An Application of Throughput Request Satisfaction Method for Maximizing Concurrent Throughput in WLAN for IoT Application System.

With the wide applications of the in systems, have become a frequently chosen communication technology due to their adaptability and affordability. In a high-density network of devices such as the , a host often meets interferences from other devices and unequal from . This results in problems between hosts communicating concurrently in WLAN.

An Enhancement of Outdoor Location-Based Augmented Reality Anchor Precision through VSLAM and Google Street View.

Outdoor applications require precise positioning for seamless integrations of virtual content into immersive experiences. However, common solutions in outdoor LAR applications rely on traditional smartphone sensor fusion methods, such as the and compasses, which often lack the accuracy needed for precise AR content alignments. In this paper, we introduce an innovative approach to enhance anchor precision in outdoor environments.

An Interface Setup Optimization Method Using a Throughput Estimation Model for Concurrently Communicating Access Points in a Wireless Local Area Network.

The has been deployed around the globe as a major Internet access medium due to its low cost and high flexibility and capacity. Unfortunately, dense wireless networks can suffer from poor performance due to high levels of radio interference resulting from adjoining . To address this problem, we studied the , which selects the maximum or minimum power supplied to each AP so that the average among the concurrently communicating APs is maximized.

A Throughput Request Satisfaction Method for Concurrently Communicating Multiple Hosts in Wireless Local Area Network.

Nowadays, the has been widely used for Internet access services around the world. Then, the or in meeting the can appear among concurrently communicating hosts with the same , which should be solved by sacrificing advantageous hosts. Previously, we studied the by adopting at the AP.

Design and Implementation of IoT Server Platform with Applications.

Nowadays, rapid developments of technologies have increased possibilities of realizing where collaborations and integrations of various are essential. However, IoT application systems have often been designed and deployed independently without considering the standards of devices, logics, and data communications. In this paper, we present the design and implementation of the called for integrating IoT application systems using standards.

An evolutionary neural network approach for module orientation problems.

A novel neural network approach called "Evolutionary Neural Network (ENN)" is presented for the module orientation problem. The goal of this NP-complete problem is to minimize the total wire length by flipping circuit modules with respect to their vertical and/or horizontal axes of symmetry. In order to achieve high quality VLSI systems, it is strongly desired to solve the problem as quickly as possible in the design cycle.

A gradual neural-network algorithm for jointly time-slot/code assignment problems in packet radio networks.

A gradual neural network (GNN) algorithm is presented for the jointly time-slot/code assignment problem (JTCAP) in a packet radio network in this paper. The goal of this newly defined problem is to find a simultaneous assignment of a time-slot and a code to each communication link, whereas time-slots and codes have been independently assigned in existing algorithms. A time/code division multiple access protocol is adopted for conflict-free communications, where packets are transmitted in repetition of fixed-length time-slots with specific codes.

A gradual neural network approach for FPGA segmented channel routing problems.

A novel neural network approach called gradual neural network (GNN) is presented for segmented channel routing in field programmable gate arrays (FPGA's). FPGA's contain predefined segmented channels for net routing, where adjacent segments in a track can be interconnected through programmable switches for longer segments. The goal of the FPGA segmented channel routing problem, known to be NP-complete, is to find a conflict-free net routing with the minimum routing cost.

A gradual neural-network approach for frequency assignment in satellite communication systems.

A novel neural-network approach called gradual neural network (GNN) is presented for a class of combinatorial optimization problems of requiring the constraint satisfaction and the goal function optimization simultaneously. The frequency assignment problem in the satellite communication system is efficiently solved by GNN as the typical problem of this class. The goal of this NP-complete problem is to minimize the cochannel interference between satellite communication systems by rearranging the frequency assignment so that they can accommodate the increasing demands.

A binary Hopfield neural-network approach for satellite broadcast scheduling problems.

This paper presents a binary Hopfield neural network approach for finding a broadcasting schedule in a low-altitude satellite system. Our neural network is composed of simple binary neurons on the synchronous parallel computation, which is greatly suitable for implementation on a digital machine. With the help of heuristic methods, the neural network of a maximum of 200000 neurons can always find near-optimum solutions on a conventional workstation in our simulations.

A parallel improvement algorithm for the bipartite subgraph problem.

The authors propose the first parallel improvement algorithm using the maximum neural network model for the bipartite subgraph problem. The goal of this NP-complete problem is to remove the minimum number of edges in a given graph such that the remaining graph is a bipartite graph. A large number of instances have been simulated to verify the proposed algorithm, with the simulation result showing that the algorithm finds a solution within 200 iteration steps and the solution quality is superior to that of the best existing algorithm.