A Steganography Based on Optimal Multi-Threshold Block Labeling

Hiding secret data in digital images is one of the major researchfields in information security.Recently,reversible data hiding in encrypted images has attracted extensive attention due to the emergence of cloud services.This paper proposes a novel reversible data hiding method in encrypted images based on an optimal multi-threshold block labeling technique(OMTBL-RDHEI).In our scheme,the content owner encrypts the cover image with block permutation,pixel permutation,and stream cipher,which preserve the in-block correlation of pixel values.After uploading to the cloud service,the data hider applies the prediction error rearrangement(PER),the optimal threshold selection(OTS),and the multi-threshold labeling(MTL)methods to obtain a compressed version of the encrypted image and embed secret data into the vacated room.The receiver can extract the secret,restore the cover image,or do both according to his/her granted authority.The proposed MTL labels blocks of the encrypted image with a list of threshold values which is optimized with OTS based on the features of the current image.Experimental results show that labeling image blocks with the optimized threshold list can efficiently enlarge the amount of vacated room and thus improve the embedding capacity of an encrypted cover image.Security level of the proposed scheme is analyzed and the embedding capacity is compared with state-of-the-art schemes.Both are concluded with satisfactory performance.

Pattern-Moving-Based Parameter Identification of Output Error Models with Multi-Threshold Quantized Observations

This paper addresses a modified auxiliary model stochastic gradient recursive parameter identification algorithm(M-AM-SGRPIA)for a class of single input single output(SISO)linear output error models with multi-threshold quantized observations.It proves the convergence of the designed algorithm.A pattern-moving-based system dynamics description method with hybrid metrics is proposed for a kind of practical single input multiple output(SIMO)or SISO nonlinear systems,and a SISO linear output error model with multi-threshold quantized observations is adopted to approximate the unknown system.The system input design is accomplished using the measurement technology of random repeatability test,and the probabilistic characteristic of the explicit metric value is employed to estimate the implicit metric value of the pattern class variable.A modified auxiliary model stochastic gradient recursive algorithm(M-AM-SGRA)is designed to identify the model parameters,and the contraction mapping principle proves its convergence.Two numerical examples are given to demonstrate the feasibility and effectiveness of the achieved identification algorithm.

Two-dimensional cross entropy multi-threshold image segmentation based on improved BBO algorithm

In order to improve the global search ability of biogeography-based optimization(BBO)algorithm in multi-threshold image segmentation,a multi-threshold image segmentation based on improved BBO algorithm is proposed.When using BBO algorithm to optimize threshold,firstly,the elitist selection operator is used to retain the optimal set of solutions.Secondly,a migration strategy based on fusion of good solution and pending solution is introduced to reduce premature convergence and invalid migration of traditional migration operations.Thirdly,to reduce the blindness of traditional mutation operations,a mutation operation through binary computation is created.Then,it is applied to the multi-threshold image segmentation of two-dimensional cross entropy.Finally,this method is used to segment the typical image and compared with two-dimensional multi-threshold segmentation based on particle swarm optimization algorithm and the two-dimensional multi-threshold image segmentation based on standard BBO algorithm.The experimental results show that the method has good convergence stability,it can effectively shorten the time of iteration,and the optimization performance is better than the standard BBO algorithm.

Multi-dimensional and Multi-threshold Airframe Damage Region Division Method Based on Correlation Optimization

In order to obtain the image of airframe damage region and provide the input data for aircraft intelligent maintenance,a multi-dimensional and multi-threshold airframe damage region division method based on correlation optimization is proposed.On the basis of airframe damage feature analysis,the multi-dimensional feature entropy is defined to realize the full fusion of multiple feature information of the image,and the division method is extended to multi-threshold to refine the damage division and reduce the impact of the damage adjacent region’s morphological changes on the division.Through the correlation parameter optimization algorithm,the problem of low efficiency of multi-dimensional multi-threshold division method is solved.Finally,the proposed method is compared and verified by instances of airframe damage image.The results show that compared with the traditional threshold division method,the damage region divided by the proposed method is complete and accurate,and the boundary is clear and coherent,which can effectively reduce the interference of many factors such as uneven luminance,chromaticity deviation,dirt attachment,image compression,and so on.The correlation optimization algorithm has high efficiency and stable convergence,and can meet the requirements of aircraft intelligent maintenance.

Multi-Threshold Algorithm Based on Havrda and Charvat Entropy for Edge Detection in Satellite Grayscale Images

Automatic edge detection of an image is considered a type of crucial information that can be extracted by applying detectors with different techniques. It is a main tool in pattern recognition, image segmentation, and scene analysis. This paper introduces an edge-detection algorithm, which generates multi-threshold values. It is based on non-Shannon measures such as Havrda & Charvat’s entropy, which is commonly used in gray level image analysis in many types of images such as satellite grayscale images. The proposed edge detection performance is compared to the previous classic methods, such as Roberts, Prewitt, and Sobel methods. Numerical results underline the robustness of the presented approach and different applications are shown.

A Semi-Vectorial Hybrid Morphological Segmentation of Multicomponent Images Based on Multithreshold Analysis of Multidimensional Compact Histogram

In this work, we propose an original approach of semi-vectorial hybrid morphological segmentation for multicomponent images or multidimensional data by analyzing compact multidimensional histograms based on different orders. Its principle consists first of segment marginally each component of the multicomponent image into different numbers of classes fixed at K. The segmentation of each component of the image uses a scalar segmentation strategy by histogram analysis;we mainly count the methods by searching for peaks or modes of the histogram and those based on a multi-thresholding of the histogram. It is the latter that we have used in this paper, it relies particularly on the multi-thresholding method of OTSU. Then, in the case where i) each component of the image admits exactly K classes, K vector thresholds are constructed by an optimal pairing of which each component of the vector thresholds are those resulting from the marginal segmentations. In addition, the multidimensional compact histogram of the multicomponent image is computed and the attribute tuples or ‘colors’ of the histogram are ordered relative to the threshold vectors to produce (K + 1) intervals in the partial order giving rise to a segmentation of the multidimensional histogram into K classes. The remaining colors of the histogram are assigned to the closest class relative to their center of gravity. ii) In the contrary case, a vectorial spatial matching between the classes of the scalar components of the image is produced to obtain an over-segmentation, then an interclass fusion is performed to obtain a maximum of K classes. Indeed, the relevance of our segmentation method has been highlighted in relation to other methods, such as K-means, using unsupervised and supervised quantitative segmentation evaluation criteria. So the robustness of our method relatively to noise has been tested.

GROUND BOUNCING NOISE REDUCTION TECHNIQUE CONSIDERING WAKE-UP DELAY IN MTCMOS CIRCUITS

Multi-Threshold CMOS(MTCMOS) is an effective technique for controlling leakage power with low delay overhead.However the large magnitude of ground bouncing noise induced by the sleep to active mode transition may cause signal integrity problem in MTCMOS circuits.We propose a methodology for reducing ground bouncing noise under the wake-up delay constraint.An improved two-stage parallel power gating structure that can suppress the ground bouncing noise through turn on sets of sleep transistors consecutively is proposed.The size of each sleep transistor is optimized by a novel sizing algorithm based on a simple discharging model.Simulation results show that the proposed techniques achieve at least 23% improvement in the product of the peak amplitude of ground bouncing noise and the wake-up time when compared with other existing techniques.

An Improved SOI CMOS Technology Based Circuit Technique for Effective Reduction of Standby Subthreshold Leakage

Silicon-on-insulator (SOI) CMOS technology is a very attractive option for implementing digital integrated circuits for low power applications. This paper presents migration of standby subthreshold leakage control technique from a bulk CMOS to SOI CMOS technology. An improved SOI CMOS technology based circuit technique for effective reduction of standby subthreshold leakage power dissipation is proposed in this paper. The proposed technique is validated through design and simulation of a one-bit full adder circuit at a temperature of 27℃, supply voltage, VDD of 0.90 V in 120 nm SOI CMOS technology. Existing standby subthreshold leakage control techniques in CMOS bulk technology are compared with the proposed technique in SOI CMOS technology. Both the proposed and existing techniques are also implemented in SOI CMOS technology and compared. Reduction in standby subthreshold leakage power dissipation by reduction factors of 54x and 45x foraone-bit full adder circuit was achieved using our proposed SOI CMOS technology based circuit technique in comparison with existing techniques such as MTCMOS technique and SCCMOS technique respectively in CMOS bulk technology. Dynamic power dissipation was also reduced significantly by using this proposed SOI CMOS technology based circuit technique. Standby subthreshold leakage power dissipation and dynamic power dissipation were also reduced significantly using the proposed circuit technique in comparison with other existing techniques, when all circuit techniques were implemented in SOI CMOS technology. All simulations were performed using Microwindver 3.1 EDA tool.

SRAM Cell Leakage Control Techniques for Ultra Low Power Application: A Survey

Low power supply operation with leakage power reduction is the prime concern in modern nano-scale CMOS memory devices. In the present scenario, low leakage memory architecture becomes more challenging, as it has 30% of the total chip power consumption. Since, the SRAM cell is low in density and most of memory processing data remain stable during the data holding operation, the stored memory data are more affected by the leakage phenomena in the circuit while the device parameters are scaled down. In this survey, origins of leakage currents in a short-channel device and various leakage control techniques for ultra-low power SRAM design are discussed. A classification of these approaches made based on their key design and functions, such as biasing technique, power gating and multi-threshold techniques. Based on our survey, we summarize the merits and demerits and challenges of these techniques. This comprehensive study will be helpful to extend the further research for future implementations.

Research on Estimation of Time Delay Difference in Passive Locating for Impulse Signal

Analyzed the relation between time delay difference and time delay estimation errors, based on the principles of three-point locating, a reformed threshold method for time delay difference estimation of impulse signals, called as amendment estimation for short, is developed by introducing channel equalization technique to its conventional version, named as direct estimation in this paper, to improve the estimation stability. After inherent relationship between time delay and phase shift of signals is analyzed, an integer period error compensation method utilized the diversities of both contribution share and contribution mode of concerned estimates is proposed under the condition of high precision phase lag estimation. Finally, a cooperative multi-threshold estimation method composed of amendment and direct estimations to process impulse signals with three thresholds is established. In sea trials data tests of passive locating, this method improves the estimation precision of time delay difference efficiently. The experiments verify the theoretical predictions.

A feature extraction and correspondence algorithm for laser range finder with sensor uncertainty

This paper presents a feature extraction and correspondence algorithm which employs a novel feature transform. Unlike conventional approaches such as Hough Transform, we employ a robust but simple approach to extract the geometrical feature under real dynamic world conditions. Multi-threshold segmentation and the split-and-merge method are employed to interpret geometrical features such as edge, concave corners, convex corners, and segments in a unified framework. The features are represented by feature tree (F-Tree) so as to compactly represent the environments and some important properties of the F-Tree are discussed in this paper. To demonstrate the validity of the approach, we show the actual experiment results which are based on real Laser Range Finder data and real time analysis. The comparative study with Hough Transform shows the advantages and the high performance of the proposed algorithm.

Ground-Based Cloud Using Exponential Entropy/Exponential Gray Entropy and UPSO

Objective and accurate classification model or method of cloud image is a prerequisite for accurate weather monitoring and forecast.Thus safety of aircraft taking off and landing and air flight can be guaranteed.Thresholding is a kind of simple and effective method of cloud classification.It can realize automated ground-based cloud detection and cloudage observation.The existing segmentation methods based on fixed threshold and single threshold cannot achieve good segmentation effect.Thus it is difficult to obtain the accurate result of cloud detection and cloudage observation.In view of the above-mentioned problems,multi-thresholding methods of ground-based cloud based on exponential entropy/exponential gray entropy and uniform searching particle swarm optimization(UPSO)are proposed.Exponential entropy and exponential gray entropy make up for the defects of undefined value and zero value in Shannon entropy.In addition,exponential gray entropy reflects the relative uniformity of gray levels within the cloud cluster and background cluster.Cloud regions and background regions of different gray level ranges can be distinguished more precisely using the multi-thresholding strategy.In order to reduce computational complexity of original exhaustive algorithm for multi-threshold selection,the UPSO algorithm is adopted.It can find the optimal thresholds quickly and accurately.As a result,the real-time processing of segmentation of groundbased cloud image can be realized.The experimental results show that,in comparison with the existing groundbased cloud image segmentation methods and multi-thresholding method based on maximum Shannon entropy,the proposed methods can extract the boundary shape,textures and details feature of cloud more clearly.Therefore,the accuracies of cloudage detection and morphology classification for ground-based cloud are both improved.

Spatiotemporal Variations of Meteorological Droughts in China During 1961–2014: An Investigation Based on Multi-Threshold Identification

As a major agricultural country, China suffers from severe meteorological drought almost every year.Previous studies have applied a single threshold to identify the onset of drought events, which may cause problems to adequately characterize long-term patterns of droughts.This study analyzes meteorological droughts in China based on a set of daily gridded(0.5° 9 0.5°) precipitation data from 1961 to 2014. By using a multi-threshold run theory approach to evaluate the monthly percentage of precipitation anomalies index(Pa), a drought events sequence was identified at each grid cell. The spatiotemporal variations of drought in China were further investigated based on statistics of the frequency, duration,severity, and intensity of all drought events. Analysis of the results show that China has five distinct meteorological drought-prone regions: the Huang-Huai-Hai Plain, Northeast China, Southwest China, South China coastal region,and Northwest China. Seasonal analysis further indicates that there are evident spatial variations in the seasonal contribution to regional drought. But overall, most contribution to annual drought events in China come from the winter. Decadal variation analysis suggests that most of China's water resource regions have undergone an increase in drought frequency, especially in the Liaohe, Haihe, and Yellow River basins, although drought duration and severity clearly have decreased after the 1960 s.

Renal Pathology Images Segmentation Based on Improved Cuckoo Search with Diffusion Mechanism and Adaptive Beta-Hill Climbing

Lupus Nephritis(LN)is a significant risk factor for morbidity and mortality in systemic lupus erythematosus,and nephropathology is still the gold standard for diagnosing LN.To assist pathologists in evaluating histopathological images of LN,a 2D Rényi entropy multi-threshold image segmentation method is proposed in this research to apply to LN images.This method is based on an improved Cuckoo Search(CS)algorithm that introduces a Diffusion Mechanism(DM)and an Adaptiveβ-Hill Climbing(AβHC)strategy called the DMCS algorithm.The DMCS algorithm is tested on 30 benchmark functions of the IEEE CEC2017 dataset.In addition,the DMCS-based multi-threshold image segmentation method is also used to segment renal pathological images.Experimental results show that adding these two strategies improves the DMCS algorithm's ability to find the optimal solution.According to the three image quality evaluation metrics:PSNR,FSIM,and SSIM,the proposed image segmentation method performs well in image segmentation experiments.Our research shows that the DMCS algorithm is a helpful image segmentation method for renal pathological images.

Performance projections for ballistic carbon nanotube FinFET at circuit level

A novel three-dimensional device structure for a carbon nanotube （CNT） fin field-effect transistor （FinFET） is proposed and evaluated. We evaluated the potential of the CNT FinFET compared with a Si FinFET at a 22-nm node at the circuit level using three performance metrics including propagation delay, total power dissipation, and energy-delay product （EDP）. Compared with a Si FinFET, the CNT FinFET presents obvious advantages in speed and EDP arising from its almost much larger current density but also results in a higher total power dissipation, especially at a low threshold voltage （V~ = 1/3Vaa）. A suitable improvement in Vth can effectively contribute to a significant suppression of leakage current and power dissipation, and then an obvious optimization is obtained in the EDP with an acceptable sacrifice in speed. In particular, CNT FinFETs with optimized threshold voltages can provide an EDP advantage of approximately 50 times over Si FinFETs under a low supply voltage （Vdd -- 0.4 V）, suggesting great potential for CNT FinFET-based integrated circuits.

Human and Bicycle Detection System Using Range Sensor

This paper presents a full-scale solution to the detection of the traffic data using laser device.Range images,gathered by a particular laser camera,are used in the multi-threshold segmentation.The multi-threshold segmentation is based on the height of the moving objects.In order to get the precise height of the moving objects,mapping of the original terrain is performed on the first step.On each layer,the clustering algorithm called iteration-self organizing data analysis techniques algorithm(ISODATA) is conducted afterwards.Kalman filtering technique is applied to recognize and track the moving objects.Extensive experiments show that these algorithms are effective in object recognition and tracking,as well as robust in the applications.

Contrasting effects of N fertilization and mowing on ecosystem multifunctionality in a meadow steppe

There is little experimental field evidence on how multiple essential land use intensification drivers(LUIDs),such as nitrogen(N)fertilization and mowing,interact to control ecosystem multifunctionality.Here,we conducted a 4-year field experiment in a meadow steppe in northeast China and evaluated the direct and indirect effects of mowing and N fertilization on a range of ecosystemfunctions associated with nutrient cycle,carbon stocks,and organic matter decomposition during the past 2 years of the experiment(2017 and 2018).Mowing had negative effects on the ecosystem multifunctionality index(EMF),carbon(C)cycle multifunctionality index(CCMF),and N cycle multifunctionality index(NCMF)in 2 years of sampling.However,in general,the responses of multifunctionality to N fertilization were ratespecific and year-dependent.N fertilization had positive effects on EMF,CCMF,NCMF,and phosphorus(P)cycle multifunctionality index(PCMF)in 2017,with the higher precipitation rate during the growing season,which was likely associated with the strong monsoon season.However,in 2018,EMF,CCMF,and NCMF increased at the lower N fertilization levels(£10 g N m^(-2) yr^(-1)),but decreased at higher N rates.N fertilization had consistent positive effects on PCMF in the 2 years of sampling.The effects of land use drivers on multifunctionality were indirectly influenced by bacterial biomass,plant richness,and soil moisture changes.Our results also indicated that the impacts of land use drivers on multifunctionality played an important role in maintaining a range of functions at low levels of functioning(<50% functional threshold).Low N fertilization levels(£10 g N m^(-2) yr^(-1))were able to reduce the negative effects of mowing on ecosystem multifunctionality while promoting plant biomass(food for livestock)and C storage.These findings are useful for designing practical strategies toward promoting multifunctionality by managing multiple LUIDs in a meadow steppe.

Ground bounce noise reduction aware combinational multi threshold CMOS circuits for nanoscale CMOS multiplier

Multi-threshold complementary metal-oxide- semiconductor （MTCMOS） is ofbn used to reduce the leakage current in idle circuit. Ground bounce noise produced during a transition mode （sleep-to-active） is an important challenge in MTCMOS. In this paper, various noise-aware combinational MTCMOS circuit was used to evaluate the ground bounce noise. An intermediate mode was applied in the sleep-to-active mode transition to reduce the charge stored on virtual lines to real ground. The dependence of ground bounce noise on voltage, transistor size and temperature was investigated with different MTCMOS circuit technique. The peak amplitude of ground bounce noise was reduced up to 78.82%. The leakage current of the circuit was decreased up to 99.73% and the active power of the circuit was reduced up to 62.32%. Simulation of multiplier with different MTCMOS circuit techniques was performed on 45nm CMOS technology.