The problem of stability analysis for a class of nonlinear discrete time systems with time varying delay is studied in this work. Such systems are modeled by delayed difference equations. Subsequently, this system is transformed into an arrow form matrix representation. Using M-matrix properties, novel sufficient stability conditions are determined. It is shown how to use our method to design a state feedback controller that stabilizes a discrete time Lure system with time varying delay and sector bounded nonlinearity. The originalities of our findings are shown in their explicit representation, using system’s parameters, as well as in their easiness to be employed. The obtained results demonstrate also that checking stability of a nonlinear discrete time systems with time varying delay can be reduced to an M-matrix test. Several examples are provided to show the effectiveness of the introduced technique.

A novel framework for record-oriented storage system for wireless sensor nodes. The proposed approach has a tiny memory footprint, and that is very useful for memory-constrained sensor nodes. It can be built for any byte-addressable memory.

The emergence of blockchain interoperability is reducing the risk of investing in blockchain by avoiding vendor lock-in, leveraging interoperation, and providing migration capabilities. However, to fully unlock the internet of blockchains, it is necessary to provide enterprise interoperability mechanisms that allow service providers to comply with different regulations, e.g., data privacy regulations. Each blockchain can be reached via a gateway, allowing to interconnect value, to provide different services, and to enable self-sovereignty. To realize this vision, we propose Hermes, a fault-tolerant middleware that connects blockchain networks and is based on the Open Digital Asset Protocol (ODAP). Hermes is crash fault-tolerant by allying a new protocol, ODAP-2PC, with a log storage API that can leverage blockchain to secure logs, providing them transparency, auditability, availability, and non-repudiation. We introduce a use case benefiting from Hermes, digital cross-jurisdiction promissory notes. We show that cross-chain transactions can be achieved securely with Hermes, given that gateways are complying with legal frameworks.

Fractional calculus can be regarded as an important supplement to integer calculus, and has been gradually applied in physics, engineering and so on. In this paper, we define the fractional magnetic gradient tensor of a magnetic dipole, and derive its analytic expressions by using the rule of the composition of fractional-order and integer-order derivatives. Then we verify the analytic expressions by comparing with the results of the numerical method. When the order α of fractional derivatives approaches zero, the fractional magnetic gradient tensor of a magnetic dipole becomes the matrix composed of three magnetic field components. When α approaches one, the fractional magnetic gradient tensor becomes the standard magnetic dipole tensor. This trend shows that fractional derivatives and integer derivatives are consistent. Magnetic gradient tensors have larger attenuation with higher derivative orders when increasing the distance between the observation point and the magnetic source. Therefore, the limited resolution of the magnetic sensors causes a large blind area in a survey, which can be compensated by measuring the fractional magnetic gradient tensor. In addition, each component of the fractional tensor is independent and has great potential of solving the multiple solution problems of the localization of a magnetic dipole.

ob-shop scheduling is an important but difficult problem arising in low-volume high-variety manufacturing. It is usually solved at the beginning of each shift with strict computational time requirements. For fast resolution of the problem, a promising direction is to formulate it in an Integer Linear Programming (ILP) form so as to take advantages of widely available ILP methods such as Branch-and-Cut (B&C). Nevertheless, computational requirements on ILP methods for existing ILP formulations are high. In this paper, a novel ILP formulation is presented. In the formulation, a set of binary indicator variables indicating whether an operation begins at a time slot on a machine group or not is selected as decision variables, and all constraints are innovatively formulated based on this set of variables. For fast resolution of large problems, our recent decomposition-and-coordination method “Surrogate Absolute-Value Lagrangian Relaxation” (SAVLR) is enhanced by using a 3-segment piecewise linear penalty function, which more accurately approximates a quadratic penalty function as compared to an absolute-value function. Testing results demonstrate that our new formulation drastically reduces the computational requirements of B&C as compared to our previous formulation. For large problems where B&C has difficulties, near-optimal solutions are efficiently obtained by using the enhanced SAVLR under the new formulation.

In recent years, the number of IoT devices has increased substantially. One of the main concerns is the interaction with high availability. While peer-to-peer (P2P) technology has been used to achieve high availability by software running in P2P hardware, a single software still acts as the center for the interaction. If the software has bugs or is out of service, all devices are affected. Aiming at this issue, this paper proposes a fully decentralized model as a high-availability solution, in which P2P software runs on a P2P hardware environment. P2P software is a set of independent software from respective participants (IoT devices) that can work cooperatively without centralized software. We use blockchain as a P2P platform because it has P2P nodes and a trustless environment, so the P2P software in this paper is P2P smart contracts. To pair cooperative smart contracts, each P2P smart contract has requirements on other smart contracts. If several smart contracts matches the requirements mutually, they are treated as a work group (a topic), and then they process each other’s requests within that group. When a single smart contract fails, the paired P2P software can be re-selected, resulting in high availability. Simulation results show that the proposed model can effectively eliminate the impact of the failure of any single smart contract.

A wireless sensor network (WSN) assisted by Unmanned Aerial Vehicles (UAVs) can be used to monitor various phenomena in remote, extensive, inaccessible, or dangerous places. The WSN on the ground can provide close-to-the-scene sensing, in-network data processing, and multi-hop communication. The UAVs can interface the ground network with a remote control station or facilitate fast and flexible data collection. To this end, the aerial links established between the UAVs and the WSN are critical. The reliability of the links depends on many factors, including Cross Technology Interference (CTI), the relative distance of the UAVs from the ground nodes, the drive quality of the UAVs, and noise. In this paper, we present experimental results addressing some of these issues. Our experiments consisted of eleven IEEE 802.15.4 compliant transceivers, nine of which were deployed on the ground in a grid topology with a further two being attached to a UAV. From careful examination of traces extracted from received packets we concluded that CTI is the most significant factor affecting the quality of aerial links. Our observations have application for deployment related decision-making and for the design of UAV-assisted data collection protocols.

We review recent work on broadband RF channelizers based on integrated optical frequency Kerr micro-combs combined with passive micro-ring resonator filters, with microcombs having channel spacings of 200GHz and 49GHz. This approach to realizing RF channelizers offers reduced complexity, size, and potential cost for a wide range of applications to microwave signal detection.

The Multiple Travelling Salesman Problem (MTSP) is among the most interesting combinatorial optimization problems because it is widely adopted in real-life applications, including robotics, transportation, networking, etc.Although the importance of this optimization problem, there is no survey dedicated to reviewing recent MTSP contributions. In this paper, we aim to fill this gap by providing a comprehensive review of existing studies on MTSP. In this survey, we focus on MTSP’s recent contributions to both classical vehicles/robots and unmanned aerial vehicles. We highlight the approaches applied to solve the MTSP as well as its application domains. We analyze the MTSP variants and propose a taxonomy and a classification of recent studies.

Soiling can cause significant losses to photovoltaic systems, and therefore it is often measured for the purposes of predicting long-term energy forecasts or for monitoring real time performance and triggering maintenance events as needed. Currently, the most common soiling monitoring technologies are soiling stations that use the electrical outputs of a regularly cleaned PV device and of a naturally soiled PV device to quantify soiling. As part of a new class of low-cost and low-maintenance soiling stations NREL has previously presented “DUSST”. DUSST projects a collimated monochromatic light source through a glass surface (exposed similarly to the PV modules that need to be monitored) and on to a light detector to measure the intensity of the transmitted light. As the glass surface naturally soils, the losses are quantified by comparing this soiled reading with a calibrated reading under baseline clean condition. This work presents the ongoing improvement of DUSST and the ongoing indoor and outdoor validation of this new soiling sensor.

Integrated Kerr micro-combs are a powerful source of multiple wavelength channels for photonic radio frequency (RF) and microwave signal processing, particularly for transversal filter systems. They offer significant advantages featuring a compact device footprint, high versatility, large numbers of wavelengths, and wide Nyquist bands. We review progress photonic RF and microwave high bandwidth temporal signal processing based on Kerr micro-combs with comb spacings from 49GHz to 200GHz. We focus on integral and fractional Hilbert transforms, differentiators as well as integrators. The future potential of optical micro-combs for RF photonic applications in terms of functionality and ability to realize integrated solutions is also discussed.

Accepted Manuscript (Postprint): L. Micheli et al., “Improved PV Soiling Extraction through the Detection of Cleanings and Change Points,” IEEE Journal of Photovoltaics, Volume: 11, Issue: 2, March 2021.

We integrate GO films with silicon-on-insulator nanowires (SOI), high index doped silica glass (Hydex) and silicon nitride (SiN) waveguides and ring resonators, to demonstrate an enhanced optical nonlinearity including Kerr nonlinearity and four-wave mixing (FWM).

The “screening” trend of modern society has been progressively increasing burden on human visual system, and visual fatigue problems are attracting growing attention. Nowadays, subjective testing is the most widely used measurement for visual fatigue; however, the low accuracy of subjective testing has been hindering its further development. Motivated by the idea of weighted scoring, this study investigated the effects of two weighted scales for measuring visual fatigue in screening tasks. Specifically, a questionnaire with 10 items collected from classic scales was performed with an eye-tracking testing in two typical screen visual fatigue experiments, i.e., searching and watching. Then the subjective scores were factor-analyzed into three subscales before attempting linear regressions, which set the dependent to two previously validated eye-tracking parameters, i.e., fixation frequency or saccade amplitude. Finally two weighted scales were obtained in assessing visual fatigue of varying levels, which demonstrated the potential to improve testing accuracy of visual fatigue with the calibration of objective measurement.

The lamprey is one of the most ancient of extant vertebrate species. It has changed relatively little in 450 million years and is considered a prototype for all vertebrates. Its primitive nervous system has been studied extensively, and the basic architecture of the central pattern generator (CPG) that produces its undulatory swimming motion is well known. Here it is shown that each segmental component of the lamprey’s CPG is a JK flip-flop, with additional excitatory inputs and feedback that cause all of the neurons’ states to oscillate. The JK flip-flop is the most widely used flip-flop design in modern electronic computational systems because of its advantageous features. This is apparently the first discovery that a known network of neurons functions as a logic circuit. The lamprey’s oscillator design is apparently new to engineering, making it an example of neuroscience and logic circuit design informing each other. A simulation based on simple neuron responses to excitation and inhibition illustrates the common period, phase relationships, and burst durations of the segmental cells’ oscillations. Simulation software for electronic logic circuits verifies the simulated neuron responses, on vastly different time scales. The simulation methods presented here may aid in further study of CPG neurophysiology. The novel architecture of the oscillating JK flip-flop may aid in the development of artificial neural network applications such as robotics.

Deep learning approaches have attracted a lot of attention in the automatic detection of Covid-19 and transfer learning is the most common approach. However, majority of the pre-trained models are trained on color images, which can cause inefficiencies when fine-tuning the models on Covid-19 images which are often grayscale. To address this issue, we propose a deep learning architecture called CovidNet which requires a relatively smaller number of parameters. CovidNet accepts grayscale images as inputs and is suitable for training with limited training dataset. Experimental results show that CovidNet outperforms other state-of-the-art deep learning models for Covid-19 detection.

Optical artificial neural networks (ONNs) have significant potential for ultra-high computing speed and energy efficiency. We report a novel approach to ONNs that uses integrated Kerr optical micro-combs. This approach is programmable and scalable and is capable of reaching ultra-high speeds. We demonstrate the basic building block ONNs — a single neuron perceptron — by mapping synapses onto 49 wavelengths to achieve an operating speed of 11.9 x 109 operations per second, or Giga-OPS, at 8 bits per operation, which equates to 95.2 gigabits/s (Gbps). We test the perceptron on handwritten-digit recognition and cancer-cell detection — achieving over 90% and 85% accuracy, respectively. By scaling the perceptron to a deep learning network using off-the-shelf telecom technology we can achieve high throughput operation for matrix multiplication for real-time massive data processing.

The extrapolated kernel least mean square algorithm (extrap-KLMS) with memory is proposed for the forecasting of future trends of COVID-19. The extrap-KLMS is derived in the framework of data-driven modelling that attempts to describe the dynamics of infectious disease by reconstructing the phase-space of the state variables in a reproducing kernel Hilbert space (RKHS). Short-time forecasting is enabled via an extrapolation of the KLMS trained model using a forward euler step, along the direction of a memory-dependent gradient estimate. A user-defined memory averaging window allows users to incorporate prior knowledge of the history of the pandemic into the gradient estimate thus providing a spectrum of scenario-based estimates of futures trends. The performance of the extrap-KLMS method is validated using data set for Malaysia, Saudi Arabia and Italy in which we highlight the flexibility of the method in capturing persistent trends of the pandemic. A situational analysis of the Malaysian third wave further demonstrate the capabilities of our method