Arrhythmia classification based on electrocardiogram (ECG) is crucial in automatic cardiovascular disease diagnosis. The classification methods used in the current practice largely depend on hand-crafted manual features. However, extracting hand-crafted manual features may introduce significant computational complexity, especially in the transform domains. In this study, an accurate method for patient-specific ECG beat classification is proposed, which adopts morphological features and timing information. As to the morphological features of heartbeat, an attention-based two-level 1-D CNN is incorporated in the proposed method to extract different grained features automatically by focusing on various parts of a heartbeat. As to the timing information, the difference between previous and post RR intervels is computed as a dynamic feature. Both the extracted morphological features and the interval difference are used by multi-layer perceptron (MLP) for classifing ECG signals. In addition, to reduce memory storage of ECG data and denoise to some extent, an adaptive heartbeat normalization technique is adopted which includes amplitude unification, resolution modification, and signal difference. Based on the MIT-BIH arrhythmia database, the proposed classification method achieved sensitivity Sen=93.4% and positive predictivity Ppr=94.9% in ventricular ectopic beat (VEB) detection, sensitivity Sen=86.3% and positive predictivity Ppr=80.0% in supraventricular ectopic beat (SVEB) detection, and overall accuracy OA=97.8% under 6-bit ECG signal resolution. Compared with the state-of-the-art automatic ECG classification methods, these results show that the proposed method acquires comparable accuracy of heartbeat classification though ECG signals are represented by lower resolution.

In video-based point cloud compression (V-PCC), the partitioning of the Coding Unit (CU) has ultra-high computational complexity. Just Noticeable Difference Model (JND) is an effective metric to guide this process. However, in this paper, it is found that the performance of traditional JND model is degraded in V-PCC. For the attribute video, due to the pixel-filling operation, the capability of brightness perception is reduced for the JND model. For the geometric video, due to the depth filling operation, the capability of depth perception is degraded in the boundary area for depth based JND models (JNDD). In this paper, a joint JND model (J_JND) is proposed for the attribute video to improve the brightness perception capacity, and an occupancy map guided JNDD model (O_JNDD) is proposed for the geometric video to improve the depth difference estimation accuracy of the boundaries. Based on the two improved JND models, a fast V-PCC Coding Unit (CU) partitioning algorithm is proposed with adaptive CU depth prediction. The experimental results show that the proposed algorithm eliminates 27.46% of total coding time at the cost of only 0.36% and 0.75% Bjontegaard Delta rate increment under the geometry Point-to-Point (D1) error and attribute Luma Peak-signal-Noise-Ratio (PSNR), respectively.

To realize transmit diversity for the time domain synchronous OFDM (TDS-OFDM) system, this letter proposes the space-time-frequency orthogonal training sequence and the corresponding flexible channel estimation methods. Simulation results indicate that an significant performance improvement could be achieved for low-density parity-check code (LDPC) coded TDS-OFDM system over multi-path fading channels.

In this paper, the closed-form expressions of signal-to-interference-plus-noise ratio (SINR) and the outage probability are derived for a maximal ratio combining (MRC) two-dimensional (2-D)-RAKE receiver with imperfect power control in a frequency-selective Nakagami fading channel. The impact of power control error (PCE) on the performance of the receiver is analyzed for all kinds of fading environments. The results of numerical derivation and simulation indicate that the performance of 2-D-RAKE receivers degrades due to imperfect power control. But when PCE is not serious, increasing the number of antennae and temporal diversity order can compensate for the performance loss. The exact performance improvement due to space-time processing varies with the PCE and the fading environment.

With the flourish of applications based on the Internet of Things (IoT), privacy issues have been attracting a lot of attentions. Although the concept of privacy homomorphism was proposed along with the birth of the well-known RSA cryptosystems, cryptographers over the world have spent about three decades for finding the first implementation of the so-called fully homomorphic encryption (FHE). Despite of, currently known FHE schemes, including the original Gentry's scheme and many subsequent improvements as well as the other alternatives, are not appropriate for IoT-oriented applications because most of them suffer from the problems of inefficient key size and noisy restraining. In addition, for providing fully support to IoT-oriented applications, symmetric fully homomorphic encryptions are also highly desirable. This survey presents an analysis on the challenges of designing secure and practical FHE for IoT, from the perspectives of lightweight requirements as well as the security requirements. In particular, some issues about designing noise-free FHE schemes would be addressed.

To prevent constraints or defects of a single sensor from malfunctions, this paper proposes a fire detection system based on the Dempster-Shafer theory with multi-sensor technology. The proposed system operates in three stages: measurement, data reception and alarm activation, where an Arduino is tasked with measuring and interpreting the readings from three types of sensors. Sensors under consideration involve smoke, light and temperature detection. All the measured data are wirelessly transmitted to the backend Raspberry Pi for subsequent processing. Within the system, the Raspberry Pi is used to determine the probability of fire events using the Dempster-Shafer theory. We investigate moderate settings of the conflict coefficient and how it plays an essential role in ensuring the plausibility of the system's deduced results. Furthermore, a MySQL database with a web server is deployed on the Raspberry Pi for backlog and data analysis purposes. In addition, the system provides three notification services, including web browsing, smartphone APP, and short message service. For validation, we collected the statistics from field tests conducted in a controllable and safe environment by emulating fire events happening during both daytime and nighttime. Each experiment undergoes the No-fire, On-fire and Post-fire phases. Experimental results show an accuracy of up to 98% in both the No-fire and On-fire phases during the daytime and an accuracy of 97% during the nighttime under reasonable conditions. When we take the three phases into account, the accuracy in the daytime and nighttime increase to 97% and 89%, respectively. Field tests validate the efficiency and accuracy of the proposed system.

This letter presents a flexible signal structure supporting localization service for time domain synchronous OFDM (TDS-OFDM) in multi-service transmission applications. Localization is treated as one specific service and the corresponding data is allocated within the physical layer pipe (PLP) of the first subframe. The concept of variable sub-carrier spacing to combat Doppler spread is also introduced for the localization service. Simulation results indicate that the proposed scheme outperforms the conventional scheme and at the same time achieves high positioning accuracy.

H.264 video codec system requires big capacity and high bandwidth of Frame Store (FS) for buffering reference frames. The up-to-date three dimensional (3D) stacked Phase change Random Access Memory (PRAM) is the promising approach for on-chip caching the reference signals, as 3D stacking offers high memory bandwidth, while PRAM possesses the advantages in terms of high density and low leakage power. However, the write endurance problem, that is a PRAM cell can only tolerant limited number of write operations, becomes the main barrier in practical applications. This paper studies the wear reduction techniques of PRAM based FS in H.264 codec system. On the basis of rate-distortion theory, the content oriented selective writing mechanisms are proposed to reduce bit updates in the reference frame buffers. With the proposed control parameter a, our methods make the quantitative trade off between the quality degradation and the PRAM lifetime prolongation. Specifically, taking a in the range of [0.2,2], experimental results demonstrate that, our methods averagely save 29.9–35.5% bit-wise write operations and reduce 52–57% power, at the cost of 12.95–20.57% BDBR bit-rate increase accordingly.

This paper proposes to obtain high-level, domain-robust representations for cross-view face recognition. Specially, we introduce Convolutional Deep Belief Networks (CDBN) as the feature learning model, and an CDBN based interpolating path between the source and target views is built to model the correlation of cross-view data. The promising results outperform other state-of-the-art methods.

This letter presents a novel multi-carrier code division multiple access (MC-CDMA) system called time domain synchronous MC-CDMA (TDS-MC-CDMA). Aided by the new training sequence (TS) with perfect autocorrelation in the time domain and flat frequency response in the frequency domain, the proposed TDS-MC-CDMA system outperform the traditional MC-CDMA system in terms of spectrum efficiency by about 10%. Simulations are carried out to demonstrate the good performance of the proposed scheme.

Remote data possession checking for cloud storage is very important, since data owners can check the integrity of outsourced data without downloading a copy to their local computers. In a previous work, Chen proposed a remote data possession checking protocol using algebraic signature and showed that it can resist against various known attacks. In this paper, we find serious security flaws in Chen's protocol, and shows that it is vulnerable to replay attack by a malicious cloud server. Finally, we propose an improved version of the protocol to guarantee secure data storage for data owners.

With the rapid development of multi-view video coding (MVC) and light field rendering (LFR), Free-View Television (FTV) has emerged as new entrainment equipment, which can bring more immersive and realistic feelings for TV viewers. In FTV broadcasting system, the TV-viewer can freely watch a realistic arbitrary view of a scene generated from a number of original views. In such a scenario, the ownership of the multi-view video should be verified not only on the original views, but also on any virtual view. However, capacities of existing watermarking schemes as copyright protection methods for LFR-based FTV are only one bit, i.e., presence or absence of the watermark, which seriously impacts its usage in practical scenarios. In this paper, we propose a robust multi-bit watermarking scheme for LFR-based free-view video. The direct-sequence code division multiple access (DS-CDMA) watermark is constructed according to the multi-bit message and embedded into DCT domain of each view frame. The message can be extracted bit-by-bit from a virtual frame generated at an arbitrary view-point with a correlation detector. Furthermore, we mathematically prove that the watermark can be detected from any virtual view. Experimental results also show that the watermark in FTV can be successfully detected from a virtual view. Moreover, the proposed watermark method is robust against common signal processing attacks, such as Gaussian filtering, salt & peppers noising, JPEG compression, and center cropping.

Face verification is the task of determining whether two given face images represent the same person or not. It is a very challenging task, as the face images, captured in the uncontrolled environments, may have large variations in illumination, expression, pose, background, etc. The crucial problem is how to compute the similarity of two face images. Metric learning has provided a viable solution to this problem. Until now, many metric learning algorithms have been proposed, but they are usually limited to learning a linear transformation. In this paper, we propose a nonlinear metric learning method, which learns an explicit mapping from the original space to an optimal subspace using deep Independent Subspace Analysis (ISA) network. Compared to the linear or kernel based metric learning methods, the proposed deep ISA network is a deep and local learning architecture, and therefore exhibits more powerful ability to learn the nature of highly variable dataset. We evaluate our method on the Labeled Faces in the Wild dataset, and results show superior performance over some state-of-the-art methods.

A BICM-ID system with 3-dimensional rotated BPSK constellation and signal space diversity (SSD) is proposed to combat the effect of Rayleigh fading. A new criterion based on mutual information is proposed to find the optimal rotation matrix, and the labeling that fits well with the outer code is presented. Simulation results show that at BER of 10-5 over a Rayleigh fading channel, with the code length of 192,000 bits and the iteration number of 100, the performance of the proposed system is only about 0.8 dB from the Gaussian-input Shannon limit and exceeds the limit constrained by the traditional QPSK input without rotation or SSD, at the spectrum efficiency of 1 bit/s/Hz.

In this paper, we propose a representation method based on local spatial strokes for scene character recognition. High-level semantic information, namely co-occurrence of several strokes is incorporated by learning a sparse dictionary, which can further restrain noise brought by single stroke detectors. The encouraging results outperform state-of-the-art algorithms.

Multi-cell cooperation is a promising technique to mitigate inter-cell interference arising from universal frequency reuse in cellular networks. Sharing channel state information (CSI) in neighboring cells can help enhance the overall system capacity at the cost of high feedback burden. In this paper, an asymmetric CSI feedback strategy is proposed for multi-cell cooperation beamforming. In order to improve the overall system performance, we optimize the limited feedback bandwidth based on the average received power from both serving and neighboring cells. Simulation results show that the proposed strategy utilizes the limited feedback bandwidth more efficiently, thereby achieving a higher sum rate.

Sampling is important for many applications in research areas such as graphics, vision, and image processing. In this paper, we present a novel stratified sampling algorithm (SSA) for the coiled tubing surface with a given probability density function. The algorithm is developed from the inverse function of the integration for the areas of the coiled tubing surface. We exploit a Hierarchical Allocation Strategy (HAS) to preserve sample stratification when generating any desirable sample numbers. This permits us to reduce variances when applying our algorithm to Monte Carlo Direct Lighting for realistic image generation. We accelerate the sampling process using a segmentation technique in the integration domain. Our algorithm thus runs 324 orders of magnitude faster when using faster SSA algorithm where the order of the magnitude is proportional to the sample numbers. Finally, we employ a parabolic interpolation technique to decrease the average errors occurred for using the segmentation technique. This permits us to produce nearly constant average errors, independent of the sample numbers. The proposed algorithm is novel, efficient in computing and feasible for realistic image generation using Monte Carlo method.

Over the last few years, Apache MapReduce has become the prevailing framework for large scale data processing. Instead of writing MapReduce programs which are too obscure to express, many developers usually adopt high level query languages, such as Hive or Pig Latin, to finish their complex queries. These languages automatically compile each query into a workflow of MapReduce jobs, so they greatly facilitate the querying and management of large datasets. One option to speed up the execution of workflows is to save the results produced previously and reuse them in the future if needed. In this paper we present SuperRack, which uses shared storage devices to store the results of each workflow and allows a new query to reuse these results in order to avoid redundant computation and hasten execution. We propose several novel techniques to improve the access and storage efficiency of the previous results. We also evaluate SuperRack to verify its feasibility and effectiveness. Experiments show that our solution outperforms Hive significantly under TPC-H benchmark and real life workloads.

In this letter, we propose a new modification to the belief propagation (BP) decoding algorithm for Finite-Geometry low-density parity-check (LDPC) codes. The modification is based on introducing feedback into the iterative process, which can break the oscillations of bit log-likelihood ratio (LLR) values. Simulations show that, with a given maximum iteration, the "feedback BP" (FBP) algorithm can achieve better performance than the conventional belief propagation algorithm.

As the number of concurrently running applications on the chip multiprocessors (CMPs) is increasing, efficient management of the shared last-level cache (LLC) is crucial to guarantee overall performance. Recent studies have shown that cache partitioning can provide benefits in throughput, fairness and quality of service. Most prior arts apply true Least Recently Used (LRU) as the underlying cache replacement policy and rely on its stack property to work properly. However, in commodity processors, pseudo-LRU policies without stack property are commonly used instead of LRU for their simplicity and low storage overhead. Therefore, this study sets out to understand whether LRU-based cache partitioning techniques can be applied to commodity processors. In this work, we instead propose a cache partitioning mechanism for two popular pseudo-LRU policies: Not Recently Used (NRU) and Binary Tree (BT). Without the help of true LRU's stack property, we propose a profiling logic that applies curve approximation methods to derive the hit curve (hit counts under varied way allocations) for an application. We then propose a hybrid partitioning mechanism, which mitigates the gap between the predicted hit curve and the actual statistics. Simulation results demonstrate that our proposal can improve throughput by 15.3% on average and outperforms the stack-estimate proposal by 12.6% on average. Similar results can be achieved in weighted speedup. For the cache configurations under study, it requires less than 0.5% storage overhead compared to the last-level cache. In addition, we also show that profiling mechanism with only one true LRU ATD achieves comparable performance and can further reduce the hardware cost by nearly two thirds compared with the hybrid mechanism.