Prediction Mode Research Articles

Simulation of Internet of Things Water Management for Efficient Rice Irrigation in Rwanda

The central role of water access for agriculture is a clear challenge anywhere in the world and particularly in areas with significant seasonal variation in rainfall such as in Eastern and Central Africa. The combination of modern sensor technologies, the Internet, and advanced irrigation equipment combined in an Internet of Things (IoT) approach allow a relatively precise control of agricultural irrigation and creating the opportunity for high efficiency of water use for agricultural demands. This IoT approach can thereby increase the resilience of agricultural systems in the face of complex demands for water use. Most previous works on agricultural IoT systems are in the context of countries with higher levels of economic development. However, in Rwanda, with a low level of economic development, the advantages of efficient water use from the application of IoT technology requires overcoming constraints such as lack of irrigation control for individual farmers, lack of access to equipment, and low reliability of power and Internet access. In this work, we describe an approach for adapting previous studies to the Rwandan context for rice (Oryza sativa) farming with irrigation. The proposed low cost system would automatically provide irrigation control according to seasonal and daily irrigational needs when the system sensors and communications are operating correctly. In cases of system component failure, the system switches to an alternative prediction mode and messages farmers with information about the faults and realistic irrigation options until the failure is corrected. We use simulations to demonstrate, for the Muvumba Rice Irrigation Project in Northeast Rwanda, how the system would respond to growth stage, effective rainfall, and evapotranspiration for both correct operation and failure scenarios.

Intra prediction based on geometry padding for omnidirectional video coding

Omnidirectional videos or 360 degree videos play an important role in virtual reality (VR) applications. In order to employ the existing video coding standards, omnidirectional videos are firstly projected onto the 2-Dimension (2D) plane, which generates the discontinuity at boundaries and may result in unexpected artifacts when lossy coding is applied. In this paper, we propose a new intra prediction method to deal with the above coding artefacts in omnidirectional videos. Different from the conventional intra prediction using the left and top reference samples, the right reference samples are derived from the reconstructed samples on the left boundary and padded for intra prediction. The proposed method applies to the planar, DC and partial angular prediction modes in intra prediction. The experimental results demonstrate a Bjontegaard-Delta-rate reduction of up to 2.97% using weighted spherical peak-signal to noise ratio (WSPSNR) quality metric for the coding tree units (CTUs) at the right boundary.

Optimised HEVC encoder intra‐only configuration

High-efficiency video coding (HEVC) is the latest video coding standard aimed to reduce the bitrate by half for the same video quality compared to H.264/AVC. This encoding performance makes HEVC more suitable for high-definition video applications. However, this performance is coupled with a high-computational complexity, which makes it hard to achieve real-time video encoding with a classic embedded processor. Multicore technology of programmable processors could be a very promising solution to overcome this computational complexity. Moreover, software optimisations by proposing fast algorithms for the most complex functions could also be an efficient solution to speed up the encoding process. In this context, this study presents a fast mode decision algorithm for the intra prediction module. This algorithm aims to reduce the number of intra prediction modes to be tested instead of performing a full intra mode search. Experimental results for all-Intra configuration show that the proposed fast intra mode decision allows saving up to 46.79% of the intra prediction time in average. Encoding performance in terms of video quality and bitrate is not significantly affected.

Convolutional neural network based low complexity HEVC intra encoder

Video coding is one of the key technologies of visual sensors. As the state-of-art video coding standard, High Efficiency Video Coding (HEVC) achieves a significant high compression ratio for video. However, it also introduces heavy computational complexity, leading to challenges in application of visual sensors. To reduce the complexity of HEVC intra encoder, this paper proposed a one-stage decision method of CU/PU partition and prediction mode for intra coding. First, the potential factors that may related to the corresponding decisions in CU/PU are explored. Based on this, a one-stage decision network (OSDN) structure is specially designed to determine these decisions. Consequently, the complexity of HEVC intra coding can be drastically reduced by avoiding the brute-force search. Then, OSDN is embedded into the HEVC reference software HM 15.0. Thresholds are set to let the encoder switch between OSDN and the original implementation in HEVC to obtain the final decisions. The experimental results show that the proposed method can reduce 73.69% intra encoding time with 0.1673 dB BD-PSNR loss on average. In addition, the trade-off between RD performance degradation and complexity reduction can be controlled by thresholds.

Photovoltaic Generation Prediction of CCIPCA Combined with LSTM

In order to remedy problems encompassing large-scale data being collected by photovoltaic (PV) stations, multiple dimensions of power prediction mode input, noise, slow model convergence speed, and poor precision, a power prediction model that combines the Candid Covariance-free Incremental Principal Component Analysis (CCIPCA) with Long Short-Term Memory (LSTM) network was proposed in this study. The corresponding model uses factor correlation coefficient to evaluate the factors that affect PV generation and obtains the most critical factor of PV generation. Then, it uses CCIPCA to reduce the dimension of PV super large-scale data to the factor dimension, avoiding the complex calculation of covariance matrix of algorithms such as Principal Component Analysis (PCA) and to some extent eliminating the influence of noise made by PV generation data acquisition equipment and transmission equipment such as sensors. The training speed and convergence speed of LSTM are improved by the dimension-reduced data. The PV generation data of a certain power station over a period is collected from SolarGIS as sample data. The model is compared with Markov chain power generation prediction model and GA-BP power generation prediction model. The experimental results indicate that the generation prediction error of the model is less than 3%.

A novel selective encryption scheme for H.264/AVC video with improved visual security

As cloud storage becomes more popular, concerns about data leakage have been increasing. Encryption techniques can be used to protect privacy of videos stored in the cloud. However, the recently proposed sketch attack for encrypted H.264/AVC video, which is based on the macroblock bitstream size (MBS), can generate the outline images of both intra-frames and inter-frames from a video encrypted by most existing encryption schemes; thus, the protection of the original video may be considered a failure. In this paper, a novel selective encryption scheme for H.264/AVC video with improved visual security is presented. Two different scrambling strategies that do not destroy the format compatibility are proposed to change the relative positions between macroblocks in intra-frames and inter-frames respectively, which in turn substantially distort the sketched outline images so that they do not disclose meaningful information. Moreover, the sign bits of non-zero DCT coefficients are encrypted to contribute to the visual security of our scheme, and an adaptive encryption key related to the intra prediction mode and the DCT coefficient distribution of each frame is employed to provide further security. The experimental results show that our encryption scheme can achieve a better visual scrambling effect with a small adverse impact on the video file size. Furthermore, the security analysis demonstrates that our scheme can successfully resist the MBS sketch attack compared with other related schemes. The proposed method is also proven secure against some other known attacks.

Adaptive CU split prediction and fast mode decision for 3D-HEVC texture coding based on just noticeable difference model

3D-high efficiency video coding (3D-HEVC) is the latest extension of HEVC standard. Through adopting novel coding tools, 3D-HEVC could improve the compression efficiency of multi-view texture video. However, with the great bitrate savings, 3D-HEVC brings the extreme complexity. To reduce the encoding time of texture video, a fast 3D-HEVC method based on just noticeable difference (JND) model is proposed in this paper. The JND model is introduced to analyze the coding tree block (CTB) characteristics of the texture image. Moreover, combining with adaptive coding unit (CU) split prediction and fast mode decision, the proposed scheme could skip some CTBs of texture video at the early stage without a normal coding procedure. Simulation results demonstrate that the proposed scheme can reduce 51.5% encoding runtime on average with negligible loss of subjective quality.

Frame-Wise Detection of Double HEVC Compression by Learning Deep Spatio-Temporal Representations in Compression Domain

Detection of double compression is regarded as one primary step in analyzing the integrity of digital videos, which is of prominent importance in video forensics. However, current methods are vulnerable with the severe lossy quantization in the recompression process such that it is challenging to obtain reliable frame-wise detection results, especially for the high efficiency video coding (HEVC) standard. In view of these issues, in this paper, a hybrid neural network is proposed to reveal abnormal frames in HEVC videos with double compression by learning robust spatio-temporal representations from coding information in the compression domain. Based on the statistical analysis of Coding Units (CUs), it is interesting to find that HEVC video streams contain “rich” coding information that could be leveraged to identify abnormal traces caused by double compression. Two types of coding information maps, including CU Size Map (CSM) and CU Prediction mode Map (CPM), are exploited. In contrast with the conventional paradigm relying on pixel-level representations of decoded frames, CSMs and CPMs of a short-time video clip are treated as the input, aiming to achieve high robustness against recompression of low quality. In our hybrid neural network, an attention-based two-stream residual network is proposed to learn hierarchical representations from CSM and CPM, which are then jointly optimized by the attention-based fusion module. Finally, the temporal variation is modeled by Long Short-Term Memory (LSTM) to obtain frame-wise detection results. We have conducted extensive experiments considering various video content and coding parameters, such as bitrates and sizes of Group of Picture. Experimental results show that our approach can obtain state-of-the-art performance compared with conventional methods, especially when videos are recompressed in the low bitrate coding scenarios.

Review on Fatigue Performance of Aluminum Alloys after Creep Age Forming

The process and characteristics of creep age forming technology was introduced. The influence of the creep age forming process on the fatigue performance on aviation aluminum alloy was discussed. Researches from three aspects were reviewed respectively–the fatigue damage mechanism of the creep age forming material, the influence of creep age forming process parameters on the performance of material fatigue, and creep age formed material life prediction mode. The trend of the creep age forming technology and the problems that may exist in its application in the field of aerospace were analysed. Finally, the major research direction was pointed out that fatigue properties of aluminum alloy materials and components based on the creep age forming technology should be studied deeply.

An efficient data hiding method using the intra prediction modes in HEVC

High efficiency video coding (HEVC) standard is highly contributive in data hiding. Combining the HEVC standard with data hiding methods is a complex task, and because the highly efficient coding process is a powerful attack, eliminating some of the original video data would not have a significant effect on obtaining the best compression rates and transmission efficiency. In this paper, an efficient secret data hiding method is proposed through intra prediction modes integrated with HEVC, where the intra prediction modes of HEVC encoded videos are applied as a secret data carrier obtained from the N smallest prediction units. Every ⌊log2[N + 1]⌋ + 1 secret bit is embedded in a set of N intra prediction modes, named carrier vectors, where 1 is added to or subtracted from one of these modes, resulting in the accomplished data hiding. Due to the fact that, at the most, only one intra prediction mode is modified and the N smallest prediction units are chosen, this method is contributive in appropriate data hiding performance and steganalysis. The experimental results indicate that the embedding capacity increases up to 50%, while obtaining the best capacity, rate and distortion trade-offs among the compared methods.

Computational complexity reduction in HEVC intra prediction using dual tree complex wavelet transform and holoentropy

High efficiency video coding (HEVC) is the newest video codec to increases the coding efficiency of its ancestor H.264/Advance Video Coding at the cost of highly increased computational complexity. In this paper, a novel method using Dual tree Complex Wavelet Transform is proposed to reduce the computational time consumption in HEVC Encoding. DTCWT provides better directional selectivity. By using DTCWT, total intra prediction modes are reduced from 35 modes to 7 modes including DC and Planer mode. The encoding process in HEVC system is performed using clustered entropy computing, which distinguishes the video information has useful outliers. The pixel variations under varying frames are clustered based on the interestingness and the outliers are removed using an advanced entropy principle called as holoentropy. Compared to the current state of the art algorithms, the experimental results show that this scheme is computationally simple and achieves superior reconstructed video quality at less computational complexity.

Implementation of PSO MPPT Technique for Wind Energy Conversion Systems

One major advantage of renewable energy is that it is sustainable and will never run out. They provide clean energy because they are non-pollutant and non-contributor to greenhouse effects and global warming. Renewable energy facilities generally require less maintenance than traditional generators. Their fuel being derived from natural and available resources reduces the costs of operation. The proposed PSO algorithm uses the dc current as the perturbing variable. The algorithm detects sudden wind speed changes indirectly through the dc-link voltage slope. The voltage slope is also used to enhance the tracking speed of the algorithm and to prevent the generator from stalling under rapid wind speed slow down conditions. The proposed method uses two modes of operation: A PSO mode with adaptive step size under slow wind speed fluctuation conditions, and a prediction mode employed under fast wind speed change conditions. The dc-link capacitor voltage slope reflects the acceleration information of the generator, which is then used to predict the next step size and direction of the current command.

A novel fast intra mode decision for versatile video coding

AbstractThe latest video coding standard Versatile Video Coding (VVC) obtains superior coding efficiency compared to the High Efficiency Video Coding (HEVC), which is achieved by incorporating more effective and complex new coding tools. In this paper, we propose a novel fast intra mode decision algorithm for VVC, including following two strategies: (1) the correlation between the optimal modes of the adjacent blocks and the modes selected in the rough modes decision (RMD) process is analyzed and applied to reduce the modes in the candidate list; (2) modes in the candidate list are sorted in ascending order according to the modes’ cost calculated in the RMD process. An early termination method is proposed for terminating the optimal prediction mode decision process based on this new order early. These two strategies are incorporated into intra coding to reduce the coding complexity. Since these two strategies do not add any additional computational complexity, the proposed fast algorithm can achieve more complexity reduction. The experimental results show that the complexity reduction of the proposed algorithm is up to 44.74% compared to VVC reference software VTM2.0, and averagely 30.59% encoding time saving with 0.86% BDBR increase.

SCC behavior and susceptibility prediction mode of SMA490BW weathering steel under cathodic potential

SMA490BW weathering steel has been used to manufacture high speed train bogie structures. However, its stress corrosion cracking (SCC) behavior was not very clearly, let alone the prediction mode. In this work, combined with fracture surface analysis by SEM, electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization analysis, the SCC behavior of SMA490BW steel in 3.5 wt.% NaCl solution under cathodic potentials from open circuit potential (OCP) to −1200 mVSCE (SCE = saturated calomel electrode) was studied using slow strain rate tensile (SSRT) method at 25 °C. The results indicated that the SCC susceptibility index (ISSRT) decreased with the applied potential negative shift from OCP (ISSRT = 0.31) to −800 mVSCE (ISSRT = 0.11) by anodic dissolution (AD) mechanism, and then sharply increased with the potential negatively increasing (ISSRT = 0.41 at −1200 mVSCE) by hydrogen embrittlement (HE) mechanism. The SCC susceptibility could be preliminarily predicted using the mode founded as a function of charge transfer resistance (Rt): This study will be helpful for the service assessment of high speed train bogie structure and other structures manufactured by SMA490BW steel.

Research on dominant vibration mode analysis of machining process of machine tools

The structural vibration and dominant vibration characteristics of the machining process of machine tools have an important influence on the machining quality and efficiency. The dominant vibration characteristics are related to two factors: the dynamic characteristics of the machine tool structure and the excitation characteristics of the system. In the dynamic process of structural vibration, the participations of various modes in a certain frequency domain are often different in the vibration process; that is to say, not all modes can be excited equally, only a certain mode or some of the modes may play a major role in the specific processing conditions and dominate. Thus, these modes are called the dominant vibration modes. Therefore, this paper proposes a method of modal response signal prediction and dominant vibration mode identification under the cutting state of machine tool and studies the dominant vibration characteristics of the machine tool structure under cutting conditions. A dynamic system modeling method based on state space and modal decoupling theory is proposed. The Kalman filter algorithm is used to predict the modal response signal of the machine tool during cutting process, and the modal participation degree is analyzed based on the predicted modal response signal. In this way, the dominant vibration mode of the machine tool structure is analyzed.

Key-Point Sequence Lossless Compression for Intelligent Video Analysis

Feature coding has been recently considered to facilitate intelligent video analysis for urban computing. Instead of raw videos, extracted features in the front-end are encoded and transmitted to the back-end for further processing. In this article, we present a lossless key-point sequence compression approach for efficient feature coding. The essence of this predict-and-encode strategy is to eliminate the spatial and temporal redundancies of key points in videos. Multiple prediction modes with an adaptive mode selection method are proposed to handle key-point sequences with various structures and motion. Experimental results validate the effectiveness of the proposed scheme on four types of widely used key-point sequences in video analysis.

Securing high-resolution train videos encoded with HEVC and inter prediction mode

This paper proposes an algorithm with different approaches for introducing hidden messages in a video under the HEVC standard with one of the most aggressive lossy compression processes: mixing intra-mode and inter-mode (I/B/P frames). Likewise, a high-capacity method for recovering the message is presented. The benefit of this proposal is that the H.265 standard is not modified. The results show that the average retrieval capacity is above 95 % and the proposed algorithm has no impact on the coding time or bitstream size when compared with normal compression.

Fast prediction mode selection and CU partition for HEVC intra coding

Compared with the predecessor H.264/advanced video coding, high-efficiency video coding (HEVC) is a new video coding standard with nearly double coding efficiency under the same coding quality. However, the computing complexity of HEVC increases sharply. To solve this problem, a fast algorithm for intra prediction mode selection based on mode grouping is proposed by reducing the number of modes entering rough mode decision. Moreover, the dual support vector machine is proposed to efficiently select the coding unit (CU) size, which is based on texture features of CU and sub-CUs including content complexity and direction complexity. By using the new CU size selection algorithm, the encoder can confirm the split for complex CU and terminate the split for simple CU in advance, so as to reduce the computing complexity of CU size selection. The experimental results show that by employing the two fast algorithms in intra coding, it can save 42.80% encoding time, with 0.98% increment in bit rate and 0.018 dB loss of peak-signal-to-noise ratio of luminance, compared with the reference software x265-1.7.

PSIONplusm Server for Accurate Multi-Label Prediction of Ion Channels and Their Types.

Computational prediction of ion channels facilitates the identification of putative ion channels from protein sequences. Several predictors of ion channels and their types were developed in the last quindecennial. While they offer reasonably accurate predictions, they also suffer a few shortcomings including lack of availability, parallel prediction mode, single-label prediction (inability to predict multiple channel subtypes), and incomplete scope (inability to predict subtypes of the voltage-gated channels). We developed a first-of-its-kind PSIONplusm method that performs sequential multi-label prediction of ion channels and their subtypes for both voltage-gated and ligand-gated channels. PSIONplusm sequentially combines the outputs produced by three support vector machine-based models from the PSIONplus predictor and is available as a webserver. Empirical tests show that PSIONplusm outperforms current methods for the multi-label prediction of the ion channel subtypes. This includes the existing single-label methods that are available to the users, a naïve multi-label predictor that combines results produced by multiple single-label methods, and methods that make predictions based on sequence alignment and domain annotations. We also found that the current methods (including PSIONplusm) fail to accurately predict a few of the least frequently occurring ion channel subtypes. Thus, new predictors should be developed when a larger quantity of annotated ion channels will be available to train predictive models.

Detection and attribution of aerosol–cloud interactions in large-domain large-eddy simulations with the ICOsahedral Non-hydrostatic model

Abstract. Clouds and aerosols contribute the largest uncertainty to current estimates and interpretations of the Earth’s changing energy budget. Here we use a new-generation large-domain large-eddy model, ICON-LEM (ICOsahedral Non-hydrostatic Large Eddy Model), to simulate the response of clouds to realistic anthropogenic perturbations in aerosols serving as cloud condensation nuclei (CCN). The novelty compared to previous studies is that (i) the LEM is run in weather prediction mode and with fully interactive land surface over a large domain and (ii) a large range of data from various sources are used for the detection and attribution. The aerosol perturbation was chosen as peak-aerosol conditions over Europe in 1985, with more than fivefold more sulfate than in 2013. Observational data from various satellite and ground-based remote sensing instruments are used, aiming at the detection and attribution of this response. The simulation was run for a selected day (2 May 2013) in which a large variety of cloud regimes was present over the selected domain of central Europe. It is first demonstrated that the aerosol fields used in the model are consistent with corresponding satellite aerosol optical depth retrievals for both 1985 (perturbed) and 2013 (reference) conditions. In comparison to retrievals from ground-based lidar for 2013, CCN profiles for the reference conditions were consistent with the observations, while the ones for the 1985 conditions were not. Similarly, the detection and attribution process was successful for droplet number concentrations: the ones simulated for the 2013 conditions were consistent with satellite as well as new ground-based lidar retrievals, while the ones for the 1985 conditions were outside the observational range. For other cloud quantities, including cloud fraction, liquid water path, cloud base altitude and cloud lifetime, the aerosol response was small compared to their natural variability. Also, large uncertainties in satellite and ground-based observations make the detection and attribution difficult for these quantities. An exception to this is the fact that at a large liquid water path value (LWP > 200 g m−2), the control simulation matches the observations, while the perturbed one shows an LWP which is too large. The model simulations allowed for quantifying the radiative forcing due to aerosol–cloud interactions, as well as the adjustments to this forcing. The latter were small compared to the variability and showed overall a small positive radiative effect. The overall effective radiative forcing (ERF) due to aerosol–cloud interactions (ERFaci) in the simulation was dominated thus by the Twomey effect and yielded for this day, region and aerosol perturbation −2.6 W m−2. Using general circulation models to scale this to a global-mean present-day vs. pre-industrial ERFaci yields a global ERFaci of −0.8 W m−2.