Miracle-dz/UniFAR-FTUs huggingface dataset:
How Young Children Learn to Program With Sensor, Action, and Logic Blocks

Electronic Blocks are a new programming environment designed specifically for children aged between 3 and 8 years. These physical, stackable blocks include sensor blocks, action blocks, and logic blocks. By connecting these blocks, children can program a wide variety of structures that interact with one another and the environment. Electronic Blocks provide young children with opportunities to program and observe dynamic behavior without having to acquire complex symbolic notation systems. Young children are able to build and debug program structures by using Electronic Blocks because the syntactic and semantic problems that confront users of conventional programming languages have been reduced.
A PSO approach for learning transition structures of Higher-Order Dynamic Bayesian Networks

Dynamic Bayesian Networks are widely used for modeling neural information flow and gene regulatory networks. Their assumption of first-order Markov, however, is recently being noted as a too restrictive assumption for some applications, specially when these have communication and processing of information at different time delays. Many authors are extending this Markov assumption to higher orders, suggesting the use of Higher-Order Dynamic Bayesian Networks (HO-DBNs). These networks, by their turn, bring some issues to be considered, mainly because of the elevated number of nodes, the necessity of great amounts of data and the high cost for learning their structures. In this work we propose an optimization technique based on Particle Swarm Optimization for learning the transition structures of HO-DBNs, trying to exploit their intrinsic characteristics to suggest efficient representations and simplifications in their learning. Also, we propose a way to introduce extra randomness in the method by reinterpreting the concepts of inertia and social/cognitive contributions to particles. We test the algorithm for correctness and performance, comparing it against a greedy algorithm for learning Bayesian structures.
Driver alertness detection using Google Glasses

This paper proposes an intelligent vehicle system (ITS) to monitor the driver driving behavior. Based on the first-person vision (FPV) technology (or Google glasses), our system can detect the vehicle exterior/interior scene from driver’s viewpoint and estimate driver gazing direction. First, we use “bag of words” image classification approach by applying FAST and BRIEF feature descriptor in the dataset. Then, we use vocabulary dictionary to encode an input image as feature vectors. Finally, we apply SVM classifier to identify whether the input image is vehicle interior scene or not to monitor the driver driving attention. Second, we find the correspondence between the images of the Google glasses and the camera mounted on the wind shield of the vehicle to estimate the gazing direction of the driver. In the experiments, we illustrate the effectiveness of our system.
Community Detection in Scale-Free Networks using Edge Weight and Modularity Optimization Method

In this paper, we propose a two-step algorithm to perform a community detection in scale-free networks. One of the main characteristics of scale-free networks is that node degree distribution follows a power law. However, during our own experiments, we encountered another sub-type of scale-free networks which we call “mixed scalefree networks”. Some communities have hub nodes and node degree follows power law distribution, while some communities do not have hub nodes and node degree follows normal distribution. For mixed scale-free networks, methods that do not specifically design for scale-free will have difficulties because of the scale-free properties. At the same time, scale-free based methods will have difficulties because some communities have node degree follows normal distribution. In this research, we propose a community detection algorithm that can work on networks that contain both types of communities at the same time. Our method can handle this case correctly because our algorithm performs both scale-free and non scale-free approaches iteratively. To evaluate our method, we use NMI Normalized Mutual Information to measure our results on both synthetic and real-world datasets comparing with both scalefree and non scale-free community detection methods. The results show that, our method outperforms baseline methods on mixed scale-free networks and scale-free networks while performs equally on networks with normal degree distribution.
Generation of cursive characters using minimum jerk model

We consider a problem of designing cursive characters and character strings. Based on the concept of 'dynamic font', we propose a new redesigning scheme to translate the typeface of predesigned Japanese characters to the omitted running style. In particular, a smoothing operation is introduced by employing a minimum hand jerk model as the generation mechanism. The design examples are included.
MOTION ARTIFACT REDUCTION IN FUNCTIONAL NEAR INFRARED SPECTROSCOPY SIGNALS BY AUTOREGRESSIVE MOVING AVERAGE MODELING BASED KALMAN FILTERING

Functional near infrared spectroscopy (fNIRS) is a technique that is used for noninvasive measurement of the oxyhemoglobin (HbO2) and deoxyhemoglobin (HHb) concentrations in the brain tissue. Since the ratio of the concentration of these two agents is correlated with the neuronal activity, fNIRS can be used for the monitoring and quantifying the cortical activity. The portability of fNIRS makes it a good candidate for studies involving subject's movement. The fNIRS measurements, however, are sensitive to artifacts generated by subject's head motion. This makes fNIRS signals less effective in such applications. In this paper, the autoregressive moving average (ARMA) modeling of the fNIRS signal is proposed for state-space representation of the signal which is then fed to the Kalman filter for estimating the motionless signal from motion corrupted signal. Results are compared to the autoregressive model (AR) based approach, which has been done previously, and show that the ARMA models outperform AR models. We attribute it to the richer structure, containing more terms indeed, of ARMA than AR. We show that the signal to noise ratio (SNR) is about 2 dB higher for ARMA based method.
An inverse dynamic-based dynamic programming method for optimal point-to-point trajectory planning of robotic manipulators

Abstract This paper introduces an inverse dynamic-based dynamic programming (IDBDP) method for solving optimal point-to-point robot trajectory planning problems. Compared with the conventional dynamic programming method, the proposed method offers several advantages. First, it eliminates the interpolation requirement. Second, the proposed method requires only inverse dynamic computations. The requirement to integrate equations of motion is thus avoided. As a result, the IDBDP method is computationally more efficient than the conventional dynamic programming method. The reliability and efficiency of the IDBDP method have been tested via computer simulations
Review Paper: Human factors of stereo displays: An update

Abstract— This updated review paper provides a selected review of a number of important perceptual and human‐factors issues that arise when stereo displays are designed and used. This review and analysis draws heavily from the basic vision literature in order to help provide insight for future design solutions for stereo displays. Issues discussed include: (1) the basics of human stereopsis (horopter, binocular disparity, binocular rivalry); (2) low‐level factors (interocular cross talk, interocular differences in luminance and contrast, accommodation‐vergence mismatch, stereoanomaly); (3) contextual factors (spatio‐temporal frequency effects, distance scaling of disparity); and (4) a high‐level cognitive factor (high‐level cue conflict). In this updated review, significant new material has been added: (1) interocular luminance and contrast differences and their effect on stereo viewing; (2) more insightful discussion of accommodative‐vergence mismatch; (3) high‐level cognitive factor. Two topics in the earlier review (visual pathways; dark focus and dark vergence) have been deleted. The paper concludes with the presentation of several recommendations for the design of stereo displays.
Model-based assist feature placement for 32nm and 22nm technology nodes using inverse mask technology

Inverse imaging has been long known to provide a true mathematical solution to the mask design problem. However, it is often times marred by problems like high run-time, mask manufacturability costs, and non-invertible models. In this paper, we propose a mask synthesis flow for advanced lithography nodes, which capitalizes on the inverse mask solution while still overcoming all the above problems. Our technique uses inverse mask technology (IMT) to calculate an inverse mask field containing all the useful information about the AF solution. This field is fed to a polygon placement algorithm to obtain initial AF placements, which are then cooptimized with the main features during an OPC/AF print-fix routine to obtain the final mask solution. The proposed flow enables process window maximization via IMT while guaranteeing fully MRC compliant masks. We present several results demonstrating the superiority of this approach. We also compare our IMT-AFs with the best AF solution obtained using extensive brute-force search (via a first principles simulator, S-litho), and prove that our solution is optimum.
A framework for model-checking timed CSP

Timed CSP is a well-known process algebra, built as an extension to Hoare's original CSP, designed to handle concurrency combined with timing considerations. It achieves this over a continuous time domain (the non-negative real numbers), which has the drawback of precluding standard model-checking approaches, as the state-space of any process is naturally a priori (uncountably) infinite. This paper shows how to circumvent this problem by translating and reinterpreting timed CSP processes within a new model of standard CSP. In this discrete model, which draws on previous work by A.W. Roscoe (1997) and A. Mukkaram (1993), timing of events is provided by the consistent and regular communication of a special tock event, analogous to the `tick' of a clock. The various parallel components of a process are therefore required to synchronise on tock, ensuring a uniform rate of passage of time. General results yielding tight bounds on the loss of information inherent to the translation are given. (4 pages)
Analysis and implementation of the speaker adaptation techniques : MAP, MLLR, and MLED

The topic of this thesis is speaker adaptation, whereby speaker-independent speech models are adapted to more closely match individual speakers by utilising a small amount of data from the targeted individual. Speaker adaptation methods specifically, the MAP, MLLR and MLED speaker adaptation methods are critically evaluated and compared. Two novel extensions of the MLED adaptation method are introduced, derived and evaluated. The first incorporates the explicit modelling of the mean speaker model in the speaker-space into the MLED framework. The second extends MLED to use basis vectors modelling inter-class variance for classes of speech models, instead of basis vectors modelling inter-speaker variance. An evaluation of the effect of two different types of feature vector PLP-cepstra and LPCCs on the performance of speaker adaptation is made, to determine which feature vector is optimal for speaker-independent systems and the adaptation thereof. 11 Stellenbosch University http://scholar.sun.ac.za
Accelerated Gaussian mixture model and its application on image segmentation

Gaussian mixture model (GMM) has been widely used for image segmentation in recent years due to its superior adaptability and simplicity of implementation. However, traditional GMM has the disadvantage of high computational complexity. In this paper an accelerated GMM is designed, for which the following approaches are adopted: establish the lookup table for Gaussian probability matrix to avoid the repetitive probability calculations on all pixels, employ the blocking detection method on each block of pixels to further decrease the complexity, change the structure of lookup table from 3D to 1D with more simple data type to reduce the space requirement. The accelerated GMM is applied on image segmentation with the help of OTSU method to decide the threshold value automatically. Our algorithm has been tested through image segmenting of flames and faces from a set of real pictures, and the experimental results prove its efficiency in segmentation precision and computational cost.
Emissivities and back-scattering coefficients of random lossy dielectric rough surfaces at microwave frequencies based on 3-dimensional numerical simulations

Presents results for full three-dimensional simulations of numerical solutions of Maxwell equations for random rough surfaces. Emissivities and back-scattering coefficients are calculated for soil surfaces and ocean surfaces at microwave frequencies. This is unlike analytical methods or empirical models in that different roughness parameters are used for different microwave frequencies and for active and passive remote sensing. With advances in computational electromagnetics and available computer resources, we calculate 3-dimensional Maxwell equation solutions with a 2-D rough surface for practical remote sensing applications. Highlights of our numerical approach are: (i) the sparse-matrix canonical grid method (SMCG) and the physics-based two-grid method (PBTG) are used. (ii) An algorithm has been implemented for parallel computing. Dense discretization of the surfaces is used because of high permittivity and the energy conservation check is verified. We present results for soil surfaces in L- and C-bands, and for ocean surfaces at 19 GHz.
Behavior of Tandem Buffers with Geometric Input and Markovian Output

A discrete-time system of infinite-capacity buffers in tandem is studied. The input process to the first buffer consists of individual arrivals characterized by a geometric distribution of the time between arrivals; and the probability that the output channel of each buffer, except the last, is transmitting at any epoch depends only on the number of digits in the buffer at that epoch. The transmitting state of the last buffer may depend more generally on the history of that buffer. The analysis shows that in equilibrium, the input processes to the subsequent buffers in the system are geometric with the same parameter as the input process to the first buffer. Therefore, each buffer in the system can be analyzed separately. Furthermore, the equilibrium state probabilities for a given buffer at a given epoch are independent of those for any other buffer in the system at the same epoch.
Navigation control for mobile robot based on vision and ultrasonic sensors

This paper treats the navigation problem of a mobile robot based on vision information and ultrasonic data. In our method, by calculating the optical flow on the images, the mobile robot can detect obstacles which exist ahead of it, further avoiding the area of obstacles, it can make the optimal trajectory to the final goal. Then, in order to generate the optimal trajectory, the distance between a mobile robot and obstacle is needed and then is obtained by evaluating a function with ultrasonic information. Through some experiments, we show how our proposed method is effective.
Text Entry in PDAs with WtX

Size is a mixed blessing for portable devices. While they feature a wide range of applications and services that can be used anywhere and anytime, their user interface is poor, hampered by the screen size. Furthermore, portable devices are meant to be used everywhere, even in uncomfortable situations (while standing up or walking for instance). Consequently, some tasks get hard to be accomplished. One of them is text entry, which is still far from being as comfortable as on desktop PCs. Different solutions have been envisaged, including small hardware keyboards, software (also called virtual) keyboards, predictive input techniques and handwriting recognition systems. In this paper we give a thorough overview of our text entry tool Word Tree version X (WtX), especially focusing on recent improvements. The new version of WtX features a handwriting recognition system and a mechanism (which we called First-Third-Last rule or simply FTL rule) forcing users to insert the characters of a word so as to improve text prediction.
A Vertical Handover Management Scheme based on Decision Modelling in Heterogeneous Wireless Networks

ABSTRACT Providing a continuous connection in heterogeneous wireless networks is a challenging job. Traditional approaches are mainly concerned to specific scenarios. These approaches are also suffering from different problems like ping-pong effect, inappropriate handover triggering, etc. Therefore, to provide a generic connectivity in heterogeneous wireless networks, we proposed a vertical handover decision scheme. The proposed scheme performs handover triggering using simple additive weighting on the data rate of a point of attachment (PoA). The user calculates the data rate required by different applications and then compares it with a predefined threshold. Similarly, the network selection is performed using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) decision model on different parameters such as delay, jitter, bit error rate, packet loss ratio, communication cost, response time, and network load. The PoA of a network calculates its rank using TOPSIS and sends it to the user. The user then selects the network with the highest rank. The proposed vertical handover decision scheme is compared with grey relational analysis and analytical hierarchy process in the context of handover rate, failed handovers, packet loss ratio, and throughput. The proposed scheme is tested in different mobility scenarios, and it is shown that our scheme provides superior performance over the existing schemes.
Real-time object detection and tracking in omni-directional surveillance using GPU

Recent technological advancements in hardware systems have made higher quality cameras. State of the art panoramic systems use them to produce videos with a resolution of 9000 x 2400 pixels at a rate of 30 frames per second (fps) [1]. Many modern applications use object tracking to determine the speed and the path taken by each object moving through a scene. The detection requires detailed pixel analysis between two frames. In fields like surveillance systems or crowd analysis, this must be achieved in real time. Graphics Processing Units (GPUs) are powerful devices with lots of processing capabilities for parallel jobs. The detection of objects in a scene requires large amount of independent pixel operations on the video frames that can be done in parallel, making GPU a good choice for the processing platform. This paper only concentrates on Background Subtraction Techniques [2] to detect the objects present in the scene. The foreground pixels are extracted from the processed frame and compared to the corresponding ones of the model. Using a connected- component detector, neighboring pixels are gathered in order to form blobs which correspond to the detected foreground objects. The new blobs are compared to the blobs formed in the previous frame to see if the corresponding object moved.
Study of a Single Neuron Fuzzy PID DC Motor Control Method

As the brush less DC motor is more and more widely used, relating control algorithm need more and more precise and intelligence. In this paper, after studying the fuzzy control and the neural network theory, a single neuron fuzzy self-adaptive PID control algorithm is presented for speed control of the brush less DC motor. The Matlab software simulation results show that the single neuron fuzzy self-adaptive PID control algorithm has more robustness, faster response speed and more excellent adaptive capacity than increment PID, and PID based on quadratic performance index learning algorithm of single neuron adaptive. Thus the algorithm can be widely applied to the actual control system.
Conflict resolution advisory for autonomous airborne separation in low-density airspace

The autonomous aircraft operations concept is discussed as a main option for the future European air traffic management system (EATMS). This concept addresses the delegation of air traffic control function to the flight deck to operate in the EATMS free-flight airspace. The feasibility of this concept is investigated within the framework of the FREER (free route experimental encounter resolution) project at EUROCONTROL. In this paper, we discuss its conflict detection, based upon the principle of "forbidden" zones, and a conflict resolution approach, using an artificial force technique. A prototype of the FREER airborne system is implemented on a network of cockpit simulators to demonstrate the concept.
New filtering method for GPS/INS integrated navigation

The error caused by linearization in the filtering process is an important factor that decrease the filtering accuracy, and if the error exceeds the allowable range it may result in filter divergence for some highly nonlinear systems. In the GPS/INS system integrated with position and velocity, though the linearization process is eliminated because of its linear model, the navigation accuracy is degraded by the correlation between the system's measurements. So the integration of range was adopted for observation model, which makes use of the original measurements of GPS, so it can improve the navigation accuracy. As this observation model is nonlinear, for the high accuracy requirement of GPS/INS integrated navigation system, the UPF (Unscented Particle Filter) algorithm was introduced to estimate the system states. The UPF is a nonlinear filer which is more suitable for nonlinear systems. The simulation was carried out under the condition of less than four GPS satellites data available, the results of which show that the estimated values with UPF are quite close to real values, and the UPF algorithm has a very good performance when it applied in the GPS/INS integrated navigation.
The research of defects detection and segmentation for weld radiographic inspection

Both digital radioscopy and radiographic testing rely on human experts to perform manual interpretation of images, and the recognition of welding defects demands the inspector's vast experience. With computer detect defects in a weld image; many countries have been made seeking the development of automatic system of inspection of welding defects. Defects were detected and segmented is key step for automatic recognition from welding image. This paper presents an algorithm of defects detection and segmentation for weld radiographic inspection which is based on wavelet analysis. The detection algorithm based on waveform analysis classifies weld defects as trough-anomaly, peak-anomaly and slanttrough- anomaly three types firstly. Aiming at to trough-anomaly and slant-trough-anomaly, weld defects inside them can be classified as crack and non-crack according to the width of two peaks in following. Different segmentation algorithms are adopted to these two types defects sequently. The experimental results show that the algorithm is very effective.
Monitoring manufacturing systems by means of Petri nets with imprecise markings

An attempt is made to introduce imprecision and uncertainty within Petri net theory in order to take into account abnormal events as well as the normal operation for the control and monitoring of flexible manufacturing systems. The uncertainty and imprecision concepts are introduced through a modification of the marking of a Petri net with objects and through time handling. The approach is based on the association of fuzzy enabling duration with the transition which results in attaching a fuzzy firing date to the transition. Some examples of possible use are presented. <<ETX>>