Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards the Development of a CAD Tool for the Implementation of High-Speed Embedded MPCs on FPGAs

Designing embedded model predictive controllers (MPCs) for systems with fast sampling rates is a tedious and multidisciplinary process. The reason is that from the high-level description of the control problem to the final implementation on a computational platform, the MPC design space covers multiple abstraction levels in which critical design decisions have to be made. Therefore, currently, the identification of the best design criteria can not be automated. Instead, in this paper we take an step towards the development of a computer-aided design (CAD) tool that enables the semi-automatic implementation of application-specific embedded MPCs on field-programmable gate arrays (FPGAs) for its application to control-engineering problems with fast sampling rates.
I-LOAM: Intensity Enhanced LiDAR Odometry and Mapping

In this paper, we introduce an extension to the existing LiDAR Odometry and Mapping (LOAM) [1] by additionally considering LiDAR intensity. In an urban environment, planar structures from buildings and roads often introduce ambiguity in a certain direction. Incorporation of the intensity value to the cost function prevents divergence occurence from this structural ambiguity, thereby yielding better odometry and mapping in terms of accuracy. Specifically, we have updated the edge and plane point correspondence search to include intensity. This simple but effective strategy shows meaningful improvement over the existing LOAM. The proposed method is validated using the KITTI dataset.
Optimal Linear Mean Square Filter for Markov Jump Linear Systems with Hidden Markov Chain and Partial Observations of the State

In this paper we make further foray on the filtering problem for Markov jump linear systems (MJLS) in the worst case scenario in which both, the state signal and the operation mode (Markov chain) are partially observables. Since the optimal nonlinear filter for the state signal in this setting is infinite dimensional (in the sense of the optimal filtering theory), an optimal linear filter has been derived in [1]. A peculiar feature of the approach used in [1] is the fact that the optimal linear filter for the state variable is derived by circumventing the need to deal with the unobservable Markov chain. Having the result derived in [1] and [2] as the linchpin for our approach, the main contribution of this paper is to derive an optimal linear filter for the operation mode in the worst case scenario. As far as the authors are aware, this is the first optimal filter for the operation mode, in this scenario. In addition, relying on Murayama's stochastic numerical method and the results in [3], we carry out a simulation to evaluate the filter performance.
Towards Computationally-Efficient Cognitive Sensor Systems for Autonomous Vehicles

Advanced driving assistance systems (ADAS) prepave regulators, consumers and corporations for the medium-term reality of autonomous driving with adaptive cruise control, collision avoidance and lane departure warning system. Various sensors like camera, RADAR and LIDAR, integrated into the vehicle assist driving. In addition, deep learning approaches are utilized in a wide range of applications ranging from object detection and scene segmentation to engine fault diagnosis and emission management to detect vehicle network intrusion. In this paper, we scope out the state of the art sensors subsystems in terms of its functionality, characteristics, specifications and communication protocol, and we describe cognitive deep learning based algorithms required for environment perception through these sensors. Subsequently, we analyze the cognitive algorithm by profiling the standard deep learning models, explore different compute platforms and possible algorithm and hardware optimization scenarios.
CTCR approach to the complete stability of TDS with multiple identical imaginary roots

The dynamics we treat here is a degenerate class of linear time-invariant time-delayed systems (LTI-TDS) with several rationally independent delays, which exhibit multiple and identical imaginary characteristic roots (MIIR) for some particular values of the delays. The stability behavior of the system within the immediate proximity of this parametric setting which creates the degenerate dynamics has recently attracted a number of investigations. We bring several new perspectives to this problem. First is a limiting study in the delay space that avoids the MIIR but extracts the local stability characteristics. Second, as the main contribution of the paper, we show how a well-studied stability paradigm, the Cluster Treatment of Characteristic Roots (CTCR) can be used to resolve this mathematically degenerate problem. In addition we present several validating case studies, especially on a simpler class of systems with a single but commensurate delay.
Flow-based QoS forwarding strategy: a practical implementation and evaluation

During the last decade we have seen an explosive growth in the deployment of cloud applications and services. In this context, one of the challenges is Quality of Service (QoS) management, which is the problem of allocating resources to the applications to guarantee a service level along dimensions such as performance, availability and reliability. Compared with the traditional best-effort service model based on BGP and the classical tunnelling alternatives (like MPLS), software-defined-networking (SDN) has the potential to provide a better QoS guarantee for cloud applications and services due to its centralized control, network-wide monitoring and flow-level scheduling. With this in mind, we propose a QoS-aware overlay routing based on the SDN architecture, which consists on the rewriting of IP address and TCP/UDP ports in order to be able to force the packets to follow the desired path on the overlay. We evaluate the proposal through both simulations and practical implementation analysis. In particular, we test our solution using two of the most popular SDN controllers: ONOS and OpenDayLight.
Experimental Prototyping of a Microgrid with Mechanical Point of Common Coupling

The smartgrid is a key technology for the sustainable and smart utilization of the renewable energies. In this paper, a prototyping of a microgrid, assembled at the Sustainable Development and Energy Saving Laboratory (SDESLAB) of the University of Palermo, is presented and discussed. In detail, the microgrid presents only one mechanical point of common coupling (PCC) with the main grid and it is electrically separated from the main grid. In this way, the voltage variations of the main grid do not affect the microgrid electric quantities behavior. In order to validate the effectiveness of the voltage and frequency control, several experimental tests and analysis have been carried out. In detail, a no-negligible 11th spatial harmonic component and switching frequency harmonics components have been detected on the microgrid output voltage. Therefore, in order to cancel these harmonic components, a LCL filter has been designed and experimental validated in its effectiveness. The study conducted represents a first stage for the experimental realization of a hybrid microgrid electrically independent of the main grid.
Towards a Secure Internet of Things: A Comprehensive Study of Second Line Defense Mechanisms

The Internet of Things (IoT) exemplifies a large network of sensing and actuating devices that have penetrated into the physical world enabling new applications like smart homes, intelligent transportation, smart healthcare and smart cities. Through IoT, these applications have consolidated in the modern world to generate, share, aggregate and analyze large amount of security-critical and privacy sensitive data. As this consolidation gets stronger, the need for security in IoT increases. With first line of defense strategies like cryptography being unsuited due to the resource constrained nature, second line of defense mechanisms are crucial to ensure security in IoT networks. This paper presents a comprehensive study of existing second line of defense mechanisms for standardized protocols in IoT networks. The paper analyzes existing mechanisms in three aspects: Intrusion Detection Systems (IDS), Intrusion Prevention Systems (IPS) and Intrusion Response Systems (IRS). We begin by providing an overview of standardized protocol stack, its layers and defensive security systems in IoT. From there, we build our narrative by presenting an extended taxonomy of IDS, IPS and IRS classifying them on their techniques, deployment, attacks, datasets, evaluation metrics and data pre-processing methods. We then thoroughly review, compare and analyze the research proposals in this context, considering the unique characteristics involved in these systems. Based on the extensive analysis of the existing defensive security systems, the paper also identifies open research challenges and directions for effective design of such systems for IoT networks, which could guide future research in the area.
Robust Structural Load Mitigation Control for Wind Turbines in Low Wind Speed Region

Wind turbines growth in size and flexibility leads to increased structural loads. These loads affect the lifetime of the turbines and increase related maintenance cost. During the past decade, advanced control strategies have been developed and adopted effectively to mitigate structural loads. However, most of the attempts focus on the above-rated region, only a few research relate to loads reduction in low wind speed region, which is below-rated. The main problem of below-rated region wind turbine control is to reduce structural loads without affecting the maximum power production. Most of the load mitigation control methods rely on the linear model linearized at a designed operating point, so control performance may deteriorate when the turbine operates at varying wind speed. In this paper, an observer-based controller, which is robust to wind speed change, is used in combination with a baseline maximum power point tracking controller to mitigate structural loads while keeping the maximum power captured. Simulation results using a high fidelity wind turbine model show that the proposed approach can realize multi-objective power and load control, and robustly make a reasonable compromise between power production maximization and structural loads reduction despite continuously varying wind speed.
Intelligent Routing Orchestration for Ultra-Low Latency Transport Networks

Autonomous driving scenarios face the need for millisecond real-time response, which has led to the study of mobile networks with high speed and ultra-low latency. Software-defined networking (SDN) is recognized as a key technology for next-generation networks because it contains advanced functions such as centralized control, software-based traffic analysis, and forwarding rules for dynamic updates. In this paper, an SDN with flexible architecture is considered and a transport component is proposed. The component based on mesh topology is an example of joint route prediction and forwarding. First, different from existing transport protocols, the component can adopt a software-defined stream access control strategy that includes an extended forwarding mechanism (retransmission) to improve the short-term response performance. Second, we evaluate the impact of route prediction on transport network performance by using offline training and prediction. The key challenge here is that a suitable model needs to be trained from a limited training sample dataset, which will dynamically update the forwarding rules based on current and historical facts (network data). By introducing a parallel neural network classifier, an intelligent route arrangement is implemented in this work. Experimental results over different traffic patterns verify the advantages of the design. Not only does it enhance the flexibility of SDN, but it also significantly reduces the signaling overhead of the transport network without reducing the network throughput.
Limits of MPCC Formulations in Direct Optimal Control with Nonsmooth Differential Equations

In this work we discuss the limits of direct methods for Optimal Control Problems (OCPs) for some classes of nonsmooth dynamic systems. We highlight the equivalence between Filippov systems and a subclass of Differential Complementarity Systems (DCSs). Direct methods for optimal control with DCSs yield Mathematical Programs with Complementarity Constraints (MPCC), which are often solved with relaxation or smoothing methods. Due to the equivalence, results from the first class transfer to the DCSs. Therefore, to get the right numerical sensitivities one has to have a step-size of h = o(σ), where σ is the relaxation or smoothing parameter in the MPCC. A possible consequence of wrong numerical sensitivities is the appearance of spurious local solutions of the discretized OCP. We demonstrate and highlight the limits of MPCC approaches in direct optimal control on a simple counterexample.
Massive MIMO Channel Estimation for Vehicular Communications: A Deep Learning Based Approach

The estimation of the massive multiple input multiple output (MIMO) channel for vehicular communications is very challenging due to the variation of the channel and the requirement of low latency. To improve the accuracy and reduce the delay of the massive MIMO channel estimation, the recently emerging and popular deep neural network is exploited in this paper to learn the sparse structural information of the MIMO channel and estimate the channel more accurately and more rapidly. Firstly, a novel deep learning based massive MIMO channel estimation (DLCE) scheme is proposed, which achieves an efficient trade-off between the accuracy and the delay in channel estimation. Furthermore, exploiting the spatial correlation of the multiple antennas channel, an enhanced scheme called spatial-correlated DLCE (SC-DLCE) is proposed to further improve the channel estimation accuracy, especially in low signal-to-noise ratio environment. Simulation results demonstrate that the two proposed schemes can significantly improve the accuracy of massive MIMO channel estimation with a much shorter processing delay in practical vehicular communications terminals compared with the state-of-the-art benchmark schemes.
New circumscription of the tribe Limoselleae (Scrophulariaceae) that includes the taxa of the tribe Manuleeae

Cladistic analyses by maximum parsimony and Bayesian inference methods of chloroplast and nuclear sequence data indicate a new position for Limosella (Scrophulariaceae). Following this result, a new circumscription of the tribe Limoselleae is presented where the tribe Manuleeae is included in Limoselleae. Further, the study discloses that the genus Sutera is paraphyletic in its present circumscription, but that the two sections of Sutera, Sutera and Chaenostoma, are monophyletic. To accommodate these findings the genus Chaenostoma is re-established. Furthermore, the genus Jamesbrittenia recently expanded by Hilliard is shown to be a highly supported monophyletic group in its current circumscription. © 2004 The Linnean Society of London, Botanical Journal of the Linnean Society, 2004, 146, 453–467.
Elastomeric Continuously Variable Transmission Combined With Twisted String Actuator

Electric motors, with a fixed reduction ratio, have a large unused operating region when mimicking muscle movements owing to the difference between the force-velocity curves of electric motors and muscles. This unused region can be reduced by changing the reduction ratio according to the external force. However, the conventional continuously variable transmissions (CVTs) are large and heavy. Elastomeric CVT (ElaCVT), a new concept relating to CVT, is designed in this study. The primary purpose of ElaCVT is to expand the operating region of a twisted string actuator (TSA) and duplicate the force-velocity curve of the muscles by passively changing the reduction ratio according to the external load applied to the end of the TSA. A combination of ElaCVT and TSA (ElaCVT-TSA) is proposed as a linear actuator that mimics the characteristics of muscles. The deformation of elastomer changes the reduction ratio without the need for complicated mechanisms. This enables the CVT to be small and lightweight so that it can be applied to various robotic systems. The performance of the ElaCVT-TSA was evaluated by means of the experiments, and the results show that the reduction ratio was passively and continuously adjusted as the external load changed. The ElaCVT has a cylindrical shape with a length of 27 mm, a diameter of 24 mm, and weighs 12 g. The reduction ratio in the maximum velocity mode is approximately 2.31 times the reduction ratio in the maximum torque mode.
Simultaneous Lightwave Information and Power Transfer via Scattering and Line-of-Sight Links

Motivated by the fact that light can traverse through water or from air to water, we address simultaneous light information and power transfer (SLIPT), which can perform wireless charging while maintaining certain communication rate. We propose the architecture with line-of-sight (LOS) link for energy transmission and non-line-of-sight (NLOS) link for communication. We propose binary-level information modulation, and analyze the energy transfer performance and the detection bit error rate (BER). We further optimize the binary-level modulation and the load resistance at the receiver, to maximize the energy transfer under the detection BER constraint. The energy and information transmission is evaluated under a realistic application scenario.
Output Adaptive Compensation of Multi-Sinusoidal Disturbance in Linear Systems with Input and State Delays *

This paper concerns the problem of adaptive disturbance compensation for linear time-invariant (LTI) systems with both state and input delays. The external disturbance is considered as unmeasurable output of a linear autonomous dynamic system (exosystem) with unknown parameters but known order. An output feedback adaptive compensator is designed on the basis of technique recently proposed in [1] for LTI systems with input delay only and modified here for the case of state delays. The solution proposed consists of a disturbance observer, disturbance parameterization and design of adjustable control tuned by an adaptation algorithm with a modified augmented error.
Classification of Financial Tickets Using Weakly Supervised Fine-Grained Networks

Facing the rapid growth in the issuance of financial tickets, traditional manual invoice reimbursement methods are imposing an increasing burden on financial accountants and consuming excessive manpower. There are too many categories of financial ticket that need to be classified with high accuracy. Therefore, we propose a Financial Ticket Classification (FTC) network based on weakly supervised fine-grained classification discriminative filter learning networks, which greatly improves the work efficiency of financial accountants. The FTC network adopts an end-to-end network structure and uses a deep convolution network to extract highly descriptive features. By using a fully convolutional network (FCN), this method reduces the depth and width of the whole network and avoids the over duplication of features and the overconsumption of system memory. To obtain more accurate classification results, we use the large-margin softmax (L-softmax) loss function, which can make the features learned in the class more compact, make it easier to separate subclasses, and effectively prevent overfitting. Experimental results show that the proposed FTC network achieves both high accuracy (up to 99.36%) and high processing speed, which perfectly meets the requirements of accurate and real-time classification for financial accounting applications.
Data-driven Buiding Climate Control Using Model Prediction and online Weather Forecast Data

This paper proposes a multi-unit building model, in which the parameters are obtained via an online identification process. The identification process is carried out on-the-fly so it can update the best model of the building units. A model predictive controller (MPC) is also employed that uses the prediction of the building model, as well as the weather forecast data and acts on the heating boiler in an optimal fashion. In addition, since the controller is designed for a multi-unit building, it is crucial to estimate the amount of the delay that takes the hot flow to reach the units. This paper presents a very simple method for the delay identification based on unscented kalman filter. For one scenario, the results of system identification as well as the design of the controller are shown in Monte Carlo simulation, and compared with the results of a traditional controller. The results show that the proposed method can save 13% in the energy consumption. The results are also obtained for 1000 simulations for different temperature scenarios, and came up with reaching up to a 25% save in the energy consumption.
Controlled atmosphere storage and edible coating effects on storage life and quality of tomatoes

Tomatoes at pink stage maturities were coated with Semperfresh edible fruit coating which is composed of sucrose esters of fatty acids, sodium carboxymethyl cellulose and mono-diglycerides of fatty acids. One group of coated and uncoated tomatoes were stored at 23 ± 2C under air atmosphere, another group was stored at 12C under air atmosphere and third group was stored at 12C, 93-95% RH in a controlled atmosphere (CA) containing 3% CO 2 , 3% O 2 and 94% N 2 . Samples of tomatoes were analyzed for firmness, weight loss, titratable acidity, pH, soluble solids, sugars, ascorbic acid and lycopene to examine changes in quality during storage. Semperfresh edible fruit coating was found to be significantly effective at both storage temperatures (23 ± 2C and 12C) in air to delay changes in firmness, titratable acidity, pH, soluble solids, sugars, ascorbic acid and lycopene. Semperfresh fruit coating reduced fruit weight loss as compared to fruit without coating but the difference between coated and uncoated tomatoes was not significant. Shelf-life of pink tomatoes was increased 3 days at 23 ± 2C and 6 days at 12C in air by coating. CA storage delayed compositional changes in tomatoes significantly as compared to air storage of coated and uncoated tomatoes. CA storage also had a significant potential for reducing weight loss and microbial spoilage. Tomatoes were stored 40 days in CA. Coated and uncoated tomatoes exhibited the same results in CA.
A Privacy Preserving Model for Fog-enabled MCC systems using 5G Connection

Privacy issues in Cloud Computing are one of the major concerns for many individuals and companies around the world, and still a challenge for research and industry. The appearance of the 5G technology and the latest advances in Mobile Cloud Computing gave way to fog-enabled systems that represent the future of current cloud systems. Accordingly, the Fog concept had been proposed in 2013 to enhance the IoT-Cloud operations in terms of latency and reliability. In this paper we analyze and categorize surveys addressing privacy issues of these complex systems, and propose PFMCC, a model for preserving Privacy in Fog-enabled Mobile Cloud Computing systems using 5G connection. The PFMCC model consists of three components interacting with each other to preserve data privacy, usage privacy, location privacy and high mobility. We also present three algorithms to perform privacy-aware computation offloading to the fog.