Abstract: Comparison of electron- and photon-impact processes as a method for determination of photo-ionization cross sections is described, discussed and shown to have many attractive features.
Abstract: Breast Cancer is the most common malignancy in women and the second leading cause of death for women all over the world. Earlier the detection of cancer, better the treatment. The diagnosis and treatment of the cancer rely on segmentation of Sonoelastographic images. Texture features has not considered for Sonoelastographic segmentation. Sonoelastographic images of 15 patients containing both benign and malignant tumorsare considered for experimentation.The images are enhanced to remove noise in order to improve contrast and emphasize tumor boundary. It is then decomposed into sub-bands using single level Daubechies wavelets varying from single co-efficient to six coefficients. The Grey Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) features are extracted and then selected by ranking it using Sequential Floating Forward Selection (SFFS) technique from each sub-band. The resultant images undergo K-Means clustering and then few post-processing steps to remove the false spots. The tumor boundary is detected from the segmented image. It is proposed that Local Binary Pattern (LBP) from the vertical coefficients of Daubechies wavelet with two coefficients is best suited for segmentation of Sonoelastographic breast images among the wavelet members using one to six coefficients for decomposition. The results are also quantified with the help of an expert radiologist. The proposed work can be used for further diagnostic process to decide if the segmented tumor is benign or malignant.
Abstract: An efficient freeway system will be essential to the
development of Africa, and interchanges are a key to that efficiency.
Around the world, many interchanges between freeways and surface
streets, called service interchanges, are of the diamond configuration,
and interchanges using roundabouts or loop ramps are also popular.
However, many diamond interchanges have serious operational
problems, interchanges with roundabouts fail at high demand levels,
and loops use lots of expensive land. Newer service interchange
designs provide other options. The most popular new interchange
design in the US at the moment is the double crossover diamond
(DCD), also known as the diverging diamond. The DCD has
enormous potential, but also has several significant limitations.
The objectives of this paper are to review new service interchange
options and to highlight some of the main features of those
alternatives. The paper tests four conventional and seven
unconventional designs using seven measures related to efficiency,
cost, and safety.
The results show that there is no superior design in all measures
investigated. The DCD is better than most designs tested on most
measures examined. However, the DCD was only superior to all
other designs for bridge width. The DCD performed relatively poorly
for capacity and for serving pedestrians. Based on the results, African
freeway designers are encouraged to investigate the full range of
alternatives that could work at the spot of interest. Diamonds and
DCDs have their niches, but some of the other designs investigated
could be optimum at some spots.
Abstract: Determination of attentional status is important because working performance and an unexpected accident is highly related with the attention. The autonomic nervous and the central nervous systems can reflect the changes in person’s attentional status. Reduced number of suitable pysiological parameters among autonomic and central nervous systems related signal parameters will be critical in optimum design of attentional devices. In this paper, we analyze the EEG (Electroencephalography) and HRV (Heart Rate Variability) signals to demonstrate the effective relation with brain signal and cardiovascular signal during event-related attention, which will be later used in selecting the minimum set of attentional parameters. Time and frequency domain parameters from HRV signal and frequency domain parameters from EEG signal are used as input to the optimum feature parameters selector.
Abstract: A strategy for the creation of a Virtual Interactive Platform (or Networking Platform) to combine the four web-baseness of expert systems on the transfer and diffusion of low-carbon technologies. It used the concept of “Open Innovation” and “Triple Helix” with regard to theories of “Green Growth” and “Carbon Footprint”. Interpreters expert systems operate on the basis of models of the “Predator-Prey” for the process of transfer and diffusion of technologies, taking into account the features caused by the need to mitigate the effects of climate change.
Abstract: The present study explores flow visualization experiments with various blunt shaped bluff bodies placed inside a circular pipe. The bodies mainly comprise of modifications of trapezoidal cylinder, most widely used in practical applications, such as vortex flowmeters. The present configuration possesses the feature of both internal and external flows with low aspect ratio. The vortex dynamics of bluff bodies in such configuration is seldom reported in the literature. Dye injection technique is employed to visualize the complex vortex formation mechanism behind the bluff bodies. The influence of orientation, slit and after body shape is studied in an attempt to obtain better understanding of the vortex formation mechanism. Various wake parameters like Strouhal number, vortex formation length and wake width are documented for these shapes. Vortex formation both with and without shear layer interaction is observed for most of the shapes.
Abstract: Real time non-invasive Brain Computer Interfaces have a significant progressive role in restoring or maintaining a quality life for medically challenged people. This manuscript provides a comprehensive review of emerging research in the field of cognitive/affective computing in context of human neural responses. The perspectives of different emotion assessment modalities like face expressions, speech, text, gestures, and human physiological responses have also been discussed. Focus has been paid to explore the ability of EEG (Electroencephalogram) signals to portray thoughts, feelings, and unspoken words. An automated workflow-based protocol to design an EEG-based real time Brain Computer Interface system for analysis and classification of human emotions elicited by external audio/visual stimuli has been proposed. The front end hardware includes a cost effective and portable Emotiv EEG Neuroheadset unit, a personal computer and a set of external stimulators. Primary signal analysis and processing of real time acquired EEG shall be performed using MATLAB based advanced brain mapping toolbox EEGLab/BCILab. This shall be followed by the development of MATLAB based self-defined algorithm to capture and characterize temporal and spectral variations in EEG under emotional stimulations. The extracted hybrid feature set shall be used to classify emotional states using artificial intelligence tools like Artificial Neural Network. The final system would result in an inexpensive, portable and more intuitive Brain Computer Interface in real time scenario to control prosthetic devices by translating different brain states into operative control signals.
Abstract: The current Hadoop block placement policy do not fairly and evenly distributes replicas of blocks written to datanodes in a Hadoop cluster.
This paper presents a new solution that helps to keep the cluster in a balanced state while an HDFS client is writing data to a file in Hadoop cluster. The solution had been implemented, and test had been conducted to evaluate its contribution to Hadoop distributed file system.
It has been found that, the solution has lowered global execution time taken by Hadoop balancer to 22 percent. It also has been found that, Hadoop balancer respectively over replicate 1.75 and 3.3 percent of all re-distributed blocks in the modified and original Hadoop clusters.
The feature that keeps the cluster in a balanced state works as a core part to Hadoop system and not just as a utility like traditional balancer. This is one of the significant achievements and uniqueness of the solution developed during the course of this research work.
Abstract: The POD makes possible to reduce the complete high-dimensional acoustic field to a low-dimensional subspace where different modes are identified and let reconstruct in a simple way a high percentage of the variance of the field.
Rotating modes are instabilities which are commonly observed in swirling flows. Such modes can appear under both cold and reacting conditions but that they have different sources: while the cold flow rotating mode is essentially hydrodynamic and corresponds to the wellknown PVC (precessing vortex core) observed in many swirled unconfined flows, the rotating structure observed for the reacting case inside the combustion chamber might be not hydrodynamically but acoustically controlled. The two transverse acoustic modes of the combustion chamber couple and create a rotating motion of the flame which leads to a self-sustained turning mode which has the features of a classical PVC but a very different source (acoustics and not hydrodynamics).
Abstract: In this paper, we present a localization of a mobile robot with localization modules which have two ceiling-view cameras in indoor environments. We propose two kinds of localization method. The one is the localization in the local space; we use the line feature and the corner feature between the ceiling and wall. The other is the localization in the large space; we use the natural features such as bulbs, structures on the ceiling. These methods are installed on the embedded module able to mount on the robot. The embedded module has two cameras to be able to localize in both the local space and the large spaces. The experiment is practiced in our indoor test-bed and a government office. The proposed method is proved by the experimental results.
Abstract: The planning in manufacturing system is becoming complicated day by day due to the expanding networks and shortage of skilled people to manage change. Consequently, faster lead time and rising demands for eco-efficient evaluation of manufacturing products and processes need exploitation of new and intelligent knowledge management concepts for manufacturing planning. This paper highlights motivation for incorporation of new features in the manufacturing planning system. Furthermore, it elaborates requirements for the development of intelligent knowledge management concept to support planning related decisions. Afterwards, the derived concept is presented in this paper considering two case studies. The first case study is concerned with the automotive ramp-up planning. The second case study specifies requirements for knowledge management system to support decisions in eco-efficient evaluation of manufacturing products and processes
Abstract: Structural interpretation of aeromagnetic data and Landsat imagery over the Middle Benue Trough was carried out to determine the depth to basement, delineate the basement morphology and relief, and the structural features within the basin. The aeromagnetic and Landsat data were subjected to various image and data enhancement and transformation routines. Results of the study revealed lineaments with trend directions in the N-S, NE-SW, NWSE and E-W directions, with the NE-SW trends been dominant. The depths to basement within the trough were established to be at 1.8, 0.3 and 0.8km, as shown from the spectral analysis plot. The Source Parameter Imaging (SPI) plot generated showed the centralsouth/ eastern portion of the study area as being deeper in contrast to the western-south-west portion. The basement morphology of the trough was interpreted as having parallel sets of micro-basins which could be considered as grabens and horsts in agreement with the general features interpreted by early workers.
Abstract: The purpose of the research is to investigate the energetic feature of the backpack load on soldier’s gait with variation of the trunk flexion angle. It is believed that the trunk flexion variation of the loaded gait may cause a significant difference in the energy cost which is often in practice in daily life. To this end, seven healthy Korea military personnel participated in the experiment and are tested under three different walking postures comprised of the small, natural and large trunk flexion. There are around 5 degree differences of waist angle between each trunk flexion. The ground reaction forces were collected from the force plates and motion kinematic data are measured by the motion capture system. Based on these data, the impulses, momentums and mechanical works done on the center of body mass (COM) during the double support phase were computed. The result shows that the push-off and heel strike impulse are not relevant to the trunk flexion change, however the mechanical work by the push-off and heel strike were changed by the trunk flexion variation. It is because the vertical velocity of the COM during the double support phase is increased significantly with an increase in the trunk flexion. Therefore, we can know that the gait efficiency of the loaded gait depends on the trunk flexion angle. Also, even though the gravitational impulse and pre-collision momentum are changed by the trunk flexion variation, the after-collision momentum is almost constant regardless of the trunk flexion variation.
Abstract: Intercalation imparts interesting features to the host graphite material. Two different types of intercalated compounds called (GIC-bisulphate or Nyex 1000 and GIC-nitrate or Nyex 3000) were tested for their adsorption capacity and ability to undergo electrochemical regeneration. It was found that Nyex 3000 showed comparatively slow kinetics along with reduced adsorption capacity to one half for acid violet 17 as adsorbate. Acid violet 17 was selected as model organic pollutant for evaluating comparative performance of said adsorbents. Both adsorbent materials showed 100% regeneration efficiency as achieved by passing a charge of 36 C g-1 at a current density of 12 mA cm-2 and a treatment time of 60 min.
Abstract: The exponential increase in the volume of medical image database has imposed new challenges to clinical routine in maintaining patient history, diagnosis, treatment and monitoring. With the advent of data mining and machine learning techniques it is possible to automate and/or assist physicians in clinical diagnosis. In this research a medical image classification framework using data mining techniques is proposed. It involves feature extraction, feature selection, feature discretization and classification. In the classification phase, the performance of the traditional kNN k nearest neighbor classifier is improved using a feature weighting scheme and a distance weighted voting instead of simple majority voting. Feature weights are calculated using the interestingness measures used in association rule mining. Experiments on the retinal fundus images show that the proposed framework improves the classification accuracy of traditional kNN from 78.57 % to 92.85 %.
Abstract: Image registration is the process of establishing point
by point correspondence between images obtained from a same
scene. This process is very useful in remote sensing, medicine,
cartography, computer vision, etc. Then, the task of registration is to
place the data into a common reference frame by estimating the
transformations between the data sets. In this work, we develop a
rigid point registration method based on the application of genetic
algorithms and Hausdorff distance. First, we extract the feature points
from both images based on the algorithm of global and local
curvature corner. After refining the feature points, we use Hausdorff
distance as similarity measure between the two data sets and for
optimizing the search space we use genetic algorithms to achieve
high computation speed for its inertial parallel. The results show the
efficiency of this method for registration of satellite images.
Abstract: The electrical power systems of aircrafts have made
serious progress in recent years because the aircrafts depend more
and more on the electricity. There is a trend in the aircraft industry to
replace hydraulic and pneumatic systems with electrical systems,
achieving more comfort and monitoring features and enlarging the
energetic efficiency. Thus, was born the concept More Electric
Aircraft. In this paper is analyzed the integration of a fuel cell into
the existing electrical generation and distribution systems of an
aircraft. The dynamic characteristics of fuel cell systems necessitate
an adaptation of the electrical power system. The architecture studied
in this paper consists of a 50kW fuel cell, a dc to dc converter and
several loads. The dc to dc converter is used to step down the fuel
cell voltage from about 625Vdc to 28Vdc.
Abstract: In this paper, an extreme learning machine with an automatic segmentation algorithm is applied to heart disorder classification by heart sound signals. From continuous heart sound signals, the starting points of the first (S1) and the second heart pulses (S2) are extracted and corrected by utilizing an inter-pulse histogram. From the corrected pulse positions, a single period of heart sound signals is extracted and converted to a feature vector including the mel-scaled filter bank energy coefficients and the envelope coefficients of uniform-sized sub-segments. An extreme learning machine is used to classify the feature vector. In our cardiac disorder classification and detection experiments with 9 cardiac disorder categories, the proposed method shows significantly better performance than multi-layer perceptron, support vector machine, and hidden Markov model; it achieves the classification accuracy of 81.6% and the detection accuracy of 96.9%.
Abstract: Utilizing echoic intension and distribution from different organs and local details of human body, ultrasonic image can catch important medical pathological changes, which unfortunately may be affected by ultrasonic speckle noise. A feature preserving ultrasonic image denoising and edge enhancement scheme is put forth, which includes two terms: anisotropic diffusion and edge enhancement, controlled by the optimum smoothing time. In this scheme, the anisotropic diffusion is governed by the local coordinate transformation and the first and the second order normal derivatives of the image, while the edge enhancement is done by the hyperbolic tangent function. Experiments on real ultrasonic images indicate effective preservation of edges, local details and ultrasonic echoic bright strips on denoising by our scheme.
Abstract: In this paper we propose a new classification method for automatic sleep scoring using an artificial neural network based decision tree. It attempts to treat sleep scoring progress as a series of two-class problems and solves them with a decision tree made up of a group of neural network classifiers, each of which uses a special feature set and is aimed at only one specific sleep stage in order to maximize the classification effect. A single electroencephalogram (EEG) signal is used for our analysis rather than depending on multiple biological signals, which makes greatly simplifies the data acquisition process. Experimental results demonstrate that the average epoch by epoch agreement between the visual and the proposed method in separating 30s wakefulness+S1, REM, S2 and SWS epochs was 88.83%. This study shows that the proposed method performed well in all the four stages, and can effectively limit error propagation at the same time. It could, therefore, be an efficient method for automatic sleep scoring. Additionally, since it requires only a small volume of data it could be suited to pervasive applications.