The pattern nulls of low profile ultrawideband (UWB) omnidirectional antennas in the main beam region cause the performance degradation of UWB wireless communication system in the null directions. In order to achieve ultrawide pattern bandwidth, a method to improve the radiation pattern of low profile UWB omnidirectional antennas is proposed in this letter. The mathematical operation of improving the radiation pattern is derived. A low profile UWB discone antenna is designed as a representative of the low profile UWB omnidirectional antennas, and the pattern characteristic is investigated. Then, the effects of the sleeves and modified cone on the antenna pattern and input impedance are analyzed using microwave simulation software. The results show that the pattern bandwidth is increased from 6.5:1 to 11.4:1. In the operating band, the pattern nulls along the entire phi plane are eliminated, and the minimum value of the pattern nulls is optimized from -35.0 to -12.4 dB. The corresponding gain exceeds 0.2 dBi in the main beam region of the theta direction 30° to 90°. The optimization is based on a constant volume of 0.774 ka. Finally, an antenna prototype is fabricated and the measurement results are consistent with the simulation results, verifying the validity of the method

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Currently different reference  models are used to manage cybersecurity, although practically none are  applicable “as is” to lower levels as they do not detail specific  procedural aspects for them. However, they urge organizations to develop  a methodological foundation to manage cybersecurity at those levels.  Although they allow organizations to adhere to a recognized standard at  the strategic level, this advantage vanishes when organizations must  define specific low-level procedures, allowing the appearance of  inconsistency at tactical and operational levels between departments of  the same organization or between organizations. The design of these  elements with the required holism and homogeneity is difficult, and this  is why generic processes focused on getting certified regarding a  standard are usually originated, but they are insufficient to obtain  effective cybersecurity because they are not focused on dealing with  real cyber threats. Because of the great responsibility of lower levels  to achieve effective cybersecurity, this lack of methodological  definition makes it difficult to adapt cybersecurity to the highly  dynamic cyber context with the required holism and strategic alignment.  Our proposal provides CyberTOMP, a process for managing cybersecurity at  lower levels, as well as a set of methodological elements that support  it. The novelty of these contributions is that they complement the  strategic standard selected by the organization, providing it with a set  of procedural elements ready to be used out of the box, contributing  those aspects required by high-level frameworks to manage cybersecurity  at lower levels, for which there is no alternative with a managerial  approach.

Increasing renewable energy penetrations bring complex feasibility and stability problems. Data-driven methods are applied in extracting and embedding these feasibility and stability rules in power system operations and planning. This paper presents a method of alternate support vector machine decision trees for rule extraction problems. The method has significant improvements to the classical decision-tree-based algorithms in terms of efficiency, stability and versatility. Finally, we apply the method to several power and energy system scenarios to show its effectiveness.

With the introduction of spectrum sharing and het-erogeneous services in next-generation networks, the base stations need to sense the wideband spectrum and identify the spectrum resources to meet the quality-of-service, bandwidth, and latency constraints. Sub-Nyquist sampling (SNS) enables digitization for sparse wideband spectrum without needing Nyquist speed analog-to-digital converters. However, SNS demands additional signal processing algorithms for spectrum reconstruction, such as the well-known orthogonal matching pursuit (OMP) algorithm. OMP is also widely used in other compressed sensing applications. The first contribution of this work is efficiently mapping the OMP algorithm on the Zynq system-on-chip (ZSoC) consisting of an ARM processor and FPGA. Experimental analysis shows a significant degradation in OMP performance for sparse spectrum. Also, OMP needs prior knowledge of spectrum sparsity. We address these challenges via deep-learning-based architectures and efficiently map them on the ZSoC platform as second contribution. Via hardware-software co-design, different versions of the proposed architecture obtained by partitioning between software (ARM processor) and hardware (FPGA) are considered. The resource, power, and execution time comparisons for given memory constraints and a wide range of word lengths are presented for these architectures.

This paper focusses on the extraction of the essential numerical functions required to characterize the parametric average-value model of synchronous machine-rectifier systems.

In this paper, we investigate multicell-coordinated beamforming for large-scale multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) communications with low-resolution data converters. In particular, we seek to minimize the total transmit power of the network under received signal-to-quantization-plus-interference-and-noise ratio constraints while minimizing per-antenna transmit power. Our primary contributions are (1) formulating the quantized downlink (DL) OFDM antenna power minimax problem and deriving its associated dual problem, (2) showing strong duality and interpreting the dual as a virtual quantized uplink (UL) OFDM problem, and (3) developing an iterative minimax algorithm to identify a feasible solution based on the dual problem with performance validation through simulations. Specifically, the dual problem requires joint optimization of virtual UL transmit power and noise covariance matrices. To solve the problem, we first derive the optimal dual solution of the UL problem for given noise covariance matrices. Then, we use the solution to compute the associated DL beamformer. Subsequently, using the DL beamformer we update the UL noise covariance   matrices via subgradient projection. Finally, we propose an iterative algorithm by repeating the steps for optimizing DL beamformers. Simulations validate the proposed algorithm in terms of the maximum antenna transmit power and peak-to-average-power ratio.

There are a total of three publicly accessible datasets used, including the Boston Children’s Hospital–MIT (CHB-MIT) dataset from the U.S. and the Freiburg (FB) and EPILEPSIAE intracranial EEG (iEEG) datasets from Germany.

This paper presents a practical approach for Q/V-band modeling for low Earth orbit satellite channels based on tools from machine learning and statistical modeling. The developed Q/V-band LEO satellite channel model is presented in two folds; (i) a real-time forecasting method using model-based deep learning, intended for real-time operation of satellite terminals, and (ii) a statistical channel simulator that generates the path loss as a time-series random process, intended for system design and research. The provided approach capitalizes on real satellite measurements that are obtained from AlphaSat’s Q/V-band transmitter at different geographic latitudes, to model the radio channel. The results show that model-based deep learning forecasting can outperform conventional statistically derived prediction methods for varying rain and elevation angle profiles. Moreover, it can also provide more accuracy in long-term prediction in comparison to current state-of-the-art machine learning approaches for radio channel prediction. Results for the statistical channel simulator is shown to produce synthetic radio excess path loss values for varying satellite passes by capitalizing on empirical statistical models obtained from real measurements.

Optimal network reconfiguration helps with the reduction of total real power losses and the improvement of bus voltages. Several optimal network reconfiguration approaches are available in literature. However, as shown by a very recent study on the IEEE 33-bus radial distribution system, these approaches fail to attain the least total real power losses. Despite that, the recent study did not consider multiple test systems and load variations. Consequently, this article addresses those unexplored areas by introducing an optimal network reconfiguration approach that minimizes the total real power losses in the standard IEEE 33- and 69-bus radial distribution systems under nominal (100%) and heavy (160%) load conditions. The developed approach, which is based on Sparrow Search Algorithm, intelligently searches for the optimum tie and sectionalizing branches that have to be switched on and off, respectively, in order to minimize the systems’ total real power losses. The search for the optimum tie and sectionalizing branches is performed in the permutations of loops created by the radial distribution systems’ tie and sectionalizing branches. For the considered two test systems,  the developed approach gave the least total real power losses and the best bus voltage than the referenced approaches.

An accurate and robust perception system is key to understanding the driving environment of autonomous driving and robots. Autonomous driving needs 3D information about objects, including the object’s location and pose, to understand the driving environment clearly. A camera sensor is widely used in autonomous driving because of its richness in color, texture, and low price. The major problem with the camera is the lack of 3D information, which is necessary to understand the 3D driving environment. Additionally, the object’s scale change and cclusion make 3D object detection more challenging. Many deep learning-based methods, such as depth estimation, have been developed to solve the lack of 3D information. This survey presents the image 3D object detection 3D bounding box encoding techniques, feature extraction techniques, and evaluation metrics of 3D object detection. The image-based methods are categorized based on the technique used to estimate an image’s depth information, and insights are added to each method. Then, state-of-the-art (SOTA) monocular and stereo camera-based methods are summarized. We also compare the performance of the selected 3D object detection models and present challenges and future directions in 3D object detection.

In this work, a methodology for developing a memory compact model is described using recurrent neural network (RNN). Compared to traditional modeling approaches, it is flexible and able to develop an accurate model based on physical data before the material physics is fully understood. A simple ReRAM-type memory model is demonstrated using Nonlinear Autoregressive with External Input (NARX) machine learning approach. To enable the neural network to capture the resistive switching characteristics of ReRAM, the output at a range of cycling voltages was captured and used as training data. The trained model is used for DC prediction under different voltage amplitudes, and the prediction results are consistent with the physical data, with MSE values below 10-9. The accuracy of the RNN-assisted ReRAM model during the read, write and erase operations is also evaluated to show the validity of the approach.

P vs NP problem turns out to be P=NP in quantum computing but remains questions of optimization in classical computing.

This research focusses on developing a computationally efficient parametric average-value model of synchronous machine-rectifier systems.

Reliable and fast channel estimation is crucial for next-generation wireless networks supporting a wide range of vehicular and low-latency services. Recently, deep learning (DL) based channel estimation has been explored as an efficient alternative to conventional least-square (LS) and linear minimum mean square error (LMMSE) approaches. Most of these DL approaches have not been realized on system-on-chip (SoC), and preliminary study shows that their complexity exceeds the complexity of the entire physical layer (PHY). The high latency of DL is another concern. This paper considers the design and implementation of deep neural network (DNN) augmented LS-based channel estimation (LSDNN) for { \color{black} preamble-based orthogonal frequency-division multiplexing (OFDM) physical layer (PHY)} on SoC. We demonstrate the gain in performance compared to the conventional LS and LMMSE approaches. Via software-hardware co-design, word-length optimization, and reconfigurable architectures, we demonstrate the superiority of the LSDNN over the LS and LMMSE for a wide range of signal-to-noise ratio (SNR), number of pilots, preamble types, and wireless channels. Further, we evaluate the performance, power, and area (PPA) of the LS and LSDNN application-specific integrated circuit (ASIC) implementations in 45 nm technology. We demonstrate that word-length optimization can substantially improve PPA for the proposed architecture in ASIC implementations.

A zero thickness model of non-uniform spatially dispersive (SD) metasurfaces is developed and demonstrated numerically for practical structures. This is an extension of [1], [2], which proposed a method of modeling uniform spatially dispersive metasurfaces by expressing their surface susceptibilities as rational polynomial functions in the spatial frequency domain. This led to the extended generalized sheet transition conditions (GSTCs) forming a set of differential equations relating the spatial derivatives of both difference and average fields around the surface that were then integrated into an integral equation (IE) solver. Here, the extended GSTCs are further developed to model non-uniform metasurfaces by approximating them as locally linear space invariant (LSI). Using this model, a semi-analytical Floquet method is derived to predict scattered fields from periodically varying metasurfaces. The extended GSTCs, Floquet method, and IE method are tested on several nonuniform surfaces consisting of short metal dipole cells of varying lengths exhibiting strong spatial dispersion. The physical surfaces are simulated in Ansys FEM-HFSS while their zero thickness equivalents, both dispersive and non-dispersive, are simulated using the Floquet and IE methods. Excellent agreement is shown between the Floquet-SD and IE-GSTC-SD methods and HFSS, demonstrating the importance of spatial dispersion to model their scattered fields.

Future communication networks must address the scarce spectrum to accommodate extensive growth of heterogeneous wireless devices. Efforts are underway to address spectrum coexistence, enhance spectrum awareness, and bolster authentication schemes. Wireless signal recognition is becoming increasingly more significant for spectrum monitoring, spectrum management, secure communications, among others. Consequently, comprehensive spectrum awareness on the edge has the potential to serve as a key enabler for the emerging beyond 5G (fifth generation) networks. State-of-the-art studies in this domain have (i) only focused on a single task - modulation or signal (protocol) classification - which in many cases is insufficient information for a system to act on, (ii) consider either radar or communication waveforms (homogeneous waveform category), and (iii) does not address edge deployment during neural network design phase. In this work, for the first time in the wireless communication domain, we exploit the potential of deep neural networks based multi-task learning (MTL) framework to simultaneously learn modulation and signal classification tasks while considering heterogeneous wireless signals such as radar and communication waveforms in the electromagnetic spectrum. The proposed MTL architecture benefits from the mutual relation between the two tasks in improving the classification accuracy as well as the learning efficiency with a lightweight neural network model. We additionally include experimental evaluations of the model with over-the-air collected samples and demonstrate first-hand insight on model compression along with deep learning pipeline for deployment on resource-constrained edge devices. We demonstrate significant computational, memory, and accuracy improvement of the proposed model over two reference architectures. In addition to modeling a lightweight MTL model suitable for resource-constrained embedded radio platforms, we provide a comprehensive heterogeneous wireless signals dataset for public use.

In this letter, a compact frequency reconfigurable beam-switching antenna based on a single-layer frequency selective face (FSS) is proposed. The proposed antenna consists of a dual-band omnidirectional antenna and a hexagonal single-layer FSS. The dual-band omnidirectional antenna is designed as a radiation source and surrounded by the FSS. Each FSS unit consists of two crossed patches and diodes. Frequency reconfigurable characteristics at 2.4 or 5 GHz are achieved by switching the states of the diodes on the single-layer FSS. With specific combinations of FSS units, the omnidirectional radiation patterns can be transformed into directional ones scanning along the entire azimuth plane. To validate the design, a prototype is fabricated and measured. The results show that the proposed single-layer FSS antenna can perform a 12-step scan along the azimuth plane at 2.4 and 5 GHz, respectively