In the realm of expanding unmanned aerial vehicle (UAV) applications and types, the precision of UAV target classification is of paramount importance. Deep learning has emerged as the linchpin of such endeavors. A new approach based on deep learning fusion technique is proposed by our team, which integrates frequency modulated continuous wave (FMCW) radar micro-Doppler signals, cadence-velocity diagram (CVD) signals and cepstrum (CEP) signals. This synthesis culminates in UAV classification with exceptional accuracy, surpassing 97%. In this paper, two deep learning fusion approaches leveraging the ResNet34 network were employed: data-level fusion and feature-level fusion. Empirical results unequivocally highlight the potency of deep learning information fusion—most notably, the fusion of the three spectrograms—exceeding 97% accuracy. This firmly underscores the pivotal role that deep learning fusion techniques play in amplifying precision in UAV target classification.

Fully Homomorphic Encryption ($\mathsf {FHE}$) allows computing over encrypted data without decrypting the corresponding ciphertexts, and it constitutes a promising cryptographic primitive to preserve data privacy in the big data computing environments. In general,$\mathsf {FHE}$schemes can be constructed by using the standard Learning with Errors ($\mathsf {LWE}$) assumption, and the current crux lies in how to achieve efficient multi-bit$\mathsf {FHE}$encryption while being leakage-resistent against attackers who may capture the information of cryptographic secret keys via side channel attacks. Based on Berkoff-Liu’s work at TCC’14, we aim to address this issue by giving a new structure of public key matrix with any number of$\mathsf {LWE}$instances, thereby avoiding the use of a straightforward composition to achieve multi-bit$\mathsf {FHE}$encryption under standard$\mathsf {LWE}$. Particularly, our scheme attains provable security.

Many cryptography techniques have been used to protect the privacy of vehicular ad hoc networks(VANETs). For example, the ring signature, one of the anonymous techniques by virtue of its unconditional anonymity, is widely used in VANETs for privacy protection. However, some of their cryptographic properties may have been weakened due to the characteristic of VANETs. In this paper, we study the impact of the influences of ring selection on the anonymity property of ring signature when applied to privacy protection in VANETs. Specifically, we introduce a novel ring selection attack aimed to compromise the anonymity of vehicles in VANETs. We then propose a ring selection algorithm based on the analysis of the ring selection attack. Furthermore, by taking the information entropy as the privacy metric, we develop a privacy analytic model to quantitatively investigate the privacy level achieved in the solution. Finally, the analytical results indicate that better privacy protection can be achieved when selecting ring members according to our ring selection algorithm.

Proxy re-encryption (PRE) is a fundamental cryptographic primitive in secure data sharing and e-mail forwarding, etc. To our knowledge, most existing efficient lattice-based PRE schemes focus on the construction of single-hop, key-private, multi-bit and chosen-ciphertext attack (CCA), etc. Few works of literature discussed the detailed multi-hop construction over lattices. Very recently, Chandran et al. (PKC'14) proposed a lattice-based PRE scheme that builds upon the key switching mechanism of Brakerski (CRYPTO'12), and pointed out that their scheme can achieve multi-hop PRE scheme by the ideal circuit family for a directed graph G. In this paper, we are still working along this line and achieving multi-hop PRE via the branching program (BP), which is one type of NC1 circuit and can be used to compute encrypted data. To our knowledge, we proposed the first multi-hop PRE scheme via BP which supports homomorphic evaluation. We also analyze the security of our scheme under decisional learning with errors (LWE) assumption.

In this paper, we use the data of China’s manufacturing listed companies from 2009 to 2018, adopt the method of propensity score matching and double difference (PSM-DID) to solve the sample’s selective bias, and select the accelerated depreciation policy of fixed assets issued by China in 2014 as a quasi-natural experiment to verify the robustness of the empirical results, which will affect the R&D investment of manufacturing enterprises and the structural tax reduction of China. This paper makes an empirical study on the effect of fixed asset investment to restrain the financialization of manufacturing enterprises. The results show that (1) accelerated depreciation policy of fixed assets significantly promotes the R&D investment and fixed asset investment of enterprises and reduces the level of enterprise financialization; (2) accelerated depreciation of fixed asset local tax policy, through guiding the R&D investment, fixed asset investment, and deferred income tax acquisition of enterprises. It guides the investment of enterprises to the real economic field, thus reducing the financial assets of enterprises. The investment has restrained the financial trend of real enterprises. The conclusion of this paper is of practical significance to support the formulation and implementation of the national structural tax reduction policy and to clarify the regulatory role and mechanism of the structural tax reduction policy.

An ultrawide band (UWB) antipodal Vivaldi antenna is designed for airborne ground penetrating radar (GPR) system. With exponential tapered slots and slot edges, the presented antenna demonstrates a wide operating band from 300 MHz to more than 2 GHz. The fabricated prototype of this antenna with a compact size of 450 mm × 600 mm × 1.5 mm has been measured in anechoic chamber. Both simulation and experimental results show that good radiation patterns with gain 4.4-11.5 dBi in the endfire direction could be achieved in the whole working band, which indicates that the proposed antenna is suitable for airborne GPR application.

A modified 2-D reverse-time migration algorithm in the energy flow domain, called EF-RTM, is proposed for short impulse borehole radar (BHR) imaging. The key of the approach is based on Poynting's theorem, which allows decomposition of the energy flux density (EFD) derived from the source and receiver wave fields in different wave-propagation directions. Then, imaging conditions, for example, zero-lag cross correlation (prestack migration) and zero-time imaging principle (poststack migration), are applied to the decomposed EFD-field components to obtain the migrated sections. The characteristics of the resulting images can be optionally combined or separately used for better BHR data imaging, interpretation, and analysis. In this paper, the EF-RTM algorithm is validated by numerical modeling and real field data and compared with the conventional RTM algorithm. All the results show that this new EF-RTM method is superior to the conventional RTM method: it inherits the high precision of RTM with additional imaging advantages, such as natural wave-field decomposition in different directions, improved migrated cross-range resolution, a better focus of the target's shape, and migration noise reduction.

The centralized and distributed models are the main system architecture of privacy protection in location-based services (LBS). In the model of centralized, the trusted third party (TTP) is used to provide location privacy, but it is usually thought as the point of attack and the bottleneck of service, which makes the distributed model attract more attention. However, although a large number of algorithms were proposed under this model, they were mainly focussed on the protection of the snapshot query and difficult to provide privacy in the continuous query. Especially that, with the difference of anonymous users, the adversary can get rid of the anonymous users and identify the issuer, and then the privacy of the issuer will be revealed. In order to deal with this problem, we propose an uncertain continuous collaborative users finding algorithm (UCCUFA), which utilizes the uncertainty cells of region grid and the collaborative users to provide query results to make the issuer hiding behind the collaborative users. In this algorithm, all query results are provided by collaborative users, and no information interacted between the issuer and the LBS server during the procedure of continuous query. At last, we utilize the security analysis and experimental results further verify the effectiveness of location privacy protection and the performance efficiency of our proposed algorithm.

Cloud computing raises new challenges for how to protect user privacy. Fully homomorphic encryption is one way to solve the problem. In this paper, we show a useful property of Dual-LWE assumption to construct Key-Switching procedure compared with LWE assumption without extra error term. Hence, we propose to construct "Errorless Key Switching" fully homomorphic encryption (FHE) scheme based on dual learning with errors (Dual-LWE) assumption. Specifically, we compile the Dual-LWE problem proposed by Gentry et.al. at STOC2008 and First-is-errorless LWE (Ferr.LWE) problem proposed by Brakerski et al. at STOC2013 into Dual-First-is-errorless LWE (Dual-Ferr.LWE) problem. Then, utilizing Dual-Ferr.LWE assumption to construct a various GPV(vGPV) scheme, and we use vGPV scheme as the fundamental building block to construct FHE with errorless key switching scheme. Lastly, under the assumption of decisional learning with errors(DLWE), we prove that our scheme is CPA secure.

In location based services (LBSs) of snapshot query, both of query and location privacy should be preserved. Most preceding schemes usually employed a trusted third party (TTP) to achieve k-anonymity, and perturb the real query and location with other k-1 users, but this scheme may be influenced by the single point failure and performance bottleneck. In order to solve this problem, another scheme to achieve k-anonymity with collaborative users through short-range communication was proposed. After deeply analyzing the inherent drawbacks of existing schemes of this type, we have found that most of them mainly focus the location privacy and usually neglect the correlation between query and location. In order to preserve the query privacy, based on the user collaborative scheme, we propose a scheme with query information divide and exchange with random users (short for QDER). This scheme can provide preserving service for both query and location privacy simultaneously. Then we analyze the attack from passive and active adversaries and use entropy to measure the privacy level. Finally, security analysis and experimental evaluations further verify the effectiveness and efficiency.

With the application and popularization of autonomic computing in the field of aerospace exploration, large-scale database management and critical network control, existing self-reflection models based on natural language or diagram can not meet the requirements of analysis and verification. In this article, two kinds of self-reflection models, one is the static and the other is dynamic, capable of model checking and quantification analysis are separately built using performance evaluation process algebra (PEPA). Besides, huge state-spaces of models cause that the traditional Markov chains implied are hard to solve, thus we use an approach of continuous state-space approximation, a recent breakthrough in the analysis of stochastic process algebra, to generate ordinary differential system (ODE) from the PEPA model avoiding state-space explosion. By analyzing the ODEs, it is found that reducing the latency time of monitoring as well as shortening the length of execution instructions plays an important role in improving the performance of self-reflection.

Wireless sensor networks (WSN) have become increasingly one of the most promising and interesting areas over the past few years. But the character of constrained resources makes it vulnerable to Denial of Service (DoS) attacks, resulting in a large number of compromised nodes. Until now, it is unrealistic to prevent DoS attacks, but compromised nodes may be designed to self-recover to reduce the harm of DoS attacks. In this paper, an agent based self-recovery model (ABSR) was presented on the use of autonomic computing, which can recover itself in the approach of anomaly monitoring, anomaly deciding and node recovery. Finally, the model is analyzed and verified with the Eclipse Plug-in PEPA Tool, which suggests that compromised nodes can self-recover effectively using ABSR.