A Memetic Algorithm for Metabolomics Based Feature Weighting Data Classification

A metaheuristic chain based memetic algorithm namely MCMA is proposed for the classification of metabolomics data. MCIVIA regards both global evolution and local search as equivalent elemental metaheuristics, and searches with a chain of metaheuristics performed al- ternatively on the target problem. A hidden Markov model based scheduling mechanism is employed in MCMA for the selection of metaheuristics. By using MCMA for op- timizing the linkage weight vectors a feature weighting al- gorithm for metabolomlcs data is formed to identify relevant metabolite features and reveal their exact relation- ships with the given physiological states. An extreme learning machine based classifier is utilized in predicting the physiological states according to the weighted metabolite features. Experimental results on real metabolomics data of clinical liver transplantation demonstrate that the proposed feature weighting and classification method obtains better performance than the other compared algorithms.     

A novel algorithm for voice conversion using Canonical Correlation Analysis

A novel algorithm for voice conversion is proposed in this paper. The mapping function of spectral vectors of the source and target speakers is calculated by the Canonical Correlation Analysis （CCA） estimation based on Gaussian mixture models. Since the spectral envelope feature remains a majority of second order statistical information contained in speech after Linear Prediction Coding （LPC） analysis, the CCA method is more suitable for spectral conversion than Minimum Mean Square Error （MMSE） because CCA explicitly considers the variance of each component of the spectral vectors during conversion procedure. Both objective evaluations and subjective listening tests are conducted. The experimental results demonstrate that the proposed scheme can achieve better performance than the previous method which uses MMSE estimation criterion.     

User-Level Sentiment Evolution Analysis in Microblog

People＇s attitudes towards public events or products may change overtime,rather than staying on the same state.Understanding how sentiments change overtime is an interesting and important problem with many applications.Given a certain public event or product,a user＇s sentiments expressed in microblog stream can be regarded as a vector.In this paper,we define a novel problem of sentiment evolution analysis,and develop a simple yet effective method to detect sentiment evolution in user-level for public events.We firstly propose a multidimensional sentiment model with hierarchical structure to model user＇s complicate sentiments.Based on this model,we use FP-growth tree algorithm to mine frequent sentiment patterns and perform sentiment evolution analysis by Kullback-Leibler divergence.Moreover,we develop an improve Affinity Propagation algorithm to detect why people change their sentiments.Experimental evaluations on real data sets show that sentiment evolution could be implemented effectively using our method proposed in this article.     

Quantification of Cranial Asymmetry in Infants by Facial Feature Extraction

In this paper, a facial feature extracting method is proposed to transform three-dimension （3D） head images of infants with deformational plagiocephaly for assessment of asymmetry. The features of 3D point clouds of an infant＇s cranium can be identified by local feature analysis and a two-phase k-means classification algorithm. The 3D images of infants with asymmetric cranium can then be aligned to the same pose. The mirrored head model obtained from the symmetry plane is compared with the original model for the measurement of asymmetry. Numerical data of the cranial volume can be reviewed by a pediatrician to adjust the treatment plan. The system can also be used to demonstrate the treatment progress.     

Vehicle Real—time Location Based on Visual Perception Model

Vehicle recognition system(VRS) plays a very important role in the field of intelligent transportation systems.A novel and intuitive method is proposed for vehicle location.The method we provide for vehicle location is based on human visual perception model technique.The perception color space HIS in this algorithm is adopted.Three color components of a color image and more potential edge patterns are integrated for solving the feature extraction problem.A fast and automatic threshold technique based on human visual perception model is also developed.The vertical edge projection and horizontal edge projection are adopted for locating left-right boundary of vehicle and top-bottom boundary of vehicle,respectively.Very promising experimental results are obtained using real-time vehicle image sequences,which have confirmed that this proposed location vehicle method is efficient and reliable,and its calculation speed meets the needs of the VRS.     

Motion-based region growing segmentation of image sequences

This paper proposes a motion-based region growing segmentation scheme for the object-based video coding, which segments an image into homogeneous regions characterized by a coherent motion. It adopts a block matching algorithm to estimate motion vectors and uses morphological tools such as open-close by reconstruction and the region-growing version of the watershed algorithm for spatial segmentation to improve the temporal segmentation. In order to determine the reliable motion vectors, this paper also proposes a change detection algorithm and a multi-candidate pro- screening motion estimation method. Preliminary simulation results demonstrate that the proposed scheme is feasible. The main advantage of the scheme is its low computational load.     

HIAWSC： An Immune Algorithm Based Heuristic Web Service Composition Framework

The introduction of of web services has led to web service composition being a focus of many researchers. Composing web services using workflows is seen as the most realistic method from an industrial viewpoint. Amongst other method, the use of natural computing methods has been proposed previously to automate web service composition. The need for a fast response when computing the most suitable sequence of services is addressed in this paper. In particular, we propose a novel heuristic immune algorithm with an efficient encoding and mutation method. The algorithm involves two steps： an immune selection operation, which is maintaining antibody population diversity and the clonal selection. The use of a vaccine during the evolution provides heuristic information that accelerates the convergence. Our experimental results illustrate that the proposed heuristic immune algorithm is very effective in improving the convergence speed. We also provide a schema analysis for this method.     

Improving Voice Activity Detection via weighting likelihood and dimension reduction

The performance of the traditional Voice Activity Detection （VAD） algorithms declines sharply in lower Signal-to-Noise Ratio （SNR） environments. In this paper, a feature weighting likelihood method is proposed for noise-robust VAD. The contribution of dynamic features to likelihood score can be increased via the method, which improves consequently the noise robustness of VAD. Divergence based dimension reduction method is proposed for saving computation, which reduces these feature dimensions with smaller divergence value at the cost of degrading the performance a little. Experimental results on Aurora Ⅱ database show that the detection performance in noise environments can remarkably be improved by the proposed method when the model trained in clean data is used to detect speech endpoints. Using weighting likelihood on the dimension-reduced features obtains comparable, even better, performance compared to original full-dimensional feature.     

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.     

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.     

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.     

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.     

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.     

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.     

SDN-Based Data Offioading for 5G Mobile Networks

The rapid growth of 3G/4G enabled devices such as smartphones and tablets in large numbers has created increased demand formobile data services.Wi-Fi offloading helps satisfy the requirements of data-rich applications and terminals with improved multi-media.Wi-Fi is an essential approach to alleviating mobile data traffic load on a cellular network because it provides extra capaci-ty and improves overall performance.In this paper,we propose an integrated LTE/Wi-Fi architecture with software-defined net-working(SDN)abstraction in mobile backhaul and enhanced components that facilitate the move towards next-generation 5G mo-bile networks.Our proposed architecture enables programmable offloading policies that take into account real-time network condi-tions as well as the status of devices and applications.This mechanism improves overall network performance by deriving real-time policies and steering traffic between cellular and Wi-Fi networks more efficiently.     

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.     

A 3.1-4.8 GHz CMOS receiver for MB-OFDM UWB

An integrated fully differential ultra-wideband CMOS receiver for 3.1-4.8 GHz MB-OFDM systems is presented. A gain controllable low noise amplifier and a merged quadrature mixer are integrated as the RF front-end. Five order Gm-C type low pass filters and VGAs are also integrated for both I and Q IF paths in the receiver. The ESD protected chip is fabricated in a Jazz 0.18μm RF CMOS process and achieves a maximum total voltage gain of 65 dB, an AGC range of 45 dB with about 6 dB/step, an averaged total noise figure of 6.4 to 8.8 dB over 3 bands and an in-band IIP3 of-5.1 dBm. The receiver occupies 2.3 mm2 and consumes 110 mA from a 1.8 V supply including test buffers and a digital module.     

一种适用于“模一”多带OFDM超宽带系统中跳频产生的3.96GHz锁相环

A fully integrated phase-locked loop（PLL） is presented for a single quadrature output frequency of 3.96 GHz.The proposed PLL can be applied to mode-1 MB-OFDM UWB hopping carrier generation.An adaptive frequency calibration loop is incorporated into the PLL.The capacitance area in the loop filter is largely reduced through a capacitor multiplier.Implemented in a CMOS process, this PLL draws 13.0 mA current from a single 1.2 V supply while occupying 0.55 mm2 die area.Measurement results show that the PLL achieves a phase noise of-70 dBc/Hz at 10 kHz offset and-113 dBc/Hz at 1 MHz offset.The integrated RMS jitter from 1 kHz to 10 MHz is 2.2 ps.The reference spur level is less than-68 dBc.