Scattering Clustering Method for Terminal Fingerprint Positioning Based on MIMO Base Station

With the rapid development of wireless communication technology, location-based services are playing an increasingly important role in people’s lives. However, as the living environment becomes more and more complex, the existence of obstructions and various scatterers makes the accuracy of traditional positioning algorithms decrease, thus, fingerprint positioning has gradually become a research hotspot in the field of positioning. This paper researches the 5th Generation (5G) fingerprint location method based on machine learning. A massive multiple-in multiple-out (MIMO) channel is constructed on the MATLAB simulation platform, from which the fingerprint information is extracted to establish a fingerprint database. Considering the huge amount of data in the fingerprint database, and under the multipath effect, the channel characteristics are mainly affected by the scatterers near the point to be located. This paper proposes a scattering-based clustering method that combines the particularity of multipath propagation for clustering. Research shows that this method has excellent clustering effects, which can effectively improve algorithm efficiency and reduce data storage pressure on the base station side. (Foundation items: Social Development Projects of Jiangsu Science and Technology Department (No.BE2018704).)

Shopping and Basket Analysis by Using an Improved Apriori Algorithm in WEKA

In the past years, e-commerce and online shopping grew fast. It became more helpful by letting people buy the desired product online. Also, to help their users to find the product of their desire easily and make the process simpler, the online shopping websites use some kinds of an algorithm to provide recommendation systems. Often, these systems use techniques like basket analyzing and association rules which is finding the relation between the products together or between users too, so apriori algorithm is one of the famous ones among the recommendation systems. Although it has some limitations while implementing which makes the algorithm less confident or even useless, Let us assume we have 100K records in the sold item list in a system in which about 10K refers to the customers buying only one or two items in their purchase. Therefore, this ten per cent will not affect finding the relation between the items, at the same time these records will make the system less efficient and take more time to analyze, in this paper, we try to show how we can improve the apriori algorithm efficiency and accuracy by some preprocessing on the dataset before applying apriori algorithm by eliminating the unnecessary records, this process helps to make the algorithm better because of reducing the number of transactions, hence finding strong relationships between items easier for the rest of the records.

Battery Management System Performance Enhancement using Single Sliding Mode Based Charge Equalization Controller

In this paper, the study aims to evolve a precision model of a Battery charge equalization controller (BCEC) that manage each cell of a lithium-ion (Li-ion) battery, monitoring a nd balancing by charging a nd discharging a series-connected Li-ion battery with several cells (n) in electric vehicle (E.V.) applications. An intelligent Sliding mode controller is evolved to activate bidirectional cell switches and regulate a chopper circuit’s direct current (DC-DC flyback converter circuit) with PWM generation. To implement a small-scale BCEC, the model consists of individual models of an E.V., cells of Li-ion battery, a fly-back converter, and a single sliding mode controller (SSMC) for charging and discharging are integrated with n series-connected cells of Li-ion-battery. The BCEC output shows that the cells are operated within a safe range, and the difference in the state of charge (SOC) is maintained to be 0.1%. Comparing the developed BCEC with the existing controllers, with the performance, control algorithm, efficiency, power loss, size, and cost.

An Efficient and Robust Improved A* Algorithm for Path Planning

Path planning plays an essential role in mobile robot navigation, and the A* algorithm is one of the best-known path planning algorithms. However, the conventional A* algorithm and the subsequent improved algorithms still have some limitations in terms of robustness and efficiency. These limitations include slow algorithm efficiency, weak robustness, and collisions when robots are traversing. In this paper, we propose an improved A*-based algorithm called EBHSA* algorithm. The EBHSA* algorithm introduces the expansion distance, bidirectional search, heuristic function optimization and smoothing into path planning. The expansion distance extends a certain distance from obstacles to improve path robustness by avoiding collisions. Bidirectional search is a strategy that searches for a path from the start node and from the goal node at the same time. Heuristic function optimization designs a new heuristic function to replace the traditional heuristic function. Smoothing improves path robustness by reducing the number of right-angle turns. Moreover, we carry out simulation tests with the EBHSA* algorithm, and the test results show that the EBHSA* algorithm has excellent performance in terms of robustness and efficiency. In addition, we transplant the EBHSA* algorithm to a robot to verify its effectiveness in the real world.

Preference Oriented Mining Techniques for Location based Point Search

With the development of internet and wireless technologies, location based search is among the most discussed topic in current era. To address issues of location based search a lot of research has been done but it mainly focused on the specific aspects of the domain like most of the studies focused, on the search of nearby restaurants, shopping malls, hospitals, stores etc., by utilizing location of users as searching criteria. Problem with these studies is that users might not be satisfied by their results and the sole reason behind this might be the absence of user preferences in the search criteria. There exists some studies which focused user preferences along with user location and query time and proposed some frameworks but they are only limited to stores and their research cannot be scaled to other points like schools, hospitals, doctors , petrol pumps, gas station etc. Moreover there exist scalability issues in their recommended algorithms along with some data credibility issues in their public evaluations strategies. Our proposed research is going to present a novel location based searching technique not only for stores but for any point. The presented solution has overcome issues faced in previous research studies and possesses capability to search for “K” nearest points which are most preferable by user, by utilizing searching time as well as query location. Our research has proposed two feedback learning algorithms and one ranking algorithm. To increase the credibility of public evaluation score, system have utilized Google ranking approach while calculating the score of the point. To make user recommendations nonvolatile along with improving recommendations algorithm efficiency, proposed system have introduced item to item collaborative filtering algorithm. Through experimental evaluations on real dataset of yelp.com presented research have shown significant gain in performance and accuracy.

Optimization of aerospace aircraft guidance to the landing area

The article considers a technique for constructing an optimal guidance procedure for an aerospace aircraft. The technique is based on the adaptation of the Pontryagin maximum principle for the considered class of problems. At the same time the guidance accuracy is ensured by solving a boundary value problem, which is periodically performed during the flight. The developed procedure for predicting the final parameters of the optimal flight according to a simplified motion model is presented, which also makes it possible to determine the value of the actual miss. A detailed mathematical description of the proposed technique is given. The feasibility of the proposed technique is ensured by minimizing the amount of computational operations. The guidance algorithm efficiency is illustrated by a numerical example with a flight simulation procedure taking into account all significant factors. The paper also provides examples of solving boundary value problems and the results of modeling the optimal guidance.

Parallel Hybrid Particle Swarm Algorithm for Workshop Scheduling Based on Spark

In hybrid mixed-flow workshop scheduling, there are problems such as mass production, mass manufacturing, mass assembly and mass synthesis of products. In order to solve these problems, combined with the Spark platform, a hybrid particle swarm algorithm that will be parallelized is proposed. Compared with the existing intelligent algorithms, the parallel hybrid particle swarm algorithm is more conducive to the realization of the global optimal solution. In the loader manufacturing workshop, the optimization goal is to minimize the maximum completion time and a parallelized hybrid particle swarm algorithm is used. The results show that in the case of relatively large batches, the parallel hybrid particle swarm algorithm can effectively obtain the scheduling plan and avoid falling into the local optimal solution. Compared with algorithm serialization, algorithm parallelization improves algorithm efficiency by 2–4 times. The larger the batches, the more obvious the algorithm parallelization improves computational efficiency.

Analysis of the Wavelet Domain Filtering Approach for Video Super-Resolution

The wavelet domain-centered algorithms for the super-resolution research area give better visual quality and have been explored by different researchers. The visual quality is achieved with increased complexity and cost as most of the systems embed different pre- and post-processing techniques. The frequency and spatial domain-based methods are the usual approaches for super-resolution with some benefits and limitations. Considering the benefits of wavelet domain processing, this paper deals with a new algorithm that depends on wavelet residues. The methodology opts for wavelet domain filtering and residue extraction to get super-resolved frames for better visuals without embedding other techniques. The avoidance of noisy high-frequency components from low-quality videos and the consideration of edge information in the frames are the main targets of the super-resolution process. This inverse process is carried with a proper combination of information present in low-frequency bands and residual information in the high-frequency components. The efficient known algorithms always have to sacrifice simplicity to achieve accuracy, but in the proposed algorithm efficiency is achieved with simplicity. The robustness of the algorithm is tested by analyzing different wavelet functions and at different noise levels. The proposed algorithm performs well in comparison to other techniques from the same domain.

Dimensionality reduction applied to logical judgments

Purpose: To study the effect of the application of the dimensionality reduction in logical judgment (or logical reasoning, logical inference) programs. Methods: Use enumeration and dimensionality reduction methods to solve logical judgment problems.The effect of the two methods is illustrated in the form of a case study. Results: For logical judgmentproblems, using enumeration method to find the best answer is a comprehensive and fundamental method, but the disadvantage is that it is computationally intensive and computationally inefficient. Compared with the ideas of parallel treatment of known conditions by enumeration method, the application of dimensionality reduction thinking was built on the basis of fully mining information for feature extraction and feature selection. Conclusions: The dimensionality reduction method was applied to the logical judgment problems, and on the basis of fully mining information, the dimensionality reduction principle of statistics were applied to stratify and merge variables with the same or similar characteristics to achieve the purpose of streamlining variables, simplifying logical judgment steps, reducing computation and improving algorithm efficiency.

A Formal Verification Method of Compilation Based on C Safety Subset

With the rapid increase in the number of wireless terminals and the openness of wireless networks, the security of wireless communication is facing serious challenges. The safety and security of computer communication have always been a research hotspot, especially the wireless communication that still has a more complex architecture which leads to more safety problems in the communication system development. In recent years, more and more wireless communication systems are applied in the safety-critical field which tends to need high safety guarantees. A compiler is an important tool for system development, and its safety and reliability have an important impact on the development of safety-critical software. As the strictest method, formal verification methods have been widely paid attention to in compiler verification, but the current formal verification methods have some problems, such as high proof complexity, weak verification ability, and low algorithm efficiency. In this paper, a compiler formal verification method based on safety C subsets is proposed. By abstracting the concept of C grammar units from safety C subsets, the formal verification of the compiler is transformed into the verification of limited C grammar units. In this paper, an axiom system of first-order logic and special axioms are introduced. On this axiom system, the semantic consistency verification of C grammar unit and target code pattern is completed by means of theorem proving, and the formal verification of the compiler is completed.