Indoor localization is a prerequisite for Location Based Services(LBS),it is research hotspot in recent years,and receive widespread attention from academia and industry.With the popularity of smart terminal,many kinds of indoor localization method which is based on WiFi and smart terminal are proposed.Compared with the previous localization method,indoor localization based on WiFi and smart terminal has advantages.Through the key factors of large scale application of indoor localization,this paper analyzes and summarizes the basic methods of indoor localization based on WiFi and smart terminal systematic.With the comparison of typical work in recent years,it points out the shortage of present work,and prospects on the difficulties and direction of the research on indoor localization based on WiFi and smart terminal.

Multi-path TCP(MPTCP) and Concurrent Multipath Transfer for SCTP(CMT-SCTP) are two technologies of the concurrent multi-path transfer for NGI,which are hot topics. Compared with the traditional TCP and SCTP,the congestion control of multi-path transport protocols will face the problem of unfairness. This paper begins with an overview of the traditional TCP congestion control algorithm,and then in view of the unfairness of concurrent multi-path transfer,basing on the resource pooling method,it introduces the slow start threshold ratio factor,increase factor,per-flow aggressiveness factor,deduces three new algorithms to solve the problem of unfairness,and carryies out the contrastive analysis through simulation.

Many dynamic binary translators chose basic block as the unit of translation and execution, the basic blocks to be translated in dynamic binary translation may have the same instructions as the other ones which have been translated. This situation consequently leads to repeating translation and incurs the unnecessary overhead. This paper focus on how to detect and eliminate the overhead caused by basic blocks overlapped redundancies from the view of dynamic binary translation optimization technology. Experiment results show there are about 5% overlapped redundancies in the course of translation. By eliminating the redundancies results in a speed up of 1%~4% in translation performance.

Each variable of the multivariate time series contains multi-scale features of different time spans. These features contain abundant information about the development process, direction and trend, and possess different importance for forecasting task. However, existing time series forecasting models cannot effectively capture multi-scale features and comprehensively evaluate the importance of multi-scale features. To this end, a new time series forecasting model (FFANet) based on multi-scale temporal Feature Fusion and dual Attention mechanism is established in this study. This model can effectively fuse and concentrate important multi-scale features to achieve better forecasting performance. First, the parallel dilated convolutional layers in the multi-scale temporal feature fusion module makes the FFANet have multiple receptive fields. Therefore, the FFANet is capable of extracting and fusing multi-scale features adaptively. Then, the dual attention module is introduced to recalibrate the fused features. The FFANet focuses on key features by normalizing the temporal and channel weights to the corresponding fused features. Experimental results on Traffic, Solar-Energy and Electricity multivariate time series datasets show that the forecasting accuracy index of the proposed model is satisfactory. Compared with the Auto Regressive(AR), Vector Auto Regressive Multilayer Perception(VARMLP), RNN-GRU, Long- and Short-term Time-series network(LSTNet-skip), Temporal Pattern Attention(TPA-LSTM), Multivariate Time Series Forecasting Graph Neural Networks(MTGNN), and Attention-based Auto Regression(AttnAR) models, the prediction accuracy improved by 30.78, 31.76, 22.81, 11.25, 7.51, 3.80 and 1.88 percentage points, respectively.

A radix-4 Montgomery modular multiplication and the optimized circuit architecture are presented. It can reduce iterations of traditional radix-2 modular multiplication to about 50%. Based on this module, the implementation of high speed RSA encryption processor follows completely parallel modular exponentiation flow with Carry Save Addition(CSA) structure to perform long integer arithmetic. This avoids the repeated interim output/output format conversion. Result shows that the optimization is technology independent and thus should suit well for not only FPGA implementation but also ASIC. This design can complete a standard 1 024 bit RSA encrypt operation with only 9 836 clock cycles. Compared to the recently proposed design in the literature, the proposed design can achieve an increase of over 50% in throughput.

This paper improves traditional collaborative filtering algorithm, proposes a new user profile based on concept hierarchy tree, which can make recommender systems still work even when users have no common rating items. In the process of similarity calculation and recommendation formation, it also uses concept hierarchy thought to generate recommendation lists by different categories, avoiding recommendation lack of diversity. Experimental results on MovieLens dataset show that the improved algorithm can provide better prediction in either accuracy or diversity aspect.

Latent Dirichlet Allocation(LDA) is presented to express the distributed probability of words. The topic keywords are extracted according to Shannon information. Words which are not distinctly in the analyzed text can be included to express the topics with the help of word clustering of background and topic words association. The topic meaning is attempted to dig out. Fast Gibbs is used to estimate the parameters. Experiments show that Fast Gibbs is 5 times faster than Gibbs and the precision is satisfactory, which shows the approach is efficient.

In order to overcome the limitation of literal matching and lacking semantic concept of the traditional keyword extraction algorithm, this paper presents a Semantic-based Keyword Extraction(SKE) algorithm for Chinese text. It uses semantic feature in the keyword extraction process and constructs word semantic similarity network and uses betweenness centrality density. Experimental results show that compared with the statistic based keyword extraction algorithm, the keywords SKE algorithm extracted are more reasonable and can represent more information of the document’s topic, and the SKE algorithm has a better performance.

In order to make the indoor mobile robot better build the accurate map from the data of 2D laser range finder with noise,an improved algorithm based on similar triangules denoising rule is proposed.Using the splitting algorithm to extract the rough line set from the preprocessing data,the data points between the two split points are denoised by improving the similar triangules denoising rule,and the denoised data are re-split,and scanning points are fitted in the data between each two split points by least square method.Experimental results show that the proposed algorithm can reduce the number of effective points which are eliminated,and the accuracy and false positive indexes are better than similar triangles denoising method and traditional split and mergeing algorithm,at the same time,the line segment merging process is basically avoided,and the robustness and accuracy of environment modeling are enhanced.

Federated learning is an emerging distributed learning framework that facilitates the collective engagement of multiple clients in global model training without sharing raw data, thereby effectively safeguarding data privacy. However, traditional federated learning still harbors latent security vulnerabilities that are susceptible to poisoning and inference attacks. Therefore, enhancing the security and model performance of federated learning has become imperative for precisely identifying malicious client behavior by employing gradient noise as a countermeasure to prevent attackers from gaining access to client data through gradient monitoring. This study proposes a robust federated learning framework that combines mechanisms for malicious client detection with Local Differential Privacy (LDP) techniques. The algorithm initially employs gradient similarity to identify and classify potentially malicious clients, thereby minimizing their adverse impact on model training tasks. Subsequently, a dynamic privacy budget based on LDP is designed, to accommodate the sensitivity of different queries and individual privacy requirements, with the objective of achieving a balance between privacy preservation and data quality. Experimental results on the MNIST, CIFAR-10, and Movie Reviews (MR) text classification datasets demonstrate that compared to the three baseline algorithms, this algorithm results in an average 3 percentage points increase in accuracy for sP-type clients, thereby achieving a higher security level with significantly enhanced model performance within the federated learning framework.

In order to research the influence of information characteristis on information spreading in social network,this paper proposes an information spreading model which combines information characteristics with memory effects.The information characteristics are measured by the attributes of information amount and information adhesion.It performs the model on regular network,small-world network,random network and BA scale-free network.Results indicate that in the speed and scope of information spreading,the attribute of information amount is more effective than the attribute of information adhesion.Under certain information characteristic,it exists a fixed value when individuals select spread information.Especially,on the regular network,the probability of spreading information can reach the maximum when individuals receive information at the second time.

Aiming at the challenges of single-text representation and low accuracy of existing video content text-representation models, a video content text-reprsentation model that integrates frame-level image and audio information is proposed.The network structure of the model includes a single-mode embedding layer based on a self attention mechanism, and learns single-mode feature parameters.Two schemes, joint-representation and cooperative-representation, are adopted to fuse high-dimensional feature vectors output from the single-mode embedding layer, so that the model can focus on different objects in the video and their interaction, thereby generating richer and more accurate video text representation.The model is pretrained through large-scale datasets, and representation information, such as video frames and audio carried by the video, are extracted and sent to the coder to realize the text representation of the video content.The experimental results on MSR-VTT and LSMDC datasets show that the BLEU4, METEOR, ROUGEL, and CIDEr scores of the proposed model are 0.386, 0.250, 0.609 and 0.463 respectively.Compared with the model released by the IIT DeIhi in the MSR-VTT challenge, the proposed model improves the indexes above by 0.082, 0.037, 0.115 and 0.257 respectively.The model in this study can effectively improve the accuracy of the video content text-representation model.

Verilog’s formal semantics using the abstract states machine are studied, and the formal definition of assignment statements and delay/event control mechanism is given. Comparing with Borger’s definition on VHDL, the key methods on how to translate Verilog description to VHDL are explained. In the end, the simulation differences before and after translation are studied.

Aiming at the factors of resource constraints that exist in the process of course schedule, this paper proposes an algorithm combining Genetic Algorithm(GA) and constraint satisfaction algorithm to solve course schedule problem. Course schedule tasks are sorted with constraint satisfaction algorithm, and a single course schedule task’s timetable is allocated and optimized with GA. In this algorithm, the result of single course schedule task is global optimal. Experimental results show that this method is feasible to improve the performance and the efficiency.

In order to figure out how memory faults affect high-performance servers, this paper presents a multi-layer fault injection method for Itanium architecture computers. HMFI(Hierarchical Memory Fault Injector) is designed and developed to inject memory faults in physical level, operating system kernel level and process level. Experimental results demonstrate the effectiveness of HMFI in evaluating the fault-tolerant properties of complex computer system.

By combining several parameters selection approaches of Support Vector Machine(SVM), this paper proposes a method that defines parameters directly by analyzing training samples. Experimental results based on several standard test data sets show that the method achieves better prediction accuracy and consumes less time compared to traditional grid search methods. It provides one way to deal with the problem of selecting parameters and time consuming in application of SVM.

A new architecture of parallel BCH encoder and decoder applied in NAND Flash Controller is proposed. In order to obviously increase the throughput of decoder, pipeline operation and prefetch decoding in group operation are applied in the design. It takes 565 cycles to correct 8 bit random error after NAND Flash’s 2 KB page read operation, which is a quarter of the time cost by prefetch & decode in page.

DNS is a critical component of the operation of Internet applications. The Internet is greatly affected if DNS is attacked. DNS spoofing is one of the most popular attack means with the character of high dormancy and good attack effection. But so far, little is done to defend the systerm against this attack. Three methods are presented to detect DNS spoofing attack, and then another three techniques are proposed to identify the bogus packets and the right ones to ensure DNS service even attacked.

This paper interprets the concepts of coreference resolution, and introduces the state-of-the-arts in coreference resolution from three aspects: corpus resources, evaluation measures and resolution algorithms. It analyzes the critical problems of coreference resolution: automatic capture and presentation of structured syntactic knowledge, automatic capture and usage of underlying semantic, cross-document coreference resolution. Based on the analysis, it explores its international research trend.

Most existing algorithms for document layout analysis are sensitive to the parameters and have weak applicability. In order to make up these deficiencies, this paper presents an algorithm of region formation based on Support Vector Machine(SVM) for analyzing Chinese document. In the proposed technique, it selects connected components called seed connected components which can be used to represent regions as the first feature for training. SVM is employed to form region since it has strong learning and generalization ability. The technique decides the reading order by exploiting the projection method. Extensive experimental results show that the proposed algorithm is more effective to analyze different kinds of document layout than the state-of-the-art methods.

A genetic algorithm to handle constrained optimization problem is proposed. This method searches the decision space of a problem through the arithmetic crossover of feasible and infeasible solutions, and performs a selection on feasible and infeasible populations respectively according to fitness and constraint violation. It uses the boundary mutation on feasible solutions and the non-uniform mutation on infeasible solutions because the solutions usually deviate from the constraint domain after the traditional mutation operation. It maintains the population diversity through dimension mutation. Numerical results show that it is an effective algorithm.

To raise the measurement accuracy of binocular stereo vision system, working principle of binocular vision system and error model in all aspects are analyzed. The calibration error and the image rectification error can be reduced by improving the accuracy of calibration template and increasing calibration images. By analyzing image recognition errors’ influence on the measurement accuracy of the visual system, the relationship between structural parameters of visual system and measurement errors is introduced. The influence of structural parameters to visual system is determined. The influence of object distance on measurement accuracy is discussed and a proper object distance scope is given.

3D face reconstruction algorithms require more than one image to realize reconstruction and their efficiency is low. Aiming at the problems, this paper proposes a 3D face reconstruction algorithm based on single frontal photo. It uses Thin Plate Spline(TPS) function to build 3D face model by establishing the point-to-point correspondence among 3D faces in the database, so that an average 3D face model is built. It uses Levenberg-Marquardt Algorithm(LMA) to optimize the shape coefficients. The input image is projected orthogonally to the 3D shape to generate the face texture. Experimental results show that the method can generate realistic 3D faces efficiently.

The stability control strategy of biped robot walking process is studied,and a crus vibration control system based on the vibration acceleration of the upper body of the robot as feedback is designed.Through virtual simulation analysis software——Automatic Dynamic Analysis of Mechanical System(ADAMS),the virtual prototype of the biped robot is built and imported into Matlab.The automatic anti-disturbance control algorithm is designed to suppress the low frequency and high frequency according to active and passive vibration attenuation.Simulation results show that active and passive vibration attenuation greatly reduces the vibration of the upper body of the robot and compensates the situation that passive vibration can not suppress the low frequency vibration,and effectively improves the stability of the robot.

To solve the navigation problem of mobile robot in unknown environment,based on cognitive development mechanism of biology,a cognitive development model is constructed for mobile robot autonomous navigation.The neural network which can dynamically adjust the structure is designed by autonomous inserting neuron.It is used to imitate the properties of biological development and to obtain the network that can match the application requirements.The asymptotic learning characteristic is imitated through the thermodynamic process and a cognitive learning algorithm is designed,which is proved to be convergence in theory.The results of experiments show that the proposed model can make the robot obtain knowledge automatically and accumulate experience from environment like animal,and learn the skill of autonomous navigation through cognitive development.

Aiming at the problem that the geometric parameters are not accurate and the absolute accuracy of the industrial robot is low,a geometric parameter calibration method based on Pose Modify Position Sensitive Detector(PMPSD) is proposed.Through the establishment of the error kinematics model,and the PSD device is used to sample the data,the pose correction principle is used to correct the end laser pose and joint angle,and the model constraint objective function is established.The geometric parameters errors are obtained by using the LM algorithm,and the nominal values of geometric parameter are corrected by the parameter errors.Experimental results show that the method avoids PSD feedback control,and can quickly realize geometric parameters calibration of industrial robots,the positioning mean error and standard deviation are 78.28% and 76.38% respectively,it effectively improves the positioning accuracy of the robot.

In order to increase the positioning accuracy of indoor positioning system based on Received Signal Strength Indicator(RSSI), a Bayesian indoor location algorithm based on RSSI is proposed. The algorithm preprocesses the RSSI signal by Gaussian filtering, uses the triangle centroid algorithm to calculate the unknown node’s initial coordinates, and handles it through Bayesian filtering to get the coordinates that is more accurate. Experimental results show that the precision of proposed algorithm can reach 98%, which reduces the positioning error.

The traditional k-means algorithm has sensitivity to the initial start center. To solve this problem, a new method is proposed to find the initial start center. First it computes the density of the area where the data object belongs to; then finds k data objects all of which are belong to high density area and the most far away to each other, using these k data objects as the initial start centers. Experiments on the standard database UCI show that the proposed method can produce a high purity clustering result and eliminate the sensitivity to the initial start centers.

This paper analyzes Chinese character structure feature and evolution, combines the character recognition process together on the basis of Chinese character database and changing in recognition target, researches and comprises the advantages, disadvantages and the application range of five different kinds of Classification recognition method such as Bayesian method, quadratic discriminate function approach, Markov model, neural network classifier and Support Vector Machine(SVM). Results shows that Chinese character recognition method including recognition should be improved on the aspects of target, feature extracting and train set preparing.

As applications programs based on MapReduce model growing up, Hadoop’s performance depends on applications. From applications, this paper analyzes the limitations and shortcomings of Hadoop architecture and storage technology, and proposes a feasible and cost-effective solution to address the problem. It builds a series tests to convince the idea. The solution is multi-level parallel, both on job and I/O levels, which makes full use of disk and network bandwidth, reduces I/O bottlenecks and improves performance.

A new reciprocal algorithm is provided to realize the traditional reciprocal division on Field Programmable Gate Array(FPGA) with high precision and fast speed. In order to solve the reciprocal of the divisor, the Taylor series expansion is used combining with searching approximation to get a fitting binomial of degree 1 with one variable in every separated section, and the Newton-Raphson iteration is used one time to improve precision. The result of timing simulation proved that the divider based on this new division algorithm can be easily realized on FPGA. The divider can get the answer of 2-34 available accuracy only six clock pulses after input variables being given, and the highest operating frequency is 86.95 MHz.

Bicubic interpolation is an effective way to get well-qualified high-resolution image, but with high calculation load. The common used interpolation methods are discussed. A new region-based bicubic interpolation is proposed. Without segmenting the image, the mean value of the four neighboring points of the interpolated point is calculated which is used to divide the image into two regions: the flat region and the complex region with more details. Two different interpolating algorithms are chosen for each region. Experimental results show that the proposed algorithm can keep the image quality equal to the original algorithm while reducing the calculation load more than 10 percents. It is useful for applications.

TFIDF is a kind of common methods used to measure the terms in a document. The method is easy but it undervalues these terms that frequently appear in the documents belonging to the same class, while those terms can represent the characteristic of the documents of this class, so higher weight is entrusted to them. The expression of IDF in TFIDF is modified to increase the weight of those terms mentioned, then is applied to the experiment to validate it. In the experiment, the improved TFIDF is used to select feature and genetic algorithm is used to train the classifier. The method is better than others and proves that the improved TFIDF method is feasible.

Primitive elements and primitive polynomial play very important roles in the theory of the finite field. It is the premise of solving the problem about code sequences and searching the primitive elements can come down to searching primitive polynomial. This paper gives a new algorithm for searching primitive polynomials in the binary field making use of the algorithm for searching the minimal polynomial, and also gives out the minimal polynomial in the searching process. It shows the efficiency analysis of the algorithm.

In order to resolve the problem of saving power in soft real-time embedded systems, this paper proposes a DVS algorithm which overcomes the uncertainty due to variable task execution times and pursues the optimal scheduling scheme during runtime. The experiment results show that this algorithm guarantees the CPU utilization and the stability of the soft real-time systems. Even in the situation of overload, the scheduling system can adjust itself automatically, and more than 99% tasks can be completed before their deadlines. The random tests to multiple task sets also demonstrate that more than 15% energy can be saved in average by this DVS algorithm.

This paper introduces the result of a test that evaluates the effectiveness of Compute Unified Device Architecture(CUDA) using NVDIA GeForce8800GT and the compiler Visual Studio 2008. It tests the speed of NVIDIA CUBLAS, CUDA kernel, common C program, Intel MKL BLAS, CUDA driver API program, FFTW and CUFFT Library in matrix-multiplication and Fast Fourier Transform(FFT). Test result of the large scale data shows that the computing ability of GPU is 25 times better than that of CPU.

Applying ElGamal signature scheme to the elliptic curve cryptosystem may introduce an undefined operation of point multiplex in elliptic curve. In order to solve the problem, this paper improves processes of signature generation and validation, using a simple algebra operation instead of multiplication operation, and presents feasibility and security analysis. Menezes Vanstone(MV) algorithm is improved to reduce its data expand rate. Experimental results show that its operation speed is faster than RSA or ECC-E. Execution efficiency and key length of the improved algorithms make them more efficient in the application of smart card computation.

Because of the accumulation of positioning errors due to the drift of Personal Dead Reckoning(PDR) for indoor localization, this paper puts forward a Micro Electro Mechanical System(MEMS) sensor integration of PDR algorithm. It includes the following three points: It obtains displacement information based on typical pedometer principle and stride length estimate, joins dynamic time window and acceleration threshold in stride calculation algorithm, and gets the angle between the walking direction and magnetic north direction with the magnetometer and gyroscope to correct the positioning accumulative error. It realizes indoor location and navigation. The result proves that the distance errors of indoor localization experiment can be controlled within 5%, and the stride calculation is highly precise, the azimuth algorithm is easy to deal with, and it can rectify error because of long time drift in certain extent.

A KNN classification algorithm based on k-nearest neighbor graph for small sample sets is presented to improve the classification accuracy, which partitions the k-nearest neighbor graph into clusters with high similarity, labels the unlabel data of each cluster with the label of the label data in the same cluster, and deletes the noise data. The sample set is expended by this method. The algorithm use the expended sample set to label the unlabel data. The presented algorithm is demonstrated with standard datasets, and the experimental results show the algorithm can enhance the accuracy of classification, reduce the influence of the value of k, and achieve a satisfying result.

The mass data from Web are distributed, heterogeneous and dynamic, so the current data mining system based on single node has developed to a bottleneck. Using the advantage of cloud computing——distributed processing and virtualization, this paper presents a Weblog analysis platform under the Hadoop’s cluster framework based on cloud computing, it also presents a hybrid algorithm which can distributed process in the cloud computing environment. To further verify the effectiveness and efficiency of the platform, it uses the improved algorithm to mine users’ preferred access path in Weblog on the platform. Experimental results show that, using distributed algorithm to process large number of Weblog files in the cluster, can significantly improve the efficiency of Web data mining.

The self-organizing maps (SOM) is an unsupervised learning algorithm, which is capable of self organization and visualization and has been used in many areas. In this paper, the background of self-organizing maps algorithm and basic algorithm are introduced, the parameter setting-up and disadvantage of SOM are analyzed. The improved algorithms have been reviewed emphatically, and research hotspot and applying fields are also discussed briefly. Finally, some remarks on future research are given.

Modeling and predicting of network traffic are important for internet management and security prediction. Considering about draggle disposal and bad service quality, different kinds of classic traffic prediction methods are reviewed. Comparison about their suitable data, modeling complexity, prediction precision and suitable scene are presented. Experimental results prove that prediction model should correlate to scene tightly. It need to choose different models according to the traffic characters and prediction target in practices.

Yao’s Millionaires’ problem is a typical problem of secure multi-party computation, but most solutions are inefficient. Based on commutative encryption scheme, this paper proposes an efficient and secure solution to millionaires’ problem, which reduces the problem to the set- intersection problem by 0-encoding and 1-encoding for private inputs. Proof of security is followed. There is no complicated modular exponentiation in this solution which only needs O(n) encryption/decryption and 4 rounds of communication. It is more efficient than other solutions.

Latent Dirichlet Allocation(LDA) model can be used for identifying topic information from large-scale document set, but the effect is not ideal for short text such as microblog. This paper proposes a microblog user model based on LDA, which divides microblog based on user and represents each user with their posted microbolgs. Thus, the standard three layers in LDA model by document-topic-word becomes a user model by user-topic-word. The model is applied to user recommendation. Experiment on real data set shows that the new provided method has a better effect. With a proper topic number, the performance is improved by nearly 10%.

To deal with the new time and space challenges of the machine learning problem algorithms from large scale data, this paper focuses on sparse-learning and categorizes the L1 regularized problem’s the-state-of-the-art solvers from the view of multi-stage and multi-step optimization schemes. It compares the algorithms’ convergence properties, time and space cost and the sparsity of these solvers. The analysis shows that those algorithms sufficiently exploiting the machine learning problem’s specific structure obtain better sparsity as well as faster convergence rate.

Geometric feature extraction from image is a crucial step in image classification and recognition algorithms. It requires to calculate the Minimum Enclosing Rectangle(MER) of the image to get some attributes, such as length and width. This paper proposes an algorithm based on the combination of the vertex chain code and the discrete Green theorem to extract MER from image. According to this algorithm, it can calculate the area, centroid, and principal axis of the image easily, only with its point coordinates from the veritical or hrizontal direction of the vertex chain code. Experimental results show that the rotation method’s speed is two times of the current algorithms, while the principal axis method is three times of the rotation method.

Two one-class classification models, one-class support vector machine and support vector data description, which are based on support vector machine and one-class classification are introduced. The internal relationship and parameters optimization of the two models are also analysed, and the exists of disadvantages and improvements of the two one-class classifiers are summarized.

Based on analyzing the shortages of K-Nearest Neighbor(KNN) algorithm in solving classification problems on imbalanced dataset, a novel KNN approach based on weight strategy(GAK-KNN) is presented. The key of GAK-KNN lies on defining a new weight assignment model, which can fully take into account the adverse effects caused by the uneven distribution of training sample between classes and within classes. The specific steps are as follows: use K-means algorithm based on Genetic Algorithm(GA) to cluster the training sample set, compute the weight for each training sample in accordance to the clustering results and weight assignment model, use the improved KNN algorithm to classify the test samples. GAK-KNN algorithm can significantly improve the identification rate of the minority samples and overall classification performance. Theoretical analysis and comprehensive experimental results on the UCI dataset con?rm the claims.

The paper introduces the light-weight stack. It describes the standard TCP/IP stack. It gives emphasis to the characters of TCP/IP stack in embedded system and design methods. A comparison between standard TCP/IP stack and light-weight TCP/IP stack is listed.

According to the features of Message Passing Interface(MPI), the cloud computing application methods based on MPI, including the MPI cloud computing algorithm design model, cloud computing principles, the core model, and the process are proposed. The distributed characteristic and parallel characteristic are introduced. Theoretical analysis results show this algorithm is feasible, effective and superior to the traditional parallel technology, and it can provide the new method to distributed and parallelize the ordinary algorithms.