The rising systems programming language Rust is fast, efficient and memory safe. However, improperly dereferencing raw pointers in Rust causes new safety problems. In this paper, we present a detailed analysis into these problems and propose a practical hybrid approach to detecting unsafe raw pointer dereferencing behaviors. Our approach employs pattern matching to identify functions that can be used to generate illegal multiple mutable references (We define them as thief function) and instruments the dereferencing operation in order to perform dynamic checking at runtime. We implement a tool named UnsafeFencer and has successfully identified 52 thief functions in 28 real-world crates*, of which 13 public functions are verified to generate multiple mutable references.

A novel algorithm is proposed for estimating the direction of arrival (DOA) of linear frequency modulated (LFM) signals for the uniform circular array (UCA). Firstly, the UCA is transformed into an equivalent virtual uniform linear array (ULA) using the mode-space algorithm. Then, the short time Fourier transform (STFT) of each element's output is worked out. We can obtain the spatial time-frequency distribution matrix of the virtual ULA by selecting the single-source time-frequency (t-f) points in the t-f plane and then get the signal subspace of the array. The characteristics nature of the Bessel function allow us to obtain the multiple invariance (MI) of the virtual ULA. So the multiple rotational invariant equation of the array can be obtained and its closed-form solution can be worked out using the multi-least-squares (MLS) criterion. Finally, the two dimensional (2-D) DOA estimation of LFM signals for UCA can be obtained. Numerical simulation results illustrate that the UCA-STFT-MI-ESPRIT algorithm proposed in this paper can improve the estimation precision greatly compared with the traditional ESPRIT-like algorithms and has much lower computational complexity than the MUSIC-like algorithms.

The cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter has emerged as a promising tool for tracking a time-varying number of targets. However, the standard CBMeMBer filter may perform poorly when measurements are coupled with sensor biases. This paper extends the CBMeMBer filter for simultaneous target tracking and sensor biases estimation by introducing the sensor translational biases into the multi-Bernoulli distribution. In the extended CBMeMBer filter, the biases are modeled as the first order Gauss-Markov process and assumed to be uncorrelated with target states. Furthermore, the sequential Monte Carlo (SMC) method is adopted to handle the non-linearity and the non-Gaussian conditions. Simulations are carried out to examine the performance of the proposed filter.

Traditional low-rank feature lose the temporal information among action sequence. To obtain the temporal information, we split an action video into multiple action subsequences and concatenate all the low-rank features of subsequences according to their time order. Then we recognize actions by learning a novel dictionary model from concatenated low-rank features. However, traditional dictionary learning models usually neglect the similarity among the coding coefficients and have bad performance in dealing with non-linearly separable data. To overcome these shortcomings, we present a novel similarity constrained discriminative kernel dictionary learning for action recognition. The effectiveness of the proposed method is verified on three benchmarks, and the experimental results show the promising results of our method for action recognition.

Traditional sparse representation-based methods for human action recognition usually pool over the entire video to form the final feature representation, neglecting any spatio-temporal information of features. To employ spatio-temporal information, we present a novel histogram representation obtained by statistics on temporal changes of sparse coding coefficients frame by frame in the spatial pyramids constructed from videos. The histograms are further fed into a support vector machine with a spatial pyramid matching kernel for final action classification. We validate our method on two benchmarks, KTH and UCF Sports, and experiment results show the effectiveness of our method in human action recognition.