Robust Adaptive LCMV Beamformer Based On An Iterative Suboptimal Solution

The main drawback of closed-form solution of linearly constrained minimum variance (CF-LCMV) beamformer is the dilemma of acquiring long observation time for stable covariance matrix estimates and short observation time to track dynamic behavior of targets, leading to poor performance including low signal-noise-ratio (SNR), low jammer-to-noise ratios (JNRs) and small number of snapshots. Additionally, CF-LCMV suffers from heavy computational burden which mainly comes from two matrix inverse operations for computing the optimal weight vector. In this paper, we derive a low-complexity Robust Adaptive LCMV beamformer based on an Iterative Suboptimal solution (RAIS-LCMV) using conjugate gradient (CG) optimization method. The merit of our proposed method is threefold. Firstly, RAIS-LCMV beamformer can reduce the complexity of CF-LCMV remarkably. Secondly, RAIS-LCMV beamformer can adjust output adaptively based on measurement and its convergence speed is comparable. Finally, RAIS-LCMV algorithm has robust performance against low SNR, JNRs, and small number of snapshots. Simulation results demonstrate the superiority of our proposed algorithms

A convexity approach to dynamic output feedback robust MPC for LPV systems with bounded disturbances

International audienceA convexity approach to dynamic output feedback robust model predictive control (OFRMPC) is proposed for linear parameter varying (LPV) systems with bounded disturbances. At each sampling time, the model parameters and disturbances are assumed to be unknown but bounded within pre-specified convex sets. Robust stability conditions on the augmented closed-loop system are derived using the techniques of robust positively invariant (RPI) set and the S-procedure. A convexity method reformulates the non-convex bilinear matrix inequalities (BMIs) problem as a convex optimization one such that the on-line computational burden is significantly reduced. The on-line optimized dynamic output feedback controller parameters steer the augmented states to converge within RPI sets and recursive feasibility of the optimization problem is guaranteed. Furthermore, bounds of the estimation error set are refreshed by updating the shape matrix of the future ellipsoidal estimation error set. The dynamic OFRMPC approach guarantees that the disturbance-free augmented closed-loop system (without consideration of disturbances) converges to the origin. In addition, when the system is subject to bounded disturbances, the augmented closed-loop system converges to a neighborhood of the origin. Two simulation examples are given to verify the effectiveness of the approach

Energy-efficient secure outsourcing decryption of attribute based encryption for mobile device in cloud computation

This is a copy of the author 's final draft version of an article published in the "Journal of ambient intelligence and humanized computing". The final publication is available at Springer via http://dx.doi.org/10.1007/s12652-017-0658-2In this paper two new ways for efficient secure outsourcing the decryption of key-policy attribute-based encryption (KP-ABE) with energy efficiency are proposed. Based on an observation about the permutation property of the access structure for the attribute based encryption schemes, we propose a high efficient way for outsourcing the decryption of KP-ABE, which is suitable for being used in mobile devices. But it can only be used for the ABE schemes having tree-like access structure for the self-enclosed system. The second way is motivated from the fact that almost all the previous work on outsourcing the decryption of KP-ABE cares little about the ciphertext length. Almost all the previous schemes for secure outsourcing the decryption of ABE have linear length ciphertext with the attributes or the policy. But transferring so long ciphertexts via wireless network for mobile phone can easily run out of battery power, therefore it can not be adapted to practical application scenarios. Thus another new scheme for outsourcing the decryption of ABE but with constant-size ciphertexts is proposed. Furthermore, our second proposal gives a new efficient way for secure outsourcing the decryptor’s secret key to the cloud, which need only one modular exponentiation while all the previous schemes need many. We evaluate the efficiency of our proposals and the results show that our proposals are practical.Peer ReviewedPostprint (author's final draft

Quantum Rotational Cryptanalysis for Preimage Recovery of Round-Reduced Keccak

This paper considers the capability of 4-round Keccak-224/256/384/512 against the cryptanlysis involved by the quantum algorithm. In order to effectively find the corresponding rotational number for the rotational counterpart of preimage, we first establish a probabilistic algorithm based on the Grover search to guess a possible rotational number by using the fixed relations of bits pairs in some coordinates. This is committed to achieving that each iteration of searching the rotational counterparts contains only one run of 4-round Keccak variant applied for the verification, which can reduce the attack complexity in the quantum setting. Based on finding the rotational number under an acceptable randomness, we construct two attack models to focus on the recovery of preimage. In the first model, the Grover’s algorithm serves as finding out a rotational counterpart of the preimage. Through 64 attempts of checking the correct rotational number, the desired preimage can be obtained. In the second model, we abstract the finding of rotational counterparts into searching vertexes on a hypercube, and then, the SKW quantum algorithm is used to deal with the finding of the vertexes acted as rotational counterparts. Compared to the recent works in classical setting, we greatly reduce the attack complexity of preimage recovery. Furthermore, the first attack model is superior to the generic quantum preimage attack for 4-round Keccak-224/256/384/512, and the second model has slightly lower attack effect but more practicality on the 4-round Keccak-512/384, that is, the model is exponentially easier to implement in quantum circuit than both our first attack model and the generic quantum preimage attack

Allocating Rotational Cryptanalysis based Preimage Attack on 4-round Keccak-224 for Quantum Setting

In this paper, we aim to present a quantum setting oriented preimage attack against 4-round Keccak-224. An important technique we called the allocating rotational cryptanalysis takes the preimage attack into the situation of 2-block preimage recovery. With the conditions on the middle state proposed by Li et al., we use the generic quantum preimage attack to deal with the finding of first preimage block. By using the newly explored propagation of rotational relations, we significantly increase the number of eigenpoints at the end of 4-round modified Keccak-f from 0 to 32, and therefore improving the accuracy of determining the rotational number for a certain rotational counterpart in the quantum setting by more than 10 orders of magnitude. On the basis of the above, we design an efficient unitary oracle operator with only twice calling of the 4-round modified Keccak-f, which costs half of previous results, to mark a rotational counterpart of the second preimage block in order that the second preimage block can be found indirectly from a quickly generated specified search space. As a result on the 4-round Keccak-224: In the classical setting, the preimage attack with the complexity decreased to 2^218 is better than the result based on the pioneered rotational cryptanalysis. In the quantum setting, the amplitude amplification driven preimage attack with a complexity of 2^110 is by far the best dedicated quantum preimage attack. Additionally, the SKW algorithm is applied to the dedicated quantum preimage attack against the 4-round Keccak-224 for the first time, which is exponentially easier to implement in quantum circuit than the former, with a complexity of 2^111

Applying 3D Polygonal Mesh Watermarking for Transmission Security Protection through Sensor Networks

Although many research works have been carried out in the area of transmission 3D data through sensor networks, the security issue of transmission remains to be unsolved. It is important to develop systems for copyright protection and digital right management (DRM). In this paper, a blind watermarking algorithm is proposed to protect the transmission security of 3D polygonal meshes through sensor networks. Our method is based on selecting prominent feature vertices (prongs) on the mesh and then embedding the same watermark into their neighborhood regions. The embedding algorithm is based on modifying the distribution of vertex norms by using quadratic programming (QP). Decoding results are obtained by a majority voting scheme over neighborhood regions of these prongs. Assuming that cropping cannot remove all prongs, we can achieve robustness against the cropping attack both theoretically and experimentally. Experiments indicate that the proposed method is also robust against noise, smoothing, and mesh simplification. The proposed method has provided a solution for 3D polygonal watermarking which is potential to withstand a variety of attacks