The 3rd Generation Partnership Project (3GPP) specification of the fifth generation (5G) New Radio (NR) allows for a highly scalable and flexible radio access technology to cater to network operators with different requirements. Such scalability and flexibilities in network configurations inevitably translate to complications in the design and implementation of 5G-NR systems. Radio access in 5G-NR is much more complex and involved than its predecessor, 4G long term evolution (LTE) and LTE-Advanced technology. Therefore, the 5G-NR specifications turn out to be quite dense. Specifically, the specifications are concise, design motivations rarely explained, and the information can be convoluted or distributed across several documents. Moreover, there are several key design details associated with the access layer procedures for any given physical layer channel, which are often omitted in the specifications. For example, design motivation aspects of initial access channels or signal generation can be quite difficult to follow or understand in 5G-NR. In this paper, all the design details associated with initial access channels and signal generation in 5G-NR specifications are laid out. The contributions of the paper are three folds. First, the design details and justifications associated with both downlink and uplink access channels are presented along with signal generation details. Secondly, receiver design aspects of NR PRACH short formats are discussed. Lastly, PRACH receiver implementation aspects and performance reports from different network operators are presented and compared with 3GPP specified Radio Performance and Protocol aspect requirements[1] for millimeter wave (mmW) access. The work in this paper is of significant value to researchers and system engineers looking to design and build initial access algorithms as part of 5G NR systems.   [1] Radio Performance and Protocol aspect requirements are specified by the 3GPP Radio Access Network working group 4, also known as RAN4.

Most cyber-attacks and data breaches in cloud infrastructure are due to human errors and misconfiguration vulnerabilities. Cloud customer-centric tools are lacking, and existing security models do not efficiently tackle these security challenges. Novel security mechanisms are imperative, therefore, we propose Risk-driven Fault Injection (RDFI) techniques to tackle these challenges. RDFI applies the principles of chaos engineering to cloud security and leverages feedback loops to execute, monitor, analyze and plan security fault injection campaigns, based on a knowledge-base. The knowledge-base consists of fault models designed from cloud security best practices and observations derived during iterative fault injection campaigns. Furthermore, the observations indicate security weaknesses and verify the correctness of security attributes (integrity, confidentiality and availability) and security controls. Ultimately this knowledge is critical in guiding security hardening efforts and risk analysis. We have designed and implemented the RDFI strategies including various chaos algorithms as a software tool: CloudStrike. Furthermore, CloudStrike has been evaluated against infrastructure deployed on two major public cloud systems: Amazon Web Service and Google Cloud Platform. The time performance linearly increases, proportional to increasing attack rates. Similarly, CPU and memory consumption rates are acceptable. Also, the analysis of vulnerabilities detected via security fault injection has been used to harden the security of cloud resources to demonstrate the value of CloudStrike. Therefore, we opine that our approaches are suitable for overcoming contemporary cloud security issues

This paper proposes a mathematical model to perform optimal operational planning of large-scale energy systems with high share of renewable energy. Furthermore, it analyses the influence of different unit commitment modelling approaches on the operational planning outcomes. The value of co-optimisation of electricity and heating sector is emphasized in this paper. The results show the influence of massive renewable penetration in the energy sector towards 2050, and how this influences generation from other sources such as thermal and hydro. Including unit commitment constraints with integer variables leads to more realistic behaviour of the units, at the cost of increasing considerably the computational time. Relaxing integer variables reduces significantly the computational time, without highly compromising the accuracy of the results. Neglecting the unit commitment constraints leads to inaccurate results in terms of underestimation of costs, curtailment, wind's and solar PV's average revenue per energy unit sold, price volatility, and to overestimation of the flexibility of thermal units. Hence, depending on the purpose of the analysis, it is recommended to consider carefully the choice of unit commitment modelling approach and acknowledge the limitations. When the focus is on prices and revenues, using unit commitment constraints with integer variables is preferable.

Deep learning (DL) has seen great success in the computer vision (CV) field, and related techniques have been used in security, healthcare, remote sensing, and many other fields. As a parallel development, visual data has become universal in daily life, easily generated by ubiquitous low-cost cameras. Therefore, exploring DL-based CV may yield useful information about objects, such as their number, locations, distribution, motion, etc. Intuitively, DL-based CV can also facilitate and improve the designs of wireless communications, especially in dynamic network scenarios. However, so far, such work is rare in the literature. The primary purpose of this article, then, is to introduce ideas about applying DL-based CV in wireless communications to bring some novel degrees of freedom to both theoretical research and engineering applications. To illustrate how DL-based CV can be applied in wireless communications, an example of using a DL-based CV with a millimeter-wave (mmWave) system is given to realize optimal mmWave multiple-input and multiple-output (MIMO) beamforming in mobile scenarios. In this example, we propose a framework to predict future beam indices from previously observed beam indices and images of street views using ResNet, 3-dimensional ResNext, and a long short-term memory network. The experimental results show that our frameworks achieve much higher accuracy than the baseline method, and that visual data can significantly improve the performance of the MIMO beamforming system. Finally, we discuss the opportunities and challenges of applying DL-based CV in wireless communications.

Waste collection and management is an integrated part of both city and village life. Lack of optimized and efficient waste collection system vastly affect public health and costs more. The prevailing traditional waste collection system is neither optimized nor efficient. Internet of Things (IoT) has been playing a great role in making human life easier by making systems smart, adequate and self sufficient. Thus, this paper proposes an IoT based efficient waste collection system with smart bins. It does real-time monitoring of the waste bins and determines which bins are to emptied in every cycle of waste collection. The system also presents an enhanced navigation system that shows the best route to collect wastes from the selected bins. Four waste bins are assumed in the city of Mount Pleasant, Michigan at random location. The proposed system decreases the travel distance by 30.76% on an average in the assumed scenario, compared to the traditional waste collection system. Thus it reduces the fuel cost and human labor making the system optimized and efficient by enabling real-time monitoring and enhanced navigation.

The growing popularity of mobility-on-demand fleets increases the importance to understand the impact of mobility-on-demand fleets on transportation networks and how to regulate them. For this purpose, transportation network simulations are required to contain corresponding routing methods. We study the trade-off between computational efficiency and routing accuracy of different approaches to routing fleets in a dynamic network simulation with endogenous edge travel times: a computationally cheap but less accurate Network Fundamental Diagram (NFD) based method and a more typical Dynamic Traffic Assignment (DTA) based method. The NFD-based approach models network dynamics with a network travel time factor that is determined by the current average network speed and scales free-flow travel times. We analyze the different computational costs of the approaches in a case study for 10,000 origin-destination (OD) pairs in a network of the city of Munich, Germany that reveals speedup factors in the range of 100. The trade-off for this is less accurate travel time estimations for individual OD pairs. Results indicate that the NFD-based approach overestimates the DTA-based travel times, especially when the network is congested. Adjusting the network travel time factor based on pre-processed DTA results, the NFD-based routing approach represents a computationally very efficient methodology that also captures traffic dynamics in an aggregated way.

We propose a new family of echo hiding procedures designed to work with audio files with ternary watermarks. Most attention is paid to the case when the image is used as a watermark. The possibility of using a melody for this purpose is also mentioned. A human is supposed to be a detector to prove the presence of a watermark in the audio file. The approach employs infinite impulse response (IIR) filters of a particular form that provides a capability insertion a few symbols into a fragment of the container. The suggested method’s payload and resistance to various attacks exceed the parameters of the classical implementation of echo hiding.

Some vehicular Internet-of-Things (IoT) applications have a strict requirement on the end-to-end delay where edge computing can be used to provide a short delay for end-users by conducing efficient caching and computing at the edge nodes. However, a fast and efficient communication route creation in multi-access vehicular environment is an underexplored research problem. In this paper, we propose a collaborative learning-based routing scheme for multi-access vehicular edge computing environment. The proposed scheme employs a reinforcement learning algorithm based on end-edge-cloud collaboration to find routes in a proactive manner with a low communication overhead. The routes are also preemptively changed based on the learned information. By integrating the “proactive’‘ and “preemptive” approach, the proposed scheme can achieve a better forwarding of packets as compared with existing alternatives. We conduct extensive and realistic computer simulations to show the performance advantage of the proposed scheme over existing baselines.

Objective: Short-term cardiovascular compensatory responses to perturbations in the circulatory system caused by haemodialysis can be investigated by spectral analysis of heart rate variability. This could provide an important variable for categorising individual patients response to haemodialysis leading to a more personalised treatment. However, data obtained over a four-hour haemodialysis treatment is significant in volume and subject to artefacts that can compromise its analysis. Methods: The Lomb-Scargle Periodogram can provide a robust method of generating power spectral density estimates for large, irregularly sampled and noisy data sets obtained in clinical settings, provided that careful attention is given to frequency limits. The effect of different pre-processing methods on the resulting power spectrum is explored with simulated and real heart rate variability data. Results: Common pre-processing methods for correcting individual artefacts in heart rate records, such as interpolation, are unreliable as they act as non-linear low-pass filters and distort the resulting spectral analysis. These distortions are present, but less apparent within patient data and can mislead clinical interpretations. Conclusion: It is more appropriate to exclude suspect data points than to edit them prior to spectral analysis via the Lomb- Scargle periodogram, and where required, de-noise the entire heart rate signal by empirical mode decomposition. The use of a False Alarm Probability metric can help establish whether spectral estimates are valid Significance: Methods established to pre-process time-invariant data prior to power spectral density estimation fail when used in conjunction with the Lomb-Scargle method.

Optimization models can be quite powerful in identifying a pathway to lowest cost zero-carbon energy systems. However, it is less obvious how to invert the models to calculate the target cost and duration of storage needed for that storage to be a significant solution. Storage is a dispatchable and flexible resource with the ability to perform many functions of grid support, further complicating the analysis. This paper complements existing papers by presenting an academic study of a simplified energy system, demonstrating a method for quantifying cost and duration targets for storage. The simplified analysis also helps to gain intuition about the synergistic relationship between storage and solar energy in a location like the state of California.

In “The 100 Percent Clean Energy Act of 2018” the California legislature set a target of 100\% of California’s electricity generated from renewable and zero-carbon sources by 2045. The California Public Utility Commission (CPUC) and other state entities now have the task of planning to meet that target. The California Energy Commission (CEC) has sponsored multiple studies and sought public input on the pathways that they are exploring. A key result of that planning process is a Reference System Portfolio (RSP) based on existing and planned electricity generating capabilities, and modeled grid build out to meet the planned targets by 2045 at the lowest cost. Although this RSP has been discussed by the CEC in a public forum, to our knowledge, it has not been presented to the photovoltaic community. Here we document the CEC’s current RSP, with emphasis on understanding their expectations for build out of solar as well as the associated need for storage and curtailment.

This is a preprint version of the manuscript submitted to IEEE on June 4, 2020. This paper gives an overview of the Artificial Intelligence (AI) applications for power electronic systems. The three distinctive life-cycle phases, design, control, and maintenance are correlated with one or more tasks to be addressed by AI, including optimization, classification, regression, and data structure exploration. The applications of four categories of AI are discussed, which are expert system, fuzzy logic, metaheuristic method, and machine learning. More than 500 publications have been reviewed to identify the common understandings, practical implementation challenges, and research opportunities in the application of AI for power electronics.

numbers of GO layers and at different pump powers. By optimizing the trade-off between the nonlinearity and loss, we obtain a significant improvement in the FWM conversion efficiency of ≈7.3 dB for a uniformly coated device with 1 layer of GO and ≈9.1 dB for a patterned device with 5 layers of GO. We also obtain a significant increase in FWM bandwidth for the patterned devices. A detailed analysis of the influence of pattern length and position on the FWM performance is performed. Based on the FWM measurements, the dependence of GO’s third-order nonlinearity on layer number and pump power is also extracted, revealing interesting physical insights about the 2D layered GO films. Finally, we obtain an enhancement in the effective nonlinear parameter of the hybrid waveguides by over a factor of 100. These results verify the enhanced nonlinear optical performance of SiN waveguides achievable by incorporating 2D layered GO films.

A simple active battery management system for lithium ion battery stacks is described, that is robust and scalable. The architecture is based on an isolation unit consisting of a small equal turns ratio high isolation transformer with two diodes. One isolation unit is connected to each series connected cell in the battery stack and enables both accurate cell voltage monitoring and active cell balancing. The output of the isolation unit is completely galvanically isolated and thus suitable for high voltage packs with stringent safely requirements such as automotive and industrial electrical vehicles. Detailed measurements results are presented and a complete prototype for a LiFePO4 16s2p 2kWh battery module is constructed and its performance measured.

Due to recent advances in laser satellite communications technology, free-space optical (FSO) links are presented as an ideal alternative to the conventional radio frequency (RF) feeder links of the geostationary satellite for next generation very high throughput satellite (VHTS) systems. In this paper, we investigate the performance of multibeam VHTS systems that account for nonlinear high power amplifiers at the transparent fixed gain satellite transponder. Specifically, we consider the forward link of such systems, where the RF user link is assumed to follow the shadowed Rician model and the FSO feeder link is modeled by the Gamma-Gamma distribution in the presence of beam wander and pointing errors where it operates under either the intensity modulation with direct detection or the heterodyne detection. Moreover, zero-forcing precoder is employed to mitigate the effect of inter-beam interference caused by the aggressive frequency reuse in the user link. The performance of the system under study is evaluated in terms of the outage probability, the average bit-error rate (BER), and the ergodic capacity that are derived in exact closed-forms in terms of the bivariate Meijer’s G function. Simple asymptotic results for the outage probability and the average BER are also obtained at high signal-to-noise ratio.

In large-scale industrial plants, alarm management system (AMS) has a critical role in safety and efficiency of the plant. High degree of connectivity in large-scale plants results in high degree of dependencies between the generated alarms, and thus in any abnormal condition, a huge number of alarms are presented to the operator. This phenomenon is known as alarm flood, which might lead to a hazardous situation if the operator cannot handle them. Therefore, an efficient alarm analysis system is required to assist the operator by detecting the sequence of alarms and the root-cause analysis between them. In this paper, a data-driven method using the alarm log file is proposed to detect the causal sequence of the alarms. In this method, an efficient alarm clustering based on time distance between the alarms is proposed to keep the timely close alarms in one cluster. This clustering approach can help to preserve the neighboring alarms in one cluster. By similarity analysis between the detected clusters, the similar clusters can form a category of alarms. Each category and the clusters inside them are further analyzed for root-cause detection by means of transfer entropy. Finally, the proposed method is evaluated with an industrial alarm data

In multi-object tracking, multiple objects generate multiple sensor measurements, which are used to estimate the objects’ state simultaneously. Since it is unknown from which object a measurement originates, a data association problem arises. Considering all possible associations is computationally infeasible for large numbers of objects and measurements. Hence, approximation methods are applied to compute the most relevant associations. Here, we focus on deterministic methods, since multi-object tracking is often applied in safety-critical areas. In this work we show that Herded Gibbs sampling, a deterministic version of Gibbs sampling, applied in the labeled multi-Bernoulli filter, yields results of the same quality as randomized Gibbs sampling while having comparable computational complexity. We conclude that it is a suitable deterministic alternative to randomized Gibbs sampling and could be a promising approach for other data association problems.

We present the design of a multipath multi-WAN-hop SD-WAN (MMS) system to realize an overlay network on top of the public internet. The MMS includes an SD-WAN system controller (SSC) and MMS gateways (MMSGs), one for each branch. The SSC is responsible for configuring the routing paths for the whole system. The MMSG uses low-cost access networks such as PON, xDSL, PLC, cable modems, and even LTE/5G to access the public internet. We propose an IP address swapping technique to realize multihop routing in the public internet. In addition, we implement IP over MPTCP (IPoMP) in MMSGs, in which a flow between a pair of branches is mapped into multiple MPTCP subflows to exploit multipath routing.