Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance of MiMo-OFDM Systems in Canal Rayleigh

Along with the development of wireless communication makes the increasing demand for obtaining quality services and better range, bandwidth-efficient and reliable system. OFDM is a multicarrier transmission technique to overcome multipath fading, which may result in intersymbol interference (ISI). On the other hand there is the antenna system with the use of multiple antennas at the transmitter and receiver that can also be overcome multipath fading,ie Multiple Input Multiple Output (MIMO) to increase channel capacity and diversity gain. MIMO techniques used in this Final adaldah Space Time Block Code. Simulation aims to simulate and analyze the MIMO OFDM technique using space-time block code against the influence of the number of subcarriers and influence user speed in AWGN channel and Rayleigh. The simulation results based on the number of subcarriers in AWGN channel, the best performance is obtained by subcarrier number 256. With BPSK and QPSK modulation obtained BER = 6 10-4 when SNR 4 dB. Simulation based on Rayleigh channel obtained when BER = 9.7 10-5 with BPSK modulation. Based on the comparison of the greater frequency doppler given doppler frequency, resulting in BER generated even greater, because the state of the canal will be getting worse.
A Representation Learning Framework for Property Graphs

Representation learning on graphs, also called graph embedding, has demonstrated its significant impact on a series of machine learning applications such as classification, prediction and recommendation. However, existing work has largely ignored the rich information contained in the properties (or attributes) of both nodes and edges of graphs in modern applications, e.g., those represented by property graphs. To date, most existing graph embedding methods either focus on plain graphs with only the graph topology, or consider properties on nodes only. We propose PGE, a graph representation learning framework that incorporates both node and edge properties into the graph embedding procedure. PGE uses node clustering to assign biases to differentiate neighbors of a node and leverages multiple data-driven matrices to aggregate the property information of neighbors sampled based on a biased strategy. PGE adopts the popular inductive model for neighborhood aggregation. We provide detailed analyses on the efficacy of our method and validate the performance of PGE by showing how PGE achieves better embedding results than the state-of-the-art graph embedding methods on benchmark applications such as node classification and link prediction over real-world datasets.
A Novel Cache-based Framework for Accelerating Real-time Flow Transmission

Real-time flow transmission is widely used in various online applications, such as video transmission, social networks, etc. Most of these tasks which require strict deadlines need to be handled by distributed systems, so the deadline guarantee of the whole system can be divided into each node. However, due to insufficient computing power or software errors, some nodes are prone to delay data processing, leading to the loss of computing deadline, resulting in the loss of the cutoff event of the whole system, and causing the data processing delay of the next link. In view of the performance loss caused by this situation, this paper proposes a cache-based processing framework to speed up the forwarding of real-time data flows. By estimating the data processing ability of nodes online, when the processing ability of nodes is out of order, some computing tasks can be mapped to other preparatory nodes online, so as to achieve fast task. Migration ensures the deadline of computing tasks on nodes. By adjusting reasonable system parameters, the processing capacity of nodes in distributed system can be accelerated, so that the processing capacity of the system can be increased dynamically and the data flow forwarding capability of the whole system can be accelerated. The results show that this method can increase the number of data forwarding by 23% and reduce the data loss rate by 31%.
Passive radar for measuring passive sensors: direct signal interference suppression on FPGA using orthogonal matching pursuit and stochastic gradient descent

Passive wireless sensors provide an attractive technology in niche applications where battery powered sensors are not applicable. Surface acoustic wave technology provides an optimum implementation of passive wireless transducers acting as cooperative targets to short range radar systems: the signal carrying the information is delayed beyond clutter, with a piezoelectric substrate converting the electromagnetic wave to an acoustic wave whose properties are dependent on the environment. We have tackled the issue of short range radar certification by considering a passive radar approach in which existing non-cooperative radiofrequency sources are used to power the sensor, and the reader is made solely of passive receivers aimed at recording the reference signal generated by the non-cooperative source and the backscattered signal returned by the sensor. A passive radar approach only requires a cross-correlation between the reference and surveillance signals to identify the time delay between the incoming and backscattered signals and hence the recovery of the physical quantity sensed by the acoustic transducer through acoustic velocity modulation. Practical sources do exhibit some short term correlation. Hence, strong copies of the reference signal with delays shorter than the one introduced by the sensor must be canceled by the receiver to allow for the weak sensor response to be extracted. This classical problem is called Direct Signal Interference (DSI) suppression. In a post-processing approach with little computation time limitation, this problem is solved using a least square error optimization approach. In the context of real time sensor measurement using a Field Programmable Gate Array (FPGA) implementation, data recording, frequency transposition and decimation are readily implemented in the gate array matrix. DSI removal appears as the limiting factor for a full FPGA implementation of the short range passive radar reader. We address the challenge by the iterative process of DSI suppression using Orthogonal Matching Pursuit (OMP) algorithm, and additionnally consider the FPGAfriendly Stochastic Gradient Descent (SGD) approach to try to recover DSI coefficients from a pipelined algorithm using streams of measurement data.
A Review of the Research on the Prediction Model of Extreme Learning Machine

With the wide application of prediction, the research of prediction algorithm and theory has made a great progress. In recent years, extreme learning machines have been used in the field of prediction, such as stock price prediction. The prediction algorithms of ELM are reviewed in this paper, which includes the single extreme learning machine prediction algorithm and the combined prediction algorithm. At the same time, the existing problems and research directions are pointed out.
Direct Predictive Speed Control With a Sliding Manifold Term for PMSM Drives

This paper proposes a new direct predictive speed control (DPSC) method for permanent-magnet synchronous machines (PMSMs), which eliminates the cascaded loops structure that is usually used in the conventional controller. A novel sliding manifold term is added to the cost function to realize the speed/current tracking. The proposed cost function is simple and easy to implement. Therefore, in comparison with the other DPSC methods, the proposed method does not need long prediction horizons, dynamically adjustable weighting factors or large weighting factors for the control variables with large time constants. The performance of the proposed method has been experimentally investigated and compared with: 1) conventional DPSC scheme and 2) a predictive current control with an outer PI controller (PI-PCC). The results demonstrated enhancement in the dynamic performance in comparison with the PI-PCC method and improvement in the steady-state response in comparison with the conventional DPSC technique.
“Youtubing” to Language Perfection in A CulinaryEnvironment

The recent years have seen the booming of online video viewing and this trend is due to the birth of YouTube which has massively contributed to the online video activity amongst the netizens. Seen as one of a powerful media to engage students’ attention and participation in language learning, this research explores how in the context of a culinary setting the YouTube videos can actually help promote students’ communicative competence. The integration of online videos in the teaching of culinary skills also hopes to propagate the use of the target language based on the urgency of increasing the exposure and use of English language both inside and outside of the classrooms. This research delves into the language aspects whereby; the verbal and non-verbal communication aspects presented in the videos are observed and as such, the use of YouTube videos in the culinary classes are hoped to meet the educational outcomes of improving the teaching and learning techniques. A set of questionnaire administered to a selected group of culinary students have shown that an otherwise mundane class of culinary skill learning has resulted in a more exciting way of learning hands-on as the respondents are activated into paying closer attention to the videos’ content and organization as well as the non-verbal communication skills in presenting videos aligned to the art of culinary exposed to them. It is indicated in the responses made by them whereby the selected media has been seen as something influential in retaining their understanding and interest of the concepts taught in their culinary classes. Hence, a powerful technology as YouTube should not be frowned upon as it places an undeniable impact which imposes on teacher-learner language learning environment.
Freeform Shape Optimization Of Regular Quad Meshes

In this paper we propose a new scheme of direct freeform shape optimization on polygonal meshes. The approach coilsists of three main ingredients: 1) surface partition of polygonal meshes into different patches; 2) a new perturbation scheme of mimicking B-spline deformation; 3) freeform shape optimization. Numerical experiments indicate the effectiveness of the proposed approach, compared to a conventional approach.
Image interpolation based on image statistics: a case study

Two novel approaches to image interpolation aimed at video service interworking, layered image coding, and image zooming are discussed. Compared to conventional interpolation filters used for image interpolation, the schemes proposed take advantage of prior knowledge of image statistics to enhance their performance. Both methods, inverse Wiener filtering (IWF) and vector interpolation (VI), use image training sequences to estimate optimal interpolation parameters. While IWF has online capabilities for parameter estimation, VI parameters need to be designed in computer intense offline optimization procedures. Both IWF and VI methods achieve superior performance compared to standard linear interpolation filters in terms of signal-to-noise ratio of the interpolated images, as well as entropy of the residual errors. <<ETX>>
Post-Disaster Response and Restauration of B-Town Network Based on Prymary Network:

"Water Distribution Systems (WDSs) represent one of most critical infrastructures of modern society. In fact, only a partial interruption of service could weaken and compromise normal urban activities. WDS failure can be mechanical (pump malfunctions or pipe breaks), hydraulic (insufficient pressure, unsupplied demand) or water quality-related (incidental or intentional contamination). To increase the ability of a system to overcome the occurrence of a failure, reliability is considered in WDS design. Traditionally, the reliability of WDSs was assured by means of densely looped layouts, to provide alternative paths for each demand node with a subsequent increase in topological and energy redundancy. Recently, a methodology to define a primary water network with the aim to suggest a group of pipes to focus on, for monitoring and maintenance was proposed. The primary network was obtained reducing the topological and energy redundancy in compliance with a minimum required level of service (in terms of nodal pressures) for all users. In case of multiple pipe failures, for example caused by an earthquake disaster, it is possible to adopt the principle of primary network to prioritize the pipes to repair or replace based on high values of edge betweenness, which are expected to maximize the functionality of the network. Therefore, in the battle of post-disaster response and restauration (BPDRR), the authors have tested the primary network principles in order to find the optimal restoration scheme for the very large network of B-Town. "
Efficiency of Synchronous and Asynchronous Buck-Converter at Low Output Current.

In this paper both synchronous and asynchronous buck-converter were designed to work in continuous conduction mode “CCM” and to deliver small load current. Then the two topologies were tested in terms of efficiency at small load current by use of different values of switching frequencies (range from 150 KHz to 1MHz) and three separated values of duty-cycle (0.4, 0.6 and 0.8). Obtained results turns out that efficiency of both synchronous and asynchronous buck-converter “switching step-down voltage regulator” responds in a negative manner to the increase in the switching frequency. However, this impact is being stronger in synchronous topology because of magnifying effect of losses related to switching frequency compared to those related to conduction when working at small load currents; this behavior makes obtained efficiency of both topologies in convergent levels when they operated to deliver small output current especially when working with higher switching frequencies. Larger duty-cycle can rise up the efficiency of both topologies.
Rethinking Robot Vision – Combining Shape and Appearance

Equipping autonomous robots with vision sensors provides a multitude of advantages by simultaneously bringing up difficulties with regard to different illumination conditions. Furthermore, especially with service robots, the objects to be handled must somehow be learned for a later manipulation. In this paper we summarise work on combining two different vision sensors, namely a laser range scanner and a monocular colour camera, for shape-capturing, detecting and tracking of objects in cluttered scenes without the need of intermediate user interaction. The use of different sensor types provides the advantage of separating the shape and the appearance of the object and therefore overcome the problem with changing illumination conditions. We describe the framework and its components of visual shape-capturing, fast 3D object detection and robust tracking as well as examples that show the feasibility of this approach.
OCC: A Smart Reply System for Efficient In-App Communications

Smart reply systems have been developed for various messaging platforms. In this paper, we introduce Uber's smart reply system: one-click-chat (OCC), which is a key enhanced feature on top of the Uber in-app chat system. It enables driver-partners to quickly respond to rider messages using smart replies. The smart replies are dynamically selected according to conversation content using machine learning algorithms. Our system consists of two major components: intent detection and reply retrieval, which are very different from standard smart reply systems where the task is to directly predict a reply. It is designed specifically for mobile applications with short and non-canonical messages. Reply retrieval utilizes pairings between intent and reply based on their popularity in chat messages as derived from historical data. For intent detection, a set of embedding and classification techniques are experimented with, and we choose to deploy a solution using unsupervised distributed embedding and nearest-neighbor classifier. It has the advantage of only requiring a small amount of labeled training data, simplicity in developing and deploying to production, and fast inference during serving and hence highly scalable. At the same time, it performs comparably with deep learning architectures such as word-level convolutional neural network. Overall, the system achieves a high accuracy of 76% on intent detection. Currently, the system is deployed in production for English-speaking countries and 71% of in-app communications between riders and driver-partners adopted the smart replies to speedup the communication process.
How Well Do Phonological Awareness and Rapid Automatized Naming Correlate With Chinese Reading Accuracy and Fluency? A Meta-Analysis

ABSTRACT Previous meta-analyses on the relationship between phonological awareness, rapid automatized naming (RAN), and reading have been conducted primarily in English, an atypical alphabetic orthography. Here, we aimed to examine the association between phonological awareness, RAN, and word reading in a nonalphabetic language (Chinese). A random-effects model analysis of data from 35 studies revealed a moderate relationship of phonological awareness with reading accuracy (r = .36) and reading fluency (r = .39). RAN also correlated significantly with reading accuracy (r = –.38) and reading fluency (r = –.51), but its relationship varied as a function of test type (graphological RAN correlated more strongly with reading than nongraphological RAN) and reading outcome (RAN correlated more strongly with reading fluency than reading accuracy). Age/grade and dialect (Mandarin vs. Cantonese) did not influence the size of the correlations. Taken together, the findings of this meta-analysis suggest that phonological awareness and RAN are universal correlates of word reading.
Ad-hoc Study on Soldiers Calibration Procedure in Virtual Reality

French Army infantrymen's are equipped today with a combat system called FELIN, which includes an infrared sighting device: the IR sight. One of the first manipulations learned by the soldier is the IR sight calibration. Currently, calibration training is a two-step process. The first step consists of practicing on a 2D WIMP software until making no mistakes. Then, the soldiers can apply his knowledge in the real situation on the shooting range. In this paper, we present an ad-hoc study of a learning method including a prototype in Virtual Reality for training on the FELIN IR sight calibration procedure. It has been experimented on real infantrymen learners in an infantry school. Results showed an attractive added value of Virtual Reality in this specific use case. It improved the learners' intrinsic motivation to repeat the training task as well as the learning efficiency. It also helped the training team to identify specific mistake types not detected by the traditional learning software.
Exploring an estimation approach for the pedestrian level of service for metro stations based on an interaction index

ABSTRACT Studies on crowd characteristics and management strategies are imperative for modern metro stations. Previous studies mainly focused on pedestrian facilities like sidewalks or crossing, while this research aims to evaluate the efficiency and safety performance of metro stations by introducing a new concept, pedestrian level of service (PLOS). The authors divide a station into separate locations in four typical types: exits, stairways, corridors, and open areas. The characteristics of pedestrians in these four types of locations are delineated and a new index called the interaction index (II) is proposed to quantify the performance. By integrating the II values of all single locations, the PLOS of the whole station can be analyzed. To validate this method, field data were collected and compared. Finally, the Caobao Road metro station in Shanghai was used as a case study for the practical application.
AN EFFICIENT TECHNIQUE FOR SQL INJECTION DETECTION AND PREVENTION

With the recent rapid increase of interactive web applications that employ back-end database services, a SQL injection attack has become one of the most serious security threats. This type of attack can compromise confidentiality and integrity of information and database. Actually, an attacker intrudes to the web application database and consequently, access to data. For preventing this type of attack different techniques have been proposed by researchers but they are not enough because most of implemented techniques cannot stop all type of attacks. In this paper our proposed technique are detection of SQL injection and prevention based on first order, second order and blind SQL injection attacks online. The proposed technique implemented in JAVA and evaluated for seven types of SQL injection attacks. Experimental results have shown that the proposed technique is efficient related to execution time overhead. Our technique need to be one second overhead to execution time. Moreover, we have compared the proposed technique with the popular web application vulnerabilities scanner techniques. The most advantages of proposed technique Its easiness to adopt by software developer, having the same syntactic structure as current popular record set retrieval methods.
Modeling the driving-point characteristic of resistive interconnect for accurate delay estimation

An efficient algorithm is presented which accounts for series resistance by computing a reduced-order approximation for the driving-point admittance of an RC tree. The algorithm consists of four rules which allow the Taylor series expansion coefficients of the driving-point admittance looking downstream of a given point in the tree to be correctly propagated further upstream. Rules 1-3 involve movement upstream, along a single branch, and past, respectively, a lumped capacitor to ground, a series lumped resistor, and a uniformly distributed RC segment. Rule 4 involves combining two or more different admittance expansions in parallel at a branch point in the tree. Using an emitter-coupled-logic clock buffer as an example, the authors demonstrate a significant improvement in accuracy. <<ETX>>
Design And Implementation Of Fast Motion Estimation In Modern Video Compression On Gpu

Motion estimation is the most compute expensive part of high definition video compression. It accounts for more than 50% of overall execution. Therefore, improving the performance of motion estimation can make significant impact on the overall performance of video compression. The performance of motion estimation can be improved in two aspects: algorithm and implementation. This thesis touches both aspects. We first propose an innovative motion estimation algorithm by replacing the traditional block matching method which comparing blocks pixel by pixel with a brand new method which based on LBP (Local Binary Pattern) code. Our new method first encodes the original video frames into LBP code and then compares the blocks only using the LBP code. Our algorithm reduces the amount of computation significantly by avoiding many pixel by pixel comparisons present in traditional block matching approaches. Using public benchmarks our experiments show our proposed motion estimation algorithm runs 5 times faster than a traditional algorithm. Furthermore, we accelerate our proposed algorithm on GPUs. Motion estimation processes of all blocks are offloaded to GPU and accelerated in parallel. Our GPU implementation runs 9 times faster than CPU implementation.
Dynamic hierarchical fuzzy systems: analysis and design using a state space model

General formulae for generic hierarchical fuzzy systems (HFS) are derived using a recursive approach and various special cases are catered for. A discrete time dynamic hierarchical fuzzy controller (HFC) is analysed and a design method for the HFC is presented. Further a sufficient condition for the stability of the combined global HFS and HFC are presented and some examples are presented to illustrate the stability theorem.