The rapid advancement of deepfake technology poses significant challenges to athletic celebrities, where altered or falsified media can impact athletes’ reputations, fan engagement, and the integrity of match broadcasting. This paper proposes a novel framework for deepfake image recognition for athletic celebrities using locality sensitive hashing (LSH). LSH, an efficient technique for high‐dimensional nearest neighbor searches, is employed to detect and differentiate deepfake images from authentic media. By extracting high‐dimensional features from images and videos using convolutional neural networks (CNNs), LSH is applied to hash similar content into clusters for quick and accurate deepfake detection. The proposed method is tested on real‐world dataset, showing promising results in terms of accuracy and computational efficiency. This research highlights the importance of integrating advanced hashing techniques like LSH in safeguarding the authenticity of digital content and provides insights into future directions for deepfake detection mechanisms.

The Internet of Vehicles (IoV), as the cornerstone of intelligent transportation systems, is gradually attracting attention and accumulated a large amount of literature. Therefore, this article employs citation analysis and topic analysis to analyze the research in the IoV field, revealing the knowledge evolution trajectory and development dynamics. The study selects 10 909 documents from the Web of Science core collection. Through main path analysis, the study identifies the knowledge trajectory of “from Intelligent transportation technology to agent and communication technology to blockchain and security.” In topic analysis, important topics, such as machine learning, autonomous vehicles, simulation, security, and smart cities are identified. Meanwhile, three topic areas machine learning and traffic prediction, simulation and autonomous vehicles, and security and road vehicles are obtained. Our research not only contribute to the advancement of academic research in the IoV field but also provide significant references for the innovation and development of related industries.

Unmanned aerial vehicles (UAVs) have made significant contributions to landslide emergency response operations due to their precise and flexible imaging capabilities. However, the conventional workflow of generating orthophotos from UAV imagery and subsequent interpretation often exceeds the critical 72-hour rescue window. To address this challenge, this paper presents a onboard landslide extraction method for original UAV images utilizing a convolutional neural network (CNN). Given the abundance of overlapping images, the CNN is trained on labeled orthophotos. To minimize discrepancies between orthophotos and original UAV images, the proposed method integrates local and non-local features. Built upon the ResNet architecture, the method incorporates modules for extracting both shallow and deep features, enabling effective fusion through self-learning. This approach mitigates the issue of accuracy degradation caused by variations between training and testing data. Furthermore, considering the necessity of deploying the CNN-based landslide extraction model on low-power embedded platforms to achieve onboard landslide extraction, this paper introduces a quantitative model compression technique. Specifically, the model’s weight and activation value data precision are linearly mapped from 32-bit floating-point type to 8-bit integer type, guided by relative entropy minimization. This results in substantial reductions in memory access and computational complexity during model inference. Experimental results demonstrate that the proposed method yields outstanding extraction performance on both the Jiuzhaigou and Bijie landslide datasets. The time taken for extracting landslides from a single 6000x4000 pixel UAV image is reduced from 109.47 seconds to 4.75 seconds, which is less than the 5.13-second interval between camera shots, thereby achieving onboard landslide extraction.

Three-dimensional point cloud registration is a crucial technique for point cloud processing. Iterative closest point (ICP) is widely used for rigid registration of point clouds because of its simplicity but suffers from slow convergence and the tendency to fall into local optimization. In this article, we proposed a new robust and fast point cloud registration method, the ICP optimized by geometric features (including normal, curvature, and point distance), called GFOICP. GFOICP statistically selects registration points by the cross entropy of geometric features of the points, then matches correspondences based on a variable distance threshold, and filters out correct correspondences using an iterative strict constraint on geometric feature similarity. In addition, geometric feature similarity is added as a constraint to the objective function to ensure strict convergence. GFOICP completes registration by iterating correspondence matching, alignment, and convergence judgment. Extensive experiments on publicly available synthetic and real-world datasets have demonstrated that GFOICP significantly improves accuracy and speed compared to standard ICP. GFOICP has similar or higher accuracy and speed than other state-of-the-art registration methods.

We present a very general algorithm for structured prediction learning that is able to efficiently handle discrete MRFs/CRFs (including both pairwise and higher-order models) so long as they can admit a decomposition into tractable subproblems. At its core, it relies on a dual decomposition principle that has been recently employed in the task of MRF optimization. By properly combining such an approach with a max-margin learning method, the proposed framework manages to reduce the training of a complex high-order MRF to the parallel training of a series of simple slave MRFs that are much easier to handle. This leads to a very efficient and general learning scheme that relies on solid mathematical principles. We thoroughly analyze its theoretical properties, and also show that it can yield learning algorithms of increasing accuracy since it naturally allows a hierarchy of convex relaxations to be used for loss-augmented MAP-MRF inference within a max-margin learning approach. Furthermore, it can be easily adapted to take advantage of the special structure that may be present in a given class of MRFs. We demonstrate the generality and flexibility of our approach by testing it on a variety of scenarios, including training of pairwise and higher-order MRFs, training by using different types of regularizers and/or different types of dissimilarity loss functions, as well as by learning of appropriate models for a variety of vision tasks (including high-order models for compact pose-invariant shape priors, knowledge-based segmentation, image denoising, stereo matching as well as high-order Potts MRFs).

In this work, we report the results of an active dispersive delay line (DDL) designed out of a distributed amplifier based transversal filter (DATF) with 12 gain cells, achieving the average gain of 2 dB and group delay variance of 3 ns from 3 to 5 GHz. Simulation based upon the measurements of a gain cell and transmission line fabricated with high speed PC Boards demonstrates that the DDL can be utilized as a real time signal processing block for an ultra-wideband (UWB) system. This work could potentially open the door for CMOS integration of DDLs.

A fully-overlapped multi-mode QC-LDPC decoder architecture, adopting improved TDMP algorithm, is presented in this paper. With symmetrical four-stage pipelining, block column and row permutations, nonzero sub-matrix reordering, sum memory odd-even partition, and read-write bypass, two phases are fully overlapped and each phase scans nonzero sub-matrices one by one in block row-wise order without access conflicts to sum memories. The sum memories store not only variable node sums but also prior messages. In this case, it saves an additional FIFO of 13 440 bits. The decoder attains 248-287 Mb/s at 150 MHz and 15 iterations.

In this paper a programmable and area-efficient decoder architecture supporting two main stream decoding algorithms for any Block-LDPC codes is presented. The novel decoder can be configured to decode in either TPMP or TDMP decoding mode according to different Block-LDPC codes. To verify our proposed architecture, a flexible LDPC decoder which supports IEEE 802.16e is implemented using a 0.13um CMOS process with a total area of 6.3 mm2 and maximum clock frequency of 260 MHz. The estimated comsumption is 270 mW when operates at 125 MHz and 1.2V supply.

The quasi-cyclic low-density parity-check (QC-LDPC) codes are widely applied in digital broadcast and communication systems. However, the decoders are still difficult to be put into practice due to their large area and high power, especially in the wireless mobile devices. This paper presents an improved all-purpose multirate iterative decoder architecture for QC-LDPC codes, which can largely reduce their area and power. The architecture implements the normalized min-sum algorithm, rearranges the original two-phase message-passing flow, and adopts an efficient quantization method for the second minimum absolute values, an optimized storing scheme for the position indexes and signs, and an elaborate clock gating technique for substantive memories and registers. It is also configurable for any regular and irregular QC-LDPC codes, and can be easily tuned up to different code rates and code word lengths. The chip is fabricated in an SMIC 0.18- six-metal-layer standard CMOS technology. It attains a throughput of 104.5 Mb/s, and dissipates an average power of 486 mW at 125 MHz, and 15 decoding iterations. The core area is only 9.76 mm2. The chip has been applied into the China digital terrestrial/television multimedia broadcasting system.

We present an approach to decomposing branching volume data into sub-branches. First, a metric is proposed for evaluating local convexities in volumetric data, and it is a criterion for global selection of tip points. Second, a multi-path growing strategy is adopted to segment the volumes based on a DFS transformation starting from the tips. Experiments show that this approach is capable of generating desirable components and reasonable segmentation boundaries of a volume.

In the paper, we present a novel approach to modeling plants from images by detecting apex features. First, an effective algorithm is proposed to extract apex features in volumetric data recovered from the images. It provides position and pose information for assigning 3D generic leaves. Then, the 3D leaf shapes are determined by an optimization based on the volume. Finally, Branches are modeled by using a particle flow approach. The proposed method is simply with limited manual intervention and has the obvious benefit of knowing a leaf by its visible apex part.