In recent years, indoor air quality is being a serious problem. The elevated levels of carbon dioxide make dramatic changes of climate. Global climate is suffering from environmental crises. High concentrations of carbon dioxide levels will have a bad effect on the environment and human may cause hypoxia, numb hands and feet, lose consciousness or even death. In this paper, we will set up a system to monitor the ambient air quality at indoor applications which using carbon dioxide detectors to combine mobile devices and networks to detect air quality. Users can get information by using a mobile phone or computer to read QR code or mobile communication confirm whether the places are safe and comfortable environments. The primary experiments proved that the system can be used to monitor the indoor air quality and temperature effectively.

In this paper, both effective bandwidth extension and distributed issues are investigated to improve the energy efficiency for the orthogonal frequency division multiplexing (OFDM) wireless communication systems. A Fuzzy based energy efficient bandwidth expansion (FBEEBE) scheme is proposed to fast infer the bandwidth extension factors (BEFs) for users based on fuzzy inference systems. The proposed fuzzy inference system can infer the BEFs from the two inputs of SNR and bandwidth requirement of users. Simulation results show that the proposed FBEEBE can improve the energy efficiency comparing to that of same bandwidth extension (SBE) scheme.

In recent years, the position location applications have increasingly. In this paper, we will use multiple Back-Propagation neural networks with genetic algorithm GA for a radio frequency identification RFID indoor location system to provide location services named indoor location with multiple neural networks and genetic algorithms ILMNGA. In Section 1, we collect received signal strength RSS information from reference points to train the neural network models. In Section 2, genetic algorithm GA is used to find the weight of each neural network based on the performance of each neural network. Finally, we input the RSS information of each tracking object into the model that will provide the location of tracking objects based on the RSS information. The location will be integrated using the weights produced by the GA. The experiment conducted our methodology can provide better accuracy than a single neural network.

Many FAQ (Frequently Asked Questions) systems have been proposed on the Internet. Users can easily obtain the information what they need, but they still have to read and organize those documents by themselves. However, they read the same contents from website in spite of men or women, elders or children. For different patients of diabetes, if a FAQ system can return personal health nursing documents that will be better. According the Department of Health Executive Yuan R.O.C. (Taiwan) to the recent statistics, most of patients lack chronic disease knowledge (e.g. diabetes, hypertension). Hospitals preach patients the knowledge of taking care of chronic disease. The knowledge can be shown by a narrator of the medical personnel, books, website to quickly help patients. However, the return of FAQ website information is fix to the users' questions. In this paper, we will set up a FAQ system based on domain ontology for diabetes health education documents and use FFCA (Fuzzy Formal Concept Analysis) method to help diabetic patients to get education documents. The primary experiments prove that our method is preciously to get users requirement documents.

Car navigation systems are now widely used as a component for intelligent transportation systems. Route planning is the most important task of car navigation systems. Though modern car navigation systems incorporate various road information, even dynamic information, to generate optimal route, but they are yet to present routes according to driver's requirements or preferences. Most of the car navigation systems present a single best route or alternate routes according to systems predefined choices which may not satisfy the driver. In this work a fuzzy genetic approach is proposed to generate alternate routes according to driver's requirement and choice with fine tuning by using feed back mechanism. A simple simulation experiment proves the effectiveness and importance of the concept for developing more user friendly car navigation system with an outline of implementation.

The sequential deletion method is generally used to locate the functional domain of a protein. With this method, in order to find the various N-terminal truncated mutants, researchers have to investigate the ATG-like codons, to design various multiplex polymerase chain reaction (PCR) forward primers and to do several PCR experiments. This web server (N-terminal Truncated Mutants Generator for cDNA) will automatically generate groups of forward PCR primers and the corresponding reverse PCR primers that can be used in a single batch of a multiplex PCR experiment to extract the various N-terminal truncated mutants. This saves much time and money for those who use the sequential deletion method in their research. This server is available at http://oblab.cs.nchu.edu.tw:8080/WebSDL/.

The fuzzy color histogram (FCH) spreads each pixel's total membership value to all histogram bins based on their color similarity. The FCH is insensitive to quantization errors. However, the FCH can state only the global properties of an image rather than the local properties. For example, it cannot depict the color complexity of an image. To characterize the color complexity of an image, this paper presents two image features - the color variances among adjacent segments (CVAAS) and the color variances of the pixels within an identical segment (CVPWIS). Both features can explain not only the color complexity but also the principal pixel colors of an image. Experimental results show that the CVAAS and CVPWIS based image retrieval systems can provide a high accuracy rate for finding out the database images that satisfy the users' requirement. Moreover, both systems can also resist the scale variances of images as well as the shift and rotation variances of segments in images.

Almost all form documents contain line segments. In this paper, we propose an efficient method to recognize the form document that contains at least one line segment. Our method is based on an efficient representation model of the form. The representation model uses three types of line segments to represent a form. All line segments are normalized and sorted after they were extracted. The normalization and sorting not only solve the form scaling problem but also provide an unified and efficient way of matching between forms. To make the recognition method more robust, a fuzzy matching is used. Using the representation model, when recognizing a skew form, only the line segments and the data fields instead of the whole form image need to be rotated. Experimental results show the effectiveness and the efficiency of the method.