Miracle-dz/UniFAR-FTUs huggingface dataset:
Gridless Channel Estimation for Mixed One-Bit Antenna Array Systems

A receiver architecture with low-resolution analog-to-digital converters (ADCs) coupled with large antenna arrays has drawn considerable interest in the millimeter wave (mm-wave) system. Although architecture with pure one-bit ADCs has low power cost, such a system presents many challenges for synchronization, channel estimation, and power level estimation. Research has been conducted recently on the so-called mixed one-bit system, in which most antennas are equipped with one-bit ADCs, and a few have high-resolution ADCs. Despite the advantages of this system, studies on how to efficiently use high-resolution outputs to aid the one-bit system are lacking. This research considers the channel estimation problem to fill this gap and proposes a two-step channel estimator by utilizing the different features of mixed outputs. The channel gain can be extracted from high-resolution ADCs, and the channel angle can be extracted by the combination of additional one-bit ADCs. The proposed estimator leverages the sparsity feature of the channel in mm-wave. In contrast to previous works that used compressive sensing techniques, which confine the estimate to the set of grid angle points and induce estimation bias, the proposed estimator is gridless and treats the angle as a continuous parameter. The simulation results demonstrate that the proposed method yields significantly lower mean square errors than the conventional maximum likelihood estimator. In addition, this paper investigates ways to further improve the channel estimate by arranging the locations of high-resolution ADCs. Several useful observations on system design are obtained through our analysis.
Crossfire Attack Detection Using Deep Learning in Software Defined ITS Networks

Recent developments in intelligent transport systems (ITS) based on smart mobility significantly improves safety and security over roads and highways. ITS networks are comprised of the Internet-connected vehicles (mobile nodes), roadside units (RSU), cellular base stations and conventional core network routers to create a complete data transmission platform that provides real-time traffic information and enable prediction of future traffic conditions. However, the heterogeneity and complexity of the underlying ITS networks raise new challenges in intrusion prevention of mobile network nodes and detection of security attacks due to such highly vulnerable mobile nodes. In this paper, we consider a new type of security attack referred to as crossfire attack, which involves a large number of compromised nodes that generate low-intensity traffic in a temporally coordinated fashion such that target links or hosts (victims) are disconnected from the rest of the network. Detection of such attacks is challenging since the attacking traffic flows are indistinguishable from the legitimate flows. With the support of software-defined networking that enables dynamic network monitoring and traffic characteristic extraction, we develop a machine learning model that can learn the temporal correlation among traffic flows traversing in the ITS network, thus differentiating legitimate flows from coordinated attacking flows. We use different deep learning algorithms to train the model and study the performance using Mininet-WiFi emulation platform. The results show that our approach achieves a detection accuracy of at least 80%.
Ego-Motion Estimation with Static Object Detections from Low Cost Radars

Ego-motion estimation plays a crucial role in the achievement of autonomous driving and advanced driver assistance systems (ADAS). Classical approaches using wheel-based odometers suffers from errors due to the variation of wheels diameter, and are not robust to slippery conditions. This paper proposes an approach using static object detections from one or multiple low cost radars to improve the ego-motion estimation. First, the method determines a 2D motion of the ego vehicle for each radar available in the car. Three steps are applied: data association, linear regression, and outliers rejection. Then, the estimated 2D motions are fused with the proprioceptive sensors of the car in order to precisely estimate the speed and the yaw rate of the ego vehicle. In the tested scenarios, our approach drastically reduces the error speed with respect to classical wheel-based approach.
Dynamic Runway Configurations and Flexible Arrival/Departure Tradeoffs in Metroplex Airports

Runway system is central to airport capacity. Its inefficient ultilization has been identified as a major source of airport congestions. This paper analyzes the patterns of demand-capacity imbalance and design a series of flexible strategies for air traffic demand management (ATDM), and then optimize runway configurations in metroplex (i.e. multi-airport system) airports, under a set of tradeoff settings for arrival and departure priorities. An optimization model with 4 imbalance cases, 11 tradeoff scenarios and 2 configuration strategies, are proposed to minimize the flight holding cost and the number of adjusted flights. The proposed evolutionary algorithm can obtain close-to optimal results with a very low computational cost. A case study of the Shanghai metroplex airports shows that, compared with the traditional static strategy, the proposed dynamic strategy can significantly reduce the number of adjusted flights. The proposed framework in this paper can be applied on pre-tactical (i.e. one-day planning) as well as tactical (i.e. 2-h rolling horizon) levels, to keep the balance between high demand and limited capacity through flexible ATDM options.
Development and Evaluation of Lane Hazard Prediction Application for Connected and Automated Vehicles (CAVs)

More than 37,000 fatalities occurred on U.S. roads in 2016. The number of both vehicle miles traveled and traffic accidents has increased in the past two years, and is mainly attributed to human error, mainly due to inattentiveness during hazardous driving situations. By crowdsourcing traffic data (e.g. vehicle position, speed, direction, etc.) through vehicle-to-vehicle (V2V) communication, connected vehicles (CV) can detect upcoming hazards at lane level with reasonable lead time. The work described in this paper aimed to develop and simulate an innovative V2V-based application to perform lane-level hazard prediction, and a corresponding driver response model. The concept of Lane Hazard Prediction (LHP) is to improve the mobility and safety of both individual users and the entire traffic system. LHP identifies the position of a downstream lane-level hazard (within seconds after it occurs) based on a spatial and temporal data mining and machine learning techniques. It then guides the LHP-equipped vehicles with recommended lateral maneuvers to avoid traffic jams resulting from the hazards. Simulation results demonstrate reliable hazard prediction, even when the V2V penetration rate is as low as 20%. A comprehensive evaluation of the developed LHP application from the perspectives of both user benefits and system benefits has been conducted over different CV penetration rates. The results demonstrate that the proposed LHP application can significantly improve both the safety and mobility performance of the equipped vehicles without compromising the mobility and safety performance of the overall traffic.
Emotion Recognition via Body Gesture: Deep Learning Model Coupled with Keyframe Selection

Many psychological research revealed that bodily gestures convey crucial information to emotion recognition. This valuable aspect, however, has not gained much the attention from the science community, compared to other modalities, such as facial expression and speech in the emotion recognition task. There are limited works on this topic. Unlike recent works which exploited the hand-crafted features, this study proposes an end-to-end deep learning approach for gesture-based emotion recognition. Firstly, we adopt the hashing method to extract the keyframes from the video. Secondly, a convolutional LSTM network is used for exploiting the sequence information. Our results exceed most of the hand-crafted results, it achieves the state-of-the-art results for end-to-end deep learning based technique for gesture-based emotion recognition on FABO dataset. The research also indicates the promise for future improvement.
Distributed Optimization for Railway Track Maintenance Operations Planning

In this paper, distributed optimization approaches are developed for the planning of maintenance operations of large-scale railway infrastructure formulated as a Mixed-Integer Linear Programming (MILP) problem. The proposed planning problem is solved using three different distributed optimization schemes: Parallel Augmented Lagrangian Relaxation (PALR), Alternating Direction Method of Multipliers (ADMM), and Distributed Robust Safe But Knowledgeable (DRSBK). The original distributed algorithms are modified to handle the non-convex nature of the optimization problem and to improve the solution quality. The results of large-scale test instances show that DRSBK can outperform the other distributed approaches, by providing the closest-to-optimum solution while requiring the lowest computation time.
Parasitic Network: Learning-Based Network Downsizing of Very Deep Neural Networks for Computer Vision

In recent research on deep neural network (DNN), network downsizing is one of practical issues for computational and memory efficiency. Specifically, downsizing or compression of networks minimizing the performance degradation becomes a critical problem for deployment of DNNs on resource-limited environments such mobile or embedded platforms. In this paper, we propose a compressed network called the parasitic network (PN) inspired by the relationship between a parasite and host in nature. The concept of the parasitic network is straightforward. The host network provides their mapping results in each layer to the PN as a feed. The PN that much shallower than the host network is trained based on given information from the host network. We demonstrate efficiency of our approach to the network downsizing in image classification and object detection problems which have conquered by the deeper and bigger networks. The experimental results show that PN can provide sample performance to their host network even though their architectural scale is much smaller.
Priority effects between annual and perennial plants

1. In many grassland communities, the identity of dominant species may be determined by initial frequency owing to priority effects. Yet, the conditions for these priority effects remain unclear. 2. Focusing on interactions between annual and perennial plants, we used a simulation model to investigate how longevity, fecundity, and the competitive ability of seedlings might affect the probability of equilibrium priority effects (alternative stable states) and transient priority effects (alternative transient states). 3. The model suggests that establishment-longevity tradeoff, in which annuals have competitive advantage over perennial seedlings, is a necessary condition for equilibrium priority effects. Equilibrium priority effects also require low fecundity in both annuals and perennials and small fitness differences, as determined by their relative fecundity, survival probability, and competitive ability at the seedling stage. 4. Transient priority effects need only small fitness differences. Furthermore, the model indicate that transient priority effects could last several decades under a range of the parameter space, exceeding the duration of typical experiments and observations. 5. Synthesis: Our results suggest that equilibrium priority effects driven by a life-history trade-off are possible but may be uncommon because they require unrealistically low fecundity. In contrast, transient priority effects should be more likely. Previous empirical findings of priority effects in grasslands may mostly indicate alternative transient, rather than stable, states, or other mechanisms than a life-history tradeoff.
An Integrated MPC Approach for FWIA Autonomous Ground Vehicles with Emergency Collision Avoidance

A novel hierarchical model predictive control (MPC) method is investigated for four-wheel-independently-actuated (FWIA) autonomous ground vehicles (AGVs) with emergency collision avoidance in this paper, where an artificial potential field (APF)-based NMPC path replanner and a feedback compensation control (FCC)-based LTV-MPC path follower are designed. Both replanning with circle decomposition of vehicle shape, and tracking with tire force maximization, are considered simultaneously to enlarge the reachable zone of path replanning and following, particularly in much aggressive situations, where the trajectories are not feasible with the conventional approaches. By the proposed control, ample space and sufficient time are available to steer appropriately and acceleratelbrake independently in such hazardous scenarios. In addition, a shorter predictive horizon is introduced to evaluate both methods in more extreme situations. The simulations modeled in the Carsim-Simulink joint platform demonstrate that the proposed approach can further improve path-replanning reachability and path-following safety in emergency collision avoidance scenarios, even in a shortsighted prediction.
Tor Traffic Classification from Raw Packet Header using Convolutional Neural Network

As the amount of network traffic is growing exponentially, traffic analysis and classification are playing a significant role for efficient resource allocation and network management. However, with emerging security technologies, this work is becoming more difficult by encrypted communication such as Tor, which is one of the most popular encryption techniques. This paper proposes an approach to classify Tor traffic using hexadecimal raw packet header and convolutional neural network model. Comparing with competitive machine learning algorithms, our approach shows a remarkable accuracy. To validate this method publicly, we use UNB-CIC Tor network traffic dataset. Based on the experiments, our approach shows 99.3% accuracy for the fractionized Tor/non-Tor traffic classification.
World-Wide Air Traffic: Route-based versus Direct

In 2016, DLR launched the project World Wide Air Traffic Management (WW-ATM). The project creates a platform for optimization and validation of world-wide concepts considering feasibility, throughput, costs- and ecological efficiency, and robustness respectively fault liability. One of the early tasks within the project was the generation of a world-wide air traffic scenario. Having reached a good maturity level with a standard route-based scenario with more than 105000 flights, this paper evaluates the potential benefits when switching to free-route airspace as already implemented in some European countries like Hungary.
Dissipating stop-and-go waves in closed and open networks via deep reinforcement learning

This article demonstrates the ability for model-free reinforcement learning (RL) techniques to generate traffic control strategies for connected and automated vehicles (CAVs) in various network geometries. This method is demonstrated to achieve near complete wave dissipation in a straight open road network with only 10% CAV penetration, while penetration rates as low as 2.5% are revealed to contribute greatly to reductions in the frequency and magnitude of formed waves. Moreover, a study of controllers generated in closed network scenarios exhibiting otherwise similar densities and perturbing behaviors confirms that closed network policies generalize to open network tasks, and presents the potential role of transfer learning in fine-tuning the parameters of these policies. Videos of the results are available at: https://sites.google. com/view/itsc-dissipating-waves.
Trajectory Planning for Automated Vehicles using Driver Models

Behavioral-specific trajectory planning for automated vehicles is an intensively explored research topic. Many situations in daily traffic, e.g. following a leading vehicle or stopping behind it, require knowledge about how the scene may evolve. In recent years, much effort has been put into developing driver models to predict traffic scenes as realistic as possible according to human behavior. In this paper, we present a method for behavioral-specific trajectory planning using dedicated driver models. The main idea is to first calculate a reference trajectory using a suitable model to achieve the desired behavior and then to incorporate this reference trajectory into an optimal control problem to obtain an acceleration- and jerk-optimal trajectory. A major strength of this method is in the small computation time, since the problem is formalized as a quadratic optimization problem and can thus be efficiently solved in real time, even for a huge number of optimization variables.
An Improved English-Thai Translation Framework for Non-timing Aligned Parallel Corpora Using Bleualign with Explicit Feedback

One significant resource for language translation using Statistical Machine Translation (SMT) is parallel corpora. SMT model works well with timing aligned parallel corpora. However, imperfectly aligned sentences in the bilingual corpus typically leads to poorer translation in the final translation after training the SMT model. A major challenge in effectively applying nontiming aligned parallel corpora in the SMT model has not been thoroughly researched. The goal of this paper is to improve the accuracy of an English to Thai Statistical Machine Translation (SMT) model by improving the sentence alignment of parallel corpora. This work proposes an improved English-Thai translation framework for non-timing aligned Parallel corpora using an improved alignment algorithm: Bleualign with explicit user feedback. The generated model can then be applied to the Moses SMT training system to generate English-Thai translation. This experiment uses both English and Thai subtitles obtained from TED (www.ted.com) to build the parallel corpora. The TED corpora sentences are not timing aligned, and this research will try to generate an alignment model to be applied on the Moses SMT training system. The result shows that the model using our proposed algorithm outperforms two traditional alignment models: Gale-Church, Bleualign with the highest BLEU score of 0.36.
Soft Meta-Cognitive Neural Network for Classification Problems

Classification problems in a sequential framework is considered as an important field in pattern recognition. One of the main concerns in these types of problems is overtraining. In metacognitive neural networks (McNN), overtraining could be avoided by using the confidence of classifier (CoC) measure, which assigns a value between [0], [1] to class label. In this paper, a more accurate measure of CoC for McNN is presented. In addition, the hinge loss function which has no particular probabilistic interpretation is replaced by cross-entropy loss, which the output layer of the Soft meta-cognitive neural network (SMcNN) is a SoftMax layer. The classification performance is improved by applying the proposed SMcNN to well-known vCI datasets and comparing the results to McNN, SVM, and some other classifiers.
A Hybrid Model of Tensor Factorization and Sentiment Utility Logistic Model for Trip Recommendation

This paper proposes a hybrid model of aspect-oriented sentiment prediction which integrates tensor factorization (TF) and sentiment utility logistic model (SULM). First, using sentiment dictionary words as seeds, the aspect or opinion words can be extended iteratively through double propagation. Accordingly, the users’ reviews could be represented as the features in user-item-aspect space, in which prediction model could be built. Various combinations of the hybrid model were proposed and evaluated on the Chinese reviews on places of interest from Trip Advisor. Experimental results show that the hybrid model can achieve better prediction performance than TF or SULM. The hybrid model also outperforms either TF or SULM while handling cold-start problem.
Control of Bidirectional Quadratic DC-DC Converters for Storage Support of DC Power Grids

Many applications require a DC bus supporting connections to several renewable energy sources, storage systems and loads. However, due to the intermittent nature of renewable energy sources and load variations it is essential to stabilize the voltage of the DC bus. Usually a battery is used to support the DC bus voltage, but their continuous charge and discharge cycling will affect his lifetime. Thus, the use of a storage system based in supercapacitors provides an interesting alternative to stabilize the DC power grid. In this context, this work presents a study of a storage system based in supercapacitors combined with a bidirectional quadratic DC-DC converter to support the DC bus. A full design of the controllers for the quadratic converter is also presented. The behaviour of the system will be tested through several simulation results.
An Efficient Precoder Structure for Dual Satellite Systems for Ubiquitous Communication

Multiple spot beams based dual satellite system with full frequency reuse across all spot beams is considered in this work. Both satellites transmit on same frequency to improve the spectrum utilization efficiency. Cooperative Zero Forcing (ZF) and cooperative regularized ZF precoding algorithms are investigated. If the users served by both satellites are closely placed, then it is shown that the proposed cooperative precoding fails due to the ill conditioned channels. Otherwise, the proposed DSS is shown to achieve better sum-rate than orthogonal transmission scheme, where both full frequency reuse based multiple spot beam satellites transmit on different frequency bands.
AggNet: Simple Aggregated Network for Real-Time Multiple Object Detection in Road Driving Scene

A real-time object detection model in road driving scene is being established as an important component technology that facilitates autonomous driving; its performance has been improving drastically due to progress in deep learning technology. However, the performances for occlusion between objects and small object detection are not yet perfect. In this paper, a simple aggregated convolution neural network (AggNet) based real-time multiple object detection model is proposed to improve the detection performance of occlusion or small objects in road driving scene. The proposed model is designed to deliver feature information of a small receptive field by improving the residual block of the traditional Residual Network (ResNet) with the simple aggregation block. Furthermore, to detect objects of various sizes effectively, an aggregated rezoom layer-based object detection method was applied instead of the conventional multi-scale feature and anchor-based object detection method. As a result of tests using KITTI data, it was confirmed that the proposed model can successfully detect small objects and object occlusions of various sizes. $1024\mathrm{x}320$ input images can be processed at 20–22 fps with an NVIDIA Titan XP GPU.
Leveraging Smart Lights for Passive Localization

Localization based on visible light is gaining significant attention. But most existing studies rely on a key requirement: the object of interest needs to carry an optical receiver (camera or photodiode). We remove this requirement and investigate the possibility of achieving accurate localization in a passive manner, that is, without requiring objects to carry any optical receiver. To achieve this goal, we exploit the reflective surfaces of objects and the unique propagation properties of LED luminaires. We present geometric models, a testbed implementation, and empirical evaluations to showcase the opportunities and challenges posed by this new type of localization. Overall, we show that our method can track with high accuracy (few centimeters) a subset of an object's trajectory and it can also identify passively the object's ID.