Object-based hyperspectral image classification using a new latent block model based on hidden Markov random fields

Pattern Analysis and Applications - Efficient high-dimensional analyses of hyperspectral datasets and their utilization within classification algorithms is a popular topic in the field of data...     

Design of bandpass–bandpass diplexers using rectangular-, T-, and L-shaped resonators for hybrid power amplifier and 5G applications

Analog Integrated Circuits and Signal Processing - In this paper, two bandpass–bandpass diplexers, based on L-, T- and rectangular-shaped resonators are designed and analyzed, which are used...     

On the modeling of bending responses of graphene-reinforced higher order annular plate via two-dimensional continuum mechanics approach

This research presents bending responses of FG-GPLRC plates based upon higher order shear deformation theory (HSDT) for various sets of boundary conditions. The rule of the mixture and modified Halpin–Tsai model are engaged to provide the effective material constant of the composite layers. By employing Hamilton’s principle, the governing equations of the structure are derived and solved with the aid of the differential quadrature method (DQM). Afterward, a parametric study is done to present the effects of three kinds of FG patterns, weight fraction of the GPLs, radius ratio, and thickness to inner radius ratio on the bending characteristics of the FG-GPLRC disk. Numerical results reveal that in the initial value of the \(Zt/h\), using more GPLs for reinforcing the structure provides an increase in the normal stresses but this matter is inverse for the higher value of the \(Zt/h\). The results show that considering the smaller radius ratio is a reason for boosting the shear stresses of the structure for each \(Zt/h\). Another consequence is that for the negative value of \(Zt/h\), it is true that by increasing \(h/{R}_{i}\) , the normal stresses increases but if there is positive value for \(Zt/h\), the radial and circumferential stresses fall down by having an increase in the \(h/{R}_{i}\).

     

A review of machine learning applications in IoT-integrated modern power systems

This paper aims to provide a systematic and comprehensive survey of state-of-the-art machine learning techniques and their potential applications in IoT-integrated power systems.     

Removal of iron from milk of lime to produce pure precipitated calcium carbonate

ABSTRACT

Milk of lime (MOL) suspension is widely utilized in many industries; however, in some of the applications, such as the production of precipitated calcium carbonate (PCC), the suspension must be of high purity and quality. In other words, the amounts of iron and magnesium should be so little so that the final PCC product would be of high quality. The effects of initial temperature, quicklime particle size, amount of initial solid present, agitation rate, and time on the recovery of hydrated lime and iron/magnesium reduction efficiency were investigated through a central composite design (CCD) of experiments. The iron reduction efficiency and the weight recovery of hydrated lime were obtained as 91% and 98.5%, respectively. The reduction of magnesium was not significant in this stage. Moreover, approximately the whole existing SiO₂ was eliminated.     

F-LEACH: a fuzzy-based data aggregation scheme for healthcare IoT systems

The Journal of Supercomputing - Internet of Things (IoT) is an emerging paradigm that consists of numerous connected and interrelated devices with embedded sensors, exchanging data with each other...     

Real time fractal image coder based on characteristic vector matching

Fractal coding algorithm has many applications including image compression. In this paper a classification scheme is presented which allows the hardware implementation of the fractal coder. High speed and low power consumption are the goal of the suggested design. The introduced method is based on binary classification of domain and range blocks. The proposed technique increases the processing speed and reduces the power consumption while the qualities of the reconstructed images are comparable with those of the available software techniques. In order to show the functionality of the proposed algorithm, the architecture was implemented on a FPGA chip. The application of the proposed hardware is shown in image compression. The resulted compression ratios, PSNR error, gate count, compression speed and power consumption are compared with the existing designs. Other applications of the proposed design are feasible in certain fields such as mass–volume database coding and also in video coder’s block matching schemes.     

Semantic partitioning of peer-to-peer search space

Peer-to-peer (P2P) networks are beginning to form the infrastructure of future applications. Computers are organized in P2P overlay networks to facilitate search queries with reasonable cost. So, scalability is a major aim in design of P2P networks. In this paper, to obtain a high factor of scalability, we partition network search space using a consistent static shared upper ontology. We name our approach semantic partition tree (SPT). All resources and queries are annotated using the upper ontology and queries are semantically routed in the overlay network. Also, each node indexes addresses of other nodes that possess contents expressible by the concept it maintains. So, our approach can be conceived as an ontology-based distributed hash table (DHT). Also, we introduce a lookup service for the network which is very scalable and independent of the network size and just depends on depth of the ontology tree. Further, we introduce a broadcast algorithm on the network. We present worst case analysis of both lookup and broadcast algorithms and measure their performance using simulation. The results show that our scheme is highly scalable and can be used in real P2P applications.     

Application of artificial intelligence to improve quality of service in computer networks

Resource sharing between book-ahead (BA) and instantaneous request (IR) reservation often results in high preemption rates for ongoing IR calls in computer networks. High IR call preemption rates cause interruptions to service continuity, which is considered detrimental in a QoS-enabled network. A number of call admission control models have been proposed in the literature to reduce preemption rates for ongoing IR calls. Many of these models use a tuning parameter to achieve certain level of preemption rate. This paper presents an artificial neural network (ANN) model to dynamically control the preemption rate of ongoing calls in a QoS-enabled network. The model maps network traffic parameters and desired operating preemption rate by network operator providing the best for the network under consideration into appropriate tuning parameter. Once trained, this model can be used to automatically estimate the tuning parameter value necessary to achieve the desired operating preemption rates. Simulation results show that the preemption rate attained by the model closely matches with the target rate.