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Smart agriculture is an enabling technology addressing the increasing challenges of efficiency, sustainability, and quality of food production. It requires rich data from the farming area at high spatial and temporal resolution. Although remote sensing systems have become readily available recently, in-situ sensing is still required to capture important properties of soil, crops, and their close environment. Ubiquitous sensor networks (USNs) provide a seamless and real-time in-situ sensing infrastructure that could overcome some limitations of smart agriculture. Resource efficiency is essential for USNs due to 1) the expected long operation time in typically resource-constrained environments, 2) the vast amount of captured and processed data, and 3) the ever-increasing application requirements. This survey comprehensively analyzes resource optimization techniques for USNs along three USN layers: the sensing, the communication & connectivity, and the processing & analysis layer. It discusses the application of these techniques in the smart agriculture domain and identifies current challenges and open research issues.

We consider the distributed detection problem of a temporally correlated random radio source signal using a wireless sensor network capable of measuring the energy of the received signals. It is well-known that optimal tests in the Neyman-Pearson setting are based on likelihood ratio tests (LRT), which, in this set-up, evaluate the quotient between the probability density functions (PDF) of the measurements when the source signal is present and absent. When the source is present, the computation of the joint PDF of the energy measurements at the nodes is a challenging problem. This is due to the statistical dependence introduced to the received signals by the propagation through fading channels of the radio signal emitted by the source. We deal with this problem using the characteristic function of the (intractable) joint PDF, and proposing an approximation to it. We derive bounds for the approximation error in two wireless propagation scenarios, slow and fast fading, and show that the proposed approximation is exponentially tight with the number of nodes when the time-bandwidth product is sufficiently high. The approximation is used as a substitute of the exact joint PDF for building an approximate LRT, which performs better than other well-known detectors, as verified by Monte Carlo simulations.

In-band full-duplex (IBFD) is an attractive technology in broadband power line communication (BB-PLC) because it helps to improve spectral efficiency. However, IBFD is challenging since it requires additional hardware and advanced signal processing to mitigate self-interference (SI) signals. SI cancelation architectures and channel estimation techniques determine the overall IBFD performance. Accurate SI channel estimation is required since imperfect SI cancelation reduces signal-to-interference-plus-noise ratio (SINR), causing an increase in data errors and a decrease in data rates. Although channel estimation can be improved by sending additional training symbols, increasing the training duration will lower data throughput. Thus, the training symbol number is an essential trade-off for IBFD performance in BB-PLC. In this paper, we investigate IBFD performance in single-input single-output (SISO), single-input multiple-output (SIMO), and multiple-input multiple-output (MIMO) communication scenarios, including the influence of the training period. By analyzing error vectors on a constellation diagram, we obtain the closed-form expressions for the symbol error probability (SEP) affected by IBFD and the training duration. Based on the obtained expressions, we propose a bit allocation algorithm to determine bit loading to ensure reliable IBFD communication. Furthermore, we suggest a procedure to compute the optimal training symbol number that maximizes throughput in IBFD. Using the proposed bit allocation strategy and a database of measured channels, we estimated the achievable bidirectional throughput and the throughput gain in IBFD compared to time division duplexing (TDD).

In this paper, we introduce the Global Multi-Phase Path Planning ($GMP^{3}$) algorithm in planner problems, which computes fast and feasible trajectories in environments with obstacles, considering physical and kinematic constraints. Our approach utilizes a Markov Decision Process (MDP) framework and high-level reinforcement learning techniques to ensure trajectory smoothness, continuity, and compliance with constraints. Through extensive simulations, we demonstrate the algorithm's effectiveness and efficiency across various scenarios. We highlight existing path planning challenges, particularly in integrating dynamic adaptability and computational efficiency. The results validate our method's convergence guarantees using Lyapunov’s stability theorem and underscore its computational advantages.

Smart Grids (SG) envision the exchange of both power and data, enabling system and customers to generate and transfer energy in a more efficient and balanced way. Among the relevant communication technologies, we find Power-Line Communications (PLC), which allow for data transmission on the electrical cables used for power delivery. Despite the hostile medium, PLC offer reliability and data rates to support exchange of control traffic, smart metering and sensor network applications. The distribution portion of the power delivery network, which we focus on in this work, is topologically complex, which makes channel prediction complicated. We show how random realistic topologies can be generated and then used to train a Machine Learning (ML) algorithm to infer PLC link quality (based on channel response) exploiting solely topology descriptors. We eventually show how precisely the communication quality can be inferred from the SG topology through ML. In doing so, we also discuss how the ML approach offers the common ground between top-down and bottom-up approaches for network characterization and how it enables smart decision making in the SG.

One of the challenging problems in sensor network systems is to estimate and track the state of a target point mass with unknown dynamics. Recent improvements in deep learning (DL) show a renewed interest in applying DL techniques to state estimation problems. However, the process noise is absent which seems to indicate that the point-mass target must be non-maneuvering, as process noise is typically as significant as the measurement noise for tracking maneuvering targets. In this paper, we propose a continuous-time (CT) model-free or model-building distributed reinforcement learning estimator (DRLE) using an integral value function in sensor networks. The DRLE algorithm is capable of learning an optimal policy from a neural value function that aims to provide the estimation of a target point mass. The proposed estimator consists of two high pass consensus filters in terms of weighted measurements and inverse-covariance matrices and a critic reinforcement learning mechanism for each node in the network. The efficiency of the proposed DRLE is shown by a simulation experiment of a network of underactuated vertical takeoff and landing aircraft with strong input coupling. The experiment highlights two advantages of DRLE: i) it does not require the dynamic model to be known, and ii) it is an order of magnitude faster than the state-dependent Riccati equation (SDRE) baseline.

We propose an optimal destination scheduling scheme to improve the physical layer security (PLS) of a power-line communication (PLC) based Internet-of-Things system in the presence of an eavesdropper. We consider a pinhole (PH) architecture for a multi-node PLC network to capture the keyhole effect in PLC. The transmitter-to-PH link is shared between the destinations and an eavesdropper which correlates all end-to-end links. The individual channel gains are assumed to follow independent log-normal statistics. Furthermore, the additive impulsive noise at each node is modeled by an independent Bernoulli-Gaussian process. Exact computable expressions for the average secrecy capacity (ASC) and the probability of intercept (POI) performance over many different networks are derived. Approximate closed-form expressions for the asymptotic ASC and POI are also provided. We find that the asymptotic ASC saturates to a constant level as transmit power increases. We observe that the PH has an adverse effect on the ASC. Although the shared link affects the ASC, it has no effect on the POI. We show that by artificially controlling the impulsive to background noise power ratio and its arrival rate at the receivers, the secrecy performance can be improved.

The beam self-cleaning effect in graded-index multimode optical fibers has several interesting potential applications, such as high-resolution nonlinear imaging and mode-locked fiber lasers. Most experimental and theoretical studies of beam self-cleaning have neglected the role of the state of polarization of light. In this work, we fill this gap by reporting extensive experimental investigations of beam self-cleaning in multimode fibers, for beams with different input states of light polarization. We found that the state of polarization undergoes a complex evolution, which may lead either to full conservation of the input state of polarization, or to nearly complete light depolarization at the fiber output. The former outcome is compelling for applications based on the beam self-cleaning effect, such as multimode mode-locked fiber lasers for micromachining, and multimode devices for microscopy and endoscopy. The latter result, instead, permits to test the limits of validity of current purely scalar theoretical approaches for describing nonlinear propagation in multimode fibers.

In this article, we propose a procedure to solve the controlled design for a class of under-actuated mechanical systems. Our proposed method can be viewed as a sub-method of the IDA-PBC or Controlled Lagrangian approaches, with a particular focus on shaping the potential energy. By emphasizing potential energy shaping, we can effectively tackle the bottleneck presented by the matching equation in these approaches. Moreover, our method leverages a suitable coordinate transformation that is inspired by the physics of the system, further enhancing its efficacy. Therefore, our design procedure is based on a coordinate transformation plus potential energy shaping in the new coordinates, and its existence and possibility of potential energy shaping can be verified via some algebraic calculations, making it constructive. To illustrate the results, we consider the cart-pole system and a recently introduced under-actuated mechanical system named swash mass pendulum (SMP) (Salamat and Tonello, 2021). The SMP consists of a pendulum made of a rigid shaft connected to a pair of cross-shafts where two swash masses can move under the action of servo-mechanisms.

In-band full-duplex (IBFD) is an attractive solution for increasing the throughput of Power Line Communication (PLC) systems. In IBFD, each network node is allowed to transmit and receive simultaneously in the same frequency band. This paper discusses the importance of defining figures of merit to analyze IBFD performance, which is often forgotten by the literature that addresses IBFD from a pure system level and signal processing perspective. This is because in IBFD hardware-related aspects are of great importance. The focus is then given to the first self-interference (SI) cancellation stage, namely the analog coupling and self-interference cancellation stage (ASICS). Two architectures are considered. A detailed analysis is offered by taking into account circuit-level aspects. A broadband multiple conductor PLC scenario is assumed to enable multiple-input multiple-output (MIMO) IBFD communication. The first considered architecture performs SI subtraction exploiting operational amplifiers. The second proposed architecture exploits the characteristics of a three ports magnetic circuit together with a signal generator to remove the SI. Design, analysis, and hardware realization of these circuits have been done to compare and show the practical feasibility of the ASICS for PLC, well known to be challenged by its line impedance matching problems and severe frequency selective channel characteristics. This paper is the first documented contribution of the IBFD ASICS for $2\times 2$ MIMO broadband PLC physically realized and tested in the field.

Several experiments and field trials have shown that deterministic components characterize the Power Line Communication (PLC) noise across multiple-conductors. This article aims at understanding the main origins of these noise components and evaluate several methods to characterize them statistically. The correlation, distance-correlation, mutual information estimation, and a proposed linear coefficient ratio, are used to analyze the noise time-series statistically. The analysis of the PLC noise deterministic components allow us to develop an enhanced noise model that includes them. The presence of deterministic noise fosters the development of coding and decoding algorithms that aim at mitigating the noise effect. New decoding techniques called Quasi-Deterministic-Decoding are detailed and tested with single-input-multiple-output (SIMO) $1\times 2$ and multiple-input-multiple-output (MIMO) $2\times 2$ transmission configurations using real PLC noise and channel realizations. The proposed model leads to a better representation of the PLC noise traces and, consequently, the development of improved coding algorithms for reliable data transmission at low signal-to-noise ratios.

The autoencoder concept has fostered the reinterpretation and the design of modern communication systems. It consists of an encoder, a channel and a decoder block that modify their internal neural structure in an end-to-end learning fashion. However, the current approach to train an autoencoder relies on the use of the cross-entropy loss function. This approach can be prone to overfitting issues and often fails to learn an optimal system and signal representation (code). In addition, less is known about the autoencoder ability to design channel capacity-approaching codes, i.e., codes that maximize the input-output mutual information under a certain power constraint. The task being even more formidable for an unknown channel for which the capacity is unknown and therefore it has to be learnt. In this paper, we address the challenge of designing capacity-approaching codes by incorporating the presence of the communication channel into a novel loss function for the autoencoder training. In particular, we exploit the mutual information between the transmitted and received signals as a regularization term in the cross-entropy loss function, with the aim of controlling the amount of information stored. By jointly maximizing the mutual information and minimizing the cross-entropy, we propose a theoretical approach that a) computes an estimate of the channel capacity and b) constructs an optimal coded signal approaching it. Theoretical considerations are made on the choice of the cost function and the ability of the proposed architecture to mitigate the overfitting problem. Simulation results offer an initial evidence of the potentiality of the proposed method.

This paper presents a control-based trajectory generation approach for unmanned aerial vehicles (UAVs) under dynamic constraints. It exploits the concept of optimal control to find closed-form differential equations that satisfy any arbitrary dynamic limitation mapped into kinematic constraints. Pontryagin’s Minimum Principle applies to derive a set of differential equations in which the dynamic environment is considered in the constrained Hamiltonian function. In particular, we aim to minimize the L2-norm of the control input avoiding dynamic obstacles, given initial and final boundary conditions. Lastly, this paper proposes a novel interpolation algorithm based on rational functions, referred to as rational recursive smooth trajectory (RRST) method. The method generates an analytic expression that approximates the control inputs, for which no closed-form solutions are in general attainable.

We consider a small helicopter structure that is maneuvered through the control of moving masses. It is referred to as a swash mass helicopter (SMH). This paper addresses the trajectory tracking control problem for the SMH, with a specific focus on the decoupling change of coordinates of both rotational and translational dynamics. We propose a control scheme in which position tracking is the primary objective, while the attitude tracking task is considered as a secondary objective. The intermediate control signals related to the attitude dynamics exploit the structural properties of the SMH and are enhanced with terms that grant a more accurate tracking of the target trajectory. The closed-loop system stability under the trajectory tracking objective is obtained following the Interconnection and Damping Assignment Passivity-Based Control (IDA-PBC) approach. In addition, the presence of external disturbances can diminish the trajectory tracking performance. For this reason, a nonlinear outer loop controller is added to the IDA-PBC to compensate the disturbances. Finally, the results of several simulations are reported to evaluate the performance of the control strategy.

We experimentally demonstrate spatial beam self-cleaning in an Yb-doped graded-index multimode fiber taper, both in passive and active configurations. The input laser beam at 1064 nm was injected for propagation from the small to the large core side of the taper, with laser diode pumping in a counterdirectional configuration. The Kerr effect permits to obtain high-beam quality amplification with no accompanying frequency conversions. As a result, our nonlinear taper amplifier may provide an important building block for multimode fiber lasers and amplifiers.

The focusing ability of the lens antennas can significantly contribute to perform precise radio localization with low complexity in a massive antennas array (MAA) system. Thus, by combining lens antennas with an MAA, we propose an approach for radio source localization through an angle of arrival (AoA) estimation in a multipath propagation scenario. We discuss two simplified antenna selection techniques to select and process the focused subsets of antennas. Then, we implement a low-complexity time of arrival estimation scheme to detect the first arrival path (FAP) that contains the actual AoA information. Finally, the FAP signal is processed to estimate the AoA very efficiently, by applying one of the traditional subspace-based methods. To further simplify the system architecture with only slight degradation in performance, we also propose a mixed analog–digital AoA estimation structure based on the application of the sum–difference signal patterns technique. The simulated root mean squared error performance curves as a function of the signal-to-noise-ratio demonstrate that the proposed approach is capable of providing good AoA estimation results while considerably reducing the overall system complexity compared with the MAA system without the lens.

In this work, we first provide an overview of the state of the art in mismatch error estimation and correction for time-interleaved analog to digital converters (TI-ADCs). Then, we present a novel pilot-based on-line adaptive timing mismatch error estimation approach for TI-ADCs in the context of an impulse radio ultra wideband (IR-UWB) receiver with correlation-based detection. We introduce the developed method and derive the expressions for both additive white Gaussian noise (AWGN) and Rayleigh multipath fading (RMPF) channels. We also derive a lower bound on the required ADC resolution to attain a certain estimation precision. Simulations show the effectiveness of the technique when combined with an adequate compensator. We analyze the estimation error behavior as a function of signal to noise ratio (SNR) and investigate the ADC performance before and after compensation. While all mismatches combined cause the effective number of bits (ENOB) to drop to 3 bits and to 6 bits when considering only timing mismatch, estimation and correction of these errors with the proposed technique can restore a close to ideal behavior. We also show the performance loss at the receiver in terms of bit error rate (BER) and how compensation is able to significantly improve performance.

A great deal of attention has been recently given to Machine Learning (ML) techniques in many different application fields. This paper provides a vision of what ML can do in Power Line Communications (PLC). We first and briefly describe classical formulations of the ML, and distinguish deterministic from statistical learning models with relevance to communications. We then discuss ML applications in PLC for each layer, namely, for characterization and modeling, for the development of physical layer algorithms, for media access control and networking. Finally, other applications of the PLC that can benefit from the usage of ML, as grid diagnostics, are analyzed. Illustrative numerical examples are reported to serve the purpose of validating the ideas and motivate future research endeavors in this stimulating signal/data processing field.

Power line communication (PLC) is a growing technology which utilizes the existing pre-installed power delivery infrastructure for data transmission. While it is true that the history of PLC technology goes back to the beginning of the last century, when the first data transmission over power lines took place for low data rate control and monitoring purposes, PLC has recently regained a considerable amount of research attention due to the dawn of the internet and the increasing need for fast connectivity. PLC is also expected to serve as a reliable communication medium for many emerging applications of Internet of Things (IoT) and Smart Grids (SGs).

The aim of this paper is to provide the theoretical fundamentals needed to monitor power grids using high-frequency sensors. In our context, network monitoring refers to the harvesting of different kinds of information: topology of the grid, load changes, presence of faults, and cable degradation. We rely on the transmission line theory to carry out a thorough analysis of how high-frequency signals, such those produced by power line communication modems, propagate through multi-conductor power networks. We also consider the presence of electrical anomalies on the network and analyze how they affect the signal propagation. In this context, we propose two models that rely on reflectometric and end-to-end measurements to extrapolate information about possible anomalies. An in-depth discussion is carried out to explain the properties of each model and measurement method, in order to enable the development of appropriate anomaly detection and location algorithms.

Non-orthogonal multiple access (NOMA) has recently been proposed for dual-hop cooperative relaying power line communication (PLC) systems. Unlike conventional NOMA-PLC schemes which deploy NOMA only at the relay, this paper proposes to enhance the performance of such systems by implementing the principle of NOMA at both the source and relaying modems. The system performance is evaluated in terms of the average sum capacity for which analytical expressions are derived for both the improved and conventional NOMA-PLC systems. Throughout our analysis, the PLC channel is assumed narrow-band modeled with log-normal amplitude distribution and the total PLC noise consists of both background and impulsive noise. Monte Carlo simulations are provided to corroborate the accuracy of our theoretical analysis. The derived expressions are utilized to examine the impact of various system parameters on the average capacity performance; this includes: impulsive noise probability, network branching, power allocation coefficients, and transmit power. The optimization problem of the power allocation coefficients is also addressed for both NOMA-PLC systems under consideration. Results reveal that significant gains in the average capacity can be attained with the improved NOMA-PLC approach compared to the conventional system. In addition, the improved system is able to meet a given performance requirement with smaller transmit power offering more relaxed electromagnetic compatibility issues associated with PLCs. Finally, it is demonstrated that optimizing the power allocation coefficients at both the source and relay modems is crucial to maximize performance.

Most, if not all, existing studies on power line communication (PLC) systems as well as industrial PLC standards are based on orthogonal multiple access schemes, such as orthogonal frequency-division multiplexing and code-division multiple access. In this paper, we propose non-orthogonal multiple access (NOMA) for decode-and-forward cooperative relaying PLC systems to achieve higher throughput and improve user fairness. To quantitatively characterize the proposed system performance, we also study conventional cooperative relaying (CCR) PLC systems. We evaluate the performance of the two systems in terms of the average capacity. In this respect, accurate analytical expressions for the average capacity are derived and validated with Monte Carlo simulations. The impact of several system parameters, such as the branching, impulsive noise probability, cable lengths, the power allocation coefficients, and input signal-to-noise ratio, is investigated. The results reveal that the performance of the proposed NOMA-PLC scheme is superior compared with that of the CCR-PLC system. It is also shown that the NOMA-PLC system can be more effective in reducing electromagnetic compatibility associated with PLC and that increasing the network branches can considerably degrade the performance. Moreover, optimizing the power allocation coefficients is found to be of utmost importance to maximize the performance of the proposed system.

This paper considers electromagnetic compatibility (EMC) aspects in the context of power line communication (PLC) systems. It offers a complete overview of both narrow band PLC and broad band PLC EMC norms. How to interpret and translate such norms and measurement procedures into typical constraints used by designers of communication systems is discussed. In particular, the constraints to the modulated signal spectrum are considered and the ability of pulse shaped OFDM (PS-OFDM), used in most of the PLC standards as IEEE P1901 and P1901.2, to fulfill them is analyzed. In addition, aiming to improve the spectrum management ability, a novel scheme named pulse shaped cyclic block filtered multitone modulation (PS-CB-FMT) is introduced and compared to PS-OFDM. It is shown that PS-CB-FMT offers better ability to fulfill the norms which translates in higher system capacity.

Supercontinuum generation in a multicore fiber in which several uncoupled cores were doped with dissimilar concentrations of germanium was studied experimentally. Germanium doping provided control over the separation between the zero-dispersion wavelength and the 1064-nm wavelength of a Q-switched Nd:YAG pump laser. Supercontinua generated independently in each core of the same piece of fiber displayed clear and repeatable differences due to the influence of germanium doping on refractive index and four-wave mixing. The spectral evolution of the subnanosecond pump pulses injected into the different cores was accurately reproduced by numerical simulations.