Motion constraint Markov network model for multi-target tracking

A typical Markov network for modeling the interaction among targets can handle the error merge problem,but it suffers from the labeling problem due to the blind competition among collaborative trackers. In this paper,we propose a motion constraint Markov network model for multi-target tracking. By augmenting the typical Markov network with an ad hoc Markov chain which carries motion constraint prior,this proposed model can overcome the blind competition and direct the label to the corresponding target even in the case of severe occlusion. In addition,the motion constraint prior is formu-lated as a local potential function and can be easily incorporated in the joint distribution representation of the novel model. Experimental results demonstrate that our model is superior to other methods in solving the error merge and labeling problems simultaneously and efficiently.     

Motion constraint Markov network model for multi-target tracking

A typical Markov network for modeling the interaction among targets can handle the error merge problem, but it suffers from the labeling problem due to the blind competition among collaborative trackers. In this paper, we propose a motion constraint Markov network model for multi-target tracking. By augmenting the typical Markov network with an ad hoc Markov chain which carries motion constraint prior, this proposed model can overcome the blind competition and direct the label to the corresponding target even in the case of severe occlusion. In addition, the motion Constraint prior is formulated as a local potential function and can be easily incorporated in the joint distribution representation of the novel model. Experimental results demonstrate that our model is superior to other methods in solving the error merge and labeling problems simultaneously and efficiently.     

Unsupervised Texture Segmentation Based on Artificial Immune Gaussian Mixture Models Network

We propose a novel unsupervised texture image segmentation scheme based on an Artificial immune Gaussian mixture models network （AIGMMN）, witch is really a mixture-of-mlxtures models. Starting from this algorithm, we coarsely group the homogeneous blocks into the known texture classes by the Fuzzy C-mean （FCM） clustering algorithm. Then we refinedly determine the heterogeneous blocks classification by AIGMMN. The new algorithm incorporates Expectation-maximization （EM） with the ideas of the Artificial immune clonal selection （AICS）, which can get a global optimal value and overcome the local maximum problems associated with the traditional EM algorithm. Moreover, the new algorithm is capable of determining the optimal number of components automatically in each subnet mixture model, which can approximate the non-gaussian multimodal densities distribution. We compare the proposed method with two common mixturebased segmentation methods for both synthetic and natural texture images, and achieve an overall improvement in performance.     

Self-Adaptive Topic Model： A Solution to the Problem of ＂Rich Topics Get Richer＂

The problem of ＂rich topics get richer＂（RTGR） is popular to the topic models,which will bring the wrong topic distribution if the distributing process has not been intervened.In standard LDA（Latent Dirichlet Allocation） model,each word in all the documents has the same statistical ability.In fact,the words have different impact towards different topics.Under the guidance of this thought,we extend ILDA（Infinite LDA） by considering the bias role of words to divide the topics.We propose a self-adaptive topic model to overcome the RTGR problem specifically.The model proposed in this paper is adapted to three questions：（1） the topic number is changeable with the collection of the documents,which is suitable for the dynamic data;（2） the words have discriminating attributes to topic distribution;（3） a selfadaptive method is used to realize the automatic re-sampling.To verify our model,we design a topic evolution analysis system which can realize the following functions：the topic classification in each cycle,the topic correlation in the adjacent cycles and the strength calculation of the sub topics in the order.The experiment both on NIPS corpus and our self-built news collections showed that the system could meet the given demand,the result was feasible.     

Optimal and Suboptimal Access and Transmission Polices for Dynamic Spectrum Access over Fading Channels in Cognitive Radio Networks

We propose a cross-layer optimal access and transmission framework for dynamic spectrum access to maximize expected long-term average throughput under power and collision constraints by a dynamic programming method namely Constrained Markov decision process （CMDP）. The optimal policy for CMDP is capable of guiding transmitter to choose an available channel and transmission rate at the beginning of each frame for its long-term goals according to current channel sensing results and prior channel fading information. The complexity of finding the optimal policy by Linear programming （LP） approach increases exponentially with the number of channels and fading levels, which incurs so-called curse of dimensionality. Therefore we propose two complexity- reduced suboptimal policies, namely, policy separation and heuristic algorithms. Finally, we compare the performances of policies by numerical results.     

New method of mining incomplete data

The data used in the process of knowledge discovery often includes noise and incomplete information. The boundaries of different classes of these data are blur and unobvious. When these data are clustered or classified, we often get the coverings instead of the partitions, and it usually makes our information system insecure. In this paper, optimal partitioning of incomplete data is researched. Firstly, the relationship of set cover and set partition is discussed, and the distance between set cover and set partition is defined. Secondly, the optimal partitioning of given cover is researched by the combing and parting method, acquiring the optimal partition from three different partitions set family is discussed. Finally, the corresponding optimal algorithm is given. The real wireless signals offten contain a lot of noise, and there are many errors in boundaries when these data is clustered based on the tradional method. In our experimant, the proposed method improves correct rate greatly, and the experimental results demonstrate the method’s validity.     

An Efficient Skeleton-Free Pose Retargetting Method for Triangular Meshes

In this paper, we present a novel skeleton-free pose retargetting method for triangular meshes. Firstly, by using our novel skeleton-free skinning technique, the overall pose of marker points from Motion capture （Mocap） is retargetted to a mesh without needing to build skeleton structures for these points. The computation complexity of our technique is independent of the number of markers, as the markers just serve as the constraints of the solution system in a least squares sense. Then, we show that our method also can retarget pose between meshes, which is useful for pose correction after various mesh editing operations. Our approach is numerically efficient, as the solution to the optimization problem can be obtained by fast solving a sparse linear system. Experimental results show that our method is effective and efficient for common applications.     

RER： A Replica Efficiency Based Replication Strategy

The technology of replication is often used in the unstructured Peer-to-peer （P2P） systems to allocate more replicas for the hot spots in order for a higher search performance. But the questions that what is the optimal number of copies for each file and what is the practicable approach to the optimal number are seldom answered in the related works. In terms of success rate, this paper firstly investigates the optimal allocation of copies for each file according to query rate with the routing algorithm of random walks. Then a practicable method is proposed to approach the optimal allocation in the unstructured P2P systems. Our results offer a new understanding of replication and our work contributes much to the content distribution in the Internet environment.     

A numerical integration-based yield estimation method for integrated circuits

A novel integration-based yield estimation method is developed for yield optimization of integrated circuits.This method tries to integrate the joint probability density function on the acceptability region directly. To achieve this goal,the simulated performance data of unknown distribution should be converted to follow a multivariate normal distribution by using Box-Cox transformation(BCT).In order to reduce the estimation variances of the model parameters of the density function,orthogonal array-based modified Latin hypercube sampling (OA-MLHS) is presented to generate samples in the disturbance space during simulations.The principle of variance reduction of model parameters estimation through OA-MLHS together with BCT is also discussed.Two yield estimation examples,a fourth-order OTA-C filter and a three-dimensional(3D) quadratic function are used for comparison of our method with Monte Carlo based methods including Latin hypercube sampling and importance sampling under several combinations of sample sizes and yield values.Extensive simulations show that our method is superior to other methods with respect to accuracy and efficiency under all of the given cases.Therefore,our method is more suitable for parametric yield optimization.     

Steering knuckle diameter measurement based on optical 3D scanning

Three-dimensional（3D） modeling of medical images is a critical part of surgical simulation. In this paper, we focus on the magnetic resonance（MR） images denoising for brain modeling reconstruction, and exploit a practical solution. We attempt to remove the noise existing in the MR imaging signal and preserve the image characteristics. A wavelet-based adaptive curve shrinkage function is presented in spherical coordinates system. The comparative experiments show that the denoising method can preserve better image details and enhance the coefficients of contours. Using these denoised images, the brain 3D visualization is given through surface triangle mesh model, which demonstrates the effectiveness of the proposed method.     

Software Radio——Software Radio： A Review of Design Considerations and Digital Hardware Choices

This paper reviews the requirements for Software Defined Radio （SDR） systems for high-speed wireless applications and compares how well the different technology choices available- from ASICs, FPGAs to digital signal processors （DSPs） and general purpose processors （GPPs） - meet them.     

Survivable Virtual Network Mapping Using Optimal Backup Topology in Virtualized SDN

Software-Defined Network architecture offers network virtualization through a hypervisor plane to share the same physical substrate among multiple virtual networks. However, for this hypervisor plane, how to map a virtual network to the physical substrate while guaranteeing the survivability in the event of failures, is extremely important. In this paper, we present an efficient virtual network mapping approach using optimal backup topology to survive a single link failure with less resource consumption. Firstly, according to whether the path splitting is supported by virtual networks, we propose the OBT-I and OBT-II algorithms respectively to generate an optimal backup topology which minimizes the total amount of bandwidth constraints. Secondly, we propose a Virtual Network Mapping algorithm with coordinated Primary and Backup Topology （VNM-PBT） to make the best of the substrate network resource. The simulation experiments show that our proposed approach can reduce the average resource consumption and execution time cost, while improving the request acceptance ratio of VNs.     

Distributed Power Control for Energy Conservation in Hybrid Cellular Network with Device-to-Device Communication

Device-to-Device （D2D） com- munication has been proposed as a promising implementation of green communication to benefit the existed cellular network. In order to limit cross-tier interference while explore the gain of short-range communication, we devise a series of distributed power control （DPC） schemes for energy conservation （EC） and enhancement of radio resource utilization in the hybrid system. Firstly, a constrained opportunistic power control model is built up to take advantage of the interference avoidance methodology in the presence of service requirement and power constraint. Then, biasing scheme and admission control are added to evade ineffective power consumption and maintain the feasibility of the system. Upon feasibility, a non-cooperative game is further formulated to exploit the profit in EC with minor influence on spectral efficiency （SE）. The convergence of the DPC schemes is validated and their performance is confirmed via simulation results.     

EEFA： Energy Efficiency Frame Aggregation Scheduling Algorithm for IEEE 802.11n Wireless Network

Packet size is restricted due to the error-prone wireless channel which drops the network energy utilization. Furthermore, the frequent packet retransmissions also lead to energy waste. In order to improve the energy efficiency of wireless networks and save the energy of wireless devices, EEFA （Energy Efficiency Frame Aggregation）, a frame aggregation based energy-efficient scheduling algorithm for IEEE 802.11n wireless network, is proposed. EEFA changes the size of aggregated frame dynamically according to the frame error rate, so as to ensure the data transmission and retransmissions completed during the TXOP and reduce energy consumption of channel contention. NS2 simulation results show that EEFA algorithm achieves better performance than the original frame-aggregation algorithm.     

应用于低中频和零中频DVB调谐器中8阶信道滤波器设计

An eighth order active-RC filter for low-IF and zero-IF DVB tuner applications is presented, which is implemented in Butterworth biquad structure. An automatic frequency tuning circuit is introduced to compensate the cut-off frequency variation using a 6-bit switched-capacitor array. Switched-resistor arrays are adopted to cover different cut-off frequencies in low-IF and zero-IF modes. Measurement results show that precise cut-off frequencies at 2.5, 3, 3.5 and 4 MHz in zero-IF mode, 5, 6, 7 and 8 MHz in low-IF mode can be achieved, 60 dB frequency attenuation can be obtained at 20 MHz, and the in-band group delay agrees well with the simulation. Two-tone testing shows the in-band IM3 achieves -52 dB and the out-band IM3 achieves -55 dB with -11 dBm input power. This proposed filter circuit, fabricated in a SMIC 0.18μm CMOS process, consumes 4 mA current with 1.8 V power supply.     

用于ZigBee的2.4GHz低功耗双增益低噪声放大器

This paper presents a fully differential dual gain low noise amplifier（DGLNA） for low power 2.45-GHz ZigBee/IEEE 802.15.4 applications.The effect of input parasitics on the inductively degenerated cascode LNA is analyzed.Circuit design details within the guidelines of the analysis are presented.The chip was implemented in SMIC 0.18-μm 1P6M RF/mixed signal CMOS process.The DGLNA achieves a maximum gain of 8 dB and a minimum gain of 1 dB with good input return loss.In high gain mode, the measured noise figure（NF） is 2.3-3 dB in the whole 2.45-GHz ISM band.The measured 1-dB compression point, IIP3 and IIP2 is-9, 1 and 33 dBm, respectively.The DGLNA consumes 2 mA of current from a 1.8 V power supply.     

具有频率调谐的GNSS接收机双模复数滤波器

A fifth/seventh order dual-mode OTA-C complex filter for global navigation satellite system receivers is implemented in a 0.18μm CMOS process.This filter can be configured as the narrow mode of a 4.4 MHz bandwidth center at 4.1 MHz or the wide mode of a 22 MHz bandwidth center at 15.42 MHz.A fully differential OTA with source degeneration is used to provide sufficient linearity.Furthermore,a ring CCO based frequency tuning scheme is proposed to reduce frequency variation.The measured results show that in narrow-band mode the image rejection ratio（IMRR）is 35 dB,the filter dissipates 0.8 mA from the 1.8 V power supply,and the out-of-band rejection is 50 dB at 6 MHz offset.In wide-band mode,IMRR is 28 dB and the filter dissipates 3.2 mA.The frequency tuning error is less than±2%.     

Large-signal characterizations of DDR IMPATT devices based on group Ⅲ-Ⅴ semiconductors at millimeter-wave and terahertz frequencies

Large-signal （L-S） characterizations of double-drift region （DDR） impact avalanche transit time （IM- PATT） devices based on group III-V semiconductors such as wurtzite （Wz） GaN, GaAs and InP have been carried out at both millimeter-wave （mm-wave） and terahertz （THz） frequency bands. A L-S simulation technique based on a non-sinusoidal voltage excitation （NSVE） model developed by the authors has been used to obtain the high frequency properties of the above mentioned devices. The effect of band-to-band tunneling on the L-S properties of the device at different mm-wave and THz frequencies are also investigated. Similar studies are also carried out for DDR IMPATTs based on the most popular semiconductor material, i.e. Si, for the sake of comparison. A compara- tive study of the devices based on conventional semiconductor materials （i.e. GaAs, InP and Si） with those based on Wz-GaN shows significantly better performance capabilities of the latter at both mm-wave and THz frequencies.     

The Data Age of Mobile Cellular Network in Germany and China： Policies,Technologies and Markets （Part I）

The UMTS auction in 2000 brought approximately 100 billion DM （Deutsche Mark） for the German National Treasury. T-Mobile （D1-Netz）, Vodafone （D2-Netz）, E-Plus （E1-Netz） and 02 （E2-Netz） have gradually evolved from GSM to full-fledged UMTS operators over the past years. The conglomerate of China Telecom was split twice. China acceded to WTO and promulgated the FITE Provisions. MIIT （Ministry of Industry and Information Technology） became the regulator and China Netcom was incorporated into China Unicorn in 2008. Most recently the layout of 3G future has been reconfirmed by MIIT. Voice service has remained the main source of income in both countries and operators have continued to focus on voice quality and network availability in their respective 2G networks. Because value-added and higher-speed data applications have been gaining market attention, 2.5G and 3G infrastructure has increasingly become the focal network strategy for the operators since the beginning of the new century. Germany has rolled out WCDMA/UMTS services on a large scale in the consumer market, while China has adopted all three 3G standards （TD-SCDMA, WCDMA/UMTS, CDMA2000）, which shall gradually capture a wider 3G subscriber base. The summary shows that the development of the cellular technology and market in Germany and China can be discussed in three distinct historical periods. The conclusion suggests that the case of the cellular technology appears to be consistent with and applicable to a number of arguments widely disputed in economics and management related to technology and innovation, such as dominant design, technology waves/ S-Curve, disruptive technologies, Technology Adoption Life Cycle.     

Recommending Friends Instantly in Location-based Mobile Social Networks

Differently from the general online social network （OSN）, location- based mobile social network （LMSN）, which seamlessly integrates mobile computing and social computing technologies, has unique characteristics of temporal, spatial and social correlation. Recommending friends instantly based on current location of users in the real world has become increasingly popular in LMSN. However, the existing friend recommendation methods based on topological structures of a social network or non-topological information such as similar user profiles cannot well address the instant making friends in the real world. In this article, we analyze users＇ check-in behavior in a real LMSN site named Gowalla. According to this analysis, we present an approach of recommending friends instantly for LMSN users by considering the real-time physical location proximity, offline behavior similarity and friendship network information in the virtual community simultaneously. This approach effectively bridges the gap between the offline behavior of users in the real world and online friendship network information in the virtual community. Finally, we use the real user check-in dataset of Gowalla to verify the effectiveness of our approach.