Miracle-dz/UniFAR-FTUs huggingface dataset:
A failure tolerant joint design for the Space Shuttle remote manipulator system: analysis and experiment

The feasibility of space-based fault-tolerant robot joint design with a dual-motor, single output differential-based mechanical drive system was studied. The mathematical model of the differential system was developed and the inherent nonlinear dynamic characteristics for the differential were reduced to linear state equations through variable substitutions. Other nonlinear phenomena in the system were also included in the model. Simulations were performed for various joint failure conditions, and results of these simulations demonstrated the capability of the drive system to sustain a single failure while remaining operational. A scaled-down hardware testbed was subsequently designed and built to validate the analytical results. The experimental failure transient responses were consistent with the simulation results. The test demonstrated a very useful design for a space-based robot joint which will be capable of sustaining a single failure and absorbing the failure disturbance, deactivating and locking-up the failed drive unit, and continuing to be operational with the remaining single drive mode. <<ETX>>
AtSNE: Efficient and Robust Visualization on GPU through Hierarchical Optimization

Visualization of high-dimensional data is a fundamental yet challenging problem in data mining. These visualization techniques are commonly used to reveal the patterns in the high-dimensional data, such as clusters and the similarity among clusters. Recently, some successful visualization tools (e.g., BH-t-SNE and LargeVis) have been developed. However, there are two limitations with them : (1) they cannot capture the global data structure well. Thus, their visualization results are sensitive to initialization, which may cause confusions to the data analysis. (2) They cannot scale to large-scale datasets. They are not suitable to be implemented on the GPU platform because their complex algorithm logic, high memory cost, and random memory access mode will lead to low hardware utilization. To address the aforementioned problems, we propose a novel visualization approach named as Anchor-t-SNE (AtSNE), which provides efficient GPU-based visualization solution for large-scale and high-dimensional data. Specifically, we generate a number of anchor points from the original data and regard them as the skeleton of the layout, which holds the global structure information. We propose a hierarchical optimization approach to optimize the positions of the anchor points and ordinary data points in the layout simultaneously. Our approach presents much better and robust visual effects on 11 public datasets, and achieve 5 to 28 times speed-up on different datasets, compared with the current state-of-the-art methods. In particular, we deliver a high-quality 2-D layout for a 20 million and 96-dimension dataset within 5 hours, while the current methods fail to give results due to running out of the memory.
The Multi-Constrained Multicast Routing Improved by Hybrid Bacteria Foraging-Particle Swarm Optimization

To solve multicast routing under multiple constraints, it is required to generate a multicast tree that ranges from a source to the destinations with minimum cost subject to several constraints. In this paper, PSO has been embedded with BFO to improve the convergence speed and avoid premature convergence that will be used for solving QoS multicast routing problem. The algorithm proposed here generates a set of delay compelled links to every destination present in the multicast group. Then the Bacteria Foraging Algorithm (BFA) selects the paths to all the destinations sensibly from the set of least delay paths to construct a multicast tree. The robustness of the algorithm being proposed had been established through the simulation. The efficiency and effectiveness of the algorithm being proposed was validated through the comparison study with other existing meta-heuristic algorithms. It shows that our proposed algorithm IBF-PSO outperforms its competitive algorithms.
Energy-Efficient Cooperative Secure Transmission in Multi-UAV-Enabled Wireless Networks

This paper investigates a multiple unmanned aerial vehicles (UAVs) enabled cooperative secure transmission scheme in the presence of multiple potential eavesdroppers. Specifically, multiple source UAVs (SUAVs) send confidential information to multiple legitimate ground users, and in the meantime, multiple jamming UAVs cooperatively transmit interference signals to multiple eavesdroppers in order to improve the legitimate users’ achievable secrecy rate. By taking into account the limited energy budget of UAV, our goal is to maximize the system secrecy energy efficiency (SEE), namely the achievable secrecy rate per energy consumption unit, by jointly optimizing the UAV trajectory, transmit power, and user scheduling under the constraints of UAV mobility as well as the maximum transmit power. The resulting optimization problem is shown to be a non-convex and mixed-integer fractional optimization problem, which is challenging to solve. We decompose the original problem into three sub-problems, and then, an efficient iterative algorithm is proposed by leveraging the block coordinate descent method and Dinkelbach method in combination with successive convex approximation techniques. Simulation results show that the proposed scheme outperforms the other benchmarks significantly in terms of the system SEE.
Accelerating Sparse Matrix Operations in Neural Networks on Graphics Processing Units

Graphics Processing Units (GPUs) are commonly used to train and evaluate neural networks efficiently. While previous work in deep learning has focused on accelerating operations on dense matrices/tensors on GPUs, efforts have concentrated on operations involving sparse data structures. Operations using sparse structures are common in natural language models at the input and output layers, because these models operate on sequences over discrete alphabets. We present two new GPU algorithms: one at the input layer, for multiplying a matrix by a few-hot vector (generalizing the more common operation of multiplication by a one-hot vector) and one at the output layer, for a fused softmax and top-N selection (commonly used in beam search). Our methods achieve speedups over state-of-the-art parallel GPU baselines of up to 7x and 50x, respectively. We also illustrate how our methods scale on different GPU architectures.
XQA: A Cross-lingual Open-domain Question Answering Dataset

Open-domain question answering (OpenQA) aims to answer questions through text retrieval and reading comprehension. Recently, lots of neural network-based models have been proposed and achieved promising results in OpenQA. However, the success of these models relies on a massive volume of training data (usually in English), which is not available in many other languages, especially for those low-resource languages. Therefore, it is essential to investigate cross-lingual OpenQA. In this paper, we construct a novel dataset XQA for cross-lingual OpenQA research. It consists of a training set in English as well as development and test sets in eight other languages. Besides, we provide several baseline systems for cross-lingual OpenQA, including two machine translation-based methods and one zero-shot cross-lingual method (multilingual BERT). Experimental results show that the multilingual BERT model achieves the best results in almost all target languages, while the performance of cross-lingual OpenQA is still much lower than that of English. Our analysis indicates that the performance of cross-lingual OpenQA is related to not only how similar the target language and English are, but also how difficult the question set of the target language is. The XQA dataset is publicly available at http://github.com/thunlp/XQA.
Exploring InfiniBand Congestion Control

Congestion Control (CC) is used to achieve high performance and good utilization of network resources during high load in lossless interconnection networks. Without CC a congestion which started from a single node can grow, spread and degrade the performance of the network. Congestion can affect both the contributors of the congestion and also other traffic flows in the network. Infiniband (IB) is one of communication standards providing support for Congestion Control. The IB standard describes the CC functionality for detecting and resolving congestion. The behavior of the IB CC mechanism depends on the values of CC parameters. The given values of the parameters will determine characteristics like how aggressive the congestion detection should be, the rate of feedback from the forwarding node detecting congestion to the contributors of the congestion and how much and for how long the contributors should lower their injection rates. But there are very few guidelines about how to set the values of the CC parameters for IB CC it to be efficient. In this thesis, an experiment of a Mesh network topology will be conducted using OmNet++ as a simulation platform. Large amount of traffic will be generated and fed to the network until a congestion is contributed. The performance will be measured when Infiniband congestion control is disable and when it is enabled. The results from those simulations will be compared and analysed. The topology‘s host-to-switch link capacities are to be increased. There will be a search for proper IB CC parameters and finally, we will learn more about how IB CC parameters influence performance by focusing on some of the parameters.
A Smart Manufacturing Service System Based on Edge Computing, Fog Computing, and Cloud Computing

The state-of-the-art technologies in new generation information technologies (New IT) greatly stimulate the development of smart manufacturing. In a smart manufacturing environment, more and more devices would be connected to the Internet so that a large volume of data can be obtained during all phases of the product lifecycle. Cloud-based smart manufacturing paradigm facilitates a new variety of applications and services to analyze a large volume of data and enable large-scale manufacturing collaboration. However, different factors, such as the network unavailability, overfull bandwidth, and latency time, restrict its availability for high-speed and low-latency real-time applications. Fog computing and edge computing extended the compute, storage, and networking capabilities of the cloud to the edge, which will respond to the above-mentioned issues. Based on cloud computing, fog computing, and edge computing, in this paper, a hierarchy reference architecture is introduced for smart manufacturing. The architecture is expected to be applied in the digital twin shop floor, which opens a bright perspective of new applications within the field of manufacturing.
Exact Error and Erasure Exponents for the Asymmetric Broadcast Channel

Consider the asymmetric broadcast channel with a random superposition codebook, which may be comprised of constant composition or i.i. d. codewords. By applying Forney’s optimal decoder for individual messages and the message pair for the receiver that decodes both messages, exact (ensemble-tight) error and erasure exponents are derived. It is shown that the optimal decoder designed to decode the pair of messages achieves the optimal trade-off between the total and undetected exponents associated with the optimal decoder for the private message. Convex optimization-based procedures to evaluate the exponents efficiently are proposed. Finally, numerical examples are presented to illustrate the results.
ANALISIS PERFORMANSI DENOISING SINYAL EEG MENGGUNAKAN METODE DISCRETE WAVELET TRANSFORM DAN ADAPTIVE FILTER

Abstrak Sinyal EEG ( Electroencephalogram ) merupakan rekaman sinyal yang dihasilkan dari medan elektrik spontan pada aktivitas neuron di dalam otak. Sinyal EEG dimanfaatkan pada bidang kesehatan untuk mendiagnosis keadaan neurologis otak, serta pada bidang teknologi seperti aplikasi Brain Computer Interface ( BCI ). Dalam perekaman sinyal EEG, terdapat noise yang tidak diinginkan sehingga dalam analisis sinyal EEG sulit mendapatkan informasi sinyal yang sebenarnya. Oleh karena itu dalam Tugas Akhir ini dirancang sebuah sistem denoising untuk menghilangkan noise agar memperoleh visualisasi sinyal yang sebenarnya. Pada penelitian ini sinyal EEG bersih akan di berikan noise AWGN (Additive White Gaussian Noise), kemudian teknik denoising yang akan dilakukan adalah menggunakan dua metode yaitu Discrete Wavelet Transforms dan Adaptive Filter. Pengujian dilakukan dalam Matlab dengan menggunakan parameter Minimum Squared Error ( MSE ), Signal-to-Noise Ratio ( SNR ), serta Peak Signal-to-Noise Ratio ( PSNR ). Berdasarkan hasil pengujian pada 5 data sinyal EEG, menunjukan untuk metode DWT ( Discrete Wavelet Transforms ) mendapatkan nilai rata – rata pada masing – masing parameter, MSE sebesar 0.0000209082, SNR sebesar 29.1607127780 dB dan PSNR sebesar 29.8262675865 dB. Dan untuk metode Adaptive Filter dengan algoritma Kalman diperoleh nilai rata – rata pada masing – masing parameter, MSE sebesar 0.0000425027, SNR sebesar 27.6136811973 dB dan PSNR sebesar 28.2792360058 dB. Kata kunci: Electroencephalogram (EEG), Denoising, Discrete wavelet Transform (DWT), Adaptive Filter. Abstract The EEG signal (Electroencephalogram) is a recording of a signal generated from a spontaneous electric field in the activity of neurons in the brain. EEG signals are used in the health field to diagnose the neurological state of the brain, as well as in areas of technology such as Brain Computer Interface (BCI) applications. In recording EEG signals, there is an undesirable noise so that in the EEG signal analysis it is difficult to get the actual signal information. Therefore in this final project is designed a denoising system to eliminate noise in order to obtain the actual signal visualization. In this study the clean EEG signal will be given AWGN noise (Additive White Gaussian Noise), then denoising technique that will be done is to use two methods of Discrete Wavelet Transforms and Adaptive Filter. The test is done in Matlab using Minimum Squared Error (MSE), Signal-to-Noise Ratio (SNR), and Peak Signalto-Noise Ratio (PSNR). Based on the results of testing on 5 EEG signal data, it shows that the DWT (Discrete Wavelet Transforms) method gets the average value of each parameter, MSE is 0.0000209082, SNR is 29.1607127780 dB and PSNR is 29.8262675865 dB. And for the Adaptive Filter method with Kalman algorithm, the average values for each parameter, MSE is 0.0000425027, SNR is 27.6136811973 dB and PSNR is 28.2792360058 dB. Keywords: Electroencephalogram (EEG), Denoising, Discrete wavelet Transform (DWT), Adaptive Filter.
Informal Archiving Systems of Digital Videos - A proposal for design principles.

Graduation year 2006 Department Media Lab Degree programme MA in New Media Author Tikkanen, Ville Title Informal archiving systems of digital videos. A proposal for design principles. Level Diploma Work Number of pages 95 UIAH Library, Media Lab Library Where deposited Confidential until Design research, informal archiving, personal media, digital preservation, everyday life, contextual design, practices, video prototyping, field research, scenarios
Towards a convivial control interface for a snake-like robot

A number of prior and actual work is available on hyper-redundant and especially snake-like mechanisms, including our project. Since mechanical conception and control of this kind of machines meet difficulties and require a certain research effort, the majority of related works focus on these areas. There is not any specific proposal about "how to interface such a robot in order to control". The paper discusses some ideas on a convivial robot-human interface envisaged for testing control algorithms of a snake-like robot. This project consists of a real-time simulation of a snake locomotion type known as lateral undulation in order to develop a prototype. Discussion of the interfacing problem follows a brief overview of snake locomotion and the presentation of our snake model and progression strategy.
A protected discharge facility for the elderly: design and validation of a working proof-of-concept

With the increasing share of elderly population worldwide, the need for assistive technologies to support clinicians in monitoring their health conditions is becoming more and more relevant. As a quantitative tool, geriatricians recently proposed the notion of frail elderly, which rapidly became a key element of clinical practices for the estimation of well-being in aging population. The evaluation of frailty is commonly based on self-reported outcomes and occasional physicians evaluations, and may therefore contain biased results. Another important aspect in the elderly population is hospitalization as a risk factor for patient’s well being and public costs. Hospitalization is the main cause of functional decline, especially in older adults. The reduction of hospitalization time may allow an improvement of elderly health conditions and a reduction of hospital costs. Furthermore, a gradual transition from a hospital environment to a home-like one, can contribute to the weaning of the patient from a condition of hospitalization to a condition of discharge to his home. The advent of new technologies allows for the design and implementation of smart environments to monitor elderly health status and activities, fulfilling all the requirements of health and safety of the patients. From these starting points, in this thesis I present data-driven methodologies to automatically evaluate one of the main aspects contributing to the frailty estimation, i.e., the motility of the subject. First I will describe a model of protected discharge facility, realized in collaboration and within the E.O. Ospedali Galliera (Genoa, Italy), where patients can be monitored by a system of sensors while physicians and nurses have the opportunity to monitor them remotely. This sensorised facility is being developed to assist elderly users after they have been dismissed from the hospital and before they are ready to go back home, with the perspective of coaching them towards a healthy lifestyle. The facility is equipped with a variety of sensors (vision, depth, ambient and wearable sensors and medical devices), but in my thesis I primarily focus on RGB-D sensors and present visual computing tools to automatically estimate motility features. I provide an extensive system assessment I carried out on
Expert maps: an alternative for integrating expert knowledge in satellite imagery classification

A new classification method which permits the integration of expert knowledge in satellite image classification is presented. This approach differs from other knowledge based ones for the following reasons: 1) it is a low-cost procedure; 2) it allows that the available experts explicitly express all their knowledge on the area to be classified, and 3) it does not have to face the problem of rule generalization over the whole area. The method is applied to a real image data set and it is shown that the achieved classification is more accurate than the classification just based on spectral data.
P2VSIM: A SIMULATION AND VISUALIZATION TOOL FOR THE P2V COMPILER

P2VSim: A Simulation and Visualization Tool for the P2V Compiler. (May 2009) Oscar Michael Almeida, B.S., Texas A&M University Chair of Advisory Committee: Dr. Jyh-Charn (Steve) Liu The Property Specification Language (PSL) is an IEEE standard which allows developers to specify precise behavioral properties of hardware designs. PSL assertions can be embedded within code written in hardware description languages (HDL) such as Verilog to monitor signals of interest. Debugging simulations at the register transfer level (RTL) is often required to verify the functionality of a design before synthesis. Traditional methods of RTL debugging can help locate failures, but do not necessarily immediately help in discovering the reasons for the failures. The P2VSim tool presents the ability to combine multiple Verilog signals not only instantaneously, but also across multiple clock cycles, producing a graphical display of the state of active PSL assertions in a given RTL simulation. When using the P2VSim tool, users will write PSL assertions directly into their Verilog source files. After the tool searches for and loads the embedded assertions, execution trace monitors for the relevant Verilog signals are dynamically generated and written back into the Verilog source code. P2VSim then invokes an RTL simulator, Modelsim, to generate a simulation execution trace, requiring that the designer has some hardware or software testbench already in place. Next, the input PSL assertions are
Review of Polar Integrated Navigation Algorithm

Developing the research of navigation and positioning in high latitude area has important political, economic and military value, and it is of great significance to realize the global navigation of inertial navigation system. In this paper, the basic algorithms suitable for polar navigation are summarized, various polar integrated navigation methods are compared, and the research of inertial / astronomical integrated navigation with small error, high accuracy, wide application is particularly introduced.
Code-switching, language crossing and mediatized translinguistic practices

Codeswitching (henceforth, CS) refers to the juxtaposition of two languages or varieties (Gumperz 1982 ). In line with a strong ideology of monolingualism, using two languages or varieties at the same time was once considered simply as “makeshifts” in communication. Studies in CS have reversed this view and contributed to our understanding of linguistics and sociolinguistics in general (see Woolard 2004 ). The fi rst part of this chapter very briefl y reviews CS studies involving Japanese. I then move on to discuss multilingual language use beyond the confi nes of CS. This is followed by discussions on mediatized translinguistic practices. I will then sketch issues of race and ethnicity and discuss how these relate to Japan. In the last part, role language and mediatized translinguistic practices in contemporary Japanese society are discussed.
An integrated receiver channel for picosecond range laser radar pulses

An integrated receiver channel for a pulsed time-of-flight (TOF) laser radar capable of receiving laser pulses having a width of 30 to 200 ps is described here. The realized semicustom bipolar array IC includes current mode gain control, amplifiers and a timing discriminator. An avalanche photodiode (APD) was used to improve the overall responsivity of the receiver. The single-shot resolution (SSR) of the receiver channel is /spl plusmn/5 ps at best, and the maximum input dynamic range is about 50 dB.
An efficient crowd-sourcing-based approach for fingerprint database updating

Fingerprint-based indoor localization has become one of the most attractive and promising techniques; however, one primary concern for this technology to be fully practical is to maintain the fingerprint database to combat harsh indoor environmental dynamics, especially in the large-scale and long-term deployment. In this article, focusing on three key problems now existing in fingerprint database updating approaches such as the mechanism for triggering updates, the collection of new fingerprints and determination of fingerprints’ location information, we propose a fuzzy map mechanism and decision methods of neighbours’ fingerprints in response to all kinds of changes in indoor environments. Meanwhile, we design a static data collecting mechanism to filter reliable information from numerous users’ inputs and propose a neighbours’ fingerprint-assisted technique to calculate the location of fingerprints. Experimental results demonstrate that the proposed solution not only improves the performance of updating the fingerprint database in real time and robustness by 40% and 50%, respectively, but also reduces the update frequency and improves mean location accuracy by over 40%.