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Brain Computer Interface (BCI) based environment control systems could facilitate life of people with neuromuscular diseases, reduces dependence on their caregivers, and improves their quality of life. As well as easy usage, low-cost, and robust system performance, mobility is an important functionality expected from a practical BCI system in real life. In this study, in order to enhance users' mobility, we propose internet based wireless communication between BCI system and home environment. We designed and implemented a prototype of an embedded low-cost, low power, easy to use web server which is employed in internet based wireless control of a BCI based home environment. The embedded web server provides remote access to the environmental control module through BCI and web interfaces. While the proposed system offers to BCI users enhanced mobility, it also provides remote control of the home environment by caregivers as well as the individuals in initial stages of neuromuscular disease. The input of BCI system is P300 potentials. We used Region Based Paradigm (RBP) as stimulus interface. Performance of the BCI system is evaluated on data recorded from 8 non-disabled subjects. The experimental results indicate that the proposed web server enables internet based wireless control of electrical home appliances successfully through BCIs.

Conventional (type-1) fuzzy logic controllers have been commonly used in various power converter applications. Generally, in these controllers, the experience and knowledge of human experts are needed to decide parameters associated with the rule base and membership functions. The rule base and the membership function parameters may often mean different things to different experts. This may cause rule uncertainty problems. Consequently, the performance of the controlled system, which is controlled with type-1 fuzzy logic controller, is undesirably affected. In this study, a type-2 fuzzy logic controller is proposed for the control of buck and boost DC-DC converters. To examine and analysis the effects of the proposed controller on the system performance, both converters are also controlled using the PI controller and conventional fuzzy logic controller. The settling time, the overshoot, the steady state error and the transient response of the converters under the load and input voltage changes are used as the performance criteria for the evaluation of the controller performance. Simulation results show that buck and boost converters controlled by type-2 fuzzy logic controller have better performance than the buck and boost converters controlled by type-1 fuzzy logic controller and PI controller.

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This paper presents a fault diagnosis system for a serial wound starter motor based on multilayer feed forward artificial neural network (ANN). Starter motor acts an Internal Combustion (IC) engine and has a vitalimportance for all vehicles. That is because, if the starter motor fault occurred, the vehicle cannot be run. Especially in emergency vehicles (ambulance, /ire engine, etc) starter motorjaults causes the faults. This ANN based .fault detection system has been developed for implementation on the emergency vehicles. Information of starter motor current is acquired and then it is practiced on a neural network fault diagnosis ("FD) system. The multilayer feed forward neural nehvork structures are used. Feed forward neural network is trained using the back propagation algorithm. NNFD system is efective in detection of six types of starter motor faults. NNFD system is able to diagnose the faults that can be seen in most frequencies in starter motors.

Attacks on web applications and web-based services were conducted using Hyper-Text Transfer Protocol (HTTP), which is also used as the communication protocol of web-based applications. Due to the dynamic structure of web applications and the fact that they have many variables, detection and prevention of web-based attacks are made more difficult. In this study, a hybrid learning-based web application firewall (WAF) model is proposed to prevent web-based attacks, by using signature-based detection (SBD) and anomaly-based detection (ABD). Detection of known web-based attacks is done by using SBD, while detection of anomaly HTTP requests is done by using ABD. Learning-based ABD is implemented by using Artificial Neural Networks (ANN). Thus, an adaptation of the model against zero-day attacks is ensured by learning-based ABD by using ANN. The proposed model is tested by using WAF 2015, CSIC 2010 and ECML-PKDD datasets which are open source datasets. According to the test results, a high mean achievement percentage (96.59 %) was obtained. Detection results are also compared to previous studies. After comparison, the proposed model promises higher performance than what the existing studies until now have to offer.