Efficiency Effect of Obstacle Margin on Line-of-Sight in Wireless Networks

Line-of-sight clarity and assurance are essential because they are considered the golden rule in wireless network planning, allowing the direct propagation path to connect the transmitter and receiver and retain the strength of the signal to be received. Despite the increasing literature on the line of sight with different scenarios, no comprehensive study focuses on the multiplicity of parameters and basic concepts that must be taken into account when studying such a topic as it affects the results and their accuracy. Therefore, this research aims to find limited values that ensure that the signal reaches the future efficiently and enhances the accuracy of these values’ results. We have designed MATLAB simulation and programming programs by Visual Basic .NET for a semi-realistic communication system. It includes all the basic parameters of this system, taking into account the environment's diversity and the characteristics of the obstacle between the transmitting station and the receiving station. Then we verified the correctness of the system's work. Moreover, we begin by analyzing and studying multiple and branching cases to achieve the goal. We get several values from the results, which are finite values, which are a useful reference for engineers and designers of wireless networks.     

A massively parallel fault-tolerant architecture for time-critical computing

Building large-scale parallel computer systems for time-critical applications is a challenging task since the designers of such systems need to consider a number of related factors such as proper support for fault tolerance, efficient task allocation and reallocation strategies, and scalability. In this paper we propose a massively parallel fault-tolerant architecture using hundreds or thousands of processors for critical applications with timing constraints. The proposed architecture is based on an interconnection network called thebisectional network. A bisectional network is isomorphic to a hypercube in that a binary hypercube network can be easily extended as a bisectional network by adding additional links. These additional links add to the network some rich topological properties such as node symmetry, small diameter, small internode distance, and partitionability. The important property of partitioning is exploited to propose a redundant task allocation and a task redistribution strategy under realtime constraints. The system is partitioned into symmetric regions (spheres) such that each sphere has a central control point. The central points, calledfault control points (FCPs), are distributed throughout the entire system in an optimal fashion and provide two-level task redundancy and efficiently redistribute the loads of failed nodes. FCPs are assigned to the processing nodes such that each node is assigned two types of FCPs for storing two redundant copies of every task present at the node. Similarly, the number of nodes assigned to each FCP is the same. For a failure-repair system environment the performance of the proposed system has been evaluated and compared with a hypercube-based system. Simulation results indicate that the proposed system can yield improved performance in the presence of a high number of node failures.     

Compact and lightweight optical torque sensor for robots with increased range

This paper describes the development of a compact and lightweight optical torque sensor for robots with increased range. The torque generated can only be measured by placing a torque sensor on each joint, which makes the joint bulky and heavy adding extra load to be carried by robots. In this paper, we propose a compact and lightweight design for the torque sensor. This sensor can measure torque of robot's links by detecting torsion. The optical technique is used to detect angular displacement in the joints. As optical technique is immune to electromagnetic interference, light weight, and it also required simple electronics. The torque can be calculated by using torque and twist angle relation. The proposed design is simulated by FEM software (ANSYS), and it shows successful measurements of the torque with a load capacity of 100 Nm, which is sufficient for the torque generated in robot's joints. The designed optical torque sensor is manufactured by electrical discharge machining (EDM). The optical torque sensor is calibrated and several experiments are conducted to ensure its feasibility with the robot.     

Abrasive waterjet cutting of cladded material: kerf taper and MRR analysis

Gas/plasma cutting of cladded materials provides inferior cut quality that demands subsequent finishing processes. Abrasive waterjet cutting could be a proficient alternate in terms of cut quality. However, the inherent problem of kerf taper and low material removal rate in comparison to the said thermal cutting processes limit its application. Therefore, potential of aforesaid machining process for cutting of stainless-clad steel is investigated with a prior focus on maximizing the material removal rate with minimum kerf taper. Abrasive mass flow, traverse speed, water pressure, and stand-off distance have been selected as input parameters. ANOVA analysis revealed that traverse rate as well as abrasive mass flow are the major contributing factors for both the responses. Optimal settings of parameters developed by S/N ratio analysis results in an improvement of 18.6% in material removal rate and 39% in the kerf taper. Moreover, regression models are developed and validated through various statistical tests.     

Machinability of titanium alloy through electric discharge machining

Titanium alloy (Ti-6Al-4V), being considered as hard-to-machine material, offers many challenges especially during conventional machining. Electric discharge machining could be a good option if it offers a good match between material removal rate and surface finish of the machined feature. The issue of appropriate selection of electrode material for good machining of Ti-6Al-4V is not yet comprehensively explored which is the core focus of this study. Moreover, the effect of pulse time ratio is thoroughly examined which is not specifically studied before. Discharge current and pulse time ratio are considered as the input variables, whereas the material removal rate and surface roughness are selected as performance measures of machinability. Copper, aluminum, brass and graphite are employed to evaluate the machining behavior. Experimental results revealed that aluminum electrode provides the lowest surface roughness, whereas the maximum material removal rate is achieved using graphite electrode. However, graphite electrode can offer high material removal rate with low surface roughness by initially employing negative tool polarity for rough machining and then positive tool polarity for fine machining.     

DETERMINATION OF SEVERAL PHYSICAL PROPERTIES OF LIGHT PETROLEUM PRODUCTS USING IR

In the present study IR spectrometric analysis was used to determine clear research octane number, heat of formation, specific gravity and mole fraction of methyl group, simultaneously in hydrocarbon mixtures. IR absorption spectrum can be used to provide information, that can be obtained by several existing test methods.     

Complexity of question/answer games

Question/Answer games (Q/A games for short) are a generalization of the Rényi–Ulam game and they are a model for information extraction in parallel. A Q/A game, G=(D,s,(_q1,…,_qk))$G=(D,s,(q_1,\dots ,q_k))$, is played on a directed acyclic graph, D=(V,E)$D=(V,E)$, with a distinguished start vertex s$s$. In the i$i$th round, Paul selects a set, _Qi⊆V$Q_i\subseteq V$, of at most _qi$q_i$ non-terminal vertices. Carole responds by choosing an outgoing edge from each vertex in _Qi$Q_i$. At the end of k$k$ rounds, Paul wins if Carole’s answers define a unique path from the root to one of the terminal vertices in D$D$.     

A Novel Solution of Using Deep Learning for White Blood Cells Classification: Enhanced Loss Function with Regularization and Weighted Loss (ELFRWL)

Deep learning has been successfully applied in classification of white blood cells (WBCs), however, accuracy and processing time are found to be less than optimal hindering it from getting its full potential. This is due to imbalanced dataset, intra-class compactness, inter-class separability and overfitting problems. The main research idea is to enhance the classification and prediction accuracy of blood images while lowering processing time through the use of deep convolutional neural network (DCNN) architecture by using the modified loss function. The proposed system consists of a deep neural convolution network (DCNN) that will improve the classification accuracy by using modified loss function along with regularization. Firstly, images are pre-processed and fed through DCNN that contains different layers with different activation function for the feature extraction and classification. Along with modified loss function with regularization, weight function aids in the classification of WBCs by considering weights of samples belonging to each class for compensating the error arising due to imbalanced dataset. The processing time will be counted by each image to check the time enhancement. The classification accuracy and processing time are achieved using the dataset-master. Our proposed solution obtains better classification performance in the given dataset comparing with other previous methods. The proposed system enhanced the classification accuracy of 98.92% from 96.1% and a decrease in processing time from 0.354 to 0.216 s. Less time will be required by our proposed solution for achieving the model convergence with 9 epochs against the current convergence time of 13.5 epochs on average, epoch is the formation white blood cells (WBCs) and the development of granular cells. The proposed solution modified loss function to solve the adverse effect caused due to imbalance dataset by considering weight and use regularization technique for overfitting problem.