An efficient all-zero-block detection method for general orthogonal transformations

Multi-stage all-zero block detection for HEVC coding using machine learning

Transform/quantization (T/Q) is one of the high complex modules in high efficiency video coding (HEVC). In this paper, all zero block (AZB) detection algorithm for HEVC is proposed to reduce the complexity of T/Q. The proposed AZB detection algorithm is based on the quantization level of the maximum transform coefficient, which is suitable for both uniform quantizer (UQ) and rate-distortion optimized quantizer (RDOQ). Experimental results show that 46% complexity of T/Q for UQ and 42% for RDOQ can be reduced with negligible loss of video quality and compression efficiency, outperforming the state-of-the-art methods.

In this thesis, several fast algorithms related to H.264/AVC, the latest and most advanced video standard, are explored. The first part of this work is focused on using SAD information to achieve early detection of all-zero blocks such that the complexity for coding these blocks can be avoided. A new mathematical model is introduced and an efficiency detection method is derived. The second part of this work is focused on using Hadamard transform information to achieve all-zero block detection, when SAD information is not available. A novel approach is proposed based on the relationship between integer transform and Hadamard transform. The final part of this work is focused on transcoding. Two fast algorithms are proposed to speed up the H.263 to H.264 inter frame transcoding. Experimental results show that the proposed algorithms can reduce the complexity of H.264 encoding and transcoding, while maintaining a similar video quality.

In this paper a new early detection algorithm for all-zero coefficient blocks before integer transformation and quantization is proposed in very low bit rate video coding for H.264/AVC standard. The all-zero detecting threshold is very critical as a tradeoff between encoder complexity and image quality. So firstly a feasible threshold range is analyzed theoretically, then based on extensive experiments on typical video sequences from the statistical opinion, a better optimal threshold is discussed, which has a better balance than the theoretically optimal threshold between mis-justice and leak-out ratio in detecting all-zero coefficient blocks. In our proposed algorithm, the sum of absolute difference (SAD) of each motion prediction error in the inter block is used, and this SAD is a bypass obtained from motion estimation process, so no additional computation is required. The simulation results show that the proposed algorithm can reduce the computational load significantly, almost without video quality loss.

In multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems, several detection schemes have been developed for high error performance and low complexity. In several detection schemes, maximum likelihood (ML) and QRD-M have optimal error performance. However, these detection schemes have very high complexity. Thus, this paper proposes reduced complexity of QRD-M detection scheme in MIMO-OFDM systems. The proposed detection scheme has two stages, i.e. partial zero-forcing (ZF) and conventional QRD-M. The proposed detection scheme has higher error performance and lower complexity than the decision feedback equalizer (DFE).

Low‐rate denial of service (LDoS) attacks exploit the congestion control mechanism to degrade the network quality of service. As a classic active queue management algorithm, random early detection (RED) algorithm is widely used to avoid network congestion. However, RED is vulnerable to LDoS attacks. LDoS attacks with well‐configured attack parameters force RED queue to fluctuate severely, thereby throttling transmission control protocol (TCP) senders’ sending rate. A feedback control model is proposed to describe the process of the congestion control, by which the congestion window and queue behaviours are analysed combined. After that, a two‐dimensional queue distribution model composed of the instantaneous queue and the average queue is designed to extract the attack feature. Moreover then, a combination of a simple distance‐based approach and an adaptive threshold algorithm is proposed to detect every LDoS attack burst. Test results of network simulator (NS)‐2 simulation and test‐bed experiments indicate that the proposed detection strategy can almost completely detect LDoS attack bursts and is especially robust to legitimate short bursts.

Congestion control in Cluster Wireless Sensor Networks (CWSNs) has drawn widespread attention and research interests. The increasing number of nodes and scale of networks cause more complex congestion control and management. Active Queue Management (AQM) is one of the major congestion control approaches in CWSNs, and Random Early Detection (RED) algorithm is commonly used to achieve high utilization in AQM. However, traditional RED algorithm depends exclusively on source-side control, which is insufficient to maintain efficiency and state stability. Specifically, when congestion occurs, deficiency of feedback will hinder the instability of the system. In this paper, we adopt the Additive-Increase Multiplicative-Decrease (AIMD) adjustment scheme and propose an improved RED algorithm by using neighbor feedback and scheduling scheme. The congestion control model is presented, which is a linear system with a non-linear feedback, and modeled by Lur’e type system. In the context of delayed Lur’e dynamical network, we adopt the concept of cluster synchronization and show that the congestion controlled system is able to achieve cluster synchronization. Sufficient conditions are derived by applying Lyapunov-Krasovskii functionals. Numerical examples are investigated to validate the effectiveness of the congestion control algorithm and the stability of the network.

Network operators are now facing bandwidth outages as well as a growing pressure to ensure good Quality of Service (QoS). An important practical issue for network service providers is to pay close attention to the load changes of network traffic, in particular, the stationary increase of load from a normal demand. Many network monitoring applications and performance analysis tools are based on the study of an aggregate measure of network traffic, e.g. number of packets in transit (NPT), which is a long-term univariate time series. To classify this type of network traffic data and detect any increase of network source load, we propose a dynamic principal component analysis (PCA) method, first to extract data features and then to detect a stationary load increase of network traffic. The proposed detection schemes are based on either the major or the minor principal components of network traffic data. To demonstrate the applications of the proposed feature extraction method and the detection schemes, we applied them to network traffic data simulated from the packet switching network (PSN) model. Additionally, we propose a combined detection scheme that uses both the major and the minor principal components. The proposed detection schemes, based on dynamic PCA, show enhanced performance in detecting an increase of network load for the simulated network traffic data. These results offer a new feature extraction method based on dynamic PCA that creates additional feature variables for event detection in a univariate time series.

Due to the dynamics and mobility of mobile ad hoc networks (MANETs), intrusion detection techniques in MANETs must be adaptive. In this work, we propose detection schemes that are suitable to detect in-band wormhole attacks. The first detection scheme uses the Sequential Probability Ratio Test (SPRT). The SPRT has been proven to be an optimal detection test when the probability distributions of both normal and abnormal behaviors are given. Furthermore, we introduce non-parametric methods, which require no training and are more adaptive to mobile scenarios. The proposed detection schemes are implemented and evaluated using a 48-node testbed and a mobile ad-hoc network emulator at the Army Research Lab. The performance and detection accuracy of various schemes are compared, especially in the presence of congestion. We provide tradeoffs analyses among detection latency and probabilities of false alarms and missed detection.

A digital image is subjected to the most manipulation. This is driven by the easy manipulating process through image editing software which is growing rapidly. These problems can be solved through the watermarking model as an active authentication system for the image. One of the most popular methods is Singular Value Decomposition (SVD) which has good imperceptibility and detection capabilities. Nevertheless, SVD has high complexity and can only utilize one singular matrix S, and ignore two orthogonal matrices. This paper proposes the use of the Walsh matrix with dyadic ordering to generate a new S matrix without the orthogonal matrices. The experimental results showed that the proposed method was able to reduce computational time by 22% and 13% compared to the SVD-based method and similar methods based on the Hadamard matrix respectively. This research can be used as a reference to speed up the computing time of the watermarking methods without compromising the level of imperceptibility and authentication.

A general method for the detection and characterization of an mRNA using an oligodeoxynucleotide probe is described. The results presented indicate that a G-dT or a dG-U base pair within a short DNA-RNA hybrid does not significantly reduce the stability of the hybrid. On this basis, we propose that 11 amino acids, including Trp and Met, can be used in selecting a peptide sequence suitable for use in designing an oligodeoxynucleotide probe complementary to a given mRNA. To test this hypothesis, we have synthesized an oligodeoxynucleotide (oligo II) complementary to the region of gastrin mRNA coding for Trp-Met-Asp-Phe and have compared its effectiveness as a hybridization probe and as a primer for the synthesis of gastrin-specific cDNA with another oligonucleotide (oligo I) complementary to the region of gastrin mRNA coding for Trp-Met-Glu-Glu. Unlike oligo I, oligo II functions as a primer in specific cDNA synthesis only when the mRNA is denatured prior to initiation of synthesis. Similarly, oligo II can be used as a specific hybridization probe for gastrin mRNA only when the RNA is denatured and partially cleaved with NaOH before hybridization. A simple procedure for denaturing gastrin mRNA to ensure efficient interaction with oligodeoxynucleotide probes is described. This procedure should be useful in studies with other oligonucleotides and mRNAs as well.

In ad hoc networks, selfish nodes deviating from the standard MAC (Medium Access Control) protocol can significantly degrade normal nodes’ performance and are usually difficult to detect. In this paper, we propose detection and defense schemes to identify and defend against MAC-layer selfish misbehavior, respectively, in IEEE 802.11 multi-hop ad hoc networks. Specifically, the non-deterministic nature of the IEEE 802.11 MAC protocol imposes great challenges to distinguishing selfish nodes from well-behaved nodes. Most traditional selfish misbehavior detection approaches are for wireless local area networks (WLANs) only. They either rely on a large amount of historical data to perform statistical detection, or employ throughput or delay models that are only valid in WLANs for detection. In contrast, we propose a realtime selfish misbehavior detection scheme for multi-hop ad hoc networks. It requires only several samples, and hence is more efficient and can adapt to channel dynamics more quickly. Then, based on the proposed detection scheme, we design three selfish misbehavior defense schemes against three typical kinds of smart selfish nodes. We find that the smart selfish nodes cannot degrade normal nodes’ performance much without getting detected. Extensive simulation results are finally presented to validate the proposed detection and defense schemes.

This paper is concerned with the outlier-resistant observer-based control problem for a class of interval type-2 (IT2) Takagi–Sugeno (T-S) fuzzy systems under adaptive event-triggered protocol (AETP). To improve the efficiency of resource utilization, AETP is mainly through an adjustable threshold parameter to dynamically schedule the data transmission, which occurs in the sensor-to-controller channel. In order to resist the abnormal measurement values, an output saturation composition is constructed in the observer. With the aid of Lyapunov theory, sufficient conditions are obtained to ensure the desired performance. Furthermore, the desired controller is designed for a class of IT2 T-S fuzzy systems. The combination of orthogonal decomposition and linear matrix inequalities is implemented to deal with the nonlinear coupling terms in the gain solving process. Finally, a numerical example is employed to justify the practicality and effectiveness of the proposed control scheme.

For turbo-coded differential phase-shift-keying (DPSK) systems under correlated flat-fading channels, a serial iterative structure of soft-input-soft-output (SISO) forward/backward MAP (FB-MAP) detection and SISO decoder is proposed to reduce the computational complexity of MAP detection without performance degradation. The simulation results show that the FB-MAP detection scheme offers the same or better bit error rate (BER) performance with lower computational complexity when compared with the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> probability (APP) demodulator, the SISO MAP detection, and the SISO multiple-symbol differential sphere decoder (MSDSD) scheme. Although the computational complexity is higher than the iterative filtered multiple differential detection (IF-MDD) scheme, the proposed detection scheme can have better BER performance.

Vector Lyapunov Functions Method in the Abstract Theory of Control