Electrical and Electronics

Safety, along with security, plays a vital role in today’s society. The goal of this project is to design an embedded safety and security system for vehicle by integrating and modifying existing modules. This system endures mainly with three modules namely Gas sensing module, Obstacle detection module and Anti Theft Alert system, these are interfaced with atmel AT89S52 microcontroller. Ultrasonic sensors transmit ultrasonic waves from its sensor head and again receives the ultrasonic waves reflected from an obstacle and instructs the microcontroller which alerts the driver with an alarm and controls the vehicle by stopping it. The gas sensor here is mounted inside the vehicle such that it senses the presence of the gases inside the vehicle cabin if there is any increase in the level of the toxic gases it informs to the microcontroller which alerts the persons inside the vehicle with an alarm and also sends a SMS to the authorized user through GSM. When an unauthorized person opens the car door, the car anti theft system becomes active and gives indication by raising an alarm that the car is being under theft.

Parameters design and characteristics optimization of a split-phase single-phase induction motor are proposed in this paper using particle swarm optimization (PSO) algorithm. The PSO considers the motor efficiency as objective function and five performance related items as constraints. The main advantages of the proposed technique are; its simple structure and its straightforward verification of maximum efficiency of induction motor for a given output power. The PSO algorithm was implemented on a test motor and therefore, a code has been provided under MATLAB software. The results show that the PSO method gives more suitable design optimization against conventional methods.

In recent VLSI technology, the occurrence of all kind of errors has become predictable. By adopting an emerging concept in VLSI design and test, error tolerance (ET), a novel error-tolerant adder (ETA) is proposed. The ETA is able to ease the strict restriction on accuracy, and performance. One important potential application of the proposed ETA is in digital signal processing systems that can tolerate certain amount of errors. To prove the feasibility of the ETA, we replaced all the common additions involved in a normal FFT algorithm with our proposed addition arithmetic. When compared to its conventional counterparts, the proposed ETA is able to attain more than 65% improvement in the Power-Delay Product (PDP).

With the advancement in technology and increase in number of vehicles it is the need of the day to recognize registered and unregistered vehicles. Extraction of number plate plays a key role in the recognition of vehicles. For this purpose, in this paper we propose a system to automatically extract digits and alphabets from the number plate of vehicle and search it in the database for its registration. The proposed approach involves four different processes that include Image smoothing, Edge detection, Image segmentation and data extraction. The result shows that the proposed approach can easily detect and extract data from the number plates. Some of the number plates and its extracted data are shown in this paper. The results can further be implemented on automatic extraction of data from shields, sign etc.

Security is the primary concern in any system especially in case of communication where everything is relies on cooperation among other. In this article we analyze the impact of the most dangerous attacks of wireless mess network called blackhole attack that can totally disrupt the network. Behavioral anomaly detection using SVM (SUPPORTED VECTOR MACHINE) is an attractive choice to monitor the suspicious activity like black hole. For the detection of balckhole attack we will uses the behavior parameters of node (relaying node) (PDR (PACKET DELIVERY RATIO), throughput), by analyzing the node behavior the malicious activity of the node can deprive the traffic from the source node.In order to prevent this kind of attack, it is crucial to detect the abnormality occurs during the attack. In conventional schemes, anomaly detection is achieved by defining the normal state from static training data. However, in mobile ad hoc networks where the network topology dynamically changes, such static training method could not be used efficiently.

With the progress of VLSI technology, delay buffer plays an important role affecting the circuit design and performance. This paper presents the design of low power buffer using clock gating and gated driver tree. Since delay buffers are accessed sequentially, it adopts a gated clock ring counter addressing scheme. The ring counter employs double edge triggered (DET) flip flops instead of traditional flip flop to half the operating frequency. Also for generating clock gating signals, combinational elements (C-element) are implemented in the control logic to avoid the increasing loading of the global clock signal. For the clock distribution network, a gated driver tree technique is used and it further reduces the power consumption. In addition, this technique is used in the input and output ports of the memory to decrease their loading. The proposed delay buffer consumes less power comparing to the conventional delay buffers

Wireless based target detection can also be used to detect the vehicles and human or animals. These systems are usually lightweight devices that automatically monitor the local activities in-situ, and transfer target detection and classification reports to the processing center through wireless. It is necessary that the monitoring system at borders must prove to be more efficient. This system based on Seismic sensor and PIR sensor is designed to find the target which is moving on the ground.Ref[1,2,3,4] In the existing system the Unattended Ground Sensors are used in target detection. The efficiency of the existing system is limited by false alarm rates. Also power consumption becomes a consideration in the existing system. In the proposed system PIR and seismic sensors are used in target detection. The PIR Sensor is used to track down whether the moving object is living body or machine. If it is living body, the seismic sensor is used to track down whether it is human being or animal based on the absorbed signals. If it is vehicle, then seismic sensor is used to classify the type of vehicle moving on the ground. All these process are monitored and controlled using PIC16F877A microcontroller. The status is sent through GSM as a message to the Mobile Section.

In this paper we interest to how we can make a multiphysics model of IM, the FEM currently represents the state-of-the-art in the numerical magnetic field computation relating to electrical machines. FEM is a numerical method to solve the partial differential equations that expresses the physical quantities of interest, in this case thermal transfer and Maxwell’s equations. This approach is possible by using special simulation package frequently exploited bout in university and industry. A simple description of each one of this famous software is presented. In this moment, with the complicity of the problem, we made a decoupling between the thermal phenomena, electromagnetic and mechanics phenomena, in the first time we considers only the transient thermal and the other phenomena are in steady state, on the other hand in the second times the thermal behavior is ignored. FEM analysis is used for study state and transient mode, thermal transient, magnetic field calculation, the magnetic flux density and vector potential of machine is obtained. In this model we including, non linear material characteristics, eddy current effect, torque-speed characteristics, and magnetic analysis are investigated. Finally some simulation results of induction motor modeled by Motor-CAD and Maxwell software are given and commented.

Existing driving behavior models have a strong emphasis on the driver’s cognitive components including aspects such as motivation, risk assessment, attention, compensation, capability, workload, individual traits and experience. Each existing model was designed specifically for a particular driving situation such as speeding or fatigue. This system defines a framework for a new context aware driving behavior model capable of predicting driver’s behavior. This approach broadens the cognitive focus of existing driving behavior models to integrate contextual information related to the vehicle, environment, driver and the interactions between them. In this system used to consist of different types of sensors such as alcohol sensor, eye blink sensor, lane detection sensor, heart beat sensor, accident sensor. And also mainly used to GSM modem, GPS receiver, RFID reader and finger print scanner. GSM and GPS based vehicle location and tracking system will provide effective, real time vehicle location, mapping and reporting this information value and adds by improving the level of service provided. The system has an “On Board Module" which resides in the vehicle to be tracked and a "Base Station" that monitors data from the various vehicles. The On Board module consists of GPS receiver, a GSM modem. The Alcohol Sensor is used to sense weather the person driving the car taken Alcohol or not and this data is also given to ADC, The ADC is used in this system because the signal comes from the Sensors are analog in nature, so we want to convert the Analog signals into digital signal for this purpose ADC is used. Heart Beat sensor used in the system is used to sense the heart beat of the person driving the vehicle and sends the data to the embedded system, and the eye blink sensor is used to sense the person driving the vehicle is sleeping or not. Accident sensor is used to identify weather the vehicle is running in normal condition or not when the accident occurs it send the information to the embedded system. The Embedded system is programmed like when it receives the signal from the signal sensor it activates the Vehicle system control system and level converter unit. And also this system consist of RFID reader and finger print sensor is used to prevent non-licensees from driving and therefore causing accidents. And identify the person going to drive the vehicle is the owner of the vehicle or not.

This paper describes a hybrid design for intrusion detection that combines anomaly detection with misuse detection. The proposed method includes an ensemble feature selecting classifier and a data mining Classifier. The former consists of four classifiers using different sets of features and each of them employs a machine learning algorithm named fuzzy belief k-NN classification algorithm. The latter applies data mining technique to automatically extract computer users’ normal behavior from training network traffic data. The outputs of ensemble feature selecting classifier and data mining classifier are then fused together to get the final decision. The experimental results indicate that hybrid approach effectively generates a more accurate intrusion detection model on detecting both normal usages and malicious activities.