Trace representation and linear complexity of binary sequences derived from Fermat quotients

We describe the trace representations of two families of binary sequences derived from the Fermat quotients modulo an odd prime p （one is the binary threshold sequences and the other is the Legendre Fermat quotient sequences） by determining the defining pairs of all binary characteristic sequences of cosets, which coincide with the sets of pre-images modulo p2 of each fixed value of Fermat quotients. From the defining pairs, we can obtain an earlier result of linear complexity for the binary threshold sequences and a new result of linear complexity for the Legendre Fermat quotient sequences under the assumption of 2p-1≠ 1 mod p2.     

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.     

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.     

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.     

Power System Stability Improvement by Using STATCOM with POC ＆ Excitation Controller

This paper presents the model of a static synchronous condenser （STATCOM） which is controlled externally by a newly designed power oscillation controller （POC） for the improvements of power system stability and damping effect of an on line power system. The proposed POC consists of two controllers （power oscillation damping ＆ proportional integral derivative PID ＆ POD）. PID parameters have been optimized by Zigler Necles close loop tuning method. Machine excitation has been controller by using excitation controller as required. Both single phase and three phase faults has been considered in the research. In this paper, a power system network is considered which is simulated in the phasor simulation method ＆ the network is simulated in three steps： without STATCOM, with STATCOM but no externally controlled, STATCOM with POC. Simulation result shows that without STATCOM, the system parameters become unstable during faults. When STATCOM is imposed in the network, then system parameters become stable. Again, when STATCOM is controlled externally by POC controllers, then system voltage ＆ power becomes stable in faster way then without controller It has been observed that the STATCOM ratings are only 20 MVA with controllers and 200 MVA without controllers. Therefore, STATCOM with POC controllers are more effective to enhance the voltage stability and increases power transmission capacity of a power system. So STATCOM with POC ＆ excitation controllers, the system performance is greatly enhanced.     

Quality-of-service driven power allocations for wireless full-duplex bidirectional links

Wireless full-duplex transmission has the potential to double the system throughput compared with the conventional half-duplex transmission.To achieve the maximum potential system throughput,optimal power allocation taking into account quality-of-service(QoS)is urgently needed.In this paper,we propose the QoS driven power allocation schemes for wireless full-duplex bidirectional links.By integrating information theory with the statistical QoS principle,we build two models–local transmit power related self-interference(LTPRS)model and local transmit power unrelated self-interference(LTPUS)model to analyze the wireless full-duplex transmission.For both of these two models,we derive the optimal power allocation schemes,which aim at maximizing the system throughput subject to the given delay-QoS constraint,over wireless bidirectional links with full-duplex transmission.The analyses and numerical results verify that our proposed power allocation scheme can efficiently support diverse QoS requirements over wireless bidirectional links.Our proposed fullduplex QoS driven power allocation schemes can obtain larger effective capacity compared with the conventional half-duplex transmission over wireless bidirectional links.     

HC-Store： putting MapReduce＇s foot in two camps

MapReduce is a popular framework for large- scale data analysis. As data access is critical for MapReduce＇s performance, some recent work has applied different storage models, such as column-store or PAX-store, to MapReduce platforms. However, the data access patterns of different queries are very different. No storage model is able to achieve the optimal performance alone. In this paper, we study how MapReduce can benefit from the presence of two different column-store models - pure column-store and PAX-store. We propose a hybrid storage system called hybrid columnstore （HC-store）. Based on the characteristics of the incoming MapReduce tasks, our storage model can determine whether to access the underlying pure column-store or PAX-store. We studied the properties of the different storage models and create a cost model to decide the data access strategy at runtime. We have implemented HC-store on top of Hadoop. Our experimental results show that HC-store is able to outperform PAX-store and column-store, especially when confronted with diverse workload.     

Generating test data for both path coverage and fault detection using genetic algorithms

The aim of software testing is to find faults in a program under test, so generating test data that can expose the faults of a program is very important. To date, current stud- ies on generating test data for path coverage do not perform well in detecting low probability faults on the covered path. The automatic generation of test data for both path coverage and fault detection using genetic algorithms is the focus of this study. To this end, the problem is first formulated as a bi-objective optimization problem with one constraint whose objectives are the number of faults detected in the traversed path and the risk level of these faults, and whose constraint is that the traversed path must be the target path. An evolution- ary algorithm is employed to solve the formulated model, and several types of fault detection methods are given. Finally, the proposed method is applied to several real-world programs, and compared with a random method and evolutionary opti- mization method in the following three aspects： the number of generations and the time consumption needed to generate desired test data, and the success rate of detecting faults. The experimental results confirm that the proposed method can effectively generate test data that not only traverse the target path but also detect faults lying in it.     

A comparative study of two formal semantics of the SIGNAL language

SIGNAL is a part of the synchronous languages family, which are broadly used in the design of safety-critical real-time systems such as avionics, space systems, and nu- clear power plants. There exist several semantics for SIG- NAL, such as denotational semantics based on traces （called trace semantics）, denotational semantics based on tags （called tagged model semantics）, operational semantics presented by structural style through an inductive definition of the set of possible transitions, operational semantics defined by syn- chronous transition systems （STS）, etc. However, there is lit- tle research about the equivalence between these semantics. In this work, we would like to prove the equivalence be- tween the trace semantics and the tagged model semantics, to get a determined and precise semantics of the SIGNAL language. These two semantics have several different defini- tions respectively, we select appropriate ones and mechanize them in the Coq platform, the Coq expressions of the abstract syntax of SIGNAL and the two semantics domains, i.e., the trace model and the tagged model, are also given. The dis- tance between these two semantics discourages a direct proof of equivalence. Instead, we transform them to an intermediate model, which mixes the features of both the trace semantics and the tagged model semantics. Finally, we get a determined and precise semantics of SIGNAL.     

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.     

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.