This paper presents a convex recovery method for block-sparse signals whose block partitions are unknown a priori. We first introduce a nonconvex penalty function, where the block partition is adapted for the signal of interest by minimizing the mixed l2/l1 norm over all possible block partitions. Then, by exploiting a variational representation of the l2 norm, we derive the proposed penalty function as a suitable convex relaxation of the nonconvex one. For a block-sparse recovery model designed with the proposed penalty, we develop an iterative algorithm which is guaranteed to converge to a globally optimal solution. Numerical experiments demonstrate the effectiveness of the proposed method.

The strict latency constraints of emerging vehicular applications make it unfeasible to forward sensing data from vehicles to the cloud for processing. To shorten network latency, Vehicular fog computing (VFC) moves computation to the edge of the Internet, with the extension to support the mobility of distributed computing entities. In other words, VFC proposes to complement stationary fog nodes co-located with cellular base stations with mobile ones carried by moving vehicles. Previous works of VFC mainly focus on optimizing the assignments of computing tasks among available fog nodes. However, capacity planning, which decides where and how much capacity to deploy, remains an open and challenging issue. The complexity of this problem comes from the mobility of vehicles, the spatio-temporal dynamics of vehicular traffic, and the computing resource demand generated by varying vehicular applications. To solve the above challenges, we propose a data-driven capacity planning framework that optimizes the deployment of stationary and mobile fog nodes to minimize the installation and operational costs under the quality-of-service constraints, taking into account the spatio-temporal variation in computing demand. Through real-world experiments, we analyze the cost efficiency potential of VFC in long term and demonstrate that the performance loss of VFC is below $6\%$ compared to stationary deployment with equal network capacity. We also analyze the impacts of traffic patterns on the potential cost saving. The results show when the traffic density is higher, more operational costs will be saved in the long run due to more dense deployment of mobile fog nodes.

This paper addresses the issue of reliability in Industrial Internet of Things (IIoT) in case of missing sensors measurements due to network or hardware problems. We propose to support the fault detection and classification modules, which are the two critical components of a monitoring system for IIoT, with a generative model. The latter is responsible of imputing missing sensor measurements so that the monitoring system performance is robust to missing data. In particular, we adopt Generative Adversarial Networks (GANs) to generate missing sensor measurements and we propose to fine-tune the training of the GAN based on the impact that the generated data have on the fault detection and classification modules. We conduct a thorough evaluation of the proposed approach using the extended Tennessee Eastman Process dataset. Results show that the GAN-imputed data mitigate the impact on the fault detection and classification even in the case of persistently missing measurements from sensors that are critical for the correct functioning of the monitoring system.

The formulation and implementation of the Love’s condition constraint for the source reconstruction method (SRM) in near-field antenna measurements is analyzed in the context of inverse problems. To this end, the SRM is first analyzed to identify the non-unique or non-radiating current sources which may be present. Next, the advantages and disadvantages of general regularization techniques, which may address the non-radiating currents, are presented which serve to motivate the use of the Love’s condition constraint. The main methods of formulating the constraint are then presented, one of which is a novel technique developed for this paper. Following this, the formulation methods are analyzed in order to predict the similarities and differences of the methods in the context of addressing the non-radiating currents of the SRM. This analysis is reinforced by simulated antenna measurements. In particular, the novel formulation method is demonstrated to provide greater reconstruction accuracy (in the examples considered), at the cost of computational complexity.

The use of face masks is an important way to fight the COVID-19 pandemic. In this paper, we envision the Smart Mask, an IoT supported platform and ecosystem aiming to prevent and control the spreading of COVID-19 and other respiratory viruses. The integration of sensing, materials, AI, wireless, IoT, and software will help gathering of health data and health-related event detection in real time from the user as well as from their environment. In larger scale, with the help of AI-based analysis for health data it is possible to predict and decrease medical costs with accurate diagnoses and treatment plans, where comparison of personal data to large-scale public data enables drawing up a personal health trajectory, for example. Key research problems for smart respiratory protective equipment are identified in addition to future research directions.

Fretting is a common problem for modular orthopaedic implants which may lead to mechanical failure of the implant or inflammatory tissue responses due excessive release of wear debris. Compressive residual stresses at the contacting surfaces may alleviate the problem. Here we investigate the potential of a surface enhancement method known as low plasticity burnishing (LPB) to increase the fretting resistance of bone-anchored implants for skeletal attachment of limb prostheses. Rotation bending fatigue tests performed on LPB treated and untreated test specimens demonstrate that the LPB treatment leads to statistically significantly increased resistance to fretting fatigue (LPB treated test specimens withstood on average 108 780 load cycles as compared with 37 845 load cycles for untreated test specimens, p = 0.004). LPB treated test specimens exhibited less wear at the modular interface as compared with untreated test specimens. This surface treatment may lead to reduced risk of fretting induced component failure with the consequence of a reduced need for implant revisions.

Left ventricular assist devices (LVADs) are mechanical pumps, which can be used to support heart failure (HF) patients as bridge to transplant and destination therapy. To automatically adjust the LVAD speed, a physiological control system needs to be designed to respond to variations of patient hemodynamics across a variety of clinical scenarios. These control systems require pressure feedback signals from the cardiovascular system. However, there are no suitable long-term implantable sensors available. In this study, a novel real-time deep convolutional neural network (CNN) for estimation of preload based on the LVAD flow was proposed. A new sensorless adaptive physiological control system for an LVAD pump was developed using the full dynamic form of model free adaptive control (FFDL-MFAC) and the proposed preload estimator to maintain the patient conditions in safe physiological ranges. The CNN model for preload estimation was trained and evaluated through 10-fold cross validation on 100 different patient conditions and the proposed sensorless control system was assessed on a new testing set of 30 different patient conditions across six different patient scenarios. The proposed preload estimator was extremely accurate with a correlation coefficient of 0.97, root mean squared error of 0.84 mmHg, reproducibility coefficient of 1.56 mmHg, coefficient of variation of 14.44 %, and bias of 0.29 mmHg for the testing dataset. The results also indicate that the proposed sensorless physiological controller works similarly to the preload-based physiological control system for LVAD using measured preload to prevent ventricular suction and pulmonary congestion. This study shows that the LVADs can respond appropriately to changing patient states and physiological demands without the need for additional pressure or flow measurements.

Traditional routing protocols employ limited information to make routing decisions which leads to slow adaptation to traffic variability and restricted support to the quality of service requirements of the applications. To address these shortcomings, in previous work, we proposed RSIR, a routing solution based on Reinforcement Learning (RL) in SoftwareDefined Networking (SDN). However, RL-based solutions usually suffer an increase in the learning process when dealing with large action and state spaces. This paper introduces a different routing approach called Deep Reinforcement Learning and SoftwareDefined Networking Intelligent Routing (DRSIR). DRSIR defines a routing algorithm based on Deep RL (DRL) in SDN that overcomes the limitations of RL-based solutions. DRSIR considers path-state metrics to produce proactive, efficient, and intelligent routing that adapts to dynamic traffic changes. DRSIR was evaluated by emulation using real and synthetic traffic matrices. The results show that this solution outperforms the routing algorithms based on the Dijkstra’s algorithm and RSIR, in relation to stretching (stretch), packet loss, and delay. Moreover, the results obtained demonstrate that DRSIR provides a practical and viable solution for routing in SDN.

Enabling ultra-reliable low-latency communication (URLLC) with stringent requirements for transmitting data packets (e.g., 99.999% reliability and 1 millisecond latency) presents considerable challenges in uplink transmissions. For each packet transmission over dynamically allocated network radio resources, the conventional random access protocols are based on a request-grant scheme. This induces excessive latency and necessitates reliable control signalling, resulting overhead. To address these problems, grant-free (GF) solutions are proposed in the fifth-generation (5G) new radio (NR). In this paper, an overview and vision of the state-of-the-art in enabling GF URLLC are presented. In particular, we first provide a comprehensive review of NR specifications and techniques for URLLC, discuss underlying principles, and highlight impeding issues of enabling GF URLLC. Furthermore, we briefly explain two key phenomena of massive multiple-input multiple-output (mMIMO) (i.e., channel hardening and favorable propagation) and build several deep insights into how celebrated mMIMO features can be exploited to enhance the performance of GF URLLC. Moving further ahead, we examine the potential of cell-free (CF) mMIMO and analyze its distinctive features and benefits over mMIMO to resolve GF URLLC issues. Finally, we identify future research directions and challenges in enabling GF URLLC with CF mMIMO. A new version of the paper has been updated on 21/08/2021

Radio wave propagation involved in Global Navigation Satellite System Reflectometry (GNSS-R) is subject to atmospheric refraction. Even for ground-based tracking stations, in applications such as coastal sea-level altimetry, the interferometric or reflection-minus-direct effect might be significant. Although atmospheric propagation delays are best investigated numerically via raytracing, including reflections, such a procedure is not trivial. We have developed simpler closed formulas to account for atmospheric refraction in ground-based GNSS-R, validated against independent raytracing. We provide specific expressions for the two components of the atmospheric interferometric delay and corresponding altimetry correction components, parameterized in terms of refractivity and bending angle. Assessment results showed excellent agreement for both components. We define the interferometric slant factor used to map interferometric zenithal delays to individual satellites.

Skeletal attachment of limb prostheses ensures load transfer between the prosthetic leg and the skeleton. For individuals with lower limb amputation, these loads may be of substantial magnitude. To optimize the design of such systems, knowledge about the structural interplay between implant design features, dimensional changes, and material properties of the implant and the surrounding bone is needed. Here, we present the results from a parametric finite element investigation on a generic bone-anchored implant system of screw design, exposed to external loads corresponding to average and high ambulatory loading. Of the investigated parameters, cortical thickness had the largest effect on the stress and strain in the bone-anchored implant and in the cortical bone. 36 % – 44 % reductions in maximum longitudinal stress in the bone-anchored implant was observed as a result of increased cortical thickness from 2 mm to 5 mm. Changes in thread depth had larger effect on the maximum stresses in the fixture and the bone than changes in thread root radius within the evaluated parameter space. Stress reductions in the percutaneous abutment were obtained by autologous transplantation of bone tissue distal to the fixture. Results from this investigation may guide structural design optimization for bone-anchored implant systems for attachment of limb prostheses.

This paper proposes a multifractal model, with the aim of providing a possible explanation for the locality phenomenon that appears in the estimation of the Hurst exponent in stationary second order temporal series representing self-similar traffic flows in current high-speed computer networks. It is shown analytically that this phenomenon occurs if the network flow consists of several components with different Hurst exponents.

Online federated learning (OFL) is a promising framework to learn a sequence of global functions using distributed sequential data at local devices. In this framework, we first introduce a {\em single} kernel-based OFL (termed S-KOFL) by incorporating the random-feature (RF) approximation, online gradient descent (OGD), and federated averaging (FedAvg) properly. However, it is nontrivial to develop a communication-efficient method with multiple kernels. One can construct a multi-kernel method (termed vM-KOFL) by following the extension principle in the centralized counterpart. This vanilla method is not practical as the communication overhead grows linearly with the size of a kernel dictionary. Moreover, this problem is not addressed via the existing communication-efficient techniques in federated learning such as quantization or sparsification. Our major contribution is to propose a novel randomized algorithm (named eM-KOFL), which can enjoy the advantage of multiple kernels while having a similar communication overhead with S-KOFL. It is theoretically proved that eM-KOFL yields the same asymptotic performance as vM-KOFL, i.e., both methods achieve an optimal sublinear regret bound. Mimicking the key principle of eM-KOFL efficiently, pM-KOFL is presented. Via numerical tests with real datasets, we demonstrate that pM-KOFL can yield the same performances as vM-KOFL and eM-KOFL on various online learning tasks while having the same communication overhead as S-KOFL. These suggest the practicality of the proposed pM-KOFL.

The performance of speech coding, speech recognition, and speech enhancement largely depends upon the accuracy of the linear prediction coefficient (LPC) of clean speech and noise in practice. Formulation of speech and noise LPC estimation as a supervised learning problem has shown considerable promise. In its simplest form, a supervised technique, typically a deep neural network (DNN) is trained to learn a mapping from noisy speech features to clean speech and noise LPCs. Training targets for DNN to clean speech and noise LPC estimation fall into four categories: line spectrum frequency (LSF), LPC power spectrum (LPC-PS), power spectrum (PS), and magnitude spectrum (MS). The choice of appropriate training target as well as the DNN method can have a significant impact on LPC estimation in practice. Motivated by this, we perform a comprehensive study on the training targets using two state-of-the-art DNN methods— residual network and temporal convolutional network (ResNet-TCN) and multi-head attention network (MHANet). This study aims to determine which training target as well as DNN method produces more accurate LPCs in practice. We train the ResNet-TCN and MHANet for each training target with a large data set. Experiments on the NOIZEUS corpus demonstrate that the LPC-PS training target with MHANet produces a lower spectral distortion (SD) level in the estimated speech LPCs in real-life noise conditions. We also construct the AKF with the estimated speech and noise LPC parameters from each training target using ResNet-TCN and MHANet. Subjective AB listening tests and seven different objective quality and intelligibility evaluation measures (CSIG, CBAK, COVL, PESQ, STOI, SegSNR, and SI-SDR) on the NOIZEUS corpus demonstrate that the AKF constructed with MHANet-LPC-PS driven speech and noise LPC parameters produced enhanced speech with higher quality and intelligibility than competing methods.

The exploitation of power inductors outside their linear region in switching converters can be achieved by raising the current until a decreasing of the inductance can be noticed. It allows using a smaller magnetic core increasing the power density of the converter. On the other hand, a detailed description of the magnetization curve including the temperature is required. Since this information is often not included in the inductor’s datasheets, this paper shows how to identify the behavior of an inductor when it is operated up to saturation and its temperature rises. In order to characterize the inductor in real operating conditions, a dedicated measurement rig has been developed. It consists of a switching converter that encompasses the inductor under test and is controlled by a virtual instrument developed in LabVIEW. The characterization system was tested by retrieving the inductance and the magnetization curves vs. current for two commercial inductors at core tem-peratures up to 105°C. The magnetic core is then characterized by the saturation current versus inductance, obtaining an expression for the whole family of inductor sharing the same core. Finally, we analyzed experimentally the thermal transient of the inductors in operating conditions con-firming the fundamental role of temperature in changing the current profiles and the core saturation condition.

Future wireless networks provide research challenges with many fold increase of smart devices and the exponential growth in mobile data traffic. The advent of highly computational and real-time applications cause huge expansion in traffic volume. The emerging need to bring data closer to users and minimizing the traffic off the macrocell base station (MBS) introduces the use of caches at the edge of the networks. Storing most popular files at the edge of mobile edge networks (MENs) in user terminals (UTs) and small base stations (SBSs) caches is a promising approach to the challenges that face data-rich wireless networks. Caching at the mobile UT allows to obtain requested contents directly from its nearby UTs caches through the device-to- device (D2D) communication. In this survey article, solutions for mobile edge computing and caching challenges in terms of energy and latency are presented. Caching in MENs and comparisons between different caching techniques in MENs are presented. An illustration of the research in cache development for wireless networks that apply intelligent and learning techniques (ILTs) in a specific domain in their design is presented. We summarize the challenges that face the design of caching system in MENs. Finally, some future research directions are discussed for the development of cache placement and cache access and delivery in MENs.

In terms of Machine Learning, the majority of the data can be grouped into four categories: numerical data, category data, time-series data, and text. We use different classifiers for different data properties, such as the Supervised; Unsupervised; and Reinforcement. Each Categorises has classifier we have tested almost all machine learning methods and make analysis among them.

We propose a novel concept for the implementation of 2-dimensional (2D) optical phased arrays (OPAs) with end-fire waveguides as antenna elements (AEs), and we present its theoretical model and experimental proof. The concept is based on the use of 3-dimensional (3D) photonic integrated circuits (PICs) with multiple waveguiding layers on the PolyBoard platform. In their simplest form, the 3D PICs comprise AEs at different layers, vertical and lateral couplers for the distribution of light among the AEs, and phase shifters for the execution of the 2D beam scanning process. Using the field equivalence principle, we model the radiated field from the single-mode waveguide of the platform at 1550 nm, and we find that the expected beam width is 12.7o. We also investigate the perturbation that is induced into propagating fields inside parallel waveguides in proximity, and we conclude that waveguide spacings down to 6 µm can be safely used for development of uniform OPAs in the PolyBoard platform. For OPAs with 6 µm pitch and 4 AEs, we find that the maximum steering angle is 14.0o and the expected angular clearance, wherein the main radiation lobe is higher than any grating lobe by at least 3, 6 and 10 dB is 10.8o, 7.6o and 2.8o, respectively. Based on our simulations, we design and fabricate single- and 2-layer PICs with 1×4 and 2×4 OPAs. The lateral pitch of the OPAs ranges from 10 down to 6 µm, while the vertical pitch is 7.2 µm. We experimentally characterize these OPAs and validate the potential of the 2-layer PICs for 2D beam scanning on the azimuthal and elevation plane. The beam profiles and the main scanning parameters such as the maximum steering angle and the relative intensity between the main and the grating lobes are found in excellent agreement with our simulations.