We consider the widely-used weighted L2 norm total variation multiplicative regularizer (MR) for both the Gauss-Newton inversion (GNI) and contrast source inversion (CSI) algorithms in microwave imaging (MWI). It is shown that the proper numerical implementation of the discretized MR operator is important for the GNI algorithm whereas the CSI algorithm is more robust with respect to different implementations of this MR. For the GNI algorithm, the MR operator should be discretized such that high spatial frequency components are not present in its nullspace, and also the resulting discrete operator is positive definite.

Demand response (DR) is one of the demand side management features under the paradigm of smart grids, wherein the consumers are encouraged to participate in the utility operations through active response to electricity price signals by altering their demand patterns. One impediment in employing demand response schemes in India is the fixed electricity tariff for the domestic consumers, which most of the Indian state utilities follow. Interestingly, the Tamil Nadu State Electricity Board (TNEB) follows an incremental block rate tariff for domestic consumers, which provides an opportunity for the implementation of proposed self–DR scheme. Hence firstly, this paper presents the design and development of a low-cost single-phase smart energy meter (SEM) which incorporates a TNEB tariff structure. Secondly, the development of an indigenous meter data management system (MDMS) software is attempted for the utility using open–source software tools. Finally, the idea of self-DR is introduced and emphasized through the coordinated operation of developed SEM and MDMS.

Polarization selective devices, such as polarizers and polarization selective resonant cavities (e.g., gratings and ring resonators), are core components for polarization control in optical systems and find wide applications in polarization-division-multiplexing, coherent optical detection, photography, liquid crystal display, and optical sensing. In this paper, we demonstrate integrated waveguide polarizers and polarization-selective micro-ring resonators (MRRs) incorporated with graphene oxide (GO). We achieve highly precise control of the placement, thickness, and length of the GO films coated on integrated photonic devices by using a solution-based, transfer-free, and layer-by-layer GO coating method followed by photolithography and lift-off processes. The latter overcomes the layer transfer fabrication limitations of 2D materials and represent a significant advance towards manufacturing integrated photonic devices incorporated with 2D materials. We measure the performance of the waveguide polarizer for different GO film thicknesses and lengths versus polarization, wavelength, and power, achieving a very high polarization dependent loss (PDL) of ~ 53.8 dB. For GO-coated integrated MRRs, we achieve an 8.3-dB polarization extinction ratio between the TE and TM resonances, with the extracted propagation loss showing good agreement with the waveguide results. Furthermore, we present layer-by-layer characterization of the linear optical properties of 2D layered GO films, including detailed measurements that conclusively determine the material loss anisotropy of the GO films as well as the relative contribution of film loss anisotropy versus polarization-dependent mode overlap, to the device performance. These results offer interesting physical insights and trends of the layered GO films from monolayer to quasi bulk like behavior and confirm the high-performance of integrated polarization selective devices incorporated with GO films.

In the coherent PHI-OTDR system, the phase signal is retrieved based on the reference point and the observation point which are off and closer to the two sides of step of the phase change. In the experiment, the optical pulse with the changed peak power, width or shape is injected into the fiber for interrogating the change of the quantitative characteristic of the measured phase signal. When the pulse width is fixed at 200 ns and its peak power is adjusted from 14 dBm to -23 dBm, the amplitude is slightly increased from 17.3575 rad to 17.4411 rad as long as the Rayleigh backscattering signal can be found in the electrical signal. Changing the pulse width from 260 ns to 80 ns when the peak power is fixed at 14 dBm, the maximum amplitude and the minimum amplitude of the measured phase signal are 17.4625 rad to 17.4509 rad, respectively. When the arbitrary shape of the optical pulse generated from the MZI structure with a changed delay fiber from 3 m to 6 m, the amplitude varies from 17.4558 rad to 17.4819 rad. For every measurement, the change of frequency is also small. And the small value of standard deviation supports the accuracy of the measurement. All the measurements show that the changed pulse nearly has no impact on the quantitative characteristic of the measured phase signal in the coherent PHI-OTDR system. Moreover, we also find that the phase signal of external event can be correctly extracted as long as the Rayleigh backscattering signal can be detected.

Abstract The integration of cognitive radio with e-healthcare systems assisted by wireless body sensor networks (WBSNs) has been regarded as an enabling approach for a new generation of pervasive healthcare services, to provide differentiated quality of service requirements and avoid harmful electromagnetic interference to primary medical devices (PMDs) over the crowded radio spectrum. Due to the sharing spectrum bands with PMDs in e-healthcare scenario using cognitive WBSNs (CWBSNs), efficient transmit power control and optimization strategies for resource-constrained secondary wearable biosensors (SWBs) play a key role in controlling the inter-network interference and improving the energy efficiency. This paper investigates the problem of dynamic power optimization for SWBs in e-healthcare leveraging CWBSNs with practical limitations, e.g., imperfect spectrum sensing and quality of physiological data sampling. We develop a distributed optimization framework of dynamic power optimization via the theory of differential game, by jointly considering utility maximization and quality of physiological data sampling for every SWB, while satisfying the evolution law of energy consumption in SWB’s battery. With the non-cooperation and cooperation relations for all SWBs in mind, we transform the differential game model into two subproblems, namely, utility maximization problem and total utility maximization problem. Utilizing Bellman’s dynamic programming, we derive a non-cooperative optimal solution for power optimization as a Nash equilibrium point for the utility maximization problem posed by competitive scenario. By exploiting Pontryagin’s maximum principle, a cooperative optimal solution is obtained for the total utility maximization problem, wherein all SWBs fully cooperate to obtain the highest total utilities. Building upon the analytical results, the actual utility distributed to each SWB is compared between the non-cooperative and cooperative schemes. Extensive simulations show that the proposed optimization framework is indeed an efficient and practical solution for power control compared with the benchmark algorithm.

An empirically based analysis of propagation characteristics in two vegetated suburban areas with different types and fractions of vegetation cover in 5G millimeter-wave bands is presented. A basic distance-dependent path loss model with a Gaussian random variance for shadow fading is utilized in accordance with the maximum-power directional and omnidirectional measurement data, therein exploiting significant path loss exponents in the presence of vegetation. In comparison with the existing ITU-R and 3GPP models, the effect of dense-leaved trees on path loss prediction is similar to that of buildings, whereas these standard models are inapplicable for sparse obstacle-line-of-sight (OLoS) links. Consequently, an azimuth-angle-based path loss characterization is proposed considering the antenna pattern, beam misalignment, and blockage effects. Moreover, several composite and cluster-level small-scale channel parameters, such as the number of clusters, delay spread, and angular spread, are extracted. Analysis of the first-arrival cluster in the OLoS setting reveals that forward scattering through foliage is still dominant and is expected to produce a larger azimuth angular spread of the arrival and compact multipath components in the time domain compared with line-of-sight and reflected clusters. Measurement results improve existing 3GPP channel models for suburban macrocell scenarios in millimeter-wave bands.

The network information system is a military information network system with evolution characteristics. Evolution is a process of replacement between disorder and order, chaos and equilibrium. Given that the concept of evolution originates from biological systems, in this article, the evolution of network information architecture is analyzed by genetic algorithms, and the network information architecture is represented by chromosomes. Besides, the genetic algorithm is also applied to find the optimal chromosome in the architecture space. The evolutionary simulation is used to predict the optimal scheme of the network information architecture and provide a reference for system construction.

A phaseless Gauss-Newton inversion (GNI) algorithm is developed for microwave imaging applications. In contrast to full-data microwave imaging inversion that uses complex (magnitude and phase) scattered field data, the proposed phaseless GNI algorithm inverts phaseless (magnitude-only) total field data. This phaseless Gauss-Newton inversion (PGNI) algorithm is augmented with three different forms of regularization, originally developed for complex GNI. First, we use the standard weighted L2 norm total variation multiplicative regularizer which is appropriate when there is no prior information about the object being imaged. We then use two other forms of regularization operators to incorporate prior information about the object being imaged into the PGNI algorithm. The first one, herein referred to as SL-PGNI, incorporates prior information about the expected relative complex permittivity values of the object of interest. The other, referred to as SP-PGNI, incorporates spatial priors (structural information) about the objects being imaged. The use of prior information aims to compensate for the lack of total field phase data. The PGNI, SL-PGNI, and SP-PGNI inversion algorithms are then tested against synthetic and experimental phaseless total field data.

As the fifth-generation (5G) mobile communication is utilizing millimeter-wave (mmWave) frequency bands, electromagnetic field (EMF) exposure emitted from a 5G mmWave mobile handset should be evaluated and compliant with the relevant EMF exposure limits in terms of peak spatial-average incident power density. In this work, a fast power density (PD) assessment method for a 5G mmWave mobile handset using the equivalent currents (EQC) method is proposed. The EQC method utilizes the intermediate-field (IF) data collected by a spherical measurement system to reconstruct the EQCs over a reconstruction surface, and then computes the PD in close proximity of the mobile handset with acceptable accuracy. The performance of the proposed method is evaluated using a mmWave mobile handset mock-up equipped with four quasi-Yagi antennas. The assessed PD results are compared with those computed using full-wave simulations and also those measured with a planar near-field (NF) scanning system. In addition, three influencing factors related to the accuracy of the EQC method, namely, the angular resolution, the phase error, and the handset position in the IF measurements, are also analyzed. The proposed method is a good candidate for fast PD assessment of EMF exposure compliance testing in the mmWave frequency range.

This paper deals with the computation of integrals of centred bivariate Gaussian densities over any domain defined as an angular sector of R^2. Based on an accessible geometrical approach of the problem, we suggest to transform the double integral into a single one, leading to a tractable closed-form expression only involving trigonometric functions. This solution can also be seen as the angular cumulative distribution of bivariate centered Gaussian variables (X,Y). We aim to provide a didactic approach of our results, and we validate them by comparing with those of the literature.

Micro-combs - optical frequency combs generated by integrated micro-cavity resonators – offer the full potential of their bulk counterparts, but in an integrated footprint. The discovery of temporal soliton states (DKS – dissipative Kerr solitons) as a means of mode-locking micro-combs has enabled breakthroughs in many fields including spectroscopy, microwave photonics, frequency synthesis, optical ranging, quantum sources, metrology and more. One of their most promising applications has been optical fibre communications where they have enabled massively parallel ultrahigh capacity multiplexed data transmission. Here, by using a new and powerful class of micro-comb called “soliton crystals”, we achieve unprecedented data transmission over standard optical fibre using a single integrated chip source. We demonstrate a line rate of 44.2 Terabits per second (Tb/s) using the telecommunications C-band at 1550nm with a spectral efficiency – a critically important performance metric - of 10.4 bits/s/Hz. Soliton crystals exhibit robust and stable generation and operation as well as a high intrinsic efficiency that, together with a low soliton micro-comb spacing of 48.9 GHz enable the use of a very high coherent data modulation format of 64 QAM (quadrature amplitude modulated). We demonstrate error free transmission over 75 km of standard optical fibre in the laboratory as well as in a field trial over an installed metropolitan optical fibre network. These experiments were greatly aided by the ability of the soliton crystals to operate without stabilization or feedback control. This work demonstrates the capability of optical soliton crystal micro-combs to perform in demanding and practical optical communications networks.

The MEMS magnetometer determines the orientation for the MEMS inertial system. Because of the large noise of the MEMS magnetometer and the interference of soft and hard iron outside, the measurement error of the MEMS magnetometer is large. To reduce the effects of the random noises, the MEMS magnetometer arrays are designed in this paper. In our design, thirty-two MEMS magnetometers are welding on a printed circuit board (PCB), which area is 5×5 cm2. The forty general-purpose input-output (GPIO) ports, which are thirty-two data ports and eight clock ports, are used to collect the data of MEMS magnetometers. Then, averaging the thirty-two measurements of the MEMS magnetometers, the random noises of the measurements of the MEMS magnetometers can be reduced. Based on the averaging operation for the collected sensors’ data, a unified measurement model for the MEMS magnetometer arrays is constructed. Using the unified measurement model, an adaptive Kalman filter is developed to estimate the unknown parameters. To validate the performance of the MEMS magnetometer arrays, the simulation and experimental tests are designed. The test results show that, comparing with the single MEMS magnetometer, the random noises of the MEMS magnetometer arrays are reduced effectively.

Decentralized edge computing techniques have been attracted strongly attentions in many applications of intelligent internet of things (IIoT). Among these applications, intelligent edge surveillance (LEDS) techniques play a very important role to recognize object feature information automatically from surveillance video by virtue of edge computing together with image processing and computer vision. Traditional centralized surveillance techniques recognize objects at the cost of high latency, high cost and also require high occupied storage. In this paper, we propose a deep learning-based LEDS technique for a specific IIoT application. First, we introduce depthwise separable convolutional to build a lightweight neural network to reduce its computational cost. Second, we combine edge computing with cloud computing to reduce network traffic. Third, we apply the proposed LEDS technique into the practical construction site for the validation of a specific IIoT application. The detection speed of our proposed lightweight neural network reaches 16 frames per second in edge devices. After cloud server fine detection, the precision of the detection reaches 89\%. In addition, the operating cost at the edge device is only one-tenth of that of the centralized server.

Space heating and cooling in buildings account for nearly 20% of energy use globally. In most buildings this energy is used to maintain the thermal comfort of the building’s human occupants by maintaining the interior air temperature at a particular set point. However, if one could maintain the human occupant’s thermal comfort while decreasing the heating or increasing the cooling set point, dramatic energy savings are possible. Here, we propose and evaluate an untapped degree of freedom in improving building efficiency: dynamically tuning the thermal emissivity of interior building surfaces at long-wave infrared wavelengths to maintain thermal comfort. We show that in cold weather conditions tuning the emissivity of interior walls, floors and ceilings to a low value (0.1) can decrease the set point temperature as much as 7°C, corresponding to an energy saving of nearly 67.7% relative to high emissivity materials (0.9). Conversely, in warm weather, high emissivity interior surfaces result in a 38.5% energy savings relative to low emissivity surfaces, highlighting the need for tunability for maximal year-round efficiency. Our results reveal the remarkable energy savings potential possible by better controlling the ubiquitous flows of heat that surround us in the form of thermal radiation.

In this research, I propose a two-variable activation function “Yamatani” that satisfies the first-degree homogeneity, and realize a super-resolution convolutional neural network that is independent of the dynamic range and symmetrical about the luminance inversion.

Layered two-dimensional (2D) graphene oxide (GO) films are integrated with micro-ring resonators (MRRs) to experimentally demonstrate enhanced nonlinear optics in the form of four-wave mixing (FWM). Both uniformly coated and patterned GO films are integrated on CMOS-compatible doped silica MRRs using a large-area, transfer-free, layer-by-layer GO coating method together with photolithography and lift-off processes, yielding precise control of the film thickness, placement, and coating length. The high Kerr nonlinearity and low loss of the GO films combined with the strong light-matter interaction within the MRRs results in a significant improvement in the FWM efficiency in the hybrid MRRs. Detailed FWM measurements are performed at different pump powers and resonant wavelengths for the uniformly coated MRRs with 1−5 layers of GO as well as the patterned devices with 10−50 layers of GO. The experimental results show good agreement with theory, achieving up to ~7.6-dB enhancement in the FWM conversion efficiency (CE) for an MRR uniformly coated with 1 layer of GO and ~10.3-dB for a patterned device with 50 layers of GO. By fitting the measured CE as a function of pump power for devices with different numbers of GO layers, we also extract the dependence of GO’s third-order nonlinearity on layer number and pump power, revealing interesting physical insights about the evolution of the layered GO films from 2D monolayers to quasi bulk-like behavior. These results confirm the high nonlinear optical performance of integrated photonic resonators incorporated with 2D layered GO films.

This paper presents a new linear optimal power flow model for three-phase unbalanced electrical distribution systems considering binary variables. The proposed formulation is a mixed-integer linear programming problem, aiming at minimizing the operational costs of the network while guaranteeing operational constraints. Two new linearizations for branch current and nodal voltage magnitudes are introduced. The proposed branch current magnitude linearization provides a discretization of the Euclidean norm through a set of intersecting planes; while the bus voltage magnitude approximation uses a linear combination of the L1 and the L∞ norm. Results were obtained for an unbalanced distribution system, in order to assess the accuracy of the linear formulation when compared to a nonlinear power flow with fixed power injections, showing errors of less than 4\% for currents and 0.005\% for voltages.

Integrated Kerr microcombs are emerging as a powerful tool as sources of multiple wavelength channels for photonic RF and microwave signal processing mainly in the context of transversal filters. They offer a compact device footprint, very high versatility, large numbers of wavelengths, and wide Nyquist bands. Here, we review recent progress on Kerr microcomb-based photonic RF and microwave reconfigurable filters, based both on transversal filter methods and on RF to optical bandwidth scaling. We compare and contrast results achieved with wide comb spacing combs (200GHz) with more finely spaced (49GHz) microcombs. The strong potential of optical micro-combs for RF photonics applications in terms of functions and integrability is also discussed. Index Terms—Microwave photonics, micro-ring resonators.