Suitability of VVC and HEVC for Video Telehealth Systems

Video compression in medical video streaming is one of the key technologies associated with mobile healthcare. Seamless delivery of medical video streams over a resource constrained network emphasizes the need of a video codec that requires minimum bitrates and maintains high perceptual quality. This paper presents a comparative study between High Efficiency Video Coding (HEVC) and its potential successor Versatile Video Coding (VVC) in the context of healthcare. A large-scale subjective experiment comprising of twenty-four non-expert participants is presented for eight different test conditions in Full High Definition (FHD) videos. The presented analysis highlights the impact of compression artefacts on the perceptual quality of HEVC and VVC processed videos. Our results and findings show that VVC clearly outperforms HEVC in terms of achieving higher compression, while maintaining high quality in FHD videos. VVC requires upto 40% less bitrate for encoding an FHD video at excellent perceptual quality. We have provided rate-quality curves for both encoders and a degree of overlap across both codecs in terms of perceptual quality. Overall, there is a 71% degree of overlap in terms of quality between VVC and HEVC compressed videos for eight different test conditions.     

Profiling Casualty Severity Levels of Road Accident Using Weighted Majority Voting

To determine the individual circumstances that account for a road traffic accident, it is crucial to consider the unplanned connections amongst various factors related to a crash that results in high casualty levels. Analysis of the road accident data concentrated mainly on categorizing accidents into different types using individually built classification methods which limit the prediction accuracy and fitness of the model. In this article, we proposed a multi-model hybrid framework of the weighted majority voting (WMV) scheme with parallel structure, which is designed by integrating individually implemented multinomial logistic regression (MLR) and multilayer perceptron (MLP) classifiers using three different accident datasets i.e., IRTAD, NCDB, and FARS. The proposed WMV hybrid scheme overtook individual classifiers in terms of modern evaluation measures like ROC, RMSE, Kappa rate, classification accuracy, and performs better than state-of-the-art approaches for the prediction of casualty severity level. Moreover, the proposed WMV hybrid scheme adds up to accident severity analysis through knowledge representation by revealing the role of different accident-related factors which expand the risk of casualty in a road crash. Critical aspects related to casualty severity recognized by the proposed WMV hybrid approach can surely support the traffic enforcement agencies to develop better road safety plans and ultimately save lives.     

Effective Classification of Synovial Sarcoma Cancer Using Structure Features and Support Vectors

In this research work, we proposed a medical image analysis framework with two separate releases whether or not Synovial Sarcoma (SS) is the cell structure for cancer. Within this framework the histopathology images are decomposed into a third-level sub-band using a two-dimensional Discrete Wavelet Transform. Subsequently, the structure features (SFs) such as Principal Components Analysis (PCA), Independent Components Analysis (ICA) and Linear Discriminant Analysis (LDA) were extracted from this sub-band image representation with the distribution of wavelet coefficients. These SFs are used as inputs of the Support Vector Machine (SVM) classifier. Also, classification of PCA + SVM, ICA + SVM, and LDA + SVM with Radial Basis Function (RBF) kernel the efficiency of the process is differentiated and compared with the best classification results. Furthermore, data collected on the internet from various histopathological centres via the Internet of Things (IoT) are stored and shared on blockchain technology across a wide range of image distribution across secure data IoT devices. Due to this, the minimum and maximum values of the kernel parameter are adjusted and updated periodically for the purpose of industrial application in device calibration. Consequently, these resolutions are presented with an excellent example of a technique for training and testing the cancer cell structure prognosis methods in spindle shaped cell (SSC) histopathological imaging databases. The performance characteristics of cross-validation are evaluated with the help of the receiver operating characteristics (ROC) curve, and significant differences in classification performance between the techniques are analyzed. The combination of LDA + SVM technique has been proven to be essential for intelligent SS cancer detection in the future, and it offers excellent classification accuracy, sensitivity, specificity.     

Local-Tetra-Patterns for Face Recognition Encoded on Spatial Pyramid Matching

Face recognition is a big challenge in the research field with a lot of problems like misalignment, illumination changes, pose variations, occlusion, and expressions. Providing a single solution to solve all these problems at a time is a challenging task. We have put some effort to provide a solution to solving all these issues by introducing a face recognition model based on local tetra patterns and spatial pyramid matching. The technique is based on a procedure where the input image is passed through an algorithm that extracts local features by using spatial pyramid matching and max-pooling. Finally, the input image is recognized using a robust kernel representation method using extracted features. The qualitative and quantitative analysis of the proposed method is carried on benchmark image datasets. Experimental results showed that the proposed method performs better in terms of standard performance evaluation parameters as compared to state-of-the-art methods on AR, ORL, LFW, and FERET face recognition datasets.     

Comparative photocatalytic performance and therapeutic applications of zinc oxide (ZnO) and neodymium-doped zinc oxide (Nd–ZnO) nanocatalysts against Acid Yellow-3 dye: kinetic and thermodynamic study of the reaction and effect of various parameters

Zinc oxide (ZnO) nanoparticles (NPs) were synthesized hydrothermally and doped with 4% Neodymium (Nd). The produced NPs were characterized using UV–Vis spectroscopy, X-ray diffraction (XRD), Energy dispersive X-ray analysis, Scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), Thermogravimetric analysis (TGA) and Photoluminescence (PL) spectroscopy. With the addition of 4% Nd, the bandgap reduced from 3.20 to 3.00 eV which confirmed successful doping with Nd which also evident from FTIR study. The XRD study showed hexagonal structure of the synthesized material, while SEM study confirmed that Nd-doped ZnO (Nd–ZnO) NPs are well dispersed as compare to ZnO. TGA study revealed that synthesized NPs were much stable to temperature and only 11.3% and 7.2% the total loss occurred during heating range (40–600 °C) in case of ZnO and Nd–ZnO NPs, respectively. The PL intensity of the visible peaks of ZnO reduced after doping with Nd. The degradation of Acid yellow-3 over both the catalysts followed first-order kinetics. The activation energy calculated for the photodegradation reaction was 43.8 and 33.7 kJ/mol using pure ZnO and Nd–ZnO NPs, respectively. About 91% and 80% dye was degraded at the time interval of 160 min using Nd–ZnO and ZnO NPs, respectively. High percent degradation of dye was found at low concentration (10 ppm) and at optimal dosage (0.035 g) of the catalyst. The rate of Acid yellow-3 dye degradation was found to increase with increase in temperature (up to 50 °C) and pH(8) of the medium. The recyclability study showed that both pure ZnO and Nd–ZnO NPs could be reused for the degradation of the given dye. With the addition of H₂O₂ up to 5 µL, the rate of reaction increased clearly indicating the effect of OH^· generation during photocatalysis. When compared with Nd–ZnO NPs at low concentrations, ZnO NPs at higher concentrations were found to be less hazardous. Both the NPs showed best antibacterial activities against Staphylococcus aureus. The hemolytic study indicated that at low concentration, pure ZnO was non-hemolytic as compared to Nd–ZnO.

     

Structural,optical and dielectric properties of aluminum-substituted SrAl2xFe12-2xO19 x = (0.0,0.2,0.4,0.6,0.8,1.0) M-type hexagonal ferrites

Journal of Materials Science: Materials in Electronics - Aluminum-substituted M-type hexaferrites with nominal composition SrAl2xFe12-2xO19 with x?=?(0.0,0.2,0.4,0.6,0.8,1.0) were...     

Egocentric visual scene description based on human-object interaction and deep spatial relations among objects

Multimedia Tools and Applications - Visual Scene interpretation is one of the major areas of research in the recent past. Recognition of human object interaction is a fundamental step...     

CDCSS: cluster-based distributed cooperative spectrum sensing model against primary user emulation (PUE) cyber attacks

In cognitive radio network, the secondary users (SUs) use the spectrum of primary users for communication which arises the security issues. The status of SUs as legitimate users is compulsory for the stability of the system. This paper addresses the issue of delay caused by a band-selection decision process that directly affects the security and performance. The model cluster-based distributed cooperative spectrum sensing is proposed. In this model, cluster heads (CHs) exchange control information with other CHs and ordinary nodes. This model significantly reduced the delay, sensing, convergence, routing, in band-selection process. This also reduces the energy consumption while sensing the spectrum which seriously leads to performance upgradation. The simulated results show the improved performance of cognitive radio networks in terms of delay, packet loss ratio and bandwidth usage as compared to cluster-based cooperative spectrum sensing model. The opportunity for primary user emulation attacker is minimized as the overall delay is reduced.