Powerful in-band interference can saturate a receiver's front-end and limit the usefulness of digital interference suppression methods that are bounded by the receiver's limited dynamic range. This is especially true for the self-interference (SI) encountered in full-duplex (FD) radios, but also in the case of strong interference between co-located radios. However, unlike in FD radios, receivers co-located with interference sources do not typically have direct access to the transmitted interference. This work analyzes the performance of a digitally-assisted analog interference mitigation method and its implementation for the suppression of frequency-modulated (FM) interference before quantization in global navigation satellite system (GNSS) receivers that are co-located with interference sources. Over-the-air measurement results are presented that illustrate the effects of interference mitigation on GPS L1 and Galileo E1 reception in a commercial off-the-shelf GNSS receiver and a software-defined GNSS receiver. The analysis covers the effects of the interference mitigation on the radio frequency (RF) front-end, acquisition, tracking, and positioning stages.

Future 5G networks will serve both terrestrial and aerial users, thanks to their network slicing and flexible numerology capabilities. The probability of Line-of-Sight (LoS) propagation will be intuitively higher for aerial users than for terrestrial users and this will provide a trade-off between increased capacity and increased interference. Our paper analyzes theoretically this trade-off and proposes solutions based on downlink multiantenna beamorming and joint optimization of the signal-to- interference ratio of multiple aerial users. It is shown that Multiple-Input-Single-Output solutions offer the most convenient tradeoff between complexity and capacity/interference performance. Simulation results are provided for mm Wave bands and low-altitude aerial vehicles.

Positioning is considered one of the most important features and enabler of various novel industry verticals in future radio systems. Since path loss or received signal strength-based measurements are widely available and accessible in the majority of wireless standards, path loss-based positioning has an important role among other positioning technologies. Conventionally path loss-based positioning has two phases; i) fitting a path loss model to training data, if such training data is available, and ii) determining link distance estimates based on the path loss model and calculating the position estimate. However, in both phases, the maximum measurable path loss is limited by measurement noise. Such immeasurable samples are called censored path loss data and such noisy data is commonly neglected in both the model fitting and in the positioning phase. In the case of censored path loss, the loss is known to be above a known threshold level and that information can be used in model fitting as well as in the positioning phase. In this paper, we examine and propose how to use censored path loss data in path loss model-based positioning and demonstrate with simulations the potential of the proposed approach for considerable improvements (over 30%) in positioning accuracy.

The emerging 5G New Radio (NR) networks are expected to enable huge improvements, e.g., in terms of capacity, number of connected devices, peak data rates and latency, compared to existing networks. At the same time, a new trend referred to as the RF convergence is aiming to jointly integrate communications and sensing functionalities into the same systems and hardware platforms. In this paper, we investigate the sensing prospects of 5G NR systems, with particular emphasis on the user equipment side and their potential for joint communications and environment mapping. To this end, a radio-based sensing approach utilizing the 5G NR uplink transmit signal and an efficient receiver processing and mapping scheme are proposed. An indoor scenario is then studied and evaluated through real-world RF measurements at 28 GHz mm-wave band, showing that impressive mapping performance can be achieved by the proposed system. The measurement data is available at a permanent open repository.

The modernization that stems from Industry 4.0 started populating the manufacturing sector with networked devices, complex sensors, and a significant proportion of physical actuation components. However, new capabilities in networked cyber-physical systems demand more complex infrastructure and algorithms and often lead to new security flaws and operational risks that increase the attack surface area exponentially. The interconnected nature of Industry 4.0-driven operations and the pace of digital transformation mean that cyberattacks can have far more extensive effects than ever before. Based on that, the core ideas of this paper are driven by the observation that cybersecurity is one of the key enablers of Industry 4.0. Having this in mind, we propose CryptoFactory - a forward-looking design of a layered-based architecture that can be used as a starting point for building secure and privacy-preserving smart factories. CryptoFactory aims to change the security outlook in smart manufacturing by discussing a set of fundamental requirements and functionality that modern factories should support in order to be resistant to both internal and external attacks. To this end, CryptoFactory first focuses on how to build trust relationships between the hardware devices in the factory. Then, we look on how to use several cryptographic approaches to allow IoT devices to securely collect, store and share their data while we also touch upon the emerging topic of secure and privacy-preserving communication and collaboration between manufacturing environments and value chains. Finally, we look into the problem of how to perform privacy-preserving analytics by leveraging Trusted Execution Environments and the promising concept of Functional Encryption.

The prospective millimeter-wave (mmWave) networks are envisioned to heavily utilize relay nodes to improve their performance in certain scenarios. In addition to the stationary mmWave relays already considered by 3GPP as one of the main focuses, the community recently started to explore the use of unmanned aerial vehicle (UAV)-based mmWave relays. These aerial nodes provide greater flexibility in terms of the relay placement in different environments as well as the ability to optimize the deployment height thus maximizing the cell performance. At the same time, the use of UAV-based relays leads to additional deployment complexity and expenditures for the network operators. In this paper, taking into account 3GPP-standardized mmWave-specific propagation, blockage, and resource allocation we compare the capacity gains brought by the static and the UAV-based mmWave relays in different scenarios. For each of the relay types, we investigate both uniform and clustered distribution of human users. The developed mathematical framework and a numerical study reveal that the highest capacity gains when utilizing the UAV-based relays instead of the static ones are observed in clustered deployments (up to 31%), while the performance difference between the UAV-based and the static mmWave relays under a uniform distribution of users is just 3%.

Deploying sub-THz frequencies for mobile communications is one timely research area, due to the availability of very wide and contiguous chunks of the radio spectrum. However, at such extremely high frequencies, there are large challenges related to, e.g., phase noise, propagation losses as well as to energy-efficiency, since generating and radiating power with reasonable efficiency is known to be far more difficult than at lower frequencies. To address the energy-efficiency and power amplifier (PA) nonlinear distortion related challenges, modulation methods and waveforms with low peak-to-average-power ratio (PAPR) are needed. To this end, a new modulation approach is formulated and proposed in this paper, referred to as constrained phase-shift keying (CPSK). The CPSK concept builds on the traditional PSK constellations, while additional constraints are applied to the time domain symbol transitions in order to control and reduce the PAPR of the resulting waveform. This new modulation is then compared with pulse-shaped π/2-BPSK and ordinary QPSK, in the discrete Fourier transform (DFT) spread orthogonal frequency division multiplexing (DFT-s-OFDM) context, in terms of the resulting PAPR distributions and the achievable maximum PA output power, subject to constraints in the passband waveform quality and out-of-band emissions. The obtained results show that the proposed CPSK approach allows for reducing the PAPR and thereon for achieving higher PA output powers, compared to QPSK, while still offering the same spectral efficiency. Overall, the CPSK concept offers a flexible modulation solution with controlled PAPR for the future sub-THz networks.

Mobile manipulators have a highly non-linear and non-convex mapping between the end-effector path and the manipulator's joints and position and orientation of the mobile base. As a result, trajectory optimization with end-effector path constraints takes the form of a difficult non-linear optimization problem. In this paper, we present the first multi-convex approximation to this difficult optimization problem that eventually reduces to solving a sequence of globally valid convex quadratic programs (QPs). The proposed optimizer rests on two novel building blocks. First, we introduce a set of auxiliary variables in which the non-linear constraints that arise out of manipulator kinematics and its coupling with the mobile base have a multi-affine form. Projecting the auxiliary variables to the space of actual configuration variables of the mobile manipulator involves a non-convex optimization. Thus, the second building block involves computing a convex surrogate for this non-convex projection. We show how large parts of the proposed optimizer can be solved in parallel providing the possibility of exploiting multi-core CPUs. We validate our trajectory optimization on different benchmark examples. Specifically, we highlight how it solves the cyclicity problem and provides a holistic approach where a diverse set of trajectories can be obtained by trading-off different aspects of manipulator and mobile base motion.

There is a strong interest in utilizing commercial cellular networks to support unmanned aerial vehicles (UAVs) to send control commands and communicate heavy traffic. Cellular networks are well suited for offering reliable and secure connections to the UAVs as well as facilitating traffic management systems to enhance safe operation. However, for the full-scale integration of UAVs that perform critical and high-risk tasks, more advanced solutions are required to improve wireless connectivity in mobile networks. In this context, integrated access and backhaul (IAB) is an attractive approach for the UAVs to enhance connectivity and traffic forwarding. In this paper, we study a novel approach to dynamic associations based on reinforcement learning at the edge of the network and compare it to alternative association algorithms. Considering the average data rate, our results indicate that the reinforcement learning methods improve the achievable data rate. The optimal parameters of the introduced algorithm are highly sensitive to the donor next generation node base (DgNB) and UAV IAB node densities, and need to be identified beforehand or estimated via a stateful search. However, its performance nearly converges to that of the ideal scheme with a full knowledge of the data rates in dense deployments of DgNBs.

This paper considers the problem of designing a resilient distributed voltage control algorithm for distribution systems with high penetration of distributed energy resources in the presence of an unknown cyber-attack. The purpose of the attack is to force the system to violate the operating voltage limit by intercepting its communication channels and inserting exogenous signals to perturb and/or modify the information being exchanged. We first review the cooperative voltage control proposed in our previous work and provide a new stability analysis for it. Next, we present a resilient cooperative voltage control algorithm by introducing a virtual system interconnected with the original system such that the voltage can be maintained within the operational limit under unknown attacks. The resiliency of the proposed algorithm is demonstrated via simulations on the IEEE 8500-node system when subjected to an attack which consists of corrupting the data being exchanged in the communication network between two generation units.

Given few noisy linear measurements of distinct misaligned modalities, we aim at recovering the underlying multimodal image using a sparsity promoting algorithm. Unlike previous multimodal sparse recovery approaches employing side information under the naive assumption of perfect calibration of modalities or of known deformation parameters, we adaptively estimate the deformation parameters from the images separately recovered from the incomplete measurements. We develop a multiscale dense registration method that proceeds alternately by finding block-wise intensity mapping models and a shift vector field which is used to obtain and refine the deformation parameters through a weighted least-squares approximation. The co-registered images are then jointly recovered in a plug-and-play framework where a collaborative filter leverages the local and nonlocal cross-modal correlations inherent to the multimodal image. Our experiments with this fully automatic registration and joint recovery pipeline show a better detection and sharper recovery of fine details which could not be separately recovered.

This paper presents a path planning approach for mobile robots in 2D spaces. The algorithm uses a quadtree decomposition where the discretization precision is improved until a path to the goal is found if one exists. The algorithm uses interval analysis-based methods to categorize the quadtree decomposition to occupied, free and partly occupied cells. The proposed algorithm is compared against other concurrent path planning algorithms, A on an ordinary quadtree, A for shortest path on a binary occupancy grid, and a Dijkstra's algorithm for lowest collision probability in a continuous-valued occupancy grid, in five different scenarios.Compared to the other methods, the main advantage of our method is achieving a compromise between driving distance, safety, and computation time. The proposed algorithm was found to require significantly fewer collision checks in all scenarios while providing sub-optimum results, based on the obstacle distance and path length criteria. The algorithm is suitable for further extension to include non-euclidean measures and for higher dimensions of configuration spaces. The proposed algorithm will be publicly available on GitHub repository.

Due to the increased rate of drone usage in various commercial and industrial fields, the need for their autonomous operation is rapidly increasing. One major aspect of autonomous movement is the ability to operate safely in an unknown environment. The majority of current works are persistently using a global positioning system (GPS) to directly find the absolute position of the drone. However, GPS accuracy might be not suitable in some applications and this solution is not applicable to all situations. In this paper, a positioning system based on monocular SLAM and inertial measurement unit (IMU) is presented. The position is calculated through the semi-direct visual odometry (SVO) method alongside IMU data, and is integrated with an extended Kalman filter (EKF) to enhance the efficiency of the algorithm. The data is then employed to control the drone without any requirement to any source of external input. The experiment results for long-distance flying paths is very promising.

A number of spatially-localised semantic parts of vehicles sensitive to pose changes are encoded their visible probabilities into a mid-level feature vector. Car pose estimation is then formulated into a regression on concatenated low-and mid-level features to continuously changing viewing angles. Each dimension of our visibility-Aware part codes separates all the training samples into two groups according to its visual existence in images, which provides additional part-specific range constraint of viewing angles. Moreover, the proposed codes can alleviate the suffering from sparse and imbalanced data distribution in the light of modelling latent dependency across angle targets. Experimental evaluation for car pose estimation on the EPFL Multi-View Car benchmark demonstrates significant improvement of our method over the state-of-The-Art regression methods, especially when only sparse and imbalanced data is available.

The blockchain technology is currently penetrating different sides of modern ICT community. Most of the devices involved in blockchain-related processes are specially designed targeting only the mining aspect. At the same time, the use of wearable and mobile devices may also become a part of blockchain operation, especially during the charging time. The paper considers the possibility of using a large number of constrained devices supporting the operation of the blockchain. The utilization of such devices is expected to improve the efficiency of the system and also to attract a more substantial number of users. Authors propose a novel consensus algorithm based on a combination of Proof-of-Work (PoW), Proof-of-Activity (PoA), and Proof-of-Stake (PoS). The paper first overviews the existing strategies and further describes the developed cryptographic primitives used to build a blockchain involving mobile devices. A brief numerical evaluation of the designed system is also provided in the paper.

A linear Gaussian state-space smoothing algorithm is presented for off-line estimation of derivatives from a sequence of noisy measurements. The algorithm uses numerically stable square-root formulas, can handle simultaneous independent measurements and non-equally spaced abscissas, and can compute state estimates at points between the data abscissas. The state space model's parameters, including driving noise intensity, measurement variance, and initial state, are determined from the given data sequence using maximum likelihood estimation computed using an expectation maximisation iteration. In tests with synthetic biomechanics data, the algorithm is found to be more accurate compared to a widely used open source automatic numerical differentiation algorithm, especially for acceleration estimation.

Cranes are required to lift and carry loads swiftly to desired positions without causing excessive swaying motion of the load. These are conflicting requirements, which make feedback control of crane systems challenging. Furthermore, variations in rope length and load mass complicate controller design, since they significantly influence swaying dynamics. This paper considers automatic control of Quanser 3DOF tower crane system using composite nonlinear feedback (CNF) methodology. To be more specific, a CNF controller is designed for the jib trolley position of the crane using partial state measurements. The performance of the CNF controller is compared with Quanser's built-in linear quadratic regulator (LQR) controller both in simulation and experimental setups. The results show that the CNF controller provides better load handling capability in terms of fast positioning of the jib trolley and damping of load swaying.

Casting silicone elastomers into 3D printed molds has seen a surge of applications in soft robots, soft manipulators, microfluidics, wearable technologies and stretchable sensors. In such devices, buried fluid channels are used to transport fluids, as fluidic actuators and as sensors with liquid metal. However, it is difficult to demold structures with buried channels or overhangs. As a solution, using sacrificial molds made of dissolvable materials has been proposed. In this paper, we evaluate different commercially available 3D printing materials as dissolvable mold materials. We tested dissolving prints made of high-impact polystyrene (HIPS), acrylonitrile butadiene styrene (ABS), polyvinyl butyral (PVB) and polyvinyl alcohol (PVA) in limonene, acetone, isopropanol/ethanol and water, respectively. We further studied the effect of magnetic stirring and ultrasonic bath on the dissolution times. Finally, we fabricated buried channels using different mold materials and silicone elastomers. The results show that at least ABS, PVB and PVA can be used as mold materials. In particular, PVA is a promising material as it is soluble in water. The studied method simplifies the fabrication of soft devices, allowing the fabrication of overhangs and buried channels in a single casting step.

This paper presents a direct model predictive power control for a series-connected modular rectifier. The topology combines a diode rectifier and an active-front-end (AFE) converter to achieve a medium voltage target. A voltage control loop regulates the total dc voltage, providing the power references to the inner direct model predictive control. Operation under the desired real and reactive power is achieved, while minimizing the converter switching frequency. Moreover, successful operation and control of the AFE converter is guaranteed thanks to a hard constraint included in the optimization problem.

This contribution presents a direct model predictive current control approach that achieves favorable performance during transients while minimizing the torque and current ripples at steady-state operation by increasing the granularity at which switching can be performed. To meet the control goals, an optimization problem is solved in real-time that decides whether only one discrete voltage space vector or a combination of two is selected. In the latter case, a variable switching point, i.e., a time instant within the control interval at which the converter switches change state, is computed. The proposed method is advantageous, e.g., for electric drives in machine tools, in which, depending on the operating point, fast dynamics and a low torque ripple are important. The approach is evaluated at the example of a two-level voltage source inverter driving a permanent magnet synchronous machine.

The decreasing prices of monitoring equipment have vastly increased the opportunities to utilize local data, and data processing for wider global web-based monitoring purposes. The possible amount of data flowing though different levels can be huge. Now the question is how to handle this opportunity in both dynamic and secure way. The paper presents a new concept to manage data for monitoring through the Internet. The concept is based on the use of Arrowhead Framework (AF) and MIMOSA data model, and selected edge, and gateway devices together with cloud computing opportunities. The concept enables the flexible and secure orchestration of run-time data sources and the utilization of computational services for various process and condition monitoring needs.

Cyber-physical-human systems naturally arise from interdependent infrastructure systems and smart connected communities. Such applications require ubiquitous information sensing and processing, intelligent machine-to-machine communication for a seamless coordination, as well as intelligent interactions between humans and machines. This chapter presents a control-theoretical framework to model heterogeneous physical dynamic systems, information and communication, as well as cooperative controls and/or distributed optimization of such interconnected systems. It is shown that efficient analytical and computational algorithms can be modularly designed and hierarchically implemented to operate and optimize cyber-physical-human systems, first to quantify individually the input–output relationship of nonlinear dynamic behaviors of every physical subsystem, then to coordinate locally both cyber-physical interactions of neighboring agents as well as physical-human interactions, and finally to dynamically model and optimize the overall networked system. The hierarchical structure makes the overall optimization and control problem scalable and solvable. Moreover, the three levels integrate individual designs and optimization, distributed cooperative optimization, and decision-making through real-time, data-driven, model-based learning and control. Specifically, one of the contributions of the chapter is to demonstrate how the combination of dissipativity theory and cooperative control serves as a natural framework and promising tools to analyze, optimize, and control such large-scale system. Application to digital power grid is investigated as an illustrative example.

Lightweight arms with electrical servomotor drives have demonstrated outstanding performance and speed in exoskeletons, prosthesis, and legged robot applications. They all share a similarity in actuation, which is based on series elastic actuators (SEAs). In SEAs, the system benefits from known compliance in the actuation that improves the overall performance, especially in contact with an environment that can have an unknown stiffness in assembly tasks. In some of these cases, harmonic drives or gears on the power transmission lines create the robot's compliance. For hydraulically actuated SEAs, Pratt and Krupp addressed the SEA challenges for lightweight hydraulic manipulators. However, this paper focuses on the design and control architecture of SEAs in heavy-duty manipulation having hydraulic load dynamics with variable stiffness or damping of fluid flexibility. This system faces challenging issues of payload dynamics and compressibility of fluid with high order system. A hydraulic SEA concept is designed, and a fifth-order state space SEA model is feedback controlled in a free space motion to demonstrate load dynamics of hydraulic actuation. In addition, a P controller and a controller based on integral of time-weighted absolute error (ITAE) are designed. The simulation results show the latter has better performance in the spring deflection of the SEA. A mixed working condition that changes from a purely inertia payload to an inertia and elastic reaction force is designed to examine the switching smoothness for varying payloads, and the control adaptability of controllers in different working conditions.

In this paper, we study the performance of a complementary filter with adaptive weights in a sensor fusion application for real-time localization of an omnidirectional field robot. The test-case robot is a large, four-wheel drive and steer (4WDS) construction vehicle with nonlinear internal dynamics and hydraulic driving and steering actuators. Our objective is to provide the vehicle's real-time controller with robust, smooth feedback that prevents unnecessary oscillations in steering, which can waste significant amounts of energy. We do so by assigning weights for measurements based on their consistency with the robot's motions. The calculations are based on two main data sources: (1) measured velocity vectors from wheel driving (odometer) and steering of the 4WDS test-case robot; and (2) data obtained from a differential global navigation satellite system on the absolute pose of the robot. We show that the sensor fusion is robust to the noise and single point failures of the sensors, while the maximum heading oscillations are reduced by 70%-95%, preserving the accuracy of the global positioning system. Moreover, we demonstrate the feasibility and efficacy of the real-time implementation of this filtering method in path-following control of the robot.

In this paper, we present a method for device localization based on the fusion of location data from Global Navigation Satellite System and data from inertial sensors. We use a Kalman filter as well as its non-linear variants for realtime position estimation, and corresponding smoothers for offline position estimation. In all filters we use information about changes of user's heading, which are computed from the acceleration and gyroscope data. Models used with Extended and Unscented Kalman filters also take into account information about step length, whereas Kalman Filter does not, because the measurement is non-linear. In order to overcome this shortcoming, we introduce a modified Kalman Filter which adjusts the state vector according to the step length measurements. Our experiments show that use of step length information does not significantly improve performance when location measurements are constantly available. However, in real situations, when location data is partially unavailable, information about step length and its appropriate integration into the filter design is important, and improve localization accuracy considerably.

A finite control set model predictive control strategy for the control of the stator currents of a synchronous reluctance motor driven by a three-level neutral point clamped inverter is presented in this paper. The presented algorithm minimizes the stator current distortions while operating the drive system at switching frequencies of a few hundred Hertz. Moreover, the power electronic converter is protected by overcurrents and/or overvoltages owing to a hard constraint imposed on the stator currents. To efficiently solve the underlying integer nonlinear optimization problem a sphere decoding algorithm serves as optimizer. To this end, a numerical calculation of the unconstrained solution of the optimization problem is proposed, along with modifications in the algorithm proposed in [1] so as to meet the above-mentioned control objectives. Simulation results show the effectiveness of the proposed control algorithm.

In this study, we propose an observer design based on inertial sensors and the finite element (FE) method to estimate the flexural states of a long-reach and highly flexible manipulator in a 3D plane of motion. Vertical and lateral dynamic bendings are considered, along with deformation due to torsion. The aim is to achieve accurate end-point positioning by using the estimated flexural degrees-of-freedom, which are formulated using an FE model. The states are reconstructed based on angular velocity measurements, which are obtained from strap-on inertial sensors placed along the flexible link. For validation, a motion-capture setup consisting of three OptiTrack cameras is used. The experiments are conducted on a hydraulic manipulator that has a single 4.5-m long flexible link with a tip mass. The validation is carried out by comparing the estimates to the OptiTrack reference measurements. The results demonstrate that this method provides satisfactory end-point positioning, while also being convenient for use in heavy-duty mobile manipulators.

Recent development in depth sensing provide various opportunities for the development of new methods for Human Robot Interaction (HRI). Collaborative robots (co-bots) are redefining HRI across the manufacturing industry. However, little work has been done yet in the field of HRI with Kinect sensor in this industry. In this paper, we will present a HRI study using nearest-point approach with Microsoft Kinect v2 sensor's depth image (RGB-D). The approach is based on the Euclidean distance which has robust properties against different environments. The study aims to improve the motion performance of Universal Robot-5 (UR5) and interaction efficiency during the possible collaboration using the Robot Operating System (ROS) framework and its tools. After the depth data from the Kinect sensor has been processed, the nearest points differences are transmitted to the robot via ROS.

The competition among manufacturers in the global markets calls for the enhancement of the agility and performance of the production process and the quality of products. As a result, the production systems should be designed in such a way to provide decision makers with visibility and analytics. To fulfill these objectives, the development of information systems in manufacturing industries has intensified in the past few years. On the other hand, the volume of data which is being generated on the shop floor is rising. To improve the efficiency of manufacturing processes, this amount of data should be analyzed by decision makers. To cope with this challenge, advanced visualization is needed to assist users to gain insight into data and make effective decisions faster. This paper describes an approach for building a role-based visualization of industrial IoT. We propose an extendible architecture that anticipates the future growth of data. By using the IoT platform introduced in this paper, selected Key Performance Indicators(KPI) can be monitored by different levels of enterprise. The prototype IoT dashboard has been implemented for a pilot production line 'Festo didactic training line' located in Seinäjoki University of Applied Sciences(SeAMK) and results have been validated.

In this paper, we propose an algorithm for complete odometry of a vehicle on a horizontal plane., that is., estimation of linear velocity vector (forward and sideslip speeds) and angular speed of a vehicle. The vehicle is equipped with an automotive Radar sensor and a vertical gyro. The Radar sensor provides radial speed and azimuth angle of number of objects in the environment. We first derive the kinematic constraints imposed on the vehicle motion and stationary points in the environment. Using the constraints we classify the points detected by the Radar to stationary and non-stationary points. It is known that using data from a single Radar., the abovementioned constraints are singular. Previous works have thus proposed the use of more than one Radar sensor., or they have neglected the sideslip speed. In our work, we then use the Radar data of the stationary objects and a gyro data to solve an optimization algorithm to calculate vehicle odometry. Experimentation has been performed with a non-road vehicle driven on a straight path and on a circular path. We report our findings and show efficacy of the algorithm in comparison to the state of art [8] as well as wheel odometry and a complete navigation solution (including GNSS) as the reference path.

This paper presents a method for improving the accuracy of extended GNSS satellite orbit predictions with convolutional neural networks (CNN). Satellite orbit predictions are used in self-assisted GNSS to reduce the Time to First Fix of a satellite positioning device. We describe the models we use to predict the satellite orbit and present the improvement method that uses CNN. The CNN estimates future prediction errors of our model and these estimates are used to correct our orbit predictions. We also describe how the neural network can be implemented into our prediction algorithm. In tests with GPS and BeiDou data, the method significantly improves orbit prediction accuracy. For example, the 68% error quantile of 7 day orbit prediction errors of GPS satellites was reduced by 45% on average.

Accurate load and response forecasts are a critical enabler for high demand response penetrations and optimization of responses and market actions. Project RESPONSE studies and develops methods to improve the forecasts. Its objectives are to improve 1) load and response forecast and optimization models based on both data-driven and physical modelling, and their hybrid models, 2) utilization of various data sources such as smart metering data, weather data, measurements from substations etc., and 3) performance criteria of load forecasting. The project applies, develops, compares, and integrates various modelling approaches including partly physical models, machine learning, modern load profiling, autoregressive models, and Kalman-filtering. It also applies non-linear constrained optimization to load responses. This paper gives an overview of the project and the results achieved so far.

Performance measurement tools and techniques have become very significant in today's industries for increasing the efficiency of their processes in order to face the competitive market. The first step towards performance measurement is the real-time monitoring and gathering of the data from the manufacturing system. Applying these performance measurement techniques on real-world industry in a way that is more general and efficient is the next challenge. This paper presents a methodology for implementing the key performance indicators defined in the ISO 22400 standard-Automation systems and integration, Key performance indicators (KPIs) for manufacturing operations management. The proposed methodology is implemented on a multi robot line simulator for measuring its performance at runtime. The approach implements a knowledge-based system within an ontology model which describes the environment, the system and the KPIs. In fact, the KPIs semantic descriptions are based on the data models presented in the Key Performance Indicators Markup Language (KPIML), which is an XML implementation of models developed by the Manufacturing Enterprise Solutions Association (MESA) international organization.

The evolution of industries and their needs towards the implementation of Industry 4.0 based systems has brought both new technological challenges and opportunities. This article proposes the adoption and deployment of cloud robotics at factories to enhance the control and monitoring of processes, such as handling materials multiple assemblies in single cells. The ultimate research objective of this research is the offloading computation and integrating cloud robotics into an industrial scenario. However, the investigation of state of the art techniques, tools and technologies, and the development of functional prototypes is beforehand required. Then, this article presents a small-scale system as a prototype that employs the Google cloud vision API as a resource that, in turn, is used by networked agents for supporting the decision-making in the process of handling material commodities at factory shop floor. The overall concept as well as the interaction between the main actors of the prototype is detailed. Finally, further research directions are discussed.

Electric vehicles have pro-environmental advantages compared to traditional automobiles, or even hybrids: they can help reducing pollution and noise levels locally, and greenhouse gas emissions globally. However, there are still many challenges that the electric vehicles must overcome before reaching level of diffusion that can have significant impact on sustainability. This paper evaluates combined sustainability of electric vehicle and small-scale energy production. We propose a framework for sustainable electric vehicle - energy prosumer integration and outline a policy mix that is needed to support adoption of both renewable energy technologies and electric vehicles.

Availability of new GPS civil signals L2C and L5 along with existed L1C/A signal and Galileo E1/E5/E6 signals has increased the potential ways to generate linear combination of signals to remove ionosphere errors and improve accuracy in carrier integer ambiguity resolution. Conventionally, a linear combination of dual frequency signals has been used to remove first order ionosphere delays incurred in signal propagation path which is a major source of range error. Out of the three civil signals in GPS and Galileo system, L5/E5 signals have advanced signal features such as higher received power, faster chip rate and lower carrier frequency than L1/E1 and L2C/E6 signals. Hence, dual frequency receiver with combination of L1/L5 and E1/E5 signals is more suitable to remove ionosphere delay and get benefit from L5/E5 signal characteristics. However, the major limitation of linear combination of signal observations is an amplification of receiver noise. To get benefit of two frequency signals, a suitable signal processing architecture is needed. By taking advantage of GPS L5/Galileo E5 signal characteristics, a dual frequency signal processing architecture is proposed with an aim to reduce the ionosphere-free signal observation noise and to enhance the L1/E1 signal tracking loop sensitivity. The L1/E1 signal tracking loop sensitivity can be enhanced by Doppler aiding from L5/E5 signal tracking loop. The low noise L5/E5 signal Doppler aid reduces the noise in the L1/E1 signal tracking loop. Moreover, two frequency signals tracked with common Doppler estimate will have common observation errors, which will get cancel in linear combination of observations i.e. ionosphere-free, wide-lane etc. Further, code phase observations can be smoothed (Hatch filter) using carrier phase observations. The carrier phase observations are limited by cycle slip. Hence, we have investigated an optimum combination of divergence-free and ionosphere-free pseudorange smoothing using dual-frequency carrier Doppler observations for GPS L1/L5 and Galileo E1/E5 signals. The cycle slip in carrier phase observations can be neglected in carrier Doppler observations. The proposed signal processing architecture incorporated in GPS L1/L5 and Galileo E1/E5 dual frequency receiver will ensure robust signal tracking and minimum pseudorange errors, suitable to a range of high accuracy standalone and code differential positioning applications. The performance of the proposed dual frequency signal processing architecture is evaluated with GPS L1/L5 signals collected from Block-IIF satellites.

Differential modulation has largely re-attracted the attention of academia and industry due to its advantages relating to simple implementation and no need for knowledge of channel state information. The present work analyzes the average bit error rate performance of dual-hop cooperative systems over generalized multipath fading conditions. The considered system is differentially modulated and is assumed to operate based on the amplify-and-forward relaying protocol. Therefore, the main advantage of the considered set up is that it does not require any channel state information neither at the relay nor at the destination nodes. Novel closed-form expressions are derived for the end-to-end error rate under asymmetric generalized multipath fading conditions, which are encountered in realistic wireless communication scenarios. These expressions are subsequently employed in quantifying the effect of generalized fading conditions on the achieved bit error rate performance. It is shown that the impact of multipath fading and shadowing effects is detrimental at both high and low signal-to-noise ratio regimes as the corresponding deviations are often close to an order of magnitude. The incurred difference is also significantly different than the conventional Rayleigh fading conditions, which verifies that accurate channel characterization is of paramount importance in the effective design of conventional and emerging wireless technologies. In addition, it indicates that differential modulation can be a suitable modulation scheme for relay systems, under certain conditions, since it can provide adequate performance at a reduced implementation complexity.

Novel composite fading models were recently proposed based on inverse gamma distributed shadowing conditions. These models were extensively shown to provide remarkable modeling of the simultaneous occurrence of multipath fading and shadowing phenomena in emerging wireless scenarios such as cellular, off-body and vehicle-to-vehicle communications. Furthermore, the algebraic representation of these models is rather tractable, which renders them convenient to handle both analytically and numerically. The present contribution presents the major theoretical and practical characteristics of the η - μ / inverse gamma composite fading model, followed by a thorough ergodic capacity analysis. To this end, novel analytic expressions are derived, which are subsequently used in the evaluation of the corresponding system performance. In this context, the offered results are compared with respective results from cases assuming conventional fading conditions, which leads to the development of numerous insights on the effect of the multipath fading and shadowing severity on the achieved capacity levels. It is expected that these results will be useful in the design of timely and highly demanding wireless technologies, such as wearable, cellular and inter-vehicular communications as well in wireless power transfer based applications in the context of the Internet of Things.

An optimum packet length selection scheme to maximize the throughput of a smart utility network (SUN) is introduced under wireless local area network (WLAN) interference system. The traditional and the investigated segmented packet collision models (PCM) are compared in terms of packet error rate (PER) and maximum achievable throughput. Furthermore, we quantify the impact of minimum mean square error (MMSE) interference mitigation for the SUN in the coexistence of WLAN interfering packets over multipath Rayleigh fading channel. The effect of the distance between the WLAN transmitter and the SUN receiver on the probability of error is also investigated.

Non-orthogonal multiple access (NOMA) has been recently proposed as a viable technology that can potentially improve the spectral efficiency of fifth generation (5G) wireless networks and beyond. However, in practical communication scenarios, transceiver architectures inevitably suffer from radio-frequency (RF) front-end related impairments that can lead to degradation of the overall system performance, with in-phase/quadrature-phase imbalance (IQI) constituting a major impairment in direct-conversion transceivers. In the present work, we quantify the effects of joint transmitter/receiver IQI on the performance of NOMA based multi-carrier (MC) systems under multipath fading conditions. Furthermore, we derive the asymptotic diversity order of the considered MC NOMA set up. Capitalizing on these results, we demonstrate that the effects of IQI differ considerably among NOMA users and depend on the underlying system parameters. For example, it is shown that the first sorted user appears more robust to IQI, which indicates that higher order users are more sensitive to the considered non-negligible impairment.

Neural decoding focuses on predicting behavior variables based on neural activities. Linear discriminant analysis (LDA) has been successfully used in pattern recognition and machine learning to find the set of discriminant vectors to characterize two or more classes of objects. However, LDA cannot be directly used for real-time neural decoding problems. In this paper, we propose an incremental LDA with online learning method to overcome this limitation. The dataflow techniques are implemented in the LIDE (LIghtweight Dataflow Environment), which provides capabilities to systematically optimize and integrate embedded software components for signal and information processing. Using these techniques along with online learning, an efficient real-time neural decoding system can be attained.

Soft mobile microrobots whose deformation can be directly controlled by an external field can adapt to move in different environments. This is the case for the light-driven microrobots based on liquid-crystal elastomers (LCEs). Here we show that the soft microrobots can move through an agarose hydrogel by means of light-controlled travelling-wave motions. This is achieved by exploiting the inherent rise of the LCE temperature above the melting temperature of the agarose gel, which facilitates penetration of the microrobot through the hydrogel. The locomotion performance is investigated as a function of the travelling-wave parameters, showing that effective propulsion can be obtained by adapting the generated motion to the specific environmental conditions.

In this study, we demonstrate the functionality and usability of a compact, pneumatically actuated, elastomeric stimulation device for mechanobiological studies. The soft mechatronic device enables high-resolution live-cell confocal fluorescent imaging during equiaxial stretching. Several single cells can be tracked and imaged repeatedly after stretching periods. For demonstration, we provide image based analysis of dynamic change of the cell body and the nucleus area and actin fiber orientation during mechanical stimulation of mouse embryonic fibroblast (MEF) cells. Additionally, we present the characteristics of the device utilizing computational simulations and experimental validation using a particle tracking method for strain field analysis.

A maximum-power-point tracking (MPPT) algorithm is essential in all controllers of solar power electronic converters due to the nonlinear current-voltage characteristics of a photovoltaic generator. One of the most widely utilized algorithms are perturbative MPPT techniques such as perturb and observe and incremental conductance methods due to their simple implementation with relatively good tracking performance. However, in order to optimize the performance of such algorithms, the design parameters - sampling frequency and perturbation step size - need to be designed in respect to interfaced power electronic converter. Recent studies have provided state-of-art MPP-tracking design rules for single and two-stage grid-connected PV systems. Unfortunately, the analysis of those studies does not provide analytical results for PV power transient response under feedback-controlled converters. This paper provides reduced-order transfer functions for the converters equipped with either I-type or PID-type controllers in order to approximate the maximum sampling or perturbation frequency for MPP-tracking algorithms. The analysis reveals the factors affecting the transient behavior similarly as in open-loop converter providing valuable tools for optimizing MPP-tracking perturbation frequency design.

The model predictive control problem of linear systems with integer inputs results in an integer optimization problem. In case of a quadratic objective function, the optimization problem can be cast as an integer least-squares (ILS) problem. Three algorithms to solve this problem are proposed in this paper. Optimality can be traded in to reduce the computation time. An industrial case study-an inverter-driven electrical drive system-is considered to demonstrate the effectiveness of the presented techniques.