Study of Lateral Spread of Ions Emitted from 2.3 kJ Plasma Focus with Hydrogen and Nitrogen Gases

The characteristics of ion beams of hydrogen and nitrogen with different filling pressures emitted from the plasma focus device of 2.3 kJ energy are investigated. CR-39 SSNTDs are employed for the registration of tracks of ions. The exposed detectors are etched in 6N NaOH solution at 70°C and then examined with an optical microscope. The ion flux is estimated to be of the order of 10^5–6 tracks/cm². The flux with the radial position does not exhibit any regular pattern of variation.     

On the application of Harris hawks optimization (HHO) algorithm to the design of microchannel heat sinks

Engineering with Computers - A novel Harris hawks optimization algorithm is applied to microchannel heat sinks for the minimization of entropy generation. In the formulation of the heat transfer...     

Multi-objective optimization of a hatchback rear end utilizing fractional factorial design algorithm

Air flow has significant effects on fuel consumption, performance, and comfort. Decreasing drag coefficient enhances fuel consumption and vehicle performance. Moreover, omitting or reducing the power of aerodynamic noise sources provides passengers comfort. In this paper, optimization of a hatchback rear end is conducted considering drag and aerodynamic noise objectives. To this end, five geometrical parameters of the hatchback rear end are chosen as design variables in two levels. Numerical simulation is applied to survey air flow features around the models in the wind tunnel. To reduce the number of runs, fraction factorial design algorithm is applied to generate layout of the simulations which decreased the number of case studies to half. Main and interaction effects of these factors on drag coefficient and acoustic power of the rear end source are derived using analysis of variance. Optimum level for each parameter is chosen considering simultaneous drag and noise goals. Finally, characteristics of air flow and acoustic power around optimum model are discussed.

     

Mental imagery classification using one-dimensional convolutional neural network for target selection in single-channel BCI-controlled mobile robot

Neural Computing and Applications - This paper introduces the use of the one-dimensional convolutional neural network (1D-CNN) for end-to-end EEG decoding with application towards a BCI system with...     

Hyperspectral imaging-based unsupervised adulterated red chili content transformation for classification: Identification of red chili adulterants

Neural Computing and Applications - Preserving red-chili quality is of utmost importance in which the authorities demand quality techniques to detect, classify, and prevent it from impurities. For...     

Deep learning controller for nonlinear system based on Lyapunov stability criterion

Neural Computing and Applications - For the current paper, the technique of feed-forward neural network deep learning controller (FFNNDLC) for the nonlinear systems is proposed. The FFNNDLC...     

Lung nodules detection using semantic segmentation and classification with optimal features

Neural Computing and Applications - Lung cancer is a deadly disease if not diagnosed in its early stages. However, early detection of lung cancer is a challenging task due to the shape and size of...     

Full-state neural network observer-based hybrid quantum diagonal recurrent neural network adaptive tracking control

Neural Computing and Applications - This study introduces a neural network (NN) adaptive tracking controller-based reinforcement learning (RL) scheme for unknown nonlinear systems. First, an...     

An FEC scheme combined with weighted scheduling to reduce multicast packet loss in IPTV over PON

Congestion is one of the most important challenges in optical networks. In a Passive Optical Network (PON), the Optical Line Terminal (OLT) is a bottleneck and congestion prone. In this paper, a framework is proposed with Forward Error Correction (FEC) at the IP layer combined with Weighted Round Robin (WRR) at the scheduling level to overcome packet-loss due to congestion in the OLT in order to achieve efficient video multicasting over PON. In the FEC scheme, Reed-Solomon (RS(n,k)) with erasure coding is used, where (n−k) erroneous symbols per n symbol blocks can be corrected. In our framework, an Internet Protocol TeleVision (IPTV) service provider uses the mentioned RS coding and generates redundant packets from regular IPTV packets in such a way that an Optical Network Unit (ONU) can recover lost packets from received packets, thus resulting in a better video quality. Simulation results show that using the proposed framework, an ONU can recover many lost packets and achieve better video quality under different traffic loads for its users. For instance, the proposed method can reduce packet loss rate by almost 55% and 10% under traffic load 0.9, respectively, compared with the Round Robin (RR) and WRR methods under symmetric traffic load. When High Receivers Queue (HRQ) traffic (i.e., traffic received by many users) is twice Low Receivers Queue (LRQ) traffic (i.e., traffic received by a small number of users), this reduction is almost 86% and 30% under traffic load 0.9. Finally, when LRQ traffic is twice HRQ traffic, the reduction in packet loss rate is almost 70% and 91% at traffic load 0.5.     

An intrinsic semantic framework for recognizing image objects

The paper proposes a new approach to find semantic meanings in visual object class structure, in line with the Gestalt laws of proximity. Micro level semantic structures are formed by line segments (arcs also approximated into line segments based on pixel deviation threshold) which are in close proximity. These structures are hierarchically combined till a semantic label can be assigned. The algorithm extracts semantic groups, their inter-relations and represents these using a graph. Invariant geometrical properties of the groups and relations are used as vertex and edge labels. A graph model captures the inter class variability by analyzing the repetitiveness of structures and relations and uses it as a weighting factor for classification. The algorithm has been tested on a standard benchmark database and compared with existing approaches.