Real Time Surge Voltage Control using Smart IoT
Efficient Transmission of FECG Signal using MIMO – OFDM
Deep Convolutional Auto-Encoders for Emotion Recognition from Facial Expressions
A Survey on Network Admission Control Solution in 6LoWPAN using Cryptographic Mechanism
A Comprehensive Review of Visible Light Communication (VLC)
Cognitive Radio Simulator for Mobile Networks: Design and Implementation
Reduced End-To-End Delay for Manets using SHSP-EA3ACK Algorithm
Light Fidelity Design for Audio Transmission Using Light Dependent Resistor
Dynamic Digital Parking System
Performance Analysis of Multi User Transmit Antenna Selection Systems over TWDP Fading Channels
Comparison of Wavelet Transforms For Denoising And Analysis Of PCG Signal
Video Shot Boundary Detection – Comparison of Color Histogram and Gist Method
Curvelets with New Quantizer for Image Compression
Comparison of Hybrid Diversity Systems Over Rayleigh Fading Channel
Design of Close Loop Dual-Band BPF Using CascadedOpen Loop Triangular Ring Resonator Loaded With Open Stubs
Routing is one of the most important challenges in Vehicular Ad hoc Networks (VANETs). These networks involve a high cost in the real world experimentation. Thus, simulation is a useful alternative in conducting such a research. In this paper, two important routing protocols (AODV and DSDV) have been considered for testing in VANET environments. Performance evaluation has been accomplished for these two routing protocols using the CityMob mobility generator that generates the required urban mobility scenario files. Then, simulation has been done based on these scenario files using the NS-2 simulator. The performance of the routing protocols has been compared based on some routing metrics (Packet Delivery Ratio and End to End Delay) for different values for the speed of vehicles. The work has been done under Linux operating system.
Many algorithms have been proposed for detecting video shot boundaries and classifying shot and shot transition types. Here we are using two different methods for comparison, using GIST , Color Histogram. Color histogram method draws the histogram for the frames and detects shot comparing these histograms but this is sensitive to illuminance and motion while the GIST uses two different properties of the video i.e color and gist for the detection of the shots. The aim of this paper is to make a comparison between two of the well-known methods used for detecting video shot boundaries. Firstly various methods are described in the preceding sections then a comparison is made about it. This paper shows that the GIST method produces good result over the other method.
Communication messages to and from mobile wireless users commonly transit combined wired and wireless subnets which are vulnerable to time variant mobile wireless channel conditions. General cases include: 1) non-dispersive free space loss, 2) non-dispersive fading, 3) time dispersive distortion, 4) frequency dispersive distortion, and 5) dual time and frequency dispersive distortion. Cognitive processing architectures (CPA) can mitigation these problematic conditions with channel state recognition (CSR) algorithms which respond to time-variant distortion without the aid of training sequences or pilot tones. They can provide efficient distortion state awareness to downstream cognitive processes; which apply near real time mitigation selections among SNR loss, flat-fading, inter-symbol interference (ISI), and/or inter-frequency interference (IFI) methods. This paper covers recent research by the authors to introduce channel state recognition (CSR) algorithms, a CSR testbed, and a reference waveform generator (RWG). The CSR testbed provides an integrated environment for algorithm verification, and the RWG provides symbol streams with controlled channel states based on calibrated symbol and channel parameters. Applying reference waveforms to CSR algorithms provides effective algorithm performance verification. This paper also surveys published wireless channel multistate hidden Markov models (HMM) revealing mature generative and recognition HMM applications for modeling wireless channel parameters. However, none have been found for recognition of channel dispersion and related conditions such as frequency selectivity and/or time selectivity. Therefore, the authors introduce a mobile wireless channel (MWC) distortion state model (DSM) and a distortion mitigation transform (DMT) linking time-variant non-dispersive, single, or dual-dispersive channel states with effective mitigation methods. Additionally, the DSM is embedded in a distortion state recognition (DSR) HMM and the CSR testbed for performance verification. Test results for the DSR algorithm demonstrate the utility of the DSM and the feasibility of CSR. Accuracy performance results agree with published standard HMM recognition accuracy in terms of sensitivity and specificity. DSR algorithm limitations are noted and provide direction for future CSR research efforts.
Development of a Character recognition system for Devnagari is difficult because (i) there are about 350 basic, modified (“matra”) and compound character shapes in the script and (ii) the characters in words are topologically connected. Here focus is on the recognition of offline handwritten Devnagari signatures that can be used in common applications like bank cheques, commercial forms, government records, bill processing systems, Postcode Recognition, Signature Verification, passport readers, offline document recognition generated by the expanding technological society. Challenges in handwritten signature recognition lie in the variation and distortion of handwritten signature or script since different people may use different style of handwriting, and direction to draw the same shape of any Devnagari character. This overview describes the nature of handwritten language, how it is translated into electronic data, and the basic concepts behind written language recognition algorithms. Handwritten Devnagari signatures are imprecise in nature as their corners are not always sharp, lines are not perfectly straight, and curves are not necessarily smooth, unlikely the printed character. An approach using Artificial Neural Network is considered for recognition of Handwritten Devnagari Signature. The learning process inherent in Neural Networks (NN) can be applied to the process of verifying handwritten signatures that are electronically captured via a stylus. This paper presents a method for verifying handwritten signatures by using NN architecture. Various static (e.g., area covered, number of elements, height, slant, etc.) (Plamondon & Srihari, 2000, p. 63-84) and dynamic (e.g., velocity, pen tip pressure, etc.) signature features are extracted and used to train the NN (Daramola & Ibiyemi, 2010, p. 48-52). Several Network topologies are tested and their accuracy is compared. Although the verification process can be thought to as a monolith component, it is recommended to divide it into loosely coupled phases (like preprocessing, feature extraction, feature matching, feature comparison and classification) allowing us to gain a better control over the precision of different components. This paper focuses on classification, the last phase in the process, covering some of the most important general approaches in the field. Each approach is evaluated for applicability in signature verification, identifying their strength and weaknesses. It is shown, that some of these weak points are common between the different approaches and can partially be eliminated with our proposed solutions. To demonstrate this, several local features are introduced and compared using different classification approaches.
This paper address the PCG signals (Phonocardiogram) and their De-noising techniques. The PCG as a kind of weak biological signal under the back ground of strong noise is easily subject to interference from noise of various sources. De-noising of PCG signal therefore, forms the primary basis for achieving non-invasive diagnosis of coronary heart disease. There are various method are available for De-noising the PCG signal but the method is most effective for the PCG signal is very much important. In this paper de- noise the 4 types of PCG signal and check the different parameters and find that which wavelet gives the maximum result for all four types PCG signals.
Lung sound signal (LSS) measurements are taken to aid in the diagnosis of various diseases. Their interpretation is difficult however due to the presence of interference generated by the heart. In this paper, the adaptive line enhancer (ALE) is employed for reducing heart sound signal (HSS) from lung sound recordings. In this paper thirteen day new born baby girl’s lung sound signal is taken as an input to an adaptive line enhancer, and for updating the weights LMS algorithm has used. This performance is done by using MATLAB 7.0. More over linear predictive FIR filter is used for detecting the interference from the input signal. The architecture is validated in MATLAB, SNR and MSE are calculated. Verilog code is written and ALE has been successfully modeled and has been synthesized using Xilinx 9.1i, cadence and synopsis. The Area, power and timing reports are compared using these three tools. The ASIC design is carried on Synopsys tools.
The goal of this work is to implement vehicle-to-infrastructure and vehicle-to-vehicle communications, creating wireless ad-hoc vehicle networks, or Vehicular Ad Hoc Networks (VANETs). The objective is to specify, design and implement embedded systems and wireless communication protocols in which distance, position and identity information is combined with mobile ad-hoc networking to create the possibility to implement all kinds of localized applications in vehicular environments.
