In this paper, a novel optical rotary sensor based on laser self-mixing interferometry is developed for the full-circle rotation measurement. The proposed sensor is convenient to use for it does not need any contact with the target or a cooperative mirror. A prototype is fabricated and tested. The measured results demonstrate a good performance compared with other optical rotary sensors, in terms of the 0.1 μrad resolution, the 2.33×10-4 linearity and 2 μrad stability over one hour. Additionally, the repeatability error is below 14.66 mrad under 9-group full-circle tests, which exhibits the potential to be instrumentalized reliably. Error analysis and limitation discussion have been also carried out. Although the accuracy needs further improvement compared with the best rotary sensor, this method has its unique advantages of high resolution, non-cooperative target sensing and electromagnetic immunity. Hence, the proposed optical rotary sensor provides a promising alternative in precise rotation measurement, tremor tracing and nano-motion monitoring. In this paper, a novel optical rotary sensor based on laser self-mixing interferometry is developed for the full-circle rotation measurement. The proposed sensor is convenient to use for it does not need any contact with the target or a cooperative mirror. A prototype is fabricated and tested. The measured results demonstrate a good performance compared with other optical rotary sensors, in terms of the 0.1 μrad resolution, the 2.33×10-4 linearity and 2 μrad stability over one hour. Additionally, the repeatability error is below 14.66 mrad under 9-group full-circle tests, which exhibits the potential to be instrumentalized reliably. Error analysis and limitation discussion have been also carried out. Although the accuracy needs further improvement compared with the best rotary sensor, this method has its unique advantages of high resolution, non-cooperative target sensing and electromagnetic immunity. Hence, the proposed optical rotary sensor provides a promising alternative in precise rotation measurement, tremor tracing and nano-motion monitoring.

Estimation of unknown noise covariances in a Kalman filter is a problem of significant practical interest in a wide array of applications. Although this problem has a long history, reliable algorithms for their estimation were scant, and necessary and sufficient conditions for identifiability of the covariances were in dispute until recently. Necessary and sufficient conditions for covariance estimation and a batch estimation algorithm. This paper presents stochastic gradient descent (SGD) algorithms for noise covariance estimation in adaptive Kalman filters that are an order of magnitude faster than the batch method for similar or better root mean square error (RMSE) and are applicable to non-stationary systems where the noise covariances can occasionally jump up or down by an unknown magnitude. The computational efficiency of the new algorithm stems from adaptive thresholds for convergence, recursive fading memory estimation of the sample cross-correlations of the innovations, and accelerated SGD algorithms. The comparative evaluation of the proposed method on a number of test cases demonstrates its computational efficiency and accuracy.

Dipole antenna is the simplest type of antenna from the theoretical point of view. However, the majority of the research on it focuses on adding supplementary structures to produce more resonant modes to achieve more functions. Here, we focus on the inherent modes of the dipole itself. Dipole antenna has alternating odd and even modes. In classical antenna theory, only odd modes are excited. The even modes are difficult to be excited due to their very high input impedances. In this paper, both odd and even modes of the dipole antenna are excited simultaneously by a single port with a novel feeding structure. Moreover, in order to tune the resonant frequencies of such multiple modes, a method by combining antithetical structures of indentations and outdentations is proposed. Its principle is explained intuitively by eigencurrent distribution. By these new techniques, all the first six modes of a dipole are excited, among which five modes are utilized for IoT applications. The antenna can cover ISM bands of 868 MHz and 2450 MHz, GNSS band of 1575 MHz, and 5G bands of 3500 MHz and 4900 MHz. This research could broaden the application of this classic antenna.

In this work, we introduce Log(v) 3LPF, a linear power flow solver for unbalanced three-phase distribution systems. Log(v) 3LPF uses a logarithmic transform of the voltage phasor to linearize the AC power flow equations around the balanced case. We incorporate the modeling of ZIP loads, transformers, capacitor banks, switches and their corresponding controls and express the network equations in matrix-vector form. With scalability in mind, special attention is given to the computation of the inverse of the system admittance matrix, Ybus. We use the Sherman-Morrison-Woodbury identity for an efficient computation of the inverse of a rank-k corrected matrix and compare the performance of this method with traditional LU decomposition methods in terms of FLOPS. We showcase the solver for a variety of network sizes, ranging from tens to thousands of nodes, and compare the Log(v) 3LPF with commercial-grade software, such as OpenDSS.

This paper proposes a novel approach to forecast congestions in high-voltage grids with high shares of distributed photovoltaic (PV) infeed. The approach is based on a physical PV model using intra-day numerical weather prediction (NWP) input data. Subsequently, probabilistic forecasts are generated based on Kernel density estimators (KDE) and Copula, describing the multivariate spatial dependencies for the marginal distributions of forecasting and approximation errors. Finally, a probabilistic power flow (PPF) using a linearized AC version is proposed, combining the benefits of high accuracy with high computational performance. To assess and quantify the overall advantages of this approach, a case study is carried out for an existing power system.

In this work, we have proposed and experimentally demonstrated a true full-duplex IO (TFD-IO) for high-speed high-density interfaces. The proposed TFD-IO can be used as an independent module that converts a unidirectional IO/interconnect to a fully bidirectional IO/interconnect, to ideally double the throughput of the high-speed interface. The TFD-IO uses a correlation-based technique to cancel the self-interference (SI) adaptively in the background. The signals transmitted from the near-end and the far-end can use independent baud-rates and signaling schemes in a TFD-IO. A proof-of-concept design of the TFD-IO module has been fabricated in a 65 nm CMOS technology, and demonstrated with bidirectional throughputs of up to 12.8\,Gb/s.

Subspace analysis is a basic tool for coping with high-dimensional data and is becoming a fundamental step in early processing of many signals elaboration tasks. Nowadays trend of collecting huge quantities of usually very redundant data by means of decentralized systems suggests these techniques be deployed as close as possible to the data sources. Regrettably, despite its conceptual simplicity, subspace analysis is ultimately equivalent to eigenspace computation and brings along non-negligible computational and memory requirements. To make this fit into typical systems operating at the edge, specialized streaming algorithms have been recently devised that we here classify and review giving them a coherent description, highlighting features and analogies, and easing comparisons. Implementation of these methods is also tested on a common edge digital hardware platform to estimate not only abstract functional and complexity features, but also more practical running times and memory footprints on which compliance with real-world applications hinges.

Converting AES Algorithm to be LWC AES using LCPoAES for Ease Installation on IoT Devices

Internet load balancing algorithms can be categorised into static and dynamic algorithms. Static algorithms like Round Robin and IP hash are rule based and do not take into account dynamic information like load on individual servers. Dynamic algorithms like Least connections take this into account and aim to distribute traffic more optimally, but lead to requirement of monitors or polling mechanisms to obtain this information. Predictive load balancing algorithms aim to remove this requirement by trying to predict load induced on servers due to requests rather than measuring it directly. We aim to provide an improved implementation of algorithm described by Patil et al.[1] and compare this implementation with a static algorithm like Round Robin in terms of performance and resource utilisation. This implementation is for a web application which does text-to-speech synthesis.

A novel channel model has been proposed for mobile millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) communications to evaluate the effect of end-user mobility. In this model the variance of number of clusters and number of rays generated from each cluster is taken into account that is novel and different from widely used channel models. Two optimum codebook based beam-tracking schemes multi-objective joint optimization codebook (MJOC) and linear hybrid combiner (LHC) have been proposed for the novel channel model and their performance for spectral efficiency (SE) is presented. Performance for the two most commonly used channel state information (CSI) estimation approaches is investigated. Finally, the relationship between the beamforming training blocks and optimal beam tracking scheme is presented.

Soil moisture influences forest health, fire occurrence and extent, and insect and pathogen impacts, creating a need for regular, globally extensive soil moisture measurements that can only be achieved by satellite-based sensors, such as NASA’s Soil Moisture Active Passive (SMAP). However, SMAP data for forested regions, which account for ~20% of land cover globally, are flagged as unreliable due to interference from vegetation water content, and forests were underrepresented in previous validation efforts, preventing an assessment of measurement accuracy in these biomes. Here we compare over twelve thousand SMAP soil moisture measurements, representing 88 site-years, to in-situ soil moisture measurements from forty National Ecological Observatory Network (NEON) sites throughout the US, half of which are forested. At unforested NEON sites, agreement with SMAP soil moisture (unbiased RMSD: 0.046 m3 m-3) was similar to previous sparse network validations (which include inflation of the metric due to spatial representativeness errors). For the forested sites, SMAP achieved a reasonable level of accuracy (unbiased RMSD: 0.06 m3 m-3 or 0.053 m3 m-3 after accounting for random representativeness errors) indicating SMAP is sensitive to changes in soil moisture in forest ecosystems. Moreover, we identified that both an index of vegetation water content and canopy height were related to mean difference, which incorporates measurement bias and representativeness bias, and suggests a potential approach to improve SMAP algorithm parameterization for forested regions. In addition, expanding the number and extent of soil moisture measurements at forested validation sites would likely further reduce mean difference by minimizing representativeness errors.

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The work presents a fast and accurate empirical stray loss model for inverter fed three-phase induction motors (IMs). The model is intended to quantify the SL both at the design stage and, normal operation of the IM. The development of the model considers the variations of frequency, switching frequency and motor’s loading. Test data of 128 operating points of two training IMs are used to develop the model. The accuracy and prediction capability of the proposed model is presented with the measured SL values for 128 operating points of another set of two IMs of the same specifications and different geometries.

Deep Reinforcement Learning (DRL) and Evolution Strategies (ESs) have surpassed human-level control in many sequential decision-making problems, yet many open challenges still exist. To get insights into the strengths and weaknesses of DRL versus ESs, an analysis of their respective capabilities and limitations is provided. After presenting their fundamental concepts and algorithms, a comparison is provided on key aspects such as scalability, exploration, adaptation to dynamic environments, and multi-agent learning. Then, the benefits of hybrid algorithms that combine concepts from DRL and ESs are highlighted. Finally, to have an indication about how they compare in real-world applications, a survey of the literature for the set of applications they support is provided.

Clinical tools involving immersive virtual reality (VR) may bring several advantages to cognitive neuroscience and neuropsychology. However, there are some technical and methodological pitfalls. The American Academy of Clinical Neuropsychology (AACN) and the National Academy of Neuropsychology (NAN) raised 8 key issues pertaining to Computerized Neuropsychological Assessment Devices. These issues pertain to: (1) the safety and effectivity; (2) the identity of the end-user; (3) the technical hardware and software features; (4) privacy and data security; (5) the psychometric properties; (6) examinee issues; (7) the use of reporting services; and (8) the reliability of the responses and results. The VR Everyday Assessment Lab (VR-EAL) is the first immersive VR neuropsychological battery with enhanced ecological validity for the assessment of everyday cognitive functions by offering a pleasant testing experience without inducing cybersickness. The VR-EAL meets the criteria of the NAN and AACN, addresses the methodological pitfalls, and brings advantages for neuropsychological testing. However, there are still shortcomings of the VR-EAL, which should be addressed. Future iterations should strive to improve the embodiment illusion in VR-EAL and the creation of an open access VR software library should be attempted. The discussed studies demonstrate the utility of VR methods in cognitive neuroscience and neuropsychology.

Directional antennas and beamforming techniques that bring promising transmission gain to the wireless links are widely incorporated in the system-level analysis of wireless networks. In many existing studies, the beamforming gain model to calculate the beamforming gain considers beamforming pattern and aligned as well as misaligned cases. However, the channel properties, e.g., the K factor and the spatial distribution of multi-paths, are neglected, which could significantly influence the beamforming gain. In this paper, a general beamforming gain model is appropriately defined, while the traditional beamforming gain model is proved to be only a special case in the proposed general model by consideringan oversimplified channel with no angular spread. In light of this, expressions of the received signal amplitude and the beamforming gain are rigorously derived for narrowband fading and wideband statistical mmWave channels, respectively. Thorough comparison between the proposed beamforming gain model and the traditional beamforming gain is provided, which demonstrates and validates that the traditional model incorrectly captures the beamforming gain and thereby, leads to inaccurate system-level network analysis. To this end, the effectiveness and importance of the proposed general beamforming gain model are revealed, particularly for millimeter-wave wideband systems.

In this paper, we propose a framework for cross-layer analysis of multi-static passive RFID systems. The model takes into account details of the shared wireless channel, including fading and capture effect, whereas, at the medium access control (MAC) layer, the anti-collision mechanism proposed in the EPC Generation 2 standard is taken as a reference. To address the complexity of the system model, we rely on a semi-analytical approach, that combines a moment matching approximation method to abstract the physical layer and Monte-Carlo simulations to describe the MAC dynamics. Furthermore, based on the space diversity feature offered by the multi-static settings, we introduce the concept of capture deiversity and propose a modification to the standard to fully support this form of diversity. Numerical results show the impact of deployment conditions and the relative positions of interrogator, tags, and detection points on the performance of tags’ identification. We show how the number of detection points impacts the system performance under various channel conditions and MAC parameters’ settings. Finally, we validate the proposed update of the MAC protocol, showing substantial performance improvement with respect to the standard collision resolution policy.

Elastic optical network (EON) is fast becoming a key solution for designing optical network with better usage of spectrum resources or other objectives of interest to tenant and/or operators. A primary concern of EONs is to protect the network against failures of its elements, because this kind of event can provoke the loss of substantial amount of traffic. In this paper, we propose a new mixed integer linear programming (MILP) formulation for protecting the network traffic against multiple link failures. The key idea is to use bandwidth squeezing together with grooming capability to provide few extra traffic for protection and guarantee a minimum bandwidth for each source–destination node pair under multiple failure events. The proposed formulation solves the virtual topology design problem jointly with the grooming, routing, modulation and spectrum allocation (RMSA) tasks. Due to the non-deterministic polynomial time (NP-hard) nature of the proposed MILP formulation, a heuristic strategy (referred to as two-step MILP) for large networks is also proposed. The solutions and performance of the proposed MILP formulation and two-step MILP analyzed through case studies in a small network. In addition, the performance of three large networks is assessed for cases scenarios where connections are under different service-level agreement (SLA). In view of proposed formulation and two-step MILP, it is possible to identify the configurations that ensure better usage of spectrum resources with different kinds of protection against single or multiple link failures.