Precise position information plays a crucial role in industries such as intelligent unmanned systems and autonomous driving. Combining the advantages of traditional precise point positioning (PPP) and real-time kinematic (RTK) technologies, the emerging PPP-RTK technique is able to provide real-time centimeter-level positioning in open environments. However, its stability is often insufficient in complex environments due to frequent interference of satellite signals. To enhance the continuity and robustness of positioning results in such challenging environments, inertial navigation system (INS), which can perceive the motion information of the carrier, is commonly integrated with global navigation satellite system (GNSS). In this paper, an optimization-based tightly-coupled multi-GNSS PPP-RTK/INS system is proposed to achieve continuous and reliable positioning in urban environments. In the proposed algorithm, an estimator based on Maximum A Posteriori (MAP) estimation is utilized to integrate multi-GNSS raw observations, inertial measurements and precise atmospheric information. Moreover, the outlier detection algorithm based on standardized residuals is applied in the proposed method to reject gross errors introduced by GNSS observations, making the positioning results more consistent with observations to get reliable ambiguity-float solutions. Then, a cascaded ambiguity resolution is conducted to obtain final precise ambiguity-fixed solutions. The experimental results show that the proposed method achieves centimeter-level horizontal position estimates in urban environments, with an accuracy improvement of over 80% compared to PPP-RTK. Besides, the proposed optimization-based algorithm represents an accuracy improvement of approximately 30% compared to the traditional extended Kalman filter-based method.

Image sharing chain detection aims to recover the sharing history of an image downloaded from online social networks (OSNs), including the ever-shared OSNs and their orders, which is an important task in the multimedia forensics community. Most of the existing algorithms directly treat the sharing chain detection as a classification problem by simply assigning a unique label to each sharing chain. Such a strategy though seems straightforward, it ignores the inherent properties of the sharing chain which can be regarded as a time sequence that carries the sharing history of an online image. In this paper, we suggest a new sharing chain detection framework via Sequence-to-Sequence (Seq2Seq) model. Different from previous classification based approaches, our model detects the sharing chain of online image progressively via a decoder. This progressive manner can fully utilize the decoded chain, which is embedded into a series of learned representations. Experimental results show that our method can detect sharing chains involving up to three OSNs, and exhibits much better performance than conventional ones.

Constrained image splicing detection and localization (CISDL) is a fundamental task of multimedia forensics, which detects splicing operation between two suspected images and localizes the spliced region on both images. Recent works regard it as a deep matching problem and have made significant progress. However, existing frameworks typically perform feature extraction and correlation matching as separate processes, which may hinder the model's ability to learn discriminative features for matching and can be susceptible to interference from ambiguous background pixels. In this work, we propose a multi-scale target-aware framework to couple feature extraction and correlation matching in a unified pipeline. In contrast to previous methods, we design a target-aware attention mechanism that jointly learns features and performs correlation matching between the probe and donor images. Our approach can effectively promote the collaborative learning of related patches, and perform mutual promotion of feature learning and correlation matching. Additionally, in order to handle scale transformations, we introduce a multi-scale projection method, which can be readily integrated into our target-aware framework that enables the attention process to be conducted between tokens containing information of varying scales. Our experiments demonstrate that our model, which uses a unified pipeline, outperforms state-of-the-art methods on several benchmark datasets and is robust against scale transformations.

We realized the bi-directional transmission of millimeter-wave (MMW) 4096-ary quadrature amplitude modulation (4096QAM) orthogonal frequency division multiplexing (OFDM) signal with frequencies of 83.5 GHz and 73.3 GHz at E-band over 2 m wireless distance with a net transmission rate of 91.46 Gbit/s. Meanwhile, we also achieved the transmission of the MMW 4096QAM OFDM signal at 117 GHz in D-band with a net transmission rate of 57.21 Gbit/s over 13.42 m wireless distance. With the help of probabilistic shaping (PS) technique and Volterra nonlinearity compensation (VNC) technology, both of the transmission systems can satisfy the 0.8 normalized generalized mutual information (NGMI) threshold with 25% soft-decision forward-error-correction (SD-FEC) overhead. In addition, the performances of different modulation formats are also be compared.