Preliminary communication Inclusion of the latest research work to a real life applications and achieving adaptive video signal post-processing is a complex and timely task. The proposed framework breaks the filtering process into several distinct phases and enables a researcher to move at a faster

A new technique based on the multilayer perceptron (MLP) neural network is proposed for blocking-artifact removal in block-coded images. The new method is based on the concept of learning-by-examples. The compressed image and its original uncompressed version are used to train the neural networks. In the developed scheme, inter-block slopes of the compressed image are used as input, the difference between the original uncompressed and the compressed image is used as desired output for training the networks. Blocking-artifact removal is realized by adding the neural network's outputs to the compressed image. The new technique has been applied to process JPEG compressed images. Experimental results show significant improvements in both visual quality and peak signal-to-noise ratio. It is also shown the present method is comparable to other state of the art techniques for quality enhancement in block-coded images.

decoder blocking artifacts also show up in the interior of the range blocks. This paper proposes an effective post-processing to reduce both kinds of these artifacts. We apply a smoothing filter adapted to the partition before each iteration of the decoding process which reduces the blocking artifacts

location-dependent nugget effect that accounts for small scale variability. Based on the dynamical forecasts by the COSMO-DE-EPS and observational data over Germany we evaluate the performance of our method and and compare it with other post-processing approaches such as geostatistical model averaging. Our

algorithm can be improved by introducing more advanced representations and novel operators in the GA, as well as designing the post-processing in an adaptive way. Results: We propose a novel framework GALF-P, consisting of Genetic Algorithm with Local Filtering (GALF) and adaptive post-processing techniques

Abstract—Malware is software designed to infiltrate or damage a computer system without the owner’s informed consent (e.g., viruses, backdoors, spyware, trojans, and worms). Nowadays, numerous attacks made by the malware pose a major security threat to computer users. Unfortunately, along with the development of the malware writing techniques, the number of file samples that need to be analyzed, named “gray list, ” on a daily basis is constantly increasing. In order to help our virus analysts, quickly and efficiently pick out the malicious executables from the “gray list,” an automatic and robust tool to analyze and classify the file samples is needed. In our previous work, we have developed an intelligent malware detection system (IMDS) by adopting associative classification method based on the analysis of application programming interface (API) execution calls. Despite its good performance in malware detection, IMDS still faces the following two challenges:

According to Wikipedia, Optical Character Recognition (OCR) “is the mechanical or electronic translation of images of handwritten or typewritten text (usually captured by a scanner) into machine-editable text. ” Even though a lot of academic research has been carried out on this topic, there are still needs for improving the accuracy and completeness of the scanned text. More specifically, scanned documents are prone to inaccuracy and error. This is especially true with large amounts of aged books in libraries across the country. Our group plans on creating a solution which will help to reduce some of these errors in scanned documents. 2. Related work

TFBS identification based on genetic algorithm with combined representations and adaptive post-processing

Sharpness enhancement is one of the post-processing stages in the consumer electronics video chain that operates in an open-loop mode. Although adaptive behavior is possible, in general there is no feedback system aimed at maximizing perceived quality. In this paper we introduce a control system

to be able to run in real time. Efficient implementations of signal adaptive filters for video post-processing can be obtained using the specialized features of the parallel BOPS DSP cores. The performance achieved by deblocking and deringing CIF and SDTV size video sequences on the MANTA^TM prototype chip