Outdoor machine vision is getting a concern nowadays. Ranging from surveillance and monitoring system to automotive system such as driver assistance system require vision application or artificial eye to keep monitoring the situations. However, most of these applications works very well during clear weather and degrade during bad weather due to the atmospheric particles mitigate the quality of vision system. This paper discuss the state of the art of image enhancement techniques used to adjust the contrast of an outdoor image degrade by fog, haze, and rain. A brief overview of bad weather will be discussed and several recent techniques on removing fog, haze and rain are discussed. � 2013 IEEE.

Reservoir water level monitoring is an important process during heavy or light rainfall to determine the volume of reserved water. Mistakes in data recording by the dam operator can lead to disasters. Data from different gauging stations are collected to determine whether to release water in the dam or not. The decision to release water is critical because it can affect the volume of water left in the dam for both drought and flood seasons. Constant water level monitoring is difficult because of the changes in water level. To overcome this issue, intelligent agent-based architecture is proposed for reservoir water level monitoring by imitating the artificial immune system. This paper presents the agent technology where agents communicate with each other concurrently by sending online data from different gauging stations to the main reservoir. One of the techniques in the artificial immune system is known as negative selection and this technique has been chosen as a water level monitoring model. � Published under licence by IOP Publishing Ltd.

Case-based reasoning (CBR) considers previous experience in form of cases to overcome new problems. It requires many solved cases in case base in order to produce a quality decision. Since today, database technology has allowed CBR to use a huge case storage therefore the case retrieval process also reflects the final decision in CBR. Traditionally, sequential indexing method has been applied to search for possible cases in case base. This technique is worked fast when the number of cases is small but it consumes more time to retrieve when the number of data grows in case base. To overcome the weakness, this study researches the nonsequential indexing called hashing as an alternative to mine large cases and faster the retrieval time in CBR. Hashing indexing searches a record by determines the index using only an entry's search key without traveling to all records. This paper presents the review of a literature and early stages of the integration hashing indexing method in CBR. The concept of hashing indexing in case retrieving process, the model development, and the preliminary algorithm testing result will be discussed in this paper. � 2010 IEEE.