Earticle

Intrusion detection system in wireless sensor network is one of the growing research areas in recent years. Wireless sensor networks (WSN) consist of tiny devices. These tiny devices have limited energy, computational power, transmission range and memory. However, wireless sensor networks are deployed mostly in open and unguarded environment. Therefore, intrusion detection is one of the important aspects for wireless sensor networks. There are two different kind of intrusion detection mechanism: anomaly based and signature based. In this paper, we mention several attacks on WSN and we primarily focus only on the anomaly based intrusion detection system. Finally, we discuss about several existing approaches to describe how they have identified security threats and implemented their intrusion detection system.

A study was made in those region that expose major area of machine learning techniques which available in data mining. There are lots of Research work is analysized in agriculture field. It is a smart techniques is needed to find the resolution over the modern method. Cotton is an significant beneficial harvest and extensively traded commodity across the world. Its yield is insightful to weather, soil as well as organization practices. This study presents a concise idea of several of the broadly capitalized machine learning technique over agricultural domain. Among the algorithms like Navie bayes, j48 ,MLP,Randomforest,Random tree, I have founded J48 gives better result over the other algorithms using cotton seed quality accuracy is best.

Recently, interesting about RFID sensor tag technology which can obtain information on the external environment to smart card is increasing worldwide. In case of sensor tag, most of sensor tag has been developed as resistive type sensor tags. Resistive type humidity sensor requires temperature revision as the influence of temperature and there are special environment to detect low humidity of 15% or less. To solve this problem, circuit was composed that capacitive sensor is interlocked to resistive type sensor tag. The composed circuit is a capacitive humidity sensor which is connected to resistive sensor tags. Using the CVC circuit, capacitive humidity sensor change is output voltage. The output voltage connects with resistive sensor tag. Implemented system's sensor value storage or real time monitoring experiment result was able to determine the usefulness. Also, developed humidity sensor tag is applied to the smart refrigerator.

Wireless sensor networks are an emerging technology for monitoring physical world. The energy constraint of Wireless sensor networks makes energy saving and Prolonging the network lifetime become the most important goals of various routing protocols. Clustering is a key technique used to extend the lifetime of a sensor network by reducing energy consumption. Also Putting few heterogeneous nodes in wireless sensor network is an effective way to increase the network lifetime and stability. The energy saving schemes for homogeneous wireless sensor networks do not perform efficiently when applied to heterogeneous wireless sensor networks. Thus, Energy efficient clustering protocols should be designed for the characteristic of heterogeneous wireless sensor networks. This paper surveys different energy efficient clustering protocols for heterogeneous wireless sensor networks and compares these protocols on various points like, location awareness, clustering method, heterogeneity level and clustering Attributes.

In this paper, we propose a new representation, called Local Min-Max Binary Pattern (LMin-MaxBP), and apply it to face recognition with single sample per class. The local appearance based methods have been successfully applied to face recognition and achieved state-of-the-art performance. The Local Binary Pattern (LBP) has been proved to be effective for image representation. The motivation for the LMin-MaxBP is to find texture information to cope with the variation due to facial expression and perspective changes as well as reducing the length of the feature vectors in LBP’s histogram to speed up the matching process. Experiments on Yale, ORL and Indian face datasets shows that the proposed approach improves the performance in the scenario of one training sample per person with significant facial expression and perspective variations with large rotation angle up to 180θ.

In this paper, we are interested in the control of a Doubly Fed Induction Machine (DFIM) operating in motor mode. The proposed structure is based on the association of passivity and fuzzy control. The main objective of this configuration is to optimally regulate the motor speed according to variable references as well as tracking of the time-varying speed, the electromagnetic torque and the flux trajectories without a state observer. Simulation results showed remarkable efficiency in terms of stability and robustness of this structure with respect to parameter variations and disturbances. Results are compared with an existing work to show up the main points of our contribution.

This paper presents the mathematical steady-state modelling of Static Synchronous Compensator (STATCOM), which is the most widely used member of Flexible Alternating Current Transmission Systems (FACTS). STATCOM Power Injection Model (PIM), derived from one voltage source representation, is presented and analyzed in detailed. A program, Flexible Alternating Current Transmission Systems Power Flow (FACTSPF) in MATLAB has been developed which extends conventional Newton-Raphson (NR) algorithm based on the Power Injection Model (PIM).The STATCOM PIM and Current Injection Model (CIM) implemented in Power System Analysis Toolbox (PSAT) are incorporated in a 5-bus system. The results obtained from simulation of the 5-bus using FACTSPF are matched with those of PSAT in acceptable tolerance and thus confirms the robustness of the PIM. The PIM model represents a robust and feasible alternative when compared with Current Injection Model (CIM) implemented in Power System Analysis Toolbox (PSAT). Keywords: FACTS; STATCOM; NR; PIM; CIM; FACTSPF; PSAT

Single walled carbon nanotube shows excellent chemical and physical properties and it has been extensively explored for novel applications. It has been shown that single walled carbon nanotube can be used to improve sensitivity of sensors such as bio sensors, gas sensors and mechanicals sensor by its unique properties. As a mechanical sensor, a mechanical force deforms the single walled carbon nanotubes and changes its electrical properties such as resistivity. The deformation results in changing of electrical structure such as band gap of single walled carbon nanotubes. In this study, we fabricate a strain sensor using single walled carbon nanotubes with an efficient structure. The structure contains aligned and multiple numbers of single walled carbon nanotubes to raise sensitivity of the strain sensor. The structure fabricated in this study is much reliable and efficient compared to the sensor with individual single walled carbon nanotube.

This paper deals with the effect of process parameters on the peak load and the energy absorption of the expansion of thin walled tubes using a die. The energy absorption and peak load during the expansion process were calculated for various punch angles (α) and with different expansion ratios (rp/ro) at different temperatures (T) by finite element analysis where rp is the punch outer radius and ro is the tube inner radius. The effect of these input parameters on the response parameters peak load, energy absorption and local buckling have been critically analyzed using Taguchi method. It is observed that the peak load and energy absorption of the expansion tube is influenced by expansion ratio (rp/ro), punch angle followed by tube temperature. The energy absorption of the expanded tube increases as the rp/ro ratio increases and also larger punch angle improves the energy absorption. Whereas the minimum expansion ratio (rp/ro) decreases the peak load at higher temperature and medium punch angle. The ANOVA performed on the experimental results revealed that the expansion ratio (rp/ro) is the most important process parameter that minimizes the peak load followed by punch angle and tube temperature. Also it is found that the expansion ratio improves the energy absorption.

This paper presents an automatic method for detecting transition zones based on multires- olution spectral analysis (MRS). The MRS is calculated over several Fast Fourier Trans- forms (FFT) of different length. It can provide a higher temporal accuracy in the upper spectral region and a better frequency resolution in the lower spectral range. We showcase the importance of this tool by attempting an automatic detection of zones of transition by calculating the Interquartile Range (IQR) of each frame of the MRS FFT. We applied our Visual Assistance of Speech Processing (VASP) System to a corpus. This corpus was in French pronounced by french speakers and has the format CiV CiV with Ci was a stop con- sonant [p t k] and V was a vowel [i e]. The results showed that the automatic detection of transition zones based on MRS provides better results compared to classical spectral analysis of the corpora used.