Best Minimum Summation Scheduler for Long Term Evolution

Resource scheduling in Long Term Evolution (LTE) is an open and rising issue. It has an enormous impact on the entire system performance. Due to the nature of LTE system, the scheduler has to be designed carefully. It has to overcome many challenges such as limited processing time and the high dynamic behavior. This paper proposes a novel scheduling policy for the MAC layer in LTE called the Best Minimum Summation (BMS). The main aim of this scheduling policy is to achieve high performance with low complexity. Three sub-schedulers have been developed. Each one of these schedulers deals with scheduling table in different dimension. The first one operates on the scheduling table through the user dimension (BMS.UE); while the second one operates on the scheduling table through the resource block dimension (BMS.RB). The third scheduler operates on the scheduling table correlating both of these dimensions (BMS.2D). All of the proposed solutions were intensively evaluated in a system level simulator. Three performance metrics were used which are throughput, error rate and fairness. The results have shown that the ability of the BMS.UE scheduler to outperform other existing schedulers of LTE.     

Augmented grasshopper optimization algorithm by differential evolution: a power scheduling application in smart homes

With the increasing number of electricity consumers, production, distribution, and consumption problems of produced energy have appeared. This paper proposed an optimization method to reduce the peak demand using smart grid capabilities. In the proposed method, a hybrid Grasshopper Optimization Algorithm (GOA) with the self-adaptive Differential Evolution (DE) is used, called HGOA. The proposed method takes advantage of the global and local search strategies from Differential Evolution and Grasshopper Optimization Algorithm. Experimental results are applied in two scenarios; the first scenario has universal inputs and several appliances. The second scenario has an expanded number of appliances. The results showed that the proposed method (HGOA) got better power scheduling arrangements and better performance than other comparative algorithms using the classical benchmark functions. Moreover, according to the computational time, it runs in constant execution time as the population is increased. The proposed method got 0.26?% enhancement compared to the other methods. Finally, we found that the proposed HGOA always got better results than the original method in the worst cases and the best cases.

     

A new dynamic cellular learning automata-based skin detector

Skin detection is a difficult and primary task in many image processing applications. Because of the diversity of various image processing tasks, there exists no optimum method that can perform properly for all applications. In this paper, we have proposed a novel skin detection algorithm that combines color and texture information of skin with cellular learning automata to detect skin-like regions in color images. Skin color regions are first detected, by using a committee structure, from among several explicit boundary skin models. Detected skin-color regions are then fed to a texture analyzer which extracts texture features via their color statistical properties and maps them to a skin probability map. This map is then used by cellular learning automata to adaptively make a decision on skin regions. Conducted experiments show that the proposed algorithm achieves the true positive rate of about 86.3% and the false positive rate of about 9.2% on Compaq skin database which shows its efficiency.     

Reconfigurable hardware implementation of a phase-correlation stereoalgorithm

This paper describes the implementation of a stereo-vision system using Field Programmable Gate Arrays (FPGAs). Reconfigurable hardware, including FPGAs, is an attractive platform for implementing vision algorithms due to its ability to exploit parallelism often found in these algorithms, and due to the speed with which applications can be developed as compared to hardware. The system outputs 8-bit, subpixel disparity estimates for 256× 360 pixel images at 30,fps. A local-weighted phase correlation algorithm for stereo disparity [Fleet, D. J.: {Int. Conf. Syst. Man Cybernetics 1:48–54 (1994)] is implemented. Despite the complexity of performing correlations on multiscale, multiorientation phase data, the system runs as much as 300 times faster in hardware than its software implementation. This paper describes the hardware platform used, the algorithm, and the issues encountered during its hardware implementation. Of particular interest is the implementation of multiscale, steerable filters, which are widely used in computer vision algorithms. Several trade-offs (reducing the number of filter orientations from three to two, using fixed-point computation, changing the location of one localized low-pass filter, and using L1 instead of L2 norms) were required to both fit the design into the available hardware and to achieve video-rate processing. Finally, results from the system are given both for synthetic data sets as well as several standard stereo-pair test images.     

Microstructure and High Temperature Impression Creep Properties of Mg–3Ca–xZr (x = 0.3, 0.6, 0.9 wt%) Alloys

The current study has investigated the influence of zirconium (Zr) addition to Mg–3Ca–xZr (x = 0.3, 0.6, 0.9 wt%) alloys prepared using argon arc melting on the microstructure and impression properties at 448–498 K under constant stress of 380 MPa. Microstructural analysis of as-cast Mg–3Ca–xZr alloys showed grain refinement with Zr addition. The observed grain refinement was attributed to the growth restriction effect of Zr in hypoperitectic Mg–3Ca–0.3 wt% Zr alloys. Heterogeneous nucleation of α-Mg in properitectic Zr during solidification resulted in grain refinement of hyperperitectic Mg–3Ca–0.6 wt% Zr and Mg–3Ca–0.9 wt% Zr alloys. The hardness of Mg–3Ca–xZr alloys increased as the amount of Zr increased due to grain refinement and solid solution strengthening of α-Mg by Zr. Creep resistance of Mg–3Ca–xZr alloys increased with the addition of Zr due to solid solution strengthening of α-Mg by Zr. The calculated activation energy (Q_a) for Mg–3Ca samples (131.49 kJ/mol) was the highest among all alloy compositions. The Q_a values for 0.3, 0.6 and 0.9 wt% Zr containing Mg–3Ca alloys were 107.22, 118.18 and 115.24 kJ/mol, respectively.     

Structural instability of carbon nanotubes embedded in viscoelastic medium and subjected to distributed tangential load

In this paper, the nonlocal Euler-Bernoulli beam model is used to predict the static and dynamic structural instability of carbon nanotubes (CNTs) subjected to a distributed tangential compressive load. The CNT is considered to be embedded in a Kelvin-Voigt viscoelastic medium. Equation of motion and boundary conditions are obtained using the extended Hamilton’s principle and the extended Galerkin’s method is applied in order to transform the resulting equations into a general eigenvalue problem. The derived equations are validated by comparing the results achieved from the new derivations with existing solutions in literature. Effects of several experimentally interesting boundary conditions are considered on the stability characteristics of the CNT. Moreover, the influences of small scale parameter and material properties of the surrounding viscoelastic medium on the stability boundaries are examined.     

Comparative study of conventional and micro WEDM based on machining of meso/micro Sized Spur Gear

This paper discusses the comparison of micro machining process using conventional and micro wire electrical discharge machining (WEDM) for fabrication of miniaturized components. Seventeen toothed miniaturized spur gear of 3.5 and 1.2 mm outside diameter were fabricated by conventional and micro WEDM respectively. The process parameters for both conventional and micro WEDM were optimized by preliminary experiments and analysis. The gears were investigated for the quality of surface finish and dimensional accuracy which were used as the criteria for the process evaluation. An average surface roughness (R_a) of 50 nm and dimensional accuracy of 0.1–1 μm were achieved in micro WEDM. Whenever applied conventional WEDM for meso/micro fabrication, a R_a surface roughness of 1.8 μm and dimensional accuracy of 2–3 μm were achieved. However, this level of surface roughness and dimensional accuracy are acceptable in many applications of micro engineering. A window of conventional WEDM consisting of low energy discharge parameters is identified for micromachining.     

Systematic approach to the correct identification of Asplenium dalhousiae (Aspleniaceae) with their medicinal uses

In present study, multiple microscope techniques were used for the systematics identification of the species Asplenium dalhousiae. The plant was collected from different phytogeographical and its natural habitat of Pakistan, where it shows higher diversity. Morphology, foliar epidermal anatomy, and spore morphological characters of the species were studied in detailed using multiple microscopic techniques through light microscopy (LM) and scanning electron microscopy (SEM). LM and SEM were used for the systematics identification of the species. Traditionally, the species is used in the ailment of many diseases, so the spore morphology, anatomical features, and morphological characters are relevant to describe the species taxonomy. The importance of multiple methods of taxonomic study (e.g., documentation and morphological characteristics) for characterizing herbs are important step in systematic certification to maintain the efficacy of herbal medicines. The aim of the present study is to examine the morphological, anatomical, and spore morphology of the species A. dalhousiae in more detailed for the correct taxonomic identification and their medicinal validation from Pakistan.     

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.     

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.