For most watermarking methods, preserving the synchronization between the watermark embedded in a digital data (image, audio or video) and the watermark detector is critical to the success of the watermark detection process. Many digital watermarking attacks exploit this fact by disturbing the synchronization of the watermark and the watermark detector, and thus disabling proper watermark detection without having to actually remove the watermark from the data. Some techniques have been proposed in the literature to deal with this problem. Most of these techniques employ methods to reverse the distortion caused by the attack and then try to detect the watermark from the repaired data. In this paper, we propose a watermarking technique that is not sensitive to synchronization. This technique uses a structured noise pattern and embeds the watermark payload into the geometrical structure of the embedded pattern.

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In this paper we present a system for automated analysis, classification and indexing of broadcast news programs. The system first analyzes the visual and the speech stream of an input news program in order to obtain an initial partitioning of the program into the so-called report segments. The analysis of the visual stream provides the boundaries of the report segments lying at the beginning and the end of each anchorperson shot. This analysis step is performed by applying an existing technique for anchorperson shot detection. The analysis of the speech stream gives the boundaries of the report segments lying in the middle of each (sufficiently long) silent interval. Then, the transcribed speech of each of the report segments is matched with the content of a large pre-specified textual topic database. This database covers a large number of topics and can be updated by the user at any time. For each topic a vast number of keywords is given, each of which is also assigned a weight that indicates the importance of a keyword for a certain topic. The result of the matching procedure is a list of probable topics per report segment, where for each topic on the list a likelihood is computed based on the number of relevant keywords found in the segment and on the weights of those keywords. The list of topics per segment is then shortened by separating the most probable from least probable topics based on their likelihood. Finally, the likelihood values of the most probable topics are used in the last system module to merge related neighboring segments into reports. The most probable topics serving as the base for the segment-merging procedure are at the same time the retrieval indexes for the reports and are used for classifying together all reports in the database that cover one and the same topic.

Despite the progress in the development of automated vehicles in the last decade, reaching the level of reliability required at large-scale deployment at an economical price and combined with safety requirements is still a long road ahead. In certain use cases, such as automated shuttles and taxis, where there is no longer even a steering wheel and pedals required, remote driving could be implemented to bridge this gap; a remote operator can take control of the vehicle in situations where it is too difficult for an automated system to determine the next actions. In logistics, it could even be implemented to solve already more pressing issues such as shortage of truck drivers, by providing more flexible working conditions and less standstill time of the truck. An important aspect of remote driving is the connection between the remote station and the vehicle. With the current roll-out of 5G mobile technology in many countries throughout the world, the implementation of remote driving comes closer to large-scale deployment. 5G could be a potential game-changer in the deployment of this technology. In this work, we examine the remote driving application and network-level performance of remote driving on a recently deployed sub-6-GHz commercial 5G stand-alone (SA) mobile network. It evaluates the influence of the 5G architecture, such as mobile edge computing (MEC) integration, local breakout, and latency on the application performance of remote driving. We describe the design, development (based on Hardware-in-the-Loop simulations), and performance evaluation of a remote driving solution, tested on both 5G and 4G mobile SA networks using two different vehicles and two different remote stations. Two test cases have been defined to evaluate the application and network performance and are evaluated based on position accuracy, relative reaction times, and distance perception. Results show the performance of the network to be sufficient for remote driving applications at relatively low speeds (<40 km/h). Network latencies compared with 4G have dropped to half. A strong correlation between latency and remote driving performance is not clearly seen and requires further evaluation taking into account the influence of the user interface.

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