Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic camera self-calibration from controlled motion sequences

In order to recover camera motion and 3-D structure from a sequence of images, points in the image plane must be related to directions in space. A least-squares algorithm is described for computing camera calibration from a series of motion sequences for which the translational direction of the camera is known. The method does not require special calibration objects or scene structure. It only requires the ability to move the camera in a given direction and to track features in the image as the camera moves. Since it is a linear least-squares method, it can include information from many sequences to produce a robust estimate of the calibration matrix, which can be updated dynamically as new measurements are taken. It uses the most general possible linear model for calibration. Experimental results from applying the algorithm to a set of real motion sequences with noisy correspondence data are given and analyzed. <<ETX>>
Surface recording of small bowel electrical activity

The present study identifies the myoelectrical signal from the small bowel serosa (electroenterogram), through bipolar electrodes placed on the abdominal surface. An electroenterogram is composed of a permanent slow wave, and a sporadic spike burst that appears when there is a mechanical contraction of the bowel. Electroenterograms were captured through electrodes sutured to bowel serosa of Beagle dogs. Simultaneously, the bioelectrical signal from the abdominal surface is captured. Internal and external biosignals were parametrized in order to obtain intestinal motility. Parameters from surface activity were correlated with parameters from electroenterogram at each point of measurement. Results indicate that spectral parameters from internal and external signals are moderately strongly correlated. The highest correlation is found in those parameters which represent the existence of the spike bursts at the bowel point nearest to the surface electrodes. In conclusion, the present study shows that the spike burst component of the small bowel electrical activity can be detected through external recordings. Since spike burst induces a bowel contraction, its identification by means of surface recording could be a useful method to define intestinal motility.
The International Workshop on Computer Architecture for Machine Perception and Sensing Comparison of Different Cooperation Strategies in the Prey-predator Problem

The paper describes two cooperating strategies among several homogeneous agents to reach a given target. In our case we used the prey-predators paradigm in which a set of agents (predators) have the purpose to reach a target (prey). The problem is addressed as an optimization problem that has been faced with two different algorithms (a genetic algorithm and a particle swam optimization algorithm). The two approaches are evaluated by using a simulator for each strategy and the results show that the strategies are very different in terms of prey-predator successes. Genetic algorithm can be used by the prey to solve at the best the problem to reach the lair, otherwise the Particle Swarm Optimization method allows the predator to reach a target in a fast way.
Tecnologías big data para análisis y recuperación de imágenes web

Se aborda el analisis web desde el punto de vista de las imagenes, empleando tecnologias big data . Las imagenes cada vez tienen mas peso en la web por lo que cualquier analisis que se realice debera considerar este tipo de informacion. Los grandes volumenes de imagenes existentes hacen necesaria la utilizacion de grandes infraestructuras de computacion para realizar este tipo de trabajos, asi como tecnologias de vision artificial especificas. Se muestran tecnologias big data que pueden ser utilizadas dentro del campo del analisis de imagenes a gran escala. Ademas, se propone una arquitectura que permite recuperar imagenes de una biblioteca de imagenes de forma eficiente y con un bajo coste computacional. Esta arquitectura puede servir como base para los analisis web e investigaciones que requieran un estudio detallado de las imagenes similares, sin la necesidad de disponer de hardware especifico para ello.
Performance and risk analysis of the Hodrick-Prescott filter

We evaluate the consistent estimator of the noise-to-signal ratio parameter, the so called DDR estimator, in the Hodrick-Prescott filter introduced in Dermoune et al. (2008, [2]) and suggest two ways to update it to make the probability distribution of the cyclical component as closer to Gaussian as possible. When comparing the results between the different estimators, both the trend and the cyclical component are analyzed to decide which of the three estimators generates the best result. It appears that in most cases the DDR estimator gives the best trend component. We then apply the filter to standard risk calculations in the sense that we compare risk figures such as Value-at-Risk and expected volatility obtained for the original time series and the filtered one. The observed variation in the ratio of these two risk figures may be useful to enhance the performance of the underlying optimal portfolio.
Partitioning algorithms for soft decision decoding of block codes

Using some little-known but very important results (Evseev's (1983) performance bound and the partitioned decoding technique), new algorithms have been devised, providing useful new performance results and complexity evaluations for practical soft-decision decoders. In addition, a deeper and clearer understanding of the problems and possibilities of this topic has emerged. <<ETX>>
Eight more Classic Machine Learning algorithms

Note to other teachers and users of these slides. Andrew would be delighted if you found this source material useful in giving your own lectures. Feel free to use these slides verbatim, or to modify them to fit your own needs. PowerPoint originals are available. If you make use of a significant portion of these slides in your own lecture, please include this message, or the following link to the source repository of Andrew’s tutorials: http://www.cs. cmu.edu/~awm/tutorials . Comments and corrections gratefully received.
Implementation of an object-oriented functional language on the multithreaded architecture

We suggest a new object-oriented functional language, OOId, which is based on a functional language Id and extended with object-oriented language features. We also discuss how to map OOId program onto DAVRID, which is a kind of multithreaded architecture based on the dataflow model. The comparison of execution time of two programs written in Id and OOId for the matrix multiplication problem, shows we can effectively run OOId program with just a little slow-down of execution time. In addition, the rate of slow-down is decreased, as the size of the matrix is larger. That means we can use good features of object-oriented language by paying just a little slow-down of execution time.
XOR retransmission in multicast error recovery

A growing number of network applications require the use of a reliable multicast protocol to disseminate data from a source to a potentially large number of receivers. We present a new error recovery mechanism, called XOR, which is based on the selective repeat ARQ protocols. The idea is to combine several NACKed packets by XORing them. The analytical and simulative result show that XOR achieves a better throughput than both 'N1' and 'N2' do.
Genetic Algorithms for Solving Linear Bilevel Programming

Bilevel programming, a tool for modelling decentralized decisions, consists of the objectives of the upper level and lower level. And numerous methods are proposed for solving this problem. In this paper, we provide a genetic algorithm method for solving the linear bilevel programming. In our algorithm, we adopted some techniques to guarantee the not only the initial chromosomes but also the chromosomes generated by genetic operators are all feasible, which greatly reduces the searching space and avoiding the difficulty to deal with the infeasible points. Furthermore, it also enhances the efficiency of the algorithm that the best offsprings are selected to replace the parents in operator procedures. Some examples are illustrative to show the feasibility and efficiency of the algorithm proposed in this paper.
Turbo-based multiuser detection

This paper describes the application of cross-entropy minimization techniques to coded multiuser detection. Research has shown that the complexity of the optimum multiuser detector is exponential in the number of users but that it offers much superior performance over the conventional detector that ignores multiple access interference (MAI). These results have been extended for the case with forward error correction (FEC) coding. This paper investigates a suboptimum receiver of lower complexity that does not significantly compromise performance when the signal is FEC-encoded.
Robust Decoding for Channels with Impulse Noise.

Data transmission over power lines is an attractive alternative to well-established wireline and wireless communica- tion technologies. One of the main challenges in accomplishing reliable power-line communication (PLC) is channel impair- ment through electromagnetic interferences, or so-called impulse noise. In this paper, we consider transmission over impulse- noise channels for a typical narrowband system architecture employing convolutional codes and Viterbi decoding. We present different decoding metrics, including new designs adopted from the multiuser detection literature, and we derive expressions for cutoff rate and bit-error rate (BER) performances of the corresponding decoders. These expressions are amenable for quick numerical evaluation and thus, constitute a valuable tool for decoder optimization and performance comparison. Our numerical and BER simulation results show that one of the proposed metrics enables robust decoding without knowledge of the statistic of the impulse noise with a performance close to that of optimum decoding, which relies on the noise statistic. It is further highlighted that, different from transmission over the Gaussian-noise channel, quadrature detection is beneficial in case of real-valued modulation and passband transmission over impulse-noise channels.
Simple Learning Classifier Machine

A learning classifier system is one of the methods for applying a genetic-based approach to machine learning applications. An enhanced version of the system that employs the Bucket-brigade algorithm to reward individuals in a chain of co-operating rules is implemented and assigned the task of learning rules for classifying simple objects. The task is to classify an object that has one or more of the following features: wing, 2-legs/wheels, 3-legs/wheels, 4-legs/wheels, big, flies into one of the following: bird, vehicle. the main goal is to exploit the ability of the algorithm to perform well in a noisy environment and its ability to make little or no assumption about its problem domain. Results are presented which show that the system was able to learn rules for the task using only a few training examples and starting with classifiers that were randomly generated. It is argued that a classifier based learning method requires little training examples and that by its use of genetic algorithms to search for new plausible rules, the method should be able to cope with changing conditions. Results show also The parallel implementation of the algorithm would speed up the training process.
Automatic Analog Model Generation For Behavioral Simulation

This paper describes a method to generate automatically, transient simulation models from a higher-level transfer function specification. The specified transfer function can be a ratio of nlh order polynomials in the s-domain or z-domain. An appropriate state-variable model is derived from the transfer functions using the phase-variable technique. The simulation models are generated for the AT&T Bell laboratories mixed-mode (analog-digital) behavioral simulator. The programs gensims and gensimz are developed to automatically generate the behavioral models for both the s-domain and z-domain respectively.
Advanced triple-channel botnets: model and implementation

Nowadays, most of research on botnet survivability only focuses on the advanced design of downstream (from botmasters to bots, used to deliver commands) command and control (C&C) channel. However, the upstream (from bots to botmasters, used to upload the collected data on victims) C&C channel remains vulnerable and low-efficiency in most of botnets to this day. To address the problem, we propose a C&C channel division scheme and then establish a Botnet Triple-Channel Model (BTM). BTM divides a traditional C&C channel into three independent sub-channels, denoting as Command Download Channel (CDC), Registration Channel (RC) and Data Upload Channel (DUC), respectively. To illuminate the feasibility and advantages, we implement a BTM botnet prototype which exploits URL Flux for CDC, Domain-flux for RC, and introduces a new approach (Cloud-based File Hosting and URL Shortening Services) for DUC. Compared with current botnets, the proposed BTM botnet will promise to be as robust as P2P botnets and as efficient as centralized botnets. The ultimate goal of our work is to increase the understanding of advanced botnets which will promote the development of more efficient countermeasures.
Ungrammaticality detection and garden path strength: A commentary on Meng and Bader"s (2000) evidence for serial parsing

We argue that the data presented by Meng and Bader (2000) and interpreted as evidence in favour of serial parsing by these authors are also naturally accounted for in a weakly interactive constraint-based parsing architecture. In this way, Meng and Bader"s “Mismatch Effect”, i.e., the observation that there appears to be a correlation between garden path strength and ungrammaticality detection, is reinterpreted as an epiphenomenon of constraint interaction and, thereby, as orthogonal to the serial vs. parallel parsing debate. New experimental evidence in favour of this alternative account is provided.
Super-resolution SAR imaging via nonlinear regressive model parameter estimation method

A novel SAR super-resolution imaging method is described Firstly, SAR image peak extraction is carried out in the image domain and the coarse feature parameter estimation is obtained. Secondly, Parameter estimation of nonlinear regressive model is carried out in the phase history domain and the fine feature parameter estimation is obtained. Finally, from the estimated parameter and based on the point-scattering model, the simulated phase history data of large dimensions is generated. By FFT imaging, higher resolution image is obtained. Experimental examples have shown that this method offer significant advantages over the FFT methods to better resolve the dominant target scatterers.
Exemplary Science Teachers' Use of Technology.

The purpose of this study is to examine exemplary science teachers’ level of computer use, their knowledge/skills in using specific computer applications for science instruction, their use of computer-related applications/tools during their instruction, how often they required their students to use those applications in or for their science class and factors influencing their decisions in using technology in the classroom. The sample of this study includes middle and high school science teachers who received the Presidential Award for Excellence in Science Teaching Award. Analysis of the survey responses indicated that exemplary science teachers have a variety of knowledge/skills in using computer related applications/tools. The most commonly used computer applications/tools are information retrieval via the Internet, presentation tools, online communication, digital cameras, and data collection probes. Results of the study revealed that students’ use of technology in their science classroom is highly correlated with the frequency of their science teachers’ use of computer applications/tools.
Generating Code for High-Level Operations through Code Composition

Abstract : A traditional compiler translates each expression or statement in a high-level language into a sequence of lower-level target statements (e.g., operations in an intermediate representation, or machine instructions), in a manner fixed by the compiler writer. The output is then subject to further optimization. This compilation strategy is called custom code generation, as the compiler generates custom code for each input construct. An alternative strategy is to generate a call to a runtime library for each high-level language construct. This approach is attractive if the source language contains complex, powerful constructs, like the distributed array assignment statement in High Performance FORTRAN (HPF). The decision between custom code generation and use of a runtime library involves tradeoffs between efficiency (performance of the generated code), maintainability (ease of developing and maintaining the algorithm), and generality (implementation of the general case, rather than merely a simplified canonical case). I introduce a new compilation strategy, high-level code composition, which combines the advantages of custom code generation and runtime libraries. The compilation of each construct is controlled by code templates, which contain both target code to be generated and compile-time control instructions that specify how the templates are composed together. The templates are external to the compiler, making them easy to write and modify.
A Single-Chip Dual-Band 22-29-GHz/77-81-GHz BiCMOS Transceiver for Automotive Radars

Integration of multi-mode multi-band transceivers on a single chip will enable low-cost millimeter-wave systems for next-generation automotive radar sensors. The first dual-band millimeter-wave transceiver operating in the 22-29-GHz and 77-81-GHz short-range automotive radar bands is designed and implemented in 0.18-? m SiGe BiCMOS technology with fT/fmax of 200/180 GHz. The transceiver chip includes a dual-band low noise amplifier, a shared downconversion chain, dual-band pulse formers, power amplifiers, a dual-band frequency synthesizer and a high-speed highly-programmable baseband pulse generator. The transceiver achieves 35/31-dB receive gain, 4.5/8-dB double side-band noise figure, >60/30-dB cross-band isolation, -114/-100.4-dBc/Hz phase noise at 1-MHz offset, and 14.5/10.5-dBm transmit power in the 24/79-GHz bands. Radar functionality is also demonstrated using a loopback measurement. The 3.9 × 1.9-mm2 24/79-GHz transceiver chip consumes 0.51/0.615 W.
Why Pascal is Not My Favorite Programming Language

The programming language Pascal has become the dominant language of instruction in computer science education. It has also strongly influenced languages developed subsequently, in particular Ada. Pascal was originally intended primarily as a teaching language, but it has been more and more often recommended as a language for serious programming as well, for example, for system programming tasks and even operating systems. Pascal, at least in its standard form, is just plain not suitable for serious programming. This paper discusses my personal discovery of some of the reasons why.
Some Problems of Professional End User Developers

By the term 'professional end user developers' we mean people such as research scientists who work in highly technical, knowledge-rich domains and who develop software in order to further their professional goals. In common with other end user developers, professional end user developers do not describe themselves as software engineers and have no formal training in software engineering. They differ from most other end user developers, however, in that learning programming languages rarely presents them with any problem. In this paper, drawing on data from field studies of different groups of professional end users, we examine the problems that such people face in meeting the demands of software development given the culture in which they work and their normal development practice. Understanding these problems is an essential prerequisite to developing tools, techniques etcetera to support professional end user development.
Automatic Generation and Optimisation of Reconfigurable Financial Monte-Carlo Simulations

Monte-Carlo simulations are used in many applications, such as option pricing and portfolio evaluation. Due to their high computational load and intrinsic parallelism, they are ideal candidates for acceleration using reconfigurable hardware. However, for maximum efficiency the hardware configuration must be parametrised to match the characteristics of both the simulation task and the platform on which it will be executed. This paper presents a methodology for the automatic implementation of Monte-Carlo simulations, starting from a high-level mathematical description of the simulation and resulting in an optimised hardware configuration for a given platform. This process automatically generates fully-pipelined hardware for maximum performance; it also maximises thread-level parallelism by instantiating multiple pipelines to optimise device utilisation. The configured hardware is used by an associated software component to execute simulations using run-time supplied parameters. The proposed methodology is demonstrated by five different Monte-Carlo simulations, including log-normal price movements, correlated asset Value-at-Risk calculation, and price movements under the GARCH model. Our results show that hardware implementations from our approach, on a Xilinx Virtex-4 XC4VSX55 FPGA at 150 MHz, can run on-average 80 times faster than software on a 2.66 GHz Xeon PC.
A larger audience, please!: encouraging people to listen to a guide robot

Tour guidance is a common task of social robots. Such a robot must be able to encourage the participation of people who are not directly interacting with it. We are particularly interested in encouraging people to overhear its interaction with others, since it has often been observed that even people who hesitate to interact with a robot are willing to observe its activity. To encourage such participation as bystanders, we developed a robot that walks backwards based on observations of human tour guides. Our developed system uses a robust human tracking system that enables a robot to guide people by walking forward/backward and allows us to scrutinize people's behavior after the experiment. We conducted a field experiment to compare the ratios of overhearing in "walking forward" and "walking backward. " The experimental results revealed that in fact people do more often overhear the robot's conversation in the "walking backward" condition.
An efficient scheduling algorithm for packet cellular networks

Scheduling algorithms are crucial components for providing quality of service (QoS) guarantees in broadband wireless networks. However, bursty channel errors and location-dependent channel capacity and errors are unique factors in wireless networks that need to be taken into consideration when applying wireline scheduling algorithms to the wireless domain. In this paper, a new scheduling algorithm for packet cellular networks, Wireless Deficit Round Robin (WDRR), is proposed. WDRR is a round robin scheduler that has low implementation complexity and stems its efficiency from its low delay bound, tight fairness index, and almost perfect isolation property. In error-prone channels, the algorithm provides short-term fairness among sessions that perceive a clean channel, long-term fairness among all sessions, ability to meet specified throughput objectives for all sessions, and graceful service degradation among sessions that received excess service. Both analysis and simulation are used to verify the WDRR properties.