The electrical short term load forecasting has been emerged as one of the most essential field of research for efficient and reliable operation of power system in last few decades. It plays very significant role in the field of scheduling, contingency analysis, load flow analysis, planning and maintenance of power system. This paper addresses a review on recently published research work on different variants of artificial neural network in the field of short term load forecasting. In particular, the hybrid networks which is a combination of neural network with stochastic learning techniques such as genetic algorithm(GA), particle swarm optimization (PSO) etc. which has been successfully applied for short term load forecasting (STLF) is discussed thoroughly.

No abstract

No abstract

No abstract

In this paper, Baseline JPEG standard using has been implemented along with encoding and decoding of gray scale images in JPEG. The first step in encoding starts by dividing the image in 8*8 blocks into sub-images on which DCT is performed which is followed by dividing the resulted matrices by a Quantization matrix. The algorithm ends by making the data one-dimensional which is done by Zigzag Coding and composed by Arithmetic coding or Huffman Coding. Reversing the process of encoding results in decoding process. At first, the received bit stream is converted back into two-dimensional matrices and multiplied by Quantization matrix and Inverse DCT is performed and the sub-images are connected to restore the image. Effect of coefficients on the image restored and difference between compression ratios has been presented in the paper.