The photographic negative-mask method of neutralizing the diffraction-halo effect in speckle photography is applied to enhance the quality of Young's fringes obtained in particle-image-velocimetry studies. The improvement of the fringes achieved with the negative-mask method is compared with improvements by the commonly used method of analyzing a contact copy of particle-image-velocimetry specklegrams. Theoretical analysis and experimental results are presented. 相似文献
Organic light-emitting transistors (OLET) evolved from the fusion of the switching functionality of field-effect transistors (FET) with the light-emitting characteristics of organic light-emitting diode (OLED) that can simplify the active-matrix pixel device architecture and hence offer a promising pathway for future flat panel and flexible display technology. This review systematically analyzes the key device/molecular engineering tactics that assist in improving the electrode edge narrow emission to wide-area emission for display applications via three different topics, that is, narrow to wide-area emission, vertical architecture, and impact of high-κ dielectric on the device performance. Source–drain electrode engineering such as symmetric/asymmetric, planar/non-planar arrangement, semitransparent nature, multilayer approach comprising charge transport, and work function modification layers enable widening the emission zone. Vertical OLET architecture offers short channel lengths with a high aperture ratio, pixel type area emission, and stable light-emitting area. Transistors utilizing high-κ dielectric materials have assisted in lowering the operating voltage, enhancing luminance and air stability. The promising development in achieving wide-area emission provides a solid basis for constructing OLET research toward display applications; however, it relies on developing highly luminescent and fast charge transporting materials, suitable semitransparent source/drain electrodes, high-κ -dielectrics, and device architectural engineering. 相似文献
The mechanism of detecting the neurodegenerative disorder from Magnetic Resonance Images (MRIs) is one of the demanding and critical process in recent days. For this purpose, the existing works introduced some of the segmentation and classification techniques, which were used to detect the abnormal region from the brain images. However, it limits the problems of over segmentation, inefficient classification, and more complexity. The early predictions and the diagnosis process of neurodegenerative-disorders were accomplished by the use of segmentation and classification approaches of various methods. The proposed methodology focused on developing an integrated segmentation and classification techniques for an accurate brain disease classification. Here, the most extensively used segmentation techniques such Particle Swarm Optimization (PSO) and Self-Organizing Map (SOM) techniques are integrated for enabling an efficient image segmentation. In addition, it segments the Grey Matter (GM), White Matter (WM) and Cerebrospinal Fluid (CSF) regions. Consequently, the most suitable features are extracted from the segmented image by using the Neighbor Intensity Pattern (NIP) extraction technique. Based on these features, the normal and abnormal regions are classified by the use of an integrated Neural Network and K-Nearest Neighbor (KNN) classification techniques. The hybridization of the work is, that it integrates the benefits of various segmentation and classification techniques, which leads to increased detection efficiency and classification accuracy. The performance of these techniques are evaluated by using two different datasets such as ADNI and PPMI, which contains more number of brain MRIs. Also, various performance parameters have been utilized to test the results of the proposed system. Moreover, the traditional classification techniques are considered to compare the results of the proposed classification technique. During experimental evaluation, the performance of the techniques are validated by using different measures, and the results are compared with other existing techniques for analyzing the efficiency of proposed mechanism. At last, the results stated that the NN-KNN outperforms the other techniques by exactly locating the affected regions. The proposed framework exhibits the higher performance of accuracy level with 98.6%, sensitivity rate of 95%, exposed 96% of specificity rate and acquires the efficient precision rate of 99.21%. In future, this work can be expanded by using some advanced techniques for classifying other brain diseases.
Multimedia Tools and Applications - With the fast growing technologies in the field of remote sensing, hyperspectral image analysis has made a great breakthrough. It provides accurate and detailed... 相似文献
Multimedia Tools and Applications - Digital images are widely distributed today over the internet and through other mediums. There are powerful digital image processing tools which have made... 相似文献
Cyber Security Operations Center (CSOC) is a service-oriented system. Analysts work in shifts, and the goal at the end of each shift is to ensure that all alerts from each sensor (client) are analyzed. The goal is often not met because the CSOC is faced with adverse conditions such as variations in alert generation rates or in the time taken to thoroughly analyze new alerts. Current practice at many CSOCs is to pre-assign analysts to sensors based on their expertise, and the alerts from the sensors are triaged, queued, and presented to analysts. Under adverse conditions, some sensors have more number of unanalyzed alerts (backlogs) than others, which results in a major security gap for the clients if left unattended. Hence, there is a need to dynamically reallocate analysts to sensors; however, there does not exist a mechanism to ensure the following objectives: (i) balancing the number of unanalyzed alerts among sensors while maximizing the number of alerts investigated by optimally reallocating analysts to sensors in a shift, (ii) ensuring desirable properties of the CSOC: minimizing the disruption to the analyst to sensor allocation made at the beginning of the shift when analysts report to work, balancing of workload among analysts, and maximizing analyst utilization. The paper presents a technical solution to achieve the objectives and answers two important research questions: (i) detection of triggers, which determines when-to reallocate, and (ii) how to optimally reallocate analysts to sensors, which enable a CSOC manager to effectively use reallocation as a decision-making tool. 相似文献
Applied Intelligence - This paper proposes an optimal tuning of fractional order proportional integral derivative (FOPID) controller for higher order process using hybrid approach. The proposed... 相似文献
Pt(II) metal complexes are known to exhibit strong solid‐state aggregation and are promising for realization of efficient emission in fabrication of organic light emitting diodes (OLED) with nondoped emitter layer. Four pyrimidine–pyrazolate based chelates, together with four isomeric Pt(II) metal complexes, namely: [Pt(pm2z)2], [Pt(tpm2z)2], [Pt(pm4z)2], and [Pt(tpm4z)2], are isolated and systematically investigated for their structure–property relationships for practical OLED applications. Detailed single molecular and aggregated structures are revealed by photophysical and mechanochromic measurements, grazing‐incidence X‐ray diffraction, and theoretical approaches. These results suggest that these Pt(II) emitters pack like a deck of playing cards under vacuum deposition, and their emission energy is not only affected by the single molecular designs, but notably influenced by their intermolecular packing interaction, i.e., Pt···Pt separations that are arranged in the order: [Pt(tpm4z)2] > [Pt(pm4z)2] > [Pt(tpm2z)2] > [Pt(pm2z)2]. Nondoped OLED with emission ranging from green to red are prepared, to which the best performances are recorded for [Pt(tpm2z)2], giving maximum external quantum efficiency (EQE) of 27.5% at 103 cd m?2, maximum luminance of 2.5 × 105 cd m?2 at 17 V, and with stable CIEx,y of (0.56, 0.44). 相似文献
Schizophrenia (SZ) is a psychiatric disorder that especially affects individuals during their adolescence. There is a need to study the subanatomical regions of SZ brain on magnetic resonance images (MRI) based on morphometry. In this work, an attempt was made to analyze alterations in structure and texture patterns in images of the SZ brain using the level-set method and Laws texture features.
Materials and methods
T1-weighted MRI of the brain from Center of Biomedical Research Excellence (COBRE) database were considered for analysis. Segmentation was carried out using the level-set method. Geometrical and Laws texture features were extracted from the segmented brain stem, corpus callosum, cerebellum, and ventricle regions to analyze pattern changes in SZ.
Results
The level-set method segmented multiple brain regions, with higher similarity and correlation values compared with an optimized method. The geometric features obtained from regions of the corpus callosum and ventricle showed significant variation (p < 0.00001) between normal and SZ brain. Laws texture feature identified a heterogeneous appearance in the brain stem, corpus callosum and ventricular regions, and features from the brain stem were correlated with Positive and Negative Syndrome Scale (PANSS) score (p < 0.005).
Conclusion
A framework of geometric and Laws texture features obtained from brain subregions can be used as a supplement for diagnosis of psychiatric disorders.
