Journal of Information Systems and Telecommunication
Year:2014 | Volume:2 | Issue:1 (5)|Papers Count:7

This paper presents a modification of Particle Swarm Optimization (PSO) technique based on cooperative behavior of swarms and learning ability of an automaton. The approach is called Cooperative Particle Swarm Optimization based on Learning Automata (CPSOLA). The CPSOLA algorithm utilizes three layers of cooperation which are intra swarm, inter swarm and inter population. There are two active populations in CPSOLA. In the primary population, the particles are placed in all swarms and each swarm consists of multiple dimensions of search space. Also there is a secondary population in CPSOLA which is used the conventional PSO’s evolution schema. In the upper layer of cooperation, the embedded Learning Automaton (LA) is responsible for deciding whether to cooperate between these two populations or not. Experiments are organized on five benchmark functions and results show notable performance and robustness of CPSOLA, cooperative behavior of swarms and successful adaptive control of populations.

Developing large and complex systems often involves many stakeholders each of which has her own expectations from the system; hence, it is difficult to write a single formal specification of the system considering all of stakeholders’ requirements at once; instead, each stakeholder can specify the system from her own viewpoint first. Then, the resulting specifications can be composed to prepare the final specification. Much work has been done so far for the specification of non-probabilistic systems regarding viewpoints (or expectations) of different stakeholders; however, because of big trend to apply formal methods on probabilistic systems, in this paper, we present an approach to compose viewpoints of different stakeholders in the specification of probabilistic systems. According to this approach, different viewpoints are separately specified using the Z notation. Then, the resulting specifications are composed using some new operators proposed in this paper. We show the applicability of the presented approach by performing it on a known case study.

UWB ranging is usually based on the time-of-arrival (TOA) estimation of the first path. There are two major challenges in TOA estimation. One challenge is to deal with multipath channel, especially in indoor environments. The other challenge is the existence of interference from other sources. In this paper, we propose a new method of TOA estimation, which is very robust against the interference. In this method, during the phase of TOA estimation, the transmitter sends its pulses in random positions within the frame. This makes the position of the interference relative to the main pulse to be random. Consequently, the energy of interference would be distributed, almost uniformly, along the frame. In energy detection methods, a constant interference along the frame does not affect the detection of arrival time and only needs the adjustment of the threshold. Simulation results in IEEE. 802.15.4a channels show that, even in presence of very strong interference, TOA estimation error of less than 3 nanoseconds is feasible with the proposed method.

Image retrieval is one of the most applicable image processing techniques which have been extensively used. Feature extraction is one of the most important procedures used for interpretation and indexing images in Content-Based Image Retrieval (CBIR) systems. Effective storage, indexing and managing a large number of image collections are critical challenges in computer systems. There are many proposed methods to overcome these problems. However, the rate of image retrieval and speed of retrieval are still interesting fields of researches. In this paper, we propose a new method based on combination of Gabor filter and Walsh transform and Wavelet Pyramid (GWWP). The Crossover Point (CP) of precision and recall are considered as metrics to evaluate and compare different methods. The Obtained results show using GWWP provides better performance in compared to with other methods.

Language modeling has many applications in a large variety of domains. Performance of this model depends on its adaptation to a particular style of data. Accordingly, adaptation methods endeavour to apply syntactic and semantic characteristics of the language for language modeling. The previous adaptation methods such as family of Dirichlet class language model (DCLM) extract class of history words. These methods due to lake of syntactic information are not suitable for high morphology languages such as Farsi. In this paper, we present an idea for using syntactic information such as part-of-speech (POS) in DCLM for combining with one of the language models of n-gram family. In our work, word clustering is based on POS of previous words and history words in DCLM. The performance of language models are evaluated on BijanKhan corpus using a hidden Markov model based ASR system. The results show that use of POS information along with history words and class of history words improves performance of language model, and decreases the perplexity on our corpus. Exploiting POS information along with DCLM, the word error rate of the ASR system decreases by 1.2% compared to DCLM.

Applying particle swarm optimization (PSO) algorithm has become a widespread heuristic technique in many fields of engineering. In this paper, we apply PSO algorithm in additive white Gaussian noise (AWGN) and multipath fading channels. In the proposed method, PSO algorithm was applied to solve joint multiuser and inter-symbol interference (ISI) suppression problems in the code-division multiple-access (CDMA) systems over multipath Rayleigh fading channel and consequently, to reduce the computational complexity. At the first stage, to initialize the POS algorithm, conventional detector (CD) was employed. Then, time-varying acceleration coefficients (TVAC) were used in the PSO algorithm. The simulation results indicated that the performance of PSO-based multiuser detection (MUD) with TVAC is promising and it is outperforming the CD.

The TCP connection management protocol sets a position for a classic Denial of Service (DoS) attack, called the SYN flooding attack. In this attack attacker sends a large number of TCP SYN segments, without completing the third handshaking step to quickly exhaust connection resources of the victim server. Therefore it keeps TCP from handling legitimate requests. This paper proposes that SYN flooding attack can be viewed metaphorically as result of an unfair scheduling that gives more opportunity to attack requests but prevents legal connections from getting services. In this paper, we present a scheduling algorithm that ejects the half connection with the longest duration, when number of half open connections reaches to the upper bound. The simulation results show that the proposed defense mechanism improves performance of the under attack system in terms of loss probability of requests and share of regular connections from system resources.