The performance evaluation of a continuous activity recognitionsystem can be viewed as a problem of how to measure the similarity of two time series; the similarity of a prediction sequence to its corresponding ground truth. In the topic of activity recognition, these time series are made up of discrete events, each representing a particular activity which the system is designed to recognise. As such events are based on real world activities, or concern changes to a user’s environment, it is often the case that they are of variable duration and have ambiguous start and stop times. This can lead to events being detected some time before or after they actually occur. It can also lead to single events being fragmented into multiple smaller events of the same class; or, alternatively, the merging of several real events into a single detected event.
We present a system for online handwritten signature veriﬁcation, approaching the problem as a two-class pattern recognition problem. A test signatureÕs authenticity is established by ﬁrst aligning it with each reference signature for the claimed user, using dynamic time warping. The distances of the test signature to the nearest, farthest and template reference signatures are normalized by the corresponding mean values obtained from the reference set, to form a three- dimensional feature vector. This feature vector is then classiﬁed into one of the two classes (genuine or forgery). A linear classiﬁer used in conjunction with the principal component analysis obtained a 1.4% error rate for a data set of 94 peo- ple and 619 test signatures (genuine signatures and skilled forgeries). Our method received the ﬁrst place at SVC2004 with a 2.8% error rate.
Image processing is any form of signal processing for which the input is an image, such as a photograph or video frame; the output of image processing may be either an image or a set of characteristics or parameters related to the image. Image processing is classified into two types. They are,
simple learning algorithm for SLFNs called extreme learning machine. Different from traditional learning algorithms the extreme learning algorithm not only provides the smaller training error but also the better performance. The input weights and hidden layer biases of SLFNs can be randomly assigned if the activation functions in the hidden layer are infinitely differentiable. Subsequent to the input weights and the hidden layer biases are chosen arbitrarily, SLFNs can be simply measured as a linear system and the output weights of SLFNs can be analytically determined through simple generalized inverse operation of the hidden layer output matrices. Based on this concept, this paper proposes a simple learning algorithm for SLFNs called extreme learning machine (ELM) whose learning speed can be thousands of times faster than traditional other learning algorithms like local binary pattern, local ternary pattern, main element analysis, k nearest algorithm, linear discriminant algorithm while obtaining better generalization performance. Different from established learning algorithms the proposed learning algorithm not only tends to reach the smallest training error but also the smallest norm of weights. ELM was initially proposed for pattern single hidden layer feed forward neural networks and has recently been extended to kernel learning as well: ELM provides a united learning platform with widespread type of feature mappings and can be applied in regression and multi-class classification applications directly. From the optimization technique point of view ELM has milder optimization constraints compared to SVM, LSSVM and so on.
The Advanced Driver Assistance System (ADAS) is actively developed by major automobile manufacturers worldwide for intelligent vehicle technology. ADAS is considered the rst step in the future development of unmanned intelligent vehicles. Currently, ADAS cannot control a vehicle independently from the driver. The system provides several working and environmen- tal situations around a vehicle which can be analyzed and determined by a microprocessor. ADAS warns drivers of trac accidents in advance. The system has more than nine functions, including a blind spot system (BSD), a Backup Parking Aid System (BPAS), a Rear Crash Collision Warning System (RCCWS), a Lane Departure detection System (LDS), a Collision Mitigation System (CMS), an Adaptive Front-lighting
The automatic recognition of human faces spans a variety of different technologies. At a highest level, the technologies are best distinguished by the input medium that is used, whether visible light, infra-red [8, 9] or three dimensional data  from stereo or other range-finding technologies. Thus far, the field has concentrated on still, visible-light, photographic images, often black and white, though much interest is now beginning to be shown in the recognition of faces in color video. Each input medium that is used for face recognition brings robustness to certain conditions, e.g. infra-red face imaging is practically invariant to lighting conditions while three dimensional data in theory is invariant to head pose. Imaging in the visible light spectrum, however, will remain the preeminent domain for research and application of face recognition because of the vast quantity of legacy data and the ubiquity and cheapness of photographic capture equipment .
In the bacterial genome of free-living Escherichia coli , the chromosomal DNA sequences are considered free from any bound proteins and exist essentially as a naked DNA. The search kinetics of dCas/single-stranded RNA was studied in living E. coli by combining single-molecule fluorescence microscopy and bulk restriction-protection assay . Binding of Cas9 is more time-consuming than simple binding of transcription factor for correct base recognition, because Cas9 binds and also unwinds the DNA double-helix to test for correct base pairing to the guide RNA. The dCas molecules were visualized by fusing with fluorescent protein YPet . DNA-bound dCas/YPet molecules are detectable as individual diffraction-limited spots after five second image acquisition time while non-bound molecules are seen as the diffuse fluorescent background . Under these conditions dCas was demonstrated to take six hours to find the correct target sequence suggesting that each potential target is bound for less than 30 milliseconds that is 20 times faster than 750 milliseconds in eukaryotic mouse cells . To achieve fast targeting, both dCas9 and its guide RNA molecules have to be present at extremely high saturating concentrations.
Abstract---Two levels security system include Face Recognition and Password based entry control. Two level security(Face recognition and PIN) can provide hi-tech security at public place including International Airport, Metro-Station, Shopping Mall, banking ATM control and home etc. It increase human computer interaction which is fast and secure. This paper addresses first level i.e. the building of face recognitionsystem by using Principal Component Analysis (PCA) method and second level Edsim51Di simulator for keypad entry and finally controlling the motor. A PCA algorithm is written on MATLAB. PCA , ,  is a statistical approach used for reducing the number of variables in face recognition. As extracting the most information (feature) contained in the images (face). In PCA, every image in the training set can be represented as a linear combination of weighted eigenvectors called as “Eigen faces”. These eigenvectors are obtained from covariance matrix of a training image set called as basis function. Recognition is performed by projecting a new image (test image) onto the subspace spanned by the Eigen faces and then classification is done by distance measure methods such as Euclidean distance. Finally, when the face is matched, MATLAB program generate the 8bit hex code which is received at the port of 8051, further this project is simulated with the help of Edsim51di, first matches face code from database and secondly password entered by keypad which is interface with 8051 to match with the stored database and motor is rotated to allow the entry. If any one condition not satisfies it display “No Entry” at LCD.
The competitive inhibition reaction (Figure 3.6 (c)) like the bimolecular experiment also obstructs association, but this time by adding a molecule which can directly compete for hydrogen bonding sites. Depending on the effectiveness of the inhibition, either the rate will be similar to the bimolecular experiment or reduced from the native experiment, with evidence remaining of the recognition mediated processes. The effectiveness of inhibition will depend on binding strength (K) between complexes of building blocks with each other and with molecules of formed template compared to association with the competitive inhibitor. If the association of building blocks with molecules of template, formed in the experiment, is substantially greater than the binding strength of the building blocks with the competitive inhibitor, reaction inhibition will be decreased and the rate will be greater than the bimolecular reaction. Results of this control reaction provide information as to whether the reaction is proceeding via a recognition mediated pathway. The final experiment is the templated reaction (Figure 3.6 (d)). This experiment is conducted in a similar fashion to the native, but prefabricated template is added at the beginning of the reaction to promote autocatalysis by removing the lag period. The removal of the lag period turns the product curve for the replicating template from sigmoidal in the native to parabolic in the templated experiment (Figure 3.6 (b) and (d)). Analysis of the results of the templated experiments informs the investigator if the reaction is indeed proceeding via a minimally replicating pathway. In conclusion all four experiments have to be studied in order to determine if the reaction is first autocatalytic, second recognition mediated and thirdly, and most importantly, whether the reaction mechanism is via self-replication.
Visiting physical therapists to the clinics for physical rehabilitation in regular basis is a very long and time- consuming trip where the final result for success is truly hard to see in daily training. That’s why technological development in traditional physical rehabilitation system is both important, interesting and its effects on patients’ time-management process is huge. In this paper we have proposed a framework for distance physical rehabilitation system using motion captured data from multiple Kinects which can interact directly with the patients, even grasp and track their movements so as to send those data back to the doctors in clinics using Windows Azure. Its goal is to coach patients through their physical therapy exercises and make those exercises a more enjoyable experience and bring physical therapy alive for them at their homes, the same way doctors interact with them in clinics.
For learning method, many researchers reported that Boosting , ,  has shown improvement on many recognition problems which iteratively learning classifiers by reweighting the data. Boosting combine all different types of features in one feature pool and homogenous classifiers are used to train those features iteratively in Boosting. Recently, most object classrecognition approaches exists in the literature use Boosting approach. However, the low level features used on those works are different.  combine three interest point detectors together with four types of local features, namely subsampled gray values, basic intensity moments, moment invariants, and SIFT.  used the others local features Gradient Location-Orientation Histogram (GLOH) and opponent angle color descriptor. Another object classrecognition approach using different features from PCA-SIFT, shape context and spatial features is presented by . Their model is a multi layer boosting system which the first boosting layer chooses the most important features from a pool of PCA-SIFT descriptors and shape-context descriptors. To improve the performance of the first boosting layer, the spatial relationships between the selected features are computed in the second layer of boosting. With this technique, the most authors only focused on local features without taking into account the shape of objects.
This classroom environment monitoring system consists of three modules. The first module will be implemented with RFID tags and RFID Reader for location tracking. Then face recognition biometric system will be implemented with Local Binary Pattern (LBP) algorithm. Raspberry Pi kit with connected web-camera will be used for capturing student’s face image. Then local binary pattern algorithm is applied on cropped image and histogram intensity value is calculated for all the training images. Once both the RFID pin and corresponding student’s face are matched, then their entry time with date, and classroom or meeting room location are send to the cloud database. Then motion sensors are activated for checking person count inside the classroom. If the person count is one then, the system initiates the relay units and actuators to turn ‘ON’ the lights, projector and fan or air-conditioning system. After completing the lecture, the staff or students may come out of the classroom. So while leaving from the classroom out time of each student is entered in the system. So all the students and staffs out time are updated to the database. If person count is become zero, the lights, projector and fan like electrical and electronics appliances will become turned ‘OFF’. The second module implementation is to view the collected information through web page. These marked attendance details can be viewed in web based framework controller. Staff can view all students attendance report for monthly-wise or for a particular date or for a selected duration. In the third module, web page design is implement to view the current status of the classroom in real time. If any person occupied in the classroom, classroom status will be occupied, otherwise the status will be available. So that, we can schedule the classroom to conduct meetings in future.
To implement this project need to have gone through a background study about various topics like the intelligent transportation system, traffic signaling system in India. In the technology wise, a brief idea about the image processing algorithms for choosing an efficient algorithm to develop according to the required standards. And also required hardware for image acquisition and embedded processes. The image acquisition is the main part in this system, because of it needs a high definition image of number plate to process it accurately. In the previous days the system will be used for the number plate detection, here we are extending by giving an importance to the emergency vehicles in the traffic signals and also vehicles jumps at the traffic signals.
The Class Rosters report lists students in the selected teacher’s course and period, along with any additional fields of student information that you specify. The information you enter in the “Roster columns” field saves automatically when you click Submit. In the future, if you want to change what student information appears on the roster, but you don’t want to delete the text you saved, copy the text in the “Heading” and “Roster columns” fields and paste the text into a Word document. Then, instead of re-typing the information every time you want to change the class roster, copy it from your Word document and paste it into the “Heading” and “Roster columns” fields.
The figure here shows the standard model adapted by any of the recognitionsystem. According to this model as the image is collected from the raw source for recognition, it goes through number of process stages. The first stage is to transform the image to the normalized form so that that the image will be mapped to the database image. After this transformation, the actual facial object is mapped over the image. This object location identification is called localization stage. After obtaining the facial object, the facial features are extracted. Now this featured image is mapped to the database images. The facial database is also present in the form of facial images. Once the featured dataset and the featured input image are obtained, the next work is to perform the recognition. The recognition is performed using algorithmic model such as PCA, LDA, Neural network, SVM etc. The maximum mapped image is identified over the database based which the recognition is verified. In this paper main focus on the challenges of the recognition process. The foremost challenge is the database adaption. The database adaptive challenges are given in this sub section.
The proposed method first converts the RGB values of the input barcode video to intensity values [Fig: 2]. These intensity values are given to the feature calculation block. Feature calculation converts pixel values to features. This is done by assigning values to each pixel. Scan lines can be used to scan barcodes and calculate pixel values from barcode intensity on a given line to a vector. Some lines of the input image are selected and it acts as scan lines. The number and position of the scan lines can be changed. In this paper uses three scan lines. The feature calculation level sets black pixels to 1 and white pixels to -1. Barcode recognition block comes after that [Fig: 2]. This block consists mainly of three modules. They are bar detect, barcode recognition and barcode comparison block. The bar detection block detects black and white bars from the barcode feature signal. It also calculates the bar width specified as input for the next block, i.e. the barcode recognition block. The barcode recognition module calculates all possible barcode values. These values are compared to the codebook generated in the barcode comparison block. Then comes the barcode validation block, which checks whether the barcode is valid or not. The display block displays the valid barcodes and recognizes them. Before displaying the intensity, values are converted back to RGB. The output will be a barcode video that identifies all barcodes [6-8].
The name MATLAB is expanded as Matrix Laboratory. MATLAB is a high performance language for technical computing. It integrates computation, visualization, and programming environment. It has sophisticated data structures, contains built-in editing and debugging tools, and supports object oriented programming. These factors make MATLAB an excellent tool for teaching and research. There are tool boxes in MATLAB for signal processing, image processing, symbolic computation, control theory, simulation, optimization, and several other applied sciences. The software part of this system is implemented using MATLAB version R2013a.
design .The simulation effects of solitary LUT based RAM s tructure offers a lot complexity every time faucet increases here we are not able to enforce single framework its quiet tricky project and discipline eating system ,the place since proposed framework helps as much as 91 MHz input sampling regularity and easy to implement with greater faucet aided through the help of decomposed RAM framework also it learned become field and fee will offer you less NOS of 45per cent to 19% when when put next to framework that's systolic. Determine 5: Simulation of proposed DA – b ased reconfigurable RAM that's decomposed framework
Ideal progress on the R2G is 12 months for an insurance business to train 60% of its appraiser staff to Platinum ProLevel 1. The timeframe will vary depending on amount of existing training, number of appraisers, etc. Once all appraiser staff members at a business or location have completed their training requirements on the Road to Gold, the business or location can submit its Gold Class application and be recognized as Gold Class.