A new technique for tracking sequences of digitized images

research-article Free Access Share on Embedded systems in real time applications, design & architecture Authors: A. L. Suseela View Profile , V. Lalith Kumar View Profile Authors Info & Claims UbiquityVolume 2005Issue AugustAugust 2005 pp 2https://doi.org/10.1145/1088431.1086450Online:01 August 2005Publication History 0citation170,093DownloadsMetricsTotal Citations0Total Downloads170,093Last 12 Months1,290Last 6 weeks56 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF

Advanced control and optimization of a thermo-energetic installations

Multimicroprocessor systems for real time applications: (eds) Gianni Conte and Dante del Corso, D Reidel Publishing Company (1985) 299 pp

Improved likelihood ratio test based voice activity detector applied to speech recognition

With the term “real-time data processing,“ we denote applications that have special emphasis on the following: • Algorithms that can be executed within a guaranteed response time window, and • Processes (that is, the performance of an algorithm) with overlap ping time windows which must nevertheless be kept in logical and timely order.

This paper presents an investigation for analysing the load carrying capacity of journal bearing in a variety of conditions using a proposed neural network (NN). The NN structure is very suitable for this kind of system. The network is capable of predicting the pressures of the experimental system. The network has parallel structure and fast learning capacity. It can be outlined from the results for both approaches, NN could be used to model journal bearing systems in real time applications.

Image registration is a vital problem in medical imaging. It has many potential applications in clinical diagnosis (e.g. diagnosis of cardiac, retinal, pelvic, renal, abdomen, liver and tissue disorders). In this paper a new technique has been proposed based on feature based image registration using SURF (Speeded up robust features) and Hessian matrix feature extraction techniques for detecting, extracting and matching corresponding points in the reference (fixed) and source image (deformed). The source image is aligned using SURF and Hessian feature extraction techniques to align with the reference image. The proposed technique is based on multimodality of images, feature based classification and non-rigid images as the images taken are from different sources like MRI, CT scan and X-ray. The proposed technique uses feature-based methods to find correspondence between image features such as points, lines and contours. The non-rigid images use fixed (source) image and also deformable (target) image of the subject and aligns target image according to source/reference image.

A new docking method for mobile robot is proposed. The desired docking state (desired position and orientation) of the robot is defined by a pre-captured reference image. The scale invariant feature transform (SIFT) algorithm and a double direction best bin first (BBF) based feature matching algorithm are used to extract the visual feedback information by matching the current view of the docking station and the reference image. The robot is aligned against the reference image based on a epipole servoing strategy. A centroid tracking is used to avoid the object escaping from the field of view. A random sample consensus (RANSAC) algorithm is used to solve the affine transform between current and reference images, and a control rule at last stage is presented to realize precise docking. No more knowledge of the scene geometry or artificial marks are needed. The experiment results of a realistic indoor environment demonstrate the effectiveness of the proposed method.

Multi-core CPUs with multiple levels of parallelism (i.e. data level, instruction level and task/core level) have become the mainstream CPUs for commodity computing systems. Based on the multi-core CPUs, in this paper we developed a high performance computing framework for AC contingency calculation (ACCC) to fully utilize the computing power of commodity systems for online and real time applications. Using Woodbury matrix identity based compensation method, we transform and pack multiple contingency cases of different outages into a fine grained vectorized data parallel programming model. We implement the data parallel programming model using SIMD instruction extension on x86 CPUs, therefore, fully taking advantages of the CPU core with SIMD floating point capability. We also implement a thread pool scheduler for ACCC on multi-core CPUs which automatically balances the computing loads across CPU cores to fully utilize the multi-core capability. We test the ACCC solver on the IEEE test systems and on the Polish 3000-bus system using a quad-core Intel Sandy Bridge CPU. The optimized ACCC solver achieves close to linear speedup (SIMD width multiply core numbers) comparing to scalar implementation and is able to solve a complete N-1 line outage AC contingency calculation of the Polish grid within one second on a commodity CPU. It enables the complete ACCC as a real-time application on commodity computing systems.

Speaker identification identifies the speaker among a set of users by matching against a set of voiceprints. In speaker identification, the identification time depends on the number of feature vectors, their dimensionality and the number of speakers. In this paper, text independent speaker identification model is developed by taking in MFCCs with VQ to obtain pressed feature vectors without losing much information, and the numbers of speakers are reduced in the test by gender detection algorithm. Gaussian Mixture Model (GMM) is used a modeling technique. Results show that proposed approach always yields better improvements in accuracy and brings almost 50% reduces in time processing.

Background/Objectives: This paper offers a clear traffic control system to pass emergency vehicles easily. In this paper to accomplishment the raise of current innovation called Automated Efficient Traffic Control System: waiting time of emergency vehicles and also used to control the traffic lights. Methods/Statistical Analysis: This makes the utilization of Radio Frequency Identifier (RFID). This makes it very tough to take away or get broken. We use RFID readerTK-5530, and PIC16F877A. A device Micro-chip to take a gander at the RFID Tag related to the vehicle. Findings: If RFID tag takes a look at fits in with the stolen automobile the Information is transmits the usage of GSM SIM300 to the police manager further, whilst ambulance is coming near to the intersection, it will touch to activity controller inside of the intersection switch on the unpractised light. Conclusion: The module makes utilization of Zigbee detachable on CC2500 and PIC16F877A. Device Micro-chip for remote correspondences among the rescue vehicle or visitor controller. It is used to sense ambulance in multidirectional and also sense the ambulance to reach the junction to junction communication with the priority of the vehicle.Keywords: Ambulance Detection, Data Collection of Vehicles, Micro Controller based System, Traffic Management

The evolution of intelligent systems and large-scale use of internet finds the way to develop smart systems for agricultural industry. To design such smart system, there is need to identify lacunas in earlier developed system in agricultural domain. This paper aims to provide brief review on developed systems for different domain of agriculture like robotic, farm monitoring, water management and crop health management, pest detection system etc. On the basis of literature reviewed, it has been analyzed that there is large scope of improvement in agricultural system for real time applications as less attention was given towards intelligence, modularity and human-centric design aspect. Agricultural industry demands more intelligent, powerful and efficient autonomous systems (Smart System) to fulfill future needs. Hence, there is a need to utilize advance technologies like Internet of Things, Machine Learning and Artificial Intelligence towards the development of smart systems for agricultural industry. Therefore this paper aim to propose smart robotic system for greenhouse management.

MONOSK: A network operating system for real time applications

The recent advances in the feature extraction techniques in recognition of handwritten digits attract researchers to work in this area. The present study includes recognition of handwritten digits using hybrid feature extraction technique including static and dynamic properties of handwritten digit images. In this paper, static properties include number of non-zero (white) pixels in square, horizontal, vertical and diagonal styles as sub regions of a binary image. The dynamic properties include features from recovery of drawing order of original image. The extraction of dynamic features include two stages: first stage recover the drawing order of an image and second stage compute the chain code directions from recovered drawing order. The algorithm for recovery of drawing order uses properties of writing behavior. The support vector machine has been used as recognition method for the proposed feature extraction scheme. We have achieved an overall error rate of 0.73 % for mnist data set including 60,000 training images and 10,000 test images. Our feature extraction technique results in feature vector length of an image equals to 356. The achieved results strengthen our proposed technique usability as error rate achieved is at par with literature (\(<\)1 %) and the length of feature vector per image is small in comparison to input feature vector length of 784 which has been commonly used in previous work. The developed system is stable and useful in real life applications.

Infrared image segmentation with 2-D maximum entropy method based on particle swarm optimization (PSO)