Computer Engineering

To evaluate the effectiveness and clinical significance of pattern visual evoked potential (P-VEP) as a predictor of occlusion therapy for patients with strabismic and amblyopia(squint eye).Methods: A total of 34 consecutive children with anisometropic squint were included in this study. All patients underwent a full initial ophthalmologic and orthoptic evaluation. P-VEP test was performed in all cases and binocular vision was tested and recorded Part-time occlusion therapy was performed by using adhesive patches. Results: The mean (±SEM) cycloplegic refractive error was +5.6 ± 0.6 diopters (D) in the squint eyes and +1.8 ± 0.2 D in the normal eye. The mean levels of best-corrected visual acuity were statistically differed between each measurement for occlusion therapy (for each, p < 0.05). The ratio of the patients with binocular vision increased after 6 months occlusion therapy and the difference was statistically significant (p<0.05). In addition, P100 amplitude improved at each visit and the difference was significant when compared with baseline values (for each, p < 0.05). Conclusions: P100 amplitude of the P-VEP test parallels the improvement in subjective visual acuity in squint eyes under occlusion therapy. Therefore, this test may be useful in monitoring the visual acuity in the preverbal or non-verbal patched patients.

In this Research, a new method has been proposed in order to classify the mental tasks which represent the Electroencephalogram (EEG) signal as time series. Time series are kind of data format which depict signal voltage varieties in time domain. Different parts of the different signals have different powers, so in first step and in the preprocessing, signal partitioning into several fixed windows is needed. Toward the extracting appropriate features from each EEG signal window, PCA algorithm is used. So for each window, a feature vector is made by PCA, and a general vector is created from these primary vectors. In order to refuse redundancy caused by non-important windows, the best combination of such vectors, that have the best results in classification, should be probed. Toward this goal, two feature extraction methods, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), are applied. K-Nearest Neighbor (KNN) was used as fitness function for PSO and GA. These methods select such windows whose combination of feature vectors are best and increase TP (true positive) of the classifier. The results show that GA and PSO improve the power of classification, but GA is more efficient.

Vision based AGV (Automatic Guided Vehicle) with RTOS (uC/OS-II) is designed and developed for controlling two wheeled differential servo motor drive. In order to meet the demand of function, reliability, cost and real time performance compared to its commercial counterpart of general purpose computer the system is implemented with RTOS. The multi-processor embedded system with distributed architecture consists of a main-controller of vehicle management based on the ARM LPC2378, and a sub-controller of vision navigation based on the DSP BF533. The embedded RTOS uC/OS-II is used to construct a software development platform, on which different functions needed are described as several tasks, and a number of system services facilitate software realization. In the practical application of device reformation, a commercial AGV product is upgraded by the embedded vehicular controller we develop, on which a sophisticated algorithm of path tracking is implemented successfully and efficiently. The experimental result demonstrates the effectivity and advantages of the embedded multi-processor controller with the RTOS uC/OS-II presented in this paper versus its commercial competitor.

Abdominal elcetrocardiograms make it possible to dertermne the fetal heart rate and to detect multuiple fetuses and are often used during labor and delivery.the background noise due to muscular activity and feotus motion, however, often had an amplitude equal to or grater than that of fetal heartbeat.A still more serious problem is the mother’s heart beat,which has an amplitude 2 to 10 times grater thatn that of the fetal heartbeat, and often interferes in recording[1]. The Maternal ECG (MECG) is the main source of interference in Fetal ECG (FECG) monitoring. The MECG is detected at all electrodes placed on the mother’s skin (thoracic and abdominal). In the case of multi-fetal pregnancies the traditional adaptive filtering technique provides a “maternal clean“ signal consisting of the two fetal ECG signals. The noise was found to be too strong for the algorithm (and the naked eye) to notice any fetal heart signal[1]. This paper briefs the implementation of Adaptive noise cancellation algorithms such as LMS algorithm and RLS algorithm using MATLAB 6 (R12) suitable for real time implementation, which can be used during measurements, is being developed using VLSI. The best solution in case of multiple fetuses is the BSS filtering which has successfully been implemented in MATLAB.

Visual cryptography is special type of technique for encipher the confidential visual information (e.g. printed text, handwritten notes, and picture) in such a way, that decipher can be performed by human visual system (HVS) without any complex process, providing high security. In this paper a simple but robust visual cryptography scheme is proposed. In this scheme the secret is encrypted using symmetric key encryption algorithm, and then this encrypted data will be hidden into an image file, divided into parts called shares and then they are distributed to the participants. Thus accomplishing both data encoding and hiding. Only piling of shares does not revile the secret until shares are stacked together in a particular fashion and provided with the key. It can be used to hide the original secret information from an intruder or an unwanted user. The shares are very safe because separately they reveal nothing about the secret image. The algorithm proposed by this scheme reduces a considerable time for encryption and decryption in a much easier way and ensures the lossless transmissions of images. The proposed encryption algorithm in this study has been tested on some images and showed good results.

Magnetic resonance Image (MRI) is a medical image method use in radiology to visualize internal body structures like tissues. MRI method provides high-quality contrast effect to visual tissues of brain and MRI contrast is enhancing appearance of tumors in brain. In this survey compared the CLIC, EM, AFCM, Bias Field Estimation, SFKFCM, MFCM and FLGMM methods for accurate brain segmentation in MRI images. CLIC method to evaluate metric tissue classification and estimate bias field minimizing energy, optimal bias field derived smoothness of CLIC method. It’s applied for fully automated applications. AFCM applied on corrupted images in reality accurate in classification and partial volume effect. MR data corrupted truth brightness variation in each component. AFCM implementation is faster than other methods one major problem in AFCM method is to look for clusters of the same shape, size and increased complexity. Bias corrected FCM method with spatial neighborhood regularization period and its time taking; lacks robustness to noise is main problem of the method. In GKFCM method with a spatial bias correlation can automatically learn parameters by prototype driven learning scheme and this method is effective more robust to noise. SFKFCM method for segmenting brain MRI images and correcting spurious variations, to improve the separability of observed data, fast clustering scheme improve speed up process and execution time is reduced improved clustering results. MFCM for adaptive segmentation and intensity correlation of MR images and automation available for the segmentation of MR images into tissues classes correcting interscan intensity inhomogenetics. MFCM leads to increased improvement in quality of 3D brain structures and visualization. GMM voxel intensities in each target region, its lack of taking spatial information and uncertainty data may produce poor segmentation result. To overcome problem in segmentation FGMM concept is proposed for uncertainty data and parameter quality improvement. The integration of the weighted GMM energy functions in the entire image and uses a truncated Gaussian kernel function to incorporate spatial constraints into local GMM employ the fuzzy membership function to balance each GMM segmentation process. FGMM reduced noise, low contrast, bias field and improved segmentation result compared with all other methods proposed by previous researches.

In any business network most important things that need to be addressed is up time. It depending on the size of the business and business network also, every minute downtime can more affect the productivity of the business employee and the business system that use the network. The address this within the adaptive security appliance (ASA) product line, Cisco offers high availability through a series of failover capabilities. When configured, they allow a deployed ASA to be mated with another ASA, which combine to offer little downtime if one of them encounters of failure [1]. This paper introduces the replication of data, it means both units carry data traffic and it also introduces how to secure our interesting traffic over the internet. VPN mainly used for security purpose we use VPN in many thing and many fields. It provides the secure and private network connection through the public internet; the VPN protects our data in many ways. VPN tunnel is an encrypted connection between our device and VPN sever.

Geospatial information gathered through different sensors and geographic objects is generally indistinct, vague and uncertain. The ambiguity turns out to be obvious due to the multi-granular formation of the multisensory satellite images and that directs to error accumulation at every stage. The main aim of this paper is to compare the K-Means and Fuzzy C-Means classification algorithm and find out the change detection in glacier classification by processing images taken over different time frames. The LANDSAT images correspond to the Himachal Pradesh region, one dated June 2005 and the other dated June 2010. To estimate the quality of remote sensing data the non-linear objective assessment parameters are used. Though the classification of glacier cover calculation, by improving the accurate geological classification, might be in a crude form but when projected on a larger scale, it can act as a great tool for research and analysis on a particular geographical location. The environment related bodies around the globe are deeply benefited from the valuable images provided by satellite imagery and their analysis help strategize different methods for environment protection in general and curb global warming in specific.

This paper proposes a novel method of designing discrete cosine transform using Vedic mathematics. Multipliers are fundamental and area intensive component in the architecture of any DSP system. In many circumstances, there are situations where the complexity and delay of the whole circuit increases because of inefficient multipliers. So it is necessary to reduce such complexity of Multipliers for efficient DSP architecture. The design of Vedic Multiplier is aimed to create such. The design is based on the Sutras of ancient Indian Vedic mathematics which was rediscovered from Vedas between 1911 and 1918 .The whole of Vedic mathematics is based on 16 sutras and manifests unified structure of mathematics. The DCT algorithm is based on ‘Urdhva-Tiryak’ sutra .It is expected that the Vedic architecture reduces the space and time hence the complexity of the multiplier when implemented in digital domain. The algorithm is implemented using verilog HDL and is tested and verified. It is found to reduce the space and increases the speed by when compared to conventional dct using array multiplier. The multiplier can be substituted for conventional multipliers in various applications. The exploration of Vedic algorithms in Digital Signal Processing may prove to be extremely advantageous. Hence it can be applied for discrete cosine transform application.

In this paper a method has been proposed that uses static partitioning for improving classification of time components of EEG signals. The main idea is that different windows of signals have different power in classification. So with removing some ineffective windows from signals, the power of classification might be increased. For finding best combination of windows, Genetic Algorithm (GA) was applied. For extracting appropriate features, Common Spatial Pattern (CSP) was derived for five class problem. It applied onto each window distinguishably, and the final feature vector was obtained from placing these feature vectors altogether. LDA was used for classifying tasks. The proposed method was applied on a dataset of five mental tasks in which 30% of dataset were used for testing system. The experimental results show that window selection by GA will increase the accuracy of algorithm. This technique increased the accuracy from 69% into 95.3% for 25 windows and into 100% for 50 windows. So with changing number of windows, the accuracy of algorithm will be changed. Another important parameter is ’m’ that is the number of spatial patterns selected by CSP.