Codebook Subsampling and Rearrangement Method for Large Scale MIMO Systems

In large scale MIMO （multiple-input multiple-output） systems, the size of codebook increases greatly when transmitters and receivers are equipped with more antennas. Thus, there are demands to select subsets of the codebook for usage to reduce the huge feedback overhead. In this paper, we propose a novel codebook subsampling method using chordal distance of different codewords and deleting them to affordable payload of PUCCH （physical uplink control channel）. Besides, we design a related codebook rearrangement algorithm to mitigate the system performance loss when there are bit errors in the feedback channel.     

Forecasting Typhoon Damage Scale with SOM Trained by Selective Presentation Learning

We previously proposed a new typhoon warning system which forecasts the likely extent of damage associated with a typhoon towards humans and buildings. The relation between typhoon data and damage data was learned by SOM （self-organizing maps） and typhoon damage scale （small, middle or large） was forecast by the SOM using typhoon data. Although average accuracy for actually small scale damage data was comparatively high （96.2%）, average accuracy for actually large scale damage data was comparatively low （65.2%）. Thus, we apply a selective presentation learning technique for improving the predictability of large scale damage by SOM. Learning data corresponding to middle and large scale damage are presented more often. Average accuracy for actually large scale damage data was increased by about 9%. The accuracy for actually large scale of numbers of fatalities and houses under water was increased by 25% and 20%, respectively.     

Multi-scale local features based on anisotropic heat diffusion and global eigen-structure

Multi-scale local feature detection enables downstream registration and recognition tasks in med- ical image analysis. This paper articulates a novel robust method for multi-scale local feature extraction on volumetric data. The central idea is the elegant unification of local/global eigen-structures within the powerful framework of anisotropic heat diffusion. First, the local vector field is constructed by way of Hessian matrix and its eigenvectors/eigenvalues. Second, anisotropic heat kernels are computed using the vector field＇s global graph Laplacian. Robust local features are manifested as extrema across multiple time scales, serving as volumetric heat kernel signature. To tackle the computational challenge for massive volumetric data, we propose a multi- resolution strategy for hierarchical feature extraction based on our feature-preserving down-sampling approach. As a result, heat kernels and local feature identification can be approximated at a coarser level first, and then are pinpointed in a localized region at a finer resolution. Another novelty of this work lies at the initial heat design directly using local eigenvalue for anisotropic heat diffusion across the volumetric domain. We conduct experiments on various medical datasets, and draw comparisons with 3D SIFT method. The diffusion property of our local features, which can be interpreted as random walks in statistics, makes our method robust to noise, and gives rise to intrinsic multi-scale characteristics.     

Towards energy-efficient storage placement in large scale sensor networks

Data storage has become an important issue for energy efficient data management in sensor networks. In this paper, we investigate the optimized storage placement problem in large scale sensor networks, aiming to achieve minimized energy cost. In order to efficiently deal with large scale deployment areas with irregular shape, we propose to utilize the hop as the computation unit instead of the node, such that computation complexity can be greatly reduced. We propose methodologies to solve the optimization problem both in situations for limited and unlimited numbers of storage units. The ultimate goal of this paper is to give fundamental guidance for optimized storage placement in large scale sensor networks. Simulation results show that our methodologies can greatly reduce the overall energy consumption compared to other strategies.     

Optimal Object Categorization under Application Specific Conditions

Day-to-day industrial computer vision applications focusing on object detection have the need of robust, fast and accurate object detection techniques. However, current state-of-the-art object categorization techniques only reach about 85% detection rate when performing in the wild detections, which try to cope with as much scene and object variation as possible. However, several industrial applications show many known characteristics like constant lighting, known camera position, constant background giving lead to several constraints on the actual algorithms. With a complete new universal object categorization framework, we want to prove the detection rate of these object categorization algorithms by exploiting the application specific knowledge which can help to reach a robust detector with detection rates of 99.9% or higher. We will use the same constraints to effectively reduce the number of false positive detections. Furthermore, we will introduce an innovative active learning system based on this application specific knowledge that will drastically reduce the amount of positive and negative training samples, leading to a shorter and more effective annotation and training phase.     

Performance Evaluation of Antenna Receive Diversity in SlMO-OFDM System over Rayleigh Fading Channel

This paper represents a comparative performance evaluation of different diversity combining techniques for a SIMO-OFDM （single-input-multiple-output orthogonal frequency division multiplexing） system over Rayleigh fading channel. OFDM is a key technique for achieving high data rates and spectral efficiency requirements for wireless communication systems. But in scattering environment, the system performances are severely degraded by the effects of multipath fading and inter-symbol interference. In wireless communication systems, antenna diversity is an important technique to combat multipath fading in order to improve the system performance and increase the channel capacity. In this paper, the performance of different diversity combining techniques-SC （selection combining）, EGC （equal gain combining） and MRC （maximal ratio combining） has been analyzed and compared in terms of SNR （signal to noise ratio） and BER （bit error rate） probability. The simulation results show that the maximal ratio combining technique provides maximum performance improvement relative to all other combining schemes by maximizing the SNR of SIMO-OFDM system at the combiner output. The analytic expressions of error probability and effective bit energy to noise ratio correlated with BPSK （binary phase shift keying） modulation have been derived and formulated for N-branch SC, EGC and MRC schemes. The BER characteristics for all three combining techniques are simulated in MATLAB （matrix laboratory） tool box for varying bit energy to noise ratio. Our results also derives that SNR can be improved if the number of receiving antenna is increased, which in turn reduces BER over a Rayleigh fading channel.     

Normalized Efficient Routing Protocol for WSN

WSNs （wireless sensor networks） consist of thousands of tiny nodes having the capability of sensing, computation, and wireless communications. Unfortunately these devices are limited energy devices, that is means we must save energy as much as possible, to increase network life time as long as possible. In this paper we introduce NEER--normalized energy efficient routing protocol that increases network life time through switching between AODV protocol that depends on request-reply routing, and MRPC that depends on residual battery in routing.     

From IP to Start-up： Building a Robust Life Science Incubator

An incubator is an organization that supports new ventures to grow and survive during the early stages. Mainly dedicated to information technology, life science may appear to be the next hot spot for incubators. Are there stabilized good practices？ Are the business models in information technology and in life science comparable when it goes to start-ups＇ incubation？ Leveraging our experience as practitioner in this field and using an inductive methodology, this paper tends to propose simple principles to help build robust incubators in life science, and to contribute to disseminate an entrepreneurial approach through an industry still dominated by blue chips.     

Real-time control of human actions using inertial sensors

Our study proposes a new local model to accurately control an avatar using six inertial sensors in real-time.Creating such a system to assist interactive control of a full-body avatar is challenging because control signals from our performance interfaces are usually inadequate to completely determine the whole body movement of human actors.We use a pre-captured motion database to construct a group of local regression models,which are used along with the control signals to synthesize whole body human movement.By synthesizing a variety of human movements based on actors’control in real-time,this study verifies the effectiveness of the proposed system.Compared with the previous models,our proposed model can synthesize more accurate results.Our system is suitable for common use because it is much cheaper than commercial motion capture systems.     

Remote-Controlled Ambidextrous Robot Hand Concept

In the development of robotic limbs, the side of members is of importance to define the shape of artificial limbs and the range of movements. It is mainly significant tbr biomedical applications concerning patients suffering arms or legs injuries, fn this paper, the concept of an ambidextrous design lbr robot hands is introduced. The fingers can curl in one xvay or another, to imitate either a right hand or a left hand. The advantages and inconveniences of different models have been investigated to optimise the range and the maximum force applied by fingers. Besides, a remote control interthce is integrated to the system, allowing both to send comrnands through internet and to display a video streaming of the ambidextrous hand as feedback. Therefore, a robotic prosthesis could be used for the first time in telerehabilitation. The main application areas targeted are physiotherapy alter strokes or management of phantom pains/br amputees by/earning to control the ambidextrous hand. A client application is also accessible on Facehook social network, making the robotic limb easily reachable for the patients. Additionally the ambidextrous hand can be used tbr robotics research as well as artistic performances.     

Optimal resource allocation for transmission diversity in multi-radio access networks： a coevolutionary genetic algorithm approach

The next generation wireless communication systems aim at supporting enhanced diversified network access and data transmission abilities via the cooperative integration and unified management of various radio access technologies(RATs).The resource allocation is the core component leading the network system and mobile terminals to the service robustness and performance maximization.In this paper,a numeric optimization model for optimizing terminals’transmission power and allocated RAT bandwidth for maximizing system capacity is proposed with the focal consideration of the multi-radio transmission diversity for parallel transmission through multiple links from diferent RATs,and diferent terminal characteristics on RAT supports.Also,we design a centralized and periodic scheduling algorithm including an improved coevolutionary genetic algorithm for efciently solving the optimization problem.Simulation results demonstrate that our propose algorithm can distinctly enhance the system performance and improve the computational efciency.     

Towards estimating computer users＇ mood from interaction behaviour with keyboard and mouse

The purpose of this exploratory research was to study the relationship between the mood of computer users and their use of keyboard and mouse to examine the possibility of creating a generic or individualized mood measure. To examine this, a field study （n = 26） and a controlled study （n = 16） were conducted. In the field study, interaction data and self-reported mood measurements were collected during normal PC use over several days. In the controlled study, participants worked on a programming task while listening to high or low arousing background music. Besides subjective mood measurement, galvanic skin response （GSR） data was also collected. Results found no generic relationship between the interaction data and the mood data. However, the re- suits of the studies found significant average correlations be- tween mood measurement and personalized regression models based on keyboard and mouse interaction data. Together the results suggest that individualized mood prediction is pos- sible from interaction behaviour with keyboard and mouse.     

A 2.4 GHz low power CMOS transceiver for LR-WPAN applications

A 2.4 GHz low power transceiver for low-rate wireless personal area network(LR-WPAN)applications is presented.The optimized low-IF receiver consists of a novel current reuse RF front-end with an inductor-less-load balun LNA and quadrature mixer,and an adaptive analog baseband to reduce power and area.It achieves-94 dBm of sensitivity,-9 dBm of IIP3 and 28 dBc of image rejection.The phase-locked loop based direct phase modulated transmitter is proposed to reduce power and deliver a+3 dBm output power.The phase noise of the low power frequency synthesizer with current reuse stacked LC-VCO achieves-107.8 dBc/Hz at 1 MHz offset.An ultra-low power nonvolatile memory is used to store configuration data and save power.The chip is implemented in a 0.18μm CMOS process,and the area is less than 2.8 mm2.The transceiver consumes only 10.98 mW in the receiving mode and 13.32 mW in the transmitting mode.     

Wideband Rectangular and Circular Microstrip Antennas Loaded with U-Shaped Slot

In this paper, a dual frequency resonance antenna is analysed by introducing U-shaped slot in a rectangular and circular patch, the results in terms of return loss, input impedance and radiation pattern are given. It is observed that various antenna parameters are obtained as a function of frequency for different value of slot length and width; it is easy to adjust the upper and the lower band by varying these different antenna parameters. The coaxial feed is used to excite the patch antenna. All the theoretical results using Matlab are compared with the simulated results obtained from Ansoft HFSS which are in close agreement. Furthermore, comparative study between our results and those available in the literature is done and showed to be in good agreement.     

Super-Resolution image Reconstruction Based on Iteration and Wavelet Transform

In this paper, we provide a super-resolution image reconstruction algorithm based on wavelet transform. Wavelet transform can separate high frequency and low frequency information of image. The more high frequency information can be obtained by using wavelet transform and the technique of image fusion. Meanwhile, reconstructed super-resolution image is produced by the iterative method. In iteration process, noise of image can be suppressed by applying method of wavelet threshold de-noising. The experiment results show that the algorithm can overcome the disadvantage of the classical interpolation method and effectively improve the resolution and PSNR of the image.     

Exploring the Interactions of Storylines from Informative News Events

Today＇s news readers can be easily overwhelmed by the numerous news articles online. To cope with information overload, online news media publishes timelines for continuously developing news topics. However, the timeline summary does not show the relationship of storylines, and is not intuitive for readers to comprehend the development of a complex news topic. In this paper, we study a novel problem of exploring the interactions of storylines in a news topic. An interaction of two storylines is signified by informative news events that play a key role in both storylines. Storyline interactions can indicate key phases of a news topic, and reveal the latent connections among various aspects of the story. We address the coherence between news articles which is not considered in traditional similarity-based methods, and discover salient storyline interactions to form a clear, global picture of the news topic. User preference can be naturally integrated into our method to generate query-specific results. Comprehensive experiments on ten news topics show the effectiveness of our method over alternative approaches.     

Joint relay selection and power allocation for amplify-and-forward two-path relaying networks

This paper presents a joint relay selection and power allocation scheme for amplify-and-forward two-path relaying networks,in which diferent relay nodes forward information symbols alternatively in adjacent time slots.Our approach is based on the maximization of the received signal-to-noise ratio under total power consumption by the transmission of the symbol.We show that in spite of inter-relay interferences,the maximization problem has a closed-form solution.Simulation results explicitly indicate that the performance of proposed approach outmatches the existing methods including equal power allocation and one-path relaying.     

Hybrid Bayesian estimation tree learning with discrete and fuzzy labels

Classical decision tree model is one of the classical machine learning models for its simplicity and effectiveness in applications. However, compared to the DT model, probability estimation trees （PETs） give a better estimation on class probability. In order to get a good probability estimation, we usually need large trees which are not desirable with respect to model transparency. Linguistic decision tree （LDT） is a PET model based on label semantics. Fuzzy labels are used for building the tree and each branch is associated with a probability distribution over classes. If there is no overlap between neighboring fuzzy labels, these fuzzy labels then become discrete labels and a LDT with discrete labels becomes a special case of the PET model. In this paper, two hybrid models by combining the naive Bayes classifier and PETs are proposed in order to build a model with good performance without losing too much transparency. The first model uses naive Bayes estimation given a PET, and the second model uses a set of small-sized PETs as estimators by assuming the independence between these trees. Empirical studies on discrete and fuzzy labels show that the first model outperforms the PET model at shallow depth, and the second model is equivalent to the naive Bayes and PET.