The next-generation IEEE 802.11be network introduces advanced features such as Multi-Link Operation (MLO) and Enhanced Orthogonal Frequency Division Multiple Access (Enhanced OFDMA), along with a new Multiple Resource Unit (MRU) mechanism that improves spectrum efficiency for multiple users under strict latency constraints. This paper proposes a QoS-aware downlink scheduling mechanism, which jointly considers the MRU type-index mapping and real-time channel conditions to optimize resource allocation in environments with heterogeneous device coexistence. Unlike existing approaches, which overlook the feasibility of MRU allocation under IEEE 802.11be constraints, the proposed scheme achieves significant improvements in total network throughput and spectrum utilization, as demonstrated through simulation. These results highlight the proposed scheduling approach as a practical and scalable solution for next-generation low-latency wireless communication.

This paper presents an optical transceiver, whose packet process is completed with deficit round robin (DRR) and RS232 interface, for optical synchronous optical networking (SONET) which can service on both asymmetric digital subscriber line (ADSL) and optical packet switching (OPS). To resolve the clock jitter, not only the cycle decision but also the reset function are used to synchronize the clock waveform. In the proposed DRR, it performs the packet process with low delay and low loss. Moreover, the RS232 interface, which is integrated with the field-programmable gate array (FPGA) board, is adopted due to its easy implementation. The processing data will be queued with DRR and be sent to electrical/optical (E/O) converter from the RS232 port on FPGA board (Transmitter). Passing through the optical fiber, the packet from transmitter is sent to the O/E converter and then received at the RS232 port on another FPGA board. The received electrical packet will be displayed on the seven-segment display of FPGA board to verify the transceiver function for SONET. Note that the proposed architecture is designed with Verilog hardware describe language (Verilog HDL). According to the measured results, the data transfer rate is 115,200 bps with the FPGA operating frequency of 50 MHz and the fiber distance of 5 km.

Scalable mobile visual classification-classifying images/videos in a large semantic space on mobile devices in real-time-is an emerging problem as observing the paradigm shift towards mobile platforms and the explosive growth of visual data. Though seeing the advances in detecting thousands of concepts in the servers, the scalability is handicapped in mobile devices due to the severe resource constraints within. However, certain emerging applications require such scalable visual classification with prompt response for detecting local contexts (e.g., Google Glass) or ensuring user satisfaction. In this work, we point out the ignored challenges for scalable mobile visual classification and provide a feasible solution. To overcome the limitations of mobile visual classification, we propose an unsupervised linear dimension reduction algorithm, kernel preserving projection (KPP), which approximates the kernel matrix of high dimensional features with low dimensional linear embedding. We further introduce sparsity to the projection matrix to ensure its compliance with mobile computing (with merely 12% non-zero entries). By inspecting the similarity of linear dimension reduction with low-rank linear distance metric and Taylor expansion of RBF kernel, we justified the feasibility for the proposed KPP method over high-dimensional features. Experimental results on three public datasets confirm that the proposed method outperforms existing dimension reduction methods. What is even more, we can greatly reduce the storage consumption and efficiently compute the classification results on the mobile devices.

The rapid development of technologies in both hardware and software have made content-based multimedia services feasible on mobile devices such as smartphones and tablets; and the strong needs for mobile visual search and recognition have been emerging. While many real applications of visual recognition require a large scale recognition systems, the same technologies that support server-based scalable visual recognition may not be feasible on mobile devices due to the resource constraints. Although the client-server framework ensures the scalability, the real-time response subjects to the limitation on network bandwidth. Therefore, the main challenge for mobile visual recognition system should be the recognition bitrate, which is the amount of data transmission under the same recognition performance. For this work, we exploit and compare various strategies such as compact features, feature compression, feature signatures by hashing, image scaling, etc., to enable low bitrate mobile visual recognition. We argue that thumbnail image is a competitive candidate for low bitrate visual recognition because it carries multiple features at once and multi-feature fusion is important as the size of semantic space increases. Our evaluations on two subsets of ImageNet, both contain more than 10,000 images with 19 and 137 categories, verify the efficacy of thumbnail images. We further suggest a new strategy that combines single (local) feature signature and the thumbnail image, which achieves significant bitrate reduction from (average) 102,570 to 4,661 bytes with merely (overall) 10% performance degradation.

We present our evaluation and analysis on Yahoo! Large-scale Flickr-tag Image Classification dataset. Our evaluations show that combining multi-features and different classification models, the MAP of tag prediction can be significantly improve over ordinary linear classification. Further analysis shows that some tags are given not because of the visual content but the meta information of images. Our experiments show that we can make more accurate prediction on certain tags using meta information without any training process, compared with visual content based classifiers. Combine the meta information, multi-features and multi-models fusion, we achieve significantly better performance than simple linear classification. We also evaluate the performance of various mid-level feature, and the results suggest that "Concept Bank" feature may be a promising direction for the task.

Microblogging as a new form of communication on Internet, has attracted the attention from researchers recently. Relying the real-time and conversational properties of microblogging, its users update their statuses and share experience within their the social network. Those characteristics also make microblogging an important tool for users to share or discuss real world events such as earth quake or sport game. In this paper, we propose a novel and flexible solution to detect and recognize real-time events from sport games based on analyzing the messages posted on microblogging services. We take Twitter as the experiment platform and collect a large-scale dataset of Twitter messages that are called tweets for 18 prominent sport games covering four types of sports in 2011. We also collect corresponding sport videos for those games. The proposed solution applies moving-threshold burst detection on the volume of tweets to detect highlights in sport games. A tf-idf-based weighting method is applied on the tweets within detected highlights for semantic extraction. According to the experiments we perform on the tweet and video datasets, we find that the proposed methods can achieve competent performance in sport event detection and recognition. Besides, our method can find non pre-defined tidbits that are difficult to detect in previous works.

Recently, several Gaussian like image representations are proposed as an alternative of the bag-of-word representation over local features. These representations are proposed to overcome the quantization error problem faced in bag-of-word representation. They are shown to be effective in different applications, the Extended Hierarchical Gaussianization reached excellent performance using single feature in VOC2009, Vector of Locally Aggregated Descriptors and Fisher Kernel reached excellent performance using only signature like representation on Holiday dataset. Despite their success and similarity, no comparative study about these representations has been made. In this paper, we perform a systematic comparison about three emerging different gaussian like representations: Extended Hierarchical Gaussianization, Fisher Kernel and Vector of Locally Aggregated Descriptors. We evaluate the performance and the influence of feature and parameters of these representations on Holiday and CC_Web_Video datasets, and several important properties about these representations have been observed during our investigation. This study provides better understanding about these gaussian like image representations that are believed to be promising in various applications.

In this paper, we focus on the random forest based concept detection system, and we intend to improve the efficiency of the system in testing phase and to save memory and storage usages by reducing the total number of trees (classifiers). However, reducing the tree number often results in poor performance. In this article, we proposed a method called tree-sharing to cope with this issue. Unlike the traditional method that treats each concept independently, our work shares the trees among concepts, and leave the most important ones from the view of whole system. Experiments on different concept sets show tree-sharing can greatly reduce the number of total trees while the performance decreases slightly. Even in the worst case, we achieve 80% of original performance with only 5% of trees.

We present a scalable mobile video Question-Answering system with locally aggregated descriptors and random projection using user-generated videos all around the world for ACM Multimedia 2011 Technicolor challenge: "precise event recognition and description from video excerpts." Our proposed system takes a video excerpt as a query and explores its canonical semantics of that. We collect a public events video dataset containing 7 topics with 1963 YouTube videos to evaluate our proposed system. The experiment results show that our proposed video feature representation outperforms a state-of-the-art near-duplicate retrieval based on color histogram. The signature generated by random projection not only ensures real-time efficiency but also achieves a competitive MAP performance with original feature.

Large-scale and high-resolution monitoring systems are ideal for many visual surveillance applications. However, existing approaches have insufficient resolution and low frame rate per second, or have high complexity and cost. We take inspiration from the human visual system and propose a multi-resolution design, e-Fovea, which provides peripheral vision with a steerable fovea that is in higher resolution. In this paper, we firstly present two user studies, with a total of 36 participants, to compare e-Fovea to two existing multi-resolution visual monitoring designs. The user study results show that for visual monitoring tasks, our e-Fovea design with steerable focus is significantly faster than existing approaches and preferred by users. We then present our design and implementation of e-Fovea, which combines both multi-resolution camera input and multi-resolution steerable projector output. Finally, we present our deployment of e-Fovea in three installations to demonstrate its feasibility.

In this demonstration, we present a real-time system that addresses three essential issues of large-scale image object retrieval: 1) image object retrieval-facilitating pseudo-objects in inverted indexing and novel object-level pseudo-relevance feedback for retrieval accuracy; 2) time efficiency-boosting the time efficiency and memory usage of object-level image retrieval by a novel inverted indexing structure and efficient query evaluation; 3) recall rate improvement--mining semantically relevant auxiliary visual features through visual and textual clusters in an unsupervised and scalable (i.e., MapReduce) manner. We are able to search over one-million image collection in respond to a user query in 121ms, with significantly better accuracy (+99%) than the traditional bag-of-words model.

Existing pedestrian and vehicle detection algorithms use 2D cues of objects, such as pixel values, color, texture, shape information or motion. The use of 3D cues in object detection, on the other hand, is not well studied in the literature. In this paper, we propose an efficient algorithm that detects pedestrian and vehicle using their 3D cues. The proposed algorithm first detects moving objects in a video frame using a background modeling technique. For each moving object, we extract its width and height in 3D space, with the aid of the intrinsic and extrinsic parameters of the camera monitoring the scene. To estimate the camera parameters, we apply a calibration-free method, which simply requires users to specify six vertices on a cuboid in the scene. Then based on its 3D cues, a object is verified whether it is a pedestrian(vehicle) or not by the class-specific Support Vector Machine (SVM). In our experiment, the proposed algorithm achieves a precision of 88.2% (89.1%) for pedestrian(vehicle) detection, at 32 frame-per-second on average upon five testing sequences.