nils-herrmann/clean_scirepeval huggingface dataset:
Low Complexity Decoding Algorithms for Rate Compatible Modulation

Rate compatible modulation (RCM) has high spectrum efficiency and achieves seamless and blind rate adaptation in wide range of channel conditions. However, due to many convolution operations at symbol nodes, the belief propagation decoding algorithm of RCM has a high level of computational complexity. In this paper, we investigate the low complexity algorithms for fast decoding of RCM. Instead of computing the outgoing messages at symbol nodes via multi-level convolutions, we first design a novel two-level computing structure (2L-RCM) for symbol nodes in the probability-domain, each level is composed of one set of multiplications followed by one set of additions. Based on 2L-RCM, we derive Log-2L-RCM decoding algorithm in the log-domain, which converts the multiplications and additions of 2L-RCM into additions and Jacobian logarithms, respectively. Furthermore, we propose some approximate algorithms to reduce the complexity of Jacobian logarithms. In particular, the improved Max-Log-RCM (IMax-Log-RCM) algorithm obtains good performance-complexity trade-off. The simulation results and the numerical analyses show that IMax-Log-RCM achieves only 0.3-dB worse decoding performance than the original decoding algorithm with much fewer additions.
Blind channel estimation and equalization using Viterbi algorithms

In this paper, a trellis based blind channel estimation and equalization technique is presented. First, blind channel estimation is accomplished by incorporating a list parallel Viterbi algorithm with the least mean square (LMS) updating approach. In this operation, multiple trellis mappings are preserved and ranked in terms of path metrics. Equivalently, multiple channel estimates are maintained and updated once a single symbol is received. Second, at a certain point, which is determined by the evolution of path metrics and the linear constraint possessed in the trellis mapping, only the best channel estimate is maintained. Third, this channel estimate is adopted to construct the whole trellis that is used by a conventional adaptive Viterbi algorithm. Signal detection and further channel updating are then conducted alternatively. To alleviate the noise impact, a small delay is introduced before the feedback of detected symbols to further update the channel estimate. Simulation has shown the overall good performance of the proposed scheme in terms of mean square error (MSE) convergence of the channel estimation, stability to the initial channel guess, and computational complexity etc.
Forecasting initial popularity of just-uploaded user-generated videos

User-generated videos (UGVs) have dominated contemporary social networking sites (SNSs). Forecasting their popularity is of great relevance to a broad range of online services. All existing studies forecast popularity of UGVs using their popularity statistics that are accumulated for a period of time after they are uploaded. Hence, there is always a substantial time lag (days to weeks) before popularity forecast can take effects. However, such a time lag is undesirable for timely popularity forecast as forecasting initial popularity during UGVs' lifetime is vitally important. In fact, we have found in our measurement that the most popular UGVs usually precede others starting from the beginning days and UGVs generally receive the highest attentions during the first few days. In this paper, we present the first exploration on forecasting initial popularity for UGVs at their uploading moment without accumulating their popularity statistics. Specifically, we first design an effective crawler framework to collect the publicly observable features of videos at their uploading moment. We then collect a representative and large YouTube video data set with 318,627 videos. Based on the data set, we select the most relevant features as predictors and design a neural network-based learning model to forecast initial popularity of just-uploaded UGVs. Experimental results validate the effectiveness of the proposed forecasting model and demonstrate the model's benefits for online services such as in-video advertising and video caching.
Mining visualness

To understand which concepts are visualizable and to what extent they can be visualized are worthwhile for multimedia and computer vision research. Unfortunately, few previous works have ever touched such topics. In this paper, we propose an unified model to automatically identify visual concepts and estimate their visual characteristics, or visualness, from a large-scale image dataset. To this end, an image heterogeneous graph is first built to integrate various visual features, and then a simultaneous ranking and clustering algorithm is introduced to generate visually and semantically compact image clusters, named visualsets. Based on the visualsets, visualizable concepts are discovered and their visualness scores are estimated. The experimental results demonstrate the effectiveness of the proposed schema.
Media Reviews

structor evaluation survey and a leamer posttest. Remember to use the training program notebook as an addition to the visual aids. Training schedules for maximum learning and retention are offered, along with sections on groups and group process, data collection, creative thinking, and problem solving. Both individual and group exercises can be found to enhance the utihzation of what is being taught. Pyzdek brings to each transparency content that reads well and that the instructor can use as is to sound both knowledgeable and natural. The price is hefty, so one needs to think seriously about the use of this thorough guide. It can be used as part of an organization’s TQM efforts or as a stand-alone SPC workshop.
Discontinuous seam cutting for enhanced video stitching

Video stitching requires proper seam cutting technique to decide the boundary of the sub video volume cropped from source videos. In theory, approaches such as 3D graph-cuts that search the entire spatiotemporal volume for a cutting surface should provide the best results. However, given the tremendous data size of the camera array video source, the 3D graph-cuts algorithm is extremely resource-demanding and impractical. In this paper, we propose a sequential seam cutting scheme, which is a dynamic programming algorithm that scans the source videos frame-by-frame, updates the pixels' spatiotemporal constraints, and gradually builds the cutting surface in low space complexity. The proposed scheme features flexible seam finding conditions based on temporal and spatial coherence as well as salience. Experimental results show that by relaxing the seam continuity constraint, the proposed video stitching can better handle abrupt motions or sharp edges in the source, reduce stitching artifacts, and render enhanced visual quality.
Long scene panorama generation for indoor environment

Long scene panorama (LSP) can provide the immersive experience for an extended view in one single photograph. The state-of-the-art image mosaicing based LSP techniques are significantly constrained by their high computational complexity, limiting their applications to platforms with rich computing resource. Slit-scanning based schemes, although implementation friendly, heavily rely on the consistency of the camera's speed and suffer from pushbroom distortion. In this paper, we propose a novel scheme which can generate the LSP for the indoor environment via consumer-grade cameras. A novel adaptive resampling scheme is introduced to generate more natural looking panoramas from videos captured by cameras with non-constant moving speed. A multiple slits replacement scheme is also developed to reduce the zigzag distortion and to handle the depth change. Experimental results show that the proposed scheme can achieve satisfactory performance with handheld cameras.
An Optimal Resource Allocation for Superposition Coding-Based Hybrid Digital–Analog System

Hybrid digital–analog (HDA) video transmission is a new cross-layer design, which can be widely used in Internet of Things. The key problem of HDA video transmission is to find the optimal resource allocation between the digital and analog part. This paper presents a new general resource allocation algorithm for superposition coding-based HDA system. On one hand, in order to achieve successful decoding in digital part, the bitrate is controlled by the quantization parameter (QP), and the channel coding rate and modulation order are determined by the signal to interference noise power ratio, in which analog part is considered as the interference. On the other hand, the overall video quality is directly determined by the mean square error of analog part, which depends jointly on the data variance of the analog part, the power allocated to the analog part and the channel noise power. We propose a prediction model to describe how the data variance of the analog part changes with the QP in the digital part. Based on the proposed model, the power allocation of two parts can be quantitatively connected to form an optimization problem. We prove the convexity of the resource allocation problem and the gradient descent method is utilized in system implementation. With extensive simulations, the proposed algorithm is validated, achieving 1.4–5.3 dB gain over the conventional digital system, and 6.2–7.4 dB gain over pseudo-analog system in peak signal-to-noise ratio.
Robust Linear Video Transmission Over Rayleigh Fading Channel

This research addresses the problem of robust linear video transmission over the Rayleigh fading channel, where only statistical channel state information (CSI) is available to the sender. We observe that discarding low-priority (LP) data and saving the channel uses for high-priority (HP) data can significantly improve the quality of the received video. We formulate an optimization problem that aims to minimize the total squared error of a multi-variant Gaussian random vector under the given bandwidth and power resources. To tame the complexity of this NP-hard problem, we analyze two sub-problems, namely power allocation and bandwidth allocation, and propose an iterative algorithm to approximate the solution. Subsequently, we propose a one-pass two-step fast algorithm that further reduces both algorithmic and computational complexity. A linear video transmission system is implemented based on the proposed algorithm. Simulations show that our system significantly outperforms Soft-Cast, and the PSNR gain at 5th percentile of 1000 test runs is between 4.0 dB and 7.5 dB under varying noise levels.
A novel 2D-3D hybrid approach to vehicle trajectory and speed estimation

We present in this paper a novel surveillance system using calibrated stereo camera pair. The system adopts a 2D-3D hybrid approach where vehicle detection and tracking are first performed in the 2D space. Then, both appearance and depth cues are incorporated into the tracking module based on a hybrid 2D-3D approach. After change detection is carried out in the original image domain, moving vehicles can be detected and tracked over time. Vehicle positions and speeds can be estimated very accurately by first retrieving the 3D coordinates of pixels associated with the tracked vehicles resulting in a 3D vehicle point cloud and fitting a cuboid to it. The proposed stereo vehicle surveillance system is capable of extracting several important driving parameters, including vehicle trajectories, speeds, and orientations. Experimental results have confirmed the efficiency and robustness of the system and its applicability to road safety applications, including speeding and drunk driving detection.
Image enhancement for low bit rate wavelet-based compression

At low bit rates, wavelet-based compression is advantageous over most block-based image compression schemes in that it does not generate visually objectionable blocking artifacts. However, the reconstructed images still suffer from loss of image details and sharpness, and coding artifacts, due to the loss of a considerable amount of information in the quantization. While in general it may be impossible to recover the information loss, some coding artifacts can be alleviated through the incorporation of image smoothness constraints using appropriate image prior model. Meanwhile, the constraints from the transform coding can be utilized to prevent further image degradation, such as oversmoothing of remaining image details. We propose a novel enhancement technique that utilizes a discontinuous image smoothness model to obtain good reconstruction in terms of both PSNR improvement and visual observation.
SNR scalable transcoding for video over wireless channels

We propose a novel scheme integrating transcoding, SNR scalability and error protection channel coding for video transmission over wireless channels. The target application for such error-resilient transcoding is to provide robust access to the pre-encoded high quality video server from mobile wireless terminals. The high bit rate of pre-encoded video sequence is reduced through transcoding to fit greatly the limited bandwidth of wireless links. The proposed transcoding is able to reduce both size of the video frames and the bit rate of the pre-encoded video. We also design the transcoder such that the transcoding output bitstream has the desired SNR scalability in order to guarantee a basic quality of video transmission under the hostile wireless transmission environment. With SNR scalable bitstream output, we are able to incorporate unequal error protection through channel coding for the base layer and the enhancement layers, respectively. Different rate of CRC/RCPC is adopted so as to simplify the implementation of a single channel decoder at the receiver. Experimental results show that the proposed integrated scheme of SNR scalable transcoding can guarantee the basic decoded visual quality under random and bursty error channel conditions and outperform the conventional single layer transcoding without channel coding.
Buffer-aware and QoS-effective resource allocation scheme in WBANs

Wireless Body Area Network (WBAN) represents one of the most promising networks to provide health applications for improving the quality of life, such as ubiquitous e-Health services and real-time health monitoring. The resource allocation of an energy-constrained, heterogeneous WBAN is a critical issue that should consider both energy efficiency and Quality of Service (QoS) requirements with the dynamic link characteristics, especially when the limited resource cannot satisfy the expected QoS requirements. In this paper, a buffer aware Energy-efficient and QoS-effective resource allocation scheme is proposed in which the sensor queue buffer states, constraints of QoS metrics and the characteristics of dynamic links are considered. Specifically, a buffer aware sensor evaluation method is designed to dynamically evaluate the sensor state with considering the sensor buffer states for improving the system performance. We then formulate the resource allocation problem for optimizing the transmission power, the transmission rate and the allocated time slots for each sensor to minimize the sum mix-cost, which is defined to characterize the energy cost and QoS cost between attainable QoS support and QoS requirements. Simulation results demonstrate the effectiveness of the buffer aware sensor evaluation method and the proposed energy-efficient and QoS-effective resource allocation scheme.
A knowledge-based approach to volumetric medical image segmentation

We propose in this paper an automatic segmentation of 3D image data based on a novel technique using adaptive K-mean clustering and knowledge-based morphological operations. The proposed adaptive K-mean clustering algorithm is capable of segmenting the regions of smoothly varying intensity distributions. Spatial constraints are incorporated in the clustering algorithm through the modeling of the regions by Gibbs random fields. Knowledge-based morphological operations are then applied to the segmented regions to identify the desired regions according to a priori anatomical knowledge of the region-of-interest. This proposed technique has been successfully applied to a sequence of cardiac CT volumetric images to generate the volumes of left ventricle chambers at 16 consecutive temporal frames. Our final automatic segmentation results compare favorably with the results obtained using manual outlining. Extensions of this approach to other applications can be readily made when a priori knowledge of the given object is available.<<ETX>>
Automatic creation of magazine-page-like social media visual summary for mobile browsing

Today, mobile users are struggling with accessing overloading and unstructured social media feeds on the severely constrained mobile display. To overcome the challenges associated with browsing social media feeds on mobile devices, we are developing an innovative scheme to automatically create and synthesize the mixed social media digest (pictures, texts and videos) into a magazine-page-like social media visual summary. Given a set of personalized social media digest, a multi-objective optimization is formulated to organize the digest into visual summary in a 9-block-partition fashion with consideration of informative delivery, aesthetic rules and visual perception principles. Each block will be optimized interactively in terms of size, position and color to best represent the overall social media digest. Extensive evaluation and analysis based on user studies demonstrate that the proposed approach is effective in presenting social media content in a visually appealing and compact way. It is expected that this visual summary will lead to much enhanced user experiences for browsing social media digest on mobile devices.
Robust Video Transmission Over MIMO-OFDM System using MDC and Space Time Codes

MIMO-OFDM is a promising technique for the broadband wireless communication system. In this paper, we propose a novel scheme that integrates multiple description coding (MDC), error resilient video coding, and unequal error protection strategy with various space time coding codes for robust video transmission over MIMO-OFDM system. The proposed MDC coder generates multiple bitstreams of equal importance which are very suitable for multiple antennas system. Furthermore, according to the contribution to the reconstructed video quality, we apply unequal error protection strategy using BLAST and STBC space time codes for each video bitstream. Experimental results have demonstrated that the proposed scheme can achieve desired tradeoff between the reconstructed video quality and the transmission efficiency
A novel interleaver for image communications with theoretical analysis of characteristics

We present a novel interleaving scheme for image communication applications. The proposed interleaver uses the shortened block codes as the row codes, so it has a trapezoid-like structure that results in less than linear rate of increase in decoding delay as we increase the interleaving degree. Unlike the universal or uniform interleaving, the trapezoid-like structure of such interleaving reduces the delay of de-interleaving and is more appropriate for image communication when low delay is required. We present a full theoretical analysis of the proposed interleaver. For better utilization of this kind of the interleaver, we also present an iterative procedure to improve the performance of the interleaver. We conclude that substantial performance improvement can be achieved with this novel interleaver.
A deadline-aware virtual contention free EDCA scheme for H.264 video over IEEE 802.11e wireless networks

Contention-based access has been the key component for 802.11 wireless networks. As the video service is becoming more popular, contention-based access has been the key limiting factor in providing satisfactory quality-of-service (QoS) for video over wireless networks, especially when the data traffic includes both real-time video and other types of traffics. A contention free burst (CFB) scheme has been developed recently [1], would result in improved video QoS, but at the expenses of best effort and background traffics. In this paper, we proposed a deadline-aware virtual contention free (DVCF) scheme for H.264 video over 802.11e wireless networks. The main contribution of this paper is to jointly consider the characteristic of video source, 802.11e protocol, and network specifications by adjusting the parameters of 802.11e protocol. As a result, the proposed scheme (DVCF EDCA) maintains the transmission of all types of traffics provided that the deadline of transported video packets can be strictly observed. In this framework, the deadline of each video packet is estimated first as in . The estimated deadline serves dual purpose: one is to guarantee the transmission of any given video packet before it expires and another one is to allow other types of data traffic to transmit when there is no pressing needs to transmit any video packet. Simulation results show that the proposed framework achieves virtually no loss for video packets and noticeable improvement in combined throughput of video and non-video traffics.
A novel video analysis approach for overtaking vehicle detection

Road safety plays a pivotal role in intelligent transportation systems. In the past decade, much attention from researchers and industries alike has been directed towards intelligent systems that can help reduce the number of on-road accidents. For obstacle detection applications, sensor-based approaches employing radar and lidar are appealing as they offer high object detection rates. However, videos are a much richer source of information and video-based approaches can offer a holistic view of the scene, bringing about new applications that are not limited to just obstacle detection. In this paper, a novel video-based framework is proposed for detecting possibly overtaking vehicles, which may ultimately lead to a potential hazard. The system relies on lane marks to guide the vehicle tracking module and leverages the information extracted from the vanishing point to correctly distinguish a vehicle passing by from the one truly attempting to overtake the ego-vehicle. The key to the success of the system lies in the accurate detection and tracking of the trajectory of the vehicle, localization of the lane marks, and estimation of the vanishing point. Experimental results obtained from running our algorithm on real-world footage confirm that the proposed system produces high detection and low false alarm rates.