A novel prognosis and segmentation of necrosis (dead cells) in contrast enhanced T1-weighted glioblastoma tumor with automatic contextual clustering

In recent years, mortality rate with high-grade tumor has been increased significantly especially with glioblastoma (GBM) brain tumor while compared to other malignant brain tumor. Here, the amount of dead cells accommodated with the tumor tissue in GBM brain tumor play a vital task and necessitate an earlier diagnosis of malignancy with the GBM tumor. It inspires to implement new automatic diagnosis system which detects the dead cells and tumor tissue with the GBM brain tumor, such that the survival rate of the diseased can easily be prognosis by the Radiologist and Neurosurgeon. The main objective of this article is to detect the amount of dead cells with respect to tumor tissue associated with the GBM brain tumor which desires the impact factor of the brain tumor. In this framework, initially, the new contrast enhancement modality is incorporated to enhance the gray information over the dead cells and the tumor tissue with the T1-weighted MRI GBM brain tumor. In this enhancement, the edges of the tumor cells and its dead cells are magnified efficiently. As the noises and outliers with MR image is unpredictable and it experiences the variable amount of noises over the local window, the contextual information over each pixel of the image is adaptively modified with respect to the amount of noise over local window using adaptive contextual clustering. The performance evaluation of the framework is investigated which exhibits the overwhelming result compared to conventional detection techniques.     

Real-time medical video processing, enabled by hardware accelerated correlations

Image processing involving correlation based filter algorithms have proved extremely useful for image enhancement, feature extraction and recognition, in a wide range of medical applications, but is almost exclusively used with still images due to the amount of computations required by the correlations. In this paper, we present two different practical methods for applying correlation-based algorithms to real-time video images, using hardware accelerated correlation, as well as our results in applying the method to optical venography. The first method employs a GPU accelerated personal computer, while the second method employs an embedded FPGA. We will discuss major difference between the two approaches, and their suitability for clinical use. The system presented detects blood vessels in human forearms in images from NIR camera setup for the use in a clinical environment.     

Design and Analysis of Microstrip Patch Antenna Using Periodic EBG Structure for C-Band Applications

Wireless Personal Communications - In this paper, an Electromagnetic Band Gap structured microstrip patch antenna is presented. The proposed antenna consists of a rectangular patch which is fed by...     

Complementary‐based coalition formation for energy microgrids

In recent years, the notion of electrical energy microgrids (MGs), in which communities share their locally generated power, has gained increasing interest. Typically, the energy generated comes from renewable resources, which means that its availability is variable, ie, sometimes there may be energy surpluses and at other times energy deficits. This energy variability can be ameliorated by trading energy with a connected electricity grid. However, since main electricity grids are subject to faults or other outages, it can be advantageous for energy MGs to form coalitions and share their energy among themselves. In this work, we present our model for the dynamic formation of such MG coalitions. In our model, MGs form coalitions on the basis of complementary weather patterns. Our agent‐based model, which is scalable and affords autonomy among the MGs participating in the coalition (agents can join and depart from coalitions at any time), features methods to reduce overall “discomfort” so that, even when all participating MGs in a coalition experience deficits, they can share energy so that their overall discomfort is reduced. We demonstrate the efficacy of our model by showing empirical studies conducted with real energy production and consumption data.     

A comprehensive analysis on the state‐of‐the‐art developments in reflectarray,transmitarray, and transmit‐reflectarray antennas

Contemplating the advancements in communication technology, the analysis of the features of reflectarray, transmitarray, and transmit‐reflectarray becomes essential for future adaptability. This article presents a thorough review of such high‐gain antennas, presenting some of the most relevant solutions published by the scientific society in the field of antennas and wave propagation. Several examples of unit cells for array implementation and complete array designs discussed in various literatures are analyzed. The analysis is focused in identifying the unit cell layouts, such as those developed using microstrip patches, frequency selective surfaces, or metamaterials. The analysis is extended to the ways of improving bandwidth, for example, true time delay elements, phase delay lines, meander lines, and so on, and the various methods used to enable reconfiguration, for example, p‐i‐n diodes, varactor diodes, or microelectromechanical systems. In addition, some antennas, which produce bidirectional beams simultaneously, are also discussed. Finally, all the models are compared against each other in order to highlight their benefits and limitations, summarizing their main characteristics, such as the frequency of operation, bandwidth, phase range, gain, aperture efficiency, sidelobe levels, cross polarization levels, and maximum beam‐steering range.     

A compact tri‐band microwave resonator for ethanol gas detection

This article presents the design, simulation, fabrication, and testing of a compact two‐port microwave resonator coated with nanomaterials for ethanol gas sensing applications. The proposed gas sensor consists of a transmission line loaded with three triangular split ring resonators for ethanol detection at three frequency bands viz. 2.2, 4.6, and 6.3 GHz. The transmission line has all‐pass characteristics in which band gaps are introduced using three split ring resonators. The TiO₂ and ZnO nanorods are used as sensitive layers for the proposed sensing application. The nanorods, which are grown on a glass substrate of thickness 1 mm, are loaded on to the two‐port microwave resonator making the device sensitive to ethanol. The microwave behavior of the sensor is analyzed using the scattering parameters. The absorption of the ethanol gas causes frequency detuning which is used to analyze the presence of ethanol and its concentration. From the experiments, it is understood that there is an increase in the frequency shift with an increase in the concentration of ethanol gas. The sensing device with ZnO as a sensitive layer showed a higher average sensitivity of 2.35 compared to TiO₂ whose average sensitivity is 1.29.     

Automatically generating taxonomy for grouping app reviews — a study of three apps

Software Quality Journal - App reviews often reflect end-users’ requests, issues or suggestions for supporting app maintenance and evolution. Hence, researchers have evaluated several...     

An efficient and automatic glioblastoma brain tumor detection using shift‐invariant shearlet transform and neural networks

The detection and segmentation of tumor region in brain image is a critical task due to the similarity between abnormal and normal region. In this article, a computer‐aided automatic detection and segmentation of brain tumor is proposed. The proposed system consists of enhancement, transformation, feature extraction, and classification. The shift‐invariant shearlet transform (SIST) is used to enhance the brain image. Further, nonsubsampled contourlet transform (NSCT) is used as multiresolution transform which transforms the spatial domain enhanced image into multiresolution image. The texture features from grey level co‐occurrence matrix (GLCM), Gabor, and discrete wavelet transform (DWT) are extracted with the approximate subband of the NSCT transformed image. These extracted features are trained and classified into either normal or glioblastoma brain image using feed forward back propagation neural networks. Further, K‐means clustering algorithm is used to segment the tumor region in classified glioblastoma brain image. The proposed method achieves 89.7% of sensitivity, 99.9% of specificity, and 99.8% of accuracy.     

A new benchmark for pose estimation with ground truth from virtual reality

The development of programming paradigms for industrial assembly currently gets fresh impetus from approaches in human demonstration and programming-by-demonstration. Major low- and mid-level prerequisites for machine vision and learning in these intelligent robotic applications are pose estimation, stereo reconstruction and action recognition. As a basis for the machine vision and learning involved, pose estimation is used for deriving object positions and orientations and thus target frames for robot execution. Our contribution introduces and applies a novel benchmark for typical multi-sensor setups and algorithms in the field of demonstration-based automated assembly. The benchmark platform is equipped with a multi-sensor setup consisting of stereo cameras and depth scanning devices (see Fig. 1). The dimensions and abilities of the platform have been chosen in order to reflect typical manual assembly tasks. Following the eRobotics methodology, a simulatable 3D representation of this platform was modelled in virtual reality. Based on a detailed camera and sensor simulation, we generated a set of benchmark images and point clouds with controlled levels of noise as well as ground truth data such as object positions and time stamps. We demonstrate the application of the benchmark to evaluate our latest developments in pose estimation, stereo reconstruction and action recognition and publish the benchmark data for objective comparison of sensor setups and algorithms in industry.