Scielo RSS <![CDATA[Journal of applied research and technology]]> vol. 12 num. 6 lang. es <![CDATA[SciELO Logo]]> <![CDATA[<b>The Effect of Added Dead Space on Optimal Neuro-Muscular Drive and Respiratory Signals under Hypercapnia</b>]]> The problem concerning the nature and the function of the dead space is of basic importance for the full comprehension of the respiratory physiology and pathophysiology. To study the effect of an imposed external dead space on the optimal respiratory control system, we simulated the optimal neuro-muscular drive and respiratory signals, including instantaneous airflow and lung volume profiles, with dead space loading under hypercapnia. The dead space measurement model by Gray was employed and the human respiratory control simulator based on an optimality hypothesis was implemented. The ventilatory control simulations were performed with external dead space loading of 0, 0.4 and 0.8 liters under rest condition (Pico2=0%) and CO2 inhalation of 3% to 7%. The optimization of the respiratory signals and model behavior of the optimal respiratory control under dead space loading and hypercapnia were verified and found to be in general agreement with experimental findings. <![CDATA[<b>Fingerprint Recognition by Multi-objective Optimization PSO Hybrid with SVM</b>]]> Researchers put efforts to discover more efficient ways to classification problems for a period of time. Recent years, the support vector machine (SVM) becomes a well-popular intelligence algorithm developed for dealing this kind of problem. In this paper, we used the core idea of multi-objective optimization to transform SVM into a new form. This form of SVM could help to solve the situation: in tradition, SVM is usually a single optimization equation, and parameters for this algorithm can only be determined by user's experience, such as penalty parameter. Therefore, our algorithm is developed to help user prevent from suffering to use this algorithm in the above condition. We use multi- objective Particle Swarm Optimization algorithm in our research and successfully proved that user do not need to use trial - and - error method to determine penalty parameter C. Finally, we apply it to NIST-4 database to assess our proposed algorithm feasibility, and the experiment results shows our method can have great results as we expect. <![CDATA[<b>Time and Energy Efficient DVS Scheduling for Real-Time Pinwheel Tasks</b>]]> Dynamic voltage/frequency scaling (DVFS) is one of the most effective techniques for reducing energy use. In this paper, we focus on the pinwheel task model to develop a variable voltage processor with d discrete voltage/speed levels. Depending on the granularity of execution unit to which voltage scaling is applied, DVFS scheduling can be defined in two categories: (i) inter-task DVFS and (ii) intra-task DVFS. In the periodic pinwheel task model, we modified the definitions of both intra- and inter-task and design their DVFS scheduling to reduce the power consumption of DVFS processors. Many previous approaches have solved DVFS problems by generating a canonical schedule in advance and thus require pseudo polynomial time and space because the length of a canonical schedule depends on the hyperperiod of the task periods and is generally of exponential length. To limit the length of the canonical schedules and predict their task execution, tasks with arbitrary periods are first transformed into harmonic periods and their key features are profiled. The proposed methods have polynomial time and space complexities, and experimental results show that, under identical assumptions, the proposed methods achieve more energy savings than the previous methods. <![CDATA[<b>Recommendation-Aware Smartphone Sensing System</b>]]> The context-aware concept is to reduce the gap between users and information systems so that the information systems actively get to understand users' context and demand and in return provide users with better experience. This study integrates the concept of context-aware with association algorithms to establish the context-aware recommendation systems (CARS). The CARS contains three modules and provides the product recommendations for users with their smartphone. First, the simple RSSI Indoor localization module (SRILM) locates the user position and detects the context information surrounding around users. Second, the Apriori recommendation module (ARM) provides effective recommended product information for users through association rules mining. The appropriate product information can be received effectiveness and greatly enhanced the recommendation service. <![CDATA[<b>Simulation and Implementation of an Integrated TDOA/AOA Monitoring System for Preventing Broadcast Interference</b>]]> The rapid development of wireless broadband communication technology has degraded the location accuracy performance of radio monitoring stations worldwide that use signal angle of arrival (AOA) location technology, and the stations in Taiwan are no exception. In this study, a Federal Communications Commission F(50, 50) broadcast propagation prediction methodology was applied to determine the coverage area of installed TDOA-based monitoring stations in Tainan (i.e., southern Taiwan) metropolitan area. The simulation results indicated that 3 TDOA-based location stations are required to achieve a coverage diameter of 20~30 km. Subsequently, 3 TDOA-based radio monitoring stations [Luzhu, Tainan Gaote, and Tainan health stations (Jinkang)] were installed to locate the radio transmitter that was the source of broadcast interference in Taiwan by monitoring the frequency modulation of broadcast stations at 88.3, 91.5, 89.1, and 91.9 MHz in the Tainan metropolitan, rural, and urban areas, respectively. In this study, the proposed integrated TDOA/AOA location technology was implemented in Taiwan for the first time according to International Telecommunications Union requirements. The location accuracy was within 950 m (50% circular error probability) under multipath conditions in the metropolitan area. <![CDATA[<b>Improvement of the EIGamal Based Remote Authentication Scheme Using Smart Cards</b>]]> Nowadays, we can easily obtain variety of services through networks. But due to the open environment, networks are vulnerable to many security threats. The remote user authentication scheme is one of the most widely used mechanisms for servers to authorize users to access the services. In 2009, Ramasamy and Muniyandi proposed a discrete logarithm based remote authentication scheme with smart cards. Their scheme provides mutual authentication and withstands the denial of service attack, forgery attack and parallel session attack. In this article, we show that their scheme is not a practical solution for remote access. It lacks key agreement mechanism and users cannot choose or update passwords freely. Moreover, their scheme cannot resist the stolen-verifier attack, off-line guessing attack, impersonation attack and smart-card-loss-attack. We propose an improved scheme to remedy the drawbacks. The improved scheme has the merits of providing mutual authentication and key agreement, while forward and backward secrecy are ensured as well. The users can choose and update their passwords freely. Furthermore, the scheme can also withstand many attacks such as the smart-card-loss-attack, the replay attack, the off-line guessing attack, the insider attack, the impersonation attack and the parallel session attack. <![CDATA[<b>A Low-Complexity Integer Frequency Offset Estimation Scheme Using Combined Training Symbols for OFDM Systems</b>]]> In orthogonal frequency division multiplexing (OFDM) systems, an integer part of a frequency offset (IFO) that causes ambiguity in data demodulation is estimated generally by comparing correlations between the received and local signals for IFO candidates. In this paper, we propose an IFO estimation scheme that provides a tradeoff between the estimation performance and the computational complexity including a conventional scheme as a special case. In the proposed scheme, template signals are formed by combining frequency-shifted training symbols, allowing the receiver to reduce the number of IFO candidates in the estimation process. Numerical results illustrate the tradeoff of the proposed scheme: The proposed scheme exhibits a tradeoff between the correct estimation probability and the computational complexity taking the number of the training symbols used to construct the template signal as a parameter. <![CDATA[<b>Cyclostationarity-Based Detection of Randomly Arriving or Departing Signals</b>]]> This paper addresses the problem of detection of randomly arriving or departing primary user (PU) signals in cognitive radio systems. The detection problem of the dynamic PU signal is modeled as a binary hypothesis testing problem where the PU signal might randomly depart or arrive during the sensing period. Then, we detect the cyclostationarity of the PU signal using a test statistic derived from the spectral autocoherence function in dynamic PU signal environments. Numerical results show that the proposed scheme offers an improved spectrum sensing performance than the conventional energy detector for dynamic PU environments. <![CDATA[<b>Three-Dimensional Image Security System Combines the Use of Smart Mapping Algorithm and Fibonacci Transformation Technique</b>]]> In this paper, a three-dimensional (3D) image security system combines the use of the smart pixel mapping (SPM) algorithm and the Fibonacci transformation technique is proposed. In order to reconstruct orthoscopic 3D images with improved image quality, a smart pixel mapping process is adopted. Based on the SPM-based computational integral imaging (CII) system, the depth-converted elemental image array (EIA) is obtained for increasing the quality of the reconstructed image. In the encryption process, the depth-converted EIA is scrambled by the Fibonacci transformation (FT) algorithm. Meanwhile, the computational integral imaging reconstruction (CIIR) technique is used to reconstruct the 3D image in the image reconstruction process. Compared with conventional CII-based 3D image encryption methods, the proposed method enable us to reconstruct high-resolution orthoscopic 3D images at long distance. To demonstrate the effectiveness of the proposed method, some numerical experiments are made to test the validity and the capability of the proposed 3D image security system. <![CDATA[<b>An Unambiguous Tracking Scheme Using Partial-Pulses for BOC Signals</b>]]> The recent researches on the tracking of binary offset carrier (BOC) modulated signals have been studied focusing on resolving the ambiguity problem caused by the multiple side-peaks in BOC autocorrelation. In this paper, we propose a novel unambiguous BOC tracking scheme with an improved tracking performance by using partial-pulses of BOC signals. Firstly, we observe that a sub-carrier consists of two partial rectangular pulses referred to as partial-pulses, and then, generate multiple partial-correlations composing the BOC autocorrelation. Finally, a correlation function with no side-peak is constructed by combining the partial-correlations, and then, a delay lock loop employing the proposed unambiguous correlation function adjusts the phase of the local BOC signals. From numerical results, it is confirmed that the proposed scheme provides a better tracking performance than the conventional schemes in terms of the tracking error standard deviation. <![CDATA[<b>Automatic Generation of Facial Expression Using Triangular Geometric Deformation</b>]]> This paper presents an image deformation algorithm and constructs an automatic facial expression generation system to generate new facial expressions in neutral state. After the users input the face image in a neutral state into the system, the system separates the possible facial areas and the image background by skin color segmentation. It then uses the morphological operation to remove noise and to capture the organs of facial expression, such as the eyes, mouth, eyebrow, and nose. The feature control points are labeled according to the feature points (FPs) defined by MPEG-4. After the designation of the deformation expression, the system also increases the image correction points based on the obtained FP coordinates. The FPs are utilized as image deformation units by triangular segmentation. The triangle is split into two vectors. The triangle points are regarded as linear combinations of two vectors, and the coefficients of the linear combinations correspond to the triangular vectors of the original image. Next, the corresponding coordinates are obtained to complete the image correction by image interpolation technology to generate the new expression. As for the proposed deformation algorithm, 10 additional correction points are generated in the positions corresponding to the FPs obtained according to MPEG-4. Obtaining the correction points within a very short operation time is easy. Using a particular triangulation for deformation can extend the material area without narrowing the unwanted material area, thus saving the filling material operation in some areas. <![CDATA[<b>Enhanced Differential Evolution Based on Adaptive Mutation and Wrapper Local Search Strategies for Global Optimization Problems</b>]]> Differential evolution (DE) is a simple, powerful optimization algorithm, which has been widely used in many areas. However, the choices of the best mutation and search strategies are difficult for the specific issues. To alleviate these drawbacks and enhance the performance of DE, in this paper, the hybrid framework based on the adaptive mutation and Wrapper Local Search (WLS) schemes, is proposed to improve searching ability to efficiently guide the evolution of the population toward the global optimum. Furthermore, the effective particle encoding representation named Particle Segment Operation-Machine Assignment (PSOMA) that we previously published is applied to always produce feasible candidate solutions for solving the Flexible Job-shop Scheduling Problem (FJSP). Experiments were conducted on comprehensive set of complex benchmarks including the unimodal, multimodal and hybrid composition function, to validate performance of the proposed method and to compare with other state-of-the art DE variants such as JDE, JADE, MDE_PBX etc. Meanwhile, the hybrid DE model incorporating PSOMA is used to solve different representative instances based on practical data for multi-objective FJSP verifications. Simulation results indicate that the proposed method performs better for the majority of the single-objective scalable benchmark functions in terms of the solution accuracy and convergence rate. In addition, the wide range of Pareto-optimal solutions and more Gantt chart decision-makings can be provided for the multi-objective FJSP combinatorial optimizations. <![CDATA[<b>Solving the Partial Differential Problems Using Maple</b>]]> This paper considers the partial differential problem of two types of multivariable functions and uses mathematical software Maple for verification. The infinite series forms of any order partial derivatives of these two types of multivariable functions can be obtained using binomial series and differentiation term by term theorem, which greatly reduce the difficulty of calculating their higher order partial derivative values. On the other hand, four examples are used to demonstrate the calculations. <![CDATA[<b>Modified Neural Network for Dynamic Control and Operation of a Hybrid Generation Systems</b>]]> This paper presents modified neural network for dynamic control and operation of a hybrid generation systems. PV and wind power are the primary power sources of the system to take full advantages of renewable energy, and the diesel-engine is used as a backup system. The simulation model of the hybrid system was developed using MATLAB Simulink. To achieve a fast and stable response for the real power control, the intelligent controller consists of a Radial Basis Function Network (RBFN) and an modified Elman Neural Network (ENN) for maximum power point tracking (MPPT). The pitch angle of wind turbine is controlled by ENN, and the PV system uses RBFN, where the output signal is used to control the DC / DC boost converters to achieve the MPPT. And the results show the hybrid generation system can effectively extract the maximum power from the PV and wind energy sources. <![CDATA[<b>Optimal Yield Rate in ACF Cutting Process of TFT-LCD Module Using Orthogonal Particle Swarm Optimization Based on Response Surface Design</b>]]> Anisotropic Conductive Film (ACF) is essential material in LCM (Liquid Crystal Module) process. It is used in bonding process to make the driving circuit conductive. Because the price of TFT-LCD is getting lower than before in recent years, the ACF has relatively higher cost ratio. The conventional long bar ACF cutting unit is changed into short bar ACF cutting unit in new bonding technology. However, the new type machine was not optimized in process control and mechanical design. Therefore, the failure rate of new ACF cutting process is much higher than the one of the conventional process. This wastes the ACF material and rework cost is considerably large. How to make the manufacturing cost down effectively and promote the product quality is the main issue to maintain competition capability for the product. Therefore, the orthogonal particle swarm optimization (OPSO) is used to analyze the optimal design problem. The ACF cutting yield rate is selected to be objective function for optimization. The quality characteristic function for yield rate is used in orthogonal particle swarm optimization. Three control factors such as plasma clean speed, ACF peeling speed and ACF cutter spring setting are selected to study the effect of the yield rate. Results show that the proposed method can provide good optimal solution to improve the ACF cutting process for TFTLCD manufacturing process. <![CDATA[<b>Efficient Workload Balancing on Heterogeneous GPUs using Mixed-Integer Non-Linear Programming</b>]]> Recently, heterogeneous system architectures are becoming mainstream for achieving high performance and power efficiency. In particular, many-core graphics processing units (GPUs) now play an important role for computing in heterogeneous architectures. However, for application designers, computational workload still needs to be distributed to heterogeneous GPUs manually and remains inefficient. In this paper, we propose a mixed integer non-linear programming (MINLP) based method for efficient workload distribution on heterogeneous GPUs by considering asymmetric capabilities of GPUs for various applications. Compared to the previous methods, the experimental results show that our proposed method improves performance and balance up to 33% and 116%, respectively. Moreover, our method only requires a few overhead while achieving high performance and load balancing. <![CDATA[<b>Robust Exponential Stability for Uncertain Discrete-Time Switched Systems with Interval Time-Varying Delay through a Switching Signal</b>]]> This paper deals with the switching signal design to robust exponential stability for uncertain discrete-time switched systems with interval time-varying delay. The lower and upper bounds of the time-varying delay are assumed to be known. By construction of a new Lyapunov-Krasovskii functional and employing linear matrix inequality, some novel sufficient conditions are proposed to guarantee the global exponential stability for such system with parametric perturbations by using a switching signal. In addition, some nonnegative inequalities are used to provide additional degrees of freedom and reduce the conservativeness of systems. Finally, some numerical examples are given to illustrate performance of the proposed design methods.