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The recently emerged symbol-level precoding (SLP) technique is a promising solution in multi-user wireless communication systems due to its ability to transform harmful multi-user interference (MUI) into useful signals, thereby improving system performance. Conventional symbol-level precoding designs have a significant computational complexity that makes their practical implementation difficult and imposes excessive computational complexity on the system. To deal with this problem, we suggest a new deep learning (DL) based approach that utilizes low-complexity designs of symbol-level precoding. This paper focuses on DL-based one-bit precoding approaches for downlink massive multiple-input multiple-output (MIMO) systems, where one-bit digital-to-analog converters (DACs) are used to reduce cost and power. Unlike previous works, the optimized one-bit precoder for multiuser massive MIMO system (HDL-O1PmMIMO) for a wide range of signal-to-noise-ratio (SNR) has a low computational complexity, making it suitable for real precoding scenarios. In this paper, we first design an unsupervised DL-based precoder (UDL-O1PmMIMO) to address the low SNR scenarios, using which we then design a hybrid DL-based precoder (HDL-O1PmMIMO) to address both low and high SNR scenarios. The method suggested in this article utilizes a novel residual DL network structure, which helps overcome the problem of training very deep networks. Additionally, a novel customized cost function, specifically for one-bit precoding in massive MIMO systems, is introduced to optimize the performance of the system in handling interference. The results of an experiment conducted on a general test set using Python and MATLAB show that the proposed approach outperforms existing methods in three aspects: it has a lower bit error rate, it takes less time to generate the precoded vector, and it is more resistant to imperfect channel estimation.

These days, extracting information from remote sensing data has a great impact on various aspects of our lives, such as infrastructure and urban planning, transportation and traffic management, forecasting and tracking natural disasters, searching for mineral resources, monitoring environmental changes, and numerous other fields. One crucial application is extracting accurate road information from aerial images, which has many practical applications ranging from our daily lives to long-term planning for transportation systems to autonomous vehicles. Deep learning models have shown great promise in image-processing tasks, specifically in accurately detecting and extracting roads from aerial images. In this study, various techniques were employed to achieve the desired performance. The model is a UNet assisted with attention blocks in the decoder part and trained with a patched, rotated, and augmented dataset that has been extracted from the DeepGlobe dataset. The preprocessing of the dataset included image and mask patching, rotation, exclusion of background-only images, and excluding images with very little road surface. Both patching and background exclusion in preprocessing as hard attention and attention blocks in the model as soft attention were deployed in order to tackle the inherently biased nature of the dataset. This combination of different techniques empowers the proposed model for superior remote sensing image segmentation performance with an accuracy level of 98.33%. In addition to achieve better performance by the model, another objective is to find the issues that cause the model's performance degradation on some image samples. Therefore, a comprehensive analysis of metrics, with a focus on precision and recall as proper metrics for biased dataset analysis, was conducted to identify potential shortcomings in the model or the dataset, and based on the result, several proposals for future work and further investigations were formulated.

Stealthy zero-dynamics attacks are a subset of false data injection attacks (FDIAs) that are especially dangerous, as they are designed to be undetectable by any intrusion detection mechanisms. This paper shows that stealthy attacks can be more destructive on power electronic dominated grids (PEDGs) than traditional power systems, by taking advantage of the low inertia property of PEDGs. The low inertia of these power grids causes the system to be prone to frequency instability when disturbances occur, meaning that an attacker can cause more harm to a system in a shorter amount of time. Another advantage to the attacker is an increased amount of telecommunication devices in PEDGs which provides the attacker with a larger attack surface. It is thus critical that we strive to design PEDGs in such a manner that we minimize their susceptibility to stealthy attacks. We provide a small signal model for a PEDG system, along with the the state space representation of the low-inertia part of the system, and we show that even without the state-space model of the whole system, a stealthy zero-dynamics attack can be constructed and can be successful on a PEDG. Results are also provided to show that strategically choosing model parameters in the design phase of the system can prevent the existence of stealthy zero-dynamics attacks.

A novel miniaturized microstrip low pass filter (LPF) with a wide stopband and sharp roll-off is presented. The designed filter is developed by realizing a T-shaped structure loaded with a stub as a resonator and open-stubs as suppressor cells to achieve a simple structure for ease of fabrication process. The new filter with a symmetrical structure has a 3 dB cut-off frequency of 2.15 GHz, the transition band is 123 dB/GHz from −3 dB to −40 dB, a stopband with an attenuation level better than −20 dB from 2.4 up to 22.1 GHz with a compact size of $0.0132 {{\lambda _{g}}^{2}}$ . The insertion loss in the passband is less than 0.3 dB and return loss is lower than −20 dB. The filter is fabricated, and the results show agreement between the simulation and measurement.

In this paper, we focus on one-bit precoding approach for downlink massive multiple-input multiple-output (MIMO) systems, where we exploit the concept of constructive interference (CI) employing deep learning (DL) techniques. One of the main performance limiting factors in wireless communication systems is interference, which needs to be minimized or mitigated. By controlling the interference signals in order to add up constructively at the receiver side, there is a possibility to improve the system performance. This paper presents a DL-based one-bit precoding scheme that improves the massive MIMO performance via CI exploitation in the presence of one-bit digital to analog converters (DAC) as a hardware impairment. More precisely, for phase shift keying signaling, we first formulate the optimization problem in order to maximize the CI effects in the case of a base station equipped with one-bit DACs. Then, after solving the optimization problem and creating a large enough dataset, a DL network is trained to do the precoding. Numerical results show that the DL-based solution approaches the performance of the conventional interference exploitation one-bit precoding schemes in the massive MIMO systems while having an order of magnitude less complexity.

This paper proposes a control scheme to force homogeneity for heterogenous network of the grid-forming (GFM) inverters in power electronics dominated grid (PEDG) to enable their aggregation and coherent dynamic interaction. Increased penetration of the renewable energy in distributed generation (DG) fashion is moving traditional power system to a highly disperse and complex heterogenous system i.e., PEDG with fleet of grid-forming and grid-following inverters. Optimal coordination, stability assessment, and situational awareness of PEDG is challenging due to numerous heterogenous inverters operating at the grid-edge that is outside the traditional utility centric power generation boundaries. Aggregation of these inverters will not be insightful due to their heterogenous characteristics. The proposed control scheme to force enclaved homogeneity (FEH) enables an insightful aggregation of GFM that can fully mimic the given physical system dynamics. The proposed FEH scheme enables coherent and homogenized dynamic interaction of GFM inverters that enhances the PEDG resiliency. Moreover, different cluster of GFM can be merged into single cluster with minimal synchronization time and frequency fluctuations. Accurate reference models can be achieved that enables effective dynamic assessment and optimal coordination which results in resilient PEDG. Several case studies provided to validate the effectiveness of proposed FEH in network of GFM. Then, GFMs aggregation and developed reference model for the PEDG system is validated via multiple comparative case studies.

In this paper, a novel method for real-time horizon-based attitude estimation using panoramic images is presented with results from real flight tests showing accurate attitude estimation for a rotary-wing aircraft in cluttered environments. The vision system is biologically inspired from the function of the Ocelli organ present in some insects and the fact that ultraviolet images provide better sky/ground contrast enhancement. A new method for panoramic sky/ground thresholding is proposed, consisting of a sky/ground masking and a sun-tracking system that works effectively even when the horizon line is difficult to detect by normal thresholding methods due to flares and other effects from the presence of the sun in the image. The use of optic flow to determine body rates is investigated using the panoramic image and the image interpolation algorithm. A Kalman filter is used to fuse the unfiltered measurements from inertial sensors and the vision system. [ABSTRACT FROM AUTHOR]

In this paper, we design and investigate a graphene magneto-Plasmon waveguide for plasmonic mode switch application. First, an analytical model based on effective index method is presented to extract waveguide properties, such as effective index, effective guide width, cutoff wavelength, and the number of guided modes. We then verify the results with the finite element method. Due to the strong magneto-optical activity of graphene under an external magnetic field, waveguide properties can be widely tuned magnetically. Here, using a combination of several parameters, including chemical potential, magnetic field, and structure width, we manipulate the waveguide properties, such as mode confinement and the number of guided modes in order to achieve desired functions. The results demonstrate that the proposed structure acts as a mode switch device. Namely, for a given chemical potential and wavelength, the guided modes are controlled by the magnetic field. Moreover, we show that the waveguide becomes non-reciprocal in the presence of a magnetic field, which enables the structure to act as a plasmonic isolator and circulator.

Recent advances in wearable devices allow non-invasive and inexpensive collection of biomedical signals including electrocardiogram (ECG), blood pressure, respiration, among others. Collection and processing of various biomarkers are expected to facilitate preventive healthcare through personalized medical applications. Since wearables are based on sizeand resource-constrained hardware, and are battery operated, they need to run lightweight algorithms to efficiently manage energy and memory. To accomplish this goal, this paper proposes SURF, a subject-adaptive unsupervised signal compressor for wearable fitness monitors. The core idea is to perform a specialized lossy compression algorithm on the ECG signal at the source (wearable device), to decrease the energy consumption required for wireless transmission and thus prolong the battery lifetime. SURF leverages unsupervised learning techniques to build and maintain, at runtime, a subject-adaptive dictionary without requiring any prior information on the signal. Dictionaries are constructed within a suitable feature space, allowing the addition and removal of code words according to the signal's dynamics (for given target fidelity and energy consumption objectives). Extensive performance evaluation results, obtained with reference ECG traces and with our own measurements from a commercial wearable wireless monitor, show the superiority of SURF against state-of-the-art techniques, including: 1) compression ratios up to 90-times; 2) reconstruction errors between 2% and 7% of the signal's range (depending on the amount of compression sought); and 3) reduction in energy consumption of up to two orders of magnitude with respect to sending the signal uncompressed, while preserving its morphology. SURF, with artifact prone ECG signals, allows for typical compression efficiencies (CE) in the range CE ∈ [40, 50], which means that the data rate of 3 kbit/s that would be required to send the uncompressed ECG trace is lowered to 60 and 75 bit/s for CE = 40 and CE = 50, respectively.