Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Single Phase bridgeless AC/DC PFC converter for Low Total Harmonics Distortion and High Power Factor

Now day’s the power factor has become a major problem in power system to improve the power quality of the grid, as power factor is affected on the grid due to the nonlinear loads connected to it. Single phase bridgeless AC/DC power factor correction (PFC) topology to improve the power factor as well as the total harmonic distortion (THD) of the utility grid is proposed. By removing the input bridge in conventional PFC converters, the control circuit is simplified; the total harmonics distortion (THD) and power factor (PF) are improved. The PI controller operates in two loops one is the outer control loop which calculates the reference current through LC filter and signal processing. Inner current loop generates PWM switching signals through the PI controller. The output of the proposed PFC topology is verified for prototype using MATLAB circuit simulations. The experimental system is developed, and the simulation results are obtained.
Cognitive Radars: A Reality?.

This paper describes some key ideas and applications of cognitive radars, highlighting the limits and the path forward. Cognitive radars are systems based on the perception-action cycle of cognition that sense the environment, learn from it relevant information about the target and the background, then adapt the radar sensor to optimally satisfy the needs of their mission according to a desired goal. The concept of cognitive radar was introduced originally for active radar only. In this paper we describe how this paradigm can be applied also to passive radar. In particular, we describe (i) cognitive active radars that work in a spectrally dense environment and change the transmitted waveform on-the-fly to avoid interference with the primary user of the channel, such as broadcast or communication systems, (ii) cognitive active radars that adjust transmit waveform parameters to achieve a specified level of target tracking performance, and (iii) cognitive passive radars, that contrary to the active radars cannot directly change the transmitted waveforms, but can instead select the best source of opportunity to improve detection and tracking performance.
Supporting Sensemaking during Collocated Collaborative Visual Analytics

Sensemaking (i.e. the process of deriving meaning from complex information to make decisions) is often cited as an important and challenging activity for collaborative technology. A key element to the success of collaborative sensemaking is effective coordination and communication within the team. It requires team members to divide the task load, communicate findings and discuss the results. Sensemaking is one of the human activities involved in visual analytics (i.e. the science of analytical reasoning facilitated by interactive visual interfaces). The inherent complexity of the sensemaking process imposes many challenges for designers. Therefore, providing effective tool support for collaborative sensemaking is a multifaceted and complex problem. Such tools should provide support for visualization as well as communication and coordination. Analysts need to organize their findings, hypotheses, and evidence, share that information with their collaborators, and coordinate work activities amongst members of the team. Sharing externalizations (i.e. any information related to the course of analysis such as insights, hypotheses,
Wireless Environment Aware Adaptive Scheduling Technique For Cellular Networks

It is now well known that employing channel knowledge based on signaling techniques in wireless mobile ad-hoc networks (MANET) system can yield large improvements in almost all performance metric. Here we proposed the adaptive scheduling, in which the work done is based upon the bandwidth information of channel to provide better quality of service (‘QoS’) to the cell-edge mobile stations. Channel information is critical based on which scheduling is carried out. The bandwidth channel information contains estimation delay, the pilot channel noise and pilot contamination. Afterwards, Zero Forcing precoding methodology has applied for removing the interference at user nodes, destination nodes and gateway side. By extending the characteristics of ZF, the modified Zero Forcing (MZF) has proposed to achieve higher throughput rate and higher spectrum efficiency. The achievable-rates of the ZF and MZF has derived under the comprehensive model of imperfect bandwidth information.
Toward social mechanisms of android science

Toward Social Mechanisms of Android Science was the first workshop at which researchers from various disciplines joined to discuss the application of very humanlike robots to the study of interaction and cognition and the social impact of this technology. It was the only workshop of the 27th Annual Conference of the Cognitive Science Society. The main conference was held at the Convention Centre, in Stresa, Italy on the 21st through to the 23rd of July. On the 25th and 26th, the Android Science workshop took place next door, at the Regina Palace Hotel on picturesque Lake Maggiore. For the purposes of android science, an android is an artificial system designed with the ultimate goal of being indistinguishable from humans in its external appearance and behavior. The original premise of the workshop is that androids that look and act like people can elicit from human subjects a range of social responses that only other people had been previously able to elicit (Ishiguro, 2005; MacDorman et al., 2005). This ability to elicit social responses enables androids to provide not just a well-controlled experimental apparatus for studying human interaction but a testbed for developing theories about how neural or cognitive processes influence interaction. Android development extends beyond the scope of engineering because, to make androids humanlike, it is necessary to investigate human interaction, and to evaluate theories of human interaction accurately, the theories need to be implemented in androids (Minato et al., 2004). Since mechanisms supporting human interaction come into play when people relate to machines, it is essential to examine their human likeness.
RAFI - A STEREO VISION BASED AUTONOMOUS MOBILE AREA MAPPING ROBOT WITH 16 DOF

RAFI is a self-driven mapping robot with 16 Degrees-of-Freedom (DOF). Its software development, structural and electrical design and mechanical construction are presented in this paper. RAFI moves with wheels having differential type motion with 2DOF. The head has 3 DOF sufficient for volumetric mapping by rotating the head in different directions and both hands have 5 DOF to empower its grip and carrying. An 8-bit microcontroller is used to control all the motors. 4 Ultrasonic-rangefinders have been used for obstacle-detection and avoidance which are also interfaced with the same microcontroller. Its head is equipped with two identical webcams for stereoscopic vision for generating 3D map of the field of view after generating disparity map. To optimize the computational speed and mapping accuracy images of 640×480 resolution are 85% compressed and dynamic programming with image pyramiding by quadpyramid without sub-pixel estimation is pursued.
Major Concerns In Clock Recovery Of Manchester Encoded Data Using a Phase Lock Loop

Name: Vinay Chitturi Date of Degree: December 2005 Institution: Oklahoma State University Location: Stillwater, Oklahoma Title of Study: Major Concerns In Clock Recovery Of Manchester Encoded Data Using A Phase Lock Loop Pages in Study: 98 Candidate for the Degree of Master of Science Major Field: Electrical Engineering Signals traveling in communication systems pick up noise and delays while traversing in their respective mediums. Also, the data is asynchronous when we take into consideration the receiver and the transmitter clocks. For effective communication between the two units there should be a mechanism of recovering the original clock and signal from the asynchronous and distorted or noisy signal – thus the need for a Clock Recovery Circuit. The target application is the recovery of clock and data from Manchester encoded data stream generated by a Manchester Encoder-Decoder designed using the Harsh Environment Cell Library developed in the MSVLSI lab. This thesis also presents the successful test results for the 16 bit (single write) Manchester encoder/decoder pair for temperatures up to 195°C. In any Clock Recovery System the PLL is the crucial block. The PLL charge pump generates the control signal for the Voltage Controlled Oscillator; any disturbance on its output adversely affects the performance of the VCO. This in turn has a significant impact on the locking behavior of the PLL. Factors contributing to the non-ideal operation are recognized and ways to minimize their effect are discussed. A new term called “frequency jump”, referring to the frequency shifts due to non-ideal charge pump operation, is coined. Owing to the presence of “kink effect” in the 0.5u Peregrine SOS process the transistors in the VCO don’t bias up to the designed levels. To ensure proper operation of the circuit the drain to source voltage across the affected transistors has to be controlled to avoid the “kink effect” from degrading performance. A technique is proposed in this thesis which attempts to prevent the drain to source voltages from becoming too high. ADVISOR’S APPROVAL: Dr. Chris Hutchens__________________
Mathematical Modeling of Biological Networks

Author(s): Mardanlou, Vahid | Advisor(s): Franco, Elisa | Abstract: We derive a coarse-grained model that captures the temporal evolution of DNA nanotube length distribution during growth experiments. The model takes into account nucleation, polymerization, joining, and fragmentation processes in the nanotube population. The continuous length distribution is segmented, and the time evolution of the nanotube concentration in each length bin is modeled using ordinary differential equations. The binning choice determines the level of coarse graining. This model can handle time varying concentration of tiles, and we foresee that it will be useful to model dynamic behaviors in other types of biomolecular polymers.We design a new biomolecular circuit with the potential for bistable behavior. We study this candidate toggle switch and find sufficient conditions on key parameters that guarantee bistability. The circuit structure is based on a positive feedback loop created by the mutual repression of two synthetic genes. Repression is generated not by direct inactivation of the promoter region, but by inactivation of the enzymes performing transcription. Inactivation is experimentally possible by designing the RNA outputs of the two genes to be inhibitory aptamers for the enzymes.In a synthetic biological network it may often be desirable to maximize or minimize parameters such as reaction rates, fluxes and total concentrations of reagents, while preserving a given dynamic behavior. We consider the problem of parameter optimization in biomolecular bistable circuits. We show that, under some assumptions often satisfied by bistable biological networks, it is possible to derive algebraic conditions on the parameters that determine when bistability occurs. These (global) algebraic conditions can be included as nonlinear constraints in a parameter optimization problem. We consider two simple case studies for which we derive bistability conditions using Sturm's theorem, and optimize their nominal parameters to improve switching speed and robustness to a subset of uncertain parameters.
Step Size Optimization of LMS Algorithm Using Particle Swarm Optimization Algorithm in System Identification

Summary System identification is the art and science of building mathematical models of dynamic systems from observed inputoutput data . This paper combines Particle Swarm Optimization Algorithm and LMS algorithm to describe the application of a Particle swarm Optimization (PSO) to the problem of parameter optimization for an adaptive Finite Impulse Response (FIR) filter. LMS algorithm computes the filter coefficients and PSO search the optimal step-size adaptively. Because step-size influences on the stability and performance, so it is necessary to apply method that can control it. However, the statistical Least Mean Squares method is faster than the genetic algorithm. For this reason we suggest using the genetic algorithm for off-line applications, and the statistical method for on-line adaptation. A hybrid method combining the advantages of both methods is proposed for real world applications.
Learning methods for multi-label classification

Multi-label classification is an extension of traditional single-label classification, where classes are not mutually exclusive, and each example can be assigned to several classes simultaneously. It is encountered in various modern applications such as scene classification and video annotation. The main objective of this thesis is the development of new techniques to address the problem of multi-label classification. The first part of this manuscript studies the problem of multi-label classification in the context of the theory of belief functions. We propose a multi-label learning method that is able to take into account relationships between labels and to classify new instances using the formalism of uncertainty representation for set-valued variables. The second part deals with the problem of prototype selection in the framework of multi-label learning. We propose an editing algorithm based on the k-nearest neighbors rule in order to purify training datasets and improve the performances of multi-label classification algorithms. Experimental results on synthetic and real-world datasets show the effectiveness of our approaches.
APPLICATION AND COMPARISON OF DIFFERENT LINEAR CLASSIFICATION METHODS FOR BREAST CANCER DIAGNOSIS

Aim: Breast cancer is one of the most common cancers among women compared to all other ones. Proper and early detection of tumor type in a patient is a critical subject that can increase the survival chance. Statistical methods, such as classification can be useful instrument for physicians as an auxiliary tool to detect the type of tumor, increase the speed, reduce the cost and time of diagnosis as well as reduce the error of physician in the diagnosis of the tumor. Methods: In this paper, we use multivariate linear regression, logistic regression, the K-nearest neighbor (KNN) method and discriminant analysis to determine tumor type in a patient using Wisconsin Breast Cancer (WBC) database. In addition to linear methods, quadratic discriminant analysis will be used. Stepwise method for variable selection is used in regression method. Results: The accurate percentage of classification on testing data, depends on selected method and the number of independent variables and at least equal to 90.8% for logistic regression and a maximum 99.6% for KKN with K=9. Our results show about 3.1% of doctors' diagnoses in the breast cancer may be incorrect. Conclusion: In General, if the assumptions used in each method is established, it can be said that the difference is not significant in choosing model type, and all of these methods are good tool for increasing precision of breast diagnosis.
A framework for synchronous and ubiquitous collaboration

With the advance of a variety of software/hardware technologies and wireless networking, there is coming a need for ubiquitous collaboration which allows people to access information systems independent of their access device and their physical capabilities and to communicate with other people in anytime and anywhere. Also, with the maturity of evolving computing paradigms and collaborative applications, a workspace for working together is being expanded from locally collocated physical place to geographically dispersed virtual place. Current virtual conferencing systems are not suitable for building integrated collaboration systems to work together in the same collaboration session. They also lack support for ubiquitous collaboration. As the number of collaborators with a large number of disparate access devices increases, the difficulties for protecting secured resources from unauthorized users as well as unsecured access devices will increase since the resources can be compromised by inadequately secured human and devices. Collaboration generally includes sharing resources. Mechanisms for dealing with consistency in application shared among collaborators will have to be considered in an unambiguous manner. In this dissertation we address a number of issues related in building a framework for synchronous and ubiquitous collaboration as well as heterogeneous community collaboration. First, to make ubiquitous collaboration more promising, we present a framework built on heterogeneous (wire, wireless) computing environment. Second, to provide a generic solution for controlling sessions and participants' presences in heterogeneous community collaboration, we present a set of session protocols defined in XML. Third, to provide a solution for controlling accesses to resources, we present a flexible and fine-grained access control mechanism based on Role Based Access Control model. Fourth, to provide a solution for maintaining shared state consistency at application level, we present a floor control mechanism which coordinates activities occurred in synchronously cooperating applications being shared among collaborators. The mechanism with strict conflict avoidance and non-optimistic locking strategy allows all participants to have the same views and data at all times. Finally, we give detailed experimental measurements to demonstrate the viability of the control mechanisms.
AN EVOLVED NEURAL NETWORK FOR FAST QUADRUPEDAL LOCOMOTION

This paper presents a modular neural network controller for fast locomotion of a quadruped robot. It was generated by artificial evolution techniques using a physical simulation of the Sony Aibo ERS-7. Co-evolution was used to develop neuromodules controlling the single legs as well as the coordination between the four legs. The final neurocontroller utilizes a central pattern generator and does not make use of available sensory inputs. In experiments with the physical robot a top walking speed of 47.34 cm/s was measured, where lateral leg movement contributed considerably to the achieved high velocity.
Voltage Stability in Power Systems using Artificial Intelligent Approaches

— The events such as the increase of load demands constantly in power systems, the continuous growth of electricity networks, the use of new technologies and the controls have led to the emergence of system stability problems in different structures. For the design and operation of the power system in the optimum level, instabilities in different ways and their relations with each other should be revealed clearly. To prevent great economic losses in the system as a result of the possible instabilities, it is necessary to predict stability criteria and do the necessary regulatory works. In this study, critical values for the system voltage stability have been obtained with the Power System Analysis Toolbox (PSAT) program. By using obtained values, artificial neural network (ANN) and least squares support vector mechanism (LSSVM) algorithms have been determined with the voltage stability critical values. The achievements of ANN and LSSVM algorithms, trained with the help of power system voltage stability critical values obtained under different conditions, on the voltage stability analysis have been shown. Therefore, voltage stability analyzes have been carried out by establishing 13 busbar power system in Elazig in the North-eastern Anatolia region. According to obtained the results, a very successful fixation of voltage stability has been carried out.
Critical resources management and scheduling under StarPU

The advanced users of StarPU have the ability to regulate the task submission to limit/control the consumption of critical resources (memory, network communication). This technique can penalize performance (parallelism deficit) and is not adapted to the concurrent management of several types of resources. The use of hierarchical tasks allow to refocus this management at the level of the scheduling engine: high-level tasks are used to express parallelism and resources requests. Once this graph is submitted, StarPU has the clairvoyance necessary to optimize the parallel execution of the application while respecting the constraints expressed.
Parallel Multi-feature Attention on Neural Sentiment Classification

The analysis of the review's sentiment polarity is a fundamental task in NLP. However, most of the existing sentiment classification models only focus on extracting features but ignore features' own differences. Additionally, these models only pay attention to content information but ignore the user's ranking preference. To address these issues, we propose a novel Parallel Multi-feature Attention (PMA) neural network which concentrates on fine-grained information between user and product level content features. Moreover, we use multi-feature, user's ranking preference included, to improve the performance of sentiment classification. Experimental results on IMDB and Yelp datasets show that PMA model achieves state-of-the-art performance.
Logic programming based formal representations for authorization and security protocols

Logic programming with answer set semantics has been considered appealing rulebased formalism language and applied in information security areas. In this thesis, we investigate the problems of authorization in distributed environments and security protocol verification and update. Authorization decisions are required in large-scale distributed environments, such as electronic commerce, remote resource sharing, etc. We adopt the trust management approach, in which authorization is viewed as a “proof of compliance” problem. We develop an authorization language AL with nonmonotonic feature as the policy and credential specification language, which can express delegation with depth control, complex subject structures, both positive and negative authorizations, and separation of duty concepts. The theoretical foundation for language AL is the answer set semantics of logic programming. We transform AL to logic programs and the authorization decisions are based on answer sets of the programs. We also explore the tractable subclasses of language AL. We implement a fine grained access control prototype system for XML resources, in which the language AL simplified from AL is the policy and credential specification language. We define XPolicy , the XML format of AL, which is a DTD for the XML policy documents. The semantics of the policy is based on the semantics of language AL. The system is implemented using Java programming. We investigate the security protocol verification problem in provable security approach. Based on logic programming with answer set semantics, we develop a unified framework for security protocol verification and update, which integrates protocol specification, verification and update. The update model is defined using forgetting techniques in logic programming. Through a case study protocol, we demonstrate an application of our approach.