3,087 research outputs found
Image enhancement using fuzzy intensity measure and adaptive clipping histogram equalization
Image enhancement aims at processing an input
image so that the visual content of the output image is more
pleasing or more useful for certain applications. Although
histogram equalization is widely used in image enhancement due
to its simplicity and effectiveness, it changes the mean brightness
of the enhanced image and introduces a high level of noise and
distortion. To address these problems, this paper proposes
image enhancement using fuzzy intensity measure and adaptive
clipping histogram equalization (FIMHE). FIMHE uses fuzzy
intensity measure to first segment the histogram of the original
image, and then clip the histogram adaptively in order to
prevent excessive image enhancement. Experiments on the
Berkeley database and CVF-UGR-Image database show that
FIMHE outperforms state-of-the-art histogram equalization
based methods
Hybrid Dy-NFIS & RLS equalization for ZCC code in optical-CDMA over multi-mode optical fiber
For long haul coherent optical fiber communication systems, it is significant to precisely monitor the quality of transmission links and optical signals. The channel capacity beyond Shannon limit of Single-mode optical fiber (SMOF) is achieved with the help of Multi-mode optical fiber (MMOF), where the signal is multiplexed in different spatial modes. To increase single-mode transmission capacity and to avoid a foreseen “capacity crunch”, researchers have been motivated to employ MMOF as an alternative. Furthermore, different multiplexing techniques could be applied in MMOF to improve the communication system. One of these techniques is the Optical Code Division Multiple Access (Optical-CDMA), which simplifies and decentralizes network controls to improve spectral efficiency and information security increasing flexibility in bandwidth granularity. This technique also allows synchronous and simultaneous transmission medium to be shared by many users. However, during the propagation of the data over the MMOF based on Optical-CDMA, an inevitable encountered issue is pulse dispersion, nonlinearity and MAI due to mode coupling. Moreover, pulse dispersion, nonlinearity and MAI are significant aspects for the evaluation of the performance of high-speed MMOF communication systems based on Optical-CDMA. This work suggests a hybrid algorithm based on nonlinear algorithm (Dynamic evolving neural fuzzy inference (Dy-NFIS)) and linear algorithm (Recursive least squares (RLS)) equalization for ZCC code in Optical-CDMA over MMOF. Root mean squared error (RMSE), mean squared error (MSE) and Structural Similarity index (SSIM) are used to measure performance results
Dynamic evolving neural fuzzy inference system equalization scheme in mode division multiplexer for optical fiber transmission
The performance of optical mode division multiplexer (MDM) is affected by inter-symbol interference (ISI), which arises from higher-order mode coupling and modal dispersion in multimode fiber (MMF). Existing equalization algorithms in MDM can mitigate linear channel impairments, but cannot tackle nonlinear channel impairments accurately. Therefore, mitigating the noise in the received signal of MDM in the presence of ISI to recover the transmitted signal is important issue. This paper aims at controlling the broadening of the signal from MDM and minimizing the undesirable noise among channels. A dynamic evolving neural fuzzy inference system (DENFIS) equalization scheme has been used to achieve this objective. Results illustrate that nonlinear DENFIS equalization scheme can improve the received distorted signal from an MDM with better accuracy than previous linear equalization schemes such as recursive‐least‐square (RLS) algorithm. Desirably, this effect allows faster data transmission rate in MDM. Additionally, the successful offline implementation of DENFIS equalization in MDM encourages future online implementation of DENFIS equalization in embedded optical systems
Bacterial Foraging Based Channel Equalizers
A channel equalizer is one of the most important subsystems in any digital
communication receiver. It is also the subsystem that consumes maximum computation
time in the receiver. Traditionally maximum-likelihood sequence estimation (MLSE) was
the most popular form of equalizer. Owing to non-stationary characteristics of the
communication channel MLSE receivers perform poorly. Under these circumstances
‘Maximum A-posteriori Probability (MAP)’ receivers also called Bayesian receivers
perform better.
Natural selection tends to eliminate animals with poor “foraging strategies” and favor the
propagation of genes of those animals that have successful foraging strategies since they
are more likely to enjoy reproductive success. After many generations, poor foraging
strategies are either eliminated or shaped into good ones (redesigned). Logically, such
evolutionary principles have led scientists in the field of “foraging theory” to
hypothesize that it is appropriate to model the activity of foraging as an optimization
process.
This thesis presents an investigation on design of bacterial foraging based channel
equalizer for digital communication. Extensive simulation studies shows that the
performance of the proposed receiver is close to optimal receiver for variety of channel
conditions. The proposed receiver also provides near optimal performance when channel
suffers from nonlinearities
Adaptive Equalization for UWB communication System based on ANFIS
Ultra-wideband (UWB) communication systems cover enormous bandwidths that have strongly low-power spectral densities. At UWB communication system with high data rate, owing to multipath propagation, the spread delay in inter symbol interference (ISI) will raise the bit error rate (BER) considerably. ISI which is formed via the UWB channels can be removed by equalization, which is one of the most significant signal processing techniques. Furthermore, LMS algorithm represents a very efficient tool for determining adaptive equalizer coefficients values in communication systems, in spite of that, the LMS adaptive equalizer encounters response diminishing besides slow convergence rate. The current paper adopts an adaptive equalizer based adaptive neuron-fuzzy inference system (ANFIS). The simulation outcomes reveal that the convergence rates as well as accuracy of identification of ANFIS based algorithm are surpass the traditional LMS algorithm, moreover, simulation outcomes prove that ISI is effectively limited and the performance of the system is clearly improved.  
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