Forensic Face Sketch Recognition Using Computer Vision

Now - a - days need for technologies for identification, detection and recognition of suspects has increased. One of the most common biometric techniques is face recognition, since face is the convenient way used by the people to identify each - other. Understanding how humans recognize face sketches drawn by artists is of significant value to both criminal investigators and forensic researchers in Computer Vision. However studies say that hand - drawn face sketches are still very limited in terms of artists and number of sketches because after any incident a forensic artist prepares a victims sketches on behalf of the descripti on provided by an eyewitness. Sometimes suspects used special mask to hide some common features of faces like nose, eyes, lips, face - color etc. but the outliner features of face biometrics one could never hide. In this work, I concentrated on some specific facial geometric feature which could be used to calculate some ratios of similarities from the template photograph database against the forensic sketches. This paper describes the design of a system for forensic face sketch recognition by a computer visi on approach like Two - Dimensional Discrete Cosine Transform (2D - DCT) and the Self - Organizing Map (SOM) Neural Network simulated in MATLAB

SOM: A Computer Vision Technique for Face Sketch Featured Database

Self - Organizing Maps (SOM) found to be an improved data management computer vision technique used for the closed matching of face vs sketch identification system based on neural network of untrained input images with trained databa se of images. Parameters for the SOM neural network are selected to be a minimum and maximum point for each row on a vector of training database. In this paper 64 minimum and 64 maximum pixel intensity values selected altogether using 8x8 image masking technique. Further for the design of SOM a set of 25 uniform image data used to create 5 different classes of a face image like left eye, right eye, nose, frontal face and lips for the training database. All the preprocessing for the image enhancement is don e in the MATLAB software

Approximate analytical solution of linear and nonlinear fractional delay differential equations using new variational iteration method

In this paper, an approximate analytical method, New Variational Iteration Method (NVIM) is introduced in this paper for the approximate analytical solution of Fractional Delay Differential Equations (FDDE). The algorithm is illustrated by studying initial value linear and nonlinear problems. The results obtained are presented and show that only few terms are required to get an approximate solution

Survey of Current Network Intrusion Detection Techniques

The significance of network security has grown enormously and a number of devices have been introduced to perk up the security of a network. NIDS is a retrofit approach for providing a sense of security in existing computers and data networks, while allowing them to operate in their current open mode. The goal of a network intrusion detection system is to identify, preferably in real time, unauthorized use, misuse and abuse of computer systems by insiders as well as from outside perpetrators. This paper presents a nomenclature of intrusion detection systems that is used to do a survey and identify a number of research prototypes.  Keywords: Security, Intrusion Detection, Misuse and Anomaly Detection, Pattern Matching

Implementation of Anomaly Based Network Intrusion Detection by Using Q-learning Technique

Network Intrusion detection System (NIDS) is an intrusion detection system that tries to discover malicious activity such as service attacks, port scans or even attempts to break into computers by monitoring network traffic. Data mining techniques make it possible to search large amounts of data for characteristic rules and patterns. If applied to network monitoring data recorded on a host or in a network, they can be used to detect intrusions, attacks or anomalies. We proposed “machine learning method”, cascading Principal Component Analysis (PCA) and the Q-learning methods to classifying anomalous and normal activities in a computer network. This paper investigates the use of PCA to reduce high dimensional data and to improve the predictive performance. On the reduced data, representing a density region of normal or anomaly instances, Q-learning strategies are applied for the creation of agents that can adapt to unknown, complex environments. We attempted to create an agent that would learn to explore an environment and collect the malicious within it. We obtained interesting results where agents were able to re-adapt their learning quickly to the new traffic and network information as compare to the other machine learning method such as supervised learning and unsupervised learning. Keywords: Intrusion, Anomaly Detection, Data Mining, KDD Cup’99, PCA, Q-learning

Analytical study of time-fractional order Klein–Gordon equation

AbstractIn this article, we study an approximate analytical solution of linear and nonlinear time-fractional order Klein–Gordon equations by using a recently developed semi analytical method referred as fractional reduced differential transform method with appropriate initial condition. In the study of fractional Klein–Gordon equation, fractional derivative is described in the Caputo sense. The validity and efficiency of the aforesaid method are illustrated by considering three computational examples. The solution profile behavior and effects of different fraction Brownian motion on solution profile of the three numerical examples are shown graphically

Cross-layer design for communication systems

Master'sMASTER OF ENGINEERIN

Effect of Shod Walking on Plantar Pressure with Varying Insole

Walking and running are very critical factors in human being’s everyday life. A human being takes more than 2,000 steps to walk 1.6 km. The human being wear a boot with insole to protect feet when walking, playing and doing various activities. The boot with insole provides significant impact on the feet during these events and transmitted through the feet due to intense force and pressure. Measurements of plantar pressure are important for diagnosing lower limb disorders, designing footwear, injury prevention and applications in sports biomechanics. The objective of this study is to investigate the plantar pressure exerted on the feet during shod walking (wearing boot with three types of insoles); to identify effective insole for reducing plantar pressure during walking (wearing same boot with three insoles). Eighteen fits, healthy male adults volunteered for this study with mean and SD (mean±SD) age (36±9) years, height (169±4) cm, and weight (71±8) kg. During experiments, each volunteer underwent 5 min of treadmill walking (4.5 km/hr speed) with wearing of boot with varying types of insoles (Low-density polyurethane (LDPU) insole 1; High-density polyurethane (HDPU), insole 2; and Silicone rubber (SR), insole 3). Plantar pressures were measured by using a foot pressure measuring device. A paired t-test was conducted to observe significant changes in plantar pressures of different foot region (P<0.05). Observations of the present study revealed that plantar pressures (N/cm²*s) were minimum during the use of LDPU insole than HDPU and SR insoles. It was also noticed that during the using of LDPU insole, less plantar pressure observed in the heel (3.84 ±1.16 in right foot) followed by forefoot (right 3.92±0.88), lateral (right 3.56±0.85), and medial foot (right 3.60±0.69). Hence, the present study suggested that using LDPU insole reducing the transfer of impact forces to the body/foot in comparison to HDPU and SR insoles during walking and minimizing the risk of foot-related injuries in long term use

Extended modified cubic B-spline algorithm for nonlinear Burgers' equation

AbstractIn this paper, an extended modified cubic B-Spline differential quadrature method is proposed to approximate the solution of the nonlinear Burgers' equation. The proposed method is used in space and a five-stage and four order strong stability-preserving time-stepping Runge–Kutta (SSP-RK54) method is used in time. The accuracy and efficiency of the method is illustrated by considering four numerical problems. The numerical results of the method are compared with some existing methods and it was found that the proposed numerical method produces acceptable results and even more accurate results in comparison with some existing methods. The stability analysis of the scheme is also carried out and was found to be unconditionally stable