This paper presents small-signal modeling, stability analysis, and controller design of a nonisolated bidirectional hybrid-modular DC-DC Converter for DC grid applications. The DC-DC converter can be used to interconnect two different DC voltage levels in a medium-/high-voltage DC grid. Half-bridge Sub-Modules (SMs) and a high-voltage valve are the main components of the converter. The high-voltage valve can be implemented via employing series-connected Insulated-Gate Bipolar Transistors (IGBTs). Operation with zero voltage switching of the involved high-voltage valve is feasible, i.e., there is no concern pertinent to dynamic voltage sharing among the series-connected IGBTs. The power is transferred from one side to another through the involved SMs, where their capacitors are connected in series across the high-voltage side, while they are connected sequentially across the low-voltage side. In this paper, the state-space averaging technique is employed to derive the small-signal model of the presented converter for controller design. Closed-form expression of the duty cycle-to-inductor current transfer function is extracted. Comparison between simulation results of the small-signal model and the detailed circuit model is presented to authenticate the accuracy of the derived small-signal model. Finally, a scaled-down prototype is used to verify the accuracy of the small-signal model.

Objective: We exploit altered patterns in brain functional connectivity as features for automatic discriminative analysis of neuropsychiatric patients. Deep learning methods have been introduced to functional network classification only very recently for fMRI, and the proposed architectures essentially focused on a single type of connectivity measure. Methods: We propose a deep convolutional neural network (CNN) framework for classification of electroencephalogram (EEG)-derived brain connectome in schizophrenia (SZ). To capture complementary aspects of disrupted connectivity in SZ, we explore combination of various connectivity features consisting of time and frequency-domain metrics of effective connectivity based on vector autoregressive model and partial directed coherence, and complex network measures of network topology. We design a novel multi-domain connectome CNN (MDC-CNN) based on a parallel ensemble of 1D and 2D CNNs to integrate the features from various domains and dimensions using different fusion strategies. We also consider an extension to dynamic brain connectivity using the recurrent neural networks. Results: Hierarchical latent representations learned by the multiple convolutional layers from EEG connectivity reveal apparent group differences between SZ and healthy controls (HC). Results on a large resting-state EEG dataset show that the proposed CNNs significantly outperform traditional support vector machine classifiers. The MDC-CNN with combined connectivity features further improves performance over single-domain CNNs using individual features, achieving remarkable accuracy of 91.69% with a decision-level fusion. Conclusion: The proposed MDC-CNN by integrating information from diverse brain connectivity descriptors is able to accurately discriminate SZ from HC. Significance: The new framework is potentially useful for developing diagnostic tools for SZ and other disorders.

In this paper, we propose a distributed algorithm that allows unmanned aerial vehicles (UAVs) to dynamically learn their optimal 3D locations and associate with ground users while maximizing the network’s sum-rate. Our approach is referred to as ’Learn-As-You-Fly’ (LAYF) algorithm. LAYF is based on a decomposition process that iteratively breaks the underlying optimization into three subproblems. First, given fixed 3D positions of UAVs, LAYF proposes a distributed matching-based association that alleviates the bottlenecks of bandwidth allocation and guarantees the required quality of service. Next, to address the 2D positions of UAVs, a modified version of K-means algorithm, with a distributed implementation, is adopted. Finally, in order to optimize the UAVs altitudes, we study a naturally defined game-theoretic version of the problem and show that under fixed UAVs 2D coordinates, a predefined association scheme, and limited interference, the UAVs altitudes game is a potential game where UAVs can maximize the limited interference sum-rate by only optimizing a local utility function. Our simulation results show that the network’s sum-rate is improved as compared to both a centralized suboptimal solution and a distributed approach that is based on closest UAVs association.

In this article, the effect of electrode material on the electrical breakdown, produced by a 500-ns duration high-voltage pulse in dry air at atmospheric pressure, is investigated. The configuration chosen is a pin-to-plane geometry with a gap distance of 2 mm. Both polarities of the high-voltage pulse have been investigated for three different pin electrodes. The reference pin is a copper wire of 50 mm length, while the two other pins are made of a highly resistive composite material of 240 kΩ /m, with two different lengths of 50 and 500 mm. The plane electrode is a tungsten plate of 3 cm diameter. The discharges obtained for the highly resistive wires (HRWs) can be categorized as resistive barrier discharges. Both electrical and optical characteristics of the discharges are presented and discussed. The current, voltage, and energy deposition are first analyzed. Then, the time-resolved phase-locked images of the discharges are presented, showing the propagation of the discharge filaments in the gap. The experimental results demonstrate a strong influence of the electrode material on the discharge characteristics, regardless of the polarity of the applied voltage. The main finding is that, for the same applied high-voltage pulse, the use of highly resistive materials significantly reduces the energy deposition into the discharge and the light emission from the discharge.

This work investigates the uplink of massive MIMO systems using multi-cell MMSE (M-MMSE) combining that was shown to yield unbounded capacity in Rayleigh fading. All intra and inter-cell channels are correlated with distinct per-user Rician factors and channel correlation matrices, pilot contamination and imperfect channel estimation. First, a closed-form approximation of the spectral efficiency (SE) is derived thus enabling to demonstrate that, under certain conditions on the correlation matrices, M-MMSE generates unbounded SE in Rician fading. Second, the impact of inter-cell LoS components is examined in favorable propagation conditions, and, interestingly, shown to be more beneficial in terms of SE than when these interfering links are entirely scattered.

In polymer coated planar Inter Digitated Electrodes (IDEs), the gas sensing sensitivity is much lower than the sensitivity of parallel plate capacitors. Here, we introduce a simple patterning step for the modification of the geometry of the dielectric substrate of the planar IDEs, and increase of the contribution of the sensitive layer to the output signal. The proposed methodology is investigated through simulation and verified by experimental data. Polymer coated IDEs with different dimensions of spatial wavelength were studied experimentally upon exposure to analytes of varying polarity. The sensing performance of the fabricated structures compare very well with theoretically estimated values obtained through finite element simulations. The maximum performance gain is also calculated by simulation demonstrating the potential of the technology.

Detrended Fluctuation Analysis (DFA) is a statistical estimation algorithm used to assess long-range temporal dependence in neural time series. The algorithm produces a single number, the DFA exponent, that reflects the strength of long-range temporal correlations in the data. No methods have been developed to generate confidence intervals for the DFA exponent for a single time series segment. Thus, we present a statistical measure of uncertainty for the DFA exponent in electroencephalographic (EEG) data via application of a moving-block bootstrap (MBB). We tested the effect of three data characteristics on the DFA exponent: (1) time series length, (2) the presence of artifacts, and (3) the presence of discontinuities. We found that signal lengths of ~5 minutes produced stable measurements of the DFA exponent and that the presence of artifacts positively biased DFA exponent distributions. In comparison, the impact of discontinuities was small, even those associated with artifact removal. We show that it is possible to combine a moving block bootstrap with DFA to obtain an accurate estimate of the DFA exponent as well as its associated confidence intervals in both simulated data and human EEG data. We applied the proposed method to human EEG data to (1) calculate a time-varying estimate of long-range temporal dependence during a sleep-wake cycle of a healthy infant and (2) compare pre- and post-treatment EEG data within individual subjects with pediatric epilepsy. Our proposed method enables dynamic tracking of the DFA exponent across the entire recording period and permits within-subject comparisons, expanding the utility of the DFA algorithm by providing a measure of certainty and formal tests of statistical significance for the estimation of long-range temporal dependence in neural data.

Visible light communications (VLC) have emerged as a strong candidate for meeting the escalating demand for high data rates. In this paper, we consider a VLC network, where multiple access points (APs) serve both energy-harvesting users (EHUs), i.e., users who harvest energy from light emitted by diodes and information users (IUs), i.e., users who gather data information. In order to jointly balance the achievable sum rate at the IUs and the energy harvested by the EHUs, the paper considers maximizing a network-wide utility, which consists of a weighted sum of the IUs sum rate and the EHUs harvested energy, subject to individual IU rate constraint, individual EHU harvested-energy constraint, and AP power constraints, so as to jointly determine the direct current (DC) bias value at each AP, and the power of the alternating-current (AC) signals of the users. A difficult non-convex optimization problem is solved using an iterative approach which relies on inner convex approximations, and compensates for the used approximations using proper outer-loop updates. The paper further considers solving the special cases of the problem, i.e., maximizing the sum rate, and maximizing the total harvested-energy, both subject to the same constraints. Numerical results highlight the significant performance improvement of the proposed algorithms, and illustrate the impacts of the network parameters on the performance trade-off between the sum rate and harvested-energy.

Magnetic Induction (MI) is an efficient wireless communication method to deploy operational internet of underground things (IoUT) for oil and gas reservoirs. The IoUT consists of underground things which are capable of sensing the underground environment and communicating with the surface. The MI-based IoUT enable many applications, such as monitoring of the oil rigs, optimized fracturing, and optimized extraction. Most of these applications are dependent on the location of the underground things and therefore require accurate localization techniques. The existing localization techniques for MI-based underground sensing networks are two-dimensional and do not characterize the achievable accuracy of the developed methods, which are both crucial and challenging tasks. Therefore, this paper proposes a novel three-dimensional (3D) localization technique based on Isometric scaling (Isomap) for future IoUT. Moreover, this paper also presents the closed-form expression of the Cramer Rao lower bound (CRLB) for the proposed technique, which takes into account the channel parameters of the underground magnetic-induction. The derived CRLB provides the suggestions for an MI-based underground localization system by associating the system parameters with the error trend. Numerical results demonstrate that localization accuracy is affected by different channel and networks parameters such as the number of underground things, ranging error variance, size of the coils, and the transmitting power. The root mean square error performance of the proposed technique shows that increase in the number of turns of the coils, transmitting power, and the number of anchors improves the performance. Results also show that the proposed technique is robust to the ranging error variance in the range of 10 to 30 %; however, a further increase in the ranging error variance does not allow to achieve acceptable accuracy. Also, the results show that the proposed technique achieves an average of 30 % better localization accuracy compare to the traditional methods.

A low-cost and additively manufactured 3D Antenna-in-Package (AiP) with quasi-isotropic radiation is proposed for a marine animals monitoring system. The antenna is based on a meandered dipole folded as a split ring resonator (SRR) structure, which can generate simultaneously a pair of orthogonal electric and magnetic dipoles, thus providing a quasi-isotropic radiation pattern. The antenna (integrated with a balun) has been inkjet-printed on a 3D-printed buoyant cone structure, which acts also as the system package to house the electronics and the battery. The antenna designed at 2.4 GHz is electrically small, with a ka = 0.49, and has a bandwidth of 70 MHz (2.9%). The measured gain deviation of the antenna (maximum to minimum) is near 3 dB in bandwidth, thus qualifying it as a quasi-isotropic antenna. Field tests of the antenna in the active state (integrated with the electronics) confirm a reliable communication range of 240 m in any direction in the azimuthal plane.