Vaccines have saved more lives than any other medical intervention throughout human history by preventing the spread of infectious diseases. However, despite several decades of research, there is no effective vaccine against fast evolving viruses such as the human immunodeficiency virus (HIV) and the hepatitis C virus (HCV). A confounding factor in the development of a HIV or HCV vaccine is that these viruses have a unique ability to make a lot of mutations in their genetic code. This enables them to escape the human immune system while retaining their ability to propagate infection. For developing a vaccine against such viruses, scientists are developing novel strategies which seek to target specific parts of the virus that are most vulnerable (i.e., where it is difficult for the virus to survive mutations) in order to induce a focused and potentially effective immune response. To determine the existence and location of such parts of HIV and HCV, initial studies have leveraged recently-available sequence data for these viruses, and looked for those positions in the genome for which the frequency of mutation was lowest. Unfortunately, vaccines based on such first-order statistics have not enjoyed much success, and there is increasing evidence suggesting that interactions between mutations is also important and must be considered when designing an effective vaccine against HIV and HCV. It is almost impossible to determine effects of interactions between all mutations experimentally as it requires performing billions of experiments. In this article, we explain how by leveraging virus sequence data, mutational interactions can be estimated using statistical techniques and incorporated in designing novel and potentially effective vaccine strategies against such fast-evolving viruses.

This paper proposes an efficient adaptive strategy to control virtual inertia of virtual synchronous generators. This adaptive virtual inertia can provide low frequency oscillation damping and simultaneously improve primary frequency control in power systems.

In wireless networks, the desired signal of a specific user is typically an undesired interfering signal for untended receivers. However, from the perspective of an entire wireless network, the coexistence of useful signals and interfering signals is unavoidable and often is beneficial in throughput terms. The useful signals and interfering signals are complementary. They are in juxtaposition to each other in the context of a single communication link, while they are in symbiosis within the framework of the networks, which can be described by Chinese traditional culture symbol of “Yin” and “Yang”. A wireless network having optimal performance must be a harmonious network, where the interfering and useful signals harmoniously coexist in an optimal balance. Interference management plays the critical role in arriving at this optimal balance.

The standard weighted L2 norm total variation multiplicative regularization (MR) term originally developed for microwave imaging algorithms is modified to take into account structural prior information, also known as spatial priors (SP), about the object being imaged. This modification adds one extra term to the integrand of the standard MR, thus, being referred to as an augmented MR (AMR). The main advantage of the proposed approach is that it requires a minimal change to the existing microwave imaging algorithms that are already equipped with the MR. Using two experimental data sets, it is shown that the proposed AMR (i) can handle partial SP, and (ii) can, to some extent, enhance the quantitative accuracy achievable from microwave imaging.

In this work, we have demonstrated the relevance of a high-index optical meta structure for sensing purposes.

Note: This paper has been accepted by the journal of neural computing and applications. A recent metaheuristic algorithm, such as Whale Optimization Algorithm (WOA), was proposed. The idea of proposing this algorithm belongs to the hunting behavior of the humpback whale. However, WOA suffers from poor performance in the exploitation phase and stagnates in the local best solution. Grey Wolf Optimization (GWO) is a very competitive algorithm comparing to other common metaheuristic algorithms as it has a super performance in the exploitation phase while it is tested on unimodal benchmark functions. Therefore, the aim of this paper is to hybridize GWO with WOA to overcome the problems. GWO can perform well in exploiting optimal solutions. In this paper, a hybridized WOA with GWO which is called WOAGWO is presented. The proposed hybridized model consists of two steps. Firstly, the hunting mechanism of GWO is embedded into the WOA exploitation phase with a new condition which is related to GWO. Secondly, a new technique is added to the exploration phase to improve the solution after each iteration. Experimentations are tested on three different standard test functions which are called benchmark functions: 23 common functions, 25 CEC2005 functions and 10 CEC2019 functions. The proposed WOAGWO is also evaluated against original WOA, GWO and three other commonly used algorithms. Results show that WOAGWO outperforms other algorithms depending on the Wilcoxon rank-sum test. Finally, WOAGWO is likewise applied to solve an engineering problem such as pressure vessel design. Then the results prove that WOAGWO achieves optimum solution which is better than WOA and Fitness Dependent Optimizer (FDO).

Demand side flexibility (DSF) is becoming an important tool for the power system operation. With air-conditioned buildings being key candidates for DSF, this paper presents a control-oriented thermal building modelling approach which is kept in the linear domain and therefore suited for the employment in convex optimization problems. Along with the building model, two DSF indicators are proposed and applied to assess the DSF potential for an office building in Singapore.

The interest of the automotive industry has progressively focused on subjects related to driver assistance systems as well as autonomous cars. In order to achieve remarkable results, cars combine a variety of sensors to perceive their surroundings robustly. Among them, radar sensors are indispensable because of their independence of light conditions and the possibility to directly measure velocity. However, radar interference is an issue that becomes prevalent with the increasing amount of radar systems in automotive scenarios. In this paper, we address this issue for frequency modulated continuous wave (FMCW) radars with fully convolutional neural networks (FCNs), a state-of-the-art deep learning technique. The interest of the automotive industry has progressively focused on subjects related to driver assistance systems as well as autonomous cars. Cars combine a variety of sensors to perceive their surroundings robustly. Among them, radar sensors are indispensable because of their independence of lighting conditions and the possibility to directly measure velocity. However, radar interference is an issue that becomes prevalent with the increasing amount of radar systems in automotive scenarios. In this paper, we address this issue for frequency modulated continuous wave (FMCW) radars with fully convolutional neural networks (FCNs), a state-of-the-art deep learning technique. We propose two FCNs that take spectrograms of the beat signals as input, and provide the corresponding clean range profiles as output. We propose two architectures for interference mitigation which outperform the classical zeroing technique. Moreover, considering the lack of databases for this task, we release as open source a large scale data set that closely replicates real world automotive scenarios for single-interference cases, allowing others to objectively compare their future work in this domain. The data set is available for download at: http://github.com/ristea/arim.

We present techniques for solving the problem of detecting element failures in phased array antennas by using a combination of a single fixed probe and an optimization of element excitations using principles derived from compressive sensing. This departs significantly from conventional techniques where the excitations are held constant and probes are instead moved spatially to collect measurements. Doing so helps us to accomplish two objectives with regards to successful fault diagnosis. First, we achieve a reduction in the number of measurements required compared to the state of the art; this reduction is particularly significant in the case of high-noise measurements where existing methods fail. Second, our techniques solve the problem of fault diagnosis in the case of real valued measurements (i.e. intensity measurement along with phase detection instead of phase measurement), which leads to simpler measurement hardware. We use nonconvex optimization algorithms to generate numerical results in support of our conclusions.

An increasing number of emerging applications, e.g., Internet of Things (IoT), vehicular communications, augmented reality, and the growing complexity due to the interoperability requirements of these systems, lead to the need to change the tools used for the modeling and analysis of those networks. Agent-Based Modeling (ABM) as a bottom-up modeling approach considers a network of autonomous agents interacting with each other, and therefore represents an ideal framework to comprehend the interactions of heterogeneous nodes in a complex environment. Here, we investigate the suitability of ABM to model the communication aspects of a road traffic management system as an example of an IoT network. We model, analyze and compare various Medium Access Control (MAC) layer protocols for two different scenarios, namely uncoordinated and coordinated. Besides, we model the scheduling mechanisms for the coordinated scenario as a high level MAC protocol by using three different approaches: Centralized Decision Maker, DESYNC and decentralized learning MAC (L-MAC). The results clearly show the importance of coordination between multiple decision makers in order to improve the information reporting error and spectrum utilization of the system.

We provide a low-level review of the computation of Sommerfeld integration theory using the singularity expansion method (SEM) to analytically estimate the short-wavelength components of the 2-dimensional Green’s function. The SEM is employed to replace the infinite tail of the spectral integral by a closed-form evaluation. The various steps in the SEM substitution and the calculations are elaborately presented and discussed with emphasis on giving the missing details often not included in the published literature.

Distribution locational marginal prices (DLMPs) have been formulated for electric distribution grids to economically dispatch distributed energy resources (DERs) while addressing operational constraints of the electric grid. This paper proposes to extend this methodology to thermal grids, i.e, district heating or cooling systems, and specifically the combined operation of thermal and electric grids. To this end, thermal and electric grid models are formulated in a linear approximate model fashion. Then, the derivation and decomposition of DLMPs is formulated based on the combined optimal operation problem, assuming a linear state space model form for flexible loads (FLs). The ability of the DLMPs to reflect operational constraints in the thermal grid is demonstrated for a test case with 22 FLs.

This paper will discuss the key applicable North American Standards defining performance, testing, installation and routine field maintenance of liquid-immersed distribution substation transformers. These transformers, typically applied in commercial and industrial power systems, can be installed either in outdoor or indoor locations, applied as remote or close-coupled to either primary or secondary switchgear, or both. Recommendations based on these standards for selection, installation and factory/field testing industry users should specify to maximize reliability, operational efficiency and safety will follow.

We demonstrate a photonic RF integrator based on an integrated soliton crystal micro-comb source. By multicasting and progressively delaying the input RF signal using a transversal structure, the input RF signal is integrated discretely. Up to 81 wavelengths are provided by the microcomb source, which enable a large integration time window of ~6.8 ns, together with a time resolution as fast as ~84 ps. We perform signal integration of a diverse range of input RF signals including Gaussian pulses with varying time widths, dual pulses with varying time intervals and a square waveform. The experimental results show good agreement with theory. These results verify our microcomb-based integrator as a competitive approach for RF signal integration with high performance and potentially lower cost and footprint.

[Submitted in IEEE J-BHI] Retinopathy refers to any damage in the retina that causes visual impairments or even blindness. Identification of retinal lesions plays a vital role in accurately grading retinopathy and for its effective treatment. Optical coherence tomography (OCT) imaging is the most popular non-invasive technique used for the retinal examination due to its ability to screen abnormalities in early stages. Many researchers have presented studies on OCT based retinal image analysis over the past. However, to our best knowledge, there is no framework yet available which can extract retinal lesions from multi-vendor OCT scans and utilize them for the intuitive grading of the human retina. To cater this lack, we propose a deep retinal analysis and grading framework (RAG-FW). RAG-FW is a hybrid convolutional framework which extracts retinal lesions such as intra-retinal fluid, sub-retinal fluid, hard exudates, drusen and chorioretinal abnormalities (including fibrotic scars and choroidal neovascular membranes) from multi-vendor OCT scans. Furthermore, it utilizes them for the lesion-influenced grading of retinopathy as per the clinical standards. RAG-FW has been trained using 113,261 retinal OCT scans from which 112,261 scans were used for training and 1,000 scans were used for the validation purposes. Furthermore, it has been rigorously tested on 43,613 scans from five highly complex publicly available datasets where it achieved the mean intersection-over-union score of 0.8055 for extracting the retinal lesions and the F1 score of 99.52% for correctly classifying the retinopathy cases. The source code of RAG-FW is available at http://biomisa.org/index.php/downloads/.

The mathematical foundations of the generalized current Green's function formalism are spelled out for the first time for the case of coupled N-body systems where each element can be either PEC or dielectric.

In this letter, we investigate the impact of wireless-powered communications when energy is harvested from multiple static and/or mobile wireless coexisting networks. In a first step, we characterize the aggregate power received by a harvester node when it harnesses the energy generated by the coexisting wireless networks. Considering that the harvester node acts as a transmitter after the harvesting duration, we derive the outage probability for such coexisting scenario. In addition, the throughput achieved by the harvester node is also characterized, and the optimal harvesting duration is identified taking into account the mobility of the coexisting networks, the features of the static networks, the energy harvesting process, as well as the communication performance between the harvester node and the receiver. Our work shows that the distribution of the power received by the harvester from the coexisting networks can be accurately approximated by an $\alpha-\mu$ distribution. Moreover, the mobility also impacts on the optimal throughput of the wireless-powered communications, which is accurately confirmed by the proposed analysis and extensive simulations.

A single-beam pre-phased 1-bit reflective metasurface is proposed to achieve single-beam patterns under normally incident plane waves. Theoretical analysis and numerical simulations are presented to show that, under normally incident waves, single-beam patterns can be achieved by introducing a fixed pre-phase distribution with two values in the 1-bit metasurface. Compared with conventional 1-bit reflective metasurfaces, the proposed scheme alleviates the inherent limitation of single-beam patterns on 1-bit reflective metasurfaces under normally incident plane waves. To verify the proposed scheme, a 1-bit unit cell is designed with a 180º ± 25º phase difference between the two states for frequencies ranging from 34.3 to 49.9 GHz, and a layer-stacking method is proposed to achieve two pre-phases with a 90-degree phase difference. As an example, three 1-bit reflective metasurfaces comprising 20×20 unit cells with single beams pointing separately at 0, 15 and 30 degrees are designed and measured over frequencies of 37.0 to 41.0 GHz; the measured sidelobe levels are less than -7.8 dB. Simulated and measured results show that the proposed pre-phased 1-bit metasurface can achieve single-beam patterns under normally incident plane waves.