The development of multimedia and digital imaging has led to high quantity of data required to represent modern imagery. This requires large disk space for storage, and long time for transmission over computer networks, and these two are relatively expensive. These factors prove the need for images compression. Image compression addresses the problem of reducing the amount of space required to represent a digital image yielding a compact representation of an image, and thereby reducing the image storage/transmission time requirements. The key idea here is to remove redundancy of data presented within an image to reduce its size without affecting the essential information of it. We are concerned with lossless image compression in this paper. Our proposed approach is a mix of a number of already existing techniques. Our approach works as follows: first, we apply the well-known Lempel-Ziv-Welch (LZW) algorithm on the image in hand. What comes out of the first step is forward to the second step where the Bose, Chaudhuri and Hocquenghem (BCH) error correction and detected algorithm is used. To improve the compression ratio, the proposed approach applies the BCH algorithms repeatedly until "inflation" is detected. The experimental results show that the proposed algorithm could achieve an excellent compression ratio without losing data when compared to the standard compression algorithms.
Air quality forecasting has acquired great significance in environmental sciences due to its adverse affects on humans and the environment. The artificial neural network is one of the most common soft computing techniques that can be applied for modeling such complex problem. This study designed air quality forecasting model using three-layer FFNN's and recurrent Elman network to forecast PM 10 air pollutant concentrations 1 day advance in Yilan County, Taiwan. Then, the optimal model is selected based on testing performance measurements (RMSE, MAE, r, IA and VAF) and learning time. This study used an hourly historical data set from 1/1/2009-31/12/2011 collected by Dongshan station. The data was entirely pre-processed and cleared form missing and outlier values then transformed into daily average values. The final results showed that the three-layer FFNN with One Step Secant (OSS) training algorithm achieved better results than Elman network with Gradient Descent adaptive learning rate (GDX) training algorithm. Where, the FFNN required the less training time and achieved better performance in forecasting PM 10 concentrations. Also, the testing performance measurements shown that the selected daily average input variables in previous day (PM 2.5 ), relative humidity, PM 10 , temperature, wind direction and speed is critical to give better forecasting accuracy. Whereas, the testing measurements RMSE = 6.23 µg/m 3 , MAE = 4.75 µg/m 3 , r = 0.943, IA = 0.964 and VAF = 88.80 in PM 10 FFNN forecasting model that used OSS training algorithm.
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