Occam language enables programs to be written as a collection of self-contained programs (tasks) which may be executed simultaneously (parallel or concurrent) or simply one after another with a built-in inter-process communication mechanism, depending whether these processes are executed on a single machine or on separate ones. Occam is a high-level language, it can be viewed as the assembly language for the Transputer. Unlike most microprocessors, e. g., the M68000, the definition of the operations of the Transputer is in terms of the Occam model and not machine language. Besides being a high performance microprocessor (half the speed of a VAX 8600), the Transputer has on its chip four (4) serial bi- directional links (each 20 Megabits per second) to provide concurrent message passing to other Transputers. The “channels” in the Occam language are mapped to these hardware links which connect by way of twisted pairs of wires to other Transputers. Transputer hardware supports concurrency by scheduling (time-slicing), in round-robin fashion, an arbitrary number of Occam concurrent processes. The language and the hardware are so designed that an Occam program consisting of a collection of concurrent processes may execute on one Transputer (via time slicing between the different concurrent processes) or be spread over many Transputers with little or no change in the Occam code. Consequently, the designer can build up his Occam program on one Transputer, and if higher performance is required, can spread the Occam processes over a network of interconnected Transputers. The original Transputer (T414), having no floating point unit and only 2 Kbytes of RAM. Inmos has developed a new, faster version of the Transputer called the (T9000). The T9000 is a 150 MIPS microprocessor with a 20 MFLOPS floating point unit.
Nicolas Garcia-Aracil is Associate Professor of Control and Systems Engineering at Miguel Hernandez University (Spain). He holds a M.Sc. in Control Engineering by the University of Murcia (1996, Spain), Master in Design, Robotics and Industrial Automation from University of Murcia (Spain) 1996-1997 and a PhD in Control Engineering by the Miguel Hernandez University of Elche (Spain). His current research interests are medical and surgical robotics, rehabilitation robotics, medical image, computer vision, human-robot Interaction and design and control of new robotic devices. Dr. Nicolas Garcia was a 2004 recipient of the Best Thesis in Roboticsˇ, National Research Prize, from the Spanish Federation of Automatic control. He is author or co-author of a broad range of research publications. He served as Program Chair of the 2012 IEEE RAS\\EMBS International Conference on Biomedical Robotics and Biomechatronics (BIOROB, Roma, Italy) and the General Chair of EURON Winter School on Rehabilitation Robotics (Elche, Spain).
In this section, the structure of our system and function- alities of each component are described. The structure of ANFIS-based implicit authentication system is presented in Fig. 2 which includes an activity monitoring module, a scor- ing module, a reference computation module and an ANFIS module. The learning capability of ANFIS allows for training of specific fuzzy inference model based on given input-output user data without manual interference. Therefore, this trained fuzzy model represents well the user’s behaviour and can then be used to make final authentication decision to provide access control. The deployment of the system can be divided into two phases: training phase and deployment phase. During the training phase, we use generated input (scores, references) and output (classification labels) variables to train ANFIS module offline. When the training is completed the deployment phase starts, this enables the system to infer the authenticity of the current user behaviour by taking in real-time computed scores and references.
Image fusion  utilizing the convergence of wavelet transform and adaptive neurofuzzy logic is employed. The outcomes are likened with the pixel based image fusion in spatial domain with fuzzy and neurofuzzy logic methodology besides with the quality assessment indices for image fusion likes entropy, Root Mean Square Error, Peak Signal to Noise Ratio and Correlation Coefficient. Investigational outcomes demonstrate that the proposed procedure is superior to the further fusion approaches. Image fusion  is essential for proficient diseases diagnosis from multimodality, multidimensional and multi parameter category of images. Despite the fact of fusing multimodality images which derives information loss, incorrect edges, dark spots in tissue part and spatial distortion problem. To report these problems, a Neuro-Fuzzy logic grounded image fusion algorithm has been suggested. Later the output fused image is endorsed by utilizing the computable measures such as mean and standard deviation etc.Proposed  an approach for image fusion established on Wavelet Transform method and Fuzzy Logic approach to progress the geometric perseverance of the input images. In this proposed algorithm, input images will be processed, then disintegrated into sub-images and then the fusion technique is accomplished consuming these images underneath the convinced standards such as Wavelet Transform method and precise image fusion rules, and lastly these sub-images are administered under Fuzzy Logic approach and reassembled into the subsequent output fused image with bountiful information
Field blast tests of performance on the real scale have high costs and many limitations. Moreover, results of experimental models show great dependency on site conditions and experiment method. Under these conditions, statistical methods and AI-based methods (articial neural networks and fuzzy systems) with available data have opened up a new world for researchers. Articial neural network and neuro-fuzzysystem, despite its low cost (relative to experimental methods used to predict blast-induced liquefaction), are ecient and reliable methods in data processing, even despite various eective parameters and their complex relations.
to establish the empirical model precisely, because the variables mentioned above are non-linear variable parameters. Classical control has not been able to satisfy the high accuracy of the control request. So in this, combination of the intelligent control with the microcontrollers is proposed. The fuzzy control transforms the control policy indicated by the human natural language into the digital or mathematical function through the fuzzy set and the fuzzy inference, and then uses the computer to realize the predetermined control.
While tending to the semantic gap, one thought is the joining of nearby security strategies. While frequently dismissed in academic research, a crucial perception about operational networks is the extent to which they vary: Numerous security limitations are a site-particular property. Action that is fine in an academic setting can be banned in an endeavor network, and even inside a solitary association, division approaches can contrast generally. In this manner, it is urgent to suit such contrasts. For an abnormality detection system, the characteristic strategy to address site-specifics has the system “learn” them amid preparing with typical traffic. In any case, one cannot just affirm this as the answer for the subject of adjusting to various locales; one needs to expressly demonstrate it since the center issue worries that such varieties can demonstrate assorted and barely noticeable. Lamentably, as a rule, security strategies are not characterized freshly on a specialized level. For instance, a domain may tolerate shared traffic that the length of it is not utilized for conveying improper substance and that it stays, “underneath the radar” regarding volume. To report an infringement of such a strategy, the intrusion detection system would need a thought of what is considered “suitable” or “horrifyingly extensive” in that specific environment; a choice out of span for any of today’s systems. Reporting only the utilization of P2P applications is likely not especially valuable unless the earth level out bans such use. As far as we can tell, such dubious rules are really regular in numerous situations, and in some cases, begin in the uncertain lawful dialect found in the “terms of administration” to which clients must concur. The fundamental test with respect to the semantic gap sees how the components the oddity detection system operates on identify with the semantics of the network environment. Specifically, for any given decision
Abstract - The use of photovoltaic cells has become quite popular in the recent times as there is a growing demand in the energy sector. In this paper a novel Adaptive Neuro-Fuzzy Inference System has been utilized for Maximum Peak Power Transfer and it has been applied for multi-junction solar cells to provide better efficiency. The algorithm is designed utilizing adaptive neural and concept of fuzzy logic. The optimal value of firing angle’s is calculated and fed to the Boost converter. The results are compared to that of an incremental conductance technique and it has been found that our approach performs quite better than its traditional counterparts in terms of transient state and the voltage magnitude.
Health informatics (HI) is an interdisciplinary domain, which attempts to merge ‘Healthcare’ and ‘Technology’, effectively. In addition to e-Health (i.e., applications of Information Communication Technology in healthcare delivery ) and genomic research (applications of bioinformatics for disease diagnosis and control ), one developing domain of HI is the application of intelligent algorithms in the clinical decision making, popularly known as ‘Artificial Intelligence in Medicine’ (AIM) . Studies have revealed that intelligent algorithms are better suited in clinical medicine than traditional methods due to its ability to (i) handle uncertainty and (ii) gain maturity with time . Thus, AIM research is mostly application-based in nature to examine the efficacies of these algorithms on clinical data, for example symptoms and signs . Its key focus is to handle (i.e., capture, quantify, analyze, and model) subjective clinical symptoms for decision making, such as screening, diagnosis, and prognostic determination .
Mobile robot needs a robust controller to adapt the fast integration between the input and output due to the navigation in uncertain environment. Due to nonlinearity property of mobile robot, it is difficult to obtain absolute mathematical model of a system for designing its controller . Amongst the various techniques available in this paradigm, Fuzzy Logic Controller offers a promising solution to handle the vague and imprecise information because Fuzzybased controller does not require mathematical model of the system .
Experts usually rely on common sense when they solve problems. They also use indistinct and indecisive terms. Then how can we represent expert knowledge that uses indistinct and unclear terms in a computer? The initiation of fuzzy logic gave the answer to this question. Fuzzy logic reflects how people think. It attempts to model our sense of words, our decision making and our common sense. Fuzzy logic as defined by  is determined as a set of mathematical principles for knowledge representation based on degrees of membership rather than on crisp membership of classical binary logic while Boolean or conventional logic uses sharp distinctions. It forces us to draw lines between members of a class and non-members. Based on the human brain, a neural network can be defined as a model of reasoning. The neural network collectively can be used to solve interesting and difficult problems because they made up of a highly connected network of individual computing elements (mimicking neurons); neural networks can generalize to solve diverse problems that have related features when trained . One hybrid system that is the most evident today is neuro-fuzzy systems which apply a combination of artificial neural networks (ANN) and fuzzy systems . ANN’s have been engaged in several applications ranging from disease diagnoses to rain forecasting. Their most noticeable feature is to learn from examples, and then adapt themselves based on actual solution
The conversion of wind kinetic energy into electrical energy is of a multidisciplinary nature, involving aerodynamics, mechanical systems, Electrical Machines, Power Electronics, Control theory and power systems. All electric-generating wind turbines, no matter what size, are composed of a few basic components: Wind turbine (rotor- the part that actually rotates in the wind), electrical generator, a Power Electronic Converter, a speed control system and a tower.
In the first step, a random number is generated at the acquisition card and PIN will be sent with the mobile robot, it is calculated using an algorithm E22, which will also be made at the acquisition card and send to mobile robot that will compare the value received with the result, once this stage is reached, the authentication key will be created at the base of the robot ID, name, random number and PIN. To secure communication, capture card launches investigation passkey robot concerned to alert him to go explore the affected areas, once it verifies the key to looking robot, the robot will automatically move to the area.
Neural networks and fuzzy systems are dynamical, parallel processing systems that approximation input-output functions . Fuzzy logic is capable of modeling ambiguity, handling vagueness and supporting human-type reasoning. Whereas, neural-networks are capable of learning from scratch, without needing any a priori involvement provided that sufficient data are available or measurable. The neuro-fuzzy systems are the most prominent legislature of hybridizations in terms of the number of practical implementations. In NFS, the fuzzy inference system is the main subject of the hybridization; neural-network adds learning to an inference engine .
In the ﬁrst simulation, the outside weather conditions are T out = 35 C and w out = 4 g/kg (RH = 10%), while Si = 300 W/m 2 . The humidity ratio set point was raised from 18 to 24 g/kg (which corresponds to a relative humidity change from 60% to 80%) at t = 100 min, with the temperature set point 30 C; then the temperature set point was decreased from 30 to 28 C at t = 200 min (humidity ratio set point 24 g/kg), the responses for set point step changes in humidity ratio and temperature are in Fig. 4 . As we see the response under pro- posed G-ANFIS controller is very smooth and nearly close to set point than that given by ANFIS without tuning .The ventilation rate and water capacity of fog system as a control signals are shown in Fig. 5 . The simulation results clearly demonstrate the interacting control was attained and the closed-loop system response is very acceptable. Moreover, the response of the G-ANFIS controller is much faster than AFISN.
In the first technical contribution of the dissertation, we focus on a setup that considers a polygonal environment with holes and a simple robot equipped only with a clock and contact (or bump) sensors called a bouncing robot. We consider that the bouncing robot has access to a map of its environment, but is initially unaware of its position and orientation within that environment. This bouncing robot is modeled in a predictable way: the robot moves in a straight line and then bounces from the environment’s boundaries by rotating in place counterclockwise through a bouncing angle. The problem of global robot localization is how the robot deduces its pose (position and orientation) following its modeled behavior. Can this bouncing robot be globally localized without even knowing its initial pose? Different methods have been proposed to address this localization problem for robots with limited sensing [OL07, EKOL08, EL13]. In this contribution, we synthesize finite automata-based combinatorial filters for the global robot localization that take less computation time and memory compared to traditional Bayesian filter- based localization approaches [TFBD01, Fox03, LDW91].
has been identified to be one of the major challenges in the medical sector. Several techniques have been used in order to automate the processes in diagnosis of diseases; such processes include incorporation of artificial neural network and fuzzy logic techniques as expert systems with the knowledge about the domain. Such expert systems help to hasten the speed of diseases diagnosis, its accuracy and efficiency. In this paper, two artificial intelligence techniques; neural network and fuzzy logic system are considered. The two techniques are used to develop a hybrid intelligent computational system for the diagnosis of thyroid diseases. The experimental results obtain depend on the inputs to the neural network and also are set using a decision making system for making decisions on the thyroid disease according to the output of the neuro-fuzzysystem.
Asselt’s categories for futures studies that more strongly emphasizes cognitive uncertainty is foresight which deals with multiple possible and plausible future (Veenman 2013). Foresight draws conclusions for the present and is therefore a broad range policy instrument that can serve various objectives (Cuhls 2000). In fact, Foresight is pre- sented in a scenario study as a rich detailed portrait of a plausible future world, or as future states of a system (Ber- rogi 1997). A scenario is not a forecast but a plausible description of what might occur (Enserink et al. 2010). In foresight studies, future images are given with two or more scenarios (Schwartz 1991; Goodwin and Wright 2010). It is uncertain which trends develop, continue or stop, and which unexpected events might happen, since multiple, alternative futures are possible in foresight analysis (Veen- man 2013). Normative, is the third category of future stud- ies of Asselt et al. (2010). In contrast to forecasting and foresight studies, normative futures studies favor norma- tiveness instead of trying to be ‘neutral’ (Veenman 2013). The normative studies include two branches: backcast- ing and critical futures studies. Backcasting is concerned with how desirable futures can be created, rather than what futures are likely to occur. In backcasting, one envi- sions a desired future endpoint, and then works backward to determine what policy measures would be required to achieve such a future. Critical future studies emphasizes that images of possible futures are not neutral but repre- sent particular desires, values, cultural assumptions and world views (Asselt et al. 2010). Such future studies sketch a future that is considered ideal, for example, a situation of peace and tolerance, or a situation where the environ- mental burden is minimised. These types of future studies do not attempt to imagine one or more possible images of the future or one or more possible images of development without a statement being made about the desirability of it. According to Asselt’s category, this paper implemented the first category, forecasting.
ABSTRACT: This paper presents an approach of direct integration scheme for wind energy conversion systems using a capacitor-clamped three-level inverter-based supercapacitor. The main idea of this paper is to increase the capacitance of the clamping capacitors with the use of supercapacitors. The supercapacitor voltage is varied within a defined range. This variable voltage changes brings about many challenges. The uneven distribution of the space vector is the first challenge. To overcome this, space vector modulation technique is proposed. This generates undistorted currents even in the presence of dynamic changes in supercapacitor voltages. The harmonics are reduced by replacing conventional PI controllers with the Neuro-Fuzzy controllers which give better performance. The control strategies of the proposed system are discussed in detail. Simulation results are presented to study the efficacy of the proposed system in reducing wind power fluctuations.