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This study investigated and simulated land use patterns in Beijing for the year 2000 and the year 2005 from the actual land use data for the year 1995 and the year 2000, respectively, by combining spatial land allocation simulation using the CLUE-S model, and numerical land demand prediction using the Markov model. The simulations for 2000 and 2005 were confirmed to be generally accurate using Kappa indices. Then the land-use scenarios for Beijing in 2015 were simulated assuming two modes of development： 1） urban development following existing trends; and 2） under a strict farmland control. The simulations suggested that under either mode, urbanized areas would expand at the expense of land for other uses. This expansion was predicted to dominate the land-use conversions between 2005 and 2015, and was expected to be accompanied by an extensive loss of farmland. The key susceptible to land-use changes were found to be located at the central urban Beijing and the surrounding regions including Yanqing County, Changping District and Fangshan District. Also, the simulations predicted a considerable expansion of urban/suburban areas in the mountainous regions of Bei- jing, suggesting a need for priority monitoring and protection

A modified Gauss-Markov model with weighted constraints was constructed by combining satellite altimeter and tide gauge records. Vertical motion rates of nine tide gauge stations around the Bohal Sea and Yellow Sea are estimated. This is the first time systematic estimates have been derived in this region. Downward trends were seen at the six tide gauge stations located at Tanggu, Longkou, Laohutan, Bayuquan, Xiaochangshan, and Yantai; with vertical motion rates of-1.82±0.50, -1.65±0.46, -0.88±0.42, -0.58±0.62, -0.13±0.43, and -0.01±0.43 mm/yr, respectively. Upward trends were seen at the three tide gauge stations located at Qinhuangdao, Huludao and Chengshantou; with vertical motion rates of 1.12±0.46, 0.55±0.49 and 0.26±0.44 mm/yr, respectively. There was significant subsidence in Tanggu and Longkou, and a rising trend in Qinhuangdao. According to our results, the rate of sea level rise calculated from these tide gauge records can be improved using a more accurate measurement of the land elevation accounting for lifting or subsidence. The model derived can be used to estimate vertical motions of tide gauge stations, and can be widely applied to revise the benchmark levels of tide gauges.

Based on four phases of TM images acquired in 1990, 1995, 2000 and 2005, this paper took Kitakyushu in Japan as a case study to analyze spatial change of land use landscape and corresponding effects on environmental issues guided by landscape ecology theory in virtue of combining technology of Remote Sensing with GIS. Firstly, land use types were divided into 6 classes （farmland, mountain, forestland, water body, urban land and unused land） according to national classification standard of land use, comprehensible ability of TM image and purpose of this study. Secondly, following the theory of landscape ecology analysis, 11 typical landscape indices were abstracted to evaluate the environmental effects and spatial feature changes of land use. Research results indicated that land use has grown more and more diversified and unbalanced, human activities have disturbed the landscape more seriously. Finally, transfer matrix of Markov was applied to forecast change process of land use in the future different periods, and then potential land use changes were also simulated from 2010 to 2050. Results showed that conversion tendency for all types of land use in Kitakyushu into urban construction land were enhanced. The study was anticipated to help local authorities better understand and address a complex land use system, and develop improved land use management strategies that could better balance urban expansion and ecological conservation.

Next location prediction is of great importance for many location-based applications. With the virtue of solid theoretical foundations, Markov-based approaches have gained success along this direction. In this paper, we seek to enhance the prediction performance by understanding the similarity between objects. In particular, we propose a novel method, called weighted Markov model （weighted-MM）, which exploits both the sequence of just-passed locations and the object similarity in mining the mobility patterns. To this end, we first train a Markov model for each object with its own trajectory records, and then quantify the similarities between different objects from two aspects： spatial locality similarity and trajectory similarity. Finally, we incorporate the object similarity into the Markov model by considering the similarity as the weight of the probability of reaching each possible next location, and return the top-rankings as results. We have conducted extensive experiments on a real dataset, and the results demonstrate significant improvements in prediction accuracy over existing solutions.

In this paper, an efficient scheme for recognition of handwritten Odia numerals using hidden markov model (HMM) has been proposed. Three different feature vectors for each of the numeral is generated through a polygonal approximation of object contour. Subsequently, aggregated feature vector for each numeral is derived from these three primary feature vectors using a fuzzy inference system. The final feature vector is divided into three levels and interpreted as three different states for HMM. Ten different three-state ergodic hidden markov models (HMMs) are thus constructed corresponding to ten numeral classes and parameters are calculated from these models. For the recognition of a probe numeral, its log-likelihood against these models are computed to decide its class label. The proposed scheme is implemented on a dataset of 2500 handwritten samples and a recognition accuracy of 96.3% has been achieved. The scheme is compared with other competent schemes.

In this paper, we have proposed and designed DPHK （data prediction based on HMM according to activity pattern knowledge mined from trajectories）, a real-time distributed predicted data collection system to solve the congestion and data loss caused by too many connections to sink node in indoor smart environment scenarios （like Smart Home, Smart Wireless Healthcare and so on）. DPHK predicts and sends predicted data at one time instead of sending the triggered data of these sensor nodes which people is going to pass in several times. Firstly, our system learns the knowl- edge of transition probability among sensor nodes from the historical binary motion data through data mining. Secondly, it stores the corresponding knowledge in each sensor node based on a special storage mechanism. Thirdly, each sensor node applies HMM （hidden Markov model） algorithm to pre- dict the sensor node locations people will arrive at according to the received message. At last, these sensor nodes send their triggered data and the predicted data to the sink node. The significances of DPHK are as follows： （a） the procedure of DPHK is distributed; （b） it effectively reduces the connection between sensor nodes and sink node. The time complexities of the proposed algorithms are analyzed and the performance is evaluated by some designed experiments in a smart environment.

In order to compensate the network-induced random delays in networked control systems (NCSs), the semi-continuous hidden Markov model (SCHMM) is introduced in this paper to model the controller-to-actuator (CA) delay in the forward network channel. The expectation maximization algorithm is used to obtain the optimal estimation of the model’s parameters, and the Viterbi algorithm is used to predict the CA delay in the current sampling period. Thus, the predicted CA delay and the measured sensor-tocontroller (SC) delay in the current sampling period are used to design an optimal controller. Under this controller, the exponentially mean square stability of the NCS is guaranteed, and the SC and CA delays are compensated. Finally, the effectiveness of the method proposed in this paper is demonstrated by a simulation example. Moreover, a comparative example is also given to illustrate the superiority of the SCHMM-based optimal controller over the discrete hidden Markov model (DHMM)-based optimal controller.

Obtaining training material for rarely used English words and common given names from countries where English is not spoken is difficult due to excessive time, storage and cost factors. By considering personal privacy, language- independent （LI） with lightweight speaker-dependent （SD） automatic speech recognition （ASR） is a convenient option to solve tile problem. The dynamic time warping （DTW） algorithm is the state-of-the-art algorithm for small-footprint SD ASR for real-time applications with limited storage and small vocabularies. These applications include voice dialing on mobile devices, menu-driven recognition, and voice control on vehicles and robotics. However, traditional DTW has several lhnitations, such as high computational complexity, constraint induced coarse approximation, and inaccuracy problems. In this paper, we introduce the merge-weighted dynamic time warping （MWDTW） algorithm. This method defines a template confidence index for measuring the similarity between merged training data and testing data, while following the core DTW process. MWDTW is simple, efficient, and easy to implement. With extensive experiments on three representative SD speech recognition datasets, we demonstrate that our method outperforms DTW, DTW on merged speech data, the hidden Markov model （HMM） significantly, and is also six times faster than DTW overall.

Markov chains are extensively used in modeling different aspects of engineering and scientific systems, such as performance of algorithms and reliability of systems. Different techniques have been developed for analyzing Markovian models, for example, Markov Chain Monte Carlo based simulation, Markov Analyzer, and more recently probabilistic model- checking. However, these techniques either do not guarantee accurate analysis or are not scalable. Higher-order-logic theorem proving is a formal method that has the ability to overcome the above mentioned limitations. However, it is not mature enough to handle all sorts of Markovian models. In this paper, we propose a formalization of Discrete-Time Markov Chain （DTMC） that facilitates formal reasoning about time-homogeneous finite-state discrete-time Markov chain. In particular, we provide a formal verification on some of its important properties, such as joint probabilities, Chapman-Kolmogorov equation, reversibility property, using higher-order logic. To demonstrate the usefulness of our work, we analyze two applications： a simplified binary communication channel and the Automatic Mail Quality Measurement protocol.

To solve the problems of ladle slag detection system (SDS), such as high cost, short service life, and inconvenient maintenance, a new SDS realization method based on hidden Markov model (HMM) was put forward. The physical process of continuous casting was analyzed, and vibration signal was considered as the main detecting signal according to the difference in shock vibration generated by molten steel and slag because of their difference in density. Automatic control experiment platform oriented to SDS was established, and vibration sensor was installed far away from molten steel, which could solve the problem of easy power consumption by the sensor. The combination of vector quantization technology with learning process parameters of HMM was optimized, and its revaluation formula was revised to enhance its recognition effectiveness. Industrial field experiments proved that this system requires low cost and little rebuilding for current devices, and its slag detection rate can exceed 95%.