Fine grinding of thermoplastics by high speed friction grinding assisted by guar gum

High speed friction grinding has been used to grind plant and food substances in water but never been explored for grinding of thermoplastics like polylactic acid (PLA), low and high density polyethylene and polypropylene. Such grinding was investigated in this work and was made possible by using 0.5% guar gum solution instead of just water because increasing the viscosity of water reduced their settling and the speed of passing through the grinder. Tensile, flexural, and impact strengths of the plastics were studied and higher grinding efficiency of PLA could be explained by its low elongation-at-break compared to low density polyethylene, high density polyethylene, and polypropylene. The microplastics (2000–45 μm) were studied for mass and particle size distributions and by scanning electron microscopy, ¹³C CP/MAS NMR, Fourier transform infrared spectroscopy, differential scanning calorimetry, and thermogravimetric analysis. In addition, viscosity of guar gum and contact angles was measured. This new technology can produce finely ground microplastics (710–45 μm) for a variety of applications.     

Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals

Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals; these signals can be recorded, processed and classified into different hand movements, which can be used to control other IoT devices. Classification of hand movements will be one step closer to applying these algorithms in real-life situations using EEG headsets. This paper uses different feature extraction techniques and sophisticated machine learning algorithms to classify hand movements from EEG brain signals to control prosthetic hands for amputated persons. To achieve good classification accuracy, denoising and feature extraction of EEG signals is a significant step. We saw a considerable increase in all the machine learning models when the moving average filter was applied to the raw EEG data. Feature extraction techniques like a fast fourier transform (FFT) and continuous wave transform (CWT) were used in this study; three types of features were extracted, i.e., FFT Features, CWT Coefficients and CWT scalogram images. We trained and compared different machine learning (ML) models like logistic regression, random forest, k-nearest neighbors (KNN), light gradient boosting machine (GBM) and XG boost on FFT and CWT features and deep learning (DL) models like VGG-16, DenseNet201 and ResNet50 trained on CWT scalogram images. XG Boost with FFT features gave the maximum accuracy of 88%.     

An Algebraic QoS-Based Resource Allocation Model for Competitive Multimedia Applications

A new class of multimedia applications require new mechanisms to consider various Quality of Services with respect to resource constraints so that they could support reliable services and utilize available resources optimally. In this paper we present a new analytical and generic resource management model that is QoS-based. The approach for resource allocation and relevant algorithms is based on a mathematically proved model that manages resource and QoS allocation intelligently so that the total system utility of is maximized. We use the constructs of application benefit functions and resource demand functions to represent the system configuration and to solve the resource allocation problems. Extensive surveys on the related work including systematic and analytical approaches are also presented.     

Robust object tracking with background-weighted local kernels

Object tracking is critical to visual surveillance, activity analysis and event/gesture recognition. The major issues to be addressed in visual tracking are illumination changes, occlusion, appearance and scale variations. In this paper, we propose a weighted fragment based approach that tackles partial occlusion. The weights are derived from the difference between the fragment and background colors. Further, a fast and yet stable model updation method is described. We also demonstrate how edge information can be merged into the mean shift framework without having to use a joint histogram. This is used for tracking objects of varying sizes. Ideas presented here are computationally simple enough to be executed in real-time and can be directly extended to a multiple object tracking system.     

Discontinuity-Preserving Surface Reconstruction Using Stochastic Differential Equations

We address the problem of reconstructing a surface from irregularly spaced sparse and noisy range data while concurrently identifying and preserving the significant discontinuities in depth. It is well known that, starting from either the probabilistic Markov random field model or the mechanical membrane or thin plate model for the surface, the solution of the reconstruction problem can be eventually reduced to the global minimization of a certain “energy” function. Requiring the preservation of depth discontinuities makes the energy function nonconvex and replete with multiple local minima. We present a new method for obtaining discontinuity-preserving reconstruction based on the numerical solution of an appropriate Ito vector stochastic differential equation (SDE). The reconstructed surface is found by following the sample path of the (stochastic) diffusion process that solves the SDE in question. Our central contribution is the demonstration of the efficacy of the stochastic differential equation technique for solving a vision problem. Through comparisions of the results of our method to those of the two well-known existingglobalminimization based reconstruction techniques, we show a significant improvement in the final reconstructions obtained.     

The Intratumor Bacterial and Fungal Microbiome Is Characterized by HPV,Smoking, and Alcohol Consumption in Head and Neck Squamous Cell Carcinoma

Head and neck squamous cell carcinoma (HNSCC) tumor phenotypes and clinical outcomes are significantly influenced by etiological agents, such as HPV infection, smoking, and alcohol consumption. Accordingly, the intratumor microbiome has been increasingly implicated in cancer progression and metastasis. However, few studies characterize the intratumor microbial landscape of HNSCC with respect to these etiological agents. In this study, we aimed to investigate the bacterial and fungal landscape of HNSCC in association with HPV infection, smoking, and alcohol consumption. RNA-sequencing data were extracted from The Cancer Genome Atlas (TCGA) regarding 449 tissue samples and 44 normal samples. Pathoscope 2.0 was used to extract the microbial reads. Microbe abundance was compared to clinical variables, oncogenic signatures, and immune-associated pathways. Our results demonstrated that a similar number of dysregulated microbes was overabundant in smokers and nonsmokers, while heavy drinkers were characterized by an underabundance of dysregulated microbes. Conversely, the majority of dysregulated microbes were overabundant in HPV+ tumor samples when compared to HPV- tumor samples. Moreover, we observed that many dysregulated microbes were associated with oncogenic and metastatic pathways, suggesting their roles in influencing carcinogenesis. These microbes provide insights regarding potential mechanisms for tumor pathogenesis and progression with respect to the three etiological agents.     

Experimental evaluation of loss perception in continuous media

Perception of multimedia quality, specified by quality-of-service (QoS) metrics, can be used by system designers to optimize customer satisfaction within resource bounds enforced by general-purpose computing platforms. Media losses, rate variations and transient synchronization losses have been suspected to affect human perception of multimedia quality. This paper presents metrics to measure such defects, and results of a series of user experiments that justify such speculations. Results of the study provide bounds on losses, rate variations and transient synchronization losses as a function of user satisfaction, in the form of Likert values. It is shown how these results can be used by algorithm designers of underlying multimedia systems.     

Robust one-time password authentication scheme using smart card for home network environment

Due to the exponential growth of the Internet users and wireless devices, interests on home networks have been enormously increased in recent days. In digital home networks, home services including remote access and control to home appliances as well as services offered by service providers are alluring. However, the remote control services cause digital home networks to have various security threats. Hence, for digital home networks, robust security services, especially remote user authentication, should be considered. This paper presents a robust and efficient authentication scheme based on strong-password approach to provide secure remote access in digital home network environments. The proposed scheme uses lightweight computation modules including hashed one-time password and hash-chaining technique along with low-cost smart card technology. It aims to satisfy several security requirements including stolen smart card attack and forward secrecy with lost smart card as well as functional requirements including no verification table and no time synchronization. Comparing with the existing representative schemes, it can be validated that the proposed scheme is more robust authentication mechanism having better security properties. We have conducted formal verification of the proposed scheme.     

Software development for the 8052AH-basic microcontroller

Application software for Intel's 8052AH-basic microcontroller can be developed in MCS basic-52, an enhanced version of basic, using the onchip basic interpreter. The paper explores the 8052AH-basic device and its language in comparison with the standard Microsoft basic and presents some software routines and other guidelines for programmers. Control-oriented applications, as opposed to conventional number-crunching applications, are emphasized. The paper also discusses how the resources of the Intellec development system can be used to facilitate the development of application software.     

Multidisciplinary approach to defining public e-procurement and evaluating its impact on procurement efficiency

Utilising the methodology of content analysis, this study uses a multidisciplinary approach to define public e-procurement. Various aspects of e-procurement have been discussed from information systems, supply chain management, electronic commerce/electronic government, and public procurement to come up with an integrated definition of public e-procurement. Following this, e-procurement assimilation has been defined and its impact on procurement efficiency has been evaluated. Following the confirmatory factor analysis in structural equation modelling, dimensional level analysis in ANOVA has been undertaken for the three forms of e-procurement technologies namely e-tendering, e-catalogue management systems, and e-marketplace. The results show the positive and significant impact of the assimilation process on procurement efficiency.