Self-calibration and visual SLAM with a multi-camera system on a micro aerial vehicle

The use of a multi-camera system enables a robot to obtain a surround view, and thus, maximize its perceptual awareness of its environment. An accurate calibration is a necessary prerequisite if vision-based simultaneous localization and mapping (vSLAM) is expected to provide reliable pose estimates for a micro aerial vehicle (MAV) with a multi-camera system. On our MAV, we set up each camera pair in a stereo configuration. We propose a novel vSLAM-based self-calibration method for a multi-sensor system that includes multiple calibrated stereo cameras and an inertial measurement unit (IMU). Our selfcalibration estimates the transform with metric scale between each camera and the IMU. Once the MAV is calibrated, the MAV is able to estimate its global pose via a multi-camera vSLAM implementation based on the generalized camera model. We propose a novel minimal and linear 3-point algorithm that uses inertial information to recover the relative motion of the MAV with metric scale. Our constant-time vSLAM implementation with loop closures runs on-board the MAV in real-time. To the best of our knowledge, no published work has demonstrated realtime on-board vSLAM with loop closures. We show experimental results in both indoor and outdoor environments. The code for both the self-calibration and vSLAM is available as a set of ROS packages at https://github.com/hengli/vmav-ros-pkg.


CAB: Connectionist Analogy Builder

The ability to make informative comparisons is central to human cognition. Comparison involves aligning two representations and placing their elements into correspondence. Detecting correspondences is a necessary component of analogical inference, recognition, categorization, schema formation, and similarity judgment. Connectionist Analogy Builder (CAB) determines correspondences through a simple iterative computation that matches elements in one representation with elements playing compatible roles in the other representation while simultaneously enforcing structural constraints. CAB shows promise as a process model of comparison as its performance can be related to human performance (e.g., solution trajectory, error patterns, time-on-task). Furthermore, CAB’s bounded working memory allows it to account for the inherent capacity limitations of human processing. CAB’s strengths are its parsimony, transparency of operations, and ability to generate performance predictions. In this paper, CAB is evaluated against benchmark phenomena from the analogy literature. © 2003 Cognitive Science Society, Inc. All rights reserved.


Co-fusion: Real-time segmentation, tracking and fusion of multiple objects

In this paper we introduce Co-Fusion, a dense SLAM system that takes a live stream of RGB-D images as input and segments the scene into different objects (using either motion or semantic cues) while simultaneously tracking and reconstructing their 3D shape in real time. We use a multiple model fitting approach where each object can move independently from the background and still be effectively tracked and its shape fused over time using only the information from pixels associated with that object label. Previous attempts to deal with dynamic scenes have typically considered moving regions as outliers, and consequently do not model their shape or track their motion over time. In contrast, we enable the robot to maintain 3D models for each of the segmented objects and to improve them over time through fusion. As a result, our system can enable a robot to maintain a scene description at the object level which has the potential to allow interactions with its working environment; even in the case of dynamic scenes.


Reverse Curriculum Generation for Reinforcement Learning

Many relevant tasks require an agent to reach a certain state, or to manipulate objects into a desired configuration. For example, we might want a robot to align and assemble a gear onto an axle or insert and turn a key in a lock. These tasks present considerable difficulties for reinforcement learning approaches, since the natural reward function for such goal-oriented tasks is sparse and prohibitive amounts of exploration are required to reach the goal and receive a learning signal. Past approaches tackle these problems by manually designing a task-specific reward shaping function to help guide the learning. Instead, we propose a method to learn these tasks without requiring any prior task knowledge other than obtaining a single state in which the task is achieved. The robot is trained in “reverse”, gradually learning to reach the goal from a set of starting positions increasingly far from the goal. Our method automatically generates a curriculum of starting positions that adapts to the agent’s performance, leading to efficient training on such tasks. We demonstrate our approach on difficult simulated fine-grained manipulation problems, not solvable by state-of-the-art reinforcement learning methods.


Error Mechanisms of the Oscillometric Fixed-Ratio Blood Pressure Measurement Method

The oscillometric fixed-ratio method is widely employed for non-invasive measurement of systolic and diastolic pressures (SP and DP) but is heuristic and prone to error. We investigated the accuracy of this method using an established mathematical model of oscillometry. First, to determine which factors materially affect the errors of the method, we applied a thorough parametric sensitivity analysis to the model. Then, to assess the impact of the significant parameters, we examined the errors over a physiologically relevant range of those parameters. The main findings of this model-based error analysis of the fixed-ratio method are that: (1) SP and DP errors drastically increase as the brachial artery stiffens over the zero trans-mural pressure regime; (2) SP and DP become overestimated and underestimated, respectively, as pulse pressure (PP) declines; (3) the impact of PP on SP and DP errors is more obvious as the brachial artery stiffens over the zero trans-mural pressure regime; and (4) SP and DP errors can be as large as 58 mmHg. Our final and main contribution is a comprehensive explanation of the mechanisms for these errors. This study may have important implications when using the fixed-ratio method, particularly in subjects with arterial disease.


2 Conditional Saliency 2 . 1 Lossy Coding

By the guidance of attention, human visual system is able to locate objects of interest in complex scene. In this paper, we propose a novel visual saliency detection method the conditional saliency for both image and video. Inspired by biological vision, the definition of visual saliency follows a strictly local approach. Given the surrounding area, the saliency is defined as the minimum uncertainty of the local region, namely the minimum conditional entropy, when the perceptional distortion is considered. To simplify the problem, we approximate the conditional entropy by the lossy coding length of multivariate Gaussian data. The final saliency map is accumulated by pixels and further segmented to detect the proto-objects. Experiments are conducted on both image and video. And the results indicate a robust and reliable feature invariance saliency.


New reverse-conducting IGBT (1200V) with revolutionary compact package

Fuji Electric developed a 1200V class RC-IGBT based on our latest thin wafer process. The performance of this RC-IGBT shows the same relationship between conduction loss and switching loss as our 6th generation conventional IGBT and FWD. In addition its trade-off can be optimized for hard switching by lifetime killer. Calculations of the hard switching inverter loss and chip junction temperature (Tj) show that the optimized RC-IGBT can handle 35% larger current density per chip area. In order to utilize the high performance characteristics of the RC-IGBT, we assembled them in our newly developed compact package. This module can handle 58% higher current than conventional 100A modules at a 51% smaller footprint.


Saliency Unified: A Deep Architecture for simultaneous Eye Fixation Prediction and Salient Object Segmentation

Human eye fixations often correlate with locations of salient objects in the scene. However, only a handful of approaches have attempted to simultaneously address the related aspects of eye fixations and object saliency. In this work, we propose a deep convolutional neural network (CNN) capable of predicting eye fixations and segmenting salient objects in a unified framework. We design the initial network layers, shared between both the tasks, such that they capture the object level semantics and the global contextual aspects of saliency, while the deeper layers of the network address task specific aspects. In addition, our network captures saliency at multiple scales via inception-style convolution blocks. Our network shows a significant improvement over the current state-of-the-art for both eye fixation prediction and salient object segmentation across a number of challenging datasets.


Center-Fed Patch Antenna Array Excited by an Inset Dielectric Waveguide for 60-GHz Applications

A patch antenna array proximity-coupled fed by the inset dielectric waveguide (IDW) is proposed in 60-GHz frequency band. The antenna array consists of four series-fed subarrays each having eight pairs of circular patch elements, four substrate integrated IDWs, and a compact four-way power divider transmitting the signal from the grounded coplanar waveguide (GCPW) to the IDWs. The IDW is realized by metallized via-hole arrays in a printed circuit board (PCB). The parallel center-fed configuration is applied to suppress the frequency-dependent beam squinting of the series-fed subarray. Measured results show that the proposed antenna exhibits a gain variation between 15.8 and 19.2 dBi, fixed main beam in the direction of θ = 0°, and the reflection coefficient |S11| ≤ -10 dB over the frequency band from 58 to 64 GHz.


A systematic review and meta-analysis of dressings used for wound healing: the efficiency of honey compared to silver on burns.

BACKGROUND
Honey has the antibacterial effect of silver without the toxic effect of silver on the skin. Even so, silver is the dominant antibacterial dressing used in wound healing.

OBJECTIVES
To evaluate the healing effects of honey dressings compared to silver dressings for acute or chronic wounds.

DESIGN
A systematic review with meta-analysis.

METHOD
The search, conducted in seven databases, resulted in six randomised controlled trial studies from South Asia focusing on antibacterial properties and healing times of honey and silver.

RESULT
Honey was more efficacious for wound healing than silver, as measured in the number of days needed for wounds to heal (pooled risk difference -20, 95% CI -0.29 to -0.11, p < .001). Honey turned out to have more antibacterial qualities than silver.

CONCLUSION
All the included studies based on burns showed the unequivocal result that honey had an even more positive effect than silver on wound healing.


A novel, compact, low-cost, impulse ground-penetrating radar for nondestructive evaluation of pavements

This paper reports on the development of a novel, compact, low-cost, impulse ground-penetrating radar (GPR) and demonstrate its use for nondestructive evaluation of pavement structures. This GPR consists of an ultrashort-monocycle-pulse transmitter (330 ps), an ultrawide-band (UWB) sampling receiver (0-6 GHz), and two UWB antennas (0.2-20 GHz)-completely designed using microwave-integrated circuits with seamless electrical connections between them. An approximate analysis is used to determine the signal loss and power budget. Performance of this GPR has been verified through the measurements of relative permittivity and thicknesses of various samples, and a good agreement between the experimental and theoretical results has been achieved.


Tracing Data Lineage Using Schema Transformation Pathways

With the increasing amount and diversity of information available on the Internet, there has been a huge growth in information systems that need to integrate data from distributed, heterogeneous data sources. Tracing the lineage of the integrated data is one of the current problems being addressed in data warehouse research. In this paper, we propose a new approach for tracing data linage based on schema transformation pathways. We show how the individual transformation steps in a transformation pathway can be used to trace the derivation of the integrated data in a step-wise fashion, thus simplifying the lineage tracing process.


Wireless sensor networks: a survey on recent developments and potential synergies

Wireless sensor network (WSN) has emerged as one of the most promising technologies for the future. This has been enabled by advances in technology and availability of small, inexpensive, and smart sensors resulting in cost effective and easily deployable WSNs. However, researchers must address a variety of challenges to facilitate the widespread deployment of WSN technology in real-world domains. In this survey, we give an overview of wireless sensor networks and their application domains including the challenges that should be addressed in order to push the technology further. Then we review the recent technologies and testbeds for WSNs. Finally, we identify several open research issues that need to be investigated in future. Our survey is different from existing surveys in that we focus on recent developments in wireless sensor network technologies. We review the leading research projects, standards and technologies, and platforms. Moreover, we highlight a recent phenomenon in WSN research that is to explore synergy between sensor networks and other technologies and explain how this can help sensor networks achieve their full potential. This paper intends to help new researchers entering the domain of WSNs by providing a comprehensive survey on recent developments.


Control of systems integrating logic, dynamics, and constraints

This paper proposes a framework for modeling and controlling systems described by interdependent physical laws, logic rules, and operating constraints, denoted as Mixed Logical Dynamical (MLD) systems. These are described by linear dynamic equations subject to linear inequalities involving real and integer variables. MLD systems include constrained linear systems, finite state machines, some classes of discrete event systems, and nonlinear systems which can be approximated by piecewise linear functions. A predictive control scheme is proposed which is able to stabilize MLD systems on desired reference trajectories while fulfilling operating constraints, and possibly take into account previous qualitative knowledge in the form of heuristic rules. Due to the presence of integer variables, the resulting on-line optimization procedures are solved through Mixed Integer Quadratic Programming (MIQP), for which efficient solvers have been recently developed. Some examples and a simulation case study on a complex gas supply system are reported.


Fabric-based actuator modules for building soft pneumatic structures with high payload-to-weight ratio

This paper introduces a new concept of building soft pneumatic structures by assembling modular units of fabric-based rotary actuators (FRAs) and beams. Upon pressurization, the inner folds of FRA would expand, which causes the FRA module to unfold, generating angular displacement. Hence, FRAs would enable mobility function of the structure and its range of motion. The modular nature of the actuator units enables customized configuration of pneumatic structures, which can be modified and scaled by selecting the appropriate modules. FRAs are also designed to be bladder-less, that is, they are made without an additional layer of inner bladder. Thus, a simple fabrication process can be used to prepare the actuators. In this paper, we studied how the performance of the FRA modules changes with their dimensions and demonstrated how a soft gripper can be constructed using these modules. The kinematic response of the actuator segments of the gripper was analyzed and a pressure control algorithm was developed to regulate the pneumatic pressure of the actuator. The modular based soft robotic gripper alone weighs about 140g. Yet, based on the grip tests, it is able to lift heavier objects (up to 2.4kg), achieving a high payload-to-weight ratio of about 1714%, which is higher than the value reported by previously developed soft pneumatic grippers using elastomeric materials. Lastly, we also demonstrated that the gripper is capable of performing two essential grasping modes, which are power grasping and fingertip grasping, for objects of various shapes.


A critical evaluation of the complex PTSD literature: implications for DSM-5.

Complex posttraumatic stress disorder (CPTSD) has been proposed as a diagnosis for capturing the diverse clusters of symptoms observed in survivors of prolonged trauma that are outside the current definition of PTSD. Introducing a new diagnosis requires a high standard of evidence, including a clear definition of the disorder, reliable and valid assessment measures, support for convergent and discriminant validity, and incremental validity with respect to implications for treatment planning and outcome. In this article, the extant literature on CPTSD is reviewed within the framework of construct validity to evaluate the proposed diagnosis on these criteria. Although the efforts in support of CPTSD have brought much needed attention to limitations in the trauma literature, we conclude that available evidence does not support a new diagnostic category at this time. Some directions for future research are suggested.


Agent based urban growth modeling framework on Apache Spark

The simulation of urban growth is an important part of urban planning and development. Due to large data and computational challenges, urban growth simulation models demand efficient data analytic frameworks for scaling them to large geographic regions. Agent-based models are widely used to observe and analyze the urban growth simulation at various scales. The incorporation of the agent-based model makes the scaling task even harder due to communication and coordination among agents. Many existing agent-based model frameworks were implemented using traditional shared and distributed memory programming models. On the other hand, Apache Spark is becoming a popular platform for distributed big data in-memory analytics. This paper presents an implementation of agent-based sub-model in Apache Spark framework. With the in-memory computation, Spark implementation outperforms the traditional distributed memory implementation using MPI. This paper provides (i) an overview of our framework capable of running urban growth simulations at a fine resolution of 30 meter grid cells, (ii) a scalable approach using Apache Spark to implement an agent-based model for simulating human decisions, and (iii) the comparative analysis of performance of Apache Spark and MPI based implementations.


KinectFaceDB: A Kinect Database for Face Recognition

The recent success of emerging RGB-D cameras such as the Kinect sensor depicts a broad prospect of 3-D data-based computer applications. However, due to the lack of a standard testing database, it is difficult to evaluate how the face recognition technology can benefit from this up-to-date imaging sensor. In order to establish the connection between the Kinect and face recognition research, in this paper, we present the first publicly available face database (i.e., KinectFaceDB1) based on the Kinect sensor. The database consists of different data modalities (well-aligned and processed 2-D, 2.5-D, 3-D, and video-based face data) and multiple facial variations. We conducted benchmark evaluations on the proposed database using standard face recognition methods, and demonstrated the gain in performance when integrating the depth data with the RGB data via score-level fusion. We also compared the 3-D images of Kinect (from the KinectFaceDB) with the traditional high-quality 3-D scans (from the FRGC database) in the context of face biometrics, which reveals the imperative needs of the proposed database for face recognition research.


Electromagnetic energy harvesting from train induced railway track vibrations

Anelectromagnetic energy harvester is designed to harness the vibrational power from railroad track deflections due to passing trains. Whereas typical existing vibration energy harvester technologies are built for low power applications of milliwatts range, the proposed harvester will be designed for higher power applications for major track-side equipment such as warning signals, switches, and health monitoring sensors, which typically require a power supply of 10 Watts or more. To achieve this goal, we implement a new patent pending motion conversion mechanism which converts irregular pulse-like bidirectional linear vibration into regulated unidirectional rotational motion. Features of the motion mechanism include bidirectional to unidirectional conversion and flywheel speed regulation, with advantages of improved reliability, efficiency, and quality of output power. It also allows production of DC power directly from bidirectional vibration without electronic diodes. Preliminary harvester prototype testing results illustrate the features and benefits of the proposed motion mechanism, showing reduction of continual system loading, regulation of generator speed, and capability for continuous DC power generation.


Sensorized pneumatic muscle for force and stiffness control

This paper presents the design and experimental validation of a soft pneumatic artificial muscle with position and force sensing capabilities. Conductive liquid-based soft sensors are embedded in a fiber-reinforced contractile actuator to measure two modes of deformation — axial strain and diametral expansion — which, together, are used to determine the stroke length and contractile force generated under internal pressure. We validate the proposed device by using data from the embedded sensors to estimate the force output of the actuator at fixed lengths and the stiffness and force output of a one degree-of-freedom hinge joint driven by an antagonist pair of the sensorized pneumatic muscles.


Spelling Error Trends and Patterns in Sindhi

Statistical error Correction technique is the most accurate and widely used approach today, but for a language like Sindhi which is a low resourced language the trained corpora’s are not available, so the statistical techniques are not possible at all. Instead a useful alternative would be to exploit various spelling error trends in Sindhi by using a Rule based approach. For designing such technique an essential prerequisite would be to study the various error patterns in a language. This paper presents various studies of spelling error trends and their types in Sindhi Language. The research shows that the error trends common to all languages are also encountered in Sindhi but their do exist some error patters that are catered specifically to a Sindhi language.


Induction motor fault diagnosis using labview

Now-a-days, manufacturing companies of electrical machines are in need of easy and early fault detection techniques as they are attire by the customers. Though many researchers have developed many fault diagnosing techniques, user friendly software with automated long measurement process is needed so that the non-technical persons also can operate to identify the faults occurred. In our work the healthy induction motor is modeled in stationary reference frame. Faulty conditions are included in the model to analyze the effect of fault in the motor using the user friendly software, LabVIEW.


Applications, challenges, and prospective in emerging body area networking technologies

Advances in wireless technology and supporting infrastructure provide unprecedented opportunity for ubiquitous real-time healthcare and fitness monitoring without constraining the activities of the user. Wirelessly connected miniaturized sensors and actuators placed in, on, and around the body form a body area network for continuous, automated, and unobtrusive monitoring of physiological signs to support medical, lifestyle and entertainment applications. BAN technology is in the early stage of development, and several research challenges have to be overcome for it to be widely accepted. In this article we study the core set of application, functional, and technical requirements of the BAN. We also discuss fundamental research challenges such as scalability (in terms of data rate, power consumption, and duty cycle), antenna design, interference mitigation, coexistence, QoS, reliability, security, privacy, and energy efficiency. Several candidate technologies poised to address the emerging BAN market are evaluated, and their merits and demerits are highlighted. A brief overview of standardization activities relevant to BANs is also presented.


Addressing Security and Privacy Risks in Mobile Applications

Applications for mobile platforms are being developed at a tremendous rate, but often without proper security implementation. Insecure mobile applications can cause serious information security and data privacy issues and can have severe repercussions on users and organizations alike.


Security Analysis of Wearable Fitness Devices ( Fitbit )

This report describes an analysis of the Fitbit Flex ecosystem. Our objectives are to describe (1) the data Fitbit collects from its users, (2) the data Fitbit provides to its users, and (3) methods of recovering data not made available to device owners. Our analysis covers four distinct attack vectors. First, we analyze the security and privacy properties of the Fitbit device itself. Next, we observe the Bluetooth traffic sent between the Fitbit device and a smartphone or personal computer during synchronization. Third, we analyze the security of the Fitbit Android app. Finally, we study the security properties of the network traffic between the Fitbit smartphone or computer application and the Fitbit web service. We provide evidence that Fitbit unnecessarily obtains information about nearby Flex devices under certain circumstances. We further show that Fitbit does not provide device owners with all of the data collected. In fact, we find evidence of per-minute activity data that is sent to the Fitbit web service but not provided to the owner. We also discovered that MAC addresses on Fitbit devices are never changed, enabling usercorrelation attacks. BTLE credentials are also exposed on the network during device pairing over TLS, which might be intercepted by MITM attacks. Finally, we demonstrate that actual user activity data is authenticated and not provided in plaintext on an end-to-end basis from the device to the Fitbit web service.


Versatile low power media access for wireless sensor networks

We propose B-MAC, a carrier sense media access protocol for wireless sensor networks that provides a flexible interface to obtain ultra low power operation, effective collision avoidance, and high channel utilization. To achieve low power operation, B-MAC employs an adaptive preamble sampling scheme to reduce duty cycle and minimize idle listening. B-MAC supports on-the-fly reconfiguration and provides bidirectional interfaces for system services to optimize performance, whether it be for throughput, latency, or power conservation. We build an analytical model of a class of sensor network applications. We use the model to show the effect of changing B-MAC's parameters and predict the behavior of sensor network applications. By comparing B-MAC to conventional 802.11-inspired protocols, specifically SMAC, we develop an experimental characterization of B-MAC over a wide range of network conditions. We show that B-MAC's flexibility results in better packet delivery rates, throughput, latency, and energy consumption than S-MAC. By deploying a real world monitoring application with multihop networking, we validate our protocol design and model. Our results illustrate the need for flexible protocols to effectively realize energy efficient sensor network applications.


Security and Privacy Issues in Wireless Sensor Networks for Healthcare Applications

The use of wireless sensor networks (WSN) in healthcare applications is growing in a fast pace. Numerous applications such as heart rate monitor, blood pressure monitor and endoscopic capsule are already in use. To address the growing use of sensor technology in this area, a new field known as wireless body area networks (WBAN or simply BAN) has emerged. As most devices and their applications are wireless in nature, security and privacy concerns are among major areas of concern. Due to direct involvement of humans also increases the sensitivity. Whether the data gathered from patients or individuals are obtained with the consent of the person or without it due to the need by the system, misuse or privacy concerns may restrict people from taking advantage of the full benefits from the system. People may not see these devices safe for daily use. There may also possibility of serious social unrest due to the fear that such devices may be used for monitoring and tracking individuals by government agencies or other private organizations. In this paper we discuss these issues and analyze in detail the problems and their possible measures.


New Beam Tracking Technique for Millimeter Wave-band Communications

In this paper, we propose an efficient beam tracking method for mobility scenario in mmWave-band communications. When the position of the mobile changes in mobility scenario , the base-station needs to perform beam training frequently to t rack the time-varying channel, thereby spending significant res ources for training beams. In order to reduce the training overhead, we propose a new beam training approach called “beam tracking” which exploits the continuous nature of time varying angle of departure (AoD) for beam selection. We show that transmissi on of only two training beams is enough to track the time-varying AoD at good accuracy. We derive the optimal selection of beam pai r which minimizes Cramer-Rao Lower Bound (CRLB) for AoD estimation averaged over statistical distribution of the AoD. Our numerical results demonstrate that the proposed beam track ing scheme produces better AoD estimation than the conventiona l beam training protocol with less training overhead. I. I NTRODUCTION The next generation wireless communication systems aim to achieve Giga bit/s throughput to support high speed multimedia data service [1], [2]. Since there exist ample amount of unutilized frequency spectrum in millimeter Wave (mmWave) band (30 GHz-300 GHz), wireless communication over mmWave band is considered as a promising solution to achieve significant leap in spectral efficiency [3]. However , one major limitation of mmWave communications is significant free space path loss, which causes large attenuation of sign al power at the receiver. Furthermore, the overall path loss ge t worse when the signal goes through obstacles, rain, foliage , and any blockage to mobile devices. Recently, active resear ch on mmWave communication has been conducted in order to overcome these limitations [1]–[5]. In mmWave band, many antenna elements can be integrated in a small form factor and hence, we can employ high directional beamforming using a large number of antennas to compensate high path loss. In order to perform high directional beamforming, it is necessary to estimate channels for all transmitter and rece iver antenna pair. While this step requires high computational c omplexity due to large number of antennas, channel estimation can be performed efficiently by using the angular domain representation of channels [6]. In angular domain, only a fe w angular bins contain the most of the received energy. Hence, if we identify the dominant angular bins (which correspond to the angle of arrival (AoA) and the angle of departure (AoD)), we can obtain the channel estimate without incurrin g computational complexity. Basically, both AoD and AoA can be estimated using so called “beam training” procedure. The base-station sends t he training beams at the designated direction and the receiver estimates the AoD/AoA based on the received signals. Widely used beam training method (called “beam cycling method”) is to allow the base-station to transmit N training beams one by one at the equally spaced directions. However, to ensure good estimate of AoD/AoA, N should be large, leading to significant training overhead. This problem becomes even more serious for the mobility scenario in mmWave communications. Since the location of mobiles keeps changing, th e base-station should transmit training beams more frequent ly to update AoD/AoA estimates, causing significant drop in data throughput [7]. Recently, several adaptive beam train ing schemes have been proposed to improve the conventional beam training method [10]–[14]. In this paper, we introduce a novel beam training method for mobility scenario in mmWave communications. Our idea is based on the observation that for mobility scenario, the AoD of the particular user does not change drastically so that continuous nature of the AoD change can be accounted to improve the efficacy of the beam training. Since this approach exploits temporal dynamics of AoD, we call such beam training scheme “beam tracking”. While the convenional method makes no assumption on the state of AoD, we use statistical distribution of the AoD given the previousl y state of AoD. Using the probabilistic model on AoD change, we derive effective beam tracking strategy which employs transmission of two training beams from the base-station. Optimal placement of two training beams in angular domain is sought by minimizing (the lower bound of) variance of the estimation error for AoD. As a result, we choose the best beam p ir from the beam codebook for the given prior knowledge on AoD. Our simulation results show that the proposed beam tracking method offers the channel estimation performance comparable to the conventional beam training methods with significantly reduced training overhead. The rest of this paper is organized as follows; In section II, we introduce the system and channel models for mmWave communications and in section III, we describe the proposed b am tracking method and the simulation results are provide d in section IV. Finally, the paper is concluded in section V. II. SYSTEM MODEL In this section, we describe the system model for mmWave communications. First, we describe the angular domain repr esentation of the mmWave channel and then we introduce the procedure for beam training and channel estimation. A. Channel Model Consider single user mmWave MIMO systems with the base-station withNb antennas and the mobile with Nm antennas. The MIMO channel model with L paths at timet is described by [8]


Curvature-controlled curve editing using piecewise clothoid curves

Two-dimensional curves are conventionally designed using splines or Bézier curves. Although formally they are C or higher, the variation of the curvature of (piecewise) polynomial curves is difficult to control; in some cases it is practically impossible to obtain the desired curvature. As an alternative we propose piecewise clothoid curves (PCCs). We show that from the design point of view they have many advantages: control points are interpolated, curvature extrema lie in the control points, and adding control points does not change the curve. We present a fast localized clothoid interpolation algorithm that can also be used for curvature smoothing, for curve fitting, for curvature blending, and even for directly editing the curvature. We give a physical interpretation of variational curvature minimization, from which we derive our scheme. Finally, we demonstrate the achievable quality with a range of examples. & 2013 Elsevier Ltd. All rights reserved.


Linking received packet to the transmitter through physical-fingerprinting of controller area network

The Controller Area Network (CAN) bus serves as a legacy protocol for in-vehicle data communication. Simplicity, robustness, and suitability for real-time systems are the salient features of the CAN bus protocol. However, it lacks the basic security features such as massage authentication, which makes it vulnerable to the spoofing attacks. In a CAN network, linking CAN packet to the sender node is a challenging task. This paper aims to address this issue by developing a framework to link each CAN packet to its source. Physical signal attributes of the received packet consisting of channel and node (or device) which contains specific unique artifacts are considered to achieve this goal. Material and design imperfections in the physical channel and digital device, which are the main contributing factors behind the device-channel specific unique artifacts, are leveraged to link the received electrical signal to the transmitter. Generally, the inimitable patterns of signals from each ECUs exist over the course of time that can manifest the stability of the proposed method. Uniqueness of the channel-device specific attributes are also investigated for time-and frequency-domain. Feature vector is made up of both time and frequency domain physical attributes and then employed to train a neural network-based classifier. Performance of the proposed fingerprinting method is evaluated by using a dataset collected from 16 different channels and four identical ECUs transmitting same message. Experimental results indicate that the proposed method achieves correct detection rates of 95.2% and 98.3% for channel and ECU classification, respectively.


Multi-objective model predictive control for grid-tied 15-level packed U cells inverter

This paper presents a multi-objective Model Predictive Control (MPC) for grid-tied 4-cell 15-level Packed U Cells (PUC) inverter. This challenging topology is characterized by high power quality with reduced number of components compared to conventional multilevel inverters. Compared to traditional PI controller, MPC is attracting more interest due to its good dynamic response and high accuracy of reference tracking, through the minimization of a flexible user-defined cost function. For the presented PUC topology, the grid current should be jointly controlled with the capacitors' voltages for ensuring proper operation of the inverter, which leads to an additional requirement of pre-charge circuits for the capacitors in case of using PI current controller (or using additional PI controllers). The proposed MPC achieves grid current injection, low current THD, unity PF, while balancing the capacitors' voltages.


Test anxiety and direction of attention.

The literature reviewed suggests an attentional interpretation, of the adverse effects which test anxiety has on task performance. During task performance the highly test-anxious person divides his attention between self-relevant and task-relevant variables, in contrast to the low-test-anxious person who focuses his attention more fully on the task. This interpretation was supported by literature from diverse areas suggesting that (a) highly anxious persons are generally more self-preoccupied than are people low in anxiety; (b) the selffocusing tendencies of highly test-anxious persons are activated in testing situations; (c) those situational conditions in which the greatest performance differences occur are ones which elicit the self-focusing tendencies of highly test-anxious subjects, and the task-focusing tendencies of low-anxious subjects; (d) research examining the relationship between anxiety and task variables suggests that anxiety reduces the range of task cues utilized in performance; (e) "worry," an attentionally demanding cognitive activity, is more debilitating of task performance than is autonomic arousal. Treatment and research implications of this attentional interpretation of test anxiety are briefly discussed.


GPS positioning in a multipath environment

We address the problem of GPS signal delay estimation in a multipath environment with a low-complexity constraint. After recalling the usual early–late estimator and its bias in a multipath propagation context, we study the maximum-likelihood estimator (MLE) based on a signal model including the parametric contribution of reflected components. It results in an efficient algorithm using the existing architecture, which is also very simple and cheap to implement. Simulations show that the results of the proposed algorithm, in a multipath environment, are similar to these of the early–late in a single-path environment. The performance are further characterized, for both MLEs (based on the single-path and multipath propagation) in terms of bias and standard deviation. The expressions of the corresponding Cramér—Rao (CR) bounds are derived in both cases to show the good performance of the estimators when unbiased.


Counter-forensics in machine learning based forgery detection

With the powerful image editing tools available today, it is very easy to create forgeries without leaving visible traces. Boundaries between host image and forgery can be concealed, illumination changed, and so on, in a naive form of counter-forensics. For this reason, most modern techniques for forgery detection rely on the statistical distribution of micro-patterns, enhanced through high-level filtering, and summarized in some image descriptor used for the final classification. In this work we propose a strategy to modify the forged image at the level of micro-patterns to fool a state-of-the-art forgery detector. Then, we investigate on the effectiveness of the proposed strategy as a function of the level of knowledge on the forgery detection algorithm. Experiments show this approach to be quite effective especially if a good prior knowledge on the detector is available.


A Multimodal, Full-Surround Vehicular Testbed for Naturalistic Studies and Benchmarking: Design, Calibration and Deployment

Recent progress in autonomous and semiautonomous driving has been made possible in part through an assortment of sensors that provide the intelligent agent with an enhanced perception of its surroundings. It has been clear for quite some while now that for intelligent vehicles to function effectively in all situations and conditions, a fusion of different sensor technologies is essential. Consequently, the availability of synchronized multi-sensory data streams are necessary to promote the development of fusion based algorithms for low, mid and high level semantic tasks. In this paper, we provide a comprehensive description of our heavily sensorized, panoramic testbed capable of providing high quality data from a slew of synchronized and calibrated sensors such as cameras, LIDARs, radars, and the IMU/GPS. The vehicle has recorded over 100 hours of real world data for a very diverse set of weather, traffic and daylight conditions. All captured data is accurately calibrated and synchronized using timestamps, and stored safely in high performance servers mounted inside the vehicle itself. Details on the testbed instrumentation, sensor layout, sensor outputs, calibration and synchronization are described in this paper.


Prototypical Networks for Few-shot Learning

We propose Prototypical Networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each new class. Prototypical Networks learn a metric space in which classification can be performed by computing distances to prototype representations of each class. Compared to recent approaches for few-shot learning, they reflect a simpler inductive bias that is beneficial in this limited-data regime, and achieve excellent results. We provide an analysis showing that some simple design decisions can yield substantial improvements over recent approaches involving complicated architectural choices and meta-learning. We further extend Prototypical Networks to zero-shot learning and achieve state-ofthe-art results on the CU-Birds dataset.


Prozessmodellierung in eGovernment-Projekten mit der eEPK

Im Rahmen der Informationssystemgestaltung unterscheiden sich die Projektbeteiligten insbesondere hinsichtlich ihrer Anforderungen an die Inhalte und Repräsentationsformen der für die Anwendungssystemund Organisationsgestaltung verwendeten Modellen. Die multiperspektivische Modellierung stellt einen viel versprechenden Ansatz dar, um diesem Umstand durch die Bereitstellung perspektivenspezifischer Sichten auf Informationsmodelle gerecht zu werden. Der Beitrag stellt eine Erweiterung ereignisgesteuerter Prozessketten durch Konfigurationsmechanismen vor, welche die Erstellung und Nutzung von Perspektiven auf Prozessmodelle unterstützt. 1 Perspektiven auf Prozessmodelle Die wissenschaftliche Diskussion über die Qualität von Prozessmodellen wurde in den letzten Jahren wesentlich durch die Entwicklung von allgemeinen Modellierungsempfehlungen, welche auf die syntaktische Korrektheit der Modelle und der Einhaltung der Grundsätze ordnungsmäßiger Modellierung bei der Erstellung von Prozessmodellen abzielen [BRS95; Ro96; BRU00], geprägt. Ein dominierendes Kriterium, das die Qualität von Prozessmodellen determiniert, ist dabei die Entsprechung der Modellkonzeption und -repräsentation mit den Anforderungen der jeweiligen Modellnutzergruppe [RS99, S. 25f.; Be02, S. 28ff.; RSD03, S. 49]. So werden z. B. innerhalb eines prozessbasierten Reorganisationsprojektes diese Modellnutzergruppen durch die verschiedenen organisatorischen Rollen der Projektteilnehmer bestimmt. Ein Sachbearbeiter benötigt hierbei lediglich die für ihn relevanten Prozessmodelle und deren Schnittstellen. Aus der Sicht des Managers der zu reorganisierenden Unternehmung sollten die Modelle in aggregierter Form vorliegen und durch erweiterte visuelle Merkmale, wie z. B. ansprechender Farboder Symboldarstellung, gekennzeichnet sein. Einem Anwendungsentwickler sollten sehr detaillierte Modelle vorgelegt werden, damit sich die aus der Reorganisation ergebenden Konsequenzen im Anwendungssystem berücksichtigt werden können. Die verschiedenen Anforderungen der Nutzergruppen resultieren aus den Differenzen ihrer subjektabhängigen Vorstellungswelten und den daraus resultierenden Problemlösungsansätzen [Be01a; Be02, S. 30ff.]. Ein Ansatz zur expliziten Vermittlung zwischen diesen Vorstellungswelten findet sich in der Bereitstellung von anforderungsgerechten Perspektiven wieder [Fr94, S. 36f.]. Perspektiven werden dadurch determiniert, welcher Modellierungszweck verfolgt wird, welche organisatorische Rolle die Nutzer einnehmen und welche persönlichen Präferenzen bzgl. der Modellkonzeption und -repräsentation bestehen [Be02, S. 38ff., RSD03, S. 49]. Die Entwicklung und Bereitstellung perspektivenspezifischer Modelle wird unter dem Begriff der multiperspektivischen Informationsmodellierung diskutiert [Fi92; RS99, S. 25f.; RSD03, S. 52]. Soll den Anforderungen mehrerer Nutzergruppen entsprochen werden, d. h. sollen mehrere Perspektiven berücksichtigt werden, ergeben sich für den Modellersteller zwei Alternativen. Modelle könnten einerseits für unterschiedliche Perspektiven redundant vorgehalten werden. Nachteile dieser Vorgehensweise sind die üblichen, mit Redundanzen verbundenen Zusatzaufwände wie erhöhter Pflegeaufwand und Gefahr von Inkonsistenzen. Andererseits kann ein Gesamtmodell derartig erstellt werden, dass es die für sämtliche Perspektiven relevanten Elemente redundanzfrei enthält. Den einzelnen Vertretern der Perspektiven werden die für sie relevanten Modelle bereitgestellt, indem ihnen eine View auf das Gesamtmodell zur Verfügung gestellt wird, die das Gesamtmodell um die nicht relevanten Inhalte reduziert. Die Maßnahmen, welche vom Modellierer zur Erstellung dieser Views durchgeführt werden müssen, können hierbei durchaus zu einer komplexen Aufgabe werden. Im Folgenden wird anhand ereignisgesteuerter Prozessketten (EPKs) [KNS92] die Umsetzung der zweiten Variante diskutiert. Dabei wird in Abschnitt 2 zunächst eine metamodellbasierte Formalisierung der EPK vorgenommen, um eine konzeptionelle Basis für die multiperspektivische Erweiterung der Modellierungstechnik durch Konfigurationsmechanismen zu schaffen. Darauf aufbauend werden in Abschnitt 3 die notwendigen Erweiterungen der Modellierungstechnik vorgenommen. Abschnitt 4 behandelt die Problematik und Notwendigkeit von Konsistenzsicherungsmechanismen, die bei der Verwendung der Konfigurationsmechanismen auftreten können. Nach einem Praxisbeispiel in Abschnitt 5 schließt der Beitrag mit einem Ausblick in Abschnitt 6. 2 Metamodellbasierte Formalisierung der EPK Das hier vorgestellte Verfahren zur Erstellung multiperspektivischer ereignisgesteuerter Prozessketten basiert auf der Manipulation von Modellstrukturen durch die Anwendung von Konfigurationsmechanismen. Da hierbei die strukturellen Veränderungen auf Ebene der Sprachspezifikation sowie deren Auswirkungen auf die Struktur der Modelle im Zentrum der Betrachtung stehen, empfiehlt es sich, die Sprache der EPK formalisiert darzustellen. Dies kann wiederum durch Informationsmodelle – sogenannte sprachorientierte Metamodelle – erfolgen [St96, S. 24ff.]. Als Notationsform eignen sich insbesondere Struktursprachen wie das Entity-Relationship-Modell [Ch76]. Ein erweiterter Dialekt dieser Modellierungssprache wird im vorliegenden Beitrag zur Metamodellierung verwendet. 2 Das zentrale Element der EPK Sprache ist das Prozesselement (vgl. Abbildung 1). Alle am Prozessgraphen beteiligten Knoten werden als Spezialisierung dieses Elements mo1 Der Begriff EPK wird im Folgenden synonym zur erweiterten ereignisgesteuerten Prozesskette (eEPK) verwendet, die neben der Ablauflogik die Annotation von benötigten Ressourcen erlaubt. 2 Es handelt sich hierbei um einen Dialekt, der die perspektivenabhängige Modifikation von Sprachen erlaubt (vgl. ausführlich [Be02, S. 77ff.]). delliert. Hierzu gehören die Elemente Prozessfunktion, Prozessereignis und Operator (vgl. Spezialisierung des Entitytyps Prozesselement). Eine Prozessfunktion beschreibt eine Aktivität innerhalb eines Prozesses. Diese Aktivitäten werden beim Eintreten eines oder einer Kombination von Ereignissen ausgeführt und führen nach ihrer Beendigung selbst wieder zu dem Eintreten von Ereignissen. Somit kann ein Ereignis als Zustandswechsel interpretiert werden. Die notwendige Kombination der Ereignisse für die Ausführung einer Aktivität sowie die Definition der Menge der eintretenden Ereignisse nach erfolgreicher Ausführung werden über die Operatoren definiert. Hierdurch lassen sich parallele Prozessstränge und deren Wiederzusammenführung im Prozessmodell abbilden. Prozessfunktion Operator Prozessereignis Prozesselement D,T (0,n) (0,n) Ressource (0,n) (0,n) D,P Organisationseinheit Fachbegriff Anwendungssystemtyp Vorgänger/ Nachfolger PERessourcenZuO (0,n) ProzessRessourcenBeziehungstyp Typ PRBeziehungstyphierarchie


Decoding of EEG Signals Using Deep Long Short-Term Memory Network in Face Recognition Task

The paper proposes a novel approach to classify the human memory response involved in the face recognition task by the utilization of event related potentials. Electroencephalographic signals are acquired when a subject engages himself/herself in familiar or unfamiliar face recognition tasks. The signals are analyzed through source Iocalization using eLORETA and artifact removal by ICA from a set of channels corresponding to those selected sources, with an ultimate aim to classify the EEG responses of familiar and unfamiliar faces. The EEG responses of the two different classes (familiar and unfamiliar face recognition)are distinguished by analyzing the Event Related Potential signals that reveal the existence of large N250 and P600 signals during familiar face recognition.The paper introduces a novel LSTM classifier network which is designed to classify the ERP signals to fulfill the prime objective of this work. The first layer of the novel LSTM network evaluates the spatial and local temporal correlations between the obtained samples of local EEG time-windows. The second layer of this network models the temporal correlations between the time-windows. An attention mechanism has been introduced in each layer of the proposed model to compute the contribution of each EEG time-window in face recognition task. Performance analysis reveals that the proposed LSTM classifier with attention mechanism outperforms the efficiency of the conventional LSTM and other classifiers with a significantly large margin. Moreover, source Iocalization using eLORETA shows the involvement of inferior temporal and frontal lobes during familiar face recognition and pre-frontal lobe during unfamiliar face recognition. Thus, the present research outcome can be used in criminal investigation, where meticulous differentiation of familiar and unfamiliar face detection by criminals can be performed from their acquired brain responses.


Algorithmic Prediction of Health-Care Costs

The rising cost of health care is one of the world’s most important problems. Accordingly, predicting such costs with accuracy is a significant first step in addressing this problem. Since the 1980s, there has been research on the predictive modeling of medical costs based on (health insurance) claims data using heuristic rules and regression methods. These methods, however, have not been appropriately validated using populations that the methods have not seen. We utilize modern data-mining methods, specifically classification trees and clustering algorithms, along with claims data from over 800,000 insured individuals over three years, to provide rigorously validated predictions of health-care costs in the third year, based on medical and cost data from the first two years. We quantify the accuracy of our predictions using unseen (out-of-sample) data from over 200,000 members. The key findings are: (a) our data-mining methods provide accurate predictions of medical costs and represent a powerful tool for prediction of health-care costs, (b) the pattern of past cost data is a strong predictor of future costs, and (c) medical information only contributes to accurate prediction of medical costs of high-cost members.


Extensive Benchmark and Survey of Modeling Methods for Scene Background Initialization

Scene background initialization is the process by which a method tries to recover the background image of a video without foreground objects in it. Having a clear understanding about which approach is more robust and/or more suited to a given scenario is of great interest to many end users or practitioners. The aim of this paper is to provide an extensive survey of scene background initialization methods as well as a novel benchmarking framework. The proposed framework involves several evaluation metrics and state-of-the-art methods, as well as the largest video data set ever made for this purpose. The data set consists of several camera-captured videos that: 1) span categories focused on various background initialization challenges; 2) are obtained with different cameras of different lengths, frame rates, spatial resolutions, lighting conditions, and levels of compression; and 3) contain indoor and outdoor scenes. The wide variety of our data set prevents our analysis from favoring a certain family of background initialization methods over others. Our evaluation framework allows us to quantitatively identify solved and unsolved issues related to scene background initialization. We also identify scenarios for which state-of-the-art methods systematically fail.


PUMA: Planning Under Uncertainty with Macro-Actions

Planning in large, partially observable domains is challenging, especially when a long-horizon lookahead is necessary to obtain a good policy. Traditional POMDP planners that plan a different potential action for each future observation can be prohibitively expensive when planning many steps ahead. An efficient solution for planning far into the future in fully observable domains is to use temporallyextended sequences of actions, or “macro-actions.” In this paper, we present a POMDP algorithm for planning under uncertainty with macro-actions (PUMA) that automatically constructs and evaluates open-loop macro-actions within forward-search planning, where the planner branches on observations only at the end of each macro-action. Additionally, we show how to incrementally refine the plan over time, resulting in an anytime algorithm that provably converges to an ǫ-optimal policy. In experiments on several large POMDP problems which require a long horizon lookahead, PUMA outperforms existing state-of-the art solvers. Most partially observable Markov decision process (POMDP) planners select actions conditioned on the prior observation at each timestep: we refer to such planners as fully-conditional. When good performance relies on considering different possible observations far into the future, both online and offline fully-conditional planners typically struggle. An extreme alternative is unconditional (or “openloop”) planning where a sequence of actions is fixed and does not depend on the observations that will be received during execution. While open-loop planning can be extremely fast and perform surprisingly well in certain domains, acting well in most real-world domains requires plans where at least some action choices are conditional on the obtained observations. This paper focuses on the significant subset of POMDP domains, including scientific exploration, target surveillance, and chronic care management, where it is possible to act well by planning using conditional sequences of openloop, fixed-length action chains, or “macro-actions.” We call this approach semi-conditional planning, in that actions are chosen based on the received observations only at the end of each macro-action. Copyright c © 2010, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. For a discussion of using open-loop planning for multi-robot tag for open-loop planning see Yu et al. (2005). We demonstrate that for certain domains, planning with macro-actions can offer performance close to fullyconditional planning at a dramatically reduced computational cost. In comparison to prior macro-action work, where a domain expert often hand-coded a good set of macro-actions for each problem, we present a technique for automatically constructing finite-length open-loop macroactions. Our approach uses sub-goal states based on immediate reward and potential information gain. We then describe how to incrementally refine an initial macro-action plan by incorporating successively shorter macro-actions. We combine these two contributions in a forward-search algorithm for planning under uncertainty with macro-actions (PUMA). PUMA is an anytime algorithm which guarantees eventual convergence to an ǫ-optimal policy, even in domains that may require close to fully-conditional plans. PUMA outperforms a state-of-the-art POMDP planner both in terms of plan quality and computational cost on two large simulated POMDP problems. However, semiconditional planning does not yield an advantage in all domains, and we provide preliminary experimental analysis towards determining a priori when planning in a semiconditional manner will be helpful. However, even in domains that are not well suited to semi-conditional planning, our anytime improvement allows PUMA to still eventually compute a good policy, suggesting that PUMA may be viable as a generic planner for large POMDP problems.


Revising Perceptual Linear Prediction (PLP)

Mel Frequency Cepstral Coefficients (MFCC) and Perceptual Linear Prediction (PLP) are the most popular acoustic features used in speech recognition. Often it depends on the task, which of the two methods leads to a better performance. In this work we develop acoustic features that combine the advantages of MFCC and PLP. Based on the observation that the techniques have many similarities, we revise the processing steps of PLP. In particular, the filter-bank, the equal-loudness pre-emphasis and the input for the linear prediction are improved. It is shown for a broadcast news transcription task and a corpus of children’s speech that the new variant of PLP performs better than both MFCC and conventional PLP for a wide range of clean and noisy acoustic conditions.


Multi-speaker modeling and speaker adaptation for DNN-based TTS synthesis

In DNN-based TTS synthesis, DNNs hidden layers can be viewed as deep transformation for linguistic features and the output layers as representation of acoustic space to regress the transformed linguistic features to acoustic parameters. The deep-layered architectures of DNN can not only represent highly-complex transformation compactly, but also take advantage of huge amount of training data. In this paper, we propose an approach to model multiple speakers TTS with a general DNN, where the same hidden layers are shared among different speakers while the output layers are composed of speaker-dependent nodes explaining the target of each speaker. The experimental results show that our approach can significantly improve the quality of synthesized speech objectively and subjectively, comparing with speech synthesized from the individual, speaker-dependent DNN-based TTS. We further transfer the hidden layers for a new speaker with limited training data and the resultant synthesized speech of the new speaker can also achieve a good quality in term of naturalness and speaker similarity.


Classroom Response Systems: A Review of the Literature.

As the frequency with which Classroom Response Systems (CRSs) are used is increasing, it becomes more and more important to define the affordances and limitations of these tools. Currently existing literature is largely either anecdotal or focuses on comparing CRS and non-CRS environments that are unequal in other aspects as well. In addition, the literature primarily describes situations in which the CRS is used to provide an individual as opposed to a group response. This article points to the need for a concerted research effort, one that rigorously explores conditions of use across diverse settings and pedagogies.


An energy-efficient dual sampling SAR ADC with reduced capacitive DAC

This paper presents an energy-efficient SAR ADC which adopts reduced MSB cycling step with dual sampling of the analog signal. By sampling and holding the analog signal asymmetrically at both input sides of comparator, the MSB cycling step can be hidden by hold mode. Benefits from this technique, not only the total capacitance of DAC is reduced by half, but also the average switching energy is reduced by 68% compared with conventional SAR ADC. Moreover, switching energy distribution is more uniform over entire output code compared with previous works.


The impact of pretend play on children's development: a review of the evidence.

Pretend play has been claimed to be crucial to children's healthy development. Here we examine evidence for this position versus 2 alternatives: Pretend play is 1 of many routes to positive developments (equifinality), and pretend play is an epiphenomenon of other factors that drive development. Evidence from several domains is considered. For language, narrative, and emotion regulation, the research conducted to date is consistent with all 3 positions but insufficient to draw conclusions. For executive function and social skills, existing research leans against the crucial causal position but is insufficient to differentiate the other 2. For reasoning, equifinality is definitely supported, ruling out a crucially causal position but still leaving open the possibility that pretend play is epiphenomenal. For problem solving, there is no compelling evidence that pretend play helps or is even a correlate. For creativity, intelligence, conservation, and theory of mind, inconsistent correlational results from sound studies and nonreplication with masked experimenters are problematic for a causal position, and some good studies favor an epiphenomenon position in which child, adult, and environment characteristics that go along with play are the true causal agents. We end by considering epiphenomenalism more deeply and discussing implications for preschool settings and further research in this domain. Our take-away message is that existing evidence does not support strong causal claims about the unique importance of pretend play for development and that much more and better research is essential for clarifying its possible role.


Adversarial Examples: Attacks on Machine Learning-based Malware Visualization Detection Methods

As the threat of malicious software (malware) becomes urgently serious, automatic malware detection techniques have received increasing attention recently, where the machine learning (ML)-based visualization detection plays a significant role. However, this leads to a fundamental problem whether such detection methods can be robust enough against various potential attacks. Even though ML algorithms show superiority to conventional ones in malware detection in terms of high efficiency and accuracy, this paper demonstrates that such ML-based malware detection methods are vulnerable to adversarial examples (AE) attacks. We propose the first AE-based attack framework, named Adversarial Texture Malware Perturbation Attacks (ATMPA), based on the gradient descent or L-norm optimization method. By introducing tiny perturbations on the transformed dataset, ML-based malware detection methods completely fail. The experimental results on the MS BIG malware dataset show that a small interference can reduce the detection rate of convolutional neural network (CNN), support vector machine (SVM) and random forest(RF)-based malware detectors down to 0 and the attack transferability can achieve up to 88.7% and 74.1% on average in different ML-based detection methods.


Block Chain based Searchable Symmetric Encryption

The mechanism for traditional Searchable Symmetric Encryption is pay-then-use. That is to say, if a user wants to search some documents that contain special keywords, he needs to pay to the server firstly, then he can enjoy search service. Under this situation, these kinds of things will happen: After the user paying the service fees, the server may either disappear because of the poor management or returning nothing. As a result, the money that the user paid cannot be brought back quickly. Another case is that the server may return incorrect document sets to the user in order to save his own cost. Once such events happen, it needs the arbitration institution to mediate which will cost a long time. Besides, to settle the disputes the user has to pay to the arbitration institution. Ideally, we deeply hope that when the user realizes the server has a tendency to cheat in the task of searching, he can immediately and automatically withdraw his money to safeguard his right. However, the existing SSE protocols cannot satisfy this demand. To solve this dilemma, we find a compromised method by introducing the block chain into SSE. Our scheme achieves three goals stated below. Firstly, when the server does not return any thing to user after he gets the search token, the user can get some compensation from the server, because the server can infer some important information from the Index and this token. Besides, the user also doesn’t pay the service charge. Secondly, if the documents that the server returns are false, the server cannot receive service fees, meanwhile, he will be punished. Lastly, when the user receives some bitcoin from server at the beginning, he may terminate the protocol. Under this situation, the server is a victim. In order to prevent such thing from happening, the server will broadcast a transaction to redeem his pledge after an appointed time. ∗Corresponding author Email addresses: tianhb@mail.sysu.edu.cn (Haibo Tian), isszhfg@mail.sysu.edu.cn (Fangguo Zhang ) Preprint submitted to Elsevier April 26, 2017


Ionospheric Time-Delay Algorithm for Single-Frequency GPS Users

The goal in designing an ionospheric time-delay correctionalgorithm for the single-frequency global positioning system userwas to include the main features of the complex behavior of theionosphere, yet require a minimum of coefficients and usercomputational time, while still yielding an rms correction of at least50 percent. The algorithm designed for this purpose, andimplemented in the GPS satellites, requires only eight coefficientssent as part of the satellite message, contains numerousapproximations designed to reduce user computationalrequirements, yet preserves the essential elements required to obtaingroup delay values along multiple satellite viewing directions.


Weighted Similarity Schemes for High Scalability in User-Based Collaborative Filtering

Similarity-based algorithms, often referred to as memory-based collaborative filtering techniques, are one of the most successful methods in recommendation systems. When explicit ratings are available, similarity is usually defined using similarity functions, such as the Pearson correlation coefficient, cosine similarity or mean square difference. These metrics assume similarity is a symmetric criterion. Therefore, two users have equal impact on each other in recommending new items. In this paper, we introduce new weighting schemes that allow us to consider new features in finding similarities between users. These weighting schemes, first, transform symmetric similarity to asymmetric similarity by considering the number of ratings given by users on non-common items. Second, they take into account the habit effects of users are regarded on rating items by measuring the proximity of the number of repetitions for each rate on common rated items. Experiments on two datasets were implemented and compared to other similarity measures. The results show that adding weighted schemes to traditional similarity measures These authors contributed equally to this work as the first author. P. Pirasteh · D. Hwang Department of Computer Engineering, Yeungnam University, Gyeongsan, Republic of Korea P. Pirasteh e-mail: parivash63@gmail.com D. Hwang e-mail: dosamhwang@gmail.com J. E. Jung ( ) Department of Computer Engineering, Chung-Ang University, Seoul, Republic of Korea e-mail: ontology.society@gmail.com significantly improve the results obtained from traditional similarity measures.


Optimization tool for direct water cooling system of high power IGBT modules

Thermal management of power electronic devices is essential for reliable system performance especially at high power levels. Since even the most efficient electronic circuit becomes hot because of ohmic losses, it is clear that cooling is needed in electronics and even more as the power increases. One of the most important activities in the thermal management and reliability improvement is the cooling system design. As industries are developing smaller power devices with higher power densities, optimized design of cooling systems with minimum thermal resistance and pressure drop become important issue for thermal design engineers. This paper aims to present a user friendly optimization tool for direct water cooling system of a high power module which enables the cooling system designer to identify the optimized solution depending on customer load profiles and available pump power. CFD simulations are implemented to find best solution for each scenario.


BER Analysis of MIMO-OFDM System using Alamouti STBC and MRC Diversity Scheme over Rayleigh Multipath Channel

Commons Attribution-Noncommercial 3.0 Unported License http://creativecommons.org/licenses/by-nc/3.0/), permitting all non commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Global Journal of Researches in Engineering Electrical and Electronics Engineering Volume 13 Issue 13 Version 1.0 Year 2013 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Inc. (USA) Online ISSN: 2249-4596 & Print ISSN: 0975-5861


A passivity based Cartesian impedance controller for flexible joint robots - part I: torque feedback and gravity compensation

In this paper a novel approach to the Cartesian impedance control problem for robots with flexible joints is presented. The proposed controller structure is based on simple physical considerations, which are motivating the extension of classical position feedback by an additional feedback of the joint torques. The torque feedback action can be interpreted as a scaling of the apparent motor inertia. Furthermore the problem of gravity compensation is addressed. Finally, it is shown that the closed loop system can be seen as a feedback interconnection of passive systems. Based on this passivity property a proof of asymptotic stability is presented.


A unified approach for motion and force control of robot manipulators: The operational space formulation

A framework for the analysis and control of manipulator systems with respect to the dynamic behavior of their end-effectors is developed. First, issues related to the description of end-effector tasks that involve constrained motion and active force control are discussed. The fundamentals of the operational space formulation are then presented, and the unified approach for motion and force control is developed. The extension of this formulation to redundant manipulator systems is also presented, constructing the end-effector equations of motion and describing their behavior with respect to joint forces. These results are used in the development of a new and systematic approach for dealing with the problems arising at kinematic singularities. At a singular configuration, the manipulator is treated as a mechanism that is redundant with respect to the motion of the end-effector in the subspace of operational space orthogonal to the singular direction.


A passivity based Cartesian impedance controller for flexible joint robots - part II: full state feedback, impedance design and experiments

The paper presents a Cartesian impedance controller for flexible joint robots based on the feedback of the complete state of the system, namely the motor position, the joint torque and their derivatives. The approach is applied to a quite general robot model, in which also a damping element is considered in parallel to the joint stiffness. Since passivity and asymptotic stability of the controller hold also for varying damping matrices, some possibilities of designing those gain matrices (depending on the actual inertia matrix) are addressed. The passivity of the controller relies on the usage of only motor side measurements for the position feedback. A method is introduced, which provides the exact desired link side stiffness based on this motor position information. Experimental results are validating the proposed controller.


Nonlinear Systems Analysis

THIS IS a completely rewritten version of the first edition which appeared in 1978. At that time the book gave a good overview of the main body of nonlinear systems and control theory, except for nonlinear optimal control theory. The emphasis was very much on the analysis of systems (in openor closed-loop configuration), instead of on the actual design of controllers. Most attention was being focused on stability issues, both from the input-output and the state-space point of view. Highlights in this theory were the concepts of input-output stability including Popov's criterion and the circle criterion (see also Desoer and Vidyasagar, 1975), the theory of Lyapunov stability, and perhaps the method of describing functions. Since the appearance of the first edition, geometric nonlinear control theory has become a prevailing trend. (In fact these developments were already initiated at the beginning of the seventies.) Geometric nonlinear control theory started as a successful approach to deal with basic system-theoretic questions in the state-space formulation of nonlinear control systems, such as controllability and observability properties and minimal realization theory. It gained strong impetus at the beginning of the eighties by the systematic study of nonlinear (state) feedback for various synthesis problems; also stimulated by the geometric approach to linear control theory of Wonham and Morse and Basile and Marro: In particular the Lie bracket conditions for controllability and feedback linearizability have become popular and powerful tools in nonlinear control. The books by Isidori (1989) and Nijmeijer and Van der Schaft (1990) are clear exponents of the achievements of geometric nonlinear control theory. In the last couple of years one can witness some developments which can be looked at as attempts to bridge the gap between nonlinear systems and control theory as put forward in the first edition of the present book on the one hand, and geometric nonlinear control theory on the other hand. Indeed, in nonlinear adaptive control as well as in nonlinear robust control (including nonlinear ~-cont ro l ) there is much need for the kind of stability concepts as exposed in the first edition of the book, while at the same time geometric nonlinear control theory offers an underlying structural framework. In particular passivity, and more generally, dissipativity concepts turn out to be extremely useful in these areas, especially within the context of the control of physical systems. Also, there has been recently some rapprochement between nonlinear input-output theory and (geometric) state-space theory. In my opinion these converging developments are very important and indeed offer promising perspectives for a truly nonlinear control design. In the present second edition Professor Vidyasagar has made an admirable attempt to include at least an introduction to geometric nonlinear control theory in an additional chapter of the book. In fact, this new chapter deals with the perhaps most 'useful' parts of geometric nonlinear control theory such as controllability, feedback linearization and input-output linearization. The other most noticeable differences with the first edition are the inclusion of some


On a New Generation of Torque Controlled Light-Weight Robots

The paper describes the recent design and development efforts in DLR ́s robotics lab towards the second generation of light-weight robots. The design of the lightweight mechanics, integrated sensors and electronics is outlined. The fully sensorized joint, with motor and link position sensors as well as joint torque sensors enables the implementation of effective vibration damping and advanced control strategies for compliant manipulation. The mechatronic approach incorporates a tight collaboration between mechanics, electronics and controller design as well. Thus we hope, that important steps towards a new generation of service and personal robots have been achieved.


Compact Zigzag-Shaped-Slit Microstrip Antenna With Circular Defected Ground Structure for Wireless Applications

In this letter, a compact zigzag-shaped-slit rectangular microstrip patch antenna with circular defected ground structure (DGS) is designed for wireless applications. The probe-fed antenna consisting of a zigzag-shaped slit, dual T-shaped slits on either sides of a rectangular patch, and circular dumbbell-shaped defected ground plane is optimized. The antenna was able to generate three separate resonances to cover both the 2.45/5.28-GHz WLAN bands and the 3.5-GHz WiMAX bands while maintaining a small overall size of 40 ×28 ×3.175 mm3. The return-loss impedance bandwidth values are enhanced significantly for three resonant frequencies. The designed antenna is characterized with better radiation patterns and potentially stable gain around 4-6 dBi over the working bands. Good agreement was obtained between measurements and simulations.


FIRST EXPERIMENTAL INVESTIGATIONS ON WHEEL-WALKING FOR IMPROVING TRIPLE-BOGIE ROVER LOCOMOTION PERFORMANCES

Deployment actuators of a triple-bogie rover locomotion platform can be used to perform Wheel-Walking (WW) manoeuvres. How WW could affect the traversing capabilities of rovers is a recurrent debate in the planetary robotics community. The Automation and Robotics Section of ESTEC has initiated a long term project to evaluate the performance of WW manoeuvres in different scenarios. This paper presents the first experimental results on this project, obtained during the test campaign run on November 2014 at the Planetary Robotics Lab (PRL) of ESTEC, and shows the performance analysis made when comparing WW with standard rolling. The PRL rover prototype ExoTeR was used to test three different scenarios: entrapment in loose soil, up-slope traverse and lander egressing. WW locomotion showed increased capabilities in all scenarios and proved its relevance and advantages for planetary exploration missions.


ISO 9241-11 Revised: What Have We Learnt About Usability Since 1998?

A revision is currently being undertaken of ISO 9241-11, published in 1998 to provide guidance on usability. ISO-9241-11 defines usability in terms of effectiveness, efficiency and satisfaction in a particular context of use. The intention was to emphasise that usability is an outcome of interaction rather than a property of a product. This is now widely accepted. However, the standard also places emphasis on usability measurement and it is now appreciated that there is more to usability evaluation than measurement. Other developments include an increasing awareness of the importance of the individual user's emotional experience as discretionary usage of complex consumer products and use of the World Wide Web have became more widespread. From an organisational perspective, it is now appreciated that usability plays an important role in managing the potentials risks that can arise from inappropriate outcomes of interaction. The revision of ISO 9241-11 takes account of these issues and other feedback.


Aerodynamic Design Optimization Studies of a Blended-Wing-Body Aircraft

Abstract The blended-wing body is an aircraft configuration that has the potential to be more efficient than conventional large transport aircraft configurations with the same capability. However, the design of the blended-wing is challenging due to the tight coupling between aerodynamic performance, trim, and stability. Other design challenges include the nature and number of the design variables involved, and the transonic flow conditions. To address these issues, we perform a series of aerodynamic shape optimization studies using Reynolds-averaged Navier–Stokes computational fluid dynamics with a Spalart–Allmaras turbulence model. A gradient-based optimization algorithm is used in conjunction with a discrete adjoint method that computes the derivatives of the aerodynamic forces. A total of 273 design variables—twist, airfoil shape, sweep, chord, and span—are considered. The drag coefficient at the cruise condition is minimized subject to lift, trim, static margin, and center plane bending moment constraints. The studies investigate the impact of the various constraints and design variables on optimized blended-wing-body configurations. The lowest drag among the trimmed and stable configurations is obtained by enforcing a 1% static margin constraint, resulting in a nearly elliptical spanwise lift distribution. Trim and static stability are investigated at both onand off-design flight conditions. The single-point designs are relatively robust to the flight conditions, but further robustness is achieved through a multi-point optimization.


Group-Sensitive Triplet Embedding for Vehicle Reidentification

The widespread use of surveillance cameras toward smart and safe cities poses the critical but challenging problem of vehicle reidentification (Re-ID). The state-of-the-art research work performed vehicle Re-ID relying on deep metric learning with a triplet network. However, most existing methods basically ignore the impact of intraclass variance-incorporated embedding on the performance of vehicle reidentification, in which robust fine-grained features for large-scale vehicle Re-ID have not been fully studied. In this paper, we propose a deep metric learning method, group-sensitive-triplet embedding (GS-TRE), to recognize and retrieve vehicles, in which intraclass variance is elegantly modeled by incorporating an intermediate representation “group” between samples and each individual vehicle in the triplet network learning. To capture the intraclass variance attributes of each individual vehicle, we utilize an online grouping method to partition samples within each vehicle ID into a few groups, and build up the triplet samples at multiple granularities across different vehicle IDs as well as different groups within the same vehicle ID to learn fine-grained features. In particular, we construct a large-scale vehicle database “PKU-Vehicle,” consisting of 10 million vehicle images captured by different surveillance cameras in several cities, to evaluate the vehicle Re-ID performance in real-world video surveillance applications. Extensive experiments over benchmark datasets VehicleID, VeRI, and CompCar have shown that the proposed GS-TRE significantly outperforms the state-of-the-art approaches for vehicle Re-ID.


Integrating technology in the classroom: a visual conceptualization of teachers' knowledge, goals and beliefs

In this paper, we devise a diagrammatic conceptualization to describe and represent the complex interplay of a teacher’s knowledge (K), goals (G) and beliefs (B) in leveraging technology effectively in the classroom. The degree of coherency between the KGB region and the affordances of the technology serves as an indicator of the teachers’ developmental progression through the initiation, implementation and maturation phases of using technology in the classroom. In our study, two teachers with differing knowledge, goals and beliefs are studied as they integrated GroupScribbles technology in their classroom lessons over a period of 1 year. Our findings reveal that the transition between the teacher’s developmental states (as indicated by coherency diagrams) is nonlinear, and thus the importance of ensuring high coherency right at the initiation stage. Support for the teacher from other teachers and researchers remains an important factor in developing the teacher’s competency to leverage the technology successfully. The stability of the KGB region further ensures smooth progression of the teacher’s effective integration of technology in the classroom.


EvalVid - A Framework for Video Transmission and Quality Evaluation

With EvalVid we present a complete framework and tool-set for evaluation of the quality of video transmitted over a real or simu lated communication network. Besides measuring QoS parameters of the underlyin g network, like loss rates, delays, and jitter, we support also a subjective vide o quality evaluation of the received video based on the frame-by-frame PSNR calcula tion. The tool-set has a modular construction, making it possible to exchange b oth the network and the codec. We present here its application for MPEG-4 as exam ple. EvalVid is targeted for researchers who want to evaluate their network designs or setups in terms of user perceived video quality. The tool-set is publi cly available [11].


Design and control of an actuated thumb exoskeleton for hand rehabilitation following stroke

Chronic hand impairment is common following stroke. This paper presents an actuated thumb exoskeleton (ATX) to facilitate research in hand rehabilitation therapy. The ATX presented in this work permits independent bi-directional actuation in each of the 5 degrees-of-freedom (DOF) of the thumb using a mechanism that has 5 active DOF and 3 passive DOF. The ATX is able to provide considerable joint torques for the user while still allowing backdrivability through flexible shaft transmission. A prototype has been built and experiments were conducted to evaluate the closed-loop position control. Further improvement and future work are discussed.


Robotic Cutting : Mechanics and Control of Knife Motion

Effectiveness of cutting is measured by the ability to achieve material fracture with smooth knife movements. The work performed by the knife overcomes the material toughness, acts against the blade-material friction, and generates shape deformation. This paper studies how to control a 2-DOF robotic arm equipped with a force/torque sensor to cut through an object in a sequence of three moves: press, push, and slice. For each move, a separate control strategy in the Cartesian space is designed to incorporate contact and/or force constraints while following some prescribed trajectory. Experiments conducted over several types of natural foods have demonstrated smooth motions like would be commanded by a human hand.


Pravastatin in elderly individuals at risk of vascular disease (PROSPER): a randomised controlled trial

BACKGROUND
Although statins reduce coronary and cerebrovascular morbidity and mortality in middle-aged individuals, their efficacy and safety in elderly people is not fully established. Our aim was to test the benefits of pravastatin treatment in an elderly cohort of men and women with, or at high risk of developing, cardiovascular disease and stroke.

METHODS
We did a randomised controlled trial in which we assigned 5804 men (n=2804) and women (n=3000) aged 70-82 years with a history of, or risk factors for, vascular disease to pravastatin (40 mg per day; n=2891) or placebo (n=2913). Baseline cholesterol concentrations ranged from 4.0 mmol/L to 9.0 mmol/L. Follow-up was 3.2 years on average and our primary endpoint was a composite of coronary death, non-fatal myocardial infarction, and fatal or non-fatal stroke. Analysis was by intention-to-treat.

FINDINGS
Pravastatin lowered LDL cholesterol concentrations by 34% and reduced the incidence of the primary endpoint to 408 events compared with 473 on placebo (hazard ratio 0.85, 95% CI 0.74-0.97, p=0.014). Coronary heart disease death and non-fatal myocardial infarction risk was also reduced (0.81, 0.69-0.94, p=0.006). Stroke risk was unaffected (1.03, 0.81-1.31, p=0.8), but the hazard ratio for transient ischaemic attack was 0.75 (0.55-1.00, p=0.051). New cancer diagnoses were more frequent on pravastatin than on placebo (1.25, 1.04-1.51, p=0.020). However, incorporation of this finding in a meta-analysis of all pravastatin and all statin trials showed no overall increase in risk. Mortality from coronary disease fell by 24% (p=0.043) in the pravastatin group. Pravastatin had no significant effect on cognitive function or disability.

INTERPRETATION
Pravastatin given for 3 years reduced the risk of coronary disease in elderly individuals. PROSPER therefore extends to elderly individuals the treatment strategy currently used in middle aged people.


A framework for evaluating quality-driven self-adaptive software systems

Over the past decade the dynamic capabilities of self-adaptive software-intensive systems have proliferated and improved significantly. To advance the field of self-adaptive and self-managing systems further and to leverage the benefits of self-adaptation, we need to develop methods and tools to assess and possibly certify adaptation properties of self-adaptive systems, not only at design time but also, and especially, at run-time. In this paper we propose a framework for evaluating quality-driven self-adaptive software systems. Our framework is based on a survey of self-adaptive system papers and a set of adaptation properties derived from control theory properties. We also establish a mapping between these properties and software quality attributes. Thus, corresponding software quality metrics can then be used to assess adaptation properties.


A multimodal adaptive session manager for physical rehabilitation exercising

Physical exercising is an essential part of any rehabilitation plan. The subject must be committed to a daily exercising routine, as well as to a frequent contact with the therapist. Rehabilitation plans can be quite expensive and time-consuming. On the other hand, tele-rehabilitation systems can be really helpful and efficient for both subjects and therapists. In this paper, we present ReAdapt, an adaptive module for a tele-rehabilitation system that takes into consideration the progress and performance of the exercising utilizing multisensing data and adjusts the session difficulty resulting to a personalized session. Multimodal data such as speech, facial expressions and body motion are being collected during the exercising and feed the system to decide on the exercise and session difficulty. We formulate the problem as a Markov Decision Process and apply a Reinforcement Learning algorithm to train and evaluate the system on simulated data.


Environmental influence in the brain, human welfare and mental health

The developing human brain is shaped by environmental exposures—for better or worse. Many exposures relevant to mental health are genuinely social in nature or believed to have social subcomponents, even those related to more complex societal or area-level influences. The nature of how these social experiences are embedded into the environment may be crucial. Here we review select neuroscience evidence on the neural correlates of adverse and protective social exposures in their environmental context, focusing on human neuroimaging data and supporting cellular and molecular studies in laboratory animals. We also propose the inclusion of innovative methods in social neuroscience research that may provide new and ecologically more valid insight into the social-environmental risk architecture of the human brain.


Compact Rep-Rate GW Pulsed Generator Based on Forming Line With Built-In High-Coupling Transformer

In this paper, a compact rep-rate GW pulsed power generator is developed. First, its three key subsystems are theoretically analyzed, engineering designed, and experimentally investigated, respectively. The emphases are put on these four problems: the theoretical analysis of the voltage distribution across the conical secondary windings of the high-coupling transformer, the investigation of the high energy storage density dielectric used in the pulse forming line, the choice of the gas flow velocity of the gas blowing system, and theoretical analysis of the passive stability of the pulsed power generator operated in rep-rate mode. Second, the developed pulsed power generator is described in detail. It has a 0.2-m diameter, a 1.0-m length, and a 20- Ω wave impedance. Across a 100- Ω resistive dummy load, it can steadily operate at a 300-kV output voltage in 50-Hz rep-rate and 250 kV in 150 Hz without gas blowing system. The average power is ~ 1 kW. Finally, the pulsed power generator is applied to drive a relativistic backward-wave oscillator, generating a high-power microwave with peak output power of 200 MW and duration (full-width at half-maximum) of 5 ns in 150-Hz rep-rate. These efforts set a good foundation for the development of a compact rep-rate pulsed power generator and show a promising application for the future.


Myanmar Spell Checker

Natural Language Processing (NLP) is one of the most important research area carried out in the world of Human Language. For every language, spell checker is an essential component of many of Office Automation Systems and Machine Translation Systems. In this paper, we develop a Myanmar Spell Checker System which can handle Typographic errors, Sequence errors, Phonetic errors, and Context errors. A Myanmar Text Corpus is created for developing Myanmar Spell checker. To check Typographic Errors, corpus look up approach is applied. Myanmar3 Unicode is applied in this system so that it can automatically reorder the character sequence. A compound misused word detection algorithm is proposed for Phonetic Errors checking and Bayesian Classifier is applied for Context Errors checking. In this system, Levenshtein Distance Algorithm is applied to improve users’ efficiency by providing a suggestion list for misspelled Myanmar Words. We provide evaluation results of the system and our approach can handle various types of Myanmar spell errors.


A Supervised Named-Entity Extraction System for Medical Text

TOKEN LEMMA POS Chunk Capitalized Token Class Dependency Sem Group Doc Type Section CUI TUI SemGroup UMLS Categ LEXIQUE SG LEXIQUE CUI DISO TOKEN ANAT TOKEN Wiki Gold label Nails nail NNS B-NP Capitalized Word ROOT DIS heent B-C1 B-T023 B-ANAT B DISO B-ANAT B-C0027342 1 1 – O : : : O – Punct P ANAT DIS heent – – – O O O 0 0 – O No no DT B-NP Capitalized Word NMOD #NA DIS heent – – – O O O 0 0 – O bed bed NN I-NP – Word NMOD #NA DIS heent – – – O O O 1 1 – B-DISORDER abnormalities abnormality NNS I-NP – Word NMOD DISO DIS heent – – – O O O 1 0 – I-DISORDER , , , O – Punct P #NA DIS heent – – – O O O 0 0 – O lunulas lunula NNS B-NP – Word AMOD #NA DIS heent – – – O O O 0 0 – O present present JJ B-ADJP – Word APPO #NA DIS heent – – – O O O 1 1 – O , , , O – Punct P ANAT DIS heent – – – O O O 0 0 – O no no DT B-NP – Word NMOD DISO DIS heent – – – O O O 0 0 – O splinters splinter NNS I-NP – Word ROOT DIS heent B-C2 C3 B-T037 B-DISO O O O 1 0 B-DISO B-DISORDER , , , I-NP – Punct P DISO DIS heent – – – O O O 0 0 – O pulses pulse NNS I-NP – Word COORD DISO DIS heent – – – O B-PHYS B-C0391850 0 0 – O I Lexical and morphological features (e.g., capitalization, lemma)