In the current heterogeneous wireless communication system, the sharp rise in energy consumption and the emergence of new service types pose great challenges to nowadays radio access network selection algorithms which do not take care of these new trends. So the proposed energy efficiency based multi-service heterogeneous access network selection algorithm-ESRS (Energy Saving Radio access network Selection) is intended to reduce the energy consumption caused by the traffic in the mobile network system composed of Base Stations (BSs) and Access Points (APs). This algorithm models the access network selection problem as a Multiple-Attribute Decision-Making (MADM) problem. To solve this problem, lots of methods are combined, including analytic Hierarchy Process (AHP), weighted grey relational analysis (GRA), entropy theory, simple additive weight (SAW), and utility function theory. There are two main steps in this algorithm. At first, the proposed algorithm gets the result of the user QoS of each network by dealing with the related QoS parameters, in which entropy theory and AHP are used to determine the QoS comprehensive weight, and the SAW is used to get each network's QoS. In addition to user QoS, parameters including user throughput, energy consumption utility and cost utility are also calculated in this step. In the second step, the fuzzy theory is used to define the weight of decision attributes, and weighted grey relational analysis (GRA) is used to calculate the network score, which determines the final choice. Because the fuzzy weight has a preference for the low energy consumption, the energy consumption of the traffic will be saved by choosing the network with the least energy consumption as much as possible. The simulation parts compared the performance of ESRS, ABE and MSNS algorithms. The numerical results show that ESRS algorithm can select the appropriate network based on the service demands and network parameters. Besides, it can effectively reduce the system energy consumption and overall cost while still maintaining a high overall QoS value and a high system throughput, when compared with the other two algorithms.

Driver behavior assessment is a hard task since it involves distinctive interconnected factors of different types. Especially in case of insurance applications, a trade-off between application cost and data accuracy remains a challenge. Data uncertainty and noises make smart-phone or low-cost sensor platforms unreliable. In order to deal with such problems, this paper proposes the combination between the Belief and Fuzzy theories with a two-level fusion based architecture. It enables the propagation of information errors from the lower to the higher level of fusion using the belief and/or the plausibility functions at the decision step. The new developed risk models of the Driver and Environment are based on the accident statistics analysis regarding each significant driving risk parameter. The developed Vehicle risk models are based on the longitudinal and lateral accelerations (G-G diagram) and the velocity to qualify the driving behavior in case of critical events (e.g. Zig-Zag scenario). In case of over-speed and/or accident scenario, the risk is evaluated using our new developed Fuzzy Inference System model based on the Equivalent Energy Speed (EES). The proposed approach and risk models are illustrated by two examples of driving scenarios using the CarSim vehicle simulator. Results have shown the validity of the developed risk models and the coherence with the a-priori risk assessment.

In this paper, we describe a new ultrasonic oscillosensor and its application in a biological information measurement system. This ultrasonic sensor has a cylindrical tank of 26 mm (diameter)20 mm (height) filled with water and an ultrasonic probe. It detects the vibration of the target object by obtaining echo signals reflected from the water surface. This sensor can noninvasively detect the vibration of a patient by placing it under a bed frame. We propose a recognition system for humans in bed. Using this sensor, we could determine whether or not a patient is in the bed. Moreover, we propose a heart rate monitoring system using this sensor. When our system was tested on four volunteers, we successfully detected a heart rate comparable to that in the case of using an electrocardiograph. Fuzzy logic plays a primary role in the recognition. Consequently, this system can noninvasively determine whether a patient is in the bed as well as their heart rate using a constraint-free and compact device.

This paper proposed a novel intelligent intrusion detection, decision, response system with fuzzy theory. This system utilized the two essential informations: times and time, of the failed login to decide automatically whether this login is a misuse user as alike as experienced system/security administrators. The database of this system isn't preestablished before working but is built and updated automatically during working. And this system is not only notification system but gives the exact and rapid decision and response to a misuse.

The Asynchronous Transfer Mode (ATM) technique has been accepted as a basis for the future B-ISDN networks. In ATM networks, all information is packetized and transferred in small packets of fixed length, called cells. The packetized information transfer, without flow control between the user and the network and the use of statistical multiplexing, results in a need of a policing mechanism to control the traffic parameters of each virtual connection in order to guarantee the required quality of service (QoS). Policing of the peak cell rate is generally not complex and can be achieved by using a cell spacer or other policing mechanisms (PMs). Monitoring of the mean cell rate is more difficult, but is intended to improve the link utilization when it has to handle bursty traffic sources. Conventional PMs, such as the Leaky Bucket Mechanism (LBM) and Window Mechanisms (WMs), are not well suited to the bursty nature of the sources supported by ATM networks, therefore intelligent PMs are needed. In this paper, we propose a Fuzzy Policing Mechanism (FPM) for multimedia applications over ATM networks. We consider the case of still picture source control. The performance evaluation via simulation shows that the FPM efficiently controls the mean cell rate of the still picture source. The proposed FPM shows a good response behavior against parameter variations and the selectivity characteristics approach very close to the ideal characteristic required for a PM. The FPM has a better characteristic compared with the LBM.

ATM networks are proposed by CCITT as the solution for the future B-ISDN. In ATM networks, the cells are transmitted between user and network without flow control, therefore, a policing mechanism (PM) is needed to check that the source traffic doesn't exceed the negotiated parameters. The sources supported by ATM networks have a bursty nature. The control of the mean cell rate of the bursty sources is intended to increase the network utilization. The conventional PMs can't efficiently monitor the mean cell rate of bursty sources, therefore new PMs are needed. In this letter, we propose a fuzzy policing mechanism (FPM). The performance evaluation via simulations shows that the FPM efficiently controls the mean cell rate of the packet voice source. The selectivity characteristics of the FPM approach the ideal characteristic required for a PM.

In this work, we introduce a fuzzy inference in conventional backpropagation learning algorithm, for networks of neuron like units. This procedure repeatedly adjusts the learning parameters and leads the system to converge at the earliest possible time. This technique is appropriate in a sense that optimum learning parameters are being applied in every learning cycle automatically, whereas the conventional backpropagation doesn't contain any well-defined rule regarding the proper determination of the value of learning parameters.

In this paper, voltage-input current-output Membership Function Circuit (MFC) and Normalization Locked Loop (NLL) are proposed. They are useful building blocks for the current-mode analog fuzzy hardware. The voltage-input current-output MFC consists of one source coupled type Operational Transconductance Amplifier (OTA). The MFC is used in the input parts of the analog fuzzy hardware system. The fuzzy hardware system can execute the singleton fuzzy control algorithm. In the algorithm, the weighted average operation is processed. When the weighted average operation is directly realized by analog circuits, a divider must be implemented. Here, the NLL circuit, which can process the weighted average operation without the divider, is implemented using one source coupled type OTA. The proposed circuits were designed by using 2 µm CMOS design rules and its operations were confirmed using SPICE simulations.