is critical for mobile robot navigation. It is a challenging task to ensure path optimality and safety in cluttered environments. We proposed an Environment Aware Dynamic Window Approach in this paper to cope with the issue. The method integrates environment characterization into Dynamic Window Approach (DWA). Two strategies are proposed in order to achieve the integration. The local goal strategy guides the robot to move through openings before approaching the final goal, which solves the local minima problem in DWA. The adaptive control strategy endows the robot to adjust its state according to the environment, which addresses path safety compared with DWA. Besides, the evaluation shows that the path generated from the proposed algorithm is safer and smoother compared with state-of-the-art algorithms.

Keywords: adaptive control, dynamic window approach, environment aware, local obstacle avoidance, mobile robots

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Authors: Bandar Alahmadi

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The fact that people tend to remember pictures better than texts, motivates researchers to develop graphical passwords as an alternative to textual passwords. Graphical passwords as such were introduced as a possible alternative to traditional text passwords, in which users prove their identity by clicking on pictures rather than typing alphanumerical text. In this paper, we present a scheme for graphical passwords that are resistant to shoulder surfing attacks and spyware attacks. The proposed scheme introduces a clicking technique to chosen images. First, the users choose a set of images, the images are then included in a grid where users can click in the cells around each image, the location of the click and the number of clicks are saved. As a result, the proposed scheme can be safe from shoulder surface and spyware attacks.

Keywords: security, password, authentication, attack, applications

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Authors: Sujay Kotwale, Ramasubba Reddy M.

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Electrocardiogram (ECG) is a non-invasive technique used to study and analyze various heart diseases. Cardiac arrhythmia is a serious heart disease which leads to death of the patients, when left untreated. An early-time detection of cardiac arrhythmia would help the doctors to do proper treatment of the heart. In the past, various algorithms and machine learning (ML) models were used to early-time detection of cardiac arrhythmia, but few of them have achieved better results. In order to improve the performance, this paper implements principal component analysis (PCA) along with XGBoost model. The PCA was implemented to the raw ECG signals which suppress redundancy information and extracted significant features. The obtained significant ECG features were fed into XGBoost model and the performance of the model was evaluated. In order to valid the proposed technique, raw ECG signals obtained from standard MIT-BIH database were employed for the analysis. The result shows that the performance of proposed method is superior to the several state-of-the-arts techniques.

Keywords: cardiac arrhythmia, electrocardiogram, principal component analysis, XGBoost

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Authors: Abdelkadir Elhaouari

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The priciest thing in human life since his birth is his freedom, basing on this idea, the conflict exists till now, the fight against oppression, injustice, tyranny and slavery, searching for freedom and political resistances, and this makes the freedom is deeply related to the defense for its existence all over years. This project attempts using any way to preserve this freedom, and building and maintaining bases and rules to organize this life. Appeal is a one of the most important method that human uses to protect his freedom, and we will mention in this thesis our attempt to clarify this aspect to the individual. We can say that the law does not know just one color or one logic, and is not based on one rule to be taken by heart, but the law is neutrality, the diversity, abstraction and diligence diversity. The penal law is a valued law and it deserves to be studied and searched more… so that to attempt to master it. Our thesis is just a brief explanation of an important point in this law, where we attempt to clarify and simplify the image to the normal person, so that he can preserve his rights, and we hope that we had succeeded to choose the right topic for that.

Keywords: appeal, penalization, judgement, criminal

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Authors: Derek Brandon G. Yu, Clyde Vincent O. Pilapil, Christine F. Peña

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College students of Cebu province have been indoors since March 2020, and a challenge encountered is the sudden shift from face to face to online learning and with the lack of empirical data on online learning on Higher Education Institutions (HEIs) in the Philippines. Sentiments on face to face and online learning will be collected from University of San Carlos (USC), School of Arts and Sciences (SAS) students regarding Mathematics in the Modern World (MMW), a General Education (GE) course. Natural Language Processing with machine learning algorithms will be used to classify the sentiments of the students. Results of the research study are the themes identified through topic modelling and the overall sentiments of the students in USC SAS

Keywords: natural language processing, online learning, sentiment analysis, topic modelling

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Authors: Tatsuya Kato, Masanobu Nagata, Hidetoshi Nakashima, Kazunori Matsuo

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Localization of mobile robots are important tasks for developing autonomous mobile robots. This paper proposes a method to estimate positions of a mobile robot using an omnidirectional camera on the robot. Landmarks for points of references are set up on a field where the robot works. The omnidirectional camera which can obtain 360 [deg] around images takes photographs of these landmarks. The positions of the robots are estimated from directions of these landmarks that are extracted from the images by image processing. This method can obtain the robot positions without accumulative position errors. Accuracy of the estimated robot positions by the proposed method are evaluated through some experiments. The results show that it can obtain the positions with small standard deviations. Therefore the method has possibilities of more accurate localization by tuning of appropriate offset parameters.

Keywords: mobile robots, localization, omnidirectional camera, estimating positions

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Authors: R. Burahee, B. Chassinat, T. de Laclos, A. Dépée, A. Sastim

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The purpose of this paper is to propose a solution to detect, predict and reconstitute rain drops in real time – during the night – using an embedded material with an infrared camera. To prevent the system from needing too high hardware resources, simple models are considered in a powerful image treatment algorithm reducing considerably calculation time in OpenCV software. Using a smart model – drops will be matched thanks to a process running through two consecutive pictures for implementing a sophisticated tracking system. With this system drops computed trajectory gives information for predicting their future location. Thanks to this technique, treatment part can be reduced. The hardware system composed by a Raspberry Pi is optimized to host efficiently this code for real time execution.

Keywords: reconstitution, prediction, detection, rain drop, real time, raspberry, infrared

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Authors: Zzibu Mark Brian

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As software systems become increasingly complex, ensuring code security is a growing concern. Traditional vulnerability detection methods often rely on manual code reviews or static analysis tools, which can be time-consuming and prone to errors. This paper presents a distinct approach to enhancing code security by leveraging artificial intelligence (AI) and machine learning (ML) techniques. Our proposed system utilizes a combination of natural language processing (NLP) and deep learning algorithms to identify and classify vulnerabilities in real-world codebases. By analyzing vast amounts of open-source code data, our AI-powered tool learns to recognize patterns and anomalies indicative of security weaknesses. We evaluated our system on a dataset of over 10,000 open-source projects, achieving an accuracy rate of 92% in detecting known vulnerabilities. Furthermore, our tool identified previously unknown vulnerabilities in popular libraries and frameworks, demonstrating its potential for improving software security.

Keywords: AI, machine language, cord security, machine leaning

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Authors: Samineh Barmaki, Ville Jokinen, Esko Kankuri

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We report a microfluidic chip that can be used to modify the gaseous microenvironment of a cell-culture in ambient atmospheric conditions. The aim of the study is to show the cellular response to nitric oxide (NO) under hypoxic (oxygen < 5%) condition. Simultaneously targeting to hypoxic and nitric oxide will provide an opportunity for NO‑based therapeutics. Studies on cellular responses to lowered oxygen concentration or to gaseous mediators are usually carried out under a specific macro environment, such as hypoxia chambers, or with specific NO donor molecules that may have additional toxic effects. In our study, the chip consists of a microfluidic layer and a cell culture well, separated by a thin gas permeable polydimethylsiloxane (PDMS) membrane. The main design goal is to separate the gas oxygen scavenger and NO donor solutions, which are often toxic, from the cell media. Two different types of gas exchangers, titled 'pool' and 'meander' were tested. We find that the pool design allows us to reach a higher level of oxygen depletion than meander (24.32 ± 19.82 %vs -3.21 ± 8.81). Our microchip design can make the cells culture more simple and makes it easy to adapt existing cell culture protocols. Our first application is utilizing the chip to create hypoxic conditions on targeted areas of cell culture. In this study, oxygen scavenger sodium sulfite generates hypoxia and its effect on human embryonic kidney cells (HEK-293). The PDMS membrane was coated with fibronectin before initiating cell cultures, and the cells were grown for 48h on the chips before initiating the gas control experiments. The hypoxia experiments were performed by pumping of O₂-depleted H₂O into the microfluidic channel with a flow-rate of 0.5 ml/h. Image-iT® reagent as an oxygen level responser was mixed with HEK-293 cells. The fluorescent signal appears on cells stained with Image-iT® hypoxia reagent (after 6h of pumping oxygen-depleted H₂O through the microfluidic channel in pool area). The exposure to different levels of O₂ can be controlled by varying the thickness of the PDMS membrane. Recently, we improved the design of the microfluidic chip, which can control the microenvironment of two different gases at the same time. The hypoxic response was also improved from the new design of microchip. The cells were grown on the thin PDMS membrane for 30 hours, and with a flowrate of 0.1 ml/h; the oxygen scavenger was pumped into the microfluidic channel. We also show that by pumping sodium nitroprusside (SNP) as a nitric oxide donor activated under light and can generate nitric oxide on top of PDMS membrane. We are aiming to show cellular microenvironment response of HEK-293 cells to both nitric oxide (by pumping SNP) and hypoxia (by pumping oxygen scavenger solution) in separated channels in one microfluidic chip.

Keywords: hypoxia, nitric oxide, microenvironment, microfluidic chip, sodium nitroprusside, SNP

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Authors: Anocha Rojanapanich

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This research aimed to identify important factors that effect customer satisfaction in boutique hotels and the important factors effecting customer loyalty in returning to boutique hotels. Furthermore, this study also aimed to study demographics, which effect variable factors. Four hundred questionnaires were completed by customers of the boutique hotels. The descriptive statistics used in this paper were percentages, means, and standard deviation (S.D.), while hypothesis testing was done using T-test, Anova, Correlation and Regression to analyze the relationship among those factors. In terms of the purpose in staying, it was found that the largest respondent was for ‘leisure purposes’. While the frequency indicated that most of the customers who stayed ‘once’in the last two years in the hotels had less concern in the hotel’s image than other groups. For customer’s perceived value and income levels had an influence on customer perceived values in both functional value price and emotional value.

Keywords: boutique hotels, customer attitude, customer satisfaction, customer loyalty

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Authors: R. Rajabpour, N. Talebbeydokhti, M. H. Ahmadi

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In recent years, new techniques for solving complex problems in engineering are proposed. One of these techniques is JPSO algorithm. With innovative changes in the nature of the jump algorithm JPSO, it is possible to construct a graph-based solution with a new algorithm called G-JPSO. In this paper, a new algorithm to solve the optimal control problem Fletcher-Powell and optimal control of pumps in water distribution network was evaluated. Optimal control of pumps comprise of optimum timetable operation (status on and off) for each of the pumps at the desired time interval. Maximum number of status on and off for each pumps imposed to the objective function as another constraint. To determine the optimal operation of pumps, a model-based optimization-simulation algorithm was developed based on G-JPSO and JPSO algorithms. The proposed algorithm results were compared well with the ant colony algorithm, genetic and JPSO results. This shows the robustness of proposed algorithm in finding near optimum solutions with reasonable computational cost.

Keywords: G-JPSO, operation, optimization, pumping station, water distribution networks

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Authors: Lusungu Ndovi

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Radio propagation from point-to-point is affected by the physical channel in many ways. A signal arriving at a destination travels through a number of different paths which are referred to as multi-paths. Research in this area of wireless communications has progressed well over the years with the research taking different angles of focus. By this is meant that some researchers focus on ways of reducing or eluding Multipath effects whilst others focus on ways of mitigating the effects of Multipath through compensation schemes. Baseband processing is seen as one field of signal processing that is cardinal to the advancement of software-defined radio technology. This has led to wide research into the carrying out certain algorithms at baseband. This paper considers compensating for Multipath for Frequency Modulated signals. The compensation process is carried out at Radio frequency (RF) and at Quadrature baseband (QBB) and the results are compared. Simulations are carried out using MatLab so as to show the benefits of working at lower QBB frequencies than at RF.

Keywords: quadrature baseband, qadio frequency, qultipath compensation, frequency qodulation, signal processing

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Authors: L. Chevez, A. Apaza, J. Rodriguez, R. Puga, H. Loro, Juan Z. Davalos

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Virtual Dimension (VD) procedure is used to analyze Hyperspectral Image (HIS) treatment-data in order to estimate the abundance of mineral components of a mining sample. Hyperspectral images coming from reflectance spectra (NIR region) are pre-treated using Standard Normal Variance (SNV) and Minimum Noise Fraction (MNF) methodologies. The endmember components are identified by the Simplex Growing Algorithm (SVG) and after adjusted to the reflectance spectra of reference-databases using Simulated Annealing (SA) methodology. The obtained abundance of minerals of the sample studied is very near to the ones obtained using XRD with a total relative error of 2%.

Keywords: hyperspectral imaging, minimum noise fraction, MNF, simplex growing algorithm, SGA, standard normal variance, SNV, virtual dimension, XRD

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Authors: Ionel Zagan, Vasile Gheorghita Gaitan

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The development of CPUs and of real-time systems based on them made it possible to use time at increasingly low resolutions. Together with the scheduling methods and algorithms, time organizing has been improved so as to respond positively to the need for optimization and to the way in which the CPU is used. This presentation contains both a detailed theoretical description and the results obtained from research on improving the performances of the nMPRA (Multi Pipeline Register Architecture) processor by implementing specific functions in hardware. The proposed CPU architecture has been developed, simulated and validated by using the FPGA Virtex-7 circuit, via a SoC project. Although the nMPRA processor hardware structure with five pipeline stages is very complex, the present paper presents and analyzes the tests dedicated to the implementation of the CPU and of the memory on-chip for instructions and data. In order to practically implement and test the entire SoC project, various tests have been performed. These tests have been performed in order to verify the drivers for peripherals and the boot module named Bootloader.

Keywords: hardware scheduler, nMPRA processor, real-time systems, scheduling methods

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Authors: Sadik Yigit

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The energy consumed in the buildings constitutes a large portion of the total energy consumption in the world. In this study, it was aimed to measure the impact of climate change on the energy consumption of residential building stock by analyzing a typical mid-rise residential building in four different climate regions of Turkey. An integrated system was developed using the "Distribution Evolutionary Algorithms in Python" tool and Energy Plus. By using the developed integrated system, the energy performance of the typical residential building was analyzed under the effect of different climate change scenarios. The results indicated that predicted overheating will be experienced in the future, which will significantly increase the cooling energy loads of the buildings. In addition, design solutions to improve the future energy performance of the buildings were proposed, considering budget constraints. The results of the study will guide researchers studying in this area of research and designers in the sector in finding climate change resilient design solutions.

Keywords: energy_efficient, residential buildings, climate change, energyplus

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Authors: Hae-Yeoun Lee

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Reversible watermarking that not only protects the copyright but also preserve the original quality of the digital content have been intensively studied. In particular, the demand for reversible watermarking has increased. In this paper, we propose a reversible watermarking scheme based on interpolation-error shifting and error precompensation. The intensity of a pixel is interpolated from the intensities of neighbouring pixels, and the difference histogram between the interpolated and the original intensities is obtained and modified to embed the watermark message. By restoring the difference histogram, the embedded watermark is extracted and the original image is recovered by compensating for the interpolation error. The overflow and underflow are prevented by error precompensation. To show the performance of the method, the proposed algorithm is compared with other methods using various test images.

Keywords: reversible watermarking, high capacity, high quality, interpolated error shifting, error precompensation

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Authors: Parampal Kaur, Deepak Aggarwal

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In the computational Grid, fault tolerance is an imperative issue to be considered during job scheduling. Due to the widespread use of resources, systems are highly prone to errors and failures. Hence, fault tolerance plays a key role in the grid to avoid the problem of unreliability. Scheduling the task to the appropriate resource is a vital requirement in computational Grid. The fittest resource scheduling algorithm searches for the appropriate resource based on the job requirements, in contrary to the general scheduling algorithms where jobs are scheduled to the resources with best performance factor. The proposed method is to improve the fault tolerance of the fittest resource scheduling algorithm by scheduling the job in coordination with job replication when the resource has low reliability. Based on the reliability index of the resource, the resource is identified as critical. The tasks are scheduled based on the criticality of the resources. Results show that the execution time of the tasks is comparatively reduced with the proposed algorithm using real-time approach rather than a simulator.

Keywords: computational grid, fault tolerance, task replication, job scheduling

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Authors: Nestor A. Garcia Fragoso, Tetyana Baydyk, Ernst Kussul

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This article contains information from our investigation in the field of voice recognition. For this purpose, we created a voice database that contains different phrases in two languages, English and Spanish, for men and women. As a classifier, the LIRA (Limited Receptive Area) grayscale neural classifier was selected. The LIRA grayscale neural classifier was developed for image recognition tasks and demonstrated good results. Therefore, we decided to develop a recognition system using this classifier for voice recognition. From a specific set of speakers, we can recognize the speaker’s voice. For this purpose, the system uses spectrograms of the voice signals as input to the system, extracts the characteristics and identifies the speaker. The results are described and analyzed in this article. The classifier can be used for speaker identification in security system or smart buildings for different types of intelligent devices.

Keywords: extreme learning, LIRA neural classifier, speaker identification, voice recognition

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Authors: Kamyar Tolouei, Ehsan Moosavi

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In open-pit mines, the long-term production scheduling optimization problem (LTPSOP) is a complicated problem that contains constraints, large datasets, and uncertainties. Uncertainty in the output is caused by several geological, economic, or technical factors. Due to its dimensions and NP-hard nature, it is usually difficult to find an ideal solution to the LTPSOP. The optimal schedule generally restricts the ore, metal, and waste tonnages, average grades, and cash flows of each period. Past decades have witnessed important measurements of long-term production scheduling and optimal algorithms since researchers have become highly cognizant of the issue. In fact, it is not possible to consider LTPSOP as a well-solved problem. Traditional production scheduling methods in open-pit mines apply an estimated orebody model to produce optimal schedules. The smoothing result of some geostatistical estimation procedures causes most of the mine schedules and production predictions to be unrealistic and imperfect. With the expansion of simulation procedures, the risks from grade uncertainty in ore reserves can be evaluated and organized through a set of equally probable orebody realizations. In this paper, to synthesize grade uncertainty into the strategic mine schedule, a stochastic integer programming framework is presented to LTPSOP. The objective function of the model is to maximize the net present value and minimize the risk of deviation from the production targets considering grade uncertainty simultaneously while satisfying all technical constraints and operational requirements. Instead of applying one estimated orebody model as input to optimize the production schedule, a set of equally probable orebody realizations are applied to synthesize grade uncertainty in the strategic mine schedule and to produce a more profitable and risk-based production schedule. A mixture of metaheuristic procedures and mathematical methods paves the way to achieve an appropriate solution. This paper introduced a hybrid model between the augmented Lagrangian relaxation (ALR) method and the metaheuristic algorithm, the Harris Hawks optimization (HHO), to solve the LTPSOP under grade uncertainty conditions. In this study, the HHO is experienced to update Lagrange coefficients. Besides, a machine learning method called Random Forest is applied to estimate gold grade in a mineral deposit. The Monte Carlo method is used as the simulation method with 20 realizations. The results specify that the progressive versions have been considerably developed in comparison with the traditional methods. The outcomes were also compared with the ALR-genetic algorithm and ALR-sub-gradient. To indicate the applicability of the model, a case study on an open-pit gold mining operation is implemented. The framework displays the capability to minimize risk and improvement in the expected net present value and financial profitability for LTPSOP. The framework could control geological risk more effectively than the traditional procedure considering grade uncertainty in the hybrid model framework.

Keywords: grade uncertainty, metaheuristic algorithms, open-pit mine, production scheduling optimization

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Authors: Amanpreet Kaur

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Wavelet transform (WT) is a powerful statistical tool used in applied mathematics for signal and image processing. The different mother, wavelet basis function, has been compared to select the optimal wavelet function that represents the electromyogram signal characteristics of upper limb amputees. Four different EMG electrode has placed on different location of shoulder muscles. Twenty one wavelet functions from different wavelet families were investigated. These functions included Daubechies (db1-db10), Symlets (sym1-sym5), Coiflets (coif1-coif5) and Discrete Meyer. Using mean square error value, the significance of the mother wavelet functions has been determined for teres, pectorals, and infraspinatus around shoulder muscles. The results show that the best mother wavelet is the db3 from the Daubechies family for efficient classification of the signal.

Keywords: Daubechies, upper limb amputation, shoulder muscles, Symlets, Coiflets

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Authors: Kapil Pandey, Vishnu Goyal

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Spatial and temporal data analysis is very well known in the field of satellite image processing. When spatial data are correlated with time, series analysis it gives the significant results in change detection studies. In this paper the GIS and Remote sensing techniques has been used to find the change detection using time series satellite imagery of Uttarakhand state during the years of 1990-2010. Natural vegetation, urban area, forest cover etc. were chosen as main landuse classes to study. Landuse/ landcover classes within several years were prepared using satellite images. Maximum likelihood supervised classification technique was adopted in this work and finally landuse change index has been generated and graphical models were used to present the changes.

Keywords: GIS, landuse/landcover, spatial and temporal data, remote sensing

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Authors: Mohammad Khodaei, Mahmood Meratien, Alireza Valanezhad, Serdar Pazarlioglu, Serdar Salman, Ikuya Watanabe

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Pore size and morphology plays a crucial role on mechanical properties of porous scaffolds. In this research, titanium scaffold was prepared using space holder technique. Sodium chloride and ammonium bicarbonate were utilized as spacer agent separately. The effect of the hardness of spacer on the cell morphology was investigated using scanning electron microscopy (SEM) and optical stereo microscopy. Image analyzing software was used to interpret the microscopic images quantitatively. It was shown that sodium chloride, due to its higher hardness, maintain its morphology during cold compaction, and cause better replication in porous scaffolds.

Keywords: Spacer, Titanium Scaffold, Pore Morphology, Space Holder Technique

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Authors: Rania Alshikhe, Vinita Jindal

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Digital innovation has played a crucial role in managing smart transportation. For this, big trajectory data collected from traveling vehicles, such as taxis through installed global positioning system (GPS)-enabled devices can be utilized. It offers an unprecedented opportunity to trace the movements of vehicles in fine spatiotemporal granularity. This paper aims to explore big trajectory data to measure the travel efficiency of road networks using the proposed statistical travel efficiency measure (STEM) across an entire city. Further, it identifies the cause of low travel efficiency by proposed least square approximation network-based causality exploration (LANCE). Finally, the resulting data analysis reveals the causes of low travel efficiency, along with the road segments that need to be optimized to improve the traffic conditions and thus minimize the average travel time from given point A to point B in the road network. Obtained results show that our proposed approach outperforms the baseline algorithms for measuring the travel efficiency of the road network.

Keywords: GPS trajectory, road network, taxi trips, digital map, big data, STEM, LANCE

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Authors: Xiuying Wang, Changliang Li, Bo Xu

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A fixed-length feature vector is required for many machine learning algorithms in NLP field. Word embeddings have been very successful at learning lexical information. However, they cannot capture the compositional meaning of sentences, which prevents them from a deeper understanding of language. In this paper, we introduce a novel hierarchical tree long short-term memory (HTLSTM) model that learns vector representations for sentences of arbitrary syntactic type and length. We propose to split one sentence into three hierarchies: short phrase, long phrase and full sentence level. The HTLSTM model gives our algorithm the potential to fully consider the hierarchical information and long-term dependencies of language. We design the experiments on both English and Chinese corpus to evaluate our model on sentiment analysis task. And the results show that our model outperforms several existing state of the art approaches significantly.

Keywords: deep learning, hierarchical tree long short-term memory, sentence representation, sentiment analysis

Procedia PDF Downloads 347