This paper presents a novel patch-based noise reduction framework designed to enhance the performance of Low Probability of Intercept (LPI) radar waveform classification. The proposed approach capitalizes on the unique characteristic of the waveform's Time-Frequency Images (TFIs) being concentrated in the central region of the image. By partitioning the noisy image into multiple patches, each patch is independently processed using convolutional autoencoders. This method effectively eliminates noise and restores the signal in low Signal-to-Noise Ratio (SNR) environments, thus mitigating interference between the signal and noise components. Simulation results demonstrate the superior performance of the proposed method, achieving an 11% improvement in accuracy compared to classifying noisy images at a -10dB SNR.

This article proposes zero-forcing (ZF) beamforming strategies that can simultaneously deal with active and passive eavesdroppers in visible light communication (VLC) systems. First, we propose a ZF beamforming scheme that steers a transmission beam to the null space of active eavesdroppers' (AEDs) channel, while simultaneously considering the SNRs for a legitimate user (UE) and passive eavesdroppers (PEDs) residing at unknown locations. To find an eigenmode related to the optimal beamforming vector, we adopt an inverse free preconditioned Krylov subspace projection method. For unfavorable VLC secrecy environments, the proposed ZF beamformer appears to be incapable of effectively coping with the PEDs due to the strict condition that the data transmission must be in the null space of the AEDs' channel matrix. Hence, an alternative beamforming scheme is proposed by relaxing the constraint on the SNRs of the AEDs. The related optimization problem is formulated to reduce the secrecy outages caused by PEDs, while simultaneously satisfying the target constraints on the SNRs of the UE and the AEDs. To simplify the mathematical complexity of the approach, Lloyd's algorithm is employed to sample the SNR field, which in turn discretizes the problem, thus making it tractable for practical implementation. The numerical results show that both the exact and relaxed ZF beamforming methods achieve superior performance in the sense of secrecy outage relative to a benchmark ZF scheme. Moreover, the proposed relaxed ZF beamforming method is shown to cope with PEDs better than the exact ZF beamforming approach for unfavorable VLC environments.

This paper proposes a novel zero-forcing (ZF) beamforming strategy that can simultaneously cope with active and passive eavesdroppers (EDs) in visible light communication systems. A related optimization problem is formulated to maximize the signal-to-noise ratio (SNR) of the legitimate user (UE) while suppressing the SNR of active ED to zero and constraining the average SNR of passive EDs. The proposed beamforming directs the transmission along a particular eigenmode related to the null space of the active ED channel and the intensity of the passive ED point process. An inverse free preconditioned Krylov subspace projection method is used to find the eigenmode. The numerical results show that the proposed ZF beamforming scheme yields better performance relative to a traditional ZF beamforming scheme in the sense of increasing the SNR of the UE and reducing the secrecy outage probability.

This paper proposes a secrecy enhancement mechanism for multiuser visible light communication (VLC) systems. Thanks to the inherent advantages of visible light that it cannot penetrate opaque walls and its channel gain largely depends on the distance, VLC systems can serve multiple users at a time with high security and dense spatial reuse. Nevertheless, in the presence of multiple users, the interference caused by other users' signals should be carefully considered when analyzing the secrecy rate and data rate performance measures. By employing a continuous LED model, we formulate an optimization problem to find the optimal set of LEDs that should be used for communication such that the average secrecy rate for the secured user is maximized while satisfying data rate requirements for the other ordinary users. Numerical results are provided to verify that the relative locations of multiple users, the spatial distribution of a random eavesdropper, and the required data rates are significant factors that affect the secrecy performance in multiuser VLC systems.

In this paper, we propose a secure visible light communication (VLC) system with a novel spatial jamming scheme, which is inspired by practical observations of indoor VLC environments. In reality, probable and approximate locations of VLC users can be anticipated by analyzing the user behavior characteristic and the layout of the room. Based on the available location knowledge of a legitimate user (UE) and an eavesdropper (ED), an LED transmitter can choose to convey data or a jamming signal. We call this strategy spatial jamming. By employing a continuous LED model, the related optimization problems are formulated and analyzed based on the signal-to-interference-plus-noise ratio and the secrecy rate, respectively. The numerical results are provided to validate the prediction that the proposed spatial jamming scheme can effectively secure a VLC transmission even when the LEDs do not know the exact location of the ED.

This paper considers physical layer security enhancement mechanisms that utilize simultaneous beamforming and jamming in visible light communication systems with a randomly located eavesdropper under the assumption that there are multiple light-emitting diode (LED) transmitters and one intended user. When an eavesdropper with an augmented front-end receiver is present, the jamming is very useful for preventing the eavesdropper from wiretapping the information since it is not possible to extract only the information component from the received signal if the jamming signal is random. Thus, in this paper, an optimization problem is formulated with a focus on the signal-to-interference-plus-noise ratio for the legitimate link, and it is solved by a heuristic method called the concave-convex procedure. Then, a ternary scheme is proposed, which is less complicated than the full (joint) scheme, and it is optimized by adopting a formulation based on an assignment problem, the solution of which is effectively obtained by the so-called tabu search procedure. In addition, the problem of maximizing the average secrecy rate is investigated by utilizing a continuous LED model, which significantly relaxes the complication that rises from calculating the expectation with respect to the location of the eavesdropper. Our analysis and simulation results show that the proposed simultaneous beamforming and jamming strategies (both joint and ternary) are good proxies for maximizing the average secrecy rate by utilizing the statistical information on the eavesdropper's random location.

Considering reflected light in physical layer security (PLS) is very important because a small portion of reflected light enables an eavesdropper (ED) to acquire legitimate information. Moreover, it would be a practical strategy for an ED to be located at an outer area of the room, where the reflection light is strong, in order to escape the vigilance of a legitimate user. Therefore, in this paper, we investigate the impact of multipath reflections on PLS in visible light communication in the presence of randomly located eavesdroppers. We apply spatial point processes to characterize randomly distributed EDs. The generalized error in signal-to-noise ratio that occurs when reflections are ignored is defined as a function of the distance between the receiver and the wall. We use this error for quantifying the domain of interest that needs to be considered from the secrecy viewpoint. Furthermore, we investigate how the reflection affects the secrecy outage probability (SOP). It is shown that the effect of the reflection on the SOP can be removed by adjusting the light emitting diode configuration. Monte Carlo simulations and numerical results are given to verify our analysis.

This paper considers secrecy enhancement mechanisms in visible light communication (VLC) systems with spatially distributed passive eavesdroppers (EDs) under the assumption that there are multiple LED transmitters and one legitimate user equipment. Based on certain amplitude constraints, we propose a beamforming scheme to improve secrecy performance. Contrary to the case where null-steering is made possible by using knowledge of the ED locations, the proposed beamforming when only statistical information about ED locations is available directs the transmission along a particular eigenmode related to the intensity of the ED process and the intended channel. Then, a LED selection scheme that is less complicated than beamforming is provided to reduce the secrecy outage probability (SOP). An approximate closed-form for the SOP is derived by using secrecy rate bounds. All the analysis is numerically verified by Monte-Carlo simulations. The analysis shows that the beamformer yields superior performance to LED selection. However, LED selection is still a highly efficient alternative scheme due to the complexity associated with the use of multiple transmitters in the full beamforming approach. These performance trends and exact relations between system parameters can be used to develop a secure VLC system in the presence of randomly distributed EDs.

Information which is related to the geographic area is being produced continuously. However, there is currently no technique that can handle large spatial data. For this reason, we developed a spatial big data platform, ORANGE, based on the Apache Hadoop. ORANGE can load the vector and raster data based on HDFS and manages metadata and creates index data using the Apache HIVE. These improvements made the platform faster process of large scale spatial data. In case of comparing the nine-Intersection Model.

For multi-hop wireless sensor networks (WSNs), time synchronization (TS) across distributed nodes is important for certain applications (i.e., surveillance, gunshot location system, etc.). In this paper, a method combining two forms of cooperative transmission (CT), digital-based Concurrent Cooperative Transmission (CCT) and analog-based Semi-Cooperative Spectrum Fusion (SCSF), is proposed and demonstrated to achieve fast and accurate time synchronization over large multihop WSNs. CCT, considered in many physical layer system studies and realistically demonstrated, enables range extension. Range extension implies that the source timestamp can be diffused to the network faster (i.e., in fewer hops) than non-CT-based TS protocols. SCSF is an analog CT method where different cooperators transmit correlated information simultaneously. The two methods combine to create a new distributed method of network TS, called the Cooperative Analog and Digital (CANDI) TS protocol, which promises significantly shorter protocol time and smaller time errors in multi-hop networks. This paper presents the algorithm description, and physical layer simulations as well as network experimental results over a line network that show the performance advantage of CANDI over TPSN.

In spatial data sets, gaps or overlaps among features are frequently found in spatial tessellations due to the non-abutting edges with adjacent features. These non-abutting edges in loose tessellations are also called inconsistent boundaries or slivers; polygons containing at least one inconsistent boundary are called inconsistent polygons or sliver polygons. The existing algorithms to solve topological inconsistencies in sliver polygons suffer from one or more of three major issues, namely determination of tolerances, excessive CPU processing time for large data sets and loss of vertex history. In this article, we introduce a new algorithm that mitigates these three issues. Our algorithm efficiently searches the features with inconsistent polygons in a given spatial data set and logically partitions them among adjacent features. The proposed algorithm employs the constrained Delaunay triangulation technique to generate labelled triangles from which inconsistent polygons with gaps and overlaps are identified using label counts. These inconsistent polygons are then partitioned using the straight skeleton method. Moreover, each of these partitioned gaps or overlaps is distributed among the adjacent features to improve the topological consistency of the spatial data sets. We experimentally verified our algorithm using the real land cadastre data set. The comparison results show that the proposed algorithm is four times faster than the existing algorithm for data sets with 200,000 edges.