Query: Cyclostationary Feature Detection in Cognitive Radio using Different Modulation Schemes

Relevant passages:
Spectrum Sensing Methods and Dynamic Spectrum Sharing in Cognitive Radio Networks: A Survey <=SEP=> Dynamic spectrum access and cognitive radio (CR) are emerging technologies to utilize the scarce frequency spectrum in an efficient and opportunistic manner. They have been receiving tremendous amount of well-deserved attention in the literature. One of the challenging problems in dynamic spectrum access for cognitive radios is to sense the spectrum in wide band regime of heterogeneous networks to identify the spectrum opportunities. Our main goal in this paper is to present the state-of-the-art research results in signal processing techniques for spectrum sensing in CR network to make automatic and wise decision by unlicensed secondary users to adapt their transmission parameters according to their operating RF environment and respect the primary users. Specifically, we present dynamic spectrum sharing models for spectrum access and signal processing techniques for spectrum sensing by categorizing them into five basic groups. We also present the comparison of different signal processing techniques from different perspectives including networking metrics for the evaluation of the methods e.g. how accurate and how fast can sense spectrum utilization given the various methods and how this will affect networking functionality, and pros and cons of all the methods in the context of networking systems. We also present the status, research challenges and perspectives of signal processing/detection techniques for spectrum sensing in cognitive radio networks.

Spectrum Sensing in Cognitive Radio Networks <=SEP=> The rising number and capacity requirements of wireless systems bring increasing demand for RF spectrum. Cognitive radio (CR) system is an emerging concept to increase the spectrum efficiency. CR system aims to enable opportunistic usage of the RF bands that are not occupied by their primary licensed users in spectrum overlay approach. In this approach, the major challenge in realizing the full potential of CR systems is to identify the spectrum opportunities in the wide band regime reliably and optimally. In the spectrum underlay approach, CR systems enable dynamic spectrum access by co-existing and transmitting simultaneously with licensed primary users without creating harmful interference to them. In this case, the challenge is to transmit with low power so as not to exceed the tolerable interference level to the primary users. Spectrum sensing and estimation is an integral part of the CR system, which is used to identify the spectrum opportunities in spectrum overlay and to identify the interference power to primary users in spectrum underlay approach. In this chapter, the authors present a comprehensive study of signal detection techniques for spectrum sensing proposed for CR systems. Specifically, they outline the state of the art research results, challenges, and future perspectives of spectrum sensing in CR systems, and also present a comparison of different methods. With this chapter, readers can have a comprehensive insight of signal processing methods of spectrum sensing for cognitive radio networks and the ongoing research and development in this area.

Signal Processing in Cognitive Radio <=SEP=> Cognitive radio allows for usage of licensed frequency bands by unlicensed users. However, these unlicensed (cognitive) users need to monitor the spectrum continuously to avoid possible interference with the licensed (primary) users. Apart from this, cognitive radio is expected to learn from its surroundings and perform functions that best serve its users. Such an adaptive technology naturally presents unique signal-processing challenges. In this paper, we describe the fundamental signal-processing aspects involved in developing a fully functional cognitive radio network, including spectrum sensing and spectrum sculpting.

Cognitive radio: brain-empowered wireless communications <=SEP=> Cognitive radio is viewed as a novel approach for improving the utilization of a precious natural resource: the radio electromagnetic spectrum. The cognitive radio, built on a software-defined radio, is defined as an intelligent wireless communication system that is aware of its environment and uses the methodology of understanding-by-building to learn from the environment and adapt to statistical variations in the input stimuli, with two primary objectives in mind: /spl middot/ highly reliable communication whenever and wherever needed; /spl middot/ efficient utilization of the radio spectrum. Following the discussion of interference temperature as a new metric for the quantification and management of interference, the paper addresses three fundamental cognitive tasks. 1) Radio-scene analysis. 2) Channel-state estimation and predictive modeling. 3) Transmit-power control and dynamic spectrum management. This work also discusses the emergent behavior of cognitive radio.

Cooperative Sensing among Cognitive Radios <=SEP=> Cognitive Radios have been advanced as a technology for the opportunistic use of under-utilized spectrum since they are able to sense the spectrum and use frequency bands if no Primary user is detected. However, the required sensitivity is very demanding since any individual radio might face a deep fade. We propose light-weight cooperation in sensing based on hard decisions to mitigate the sensitivity requirements on individual radios. We show that the "link budget" that system designers have to reserve for fading is a significant function of the required probability of detection. Even a few cooperating users (~10-20) facing independent fades are enough to achieve practical threshold levels by drastically reducing individual detection requirements. Hard decisions perform almost as well as soft decisions in achieving these gains. Cooperative gains in a environment where shadowing is correlated, is limited by the cooperation footprint (area in which users cooperate). In essence, a few independent users are more robust than many correlated users. Unfortunately, cooperative gain is very sensitive to adversarial/failing Cognitive Radios. Radios that fail in a known way (always report the presence/absence of a Primary user) can be compensated for by censoring them. On the other hand, radios that fail in unmodeled ways or may be malicious, introduce a bound on achievable sensitivity reductions. As a rule of thumb, if we believe that 1/N users can fail in an unknown way, then the cooperation gains are limited to what is possible with N trusted users.

An intrusion detection system against malicious attacks on the communication network of driverless cars <=SEP=> Vehicular ad hoc networking (VANET) have become a significant technology in the current years because of the emerging generation of self-driving cars such as Google driverless cars. VANET have more vulnerabilities compared to other networks such as wired networks, because these networks are an autonomous collection of mobile vehicles and there is no fixed security infrastructure, no high dynamic topology and the open wireless medium makes them more vulnerable to attacks. It is important to design new approaches and mechanisms to rise the security these networks and protect them from attacks. In this paper, we design an intrusion detection mechanism for the VANETs using Artificial Neural Networks (ANNs) to detect Denial of Service (DoS) attacks. The main role of IDS is to detect the attack using a data generated from the network behavior such as a trace file. The IDSs use the features extracted from the trace file as auditable data. In this paper, we propose anomaly and misuse detection to detect the malicious attack.

Modelling and testing consumer trust dimensions in e-commerce <=SEP=> Prior research has found trust to play a significant role in shaping purchase intentions of a consumer. However there has been limited research where consumer trust dimensions have been empirically defined and tested. In this paper we empirically test a path model such that Internet vendors would have adequate solutions to increase trust. The path model presented in this paper measures the three main dimensions of trust, i.e. competence, integrity, and benevolence. And assesses the influence of overall trust of consumers. The paper also analyses how various sources of trust, i.e. consumer characteristics, firm characteristic, website infrastructure and interactions with consumers, influence dimensions of trust. The model is tested using 365 valid responses. Findings suggest that consumers with high overall trust demonstrate a higher intention to purchase online. © 2017 Elsevier Ltd. All rights reserved.

A Flexible Technique for Accurate Omnidirectional Camera Calibration and Structure from Motion <=SEP=> In this paper, we present a flexible new technique for single viewpoint omnidirectional camera calibration. The proposed method only requires the camera to observe a planar pattern shown at a few different orientations. Either the camera or the planar pattern can be freely moved. No a priori knowledge of the motion is required, nor a specific model of the omnidirectional sensor. The only assumption is that the image projection function can be described by a Taylor series expansion whose coefficients are estimated by solving a two-step least-squares linear minimization problem. To test the proposed technique, we calibrated a panoramic camera having a field of view greater than 200 in the vertical direction, and we obtained very good results. To investigate the accuracy of the calibration, we also used the estimated omni-camera model in a structure from motion experiment. We obtained a 3D metric reconstruction of a scene from two highly distorted omnidirectional images by using image correspondences only. Compared with classical techniques, which rely on a specific parametric model of the omnidirectional camera, the proposed procedure is independent of the sensor, easy to use, and flexible.

Time Series Data Classification Using Recurrent Neural Network with Ensemble Learning <=SEP=> In statistics and signal processing, a time series is a sequence of data points, measured typically at successive times, spaced apart at uniform time intervals. Time series prediction is the use of a model to predict future events based on known past events; to predict future data points before they are measured. Solutions in such cases can be provided by non-parametric regression methods, of which each neural network based predictor is a class. As a learning method of time series data with neural network, Elman type Recurrent Neural Network has been known. In this paper, we propose the multi RNN. In order to verify the effectiveness of our proposed method, we experimented by the simple artificial data and the heart pulse wave data.

Evidence-based strategies in weight-loss mobile apps. <=SEP=> BACKGROUND
Physicians have limited time for weight-loss counseling, and there is a lack of resources to which they can refer patients for assistance with weight loss. Weight-loss mobile applications (apps) have the potential to be a helpful tool, but the extent to which they include the behavioral strategies included in evidence-based interventions is unknown.


PURPOSE
The primary aims of the study were to determine the degree to which commercial weight-loss mobile apps include the behavioral strategies included in evidence-based weight-loss interventions, and to identify features that enhance behavioral strategies via technology.


METHODS
Thirty weight-loss mobile apps, available on iPhone and/or Android platforms, were coded for whether they included any of 20 behavioral strategies derived from an evidence-based weight-loss program (i.e., Diabetes Prevention Program). Data on available apps were collected in January 2012; data were analyzed in June 2012.


RESULTS
The apps included on average 18.83% (SD=13.24; range=0%-65%) of the 20 strategies. Seven of the strategies were not found in any app. The most common technology-enhanced features were barcode scanners (56.7%) and a social network (46.7%).


CONCLUSIONS
Weight-loss mobile apps typically included only a minority of the behavioral strategies found in evidence-based weight-loss interventions. Behavioral strategies that help improve motivation, reduce stress, and assist with problem solving were missing across apps. Inclusion of additional strategies could make apps more helpful to users who have motivational challenges.

Pipsqueak: Lean Lambdas with Large Libraries <=SEP=> Microservices are usually fast to deploy because each microservice is small, and thus each can be installed and started quickly. Unfortunately, lean microservices that depend on large libraries will start slowly and harm elasticity. In this paper, we explore the challenges of lean microservices that rely on large libraries in the context of Python packages and the OpenLambda serverless computing platform. We analyze the package types and compressibility of libraries distributed via the Python Package Index and propose PipBench, a new tool for evaluating package support. We also propose Pipsqueak, a package-aware compute platform based on OpenLambda.

True Inline Cross-Coupled Coaxial Cavity Filters <=SEP=> In this paper, a novel true inline configuration for cross-coupled coaxial cavity filters is presented, which is characterized by a simple and compact structure, improved performance, and good tunability. Instead of using folded structures, dedicated coupling probes, or extra cavities, as required by conventional techniques, cross coupling is realized by changing the orientation of selected resonators. Sequential coupling between adjacent resonators and cross coupling between nonadjacent resonators are effectively controlled by introducing small metal plates at different locations. A six-pole bandpass filter with two transmission zeros was built and tested. The measurement and simulation results agree very well, demonstrating feasibility of the inline filter configuration. It enables compact design with improved resonator Q compared to conventional combline filters. Furthermore, cross coupling can be readily adjusted using tuning elements.

Bridgeless half-bridge AC-DC converter with series-connected two transformers <=SEP=> This paper proposes a bridgeless half-bridge ac-dc converter with series-connected two transformers. The proposed converter integrates the operation of the bridgeless power factor correction (PFC) boost rectifier and the asymmetrical pulse-width modulation half-bridge dc-dc converter. The proposed converter provides an isolated dc output voltage without any full-bridge diode rectifier. Conduction losses are lowered by eliminating the full-bridge diode rectifier. Switching losses are reduced by achieving zero-voltage switching of the power switches. To improve the power efficiency of the bridgeless half-bridge ac-dc converter, this paper presents the study results about the bridgeless half-bridge ac-dc converter with the series-connected two transformers. By using series-connected two transformers, the proposed convert has a simpler power circuit. The proposed converter gives a high efficiency, high power factor, and low cost. The proposed converter achieves a high efficiency of 93.5 % for 250 W (50 V / 5 A) output power at 90 Vrms line voltage.

Phase Current Sensor and Short-Circuit Detection based on Rogowski Coils Integrated on Gate Driver for 1.2 kV SiC MOSFET Half-Bridge Module <=SEP=> Silicon-carbide (SiC) MOSFETs are enabling electrical vehicle motor drives to meet the demands of higher power density, efficiency, and lower system cost. Hence, this paper seeks to explore the benefits that a gate-driver-level intelligence can contribute to SiC-based power inverters. The intelligence is brought by PCB-embedded Rogowski switch-current sensors (RSCS) integrated on the gate driver of a 1.2 kV, 300 A SiC MOSFET half-bridge module. They collect two MOSFET switch currents in a manner of high magnitude, high bandwidth, and solid signal isolation. The switch-current signals are used for short-circuit detection under various fault impedances, as well as for phase-current reconstruction by subtracting one switch current from another. The fundamentals and noise-immunity design of the gate driver containing the RSCS are presented in the paper and can be applied to any half-bridge power module. A three-phase inverter prototype has been built and operated in continuous PWM mode. On this setup, the performance and limitations of the short-circuit detection and phase-current reconstruction are experimentally validated by comparing with commercial current probes and Hall sensors.

Design of a CMOS bandgap Reference Circuit with a Wide temperature Range, High Precision and Low temperature coefficient <=SEP=> This paper presents an approach to the design of a high-precision CMOS voltage reference. The proposed circuit is designed for TSMC 0.35 m standard CMOS process. We design the ̄rstorder temperature compensation bandgap voltage reference circuit. The proposed post-simulated circuit delivers an output voltage of 0.596V and achieves the reported temperature coe±cient (TC) of 3.96 ppm/ C within the temperature range from 60 C to 130 C when the supply voltage is 1.8V. When simulated in a smaller temperature range from 40 C to 80 C, the circuit achieves the lowest reported TC of 2.09 ppm/ C. The reference current is 16.586 A. This circuit provides good performances in a wide range of temperature with very small TC.

A brief survey on deep belief networks and introducing a new object oriented MATLAB toolbox (DeeBNet) <=SEP=> Nowadays, this is very popular to use the deep architectures in machine learning. Deep Belief Networks (DBNs) are deep architectures that use stack of Restricted Boltzmann Machines (RBM) to create a powerful generative model using training data. DBNs have many ability like feature extraction and classification that are used in many applications like image processing, speech processing and etc. This paper introduces a new object oriented MATLAB toolbox with most of abilities needed for the implementation of DBNs. According to the results of the experiments conducted on MNIST (image), ISOLET (speech), and 20 Newsgroups (text) datasets, it was shown that the toolbox can learn automatically a good representation of the input from unlabeled data with better discrimination between different classes. Also on all datasets, the obtained classification errors are comparable to those of state of the art classifiers. In addition, the toolbox supports different sampling methods (e.g. Gibbs, CD, PCD and our new FEPCD method), different sparsity methods (quadratic, rate distortion and our new normal method), different RBM types (generative and discriminative), using GPU, etc. The toolbox is a user-friendly open source software and is freely available on the website http://ceit.aut.ac.ir/~keyvanrad/DeeBNet%20Toolbox.html .

Detection and Visualization of Android Malware Behavior <=SEP=> Malware analysts still need to manually inspect malware samples that are considered suspicious by heuristic rules. They dissect software pieces and look for malware evidence in the code. The increasing number of malicious applications targeting Android devices raises the demand for analyzing them to find where the malcode is triggered when user interacts with them. In this paper a framework to monitor and visualize Android applications’ anomalous function calls is described. Our approach includes platformindependent application instrumentation, introducing hooks in order to trace restricted API functions used at runtime of the application. These function calls are collected at a central server where the application behavior filtering and a visualization take place. This can help Android malware analysts in visually inspecting what the application under study does, easily identifying such malicious functions.

Discriminatively trained recurrent neural networks for single-channel speech separation <=SEP=> This paper describes an in-depth investigation of training criteria, network architectures and feature representations for regression-based single-channel speech separation with deep neural networks (DNNs). We use a generic discriminative training criterion corresponding to optimal source reconstruction from time-frequency masks, and introduce its application to speech separation in a reduced feature space (Mel domain). A comparative evaluation of time-frequency mask estimation by DNNs, recurrent DNNs and non-negative matrix factorization on the 2nd CHiME Speech Separation and Recognition Challenge shows consistent improvements by discriminative training, whereas long short-term memory recurrent DNNs obtain the overall best results. Furthermore, our results confirm the importance of fine-tuning the feature representation for DNN training.

Accurate Whole Human Genome Sequencing using Reversible Terminator Chemistry <=SEP=> DNA sequence information underpins genetic research, enabling discoveries of important biological or medical benefit. Sequencing projects have traditionally used long (400–800 base pair) reads, but the existence of reference sequences for the human and many other genomes makes it possible to develop new, fast approaches to re-sequencing, whereby shorter reads are compared to a reference to identify intraspecies genetic variation. Here we report an approach that generates several billion bases of accurate nucleotide sequence per experiment at low cost. Single molecules of DNA are attached to a flat surface, amplified in situ and used as templates for synthetic sequencing with fluorescent reversible terminator deoxyribonucleotides. Images of the surface are analysed to generate high-quality sequence. We demonstrate application of this approach to human genome sequencing on flow-sorted X chromosomes and then scale the approach to determine the genome sequence of a male Yoruba from Ibadan, Nigeria. We build an accurate consensus sequence from >30× average depth of paired 35-base reads. We characterize four million single-nucleotide polymorphisms and four hundred thousand structural variants, many of which were previously unknown. Our approach is effective for accurate, rapid and economical whole-genome re-sequencing and many other biomedical applications.

Model Reference Adaptive Control-Based Speed Control of Brushless DC Motors With Low-Resolution Hall-Effect Sensors <=SEP=> A control system with a novel speed estimation approach based on model reference adaptive control (MRAC) is presented for low cost brushless dc motor drives with low-resolution hall sensors. The back EMF is usually used to estimate speed. But the estimation result is not accurate enough at low speeds because of the divided voltage of stator resistors and too small back EMF. Moreover, the stator resistor is always varying with the motor's temperature. A speed estimation algorithm based on MRAC was proposed to correct the speed error estimated by using back EMF. The proposed algorithm's most innovative feature is its adaptability to the entire speed range including low speeds and high speeds and temperature and different motors do not affect the accuracy of the estimation result. The effectiveness of the algorithm was verified through simulations and experiments.

Interpretation and trust: designing model-driven visualizations for text analysis <=SEP=> Statistical topic models can help analysts discover patterns in large text corpora by identifying recurring sets of words and enabling exploration by topical concepts. However, understanding and validating the output of these models can itself be a challenging analysis task. In this paper, we offer two design considerations - interpretation and trust - for designing visualizations based on data-driven models. Interpretation refers to the facility with which an analyst makes inferences about the data through the lens of a model abstraction. Trust refers to the actual and perceived accuracy of an analyst's inferences. These considerations derive from our experiences developing the Stanford Dissertation Browser, a tool for exploring over 9,000 Ph.D. theses by topical similarity, and a subsequent review of existing literature. We contribute a novel similarity measure for text collections based on a notion of "word-borrowing" that arose from an iterative design process. Based on our experiences and a literature review, we distill a set of design recommendations and describe how they promote interpretable and trustworthy visual analysis tools.

Pleasure and orgasm in women with Female Genital Mutilation/Cutting (FGM/C). <=SEP=> INTRODUCTION
Female genital mutilation/cutting (FGM/C) violates human rights. FGM/C women's sexuality is not well known and often it is neglected by gynecologists, urologists, and sexologists. In mutilated/cut women, some fundamental structures for orgasm have not been excised.


AIM
The aim of this report is to describe and analyze the results of four investigations on sexual functioning in different groups of cut women.


MAIN OUTCOME MEASURE



INSTRUMENTS
semistructured interviews and the Female Sexual Function Index (FSFI).


METHODS



SAMPLE
137 adult women affected by different types of FGM/C; 58 young FGM/C ladies living in the West; 57 infibulated women; 15 infibulated women after the operation of defibulation.


RESULTS
The group of 137 women, affected by different types of FGM/C, reported orgasm in almost 86%, always 69.23%; 58 mutilated young women reported orgasm in 91.43%, always 8.57%; after defibulation 14 out of 15 infibulated women reported orgasm; the group of 57 infibulated women investigated with the FSFI questionnaire showed significant differences between group of study and an equivalent group of control in desire, arousal, orgasm, and satisfaction with mean scores higher in the group of mutilated women. No significant differences were observed between the two groups in lubrication and pain.


CONCLUSION
Embryology, anatomy, and physiology of female erectile organs are neglected in specialist textbooks. In infibulated women, some erectile structures fundamental for orgasm have not been excised. Cultural influence can change the perception of pleasure, as well as social acceptance. Every woman has the right to have sexual health and to feel sexual pleasure for full psychophysical well-being of the person. In accordance with other research, the present study reports that FGM/C women can also have the possibility of reaching an orgasm. Therefore, FGM/C women with sexual dysfunctions can and must be cured; they have the right to have an appropriate sexual therapy.

Midpalatal implants vs headgear for orthodontic anchorage--a randomized clinical trial: cephalometric results. <=SEP=> INTRODUCTION
The purpose of this study was to compare the clinical effectiveness of the midpalatal implant as a method of reinforcing anchorage during orthodontic treatment with that of conventional extraoral anchorage. This was a prospective, randomized, clinical trial at Chesterfield and North Derbyshire Royal Hospital NHS Trust and the Charles Clifford Dental Hospital, Sheffield, in the United Kingdom.


METHODS
Fifty-one orthodontic patients between the ages of 12 and 39, with Class II Division 1 malocclusion and absolute anchorage requirements, were randomly allocated to receive either a midpalatal implant or headgear to reinforce orthodontic anchorage. The main outcome was to compare the mesial movement of the molars and the incisors of the 2 treatment groups between the start and the end of anchorage reinforcement as measured from cephalometric radiographs.


RESULTS
The reproducibility of the measuring technique was acceptable. There were significant differences between T1 and T2 in the implant group for the positions of the maxillary central incisor (P <.001), the maxillary molar (P = .009), and the mandibular molar (P <.001). There were significant differences between T1 and T2 in the headgear group for the positions of the mandibular central incisor (P <.045), the maxillary molar (P <.001), and the mandibular molar (P <.001). All skeletal and dental points moved mesially more in the headgear group during treatment than in the implant group. These ranged from an average of 0.5 mm more mesially for the mandibular permanent molar to 1.5 mm more mesially for the maxillary molar and the mandibular base. No treatment changes between the groups were statistically significant.


CONCLUSIONS
Midpalatal implants are an acceptable technique for reinforcing anchorage in orthodontic patients.

Electronic Game Play and School Performance of Adolescents in Southern Thailand <=SEP=> Increasing exposure of children and adolescents to electronic media is a worldwide phenomenon, including in Thailand. To date, few studies examine electronic game play in Thai adolescents. Our research describes the prevalence of electronic game play and examines associations between the time spent engaged in electronic game play and school performance of adolescents in Hat-Yai municipality. A cross-sectional study was conducted among 1,492 adolescents from four secondary schools and one commercial college from January through March 2007, using questionnaires for collecting information about demographic data, school grades, and electronic game play behaviors. The prevalence of electronic game play was 75% in boys and 59% in girls. Twenty-two percent of boys and 8.7% of girls played electronic games every day with more than 2 hours per session. The two most common places of game play were at game shops (71%), followed by at their own home (70%). Using linear regression analysis, the "low user or less than 2 hours per session" game players and females were less likely to have school grades below 3.00 with adjusted odds ratios of 0.44 (95% CI 0.25-0.80, p = 0.004) and 0.49 (95% CI 0.30-0.76, p = 0.005) respectively. This study finds that excessive playing of electronic games is associated with school grades below 3.00.

Implant Removal after Percutaneous Short Segment Fixation for Thoracolumbar Burst Fracture : Does It Preserve Motion? <=SEP=> OBJECTIVE
The purpose of this study was to evaluate the efficacy of implant removal of percutaneous short segment fixation after vertebral fracture consolidation in terms of motion preservation.


METHODS
Between May 2007 and January 2011, 44 patients underwent percutaneous short segment screw fixation due to a thoracolumbar burst fracture. Sixteen of these patients, who underwent implant removal 12 months after screw fixation, were enrolled in this study. Motor power was intact in all patients, despite significant vertebral height loss and canal compromise. The patients were divided into two groups by degree of osteoporosis : Group A (n=8), the non-osteoporotic group, and Group B (n=8), the osteoporotic group. Imaging and clinical findings including vertebral height loss, kyphotic angle, range of motion (ROM), and complications were analyzed.


RESULTS
Significant pain relief was achieved in both groups at final follow-up versus preoperative values. In terms of vertebral height loss, both groups showed significant improvement at 12 months after screw fixation and restored vertebral height was maintained to final follow-up in spite of some correction loss. ROM (measured using Cobb's method) in flexion and extension in Group A was 10.5° (19.5/9.0°) at last follow-up, and in Group B was 10.2° (18.8/8.6°) at last follow-up. Both groups showed marked improvement in ROM as compared with the screw fixation state, which was considered motionless.


CONCLUSION
Removal of percutaneous implants after vertebral fracture consolidation can be an effective treatment to preserve motion regardless of osteoporosis for thoracolumbar burst fractures.

The effects of monetary and social rewards on task performance in children and adolescents: liking is not enough. <=SEP=> The current study compared the effects of reward anticipation on task performance in children and adolescents (8-16 years old) using monetary and various social rewards. Eighty-five typically developing children undertook the Monetary Incentive Delay (MID) task. Of these 44 also undertook the Social Incentive Delay (SID-basic) task where social reward was operationalized as a smiling face and spoken compliments. Forty-one children participated in the SID-plus where points were added to a pictogram with written compliments. In a preparatory validation study participants were asked howmuch they liked the SID-basic rewards.Results showed that there was an effect of reward size on accuracy and RT in both the MID task and SID-plus, but not SID-basic. Subjective value of the SID-basic rewards was rated higher with hypothesized increasing reward intensity. In conclusion, although the social rewards in SID-basic were liked by children andadolescents in the validation study, they had no effect on the behaviour. Only when points were added (SID-plus), anticipated social reward affected task performance. Thus our results highlight (i) the difference between likeability andreinforcing quality and (ii) the need for a quantifiable element to rewards for themto be reinforcing for children.

Subgoal-labeled instructional material improves performance and transfer in learning to develop mobile applications <=SEP=> Mental models are mental representations of how an action changes a problem state. Creating a mental model early in the learning process is a strong predictor of success in computer science classes. One major problem in computer science education, however, is that novices have difficulty creating mental models perhaps because of the cognitive overload caused by traditional teaching methods. The present study employed subgoal-labeled instructional materials to promote the creation of mental models when teaching novices to program in Android App Inventor. Utilizing this and other well-established educational tools, such as scaffolding, to reduce cognitive load in computer science education improved the performance of participants on novel tasks when learning to develop mobile applications.

Predicting Knowledge Sharing Intention Based on Theory of Reasoned Action Framework : An Empirical Study on Higher Education Institution <=SEP=> Based on theory of reasoned action (TRA) theoretical framework, this study try to predict individual knowledge sharing intention on higher education institution context. This study also integrate some potential factors (channel richness, psychological forces and organizational climate factors) which supporting individuals' knowledge-sharing intentions Through a field survey of 242 lecturer on public and privateuniversity at Purwokerto and Yogyakarta city, this study confirm our hypothesis that attitudes toward knowledge sharing and subjective norms with regard to knowledge sharing as well as organizational climate affect individuals' intentions to share knowledge. This study showed thatPerceived Reciprocal Benefits, Perceived Enjoyment in Helping Others, Perceived Reputation Enhancement and channel richness affect individuals'attitudes toward knowledge sharing while organizational climate affect subjective norms. Overall, the results of the study advance prior research in the area of knowledge sharing by shedding light on the determinants of knowledge sharing behaviors of knowledge workers. These insights could be used by university in developing work environments that are conducive for knowledge worker to share their knowledge. Keyword: Knowledge sharing intention, TRA, Knowledge worker, attitude, university.

Gradient Domain Guided Image Filtering <=SEP=> Guided image filter (GIF) is a well-known local filter for its edge-preserving property and low computational complexity. Unfortunately, the GIF may suffer from halo artifacts, because the local linear model used in the GIF cannot represent the image well near some edges. In this paper, a gradient domain GIF is proposed by incorporating an explicit first-order edge-aware constraint. The edge-aware constraint makes edges be preserved better. To illustrate the efficiency of the proposed filter, the proposed gradient domain GIF is applied for single-image detail enhancement, tone mapping of high dynamic range images and image saliency detection. Both theoretical analysis and experimental results prove that the proposed gradient domain GIF can produce better resultant images, especially near the edges, where halos appear in the original GIF.

Dead Code Detection Method Based on Program Slicing <=SEP=> Malicious code writers often insert dead code into the source code to prevent reverse engineer analysis. There are few methods for detecting dead code used in conjunction with opaque predicates. In this paper, a method based on program slicing for dead code detection is presented, which combines static slicing analysis and dynamic slicing analysis. Further, we design a dead code detection framework in the LLVM compiler infrastructure. The proposed method is used to detect the dead code inserted in some benchmark programs. The experimental results show that the proposed method has high detection rate.