Decades of advances in computer architecture, software-intensive applications and system integration have created significant challenges for embedded systems designers and test engineers. Intrusive software instrumentation and breakpoint-based debugging are often viewed as the primary options for observing operational system internals. This narrow sight creates complicated test flows and convoluted debugging procedures. Modern embedded computing systems offer Embedded Trace as the technological answer to the encountered observability conundrum. Although an integral part of virtually all modern processors, it is frequently overlooked. Its technical foundations are little known to application engineers, test engineers, and project managers. This article explains Embedded Trace as an essential technology in the testing and debugging toolbox. It highlights its capabilities, limitations and opportunities.

We theoretically investigate and optimize the performance of four-wave mixing (FWM) in microring resonators (MRRs) integrated with two-dimensional (2D) layered graphene oxide (GO) films. Owing to the interaction between the MRRs and the highly nonlinear GO films as well as to the resonant enhancement effect, the FWM efficiency in GO-coated MRRs can be significantly improved. Based on previous experiments, we perform detailed analysis for the influence of the GO film parameters and MRR coupling strength on the FWM conversion efficiency (CE) of the hybrid MRRs. By optimizing the device parameters to balance the trade-off between the Kerr nonlinearity and loss, we achieve a high CE enhancement of ~18.6 dB relative to the uncoated MRR, which is ~8.3 dB higher than previous experimental results. The influence of photo-thermal changes in the GO films as well as variations in the MRR parameters such as the ring radius and waveguide dispersion on the FWM performance is also discussed. These results highlight the significantly improved FWM performance that can be achieved in MRRs incorporating GO films and provide a guide for optimizing their FWM performance.

All experiments are implemented in Python, using the PyTorch and the Torch-DCT libraries under the Google Colab environment. The Intel(R) Xeon(R) CPU @ 2.00GHz and a Tesla V100-SXM2-16GB GPU were assignment to the Google Colab runtime when profiling the DOT models. It should be noted that the current stable version of the PyTorch library, version 1.8.1, offers only the implementation of the FFT algorithm. Therefore, the implementations of the Hartley and Cosine transforms, listed in Table 1, are not implemented using the same optimizations (algorithm and code wise) adopted in the FFT. We benchmark the DOT methods using the LENET-5 network shown in Figure 10. The ReLU activation function is adopted a non-linear operation across the entire architecture. In this network, the convolutional operations have a kernel of size K = 5. The convolution is of type “valid”, i.e., padding is not applied to the input. Hence the output size M of each layer is smaller than its input size N, that is M=N−K+1. The optimizers used in our experiments are Adam, SGD, SGD with Momentum of 0.9, and RMSProp with α = 0.99. The StepLR scheduler is used with a step size of 20 epochs and a γ = 0.5. We train our model for 40 epochs using a mini-batch of size 128 and a learning rate of 0.001. Five datasets are used in order to benchmark the proposed DOT methods. Among them, we have the MNIST dataset and some variants of the MNIST dataset such as EMNIST, KMNIST and Fashion-MNIST. Additionally, a more complex dataset, CIFAR-10 is also used in our benchmark.

Cloud computing depends on the dynamic allocation and release of resources, on demand, to meet heterogeneous computing needs. This is challenging for cloud data centers, which process huge amounts of data characterised by its high volume, velocity, variety and veracity (4Vs model). Managing such a workload is increasingly difficult using state-of-the-art methods for monitoring and adaptation, which typically react to service failures after the fact. To address this, we seek to develop proactive methods for predicting future resource exhaustion and cloud service failures. Our work uses a realistic test bed in the cloud, which is instrumented to monitor and analyze resource usage. In this paper, we employed the optimal Kalman filtering technique to build a predictive and analytic framework for cloud server KPIs, based on historical data. Our k-step-ahead predictions on historical data yielded a prediction accuracy of 95.59%. The information generated from the framework can best be used for optimal resources provisioning, admission control and cloud SLA management.

This document provides a formal proof and supple- mentary information of the paper: Class-Incremental Learning for Wireless Device Identification in IoT. The original paper focuses on providing a novel and efficient incremental learning algorithm. In this document, we explicitly explain why the mem- ory representations (latent device fingerprints in our application) in Artificial Neural Networks approximate orthogonality with insights for the invention of our Channel Separation Incremental Learning algorithm.

Cyber-physical systems (CPSs) are complex and exhibit both continuous and discrete dynamics, hence it is difficult to guarantee that they satisfy given specifications, i.e., the properties that must be fulfilled by the system. Falsification of temporal logic properties is a testing approach that searches for counterexamples of a given specification, which can be used to increase the confidence that a CPS does fulfill its specifications. Falsification can be done using random search methods or optimization methods. In this paper, a method based on combining random parameters together with considering extreme combinations of parameter values is proposed. Evaluation results on benchmark problems show that this method performs well on many of the problems. Optimization methods are needed when optimization-free methods do not perform well in falsification. The efficiency of the falsification is affected by the optimization methods used to search for inputs that might falsify the specifications. This paper presents a new optimization method for falsification, Line-search falsification, where optimization is done over line segments through a vector of inputs in the n-dimensional parameter space. The evaluation results on the benchmark problems show that using this method improves the falsification performance by reducing the number of simulations necessary to falsify a specification.

In this paper, we present the ergodic sum secrecy rate (ESSR) analysis of an underlay spectrum sharing non-orthogonal multiple access (NOMA) system. We consider the scenario where the power transmitted by the secondary transmitter (ST) is constrained by the peak tolerable interference at multiple primary receivers (PRs) as well as the maximum transmit power of the ST. The effect of channel estimation error is also taken into account in our analysis. We derive exact and asymptotic closed-form expressions for the ESSR of the downlink NOMA system, and show that the performance can be classified into two distinct regimes, i.e., it is dictated either by the interference constraint or by the power constraint. Our results confirm the superiority of the NOMA-based system over its orthogonal multiple access (OMA) based counterpart. More interestingly, our results show that NOMA helps in maintaining the secrecy rate of the strong user while significantly enhancing the secrecy performance of the weak user as compared to OMA. The correctness of the proposed investigation is corroborated through Monte Carlo simulation.

In the age of next-generation computer, the role of the cloud, the internet and smart devices will become stronger. These days we all know the word smart well. This word is often used in our daily lives. The Internet of Things (IoT) will generate a variety of information from a variety of resources. It can store big data in the cloud. Fog computing acts as a signal between cloud and IoT. Fog extensions in this framework apply to material under IoT. IoT devices are called Fog nodes, which can be accessed anywhere within the network range. A blockchain is a novel way of recording in a secure sequence. Creating a new framework in the development of Internet of Things is one of the critical problems of wireless communication where solving such a problem can lead to continued growth in the use and popularity of IoT. Proposed research creates a framework for providing a framework for middleware on the internet of smart devices network for the internet of things using blockchains technology. Our great offering connects new research that integrates blockchains into the Internet of Things and provides secure Internet connection for smart devices. Blockchain (BC) Internet of Things (IoT) is a new technology that works with low-level, distributed, public and real-time leaders to maintain transactions between IoT sites. A blockchain is a series of blocks, each block being linked to its previous blocks. All blocks have cryptographic hash code, previous block hash, and its data. Transactions in BC are the basic components used to transfer data between IoT nodes. IoT nodes are a variety of portable but smart devices with embedded sensors, actuators, systems and the ability to communicate with other IoT nodes. The role of BC in IoT is to provide a process for processing secure data records using IoT nodes. BC is a protected technology that can be used publicly and openly. IoT requires this type of technology to allow secure communication between IoT nodes in different environments. Events in BC can be tracked and monitored by anyone who is certified to communicate within IoT.

This paper presents a quantum-assisted index modulation for next-generation IoT wireless networks. The NP-hard index selection problem is first formulated by a quadratic unconstrained binary optimization (QUBO) problem consisting of constraints of feasible solutions. To minimize the number of qubits required for a quantum circuit, this formulation is then simplified by a dictionary-based approach that partially exploits a classical computer. For both formulations, the numbers of required qubits and non-zero elements in QUBO matrices are analyzed algebraically, and found to be in close agreement with the actual measurement. It is observed that the Grover adaptive search can provide the quantum speedup for the index selection problem. This promising result implies that the on-off structure of index modulation is suitable for quantum computation, and future fault-tolerant quantum computers may be useful for obtaining high-performance index activation patterns. (c) IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Naoki Ishikawa, “Quantum speedup for index modulation,” IEEE Access, vol. 9, pp. 111114-111124, Aug. 2021. DOI: 10.1109/ACCESS.2021.3103207

Denoising of signals in an Internet-of-Things (IoT) network is critically challenging owing to the diverse nature of the nodes generating them, environments through which they travel, characteristics of noise plaguing the signals and the applications they cater to. In order to address the abovementioned challenges, we conceptualize a generalized framework combining wavelet packet transform (WPT) and energy correlation analysis. WPT decomposes both the low-frequency and high-frequency components of the received signals in different time scales and wavelet spaces. Noise components are identified, removed through filtering and the signal components are predicted back after filtering using inverse wavelet packet transform (IWPT). Next energy of the reconstructed signal components are compared with that of the original transmitted signal to modify the characteristics of the decomposed signal components. Using the modified details, the signal components are reconstructed back again and the noise components are filtered out. This process is repeated until noise is completely removed. Initial results suggest that, our proposed framework offers improvement in error probability performance of a medium-scale IoT network over traditional discrete wavelet transform (DWT) and WPT based techniques by around 3 dB and 7 dB respectively.

A Digital Twin is defined as a digital replica of a real entity in the physical world. In this study, the Digital Twin simulation is developed for connected and automated vehicles (CAVs) by leveraging the Unity game engine. A Digital Twin simulation architecture is proposed, which contains the physical world and the digital world. Particularly, the digital world consists of three layers, where the Unity game objects are built to simulate the “hardware”, the Unity scripting API are used to simulate the “software”, and external tools (e.g., SUMO, MATLAB, python, and/or AWS) are leveraged to enhance the simulation functionalities. A case study of personazlied adaptive cruise control (P-ACC) is conducted to showcase the effectiveness of the proposed Digital Twin simulation, where the ACC system can be designed to satisfy each driver’s preference with the help of cloud computing.

Identification of when and where moving areas intersect is an important problem in maritime operations and air traffic control. This problem can become particularly complicated when considering large numbers of objects, and when taking into account the curvature of the earth. In this paper, we present an approach to conflict identification as a series of stages where the earlier stages are fast, but may result in a false detection of a conflict. These early stages are used to reduce the number of potential conflict pairs for the later stages, which are slower, but more precise. The stages use R-trees, polygon intersection, linear projection and nonlinear programming. Our approach is generally applicable to objects moving in piece-wise straight lines on a 2D plane, and we present a specific case where the Mercator Projection is used to transform objects moving along rhumb lines on the earth into straight lines to fit in our approach. We present several examples to demonstrate our methods, as well as to quantify the empirical time complexity by using randomly generated areas.

In the last years, temporal metamaterials have been exploited as a novel platform for conceiving several electromagnetic and optical devices based on the anomalous scattering response achieved at a single or multiple sudden change of their medium properties, which however is difficult to carry out in a realistic scenario In this letter we investigate on the possibility to emulate the scattering response of a temporal metamaterial without acting on the medium properties, but on the effective refractive index and wave impedance perceived by the wave during the propagation within a guiding structure rather than the actual material, by acting on its boundaries. The work presents in closed form the scattering coefficients achieved when the boundary properties are suddenly modified for inducing an effective temporal interface. In this framework, temporally controlled metasurfaces can be used to implement the proposed concept, giving an easier path also to the design and realization of novel devices at microwave and optical frequencies.

This paper proposes a new method for very fast extraction of symmetrical components.

Free-space optical (FSO) systems are able to offer the high data-rate, secure, and cost-efficient communication links required for applications such as wireless front- and backhauling for 5G and 6G communication networks. Despite the substantial advancement of FSO systems over the past decades, the requirement of a line-of-sight connection between transmitter and receiver remains a key limiting factor for their deployment. In this paper, we discuss the potential role of intelligent reflecting surfaces (IRSs) as a solution to relax this requirement. We present an overview of existing optical IRS technologies; compare optical IRSs with radio-frequency IRSs and optical relays; and identify various open problems for future research on IRS-assisted FSO communications.

In cross-chain scenarios, there are different blockchains, which need to cooperate. Cooperation among different blockchains is done by smart contracts that work together to complete cross-chain tasks. When numerous cooperative smart contracts are involved, smart contracts form a complex interaction network, which makes it difficult to evaluate the cooperation. It needs a common model to quantitatively analyze the cross-chain cooperation of associated smart contracts. In this paper, we model the cooperation among smart contracts as conditions and their corresponding actions, the condition-trigger model. Then we propose the method to calculate the cooperation probabilities by the graph weight. As the edge weight lacks the information of interaction probabilities, we introduce the dimension of the edge weight to calculate the probabilities. Finally, we verify the proposed condition-trigger model and its different types. It demonstrates that our proposed methods can effectively analyze the cross-chain cooperation among smart contracts.

Multi-access edge computing (MEC) has been recently considered in challenging environments lacking available terrestrial infrastructures by extending the computing resources to the air for further enhancing the computation capability of the new aerial user equipment (AUE). Additionally, wireless power transfer (WPT) is a promising solution to prolong the battery lifetime of energy-constrained wireless devices like AUEs. In this paper, we investigate the integration of laser-beamed WPT in the high-altitude platform (HAP) aided MEC systems for the HAP-connected AUEs. By discretizing the three-dimensional coverage space of the HAP, we present a multi-tier tile grid-based spatial structure to provide aerial locations for laser charging. With this setup, we identify a new privacy vulnerability caused by the openness during the air-to-air transmission of WPT signaling messages in the presence of a terrestrial adversary. A privacy-aware laser-powered aerial MEC framework is developed that addresses this vulnerability and enhances the location privacy of AUEs for laser WPT. Specifically, the interaction between the HAP as a defender and the adversary in their tile grid allocation as charging locations to AUEs is formulated as a Colonel Blotto game, which models the competition of the players for limited resources over multiple battlefields for a finite time horizon. Moreover, we derive the mixed-strategy Nash equilibria of the tile grid allocation game for both symmetric and asymmetric tile grids between the defender and the adversary. Simulations results show that the proposed framework significantly outperforms the design baselines with a given privacy protection level in terms of system-wide expected total utilities.

This manuscript investigates adaptive Kalman filter problem of of linear systems with multiplicative and additive noises. The main contributions are stated in two aspects. Firstly, compared with the estimation problem of linear systems with additive noises, we propose an algorithm that is applicable to the linear systems with both additive and multiplicative noises. To solve the technical issue raised by the multiplicative noise, a variational Bayesian approach is proposed. Moreover, the proposed approach is capable of estimating the multiplicative and additive measurement noise covariances as a whole, while the existing algorithms often operate in a separate way. Secondly, in contrast with existing literature, where the covariance of the multiplicative noise is assumed to be fixed and known, we focus on the situation that the covariances of both additive and multiplicative noises are time-varying and unknown. Towards this end, a novel adaptive Kalman filter is proposed to jointly estimate the covariances of multiplicative and additive noises based on projection formula and a VB approach.