Hybrid information technology

11 th International Conference on Information Technology Convergence and Services (ITCSS 2025) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of all areas of Information Technology Convergence and services. The conference focuses on all technical and practical aspects of Information Technology Convergence and Services. Authors are solicited to contribute to the Conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to.

In the present RF communication scenario there is a great demand of multi band antennas with high gain, large bandwidth for the applications like Wi-Fi, WLAN, MIMO, BAN etc. Gradually, the size of handheld communication devices is becoming portable which ultimately leads to limit the space availability for antenna installation. Thus, it is a challenging task to design a compact size antenna meeting all the requirements regarding number of frequency bands, gain, and bandwidth. Metamaterial loading is found to be a significant technique for size reduction as well as enhancement of antenna performance parameters such as gain, directivity, bandwidth and efficiency.

Call for Papers
11 th International Conference on Information Technology Convergence and Services (ITCSS 2025) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of all areas of Information Technology Convergence and services. The conference focuses on all technical and practical aspects of Information Technology Convergence and Services. Authors are solicited to contribute to the Conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to.

Robust controller design for nonlinear systems with unknown dynamics (delay system) the impact of intense activity between connections can be considered as a challenge in this research. In order to reduce delays in the system resistant and the non-linear technique called variable structure control method is used. Variable structure control method is a type of non-linear controllers, although variable structure control technique, control is stable, with high reliability in environments that respond favorably almost unknown but has substantial limitations fluctuations (vibrations) high-frequency is. To fix this problem, saturated nonlinear function method was used. Many nonlinear controllers need to chip control with low volume, functionality, accuracy, and speed are high. Programmable integrated circuits are capable of solving these challenges. Programmable integrated circuits to design nonlinear variable structure controller optimized linear controller on a single chip is used. Having designed to speed up the response of the SPARTAN-3E in Xilinx programmable integrated circuit used. This technique is designed to control the programmable integrated circuit SPARTAN-3E, computational speed controller is 30.2 ns and a maximum frequency of 63.7 MHZ circuit.

Robust controller design for nonlinear systems with unknown dynamics (delay system) the impact of intense activity between connections can be considered as a challenge in this research. In order to reduce delays in the system resistant and the non-linear technique called variable structure control method is used. Variable structure control method is a type of non-linear controllers, although variable structure control technique, control is stable, with high reliability in environments that respond favorably almost unknown but has substantial limitations fluctuations (vibrations) high-frequency is. To fix this problem, saturated nonlinear function method was used. Many nonlinear controllers need to chip control with low volume, functionality, accuracy, and speed are high. Programmable integrated circuits are capable of solving these challenges. Programmable integrated circuits to design nonlinear variable structure controller optimized linear controller on a single chip is used. Having designed to speed up the response of the SPARTAN-3E in Xilinx programmable integrated circuit used. This technique is designed to control the programmable integrated circuit SPARTAN-3E, computational speed controller is 30.2 ns and a maximum frequency of 63.7 MHZ circuit.

The past two decades have seen the incorporation of robotics into medical applications. From a manufacturing perspective, robots have been used in pharmaceuticals, preparing medications. But on more novel levels, robots have been used in service roles, surgery, and prosthetics. The capability of high-precision operation in manufacturing settings gave the medical industry high hopes that robots could be used to assist in surgery. Not only are robots capable of much higher precision than a human, they are not susceptible to human factors, such as trembling and sneezing, that are undesirable in the surgery room. Another advantage to robots in medicine is the ability to perform surgery with very small incisions, which results in minimal scar tissue, and dramatically reduced recovery times. The popularity of these minimally invasive surgical (MIS) procedures has enabled the incorporation of robots in surgeries. In this paper, model reference computed torque controller is recommended for four degrees of freedom serial links robot manipulator, which modeled in SIMSCAPE. To design stable controller conventional computed torque controller is recommended. It is a nonlinear, stable, and reliable controller. The proposed approach effectively combines of design methods from nonlinear controller, and linear Proportional-Derivative (PD) control to improve the performance and stability. This paper has two important objectives: a) study on modeling of 4 degrees of freedom (DOF) based on Simscape software and b) design PD model reference computed torque controller to improve the sensitivity of surgical robot manipulator.

The dynamics of a first order delay system is highly nonlinear, time variant, uncertain and coupling effects. The main objectives to control of first order delay system are time response and acceleration measurements. The problem of acceleration measurements can be reduced, based on design sensor-less Proportional-Integral-Derivative (PID) filter controller in this research. Assuming unstructured uncertainties and structure uncertainties can be defined into one term and considered as an uncertainty and external disturbance, the problem of computation burden and large number of parameters can be solved to some extent. To solve the uncertainties acceleration measurements play an important role. In order to design sensorless PID filter controller, an accurate PD surface and the derivative of PD surface plays important role. To design an accurate PD surface, stable and tuning surface slope is needed to form the structure of main PID controller. In this algorithm, the derivative of PD surface computes the second derivation of error. Regarding to this method, the challenge of system uncertainties and time response have been solved based on sensor-less acceleration linear filter controller. As this point if s = K1e + e + K2Σe is chosen as desired surface, if the dynamic of first order delay is derived to surface then the linearization can be realized. Because, when the system dynamic is on the surface is used the derivative of surface S = K1e + e + K2e is equal to the zero that is a decoupled and linearized closed-loop systems dynamics. Linearization and decoupling by the above method can be obtained in spite of the quality of the first order delay dynamic model.

Uncertain or complicated systems are difficult to control. Modeling the system uncertainties is an especial topics in most of engineering field. On the other hand, since system has uncertainty, design stable and robust controller is crucial importance in control engineering. To solve this challenge nonlinear control technique is the best choice. Sliding mode control is one important type of robust control. Model imprecision may come from actual uncertainty about the plant or from a purposeful simplification of the system's dynamics. Modeling inaccuracies can cause strong adverse effects on the control design of nonlinear systems. For the class of systems to which it applies, sliding mode controller design provides a systematic approach to the problem of maintaining stability and consistent performance in the face of modeling imprecision. However, sliding mode controller is a robust and stable controller but it has an important challenge called, chattering phenomenon. This research focuses on the comparative between three methods to eliminate/reduce the chattering.

Robotic surgery, computer-assisted surgery, and robotically-assisted surgery are terms for technological developments that use robotic systems to aid in surgical procedures. Robotically-assisted surgery was developed to overcome the limitations of minimallyinvasive surgery and to enhance the capabilities of surgeons performing open surgery. In the case of robotically-assisted minimally-invasive surgery, instead of directly moving the instruments, the surgeon uses one of two methods to control the instruments; either a direct telemanipulator or through computer control. A telemanipulator is a remote manipulator that allows the surgeon to perform the normal movements associated with the surgery whilst the robotic arms carry out those movements using end-effectors and manipulators to perform the actual surgery on the patient. In computer-controlled systems the surgeon uses a computer to control the robotic arms and its end-effectors, though these systems can also still use telemanipulators for their input. One advantage of using the computerised method is that the surgeon does not have to be present, but can be anywhere in the world, leading to the possibility for remote surgery. The multi degrees of freedom actuator is an important joint, which has attracted worldwide developing interests for its medical, industry and aerospace applications. This paper addresses the problem of trajectory tracking of three dimensions joint in the presence of model uncertainties and external disturbances. An adaptive fuzzy sliding mode controller (AFLSMC) is proposed to steer a three dimension joint along a desired trajectory precisely. First, the dynamics model of a three dimension joint is formulated and the trajectory tracking problem is described. Second, a sliding mode controller (SMC) is designed to track a time-varying trajectory. The fuzzy logic system (FLS) is employed to approximate the uncertain model of the three dimension joint, with the tracking error and its derivatives and the commanded trajectory and its derivatives as FLS inputs and the approximation of the uncertain model as FLS output. And a fuzzy logic system is also adopted to attenuate the chattering results from the SMC. The control gains are tuned synchronously with the sliding surface according to fuzzy rules, with switching sliding surface as fuzzy logic inputs and control gains as fuzzy logic outputs. The stability and convergence of the closed-loop controller is proven using the Lyapunov stability theorem. Finally, the effectiveness and robustness of the proposed controller are demonstrated via simulation results. Contrasting simulation results indicate that the AFLSMC attenuates the chattering effectively and has better performance against the SMC.

Gas turbines can be found in many industrial application areas. Gas turbine generation is limited by some undesirable effects which can be incorporated as operational constraints. Because of importance of energy, optimization of power generation systems is necessary. In order to achieving higher efficiencies, some propositions are preferred such as recovery of heat from exhaust gases in a regenerator, utilization of intercooler in a multistage compressor, steam injection to combustion chamber and etc. In this article multi-objective particle swarm optimization are employed for Pareto approach optimization of Gas Turbine cycle. In the multi-objective optimization a number of conflicting objective functions are to be optimized simultaneously. Multi-objective optimization offers a candidate scheme whose solution can satisfy the foregoing major requirements. At the first stage single objective optimization has been investigated and then MOPSO has been used for multi-objective optimization. The sets of selected decision variables based on this Pareto front, will cause the best possible combination of corresponding objective functions. The obtained results show that the output of multi-objective optimization scheme confirms that of single objective results.

The location of FACTS device with respect to the distance relay has a significant effect on the apparent impedance seen by the protective relay. This can be analysed by means of comparing the actual measured impedance at the relaying point and the ideal tripping characteristics of the relay. Two case locations of the Thyristor Controlled Series Capacitor (TCSC) on the Ikeja West to Benin transmission line in the Nigeria power system were considered with respect to the concerned distance relay, i.e. at near end and midpoint. The effects of installation of TCSC at different locations on the transmission line distance relay were investigated under different fault conditions by the simulation model of the TCSC, simulated in the Nigeria power system using MATLAB/ Simulink software. The simulation results were discussed and used to compare the impact of TCSC located at various points on the transmission line with respect to a fault.

In this study, the removal of sunset yellow dye from aqueous solutions using low-cost materials as adsorbents such as modified Rice husk (MRH) and sugarcane bagasse (SB) by a batch system were investigated. Experiments were carried out as a function of contact time, initial concentration, pH and adsorbent dosages. The equilibrium adsorption of sunset yellow dye on adsorbents was analyzed by Langmuir, Freundlich and Temkin models. The results indicate that the Freundlich model provides the best correlation for sunset yellow-MRH system and the Langmuir model provide the best correlation for sunset yellow-SB system and also pseudo-second-order model was best applicable to the sorption data. The results revealed that the sunset yellow is considerably adsorbed on modified rice husk and sugarcane bagasse and it could be an economical method for the removal of sunset yellow from aqueous systems.

Power Stringent Static Random Access Memory (SRAM) design is very much essential in embedded systems such as biomedical implants, automotive electronics and energy harvesting devices in which battery life, input power and execution delay are of main concern. With reduced supply voltage, SRAM cell design will go through severe stability issues. In this paper, we present a highly stable average nT SRAM cell for ultra-low power in 125nm technology. The distinct difference between the proposed technique and other conventional methods is about the data independent leakage in the read bit line which is achieved by newly introduced block mask transistors. An average 6.5T SRAM and average 8T SRAM are designed and compared with 6T SRAM, 8T SRAM, 9T SRAM, 10T SRAM and 14T SRAM cells. The result indicates that there is an appreciable decrease in power consumption and delay.

Electric loads steadily increase each year due to economic and population expansion, placing electric systems close to their stability limits. Also, the electrical network is a sophisticated system with various control, protection devices, and power electronics. Any malfunction in these components can disrupt system reliability. Therefore, ensuring safe and dependable power system operation requires continuous and precise voltage stability monitoring to prevent instability and cascading outages. Despite the existence of numerous stability indexes, their limitations often make them ineffective under varying operating conditions. In response to these challenges, this article proposes a modern stability assessment index (MSAI) designed specifically to assess voltage stability in different operating scenarios. The MSAI aims to accurately assess voltage stability and stress levels in power lines, enabling operators to proactively manage system reliability. One key advantage of the MSAI is its ability to determine the maximum allowable load and loading margin, which is crucial for maintaining stable operation under changing load conditions. In order to validate the effectiveness of the proposed index, extensive evaluations are conducted using IEEE 30, 57, and 118 bus test systems. The study meticulously compares the performance of the MSAI with other prominent stability indicators, highlighting its superiority in providing predictive insights into areas prone to stress and potential contingencies. By empowering operators with preemptive correction capabilities, the MSAI reduces the risk of successive outages and enhances overall system flexibility. The voltage stability assessment and contingency ranking were evaluated using the proposed MSAI via a smooth method in the MATLAB environment.

Multiplier is the major component for processing of large amount of data in DSP applications. Using different recoding schemes in Fused Add-Multiply (FAM) design for the reduction of power and look up tables. The performance of 16-bit signed and unsigned multipliers were designed and obtained results are tabulated using Efficient Modified Booth Recoding (EMBR) techniques, which can be used for low power applications.

With an increasing demand for power hungry data intensive computing, design methodologies with low power consumption are increasingly gaining prominence in the industry. Most of the systems operate on critical and noncritical data both. An attempt to generate a precision result results in excessive power consumption and results in a slower system. An attempt to generate a precision result results in excessive power consumption and results in a slower system. For non-critical data, approximate computing circuits significantly reduce the circuit complexity and hence power consumption. For non-critical data, approximate computing circuits significantly reduce the circuit complexity and hence power consumption. In this paper, a novel approximate single precision floating point adder is proposed with an approximate mantissa adder. The mantissa adder is designed with three 8-bit full adder blocks.

Cloud computing is a computing paradigm where applications, resources and services are provided over the internet. Software and hardware can be used to pay as service basis, without buying them. The key role of scheduling is to manage different tasks in different cloud environment. Cloud computing service providers use the available resources efficiently to achieve maximum profit. This makes task scheduling as a challenging issue for cloud service providers. This paper gives an introduction about cloud computing, various existing scheduling algorithms in different task scheduling environments, existing problem and the future suggestions in existing algorithms.

This paper presents a new complexity metric for Object-Oriented (OO) design measurement to find at the design stage whether the classes become more complex, Moderate complex or less complex. The proposed metric is theoretically evaluated against the Weyuker's properties as well as empirically evaluated against three open source software system. Furthermore, for validating the validity of new complexity metric, the paper has also presented the comparison of proposed metric with some well known complexity metrics like Weighted Method per Class (WMC) metric of Chidamber and Kemerer (CK), Class Complexity (CC) metric of Balasubramanian, and Complexity Metric for OO Design (CMOOD) metric of Rajnish and Bhattacherjee (RB) against the same three open source software system..Automated tool were used to generate the metric values and for analyzing the results, IBM SPSS software used. The results in this paper indicates that the new complexity metric is correlated well with existing complexity metrics and may be used as predictors of complexity of class.

We adapt the rough set-based approach to deal with the gene expression data, where the problem is a huge amount of genes (attributes) a∈A versus small amount of experiments (objects) u∈U. We perform the gene reduction using standard rough set methodology based on approximate decision reducts applied against specially prepared data. We use rough discretization-Every pair of objects (x,y)∈UxU yields a new object, which takes values "≥a(x)" if and only if a(y)≥a(x); and "<a(x)" otherwise; over original genes-attributes a∈A. In this way: 1) We work with desired, larger number of objects improving credibility of the obtained reducts; 2) We produce more decision rules, which vote during classification of new observations; 3) We avoid an issue of discretization of real-valued attributes, difficult and leading to unpredictable results in case of any data sets having much more attributes than objects. We illustrate our method by analysis of the gene expression data related to breast cancer.

Considering the cutting stock problem and lot sizing problem for tower transmission industry, we present the new effective mathematical simulation for One-Dimensional Cutting Stock Problem (1D-CSP) by applying heuristic approach. This approach works within pre-defined trim except at the end of cutting process. The pre-defined trim is called the sustainable trim which is based on average order lengths and given stock lengths. By considering the constraints like space, manpower etc., the main objective of this study is to minimize the trim loss.

The past two decades have seen the incorporation of robotics into medical applications. From a manufacturing perspective, robots have been used in pharmaceuticals, preparing medications. But on more novel levels, robots have been used in service roles, surgery, and prosthetics. The capability of high-precision operation in manufacturing settings gave the medical industry high hopes that robots could be used to assist in surgery. Not only are robots capable of much higher precision than a human, they are not susceptible to human factors, such as trembling and sneezing, that are undesirable in the surgery room. Another advantage to robots in medicine is the ability to perform surgery with very small incisions, which results in minimal scar tissue, and dramatically reduced recovery times. The popularity of these minimally invasive surgical (MIS) procedures has enabled the incorporation of robots in surgeries. In this paper, model reference computed torque controller is recommended for four degrees of freedom serial links robot manipulator, which modeled in SIMSCAPE. To design stable controller conventional computed torque controller is recommended. It is a nonlinear, stable, and reliable controller. The proposed approach effectively combines of design methods from nonlinear controller, and linear Proportional-Derivative (PD) control to improve the performance and stability. This paper has two important objectives: a) study on modeling of 4 degrees of freedom (DOF) based on Simscape software and b) design PD model reference computed torque controller to improve the sensitivity of surgical robot manipulator.

In this paper, we propose Error Resilient SPIHT coded MIMO system to address the issue of image transmission and reception over noisy Rayleigh Fading channel. Set Partitioning in Hierarchical Trees (SPIHT) is chosen as the compression algorithm since it offers the lossless compression and excellent rate distortion in noiseless environment. SPIHT encoded data is modulated in the manner that it will provide Unequal Error Protection (UEP) to achieve error resilience. In proposed method, SPIHT encoded data is divided into the low priority stream (LP) and the high priority stream (HP) to achieve UEP and as a result it yields high PSNR value. These streams are modulated using M-QAM modulation scheme and transmitted over Multiple Input Multiple Output (MIMO) system to achieve better diversity. The adaptive value of M is determined by the proposed algorithm. Simulation results depicts that PSNR of received images are better as compared to that reported by Fowdur et al. [12].

Considering the cutting stock problem and lot sizing problem for tower transmission industry, we present the new effective mathematical simulation for One-Dimensional Cutting Stock Problem (1D-CSP) by applying heuristic approach. This approach works within pre-defined trim except at the end of cutting process. The pre-defined trim is called the sustainable trim which is based on average order lengths and given stock lengths. By considering the constraints like space, manpower etc., the main objective of this study is to minimize the trim loss.

High frequency switched-mode DC-DC converters are widely used in industry such as battery charging, renewable energy, fuel-cell, power factor correction, LED lighting applications,  due to their low response time, easy controlling and high power density. However, at high switching frequency, switching losses, electromagnetic interference (EMI) and lower efficiency become substantial problem. These problems must be eliminated or reduced by using additional passive and active snubber cells in order to operate the converter with soft switching (SS) instead of hard switching (HS). In this paper, properties of soft switching methods named as zero voltage transition (ZVT), zero current transition (ZCT), and zero voltage zero current transition (ZVZCT) developed by combining the ZVT and ZCT, zero voltage switching (ZVS) and zero current switching (ZCS) are studied and discussed, simulations of soft switching DC to DC boost converters including these methods are accomplished for 500W and 1k...

Floating point multiplication is.a key.Significance to. many.modern.applications.such as image processing, signal processing etc.The performance of DSP systems usually decided with the performance.of.the.multiplier.because.the.multiplier is the slowest.constituent in the.system..As, the multiplication dominates the execution time of most of the DSP application, hence a high speed multiplier is much preferred. This paper proposes an effective scheme for floating point multiplication that offers a superior implementation in terms of delay and area. A group of Karatsuba and Urdhva-Tiryagbhyam algorithm is used to implement a binary multiplier for mantissa multiplication. The IEEE 754 format used for the representation of a floating point number. The algorithms are implemented with Verilog (HDL) and targeted on FPGA Vertex 5 board and for the simulation purpose Xilinx ISE simulator is used and a table of delay performance, device utilization is made.

As mobiles become cheaper and gain popularity, mobile applications are developing quickly and trying to utilize more techniques to make these applications more efficient and usable. Augmented reality is a live direct or indirect view of a physical, real-world environment whose elements are augmented (or supplemented) by computer-generated sensory input such as sound, video, graphics or GPS data. This research develops an android mobile application that utilizes real-time image processing techniques to provide simulated functionality to a hand drawing of piano keys. A piano keyboard with fourteen white keys and ten black keys is drawn used a black thick pen on paper, and the mobile is set in an angel that gives the optimum view of the piano keyboard when the camera is turned on. When the application is started, a camera shot of the piano, without the presence of hands or fingers in the view, is taken. The user can then start playing on his drawn piano. The application detects keystrokes and decides which key is pressed; and then plays the corresponding tone. The application is developed using OpenCV library which focuses on real-time image processing and computer vision, since the target devices with android platform, a Java program is used to port the C++ code to android. The application is tested on multiple android-based devices with different specifications under good lighting conditions. The empirical results are impressive and comparable across different devices, despite all changes in lighting and background; all devices exhibited the same level of accuracy in detecting fingers and drawn pianos.

Haze-level discriminators are crucial for autonomous vehicles to handle segmentation tasks successfully in hazy and foggy outdoor environments. Deep learning (DL) networks trained to segment clear images exhibit more false positives and fail to recognize the pixel patterns for the class categories when faced with hazy images. To address this issue, we propose a novel dehazing scheme called Adaptive Dehazing (AD) that separates the unacceptable hazy images and applies the dehazing technique only to those images before passing them to the DL for segmentation. We define various thresholds to classify hazy images into four categories: heavy, moderate, slight, and clear. Additionally, we implement a hazy image generator to create hazy synthetic images that replicate actual hazy road scene conditions for testing the proposed algorithm. Moreover, we use the Explainable Artificial Intelligence (XAI) method to understand the feature selected by different layers in the network before and after applying the AD and their contribution to obtaining a final segmented output. Extensive experiments demonstrate both quantitatively and qualitatively the performance improvement in the segmentation task by achieving an optimal balance between Intersection over Union (IoU) and pixel accuracy (PA) metrics of the segmented categories. The improvement in IoU metrics shows that the AD scheme significantly outperforms the previous state-of-the-art dehazing methods.

Cloud computing is the most important technology at the present time, in terms of reducing applications costs and makes them more scalable and flexible. As the cloud currency is based on building virtualization technology, so it can secure a large-scale environment with limited security capacity such as the cloud. Where, Malicious activities lead the attackers to penetrate virtualization technologies that endanger the infrastructure, and then enabling attacker access to other virtual machines which running on the same vulnerable device. The proposed work in this paper is to review and discuss the attacks and intrusions that allow a malicious virtual machine (VM) to penetrate hypervisor, especially the technologies that malicious virtual machines work on, to steal more than their allocated quota from material resources, and the use of side channels to steal data and Passing buffer barriers between virtual machines. This paper is based on the Security Study of Cloud Hypervisors and classification of vulnerabilities, security issues, and possible solutions that virtual machines are exposed to. Therefore, we aim to provide researchers, academics, and industry with a better understanding of all attacks and defense mechanisms to protect cloud security. and work on building a new security architecture in a virtual technology based on hypervisor to protect and ensure the security of the cloud.

Cloud computing is the most important technology at the present time, in terms of reducing applications costs and makes them more scalable and flexible. As the cloud currency is based on building virtualization technology, so it can secure a large-scale environment with limited security capacity such as the cloud. Where, Malicious activities lead the attackers to penetrate virtualization technologies that endanger the infrastructure, and then enabling attacker access to other virtual machines which running on the same vulnerable device. The proposed work in this paper is to review and discuss the attacks and intrusions that allow a malicious virtual machine (VM) to penetrate hypervisor, especially the technologies that malicious virtual machines work on, to steal more than their allocated quota from material resources, and the use of side channels to steal data and Passing buffer barriers between virtual machines. This paper is based on the Security Study of Cloud Hypervisors and cl...

This paper presents the resonant characteristics of rectangular microstrip antenna printed on anisotropic substrate. The anisotropic substrates are characterized by both permittivity and permeability tensors. The analysis is performed in the spectral domain method by using Green's function technique. Numerical results concerning the effects of electric anisotropy and antennas parameters on the resonant characteristics of rectangular microstrip antenna are presented and discussed. Results are compared with previously published data and are found to be in good agreement.

Floating point arithmetic is widely used in many areas. IEEE Standard 754 floating
point is the most common representation today for real numbers on computers. IEEE
standards specify a set of floating-point formats for single precision and double precision. The
representation of very large or very small values, is carried out using the IEEE-754 floatingpoint
standard. This work intends to develop an Verilog implementation of a floating-point
arithmetic unit which can perform addition, subtraction and multiplication operations on 32-
bit operands using IEEE 754-2008 representations. Low power consumption and smaller area
are some of the most important criteria for the fabrication high performance systems. The
algorithms will be model in Verilog and synthesized using Cadence RTL complier using
Cadence 45nm GPDK.

With the rise in population and increase in older people Wireless Body Sensor Networks can prove to be beneficial in providing medical service to people who require continuous monitoring and care. Development in technologies for Wireless Communication has led to sensor nodes which can be worn over the body, implanted and embedded over the body. These are small sized devices which can perform processing on the signals sensed from the human body and then communicate it to required destination where this data can be used for various research purposes or other diagnosis. In the past much of the research work concerned with Body Area Networks deals with the designing of Sensor nodes, miniaturizing the nodes, various protocols related to communication and routing. This paper gives an overview of Wireless Body Sensor Networks, devices used, its architecture, protocol stack, issues, topology, WBSN standard, challenges. Some protocols and security schemes for Wireless Body Sensor Networks have also been discussed.

With the rise in population and increase in older people Wireless Body Sensor Networks can prove to be beneficial in providing medical service to people who require continuous monitoring and care. Development in technologies for Wireless Communication has led to sensor nodes which can be worn over the body, implanted and embedded over the body. These are small sized devices which can perform processing on the signals sensed from the human body and then communicate it to required destination where this data can be used for various research purposes or other diagnosis. In the past much of the research work concerned with Body Area Networks deals with the designing of Sensor nodes, miniaturizing the nodes, various protocols related to communication and routing. This paper gives an overview of Wireless Body Sensor Networks, devices used, its architecture, protocol stack, issues, topology, WBSN standard, challenges. Some protocols and security schemes for Wireless Body Sensor Networks have also been discussed.

There are many characteristics that can be calculated by the movement of people, such as distance, speed, slope and difference in length or height for each type of the movement. For the purpose of finding the most important of which features and high priority weight in the classification of the movement of people, we have been employed Taguchi method to determine the weight of each characteristic and the extent of their influence and weight in determining the behavior and the type of people movement. In this paper we employ Taguchi method in video tracking application to determine high value of weight for best parameters. We have been relying on the formula Signal to Noise Ratio (S/n) in order to find the most effective characteristic.

In fraud detection paradigms, the role of predictor variables cannot be overemphasized particularly when analytical toolssuch as statistical, machine learning and artificial intelligent tools are employed. These variables or attributes are used to organize records of data in database tables. The combination of the values of these attributes usually affects which class of a target variable a record or an observation would belong. In this paper, we propose an algorithm and employ spreadsheet 'count' 'count if' and 'filtering' functionalities (techniques) to take toll on how the individual attribute may affect the prediction of the class of an observation in an insurance dataset of 5000 observations. The analysis showed that indeed, the individual predictor attribute affects the outcome of the target variable (legal or fraudulent) differently.

In fraud detection paradigms, the role of predictor variables cannot be overemphasized particularly when analytical toolssuch as statistical, machine learning and artificial intelligent tools are employed. These variables or attributes are used to organize records of data in database tables. The combination of the values of these attributes usually affects which class of a target variable a record or an observation would belong. In this paper, we propose an algorithm and employ spreadsheet 'count' 'count if' and 'filtering' functionalities (techniques) to take toll on how the individual attribute may affect the prediction of the class of an observation in an insurance dataset of 5000 observations. The analysis showed that indeed, the individual predictor attribute affects the outcome of the target variable (legal or fraudulent) differently.

Une instance du probleme est un graphe, un ensemble F de sommets interdits et un ensemble R de sommets obligatoires. Nous montrons que construire un vertex cover, connexe ou pas, de taille minimale, contenant tous les sommets de R et aucun sommet de F, peut etre 2-approche (s&#39;il existe). Nous montrons aussi que decider s&#39;il existe ou pas un ensemble dominant independant contenant tout R et aucun sommet de F est NP-complet.

This study evaluates the use of Multi-Layer Perceptron (MLP) neural network models to forecast water levels of a gauging station located at the Kuala Lumpur city centre in Malaysia using records of upstream multiple stations. Cross correlation analysis of water level data was performed to determine the input vectors which include the current and antecedent water level data of the upstream stations to ensure that of the data available, the most influential values corresponding to different lags are selected for analysis. Twelve well recorded storm events were used to train, test and validate the MLP models. The best performance based on MSE, MAE and R² was achieved with a model of 15 input vectors of upstream current and antecedent water levels, 7 hidden nodes and an output vector for the station at Kuala Lumpur centre. The R² values for training, testing and validation datasets are 0.81,0.85 and 0.85 respectively.

Network Security is a supreme worry today for all computer users and organizations. It is the center of focus in all large and small networks. Extensively used internet protocols do not have promising mechanisms for protection against malicious activities. So the network attackers manage to shape and modify these protocols to launch various kinds of attacks on the legitimate clients. These attacks can have threatening effects on personal as well as professional aspects of clients. This paper proposes a fuzzy logic system to analyse and examine the susceptance of network clients towards various types of precarious attacks. The factors affecting the susceptibility have been carefully shortlisted by an extensive survey into journals, magazines and various research papers by leading authors in this field.

This paper presents a nature-inspired meta-heuristic, called a stochastic fractal search based method (SFS) for coping with complex economic load dispatch (ELD) problem. Two SFS methods are introduced in the paper by employing two different random walk generators for diffusion process in which SFS with Gaussian random walk is called SFS-Gauss and SFS with Levy Flight random walk is called SFS-Levy. The performance of the two applied methods is investigated comparing results obtained from three test system. These systems with 6, 10, and 20 units with different objective function forms and different constraints are inspected. Numerical result comparison can confirm that the applied approach has better solution quality and fast convergence time when compared with some recently published standard, modified, and hybrid methods. This elucidates that the two SFS methods are very favorable for solving the ELD problem.

The product reviews and the blogs play a vital role in giving the insight to end user for making a decision. Direct impact of reviews and ratings on the sale of the product raises a strong possibility of fake reviews. E-commerce sites are often indulged in writing fake reviews to promote/demote particular products and services. These fictitious opinions that are written to sound authentic are known as deceptive opinion/review spam. Review spam detection has received significant attention in both business and academia due to the potential impact fake reviews can have on consumer behaviour and purchasing decisions. To curb this issue many e-commerce companies have even started to certify the reviewers. But it covers an only small chunk of reviewers, so this technique couldn't be enough to deal with the problem of deceptive opinion spamming. Manually, it is difficult to detect these deceptive opinions. This work primarily focuses on enhancing the accuracy of existing deceptive opinion spam classifiers using psycholinguistic/sociolinguistic deceptive clues. We have formulated this problem in different ways and solve them with many machine learning techniques. This work carried out up on the publicly available gold standard corpus of deceptive opinion spam and achieved up to 92 percent cross-validation accuracy in restaurants and around 94 percent in hotels domain by the final classifier. A detail comparative results analysis has been done for all used machine learning algorithms. Keyword-Opinion Spamming, Opinion Mining, Web Mining, Psycholinguistic Features, and Machine Learning I. INTRODUCTION Opinion spamming can be defined as writing fake reviews that try to mislead human readers deliberately by giving undeserving positive opinions or false negative opinions to promote or demote some target products, services or organizations. People with malicious intentions post fake opinions without disclosing their true identity, also known as opinion spammer. Opinion spam can be broadly classified as disruptive opinion and deceptive opinion spam. Most of the previous work has focused on disruptive opinion spam, which is in the form of advertisement and other irrelevant non-opinion text. But deceptive opinion where people intentionally try to mislead others by writing fake reviews, remained a less explored field. Disruptive opinion spam can easily be identified and ignored by the user as they have quite distinguishable features that correspond to the advertisement and other commercial interests. On the other hand deceptive opinions are neither identifiable by a human reader nor even easily ignored as they have a serious impact on revenue generation and reputation. A study conducted on the impact of consumer reviews in restaurant domain finds that one-star increase in Yelp rating leads to a 5-9 percent increase in revenue [1]. Several high-profile cases have been reported in the news. The main motive behind the spamming is the monetary benefits. Opinion spam classifier can be seen as a two class text classification problem , however it is different from general text classification in terms of features. Traditional text classifiers mainly use syntactic, semantic, statistical etc. feature for classification purpose. Such features may be useful for classifying spam opinions also. But for detecting deceptive opinions, we need to keep in mind that these opinions are intentional, so a link needs to be established between use of regular words and deceptive behaviour to catch spammers. The problem of linking the opinion and opinion holder (reviewer) behaviour is not an easy task. Moreover, the task becomes more difficult in absence of information regarding opinion holder in most of the cases. To build a spam review detection model, researchers may use reviews or reviewer's characteristics. But in most of the cases and domains, they have to rely on review text due of unavailability of reviewer details. Most opinions are found in form of reviews so opinion and review is used interchangeably.

Online product reviews have become the major source of information for the end users to make purchasing decisions. Companies/individuals often hire people for writing fake reviews to increase the sale of their products. These individuals are known as opinion spammers and their activities are known as opinion spamming. Manually it is difficult for a human being to detect these deceptive reviews. Features play a major role to build effective deceptive reviews detection classifiers. We have observed human behavior through reviews, blogs datasets, and transferred these observations into features.Towards the end, we have built automated deceptive reviews classifiers using document level and aspect level domain independent features. We have performed our experiments in hotels domain. We achieved around 93 percent accuracy on Myle Ott's gold standard dataset [1] and up to 86 percent accuracy on the self-crawled Yelp 1 dataset.

In this study, a Fractional-Order Sliding Mode Control scheme is proposed for trajectory tracking control of Delta parallel robot. The proposed controller is compared with both integer-order Proportional-Derivative controller and integer-order Sliding Mode Controller with Computed Torque Control method. The forward kinematics, inverse kinematics and dynamic of Delta parallel robot are described. A Solidworks/Matlab/SimScape/Multibody model of Delta parallel robot is generated and used for dynamic parameter estimation and validation of the proposed method. Particle Swarm Optimization algorithm is utilized for dynamic parameter estimation of Delta parallel robot. The validation of the proposed method is evaluated for three different trajectories. External disturbances, noise and also various payloads are considered in testing robustness of control techniques. The results of the robustness tests confirm higher performance of FOSMC than two other control schemes. INDEX TERMS Delta parallel robot, fractional-order sliding mode control, trajectory tracking, parameter estimation.

Medical care requires intensively Image segmentation which a significant tool used to extract origin of interest from the background. Different segmentation techniques are deployed in medical images leading to an essential developments in both diagnosis and detection process. Such development has managed to assist specialists and doctors specified in the medical care system to diagnose the patients accurately. This study will propose a more developed methodology via incorporating cluster representatives and second derivative filter technique on Carcinoma image. The conventional segmentation algorithm is widely deployed in medical care image, in spite of its advantages; being able to produce a complete division of the image, but it contains major drawbacks represented by the over-segmentations and sensitivity to false edges. This study will expose these major problems of the conventional algorithm and fixing these problems via deploying Cluster Representatives; that uses a set of pixels as representatives that represent the clusters and producing advanced results. The other process deployed is a second derivative filter applied to the segmented image resulting of the demolition of unwanted region from the image. Nevertheless, results of this study has proved that the number of partitions in medical care images are much fewer when comparing partitions produced by deploying the traditional algorithm deployed in medical care images. Such result will assist experts to easily diagnose health problems in a more accurate measure.

Problem eno-dimenzionalnega razreza ima v praksi veliko pojavnih oblik. V največ primerih je definiran kot problem, pri katerem je treba v naprej znano število naročenih palic razrezati iz znane zaloge palic (različno število različnih dolžin palic). Gre za zadovoljevanje naročil kupcev, ki izhajajo iz potreb trga. Proces optimizacije eno-dimenzionalnega razreza materiala temelji na porabi zalog z namenom izpolnjevanja naročila, pri tem pa upošteva ekonomski cilj minimiziranja stroškov. Eden glavnih razlogov, da podjetje optimizira proces razreza enodimenzionalnega materiala, je v minimizaciji materiala, ki predstavlja neuporaben ostanek. Neuporaben ostanek materiala je izguba, ki jo želimo zmanjšati. V nekaterih primerih, ko je ostanek dovolj velik, se ga vrne v skladišče in ponovno uporabi (Alfieri et al., 2007; Cherri et al., 2009). Oznaka problema eno-dimenzionalnega razreza z uporabnim ostankom je 1DCSPUL (angl. One-dimensional Cutting Stock Problem with Usable Leftover). Pri rezanju materiala je zelo pomembno, kako velik je ostanek rezanja, saj se premajhen ostanek smatra kot odpadek in zavrže, to pa vpliva na stroške podjetja. Smotrno je, da je ostanek, ki se zavrže, čim manjši oz. tako majhen, kot je to mogoče (Gradišar & Jesenko, 1996). Odločevalci se pri reševanju 1DCSPUL odločijo, kje je meja, nad katero se ostanek vrne nazaj v skladišče, in pod katero predstavlja izgubo pri razrezu. Če upoštevamo vračanje neuporabljenega materiala v skladišče, potem minimizacija ostanka ne predstavlja nujno najboljše rešitve za podjetje (Cherri et al., 2009). Pri 1DCSPUL gre za proces, pri katerem imamo na zalogi standardne in nestandardne dolžine. Nestandardne dolžine predstavljajo uporabni ostanki prejšnjih naročil. Poleg optimizacijske metode lahko na višino neuporabnega ostanka oziroma izgube vpliva tudi narava problema, ki jo je pri reševanju treba upoštevati. Tako je iz praktičnega primera, predstavljenega v Erjavec et al. (2009) razvidno, da je povprečna izguba pri 1DCSPUL enaka 15 %. Razlog je v nizkem razmerju med povprečno dolžino na zalogi in povprečno dolžino naročil. Razmerje med povprečno dolžino na zalogi in povprečno dolžino naročil je nizko, če je manjše od nekega praga. V praktičnem primeru, opisanem v Erjavec et al. (2009), je ta enak 3, zato je takšen prag upoštevan v doktorski disertaciji. Predstavljena sta razvoj in podroben opis nove metode za reševanje 1DCSPUL za primere, ko je razmerje med povprečno dolžino na zalogi in povprečno dolžino naročil manjše od 3. Učinkovitost nove metode je na primerih iz literature preverjena s primerjavo s podobnimi metodami. Nova metoda je podana na način, ki omogoča enostavno integracijo v obstoječe informacijske sisteme podjetij. Ključne besede Optimizacija, eno-dimenzionalni problem razreza, uporabni ostanek, hevristične metode, simulacije ONE-DIMENSIONAL CUTTING STOCK OPTIMIZATION WITH USABLE LEFTOVER: A CASE OF LOW STOCK-TO-ORDER RATIO SUMMARY One-dimensional cutting stock problem occurs in many forms in practice. It is mostly defined as a problem, where a number of ordered items, known in advance, must be cut from a known stock of bars. The goal is to satisfy customers' orders, deriving from the market needs. The process of optimizing the one-dimensional cutting of material makes the use of stock bars more efficient in order to fulfill the customer's order. It follows the economic goal of minimizing the costs. Companies optimize the process of cutting one-dimensional material due to minimization of material that is treated as an unusable leftover. Leftover material thus represents a loss and should be reduced. If the leftover is of enough size it can be returned back on stock and reused (Alfieri, van de Velde, & Woeginger, 2007; Cherri, Arenales, & Yanasse, 2009). Such problem is known as one-dimensional cutting stock problem with usable leftover (1DCSPUL).

Twitter is a social networking site where a large number of users are actively present. Data with hashtags are popular widely on twitter, hence twitter has large amounts of datasets where user tweet their reviews. These sentiments are used to understand what opinion do people have about a product or service through their tweets. The datasets used in this system for extracting tweets are ‘UBER & OLA’. We understand reviews of people towards different products or services which in turn gives business insights as to what changes can be done or incorporated. It also helps us in the analysis of market trends and monetization. In this paper, we propose two models for sentiment analysis based on Naïve Bayes and Support Vector Machine (SVM). Its purpose is to analyze sentiments more effectively. This system uses Rstatistical programming language to generate outputs. Further, this paper represents the outputs in Word Cloud. The two classifier algorithms are machine learning algorithms in whi...

The majority of image-processing techniques interact treating the impression as being a two dimensional signal and applying normal signal-processing techniques to it. Within this document we offer a new standard approach to have the hidden restrictions which aren't revealed by the Sobel together Canny filters together in face-recognition environment. We've also showed the effect of the contrast and threshold around the photos. In the same manner, we have shown our techniques about the video. Thus, many beautiful contrast and the hidden data are shared.

Spectrum sensing is a key functionalities of Cognitive radio networks (CRN). There are several methods to improve sensing techniques. In this paper, we present a robust detector using signal to noise ratio (SNR) with adaptive threshold (ADT) scheme in Cognitive Radio Networks (CRN). This technique has two detectors, energy detector (ED)&EDwithADT detector, out of these two detectors only one will perform sensing operation at a time. Selection of detector depends on condition between estimated SNR value (S e) and threshold (γ). Numerical results show that proposed ESNR_ADT scheme optimizes detection performance and outperforms the cyclostationary based sensing method and adaptive spectrum sensing (SS) by 30.5 % and 30 % at-10 dB signal to noise ratio (SNR) respectively. It is also shown that the proposed scheme yields lesser sensing time than cyclostationary detection and adaptive SS scheme in the order of 5.2 ms and 1.0 ms at-20 dB SNR respectively.