Reflection waveform inversion (RWI) updates the low- to mid-wavenumber components of the velocity model accurately by projecting the waveform errors between observed and synthetic data onto the reflection wave paths. However, the synthetic data, generated with the aid of migration/demigration, exhibit unexpected waveform deviations from the observed data due to unknown source wavelets, potentially interfering with inversion outcomes. To address this issue, we propose a source-independent RWI (SI-RWI) method. Initially, the equivalent source spectrum of the migration/demigration process is derived in the frequency domain. Subsequently, the misfit function for RWI is designed to ensure that the observed and synthetic data share the same equivalent source spectrum. Finally, based on this novel misfit function, an RWI method is formulated that does not rely on the phase distortions of source wavelets. The proposed approach has been demonstrated successfully using 2D examples.

Integrating dense channel fingerprints into deep learning (DL) becomes a promising way to realize precise three-dimensional (3D) indoor localization. However, most existing methods are frequency-dependent, which limits the localization precision when operating in different frequency bands. To address this challenge, this paper proposes a masked Transformer encoder (MTE) model capable of using the channel state information (CSI) data of an arbitrary number of sub-channels (frequency bands) as input. The proposed MTE model can locate a UE using frequency-scalable CSI data, to realize resilient localization. We first introduce how to transform CSI data into sequential data suitable for Transformer-based models, with length of the sequence determined by the number of sub-channels. Based on this, an MTE model is designed to achieve resilient FP localization with frequency-scalability, i.e., capable of processing the CSI data of an arbitrary number of sub-channels. Next, we construct a 3D CSI FP dataset using ray-tracing (RT) simulations based on real-world indoor scenarios and versatile electromagnetic (EM) coefficients. The reliability of the dataset is verified by measurement data. Extensive experiments demonstrate that the MTE model outperforms many state-of-the-art baselines, classical time-series models, and alternative Transformer-based methods, especially under arbitrary sub-channel CSI data. Moreover, we demonstrate that the MTE model also offers many advantages in terms of training and storage costs through comparisons with conventional models.

Fingerprint-based localization methods are regarded as a promising solution in sixth-generation (6G) wireless communication because of their ubiquitous infrastructure and high precision in lab-level experiments. However, received signal strength (RSS) instability and fingerprint spatial ambiguity (FSA) significantly undermine the precision of localization methods under real-world applications. Signal spatial difference (SSD) is one of the approaches which can effectively address RSS instability and FSA in fingerprint localization. However, few of these approaches are effectively incorporated into localization methods. In this paper, we propose an SSD-based advantage actor-critic (A2C) method, SSD-A2C, for 3D indoor localization. This method is the first to combine SSD and reinforcement learning (RL), achieving a highly efficient method that can address FSA and RSS instability. A 3D indoor localization simulation environment is developed based on ray-tracing (RT) simulation results of two real-world indoor scenarios, and the proposed method is trained and evaluated by it. Experiment results proved the excellent performances of SSD-A2C in minor localization errors and a high success rate of predicting the desired location. SSD is found to be more appropriate than RSS for RL. The strengths, shortcomings, and future research directions of the proposed method are also discussed in this paper.

In the field of financial analysis, investment research analysts depend on a detailed understanding of complex financial documents to guide their decision-making process. Charts, while providing visual insights into data, present challenges in summarization. To address this issue, we present a novel approach that leverages contextual awareness, both in terms of textual semantics and visual perception. Our method begins with object detection technology to accurately locate and identify charts. Subsequently, a pre-trained language model is employed for vectorizing text and chart captions, enabling effective correlation between charts and their textual descriptions. Utilizing a large language model and strategic prompt engineering, we generate concise yet informative chart summaries, and incorporate visual saliency to assign scores, quantifying the importance of each chart for more effective data interpretation. Our study, supported by dedicated datasets, validates efficiency and accuracy improvements in financial analysis, expediting well-informed investment decisions.

With the emergence of communication services with stringent requirements such as autonomous driving or on-flight Internet, the sixth-generation (6G) wireless network is envisaged to become an enabling technology for future transportation systems. In this paper, two ways of interactions between 6G networks and transportation are extensively investigated. On one hand, the new usage scenarios and capabilities of 6G over existing cellular networks are firstly highlighted. Then, its potential in seamless and ubiquitous connectivity across the heterogeneous space-air-ground transportation systems is demonstrated, where railways, airplanes, high-altitude platforms and satellites are investigated. On the other hand, we reveal that the introduction of 6G guarantees a more intelligent, efficient and secure transportation system. Specifically, technical analysis on how 6G can empower future transportation is provided, based on the latest research and standardization progresses in localization, integrated sensing and communications, and security. The technical challenges and insights for a road ahead are also summarized for possible inspirations on 6G enabled advanced transportation.

To realize intelligent connection of everything and the digital twin (DT) of the physical world in 6G and beyond, new communication and sensing solutions are demanded. The potential of multiple spectrums is maximized for various applications and scenarios. In such a context, an accurate, efficient, and pervasive multi-spectrum propagation model is needed as a critical and unified baseline for testing the performance of the solutions in various scenarios. This work presents ray-tracing (RT) oriented methods for the DT presentation of radio propagation at multiple frequency bands from microwave to visible light. The material- and field-measurement-based approaches are proposed to characterize the electromagnetic properties of materials. On that basis, the propagation mechanisms are developed and validated, and the corresponding parameters are inverted. For the real-time simulation demand, RT and artificial intelligence (AI) algorithms are fused to develop a super-resolution modeling method. The experimental results indicate that the proposed method outperforms the baseline model regarding stability and accuracy. It can significantly reduce the computation time with comparable accuracy to the RT-only approach. The proposed methodologies and the in-depth discussions in this work are expected to pave the way to realize the DT of multi-spectrum propagation for evaluating 6G and beyond technologies.

Channel modeling has always been the core part in communication system design and development, especially in 5G and 6G era. Traditional approaches like stochastic channel modeling and ray-tracing (RT) based channel modeling depend heavily on measurement data or simulation, which are usually expensive and time consuming. In this paper, we propose a novel super resolution (SR) model for generating channel character-istics data. The model is based on multi-task learning (MTL) convolutional neural networks (CNN) with residual connection. Experiments demonstrate that the proposed SR model could achieve excellent performances in mean absolute error and standard deviation of error. Advantages of the proposed model are demonstrated in comparisons with other state-of-the-art deep learning models. Ablation study also proved the necessity of multi-task learning and techniques in model design. The contribution in this paper could be helpful in channel modeling, network optimization, positioning and other wireless channel characteristics related work by largely reducing workload of simulation or measurement.

Operators of online software services periodically collect various measures with many attributes. When a measure becomes abnormal, indicating service problems such as reliability degrade, operators would like to rapidly and accurately localize the root cause attribute combinations within a huge multi-dimensional search space. Unfortunately, previous approaches are not generic or robust in that they all suffer from impractical root cause assumptions, handling only directly collected measures but not derived ones, handling only anomalies with signicant magnitudes but not those insignicant but important ones, requiring manual parameter ne-tuning, or being too slow. This paper proposes a generic and robust multi-dimensional root cause localization approach, Squeeze, that overcomes all above limitations, the first in the literature. Through our novel bottom-up then top-down searching strategy and the techniques based on our proposed generalized ripple effect and generalized potential score, Squeeze is able to reach a good trade off between search speed and accuracy in a generic and robust manner. Case studies in several banks and an Internet company show that Squeeze can localize root causes much more rapidly and accurately than the traditional manual analysis. Furthermore, our extensive experiments on semi-synthetic datasets show that the F1-score of Squeeze outperforms previous approaches by 0.4 on average, while its localization time is only about 10 seconds.

Acoustic signals contain rich information of the environment. They can be used for detecting anomalous events such as in automated machine monitoring. In this demonstration, we present our acoustic anomaly detection system that captures acoustic signals and classifies them using machine learning techniques. Our system includes a server for sound management and model training, a mobile client for sound capturing and real-time classification, and a workbench that acts as a user interface. We will show the full operational pipeline of our system in this demonstration.

Presence, broadly defined as an event publish-notification infrastructure for converged applications, has emerged as a key mechanism for collecting and disseminating context attributes for next-generation services in both enterprise and provider domains. Current presence-based solutions and products lack in the ability to a) support flexible user-defined queries over dynamic presence data and b) derive composite presence from multiple provider domains. Accordingly, current uses of context are limited to individual domains/organizations and do not provide a programmable mechanism for rapid creation of context-aware services. This paper describes a presence virtualization architecture, where a Virtualized Presence Server receives customizable queries from multiple presence clients, retrieves the necessary data from the base presence servers, applies the required virtualization logic and notifies the presence clients. To support both query expressiveness and computational efficiency, virtualization queries are structured to separately identify both the XSLT-based transformation primitives and the presence sources over which the transformation occurs. For improved scalability, the proposed architecture offloads the XSLT-related processing to a high-performance XML processing engine. We describe our current implementation and present performance results that attest to the promise of this virtualization approach.

Session Initiation Protocol (SIP) has begun to be widely deployed for multiple services such as VoIP, Instant Messaging and Presence. Each of these services uses different SIP messages, and depending on the value of a service, e.g. revenue, the associated messages may need to be prioritized accordingly. Even within the same service, different messages may be assigned different priorities. In this paper, we present the design and implementation of a programmable classification engine for SIP messages in the Linux kernel. This design uses a novel algorithm that in addition to classifying messages can extract and maintain state information across multiple messages. We apply the classifier for overload control using operator-specified rules for categorizing messages and associated actions, augmented with a protocol-level understanding of SIP message structure. When faced with loads beyond their capacity (e.g., during catastrophic situations and major network outages), SIP servers must drop messages. It is therefore desirable that the server process high-value messages in preference to lower-value messages. We evaluated our in-kernel classifier implementation with an open source SIP server (SER) for such an overload scenario. The workload consists of a mix of call setup and call handoff messages, and the classifier is programmed with rules that prioritize handoffs over call setups. We show that, while SER can process about 40K messages/sec (in a FIFO manner), our classifier can examine and prioritize 105K messages/sec during overload. With the classifier operating at peak throughput, SER's processing rate drops to 31.6K messages/sec, but all of the available high-value messages are processed.

We present a client/server-based un-intrusive collaborative multimedia application - called eMeeting - that extracts and archives salient visual information from seminars, while allowing interaction among presenters and the audience in real time. The application uses APIs of the Lotus Sametime/spl reg/ system such as secure login, directory, meeting and multimedia services, to which we have added our own extensions. These extensions use the binary messaging channel of Sametime and allow us to add stroke-based chat as well as stroke-based annotations of slides. This permits communication among all clients. In parallel with the services just mentioned and in the same Java application, users of eMeeting are presented with slides that the system extracts from the presenter's visual material using an automatic recognition algorithm that classifies video frames as slides. At this time we assume that the system may or may not have access to the visual content in electronic form. Users can follow the seminar in the form of presenter's video, and as a series of extracted images. The server transmits the slides cyclically to each user using IP multicast giving each client the opportunity to select any previous slide. Thus, late joiners can browse slides that have been accumulated by the system.

Discrete wavelet transform is an ideal tool for multi-resolution representation of image signals. Some promising results have been recently reported on the application of wavelet transform for image compression. In this paper, we propose a new wavelet coding technique for image compression. The proposed scheme has the advantages of improved coding performance and reduced computational complexity. The input image is first decomposed into a pyramid structure with three layers using a 2-D wavelet transform. A block size of 2m - 3 (m equals 1, 2, 3) is used for each orientation sub-image at the m-th layer to form 64-D vectors by combining the corresponding blocks in all the sub-images. The 64-D vectors are then encoded using 16-D non-linear interpolative vector quantization (NIVQ). At the decoder, the indices are used to reconstruct the 64-D vectors directly from a 64-D codebook designed using a non-linear interpolative technique. The proposed scheme not only exploits the correlation among the wavelet sub-images but also preserves the high frequency sub-images. Simulation results show that the reconstructed image of a superior quality can be obtained at a compression ratio of about 100:1.