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Due to their convenience, water measuring structures have become an important means of measuring water in irrigation canal systems However, relevant research on upstream and downstream water-depth monitoring point locations is scarce. Our study aims to determine the functional relationship between the locations of the water-depth monitoring points and the opening width of the sluice. We established 14 trunk-channel and branch-channel hydrodynamic models. The locations of the water-depth monitoring points for the upstream and downstream reaches and their hydraulic characteristics were assessed using a numerical simulation and hydraulic test. The results showed that the locations of the upstream and downstream water-depth monitoring points were, respectively, 16.26 and 15.51 times the width of the sluice. The average error between the calculated flow rate and the simulated value was 14.37%, the average error between the flow rates calculated by the modified and the simulated values was 3.36%. To further verify the accuracy of the modified discharge calculation formula, by comparing the measured values, we reduced the average error of the modified formula by 19.29% compared with the standard formula. This research provides new insights into optimizing water measurements in irrigation canal systems. The results provide an engineering basis for the site selection of water-depth monitoring points that is suitable to be widely applied in the field.

The physical properties of maize seeds are closely related to food processing and production. To study and evaluate the characteristics of maize seeds, typical maize seeds in a cold region of North China were used as test varieties. A variety of agricultural material test benches were built to measure the maize seeds’ physical parameters, such as thousand-grain weight, moisture content, triaxial arithmetic mean particle size, coefficient of static friction, coefficient of rolling friction, angle of natural repose, coefficient of restitution, and stiffness coefficient. Principal component and cluster comprehensive analyses were used to simplify the characteristic parameter index used to judge the comprehensive score of maize seeds. The results showed that there were significant differences in the main physical characteristics parameters of the typical maize varieties in this cold area, and there were different degrees of correlation among the physical characteristics. Principal component analysis was used to extract the first three principal component factors, whose cumulative contribution rate was over 80%, representing most of the information of the original eight physical characteristic parameters, and had good representativeness and objectivity. According to the test results, the classification standard of the evaluation of the physical characteristics of 15 kinds of maize seeds were determined, and appropriate evaluations were conducted. The 15 kinds of maize seeds were clustered into four groups by cluster analysis, and the physical characteristics of each groups were different. This study provides a new idea for the evaluation and analysis of the physical properties of agricultural materials, and provides a new method for the screening and classification of food processing raw materials.

Macular edema is considered as a major cause of visual loss and blindness in patients with ocular fundus diseases. Optical coherence tomography (OCT) is a non-invasive imaging technique, which has been widely applied for diagnosing macular edema due to its non-invasive and high resolution properties. However, the practical applications remain challenges due to the distorted retinal morphology and blurred boundaries near macular edema. Herein, we developed a novel deep learning model for the segmentation of macular edema in OCT images based on DeepLab framework (OCT-DeepLab). In this model, we used atrous spatial pyramid pooling (ASPP) to detect macular edema at multiple features and used the fully connected conditional random field (CRF) to refine the boundary of macular edema. OCT-DeepLab model was compared against the traditional hand-crafted methods (C-V and SBG) and the end-to-end methods (FCN, PSPnet, and U-net) to estimate the segmentation performance. OCT-DeepLab showed great advantage over the hand-crafted methods (C-V and SBG) and end-to-end methods (FCN, PSPnet, and U-net) as shown by higher precision, sensitivity, specificity, and F1-score. The segmentation performance of OCT-DeepLab was comparable to that of manual label, with an average area under the curve (AUC) of 0.963, which was superior to other end-to-end methods (FCN, PSPnet, and U-net). Collectively, OCT-DeepLab model is suitable for the segmentation of macular edema and assist ophthalmologists in the management of ocular disease.

Objective To investigate macroprolactinemia caused by antipsychotics and its clinical significance. Methods A total of 133 patients with schizophrenia were selected, all of whom were treated with either risperidone or amisulpride alone. The levels of total prolactin (T-PRL) and macroprolactin (MPRL) were measured before treatment as well as the second, fourth, and sixth weeks of treatment. Results After 2 weeks of treatment, 75.09% (100/133) of the patients met the diagnostic criteria for hyperprolactinemia, the incidence of macroprolactinemia was 43% (43/100), and MPRL levels were positively correlated T-PRL levels. Conclusion Risperidone and amisulpride caused hyperprolactinemia and macroprolactinemia; thus, detection of MPRL in the clinical setting should be performed as this phenomenon appears early in treatment (the second week) and continues, that can avoid unnecessary examination and treatment for asymptomatic patients with macroprolactinemia.

Mill chatter in tandem cold rolling mill is a major rejection to the quality and production of the strips. In most mill vibration models, either the roll mass is usually limited to vibrate in vertical direction and vertical-horizontal directions, or the multiple rolls system is simplified to a single mass system. However, the torsional chatter is also a typical type of mill chatter, and the presence of intermediate roll and backup roll will affect the overall vibration of the mill structure system. In this paper, a newly cold rolling mill vibration model coupled with the dynamic rolling processing model and nonlinear vibration model is proposed with the consideration of dynamic coupling and nonlinear characteristics of the rolling process, multiroll equilibrium, and roll movement in both vertical-horizontal-torsional directions. By using Hopf bifurcation theorem and Routh–Hurwitz determinant, the existence of the Hopf bifurcation point of the mill vibration system and bifurcation characteristics are analyzed. At last, the influence of different rolling conditions on the stability of the coupled mill system is investigated, and these results can also be used to design an optimum rolling schedule and determine the appearance of mill chatter under certain rolling conditions.

Optical coherence tomography (OCT) provides the visualization of macular edema which can assist ophthalmologists in the diagnosis of ocular diseases. Macular edema is a major cause of vision loss in patients with retinal vein occlusion (RVO). However, manual delineation of macular edema is a laborious and time-consuming task. This study proposes a joint model for automatic delineation of macular edema in OCT images. This model consists of two steps: image enhancement using a bioinspired algorithm and macular edema segmentation using a Gaussian-filtering regularized level set (SBGFRLS) algorithm. We then evaluated the delineation efficiency using the following parameters: accuracy, precision, sensitivity, specificity, Dice’s similarity coefficient, IOU, and kappa coefficient. Compared with the traditional level set algorithms, including C-V and GAC, the proposed model had higher efficiency in macular edema delineation as shown by reduced processing time and iteration times. Moreover, the accuracy, precision, sensitivity, specificity, Dice’s similarity coefficient, IOU, and kappa coefficient for macular edema delineation could reach 99.7%, 97.8%, 96.0%, 99.0%, 96.9%, 94.0%, and 96.8%, respectively. More importantly, the proposed model had comparable precision but shorter processing time compared with manual delineation. Collectively, this study provides a novel model for the delineation of macular edema in OCT images, which can assist the ophthalmologists for the screening and diagnosis of retinal diseases.

According to the orientation of vocational-oriented higher education and the requirements of Shanghai Municipal Education Commission for first-class undergraduate training of practical talents, the main direction of laboratory construction in universities tends to be “integration of production and education”. In particular, the experimental training platform constructed should meet the production process and production equipment status of actual manufacturing enterprises. Therefore, after three-year construction, the laboratory of Shanghai Polytechnic University Intelligent Manufacturing Factory is characterized by digital twin technology and virtual simulation technology of intelligent manufacturing, with the goal of integrating production and education. It is planned to build the laboratory into an a intelligent manufacturing demonstration center with integrated teaching and scientific research, reasonable system, open, sharing and distinctive features.

By thermogravimetric (TG) experiments under 5 oxygen concentrations of 21.0, 40.0, 50.0, 60.0, 80.0% and 5 heating rates of 20.0, 30.0, 40.0, 50.0, 60.0 ℃/min, the combustion characteristics of jet coal and influences of oxygen concentrations and heating rates on them were investigated. Results show that the pyrolysis processes of jet coals consist of 3 segments of water evaporation, structure oxidation and mass-gaining, self-ignition and induced combustion and carbonization that lie in temperature ranges of 65.0~150.0 ℃, 150.0~290.0 ℃ and 290.0~650.0 ℃, respectively. Huainan jet coal samples burn more easily as universal ignition index increases. At constant heating rate, self-ignition temperature tends to decrease by 10.1~19.4 % with increasing oxygen concentration from 21.0 % to 80.0 %. However, the variation trend of oxidation process includes a slight rise when variation trend begins to fall. At constant oxygen concentration, the self-ignition temperature transformes into high temperature direction and lead to thermal lag effect. Values of Coal-oxidation time at various oxygen concentration levels decreased by 63.1~65.8 % at heating rates from 20.0 to 60.0 ℃/min. Accordingly, the coal-oxidation process is accelerated. A new index (R S ) named coal-oxidation difficulty level index to evaluate the lag effect of coal-oxidation process was proposed. Fitting results of three dimensionless parameters confirmed that the risk of spontaneous combustion determined by the coal-oxidation process still remains growing up although self-ignition temperature produces thermal lags with increasing heating rates.

An artificial neural network (ANN) optimized by genetic algorithm (GA) is an established prediction model of bending force in hot strip rolling. The data are collected from factory of steel manufacture. Entrance temperature and thickness, exit thickness, strip width, rolling force, rolling speed, roll shifting, target profile, and yield strength of strip are selected to be independent variables as network inputs. MATLAB software is utilized for establishing GA-ANN model and achieving the purpose of obtaining the bending force as results of setup model, as well as the GA method is used to optimize the initial weights and biases of the backpropagation neural network. Mean absolute error (MAE), mean absolute percentage error (MAPE), root mean squared error (RMSE), and correlation coefficient are adapted to evaluate the performance of the model. The predictive results are compared with the measured results to verify the accuracy of the GA-ANN prediction model. It is found that the optimization effect is the best with the population size 40 crossover probability of 0.7 and the mutation probability of 0.05 at the same time, the fitness function value can reach 80.7. In addition, the ANN architecture 9-11-1 trained with Bayesian regulation “trainbr” function has the best performance with mean absolute error of 0.01 and correlation coefficient of 0.983. With a deeper understanding of neural networks through the analysis of the GA-ANN model, the proposed model can be flexibly used for on-line controlling and rolling schedule optimizing.

We present a human-head-orientation estimation approach which enables effective estimation of head orientations of multiple individuals appeared within the same scene. Our approach bases deep representation in order to obtain adequate visual features of head orientations. To boost the estimation performance, we propose a conditional random field which fuses shallow feature, deep feature and spatial-temporal contextual cues, where the fusing parameters are learned from data via structured support vector machine. We demonstrate that the three components of fusion are complementary to each other in terms of head orientation estimation. Meanwhile, the proposed spatial-temporal field outperforms the state-of-the-art significantly on public dataset.

The compression connector of a cable joint is one of the major components causing joint overheating. This paper proposed a new model to determine the connection resistance of the compression connector. It innovatively integrated electrical contacts model analysis (ECMA) with finite element analysis (FEA) in the modeling. The compacted stranded structure of the cable conductor was taken into account in the proposed model. The streamline distortion effect on the connection resistance was also established. To verify the applicability of the proposed model, the connection resistances of five compression connectors with different cross sections were measured. The modeling results and measurement results were in close agreement with each other.

An increasing number of unknown islands, an important resource for human development, is identified based on segmentation of remote sensing image. Different from traditional digital image, remote sensing image has significant characteristics, such as multi-band, multi-source, and multi-granularity. Thus, the segmentation theory based on traditional digital image is not suitable for remote sensing image. Here, the segmentation algorithm (Otsu), which is a common method for traditional digital image, was improved in two aspects: (1) Based on PCA and band fusion, the Otsu algorithm with one-dimensional image was extended to multi-dimensional ones; (2) By optimizing the threshold value, the Otsu algorithm for single feature extraction was extended to multi-granularity extraction. Taking the island segmentation from remote sensing image as an example, the improved Otsu algorithm was compared with the traditional Otsu: 1) Through using PCA algorithm, multi-band remote sensing image was reduced to effective 3–4 new bands; 2) Through different threshold settings, the objects in the remote sensing image are divided into different classes; 3) The improved Otsu algorithm reduces the computational complexity, taking the threshold value of 2 as an example, the time efficiency is improved by 42.15%.

Purpose– The purpose of this paper is to present a distance accuracy-based industrial robot kinematic calibration model. Nowadays, the repeatability of the industrial robot is high, while the absolute positioning accuracy and distance accuracy are low. Many factors affect the absolute positioning accuracy and distance accuracy, and the calibration method of the industrial robot is an important factor. When the traditional calibration methods are applied on the industrial robot, the accumulative error will be involved according to the transformation between the measurement coordinate and the robot base coordinate.Design/methodology/approach– In this manuscript, a distance accuracy-based industrial robot kinematic calibration model is proposed. First, a simplified kinematic model of the robot by using the modified Denavit–Hartenberg (MDH) method is introduced, then the proposed distance error-based calibration model is presented; the experiment is set up in the next section.Findings– The experimental results show that the proposed calibration model based on MDH and distance error can improve the distance accuracy and absolute position accuracy dramatically.Originality/value– The proposed calibration model based on MDH and distance error can improve the distance accuracy and absolute position accuracy dramatically.

Dry etching has now become one of the key processes of high ratio of depth to width microstructure and fine patterning. This paper presents a new dry etching technology - multilayer reactive ion etching technology (MRIE). By taking full advantage of the spatial layout of the chamber, arranging multi-layer electrodes and transporting the discharged gas by a layered air supply device, the function of simultaneous etching in every reaction chamber (layer) is realized. This method can significantly enhance the productivity. Taking the photoresist etching by MRIE as an example, the law and mechanism influencing the etching rate and uniformity were analyzed for different conditions. The result shows that when plate distance is 50/55/60 mm (from bottom to top), and vacuum degree, ratio of O2 to Ar, RF source power, and continuous etching time are respectively 40 Pa, 1/2, 600 W, and 20 min, the optimal process is achieved. The average etching rate and uniformity are 143.93 ?/min and 9.8%, respectively.

Polymeric nanomaterials emerge as key building blocks for engineering materials in a variety of applications. In particular, the high modulus polymeric nanofibers are suitable to prepare flexible yet strong membrane separators to prevent the growth and penetration of lithium dendrites for safe and reliable high energy lithium metal-based batteries. High ionic conductance, scalability, and low cost are other required attributes of the separator important for practical implementations. Available materials so far are difficult to comply with such stringent criteria. Here, we demonstrate a high-yield exfoliation of ultrastrong poly(p-phenylene benzobisoxazole) nanofibers from the Zylon microfibers. A highly scalable blade casting process is used to assemble these nanofibers into nanoporous membranes. These membranes possess ultimate strengths of 525 MPa, Young's moduli of 20 GPa, thermal stability up to 600 °C, and impressively low ionic resistance, enabling their use as dendrite-suppressing membrane separators in electrochemical cells. With such high-performance separators, reliable lithium-metal based batteries operated at 150 °C are also demonstrated. Those polyoxyzole nanofibers would enrich the existing library of strong nanomaterials and serve as a promising material for large-scale and cost-effective safe energy storage.

In recent years, there have been frequent terrorist attacks on urban rail transit systems around the world, which have exposed loopholes in such measures as security inspection and prevention. The urban rail transit system is easily targeted due to its relative openness, unthoroughness of security inspection and seriousness of the damage consequences. It is also a risk zone of non-traditional security and a perfect place for terrorists to create indiscriminate killing cases. Based on field research and combining with the practical problems of rail transit at home and abroad, this paper analyzes the terrorist risks such as in urban rail transit platforms, surrounding areas and stations, and puts forward the mechanism construction and security technology prevention system of perfecting the urban rail transit operation department and related early warning countermeasures. At the same time, by exploring ways to prevent and control terrorist attacks, and improving the sensitivity and effectiveness of prevention and control, this study has certain reference significance for guiding China in the fight against terrorism in cities.

The effect of image processing is determined by the amount of information provided by the image files. With the development of integrated circuit and sensor technology, the image information acquisition technology has been greatly improved, we can obtain higher and higher image quality, and it is possible that image files contain more and more abundant information. The realization of technologies such as image target detection and recognition, image and video retrieval, image segmentation, matching and understanding has a high dependence on image quality. By use of a series of sensors or instruments, we collect relevant data and add relevant information in image files, adding some information to traditional image files, such as: complex environmental information (smoke, fog, rain, clouds, snow, etc.), feature targets Distance, brightness, etc., are conducive to the processing of graphic feature data. The paper analyzes the hierarchical structure of image information, the necessity and feasibility of the increase of image file information, how to increase the relevant information in the image file format, and the significance of the rich information image file format.

A new method of defocus length detection based on optical modulator is presented after analyzing traditional detection methods. Using this method, the convergent beam is modulated with the piezoelectric ceramic driving deformable mirrors, and received by photoelectric detectors through a pinhole or slit. Then the defocus length can be obtained after correlation detection. The method can obtain defocus length and direction by one detector at the same time, and the defocus voltage varies linearly with defocus length. Experiments were conducted, in which the beam was modulated by a 100 Hz sine signal. Detected voltages within 4 mm before and 2 mm behind the focal plane were obtained, which are consistent with theoretical derivation. The technique can be applied to circular symmetric and non-c

Traditional image descriptors tend to utilize integral derivative characterizing local features, like orientation histogram. However, integral-based derivative has a disadvantage in describing image texture details in smooth area. In this paper, we propose a novel framework for reestablishing orientation histogram based on adaptive fractional derivative, which is better at representing local feature. Then a general weighting scheme for orientation histogram is developed, which improves the accuracy of keypoints description. Finally, we demonstrate the utility of our formulation in implementing solutions for various keypoints descriptions tasks. To exercise our framework we have created a new SIFT and SURF application over images.

Ground Based Augmentation System (GBAS) can strongly support aircraft’s precise approach and landing, and integrity monitoring is not only the difficulty of the system realization but also the key point to safeguard the flight. The ionosphere grads is the uppermost element that influence the GBAS integrity. Several algorithms are used together to achieve the ionosphere grads monitoring and ensure the ionosphere grads in an acceptable limit, ultimately guarantee GBAS integrity. In this paper, the theory basis of the ionosphere grads monitoring algorithm is analyzed firstly, then an improved algorithm is proposed, which can increase ionosphere grads monitoring sensitivity effectively. The comparable experimental analysis of present and improved algorithm is given; the results show that multi-monitoring algorithm can monitor ionosphere grads from different aspects, while the improved algorithm can increase monitoring effect apparently.

It is aware that big data has gathered tremendous attentions from academic research institutes, governments, and enterprises in all aspects of information sciences. With the development of diversity of marine data acquisition techniques, marine data grow exponentially in last decade, which formsmarine big data. As an innovation, marine big data is a double-edged sword. On the one hand, there are many potential and highly useful values hidden in the huge volume of marine data, which is widely used in marine-related fields, such as tsunami and red-tide warning, prevention, and forecasting, disaster inversion, and visualization modeling after disasters. There is no doubt that the future competitions in marine sciences and technologies will surely converge into the marine data explorations. On the other hand, marine big data also brings about many new challenges in data management, such as the difficulties in data capture, storage, analysis, and applications, as well as data quality control and data security. To highlight theoretical methodologies and practical applications of marine big data, this paper illustrates a broad view about marine big data and its management, makes a survey on key methods and models, introduces an engineering instance that demonstrates the management architecture, and discusses the existing challenges.

A novel two-step process was developed to synthesize and deposit SiO 2 /TiO 2 multilayer films onto the cotton fibers. In the first step, SiO 2 particles on cotton fiber surface were synthesized via tetraethoxysilane hydrolysis in the presence of cotton fibers, in order to protect the fibers against photo-catalytic decomposition by TiO 2 nanoparticles. In the second step, the growth of TiO 2 nanoparticles into the modified cotton fiber surface was carried out via a sol–gel method at the temperature as low as 100 °C. The as-obtained SiO 2 /TiO 2 multilayer films coated on cotton fibers were characterized by scanning electron microscopy, X-ray photoelectron spectroscopy, atomic force microscopy and X-ray diffraction, respectively.

Cascaded exploitation of diversion-type small hydropower (SHP) offers a source of new energy as well as socioeconomic benefits; however, it inevitably causes environmental disturbance and damage. Previous studies on the cumulative effect of cascaded diversion SHP rarely discussed using quantitative analysis method. In this paper, the ecological footprint analysis approach is proposed to assess the positive and negative impacts of cascaded diversion SHP on environment of a small-scale river in Southwest China. Positive impact is defined as ecological supply footprint (ESF), which refers to vegetation protection by replacing firewood with SHP. Negative impact is defined as ecological loss footprint (ELF), which includes fish and net primary productivity loss, vegetation destruction and soil erosion. With the raising in the number (n>4) of diversion SHP stations, the difference between ELF and ESF increases remarkably, suggesting that the adverse impacts of cascaded diversion SHP accumulate in the study area. Compared with vegetation destruction and soil erosion, the cumulative loss of fish and net productivity is the most important aspect of the adverse impacts which needs more attentions.

The artificial bee colony exists premature and convergent stagnation phenomenons when dealing with high dimension and complex problem. An effective dual-populations artificial bee colony (DABC) algorithm is presented to solve numerical optimization problem in this paper. This algorithm consists of two populations which uses different optimization strategy respectively. In one population that implements population global exploration, the employed bees and the onlookers adopt a combination of two strategies, i.e., artificial bee colony (ABC) operator and differential evolution (DE/rand/1) operators, and the scouts employ random search, which is good at population diversity. While in another population that aims to achieve solutions local exploitation, the employed bees and the onlookers use DE/best/2 differential operators, and the scouts employ adaptive search around the optimum to update nectar position, which is beneficial to quicken convergent speed. Two populations evolve independently with different optimization strategies, and the optimal nectars information of two populations are combined every certain generations to strive for a well balance between the global exploration and the local exploitation. In additional, during the onlookers selection nectar sources, non-linear ranking selection strategy is employed to alleviated premature problem that caused by super nectar in the colony. The experimental results of benchmark functions show that the DABC algorithm is superior to several other methods for the convergence speed and solution precision, which indicates this new algorithm is an effective approach for solving global optimization problems.

In this paper we propose an intelligent adaptive retrofit reconfigurable controller in which the full system states are reliably measurable and available for feedback and diagnosis. The proposed approach retains the existing nominal controller and adds a suitably chosen signal that assures that the desired closed-loop performance is achieved despite the failure. The approach is based on the properties of the system controlled by the nominal controller, and judiciously chosen adaptation signals. Furthermore, some representative methods of intelligent reconfigurable control are introduced, especially intelligent fault diagnosis and intelligent reconfigurable control strategies based on adaptive neural networks and fuzzy reasoning has been discussed in details, which has ensure that the aircraft has retain its flying qualities to a satisfactory level even to the presence of severe control failures. The ability to control aircraft is shown by the simulation results with the intelligent reconfigurable controller using the adaptive fuzzy-neural reasoning of the aircraft.

Space rendezvous and docking (RVD) is a difficult and critical mission in spacecraft. The navigation and control (GNC) software plays an important role to complete the task. The functions of RVD control software are complex and timing sequence requirement is strict, which has presented extremely high requirements for software testing. This paper introduces FPGA design method and timing sequence analysis technology to capture timing sequence of RVD key data and trace phase relation of uplink and downlink signal. Furthermore, this paper has improved fault trigger and uplink injection method. This testing environment plays an important role in development process of RVD software.

In order to exploit solar energy resources, and improve agricultural production and, this paper make a analyses and calculation about the distribution of direct solar radiation in in Yanchi country of Ningxia by using the geographic information system(ViewGIS3.0). In addition, we combined with meteorological data to ascertain the period of effectively using solar radiation resource for agricultural production in this area. in the area.

In this paper, simulation model of a wheeled vehicle is established firstly by using dynamics simulation software of mechanical system ADAMS. Secondly, through setting the initial condition and inputting various parameters, simulation analysis of skid-steer during the unilateral braking process is performed. In addition, the results are contrasted. Finally, the minimum turning track radius is defined.

In current high-speed network, Peer-to-Peer(P2P) applications have overtaken Web applications as the major contribution on the Internet. Thereby, how to identify P2P traffic in real-time accurately and efficiently is a key step for network management. In this paper, we highlight the importance of applying data stream method in traffic classification to achieve real-time P2P traffic identification. We not only introduce a VFDT-based real-time highspeed traffic classification method, but also take thoroughly analysis on how to select a reasonable tie confidence (TieC), minimum gathering flow (MinGF) and category number (CaNum). Meanwhile, analysis has been done to ascertain the packet's interval which is used to calculate flow's real-time attribute. Experiment results have shown that when TieC is less than threshold, the larger TieC is, the better accuracy of identification is; when TieC exceeds threshold, decision trees are the same. Concerning MinGF and CaNum, although the smaller both of them are, the better performance of decision tree is, the value of them must be properly set according to requirements of classification system.

With the development of remote sensing technique, onboard data compression has become an urgent need and a lot of study has been directed toward the development of efficient techniques. In this paper, the construction approach of integer Haar is discussed briefly, and a simple image compression scheme based on the integer Haar wavelet transform and block DPCM is proposed. The scheme can be easily designed for data processing in real-time systems of remote sensing with parallel algorithms. Simulation experimental results demonstrate that the proposed approach is a efficient image compression method.

Objectives To investigate the impact of prenatal nicotine exposure on the birth weight, body shape at birth and postnatal growth of the offspring, and the expression of myocardial N-cadherin. Background Epidemiological studies have demonstrated that low birth weight, relative increased head circumference, and decreased abdominal circumference at birth, and postnatal catch-up growth by the suboptimal intrauterine environment are associated with increased risk of death from cardiovascular disease. The influence of prenatal nicotine exposure on growth character and impact on the heart of the offspring subject is still subject to discussion. N-cadherin plays a critical role in myocardial development and function and is associated with the development of heart failure. Studies have shown a nicotine receptor mediated pathway was associated with the modulation of N-cadherin expression. In summary, we hypothesise that prenatal treatment of nicotine might alter myocardial N-cadherin expression which may predispose for impaired cardiac function in later life. Methods Sprague–Dawley rats were infused with nicotine (12 mg/kg/day) or saline via subcutaneous osmotic mini-pumps throughout the gestation. We weighed brain, heart, lung, liver, and kidney of one randomly selected pup of each litter within 3–12 h after birth. The litter size was then reduced to eight pups to ensure equal nutrient access. Body weight was measured every 2 days after birth. Heart samples from the offspring at birth and 45 days of age were serially sliced for measuring N-cadherin by Immuno-histochemistry staining and analysed by western blot for N-cadherin expression. Results The birth weights of prenatal nicotine exposure offspring were significantly reduced compared with control offspring (6.72±0.38 g vs 7.40±0.35; p 0.05). Brain weight-to-body weight was significantly higher, and the liver weight-to-body weight ratio was prominently lower in maternal hypoxia offspring compared with control, indicating a relative increased head circumference and decreased abdominal circumference at birth. But there were no obvious differences in mean weight ratios of heart, lung, and kidney. N-cadherin was expressed in both atrium and ventriculium, predominantly localised to adherent junctions in intercalated discs. There was a significant down-regulated protein expression of N-cadherin in prenatal nicotine exposure offspring when compare with control. Conclusions Prenatal nicotine exposure resulted in intrauterine growth restriction with disproportion in neonatal organ size as a sign of relative increased head circumference and decreased abdominal circumference at birth of offspring who exhibited a postnatal catch-up growth. These growth characters are associated with a higher risk of cardiovascular disease. Furthermore, we demonstrate a down-regulated protein expression of N-cadherin in the myocardium from prenatal nicotine exposure offspring, which might predispose for impaired cardiac function in later life.