Using heterogeneous satellites for passive detection of moving aerial target

Passive detection of a moving aerial target is critical for intelligent surveillance. Its implementation can use signals transmitted from satellites. Nowadays, various types of satellites co-exist which can be used for passive detection. As a result, a satellite signal receiver may receive signals from multiple heterogeneous satellites, causing difficult in echo signal detection. In this paper, a passive moving aerial target detection method leveraging signals from multiple heterogeneous satellites is proposed. In the proposed method, a plurality of direct wave signals is separated in a reference channel first. Then, an adaptive filter with normalized least-mean-square (NLMS) is adopted to suppress direct-path interference (DPI) and multi-path interference (MPI) in a surveillance channel. Next, the maximum values of the cross ambiguity function (CAF) and the fourth order cyclic cumulants cross ambiguity function (FOCCCAF) correspond into each separated direct wave signal and echo signal will be utilized as the detection statistic of each distributed sensor. Finally, final detection probabilities are calculated by decision fusion based on results from distributed sensors. To evaluate the performance of the proposed method, extensive simulation studies are conducted. The corresponding simulation results show that the proposed fusion detection method can significantly improve the reliability of moving aerial target detection using multiple heterogeneous satellites. Moveover, we also show that the proposed detection method is able to significantly improve the detection performance by using multiple collaborative heterogeneous satellites

Public Perceptions of Preservation Policies and Practices in Historic Residential Neighborhood: A Case of Dongsi, Beijing, China

This project is an interdisciplinary, qualitative study of historic preservation policies and practices in an 800-year-old hutong and courtyard house neighborhood that is designated as a historical and cultural conservation area in the inner city of Beijing. Utilizing the people-centered approach in heritage conservation, this project joins the ongoing international conversation and efforts to expand the scope, relevance, and significance of historic preservation as a field, by bringing intangible aspects, such as living heritage traditions and practices, into the discussion. Interviews and analysis of policy background are used to access the long-term residents’ perceptions of preservation policies and practices in the context of their lived experience and their relationship with other neighborhood stakeholders. Topics of discussion include neighborhood values, conservation issues related to life quality concerns in the neighborhood, as well as involvement and engagement initiatives led by the government and their effectiveness. The goal of this project is to use a local case study to examine how preservation can benefit people’s wellbeing in general, in addition to protecting the built environment of a historic neighborhood. This project situates global conservation issues into the framework of social and urban development in contemporary China, and provides recommendations for more effective community engagement strategies for future policy makers and professionals

Passive detection of moving aerial target based on multiple collaborative GPS satellites

Passive localization is an important part of intelligent surveillance in security and emergency applications. Nowadays, Global Navigation Satellite Systems (GNSSs) have been widely deployed. As a result, the satellite signal receiver may receive multiple GPS signals simultaneously, incurring echo signal detection failure. Therefore, in this paper, a passive method leveraging signals from multiple GPS satellites is proposed for moving aerial target detection. In passive detection, the first challenge is the interference caused by multiple GPS signals transmitted upon the same spectrum resources. To address this issue, successive interference cancellation (SIC) is utilized to separate and reconstruct multiple GPS signals on the reference channel. Moreover, on the monitoring channel, direct wave and multi-path interference are eliminated by extensive cancellation algorithm (ECA). After interference from multiple GPS signals is suppressed, the cycle cross ambiguity function (CCAF) of the signal on the monitoring channel is calculated and coordinate transformation method is adopted to map multiple groups of different time delay-Doppler spectrum into the distance−velocity spectrum. The detection statistics are calculated by the superposition of multiple groups of distance-velocity spectrum. Finally, the echo signal is detected based on a properly defined adaptive detection threshold. Simulation results demonstrate the effectiveness of our proposed method. They show that the detection probability of our proposed method can reach 99%, when the echo signal signal-to-noise ratio (SNR) is only −64 dB. Moreover, our proposed method can achieve 5 dB improvement over the detection method using a single GPS satellite

Reliability of plasma-etched copper lines on a glass substrate

Copper (Cu) thin films can be etched into 0.3 micrometer Cu patterns, as shown in Figure 1. This process has been used in the fabrication of large-area thin film transistor (TFT) arrays for LCDs, interconnect lines in high density BiCMOS circuits, and source, drain and gate electrodes of a-Si:H TFTs (1,2). The reliability of the Cu line is usually investigated with the isothermal electromigration (EM) method (3). In almost all studies, the Cu line was prepared on the silicon substrate coated with a dielectric layer. There are few studies on Cu line lifetime on the glass substrate. Also, the EM failure investigation was focused on the line broken time, which could be influenced by the edge roughness, step coverage, current density, etc. (4). The physical and structure changes of the Cu line during the EM stress time are often neglected (5). In this paper, authors discuss the application of the plasma etched Cu line for the SSI-LED array. It allows the driving of the individual device for light emitting at specified conditions, which enables applications in displays, optical interconnects, etc. The transformation of the Cu line from a continuous pattern to the broken state will be reviewed. The temperature change with respect to the stress current density and the lifetime will be discussed. In summary, the room temperature plasma-based Cu etch process can be applied to a wide range of electronic and optoelectronic products. The understanding of the reliability of the Cu line is important for these applications. Please click Additional Files below to see the full abstract

Signal estimation in cognitive satellite networks for satellite-based industrial internet of things

Satellite industrial Internet of Things (IIoT) plays an important role in industrial manufactures without requiring the support of terrestrial infrastructures. However, due to the scarcity of spectrum resources, existing satellite frequency bands cannot satisfy the demand of IIoT, which have to explore other available spectrum resources. Cognitive satellite networks are promising technologies and have the potential to alleviate the shortage of spectrum resources and enhance spectrum efficiency by sharing both spectral and spatial degrees of freedom. For effective signal estimations, multiple features of wireless signals are needed at receivers, the transmissions of which may cause considerable overhead. To mitigate the overhead, part of parameters, such as modulation order, constellation type, and signal to noise ratio (SNR), could be obtained at receivers through signal estimation rather than transmissions from transmitters to receivers. In this article, a grid method is utilized to process the constellation map to obtain its equivalent probability density function. Then, binary feature matrix of the probability density function is employed to construct a cost function to estimate the modulation order and constellation type for multiple quadrature amplitude modulation (MQAM) signal. Finally, an improved M 2 M ∞ method is adopted to realize the SNR estimation of MQAM. Simulation results show that the proposed method is able to accurately estimate the modulation order, constellation type, and SNR of MQAM signal, and these features are extremely useful in satellite-based IIoT

Attacking Modulation Recognition With Adversarial Federated Learning in Cognitive Radio-Enabled IoT

Internet of Things (IoT) based on cognitive radio (CR) exhibits strong dynamic sensing and intelligent decision-making capabilities by effectively utilizing spectrum resources. The federal learning (FL) framework based modulation recognition (MR) is an essential component, but its use of uninterpretable deep learning (DL) introduces security risks. This paper combines traditional signal interference methods and data poisoning in FL to propose a new adversarial attack approach. The poisoning attack in distributed frameworks manipulates the global model by controlling malicious users, which is not only covert but also highly impactful. The carefully designed pseudo-noise in MR is also extremely difficult to detect. The combination of these two techniques can generate a greater security threat. We have further advanced our proposal with the introduction of the new adversarial attack method called "Chaotic Poisoning Attack" to reduce the recognition accuracy of the FL-based MR system. We establish effective attack conditions, and simulation results demonstrate that our method can cause a decrease of approximately 80% in the accuracy of the local model under weak perturbations and a decrease of around 20% in the accuracy of the global model. Compared to white-box attack methods, our method exhibits superior performance and transferability

Multiuser adversarial attack on deep learning for OFDM detection

Adversarial attack has been widely used to degrade the performance of deep learning (DL), especially in the field of communications. In this letter, we evaluate different white-box and black-box adversarial attack algorithms for a DL-based multiuser orthogonal frequency division multiplexing (OFDM) detector subject to multiuser adversarial attack. The bit error rates under different adversarial attacks are compared. The results show that, the perturbation efficiency of adversarial attack is higher than conventional multiuser interference. Virtual adversarial methods (VAM) and zeroth-order-optimization (ZOO) attacks perform the best among white-box and black-box methods, respectively. They are also effective when the attack changes the starting time. Additionally, adding the number of attackers is found useful to improve the VAM attack but not for ZOO. This work shows that adversarial attack is powerful to generate adversarial against multiuser OFDM communications

Spectrum and energy efficient multi-antenna spectrum sensing for green UAV communication

Unmanned Aerial Vehicle (UAV) communication is a promising technology that provides swift and flexible on-demand wireless connectivity for devices without infrastructure support. With recent developments in UAVs, spectrum and energy efficient green UAV communication has become crucial. To deal with this issue, Spectrum Sharing Policy (SSP) is introduced to support green UAV communication. Spectrum sensing in SSP must be carefully formulated to control interference to the primary users and ground communications. In this paper, we propose spectrum sensing for opportunistic spectrum access in green UAV communication to improve the spectrum utilization efficiency. Different from most existing works, we focus on the problem of spectrum sensing of randomly arriving primary signals in the presence of non-Gaussian noise/interference. We propose a novel and improved p-norm-based spectrum sensing scheme to improve the spectrum utilization efficiency in green UAV communication. Firstly, we construct the p-norm decision statistic based on the assumption that the random arrivals of signals follow a Poisson process. Then, we analyze and derive the approximate analytical expressions of the false-alarm and detection probabilities by utilizing the central limit theorem. Simulation results illustrate the validity and superiority of the proposed scheme when the primary signals are corrupted by additive non-Gaussian noise and arrive randomly during spectrum sensing in the green UAV communication

Attacking Spectrum Sensing With Adversarial Deep Learning in Cognitive Radio-Enabled Internet of Things

Cognitive radio-based Internet of Things (CR-IoT) network provides a solution for IoT devices to efficiently utilize spectrum resources. Spectrum sensing is a critical problem in CR-IoT network, which has been investigated extensively based on deep learning (DL). Despite the unique advantages of DL in spectrum sensing, the black-box and unexplained properties of deep neural networks may lead to many security risks. This article considers the fusion of traditional interference methods and data poisoning which is an attack method on the training data of a machine learning tool. We propose a new adversarial attack for reducing the sensing accuracy in DL-based spectrum sensing systems. We introduce a novel design of jamming waveform whose interference capability is reinforced by data poisoning. Simulation results show that significant performance enhancement and higher mobility can be achieved compared with traditional white-box attack methods