Non-line-of-sight (NLOS) moving target imaging is an emerging and challenging technology with potential applications in autonomous driving, security detection, disaster response, and more. In this article, a novel algorithm dubbed NLOS detail recovery via alternating direction method of multipliers (NDR-ADMMs) is proposed for NLOS moving target imaging. In our scheme, the static clutter filter (SCF) we proposed is utilized for NLOS clutter suppression, which facilitates hidden motion target echo extraction. To address the sparsity of echoes caused by scene complexity and target motion, we introduce a regularization constraint termed detail-aware regularization (DAR), which enhances details and suppresses noise in NLOS scenes by incorporating information from neighboring cells and expanding the receptive field of the image. Then, we propose the NDR-ADMM that combines DAR,$\ell _{1}$-norm, and ADMM to reconstruct high-resolution NLOS moving target images. Further, the corresponding fast version, NDR-ADMM+, is derived by mapping the NDR-ADMM to the adaptive parameter learning network for improving robustness and convergence. Finally, the proposed NDR-ADMM and NDR-ADMM+ are verified by simulated data and measured data we collected via millimeter-wave (MMW) radar in various NLOS scenarios. Compared to other state-of-the-art methods, NDR-ADMM+ demonstrates superior performance, robustness, and noise immunity, with NDR-ADMM following closely behind. This is attributed to DAR’s ability to capture details and suppress interference. Additionally, NDR-ADMM+ and AF-AMPnet offer the fastest processing speeds.

Surface cracks in the raceway of hub bearings pose a significant safety threat. Magnetic particle testing (MPT) can be used to locate these cracks regardless of the influences caused by raceway structure. However, crack identification and evaluation in conventional MPT are quite challenging due to the low correlation between magnetic particle indication (MPI) and inspection images. To address this problem, this article introduces a dynamical MPI measuring method through 3-D vision. Specifically, a scanning 3-D vision system, an approach to forming MPI, and a curvature feature–based algorithm for MPI identification are newly proposed. 3-D measurement directly captures the spatial aggregation state of the magnetic particles, thereby enabling the connection between MPI and the cause of it, which is the leakage magnetic field above cracks. Starting from the electromagnetic field, we analyze the potential features of the 3-D profile of MPI. Magnetic force serves as a link, providing a theoretical basis for the identification and evaluation. Experiments show that our method successfully detects artificial notches with depths of 0.5–2.5 mm and natural microcracks with a maximum width of 40μm and differentiates between variations in crack depths of 0.5 mm. The sensitivity, stability, and evaluation ability can be demonstrated.

The detection of obscured vehicle targets and non-line-of-sight imaging by vehicle-mounted radar systems have broad application prospects in the field of urban traffic and autonomous driving. In this paper, a non-line-of-sight (NLOS) model and synthetic aperture radar (SAR) imaging method are proposed to perform millimeter wave imaging of obscured vehicle targets using electromagnetic wave reflection echoes from the road surface. Then, the NLOS echoes are imaged in two dimensions with high accuracy by compressed sensing algorithm (CSA). Finally, an experimental system for NLOS vehicle targets was developed using TI millimeter-wave radar. The feasibility of millimeter-wave NLOS radar imaging and the effectiveness of the proposed algorithm are experimentally verified, and high-precision 2D imaging results of obscured vehicle targets are obtained.

Non-line-of-sight (NLOS) imaging of moving targets is of tremendous interest in the fields of urban sensing and autonomous driving. In this paper, a novel NLOS inverse synthetic aperture radar (ISAR) imaging method is proposed for moving targets by automotive millimeter-wave (MMW). In this scheme, an imaging model of the moving target in urban scenes is developed and analyzed. A low-frequency filtering method is employed to remove stationary interfering signals, a typical threshold method is applied to extract the triple-reflected echo of a hidden moving target, and the range migration algorithm (RMA) is utilized to achieve envelope alignment. Then, the well-focused image of the moving target is achieved by the polar format algorithm (PFA) with Prominent Point Processing (PPP) autofocus algorithm. Finally, an outfield experimental system for the obscured moving targets is built by TI MMW sensors. The results demonstrate our method can provide a high-resolution image of the moving target.

We present a system for performing arbitrary sequences of filesystem operations and provably detecting any violation of serializable isolation semantics, i.e. any interleaving of attacker and defender actions is equivalent to a non-interleaved sequence of attacker and defender actions. Thus, our system provides a provably secure defense against all UNIX file-name race conditions, including the infamous access/open race. Our solution operates entirely in user-space and is portable to any POSIX.1-2008 system, making it usable today. Developers can adopt our solution selectively, using it for security-critical code and using the standard POSIX interface for non-security-critical parts of their programs. Furthermore, the proofs of correctness suggest several simple improvements to the POSIX standard.

Fingerprinting attacks have emerged as a serious threat against privacy mechanisms, such as SSL, Tor, and encrypting tunnels. Researchers have proposed numerous attacks and defenses, and the Tor project now includes both network- and browser-level defenses against these attacks, but published defenses have high overhead, poor security, or both.

We present a novel web page fingerprinting attack that is able to defeat several recently proposed defenses against traffic analysis attacks, including the application-level defenses HTTPOS and randomized pipelining over Tor. Regardless of the defense scheme, our attack was able to guess which of 100 web pages a victim was visiting at least 50% of the time and, with some defenses, over 90% of the time. Our attack is based on a simple model of network behavior and out-performs previously proposed ad hoc attacks. We then build a web site fingerprinting attack that is able to identify whether a victim is visiting a particular web site with over 90% accuracy in our experiments.

We defeat two proposed Unix file-system race condition defense mechanisms. First, we attack the probabilistic defense mechanism of Tsafrir, et al., published at USENIX FAST 2008. We then show that the same attack breaks the kernel-based dynamic race detector of Tsyrklevich and Yee, published at USENIX Security 2003. We then argue that all kernel-based dynamic race detectors must have a model of the programs they protect or provide imperfect protection. The techniques we develop for performing these attacks work on multiple Unix operating systems, on uni- and multi-processors, and are useful for exploiting most Unix file-system races. We conclude that programmers should use provably-secure methods for avoiding race conditions when accessing the file-system.