Miracle-dz/UniFAR-FTUs huggingface dataset:
Poster: online adaptive sampling for network delay measurement via matrix completion

End-to-end network delay plays an important role in distributed services. Unfortunately, it is infeasible to know all node-pair delay information in practice due to the quadratic growth of overhead by active probing. In this paper, we leverage the state-of-the-art matrix completion technology for better network delay estimation from limited measurements. Specifically, we formulate the matrix completion problem as a nuclear norm minimization problem which can be solved via convex optimization. We propose an online adaptive sampling strategy for network delay measurement. The key idea is to sample the elements with larger leverage scores to maintain characteristic of important rows or columns of the matrix. The number of samples is adaptively determined by a proposed stopping criterion. A preliminary simulation result based on real-world network delay datasets indicates that our proposed sampling algorithm is capable of providing better performance (smaller estimation error and less convergence stress) at less cost (fewer samples and shorter processing time) than other traditionally used algorithms.
Directed-Driven 8-Phase Magnetically Levitated Rotary Table Based on an Analytical-Numerical Model

The magnetically levitated rotary table (MLRT) has the potential to be applied in many industrial applications due to its excellent rotation ability and good positioning accuracy. This paper presents the prototype of a novel MLRT with a simple and compact structure. The table employs a circular permanent magnet (PM) array and a circular coil array to generate large scale rotation around the vertical axis and limited movement in the other directions. This MLRT has a higher current carrying capacity and simpler commutation law than the existing MLRT. Furthermore, different from the previous works, the magnetic force model presented in this paper is obtained by a novel analytical-numerical method that considers the clearances in the circular PM array. Thus, the predicted magnetic force and torque are more accurate than those from the existing modeling method. The translation and rotation of the table are measured by six laser sensors, which are distributed around and below the mover respectively. The PID controller is used to stabilize the system. Experiments related to the motion decoupling, trajectory tracking and load change are carried out to verify the motion performance of the novel rotary table. Compared with the existing magnetic force model, the experimental results illustrate that the novel magnetic force model, which considers the clearances in a circular PM array, is more suitable for motion control of the MLRT.
Communication MAC Protocol for Coexisting Wireless Devices and Backscatter Tags

Conventional Wi-Fi devices, Radio Frequency (RF) energy harvesting devices, and smart sensor devices could coexist in a network. Backscatter communication paves a way to provide battery-free communications for the IoT network. A network can be more susceptible to collision by RF transmissions and backscatter communications in a dense energy harvesting network. In this paper, we propose an Opportunistic Backscatter Communication Medium access control (OBM) in an energy harvesting IoT network. It gives tags opportunities to exploit the wireless power transmitted by the Hybrid Access Point (HAP) to communicate with the reader. The reader excites the tags for contention when it finds the RF energy signal of the HAP. The backscatter tags opportunistically utilize it for contention and communicate with the reader. The wireless devices and the backscatter tags use contention mechanisms for mitigating collisions and accessing the channel. The proposed OBM protocol shows performance improvement over the conventional schemes.
A Novel Multi-Feature Joint Learning Method for Fast Polarimetric SAR Terrain Classification

Polarimetric synthetic aperture radar (PolSAR) image classification is one of the most important study areas for PolSAR image processing. Many kinds of PolSAR features can be extracted for PolSAR image classification, such as the scattering, polarimetric or image features. However, it is difficult to improve the classification accuracy of PolSAR images by using all these low-level features directly, since they may conflict with each other for classification. Hence, how to joint learn these low-level features to obtain high-level discriminating features is a challenging task. To solve this problem, a novel fast multi-feature joint learning method(fMF-JLC) is proposed for PolSAR image classification. The proposed method extract three kinds of low-level features of PolSAR data at first. Then, a multi-feature joint sparse representation model(MF-JSR) is proposed by designing joint sparse constraints on the extracted features above. Moreover, the joint sparse features are further compressed to overcome the dimension curse and acquire semantic features by the topic model. By this way, the low-level features are fused and discriminating high-level features are acquired. However, the pixel-wise feature learning method is time consuming. To speed the proposed method, a superpixel-based fast learning method is designed by involving the contextual relationship. Experiments are taken on three sets of real PolSAR data with different sensors and bands, and several compared methods are used to verify the effectiveness of the proposed method. The experimental results illustrate that the proposed method can obtain better performance than the state-of-art methods, especially for the heterogenous areas.
Clinical Pathway Analysis Using Process Mining and Discrete-Event Simulation: an Application to Incisional Hernia

An incisional hernia (IH) is a ventral hernia that develops after surgical trauma to the abdominal wall, a laparotomy. IH repair is a common surgery that can generate chronic pain, decreased quality of life, and significant healthcare costs caused by hospital readmissions. The goal of this study is to analyze the clinical pathway of patients having an IH using a medico-administrative database. After a preliminary statistical analysis, a process mining approach is proposed to extract the most significant pathways from the database. The resulting causal net is converted into a statechart model that can be simulated. The model is used to understand times of occurrence of complications and associated costs. It enables the simulation of what-if scenarios to propose an improved care pathway for patients who are the most exposed, using in particular prophylactic mesh at the time of abdominal wall closure during a laparotomy on hospitalization costs and readmissions.
On-site Assembly of Modular Building Using Discrete Event Simulation

With the continuous development of industrialization in building construction, modular construction and off-site prefabrication methods have been applied much more thoroughly and comprehensively to achieve higher efficiency and better quality control as the major building components are able to be produced in a factory setting, which reduces the influence of uncontrolled factors. This paper, while employing discrete event simulation, uses Simpony.NET tool to model the process of transporting modules and assembling them on the construction site of a future multi-residential project. By adding more details, such as the weather and traffic conditions, the simulation results can become more accurate. In addition, as most simulation models for modular construction processes focus mainly on the assembly of modules on site, this paper also quantified the weather influence in terms of project duration and manpower utilization. Furthermore, the simulation model could also provide a general guide for the comparison of various scenarios.
Optimized Weight Maps and Fusion for Underwater Image Enhancement

The significant growth in technology advancement leads to underwater videos and images capturing for different purposes. However, the captured underwater images or videos suffer from the low contrast, color distortion, and haziness, thus it is required to enhance the quality of such images or videos for further analysis. The image enhancement techniques already proposed, however such methods are not applicable for the underwater images with the different physical properties. It is challenging research problem to optimize the underwater image quality. In this paper, first attempt towards the restoration of underwater image using the fusion based approach proposed. Previously, the weight maps are computed and fused to enhance the quality of images, but the weight maps introduces the artefacts while performing the fusion, hence to overcome that problem, the optimized fusion technique of weight maps for underwater image enhancement designed. The multi-step fusion approach works independently on two derived images from the original image. Then to optimize the visibility of underwater image, the three weight maps such as saliency, luminance, and chromaticity computed. These weight maps are fused in multi-step manner to overcome the challenge of artefacts and generate the final restored underwater image. The results prove the effectiveness of proposed model compared to single step fusion technique.
Using Computational Intelligence for Smart Device Operation Monitoring

Digital Manufacturing is at the core of the industry 4.0 revolution, owing to smart manufacturing and smart factories, which would allow an unprecedented degree of flexibility and visibility into manufacturing processes from how parts are designed to how they are manufactured, assembled, shipped and used. HP Inc. with the introduction of Multi Jet Fusion (MJF) technology is at the fore-front of this revolution with technology that enables great versatility around material, form factor, color etc. at industrial scale. A key challenge with 3D Printing is being able to control the process for output consistency and in-spec operation. In this paper, we detail how HP is using Big Data and Machine Learning techniques to analyze the swaths of data from our 3D Printing devices to be able to tell, with minimal supervision or context, when a machine might stray into unchartered territory that may impact machine operation and/or quality of outcome. We demonstrate using Auto-encoder based deep learning models, operating on both structured and unstructured data, to flag if the values for the parameter values or imagery (in unstructured case) is deviating from what we saw before (for the same parameter value). The work described would be candidate for rolling into a 3D Print device process monitoring solution.
Design of Novel Multipliers-Vedic and Shift-Add for IEEE 754-2008 Single Precision Floating-Point Unit in High Speed Applications

IEEE 754-2008 is a standard that provides format for representation of binary floating-point numbers in computers. The multiplier module is a very crucial part of high-speed applications using the mentioned standard in single precision Floating-point Unit (FPU). So, in this paper we have designed multiplier units using some existing algorithms such as shift-add and Vedic multipliers. The multipliers are simulated in Vivado tool which can be implemented in Zed board. After the successful simulations, the time delay and the number of Look-Up Tables were found to have high values for these multipliers. Therefore, three new algorithms were proposed: a shift-add, Vedic design-1 and a Vedic design-2. This paper presents a novel architecture of a multiplier module, which provides a reduced maximum combinational path delay, simulated in Vivado and will be implemented in Zed Board. The comparative analysis of the various multipliers like Vedic and shift-add multipliers with the proposed Vedic and modified shift-add multiplier have been done for the aforementioned architecture. The proposed shift-add design has a huge area usage of 1.17 % while the proposed Vedic design-1 has a comparatively lower area usage of 0.577 %. The new shift-add technique was found to a have time delay of 41.3723 nanoseconds while the Vedic design-1 has significantly higher delay of 44.973 nanoseconds.
Fuel-Optimal Energy Management Strategy for a Power-Split Powertrain via Convex Optimization

Hybrid power-split powertrains are getting more attention as they present great efficiency and performance. These powertrains connect components by planetary gear sets. Energy management strategy of the powertrain determines the power flow through each component. This paper presents a framework to address the problem of the fuel-optimal control strategy for an energy management strategy of power-split powertrains using convex optimization. To do this, we present a novel model for the power unit and other components using convex equations and constraints. We then use numerical solvers for the convex problems to efficiently solve the optimal control problem. Convex solver shows better performance in considerably less amount of time for the tested driving cycles, compared to the dynamic programming. This makes it suitable for design cases which need to iteratively solve the optimal control problem for different initiations in the design space. Another application is using the presented optimization-based approach to obtain the control input of the powertrain in the real-time applications.
Multi-Agent Reinforcement Learning Based on Clustering in Two-Player Games

Non-stationary environment is general in real environment, including adversarial environment and multi-agent problem. Multi-agent environment is a typical non-stationary environment. Each agent of the shared environment must learn a efficient interaction for maximizing the expected reward. Independent reinforcement learning (InRL) is the simplest form in which each agent treats other agents as part of environment. In this paper, we present Max-Mean-Learning-Win-or-Learn-Fast (MML-WoLF), which is an independent on-policy learning algorithm based on reinforcement clustering. A variational auto-encoder method based on reinforcement learning is proposed to extract features for unsupervised clustering. Based on clustering results, MML-WoLF uses statistics and the dominated factor to calculate the values of the states that belong to a certain category. The agent policy is iteratively updated by the value. We apply our algorithm to multi-agent problems including matrix-game, grid world, and continuous world game. The clustering results are able to show the strategies distribution under the agent’s current policy. The experiment results suggest that our method significantly improves average performance over other independent learning algorithms in multi-agent problems.
Learning Deep Spatiotemporal Feature for Engagement Recognition of Online Courses

This paper focuses on the study of engagement recognition of online courses from students’ appearance and behavioral information using deep learning methods. Automatic engagement recognition can be applied to developing effective online instructional and assessment strategies for promoting learning. In this paper, we make two contributions. First, we propose a Convolutional 3D (C3D) neural networks-based approach to automatic engagement recognition, which models both the appearance and motion information in videos and recognize student engagement automatically. Second, we introduce the Focal Loss to address the class-imbalanced data distribution problem in engagement recognition by adaptively decreasing the weight of high engagement samples while increasing the weight of low engagement samples in deep spatiotemporal feature learning. Experiments on the DAiSEE dataset show the effectiveness of our method in comparison with the state-of-the-art automatic engagement recognition methods.
Evaluation of video streaming capacity of UAVs with respect to channel variation in 4G-LTE Surveillance Architecture

Nobody can deny the importance of technology in every sphere of life. The latest trends in technology ensure better surveillance which could not be possible ever before. Drones with attached cameras also known as Unmanned Aerial Vehicles (UAVs) are one of the example used in surveillance operations. These camera mounted spies have the ability to capture real-time video of targeted areas in the monitored zone and stream the captured video to the location where all activities are observed by the administrative body. This setup provides the platform of taking prompt action in time if required. To enhance the efficiency of UAVs in a surveillance framework, it is very essential to understand the circumstances where the video-streaming capacity of UAVs are affected. In this paper, we propose a UAV-based surveillance architecture over 4G-LTE Network and we try to explore the fact that how variation in transmitting channel effects the video streaming capacity of UAVs. For this purpose, we take advantage of the physical layer attribute Reference Signal Receive Power (RSRP) which is used to measure the channel strength of wireless link at UAVs. This cross-layer information is helpful in upgrading the performance of the upper layer application used for surveillance purposes. For simulation, we have selected NS-3 which is the most trusted network simulator in the research community. Simulation results prove that variation in the channel has a direct link with the transmitting capacity of UAVs.
Latency and consistent flow migration: relax for lossless updates

Consistency in network updates is a nascent research area, especially in the context of traffic engineering or Software Defined Networks. Various approaches have been proposed and implemented in the problem space of flow migration and congestion, primarily focusing on different flows not breaking the bandwidth capacities of the used links during updates. However, current network update techniques overlook the effect of flows congesting their own path during a network update due to latency on the links. Furthermore, while congestion will be resolved eventually after the network update, the buffers of the affected routers can be filled for a long time period, leading to the following paradox: a flow is moved to a path with less latency, but the latency stays the same! As flows are often migrated because of latency concerns, this is highly undesirable. We show that these effects occur already in a small topology in practice, causing packet loss due to overfull buffers. Furthermore, we prove that finding a lossless flow migration is NP-hard, already for a single (splittable) flow on directed acyclic graphs. Nonetheless, we can relax latency requirements to still obtain lossless flow migration. To this end, we show how to adapt current systems such as SWAN or Dionysus [SIGCOMM’13/’14], also developing our own polynomial time schedule algorithm, and discussing future consistent flow migration technique adaptations.
A fast inverse approach for the quantification of set-theoretical uncertainty

This paper concerns a machine learning approach for the inverse quantification of set-theoretical uncertainty. Inverse uncertainty quantification (e.g., using Bayesian or interval methodologies) is usually obtained following a process where a distance metric between a set of predicted and measured model responses is iteratively minimized. Consequently, the corresponding computational effort is large and usually unpredictable, leading to an intractable situation for real-time applications (e.g., as is commonly encountered in process control problems). To achieve a real-time solution to this inverse problem, machine learning is applied to train a deep neural network, consisting of multilayer auto-encoders and a shallow neural network, by means of a numerically generated data set that captures typical uncertainty in the model parameters. The method is applied to the challenging DLR AIRMOD problem and it is shown that the obtained accuracy is comparable to existing methods in literature, albeit at a fraction of their computational cost.
On Using Graph Signal Processing for Electrical Load Disaggregation

Graph Signal Processing (GSP) is an emerging field in data science and is being increasingly used for different applications in various domains like vision, biomedical and sensor networks, etc. GSP analyses and transforms signals defined on the vertices of a graph. In this paper, GSP is applied to the problem of source separation, in particular, electrical load disaggregation where, given smart meter measurements, it is required to estimate the contribution of different loads which could have resulted in those measurements. The proposed strategy based on GSP is formulated as a regularization on the graph whereby appropriately maximizing the smoothness of the underlying graph signal, electrical loads are disaggregated iteratively. The proposed optimization problem is solved in a greedy way and a closed-form solution is presented. Experimental results using publicly available REDD are presented to demonstrate the potential of the technique for disaggregating loads from low rate aggregate power measurements sampled at 1 minute and 15 minutes intervals.
Designing a Controller Circuit for Three Phase Inverter in PV application

In this paper a three phase voltage source inverter (VSI) has been simulated with controller circuit that can be used in future for hardware implementation using dSPACE 1104 controller in photovoltaic application. The dSPACE controller can be used, as it can link the MATLAB Simulink model directly into a real time operating prototype. The switching of IGBTs will be controlled by generating sinusoidal pulse width modulation (SPWM) signals with proportional integral (PI) controller circuit and parks transformation. The three phase inverter ac output voltage will be stabilized with the help of controller circuit attached to it in simulation model. Here DC voltage source is taken as the photovoltaic generator that supplying power to the three phase inverter. With this model the simulation output is showing a result of 2.83% of total harmonic distortion and supports that the controller circuit used is improving inverter ac voltage output to a great extent. This control system can be transformed into a prototype model with the dSPACE controller board as suggested in this paper.
Semi-supeprvised Topic Model for Sequential Data: A Genetic Algorithm Approach

Semi-supervised learning in topic models increase accuracy of topic predictions by introducing labeled data to guide the learning process. Inference algorithm in topic models is used for approximation of posteriori. This paper adapts the incremental naïve bayesian classification algorithm to sequentially analyse a set of test documents and classify them. To overcome the challenges of biased learning and inaccurate inferences the paper proposes a genetic algorithm approach to semi-supervised learning by introducing user's top hourly searched topic as labeled data. The learning process is continuous, labeled data is introduced into the population every hour while simultaneously removing the least fit based on the mean attribute to maintain initial population size. We successfully tested the functionality of the model with a small set of domain related papers. The results showed that genetic algorithm optimises the topic model through continuous learning by reducing the computation time complexity from O(n2) to O(n).
Paper Title (use style: paper title)

The application of satellite-based positioning is of great importance in novel intelligent rail systems. However, the positioning performance of GNSS (Global Navigation Satellite System) cannot meet the requirements of safety-related railway systems like train control and the collision early warning system. To enhance the receiver-level integrity monitoring based on the redundancy of satellite measurements, a hybrid fault detection method for GNSS is presented under an integrated architecture with GNSS and INS (Inertial Navigation System). An expanded residual-based fault detection logic is designed based on a robust unscented Kalman filtering algorithm. Results of simulation test by fault injection demonstrate the fault detection performance. Keywords—Fault detection, satellite navigation, train collision early warning, inertial navigation, integrity monitoring.