Automatic 2D/2D Registration using Multiresolution Pyramid based Mutual Information in Image Guided Radiation Therapy

Medical image registration is the key technology in image guided radiation therapy (IGRT) systems. On the basis of the previous work on our IGRT prototype with a biorthogonal x-ray imaging system, we described a method focused on the 2D/2D rigid-body registration using multiresolution pyramid based mutual information in this paper. Three key steps were involved in the method : firstly, four 2D images were obtained including two x-ray projection images and two digital reconstructed radiographies(DRRs ) as the input for the registration ; Secondly, each pair of the corresponding x-ray image and DRR image were matched using multiresolution pyramid based mutual information under the ITK registration framework ; Thirdly, we got the final couch offset through a coordinate transformation by calculating the translations acquired from the two pairs of the images. A simulation example of a parotid gland tumor case and a clinical example of an anthropomorphic head phantom were employed in the verification tests. In addition, the influence of different CT slice thickness were tested. The simulation results showed that the positioning errors were 0.068±0.070, 0.072±0.098, 0.154±0.176mm along three axes which were lateral, longitudinal and vertical. The clinical test indicated that the positioning errors of the planned isocenter were 0.066, 0.07, 2.06mm on average with a CT slice thickness of 2.5mm. It can be concluded that our method with its verified accuracy and robustness can be effectively used in IGRT systems for patient setup.

Effect of Addition the Dune Sand Powder on Development of Compressive Strength and Hydration of Cement Pastes

In this paper, the effect of addition the dune sand powder (DSP) on development of compressive strength and hydration of cement pastes was investigated as a function of water/binder ratio, was varied, on the one hand, the percentage of DSP and on the other, the fineness of DSP. In order to understand better the pozzolanic effect of dune sand powder in cement pastes, we followed the mixtures hydration (50% Pure Lime + 50% DSP) by X-ray diffraction. These mixtures the pastes present a hydraulic setting which is due to the formation of a C-S-H phase (calcium silicate hydrate). The latter is semi-crystallized. This study is a simplified approach to that of the mixtures (80% ordinary Portland cement + 20% DSP), in which the main reaction is the fixing of the lime coming from the cement hydration in the presence of DSP, to form calcium silicate hydrate semi-crystallized of second generation. The results proved that up to (20% DSP) as Portland cement replacement could be used with a fineness of 4000 cm²/g without affecting adversely the compressive strength. After 28 days, the compressive strength at 5, 10 and 15% DSP is superior to Portland cement, with an optimum effect for a percentage of the order of 5% to 10% irrespective of the w/b ratio and fineness of DSP.

Classification of Acoustic Emission Based Partial Discharge in Oil Pressboard Insulation System Using Wavelet Analysis

Insulation used in transformer is mostly oil pressboard insulation. Insulation failure is one of the major causes of catastrophic failure of transformers. It is established that partial discharges (PD) cause insulation degradation and premature failure of insulation. Online monitoring of PDs can reduce the risk of catastrophic failure of transformers. There are different techniques of partial discharge measurement like, electrical, optical, acoustic, opto-acoustic and ultra high frequency (UHF). Being non invasive and non interference prone, acoustic emission technique is advantageous for online PD measurement. Acoustic detection of p.d. is based on the retrieval and analysis of mechanical or pressure signals produced by partial discharges. Partial discharges are classified according to the origin of discharges. Their effects on insulation deterioration are different for different types. This paper reports experimental results and analysis for classification of partial discharges using acoustic emission signal of laboratory simulated partial discharges in oil pressboard insulation system using three different electrode systems. Acoustic emission signal produced by PD are detected by sensors mounted on the experimental tank surface, stored on an oscilloscope and fed to computer for further analysis. The measured AE signals are analyzed using discrete wavelet transform analysis and wavelet packet analysis. Energy distribution in different frequency bands of discrete wavelet decomposed signal and wavelet packet decomposed signal is calculated. These analyses show a distinct feature useful for PD classification. Wavelet packet analysis can sort out any misclassification arising out of DWT in most cases.

Robust Face Recognition Using Eigen Faces and Karhunen-Loeve Algorithm

The current research paper is an implementation of Eigen Faces and Karhunen-Loeve Algorithm for face recognition. The designed program works in a manner where a unique identification number is given to each face under trial. These faces are kept in a database from where any particular face can be matched and found out of the available test faces. The Karhunen –Loeve Algorithm has been implemented to find out the appropriate right face (with same features) with respect to given input image as test data image having unique identification number. The procedure involves usage of Eigen faces for the recognition of faces.

Matching Pursuit based Removal of Cardiac Pulse-Related Artifacts in EEG/fMRI

Cardiac pulse-related artifacts in the EEG recorded simultaneously with fMRI are complex and highly variable. Their effective removal is an unsolved problem. Our aim is to develop an adaptive removal algorithm based on the matching pursuit (MP) technique and to compare it to established methods using a visual evoked potential (VEP). We recorded the VEP inside the static magnetic field of an MR scanner (with artifacts) as well as in an electrically shielded room (artifact free). The MP-based artifact removal outperformed average artifact subtraction (AAS) and optimal basis set removal (OBS) in terms of restoring the EEG field map topography of the VEP. Subsequently, a dipole model was fitted to the VEP under each condition using a realistic boundary element head model. The source location of the VEP recorded inside the MR scanner was closest to that of the artifact free VEP after cleaning with the MP-based algorithm as well as with AAS. While none of the tested algorithms offered complete removal, MP showed promising results due to its ability to adapt to variations of latency, frequency and amplitude of individual artifact occurrences while still utilizing a common template.

Predicting Oil Content of Fresh Palm Fruit Using Transmission-Mode Ultrasonic Technique

In this paper, an ultrasonic technique is proposed to predict oil content in a fresh palm fruit. This is accomplished by measuring the attenuation based on ultrasonic transmission mode. Several palm fruit samples with known oil content by Soxhlet extraction (ISO9001:2008) were tested with our ultrasonic measurement. Amplitude attenuation data results for all palm samples were collected. The Feedforward Neural Networks (FNNs) are applied to predict the oil content for the samples. The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of the FNN model for predicting oil content percentage are 7.6186 and 5.2287 with the correlation coefficient (R) of 0.9193.

Security Risk Analysis Based on the Policy Formalization and the Modeling of Big Systems

Security risk models have been successful in estimating the likelihood of attack for simple security threats. However, modeling complex system and their security risk is even a challenge. Many methods have been proposed to face this problem. Often difficult to manipulate, and not enough all-embracing they are not as famous as they should with administrators and deciders. We propose in this paper a new tool to model big systems on purpose. The software, takes into account attack threats and security strength.

Wireless Neural Stimulator with Adjustable Electrical Quantity

The neural stimulation has been gaining much interest in neuromodulation research and clinical trials. For efficiency, there is a need for variable electrical stimulation such as current and voltage stimuli as well as wireless framework. In this regard, we develop the wireless neural stimulator capable of voltage and current stimuli. The system consists of ZigBee which is a wireless communication module and stimulus generator. The stimulus generator with 8-bits resolution enable both mono-polar and bi-polar waveform in voltage (-3.3~3.3V) and current(-330~330µA) stimulus mode which is controllable. The experimental results suggest that the proposed neural stimulator can play a role as an effective approach for neuromodulation.

A New Automatic System of Cell Colony Counting

The counting process of cell colonies is always a long and laborious process that is dependent on the judgment and ability of the operator. The judgment of the operator in counting can vary in relation to fatigue. Moreover, since this activity is time consuming it can limit the usable number of dishes for each experiment. For these purposes, it is necessary that an automatic system of cell colony counting is used. This article introduces a new automatic system of counting based on the elaboration of the digital images of cellular colonies grown on petri dishes. This system is mainly based on the algorithms of region-growing for the recognition of the regions of interest (ROI) in the image and a Sanger neural net for the characterization of such regions. The better final classification is supplied from a Feed-Forward Neural Net (FF-NN) and confronted with the K-Nearest Neighbour (K-NN) and a Linear Discriminative Function (LDF). The preliminary results are shown.

Salt-Tolerance of Tissue-Cultured Date Palm Cultivars under Controlled Environment

A study was conducted in greenhouse environment to determine the response of five tissue-cultured date palm cultivars, Al- Ahamad, Nabusaif, Barhee, Khalas, and Kasab to irrigation water salinity of 1.6, 5, 10, or 20 dS/ m. The salinity level of 1.6dS/m, was used as a control. The effects of high salinity on plant survival were manifested at 360 days after planting (DAP) onwards. Three cultivars, Khalas, Kasab and Barhee were able to tolerate 10 dS/m salinity level at 24 months after the start of study. Khalas tolerated the highest salinity level of 20 dS/ m and 'Nabusaif' was found to be the least tolerant cv. The average heights of palms and the number of fronds were decreased with increasing salinity levels as time progressed.

Behavior of Cu-WC-Ti Metal Composite Afterusing Planetary Ball Milling

Copper based composites reinforced with WC and Ti particles were prepared using planetary ball-mill. The experiment was designed by using Taguchi technique and milling was carried out in an air for several hours. The powder was characterized before and after milling using the SEM, TEM and X-ray for microstructure and for possible new phases. Microstructures show that milled particles size and reduction in particle size depend on many parameters. The distance d between planes of atoms estimated from X-ray powder diffraction data and TEM image. X-ray diffraction patterns of the milled powder did not show clearly any new peak or energy shift, but the TEM images show a significant change in crystalline structure of corporate on titanium in the composites.

Segmentation and Recognition of Handwritten Numeric Chains

In this paper we present an off line system for the recognition of the handwritten numeric chains. Our work is divided in two big parts. The first part is the realization of a recognition system of the isolated handwritten digits. In this case the study is based mainly on the evaluation of neural network performances, trained with the gradient back propagation algorithm. The used parameters to form the input vector of the neural network are extracted on the binary images of the digits by several methods: the distribution sequence, the Barr features and the centred moments of the different projections and profiles. The second part is the extension of our system for the reading of the handwritten numeric chains constituted of a variable number of digits. The vertical projection is used to segment the numeric chain at isolated digits and every digit (or segment) will be presented separately to the entry of the system achieved in the first part (recognition system of the isolated handwritten digits). The result of the recognition of the numeric chain will be displayed at the exit of the global system.

Flight Control of TUAV with Coaxial Rotor and Ducted Fan Configuration by NARMA-L2 Controllers for Enhanced Situational Awareness

This paper focuses on a critical component of the situational awareness (SA), the control of autonomous vertical flight for tactical unmanned aerial vehicle (TUAV). With the SA strategy, we proposed a two stage flight control procedure using two autonomous control subsystems to address the dynamics variation and performance requirement difference in initial and final stages of flight trajectory for an unmanned helicopter model with coaxial rotor and ducted fan configuration. This control strategy for chosen model of TUAV has been verified by simulation of hovering maneuvers using software package Simulink and demonstrated good performance for fast stabilization of engines in hovering, consequently, fast SA with economy in energy can be asserted during search-and-rescue operations.

Increase of Peroxidase Activity of Haptoglobin (2-2)-Hemoglobin at Pathologic Temperature and Presence of Antibiotics

Free Hemoglobin promotes the accumulation of hydroxyl radicals by the heme iron, which can react with endogenous hydrogen peroxide to produce free radicals which may cause severe oxidative cell damage. Haptoglobin binds to Hemoglobin strongly and Haptoglobin-Hemoglobin binding is irreversible. Peroxidase activity of Haptoglobin(2-2)-Hemoglobin complex was assayed by following increase of absorption of produced tetraguaiacol as the second substrate of Haptoglobin-Hemoglobin complex at 470 nm and 42°C by UV-Vis spectrophotometer. The results have shown that peroxidase activity of Haptoglobin(2-2)-Hemoglobin complex is modulated via homotropic effect of hydrogen peroxide as allostric substrate. On the other hand antioxidant property of Haptoglobin(2- 2)-Hemoglobin was increased via heterotropic effect of the two drugs (especially ampicillin) on peroxidase activity of the complex. Both drugs also have mild effect on quality of homotropic property of peroxidase activity of Haptoglobin(2-2)-Hemoglobin complex. Therefore, in vitro studies show that the two drugs may help Hp-Hb complex to remove hydrogen peroxide from serum at pathologic temperature ature (42 C).

Modeling Peer-to-Peer Networks with Interest-Based Clusters

In the world of Peer-to-Peer (P2P) networking different protocols have been developed to make the resource sharing or information retrieval more efficient. The SemPeer protocol is a new layer on Gnutella that transforms the connections of the nodes based on semantic information to make information retrieval more efficient. However, this transformation causes high clustering in the network that decreases the number of nodes reached, therefore the probability of finding a document is also decreased. In this paper we describe a mathematical model for the Gnutella and SemPeer protocols that captures clustering-related issues, followed by a proposition to modify the SemPeer protocol to achieve moderate clustering. This modification is a sort of link management for the individual nodes that allows the SemPeer protocol to be more efficient, because the probability of a successful query in the P2P network is reasonably increased. For the validation of the models, we evaluated a series of simulations that supported our results.

Real-Time Testing of Steel Strip Welds based on Bayesian Decision Theory

One of the main trouble in a steel strip manufacturing line is the breakage of whatever weld carried out between steel coils, that are used to produce the continuous strip to be processed. A weld breakage results in a several hours stop of the manufacturing line. In this process the damages caused by the breakage must be repaired. After the reparation and in order to go on with the production it will be necessary a restarting process of the line. For minimizing this problem, a human operator must inspect visually and manually each weld in order to avoid its breakage during the manufacturing process. The work presented in this paper is based on the Bayesian decision theory and it presents an approach to detect, on real-time, steel strip defective welds. This approach is based on quantifying the tradeoffs between various classification decisions using probability and the costs that accompany such decisions.

Lateral Pressure in Squat Silos under Eccentric Discharge

The influence of eccentric discharge of stored solids in squat silos has been highly valued by many researchers. However, calculation method of lateral pressure under eccentric flowing still needs to be deeply studied. In particular, the lateral pressure distribution on vertical wall could not be accurately recognized mainly because of its asymmetry. In order to build mechanical model of lateral pressure, flow channel and flow pattern of stored solids in squat silo are studied. In this passage, based on Janssen-s theory, the method for calculating lateral static pressure in squat silos after eccentric discharge is proposed. Calculative formulae are deduced for each of three possible cases. This method is also focusing on unsymmetrical distribution characteristic of silo wall normal pressure. Finite element model is used to analysis and compare the results of lateral pressure and the numerical results illustrate the practicability of the theoretical method.

A Genetic Algorithm for Clustering on Image Data

Clustering is the process of subdividing an input data set into a desired number of subgroups so that members of the same subgroup are similar and members of different subgroups have diverse properties. Many heuristic algorithms have been applied to the clustering problem, which is known to be NP Hard. Genetic algorithms have been used in a wide variety of fields to perform clustering, however, the technique normally has a long running time in terms of input set size. This paper proposes an efficient genetic algorithm for clustering on very large data sets, especially on image data sets. The genetic algorithm uses the most time efficient techniques along with preprocessing of the input data set. We test our algorithm on both artificial and real image data sets, both of which are of large size. The experimental results show that our algorithm outperforms the k-means algorithm in terms of running time as well as the quality of the clustering.

Online Brands: A Comparative Study of World Top Ranked Universities with Science and Technology Programs

University websites are considered as one of the brand primary touch points for multiple stakeholders, but most of them did not have great designs to create favorable impressions. Some of the elements that web designers should carefully consider are the appearance, the content, the functionality, usability and search engine optimization. However, priority should be placed on website simplicity and negative space. In terms of content, previous research suggests that universities should include reputation, learning environment, graduate career prospects, image destination, cultural integration, and virtual tour on their websites. The study examines how top 200 world ranking science and technology-based universities present their brands online and whether the websites capture the content dimensions. Content analysis of the websites revealed that the top ranking universities captured these dimensions at varying degree. Besides, the UK-based university had better priority on website simplicity and negative space compared to the Malaysian-based university.

Face Image Coding Using Face Prototyping

In this paper we present a novel approach for face image coding. The proposed method makes a use of the features of video encoders like motion prediction. At first encoder selects appropriate prototype from the database and warps it according to features of encoding face. Warped prototype is placed as first I frame. Encoding face is placed as second frame as P frame type. Information about features positions, color change, selected prototype and data flow of P frame will be sent to decoder. The condition is both encoder and decoder own the same database of prototypes. We have run experiment with H.264 video encoder and obtained results were compared to results achieved by JPEG and JPEG2000. Obtained results show that our approach is able to achieve 3 times lower bitrate and two times higher PSNR in comparison with JPEG. According to comparison with JPEG2000 the bitrate was very similar, but subjective quality achieved by proposed method is better.