Familiar/unfamiliar face classification from EEG signals by utilizing pairwise distant channels and distinctive time interval

The aim of the study is to classify single trial electroencephalogram and to estimate active regions/locations on skull in unfamiliar/familiar face recognition task. For this purpose, electroencephalographic signals were acquired from ten subjects in different sessions. Sixty-one familiar and fifty-nine unfamiliar face stimuli were shown to the subjects in the experiments. Since channel responses are different for familiar and unfamiliar classes, the channels discriminating the classes were investigated. To do so, three distances and four similarity measures were employed to assess the most distant channel pairs between familiar and unfamiliar classes for a 1-s time duration; 0.6 s from the stimulus to 1.6 s in a channel selection process. It is experimentally observed that this time interval is maintaining the greatest distance between two categories. The electroencephalographic signals were classified using the determined channels and time interval to measure accuracy. The best classification accuracy was 81.30% and was obtained with the Pearson correlation as channel selection method. The most discriminative channel pairs were selected from prefrontal regions.     

Neuro-adaptive cooperative tracking control with prescribed performance of unknown higher-order nonlinear multi-agent systems

This paper is concerned with the design of a distributed cooperative synchronisation controller for a class of higher-order nonlinear multi-agent systems. The objective is to achieve synchronisation and satisfy a predefined time-based performance. Dynamics of the agents (also called the nodes) are assumed to be unknown to the controller and are estimated using neural networks. The proposed robust neuro-adaptive controller drives different states of nodes systematically to synchronise with the state of the leader node within the constraints of the prescribed performance. The nodes are connected through a weighted directed graph with a time-invariant topology. Only few nodes have access to the leader. Lyapunov-based stability proofs demonstrate that the multi-agent system is uniformly ultimately bounded stable. Highly nonlinear heterogeneous networked systems with uncertain parameters and external disturbances were used to validate the robustness and performance of the new novel approach. Simulation results considered two different examples: single-input single-output and multi-input multi-output, which demonstrate the effectiveness of the proposed controller.     

Comparison of total ozone column observations from space-borne Ozone Monitoring Instrument with ground-based Dobson Ozone Spectrophotometer measurements at an urban location in Indo-Gangetic Basin

This article presents a comparison analysis of OMIT (Ozone Monitoring Instrument retrieved overpass total ozone column (TOC)), and DOST (Dobson Ozone Spectrophotometer observed TOC) over Delhi during a period from October 2004 to June 2011. Megacity Delhi, located in Indo-Gangetic Basin, is an important site for comparison of ground-based and satellite retrieved TOCs due to significant anthropogenic emissions of ozone precursors, large shift in seasons, and large-scale crop residue burning in the region. DOST and OMIT data show an overall bias of 3.07% and significant correlation with coefficient of determination R² = 0.73. Large seasonal fluctuations in the biases and correlations have been observed ranging from 2.46% (winter) to 3.82% (spring), and R² = 0.84 (winter) to R² = 0.09 (summer), respectively. The large biases are attributed to changes in temperature, cloud cover, pollutants emissions from urban area, and crop-residue burning events. We also find notable variations in correlations between the datasets due to the varying burden of absorbing aerosols from open field crop-residue burning. The R² has changed from 0.67 (for aerosol optical depth, AOD 1.5–3.5) to 0.77 (for AOD 0–0.99). The dependence of the bias on solar zenith angle, cloud fraction, and satellite distance is also discussed. A simple linear regression analysis is applied to check the linkage between DOST and OMIT. The influence of atmospheric air temperature and relative humidity on OMIT at different pressure levels between 1000 and 20 hPa has been discussed.     

Process cycle optimization in press forming of paperboard

Three‐dimensional forming of paperboard is commonly done by a press forming process, which has been widely researched. However, the process cycle of the press forming of paperboard trays has not yet been completely optimized due to limited adjustability and unsophisticated mechanical structure of commonly used forming equipment. The object of this study is to optimize the force curve of stroke to improve product quality without compromising the production rate using LUT Adjustable Packaging Line prototype for practical evaluation of formability and multibody dynamics software MSC Adams for simulation. Several process parameters were investigated with different process cycles to study their effect on the quality of the formed products. With optimized male mould speed, acceleration and speed of the blank could be reduced, which led to significantly reduced rupturing tendency without compromising the production speed. It was also found that a constant blank holding force should be used to achieve acceptable tray quality, although this results in a significantly increased surface pressure applied to the formed substrate. It was also discovered that a novel male mould attachment, which included a pressing force adjustment system that utilized a spring set with an adjustable preload, made it possible to control the force distribution between different forming tools during the dwelling phase of the process cycle in an improved way. Utilization of the method used in this paper leads to a better tray quality without compromising the production efficiency. Furthermore, this can increase the number of possible package applications for sustainable materials, such as paperboard.     

Deep Learning and Machine Learning-Based Model for Conversational Sentiment Classification

In the current era of the internet, people use online media for conversation, discussion, chatting, and other similar purposes. Analysis of such material where more than one person is involved has a spate challenge as compared to other text analysis tasks. There are several approaches to identify users’ emotions from the conversational text for the English language, however regional or low resource languages have been neglected. The Urdu language is one of them and despite being used by millions of users across the globe, with the best of our knowledge there exists no work on dialogue analysis in the Urdu language. Therefore, in this paper, we have proposed a model which utilizes deep learning and machine learning approaches for the classification of users’ emotions from the text. To accomplish this task, we have first created a dataset for the Urdu language with the help of existing English language datasets for dialogue analysis. After that, we have preprocessed the data and selected dialogues with common emotions. Once the dataset is prepared, we have used different deep learning and machine learning techniques for the classification of emotion. We have tuned the algorithms according to the Urdu language datasets. The experimental evaluation has shown encouraging results with 67% accuracy for the Urdu dialogue datasets, more than 10, 000 dialogues are classified into five emotions i.e., joy, fear, anger, sadness, and neutral. We believe that this is the first effort for emotion detection from the conversational text in the Urdu language domain.     

Degradable Bottlebrush Polypeptides and the Impact of their Architecture on Cell Uptake,Pharmacokinetics, and Biodistribution In Vivo

Bottlebrush polymers are highly promising as unimolecular nanomedicines due to their unique control over the critical parameters of size, shape and chemical function. However, since they are prepared from biopersistent carbon backbones, most known bottlebrush polymers are non-degradable and thus unsuitable for systemic therapeutic administration. Herein, we report the design and synthesis of novel poly(organo)phosphazene-g-poly(α-glutamate) (PPz-g-PGA) bottlebrush polymers with exceptional control over their structure and molecular dimensions (Dh ≈ 15–50 nm). These single macromolecules show outstanding aqueous solubility, ultra-high multivalency and biodegradability, making them ideal as nanomedicines. While well-established in polymer therapeutics, it has hitherto not been possible to prepare defined single macromolecules of PGA in these nanosized dimensions. A direct correlation was observed between the macromolecular dimensions of the bottlebrush polymers and their intracellular uptake in CT26 colon cancer cells. Furthermore, the bottlebrush macromolecular structure visibly enhanced the pharmacokinetics by reducing renal clearance and extending plasma half-lives. Real-time analysis of the biodistribution dynamics showed architecture-driven organ distribution and enhanced tumor accumulation. This work, therefore, introduces a robust, controlled synthesis route to bottlebrush polypeptides, overcoming limitations of current polymer-based nanomedicines and, in doing so, offers valuable insights into the influence of architecture on the in vivo performance of nanomedicines.     

Deep learning neural network for texture feature extraction in oral cancer: enhanced loss function

Multimedia Tools and Applications - The use of a binary classifier like the sigmoid function and loss functions reduces the accuracy of deep learning algorithms. This research aims to increase the...     

Image and video denoising by combining unsupervised bounded generalized gaussian mixture modeling and spatial information

Multimedia Tools and Applications - In recent years, a great deal of effort has been expended on developing robust solutions for images quality degradation caused mainly by noise. In this paper, we...     

Multi- Objective Optimal Design of on- Demand Pressurized Irrigation Networks

In the context of water as an economic good, from the use of water, one can derive a value, which can be affected by the reliability of supply. On-demand irrigation systems provide valuable water to skilled farmers who have the capacity to maximize economic value of water. In this study, simultaneous optimization of on-demand irrigation network layout and pipe sizes is considered taking into account both investment and annual energy costs. The optimization problem is formulated as a problem of searching for the upstream head value, which minimizes the total cost (investment and energy costs) of the system. The investment and annual energy costs are obtained in two separate phases. Max–Min ant system (MMAS) algorithm is used to obtain the minimum cost design considering layout and pipe diameters of the network simultaneously. Clement methodology is used to determine flow rates of pipelines at the peak period of irrigation requirements. The applicability of the proposed method is showed by re-designing a real world example from literature.