Qingzhong Liang

Designing electronic circuits by means of gene expression programming II

A major bottleneck in the evolutionary design of electronic circuits is the problem of scale. This refers to the very fast growth of the number of gates, used in the target circuit, as the number of inputs of the evolved logic function increases. Another related obstacle is the time required to calculate the fitness value of a circuit. In this paper, We propose a new means (Gene Expression Programming) for designing electronic circuits and introduces the encoding of the circuit as a chromosome, the genetic operators and the fitness function. From the case studies show this means has proved to be efficient to the electronic circuit and the evolution speed is fast. The experiments results show that we have attained the better results.

Autonomous robot path planning based on swarm intelligence and stream functions

This paper addresses a new approach to navigate mobile robot in static or dynamic surroundings based on particle swarm optimization (PSO) and stream functions (or potential flows). Stream functions, which are introduced from hydrodynamics, are employed to guide the autonomous robot to evade the obstacles. PSO is applied to generate each optimal step from initial position to the goal location; furthermore, it can solve the stagnation point problem that exists in potential flows. The simulation results demonstrate that the approach is flexible and effective.

The effect of critical transmission range in epidemic data propagation for mobile ad-hoc social network

In this paper, we study the information dissemination in mobile ad-hoc network, where mobile nodes are randomly and independently distributed with a given density on a square. Nodes in network move following a random direction mobility (RDM) model. One piece of information is disseminated from source to all other nodes in the network, utilizing the basic epidemic routing protocol. We develop an analytical model based on Ordinary Differential Equation (ODE) approach, in which we take the transmission range as the critical and intuitive system parameter instead of pair-wise meeting rate. Typically, we proceed to study the impacts of overlap among the moving informed nodes on the percolation ratio and the delivery delay. The analytical mode is verified by simulations. This research captures the characteristics of information disseminated in mobile ad-hoc network.

Representations of Evolutionary Electronics

For the evolutionary algorithm, the representation of the electronic circuit has two methods, one kind is code with the electronic circuit solution space, the other is code with the problem space. How weighs one representation quality may think the following questions? The first is the code method should as far as possible complete, it is say for the significance solution circuit or the optimize solution obtains in the problem space may represented by this code method. The second is the code method should speeds up the convergence speed of the algorithm search. The hardware representation methods mainly include binary bit string representation, tree representation, Cartesian Genetic Programming representation and other representations. In this paper, we will introduce the representations of the binary bit string and Cartesian Genetic Programming in detail, then give some examples of the two representations.

Self-adapting control parameters with multi-parent crossover in differential evolution algorithm

The performance of differential evolution DE algorithm is influenced by the setting of control parameters, which is quite dependent on the problem and difficult to be determined. Therefore, the studies on parameter adaptation mechanisms have gradually become more popular. In this paper, we present a self-adaptive DE algorithm GaDE, in which the adaptation of amplification factor and crossover rate is executed with a multi-parent crossover, while the adaptation timing is decided by the comparative result between the target vector and its offspring. The performance of GaDE algorithm is evaluated on a suite of bound-constrained numerical optimisation problems. The results show that our algorithm is better than, or at least comparable to, the canonical DE, and the two other adaptive DE algorithms.

A GPS Information Sharing System Based on Bluetooth Technology

The traditional GPS positioning technology is susceptible to buildings which incur severe signal attenuation and therefore the current actual GPS location information could not be directly obtained for a mobile device in some cases. The Bluetooth technology has attracted widespread attention recently due to its low-cost and strong anti- interference features. In this paper, we combined the Bluetooth wireless transmission and GPS positioning technologies to design and develop a GPS information sharing system, named GISS. The experimental results showed that the system is easy to use and achieves a flexible positioning capability of mobile devices.

Encounter Probability Aware Task Assignment in Mobile Crowdsensing

Wireless Sensor Networks (WSNs) have become essential parts in various smart city projects. However, the application-specific WSN deployment is constrained by its high cost, low flexibility and hard management. To address these limitations, a complementary promising solution, known as mobile crowdsensing, is proposed. Mobile crowdsensing leverages the surge of mobile devices as well as the sensors attached to them to opportunistically and cooperatively conduct sensing tasks. Thanks to the crowdness and mobility of mobile devices, mobile crowdsensing is able to enlarge the sensing scale and granularity. Existing mobile crowdsensing techniques are usually centralized methods and rely on infrastructure communications. Witnessing the development of Device-to-Device (D2D) communications, it is ideal to explore such abilities such that the sensing tasks can be conducted in a distributed manner as well as an infrastructureless way. Via D2D, all participated nodes can directly assign tasks to encountered nodes. In this paper, aided by the encounter relationship among mobile nodes, we study the time minimization task assignment problem in mobile crowdsensing. Specially, we propose offline and online algorithms based on historic encounter information and real-time assigned task execution time, respectively. Real-world trace based experiments validate the efficiency of our proposal.

SAPSN: A Sensor Network for Signal Acquisition and Processing

Software defined wireless sensor network can be adapted to different application needs through dynamic programming. In this paper, we propose a signal acquisition and processing wireless sensor network (SAPSN). SAPSN consists of sampling nodes, processing nodes and remote controllers. At first, the sampling node completes the local signal sampling by analog-digital conversion. Next, according to the different demand from the remote controller, processing node completes time domain or frequency domain analysis of signal processing, and transfers the results back to the remote controller. Finally, the application in remote controller will display the results according to different users needs. SAPSN is capable of time domain or frequency domain signal analysis and processing, depending on different application requirements. In this paper, we present the concept underlying SAPSN, its architecture. We also present preliminary experimental results.

Stochastic Analysis of Epidemic Routing Based Anycast in Throwbox-Equipped DTNs

Delay Tolerant Network (DTN) has been regarded as an important complementary networking paradigm to existing infrastructure-based networks. Without infrastructure support, the mobile nodes in DTNs opportunistically explore the communication opportunities to disseminate messages. As a result, the DTNs are characterized and challenged by the intermittent connectivities. To enhance the connectivity in DTNs, pioneering researchers advocate deploying throw boxes as static relay nodes in DTNs. Existing studies on throw box-enhanced DTNs are mainly about unicast where a message is transmitted from one node to another. While in this paper, we mainly focus on any cast where a message is intended to be disseminated to a fixed number of mobile nodes in the network. We are interested in analyzing the delivery performance of any cast in throw box-equipped DTNs. To address this issue, we first propose a two-dimensional Markov Chain to describe the network dynamics and then use Ordinary Differential Equation (ODEs) to formally analyze such dynamics. Simulation results validate the high accuracy of our analysis.

Multi-label Classification based on Particle Swarm Algorithm

Multi-label classification is a generalization of single-label classification, and its samples belong to multiple labels. The K-nearest neighbor algorithm can solve this problem as an optimization problem. It finds the optimum solution by caculating the distance between each sample in general. But in fact, the distance of K-nearest neighbor algorithm may be miscalculated due to the caused by the redundant or irrelevant characteristic value. In order to solve this problem, in this paper, we propose a novel method that uses the particle swarm algorithm to optimize the feature weights to improve the accuracy of distance calculation. As a result, it can improve classification accuracy further. The experimental results show that applying particle swarm algorithms optimization technique to improving K-nearest neighbor algorithm for multi-label classification problem, can improve the accuracy of classification effectively.