Miracle-dz/UniFAR-FTUs huggingface dataset:
Fuzzy logic implementation for photovoltaic maximum power tracking

The paper presents a simulation study and an experimental implementation of a fuzzy logic controller (FLC) for Cuk converter in a stand alone photovoltaic (PV) energy scheme. DC-DC converters are used to convert the unregulated DC input into a regulated DC output at a desired voltage level. A FL algorithm is selected and used to control the PV nonlinear system. The control objective for Cuk converter is to move the operating point of the PV system to its peak power point. A system that consists of a PV generator, converter, AC PWM inverter and load models is simulated, analyzed, and experimentally implemented. The simulated system with FLC is investigated at different solar insolation levels. The fixed structure FLC shows a robust performance when applied on a wide operating range of the proposed converter and AC PWM inverter.
Fractional Delay Filter Based Wideband Self-Interference Cancellation

This paper introduce Fractional Delay Filter (FD) into SelfInterference Cancellation based Full-Duplex communication system. The paper explains that an accurate delay of baseband signal in cancellation channel is very important for the performance of self-interference cancellation. It emphasise that a delay other than a phase alignment can extend the cancellation to wideband with reasonable performance. Simulations proved the feasibility of application of FD in self-interference based Full-Duplex system.
PARALLEL LOW-OVERHEAD DATA COLLECTION FRAMEWORK FOR A RESOURCE CENTRIC PERFORMANCE ANALYSIS TOOL

With the advent of multicore technology, computer systems have shifted to a new height of parallelism and computational power. Ramping up the frequency to increase performance on single processor has become a thing of the past. Nowadays, everyday computers are powered by multiple cores that share resources such as memory, network and I/O components. Moreover, they can run a larger gamut of applications at much higher speed. The increase in computational power is not reflected in the usability of such systems. The usage of these components and resources in an effective manner puts an extremely high burden to the programmer and the system software, which increases the complexity in programming models and runtime systems. To alleviate this burden, there is a need for tools that can identify parallel sections in the code, identify bottlenecks and provides hint to the programmers to improve the performance of the overall computing system. Moreover, the tool has to be able to pinpoint resource contentions so we know where uneven distributions of resources are. To address this issue, we introduced a tool called Memory Observant Data Analysis (MODA) [44]. MODA is a performance analysis tool that helps users analyze resource usage and alleviate resource conflicts by pinpointing performance issues in an algorithmic as well as in an architectural level. The main challenge of any performance analysis tool is the tool performance itself. Performance analysis tool needs to be such that it introduces minimal to no perturbation of application behavior. This requires analysis tool to achieve information during runtime with a very minimal overhead. This is not easy to achieve because
CTCDM: A Complete Time Constraint Based Delegation Model

Time-based delegation transfers privileges between users and reduce the work load of administrators. But there are defectives on the researches of time-based delegation model: incomplete time constraints and name space explosion problem of delegation model. To solve these two problems, following researches has been done: firstly, this paper proposes a time constraint policy including time representation and the time state change algorithm; secondly, based on the time constraint policy, this paper puts forward the Complete Time Constraint Based Delegation Model(CTCDM) to solve the name explosion problem from a certain extent.
Design of standard cells used in low-power ASIC's exploiting the multiple-supply-voltage scheme

ASIC design utilizing the multiple-supply-voltage (MSV) scheme has been shown to be efficient in reducing the power consumption. A new layout style of standard cells to be used in ASIC designs is proposed to effectively exploit the advantages afforded by the MSV scheme. Each standard cell is designed to use two power rails that are fed with different supply voltages. Then, the cells can be butted together arbitrarily no matter whether the cells are supplied from a high or low voltage, and the existing P&R tool can place and route the circuit as usual. As compared to the design with only one supply voltage, the average saving of power consumption of the new design (using the new cells and adopting the MSV scheme) is over 30%, but the average area overhead is only about 8%. Meanwhile, the average interconnection length is only increased by about 7.5%.
Software Implementation of CCSDS Recommended Hyperspectral Lossless Image Compression

HyperSpectral Imagers (HySI) are used in the spacecraft or aircrafts to get minute characteristics of target element through capturing image in a large number of narrow and contiguous bands. HySI data represented as data cube with two dimensions representing spatial distribution and third dimension providing band information is huge in volume and challenging task to handle. Hence onboard compression becomes a necessary for optimal usage of onboard storage and downlink bandwidth. CCSDS recommended 123.0-B-1 standard(2) has been released with onboard compression scheme of hyperspectral data. The scheme is based on Fast Lossless algorithm and consists of two main functional blocks namely Predictor and Encoder. Predictor algorithm can be implemented in two modes ‗Full Neighborhood Oriented' and ‗Reduced Column Oriented'. Encoder algorithm also defines two options ‗sample-adaptive' and ‗block-adaptive'. We have developed a MATLAB based model implementing the compression scheme with all options defined by the standard. Decompression model is also developed for getting back actual data and end to end verification. Four sets of HySI data (AVIRIS, Hyperion, Chandrayan-1 and FTIS) have been applied as input to the developed model for evaluation of the model. Compression ratio achieved is between 2 to 3 and lossless compression is ensured for each set of data as Mean Square Error (MSE) is zero for all hyperspectral images. Also visual reconstruction of decompressed data matches with original ones. In this paper we have discussed algorithm implementation methodology and results.
Research and development on on-board server for Internet ITS

We have developed an on-board server with HTTP server capability and servlet implementation and established a standard scheme to distribute information to vehicles using the on-board server. We have also developed communication media switching capability using the Mobile IPv6. All these capabilities are implemented on the Linux operating system. Currently, we are conducting a verification test in metropolitan Tokyo and Nagoya using 2000 vehicles equipped with this on-board server.
Improving the Design of Existing Code

class Price { abstract int getPriceCode(); // 取得價格代號 } class ChildrensPrice extends Price { int getPriceCode() { return Movie.CHILDRENS; } } class NewReleasePrice extends Price { int getPriceCode() { return Movie. NEW_RELEASE; } } class RegularPrice extends Price { int getPriceCode() { return Movie.REGULAR; } } 現在我可以編譯這些新 classes了。
3d object retrieval approach based on directed acyclic graph lightfield feature

A new 3D object retrieval methodology is proposed by exploiting a novel directed acyclic graph lightfield feature (DLF), in which the so-called lightfield is made up of around 2000-5000 colour views. The descriptor overcomes the disadvantages of the existing view-based 3D object retrieval methods. Benefiting from shock graph and Bayesian network learning, this DLF is simple, accurate and noise robust as compared to existing methods. Results of experiments show that the proposed method is superior to others.
On the Memory Artifacts of the Tor Browser Bundle

Tor is one of the most famous privacy-preserving tools. It creates virtual encrypted tunnels to convey users’ data. Tor users improve their privacy against people watching their activities or doing trac analysis. They enjoy being anonymous while browsing the web or chatting with friends. Tor is being used by variety of people ranging from ordinary individuals to journalists or even governmental organizations. This paper investigates the memory artifacts of the Tor browser Bundle, which is specifically pre-configured to use the Tor network. Although it is hard to analyze Tor’s data while being in transit, information is fully exposed in the clients’ machines after delivery. That is all data must go through the memory before being processed. We use Tor browser in dierent experiments to check the possibility of recovering data remnants from the memory. This paper shows that Tor works pretty good in destroying the involved data prior getting closed.
Design of Hollow-Rotor Brushless DC Motor

This paper discusses about design of hollow-rotor Brushless DC (BLDC) motor. A conventional BLDC motor has more leakage flux circling at the end of the permanent magnet that will limit torque. To overcome this problem, a new BLDC model known as hollow-rotor is proposed. The objective of this research is to design a hollow-rotor motor that will have higher torque density compared to conventional BLDC motor using Finite Element Method (FEM). In addition, performance analysis of the proposed hollow-rotor has also been carried out. For validation, the result of FEM is compared with the measurement result. It shows that, the simulation result has good agreement with the measurement result. For comparison, hollow-rotor shows higher torque density compared to conventional BLDC motor. As a conclusion, this paper provides guidelines and analysis in designing high torque hollow-rotor motor.
Designing a Brain-Computer Interface to Chess

Brain-computer interfaces (BCI) oer an additional communication channel, which may be applied to control a chess game. This paper describes the design of a brain-computer interface to chess based on a 8◊8 P300 speller. The system encodes a move in the chess game as selecting a start and a destination position on the chess board. Most of the design can be implemented using existing hardware and software. Move selection is expected to take about 30 seconds per move at over 95% accuracy.
From Intuitive Descriptions of Biochemical Systems to Their Formal Analysis

The recent interest in using techniques coming from concurrency theory to model biological and biochemical systems, gave rise to issues not considered in the traditional application field of concurrency theory, that is the modelling of distributed systems of computers. Like distributed systems, biological systems are made of many interacting entities; differently from distributed systems, most biological interactions are not completely known. Hence, modelling languages should be able to accommodate partial knowledge and allow users to construct models incrementally when new knowledge is acquired. Another major issue is to define modelling languages that are easy to understand by non-computer scientists, and to develop user friendly tools for modelling, analysis and simulation of biological systems. Finally, standardisation of modelling languages and interoperability of tools is needed to use the huge amount of data coming from wet experiments and the mathematical models which are available in biological databases. The main contributions of this thesis are the definition of an high level textual modelling language, of its automatic translation into a Beta-binders extension, and of rules to infer causal and spatial dependencies between Beta-binders transitions. The overall aim is provide modellers with a framework which allows them to easily describe biochemical systems and automatically perform simulation and formal analysis of models.
The influence of sentence complexity and working memory on relative clause ambiguity resolution

The present study aims to investigate the influence of sentence complexity on the relative clause ambiguity resolution preferences of native English speakers through the manipulation of three factors: relative clause length, relative clause position, and relative pronoun extraction site. The results of an offline questionnaire probing relative clause attachment preferences found significant effects of relative clause length and position, suggesting that syntactic complexity is a factor that influences ambiguity resolution strategies within a language. In addition, the influence of individual working memory capacity on relative clause processing was also examined, with the results showing a significant negative correlation between working memory capacity and relative clause attachment preference
Computer Aided Detection OfIschemic Stroke Using Cellular Automata

2 ABSTRACT: Computed tomography images are widely used in the diagnosis of ischemic stroke because of its faster acquisition and compatibility with most life support devices. This paper presents a new approach to automated detection of ischemic stroke using cellular automata, midline shift and image feature characteristics, which separate the ischemic stroke region from healthy tissues in computed tomography images. The proposed method consists of five stages namely, pre- processing, segmentation, tracing midline of the brain, extraction of texture features and classification. The application of the proposed method for early detection of ischemic stroke is demonstrated to improve efficiency and accuracy of clinical practice. The results are quantitatively evaluated by a human expert. A classification with accuracy of 98%,has been obtained by SVM.
Variations on the Boltzmann Machine Learning Algorithm

Abstract : Boltzmann Machines learn to model the structure of an environment by modifying internal weights. The algorithm used for changing a weight depends on collecting statistics about the behavior of the two units that the weight connects. The success and speed of the algorithm depends on the accuracy of the statistics, the size of the weight changes, and the way in which the accuracy of the machine's model varies as the weights are changed. This paper presents theoretical analysis and empirical results that can be used to select more effective parameters for the learning algorithm. The Boltzmann Machine learns to make its model of its environment correspond to the actual environment in which it is placed. An environment is a probability distribution of patterns over a subset of the units called the visible units. The environment can be specified explicitly as a list of ordered pairs, giving a pattern, V a' over the visible units, and a probability for the occurrence of the pattern. The machine's model is just the probability distribution it would produce over the visible units if it were allowed to run freely without any environmental input. This probability distribution is not stored directly. Instead, it is specified implicitly by the magnitude of the weights in the machine. For a machine architecture with v visible units, there are 2(v) possible patterns. A machine whose weights were all zero would implicitly specify an environment where each of these patterns had probability 2-v, since the energy of all states would be the same.
Enhancing Text Categorization Using External Knowledge Repositories

Digital documents are ever increasing and identifying the relevant document related to a specific topic is really a challenging task. The process of searching for the relevant document becomes too expensive as it has to search every document in the entire collection and hence results in huge computational as well as time complexity. In such situations, To make the search fast, efficient and narrow focused, it is quiet essential to organize the documents, possibly in a hierarchy, depending on its textual content. This leads to the topic spotting problem, popularly known as Text Categorization, the process of assigning tags to documents with one or more predefined class labels which are, simply saying, themes or topics relevant to its content. Categorizing the documents based on the presence or absence of words posses many restrictions and may not able to capture the world knowledge behind the text fragments. To capture such type of common sense and domain specific knowledge, additional features related to the local context of the given text fragment must be generated. Such local contexts could possibly be generated using popular open source as well as domain specific external knowledge repositories like Wikipedia, Yahoo directories, Open Directory Project(DMOZ), etc. The deep understanding of the document text with background knowledge refinement will improve the existing induction algorithms for text categorization. In this thesis, we have proposed methods for enhancing text categorization using external knowledge repository like wikipedia. In the first technique, we have extracted wikipedia concepts from wikimedia XML dumps. Then the given text documents are augmented with newly generated features present in the wiki concept vectors and categorization is performed with these augmented text documents. The experiment is repeated with wiki hyperlink text vectors and categorization accuracy is observed. Finally we have taken up the wiki knowledge concepts from the processed wikipedia knowledge repository and formed the clusters of words taken from knowledge concepts. Each cluster is assigned with a cluster ID. Using these cluster IDs, the text documents were
Spatial self-organization in large populations of mobile robots

A homogeneous population of robots is to be realized for applications such us material transportation, hazardous material handling, planetary exploration, etc. Several algorithms for spatial self-organization of this swarm are given. Self-organizing agents can arrange themselves geometrically in two- and three-dimensional space using only local information. The method is a distributed one: each agent uses only information obtained by its own sensors. Algorithms are based on feasible assumptions on sensing capability. It is also shown possible to divide such a population into groups around goals by communicating minimal data. The self-organizing characteristic of the swarm provide a modular, adaptive and dynamic system. The approach is useful when a central controller is not feasible. Performance of the algorithms is shown with simulations. <<ETX>>
A channel recovery method in TDMA wireless systems

A single base repeater failure in TDMA wireless systems causes all active calls on this base repeater to be dropped. In order to increase the system end-to-end availability, an RF channel recovery method for TDMA wireless systems is proposed in this paper. By applying the method, when a base repeater fails, the channels of active calls carried by the base repeater are replaced by working channels in the channel pool of the base site. All the active calls continue without the intervention of end users. The method deals not only with the failure and recovery of base repeaters, but also with the channel failure recovery in handoff processes, transient channel failures, and new call setup processes. To predict system availability and performance, hierarchical stochastic reward net (SRN) models are developed for analyzing channel allocation, base repeater failure/repair, and channel recovery. The results show that the recovery method can nearly eliminate dropped calls when the traffic load is light, and can dramatically reduce dropped calls when the traffic load is normal. The price we pay is a slightly increased blocking probability, nearly transparent to the end users.
Pedagogy-driven Design for Online Language Teaching and Learning

This article discusses some pedagogical implications of a research project carried out at the University of Antwerp between 1996 and 2004. Its objective was to explore the boundaries of a pedagogy-driven approach in research-based research-oriented CALL system design. The starting point was the observation of a serious decrease in linguistic-didactic functionalities and in overall interactivity of online language learning programs compared to applications developed earlier on CD-ROM. The resulting research question was the following: which software tools, components, and protocols are most efficient for designing, developing, and implementing online interactive language courseware? Besides deliverables in terms of design models, object models, architectures, and frameworks, this project also yielded relevant pedagogical conclusions for online language pedagogy, the role of the teacher, and pre-service and in-service training. These conclusions should be read as provisional suggestions which can lead to new working hypotheses in the field of online language learning and teaching.
Design combination in integrated flight control

Control integration makes engineering and business sense, to reduce cost and design cycling time, and to improve performance. We select two specific autopilot control channels, namely the pitch attitude and speed control, to investigate the special design challenges that one may face during system integration. Two integration approaches are proposed, and compared through an example of Boeing 747 transport. The simulation results justify the theoretical analysis.
Generation of broadband speech from narrowband speech based on linear mapping

In this paper, a method for generating broadband speech from band-limited speech using spectral linear mapping is proposed. This method is based on LPC analysis and synthesis. It first extracts sound source information (residual waveforms) and vocal tract information (spectral envelopes) from input speech, performs linear mapping of the vocal tract information and nonlinear processing of the sound source information for broadband conversion, and finally generates broadband speech from both by LPC synthesis. The spectral envelope is made broadband by linear mapping, the spectral space is divided into a number of subspaces, and a narrowband spectrum is converted into a broadband spectrum by a conversion (convert) matrix of each subspace. A conversion (convert) matrix is estimated using training speech such that the mean square error between the postconversion spectrum and the target broadband spectrum is minimized. Spectral distortions of the proposed method were compared experimentally with codebook mapping and neural network methods and it has been verified that the proposed method with linear mapping gives performance not inferior to that of the other two methods. In addition, the proposed method has been verified to have the effect of giving a band feeling in subject evaluation tests. © 2002 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 85(8): 44–53, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjb.10065
Application of the genetic algorithm in modeling RF on-chip inductors

The genetic algorithm (GA) with parameter confinement has been introduced into the extraction of model parameters for RF on-chip inductors. The details of applying the GA in extracting the model's parameters are discussed. For a set of inductors, the meaningfulness of extracted parameters is considered in the procedure of extraction. The S-parameter, inductance, and Q value can be set as the fitting targets simultaneously in this study. The values of model parameters are extracted by a fully automatic programmed GA procedure, which show excellent quality of fit when compared with the measured data.