Orthogonal frequency-division multiplexing (OFDM) is a popular air interface technology that is adopted as a standard modulation scheme for 4G communication systems owing to its excellent spectral efficiency. For OFDM systems, synchronization problems have received much attention along with peak-to-average power ratio (PAPR) reduction. In addition to frequency offset estimation, frame synchronization is a challenging problem that must be solved to achieve optimal system performance. In this paper, we present a maximum likelihood (ML) frame synchronizer for OFDM systems. The synchronizer exploits a synchronization word and cyclic prefixes together to improve the synchronization performance. Numerical results show that the performance of the proposed frame synchronizer is better than that of conventional schemes. The proposed synchronizer can be used as a reference for evaluating the performance of other suboptimal frame synchronizers. We also modify the proposed frame synchronizer to reduce the implementation complexity and propose a near-ML synchronizer for time-varying fading channels.

In this paper, we propose a novel gait recognition framework - Spherical Space Model with Human Point Clouds (SSM-HPC) to recognize front view of human gait. A new gait representation - Marching in Place (MIP) gait is also introduced which preserves the spatiotemporal characteristics of individual gait manner. In comparison with the previous studies on gait recognition which usually use human silhouette images from image sequences, this research applies three dimensional (3D) point clouds data of human body obtained from stereo camera. The proposed framework exhibits gait recognition rates superior to those of other gait recognition methods.

Extracting named entities (NEs) and their relations is more difficult in Thai than in other languages due to several Thai specific characteristics, including no explicit boundaries for words, phrases and sentences; few case markers and modifier clues; high ambiguity in compound words and serial verbs; and flexible word orders. Unlike most previous works which focused on NE relations of specific actions, such as work_for, live_in, located_in, and kill, this paper proposes more general types of NE relations, called predicate-oriented relation (PoR), where an extracted action part (verb) is used as a core component to associate related named entities extracted from Thai Texts. Lacking a practical parser for the Thai language, we present three types of surface features, i.e. punctuation marks (such as token spaces), entity types and the number of entities and then apply five alternative commonly used learning schemes to investigate their performance on predicate-oriented relation extraction. The experimental results show that our approach achieves the F-measure of 97.76%, 99.19%, 95.00% and 93.50% on four different types of predicate-oriented relation (action-location, location-action, action-person and person-action) in crime-related news documents using a data set of 1,736 entity pairs. The effects of NE extraction techniques, feature sets and class unbalance on the performance of relation extraction are explored.

This letter investigates the training convergence in range-based cooperative positioning with stochastic positional knowledge. Firstly, a closed-form of squared position-error bound (SPEB) is derived with error-free ranging. Using the derived closed-form, it is proved that the SPEB reaches its minimum when at least 2 out of N (> 2) agents send training sequences. Finally, numerical results are provided to elaborate the theoretical analysis with zero-mean Gaussian ranging errors.

In this paper, we present an automatic vision-based traffic sign recognition system, which can detect and classify traffic signs at long distance under different lighting conditions. To realize this purpose, the traffic sign recognition is developed in an originally proposed dual-focal active camera system. In this system, a telephoto camera is equipped as an assistant of a wide angle camera. The telephoto camera can capture a high accuracy image for an object of interest in the view field of the wide angle camera. The image from the telephoto camera provides enough information for recognition when the accuracy of traffic sign is low from the wide angle camera. In the proposed system, the traffic sign detection and classification are processed separately for different images from the wide angle camera and telephoto camera. Besides, in order to detect traffic sign from complex background in different lighting conditions, we propose a type of color transformation which is invariant to light changing. This color transformation is conducted to highlight the pattern of traffic signs by reducing the complexity of background. Based on the color transformation, a multi-resolution detector with cascade mode is trained and used to locate traffic signs at low resolution in the image from the wide angle camera. After detection, the system actively captures a high accuracy image of each detected traffic sign by controlling the direction and exposure time of the telephoto camera based on the information from the wide angle camera. Moreover, in classification, a hierarchical classifier is constructed and used to recognize the detected traffic signs in the high accuracy image from the telephoto camera. Finally, based on the proposed system, a set of experiments in the domain of traffic sign recognition is presented. The experimental results demonstrate that the proposed system can effectively recognize traffic signs at low resolution in different lighting conditions.

Existing pose estimation algorithms suffer from either low performance or heavy computation cost. In this letter, we present an approach to improve the attractive algorithm called Orthogonal Iteration. A new form of fundamental equations is derived which reduces the computation cost significantly. And paraperspective camera model is used instead of weak perspective camera model during initialization which improves the stability. Experiment results validate the accuracy and stability of the proposed algorithm and show that its computational complexity is favorably compare to the O(n) non-iterative algorithm.

It is known that the optimal sensor coverage of a mission space is performed by a Voronoi partition, which is called a Voronoi coverage problem. We consider the case that the mission space has several obstacles where mobile sensors cannot be deployed and search an optimal deployment to maximize the sensing performance. Inspired by the potential field method, we introduce a repulsive potential for obstacle avoidance and define the objective function by a combination of two functions: one for evaluation of the sensing performance and the other for obstacle avoidance. We introduce a space where a sensor can move, called its moving space. In general, a moving space may not coincide with the mission space. We assume that the respective moving spaces of each sensor may differ from each other. By introducing a barycentric coordinate over the moving space, we show that the Voronoi coverage problem to maximize the objective function is transformed into a potential game. In potential games, local maximizers of a potential function are stable equilibrium points of the corresponding replicator dynamics. We propose a distributed sensor coverage control method based on the replicator dynamics to search a local maximizer of the objective function and a path to it. Using simulations, we also compare the proposed method with the Lloyd and TangentBug algorithm proposed by Breitenmoser et al.

This paper investigates the non-data-aided (NDA) carrier frequency estimation of amplitude and phase shift keying (APSK) signals. The true Cramer-Rao bound (CRB) for NDA frequency estimation of APSK signals are derived and evaluated numerically. Characteristic and jitter variance of NDA Luise and Reggiannini (L&R) frequency estimator are analyzed. Verified by Monte Carlo simulations, the analytical results are shown to be accurate for medium-to-high signal-to-noise ratio (SNR) values. Using the proposed closed-form expression, parameters of the algorithm are optimized efficiently to minimize the jitter variance.

In this paper, we consider the problem of waveform optimization for multi-input multi-output (MIMO) radar in the presence of signal-dependent noise. A novel diagonal loading (DL) based method is proposed to optimize the waveform covariance matrix (WCM) for minimizing the Cramer-Rao bound (CRB) which improves the performance of parameter estimation. The resulting nonlinear optimization problem is solved by resorting to a convex relaxation that belongs to the semidefinite programming (SDP) class. An optimal solution to the initial problem is then constructed through a suitable approximation to an optimal solution of the relaxed one (in a least squares (LS) sense). Numerical results show that the performance of parameter estimation can be improved considerably by the proposed method compared to uncorrelated waveforms.

Lazy-task creation is an efficient method of overcoming the overhead of the grain-size problem in parallel computing. Work stealing is an effective load balancing strategy for parallel computing. In this paper, we present dynamic work stealing strategies in a lazy-task creation technique for efficient fine-grain task scheduling. The basic idea is to control load balancing granularity depending on the number of task parents in a stack. The dynamic-length strategy of work stealing uses run-time information, which is information on the load of the victim, to determine the number of tasks that a thief is allowed to steal. We compare it with the bottommost first work stealing strategy used in StackThread/MP, and the fixed-length strategy of work stealing, where a thief requests to steal a fixed number of tasks, as well as other multithreaded frameworks such as Cilk and OpenMP task implementations. The experiments show that the dynamic-length strategy of work stealing performs well in irregular workloads such as in UTS benchmarks, as well as in regular workloads such as Fibonacci, Strassen's matrix multiplication, FFT, and Sparse-LU factorization. The dynamic-length strategy works better than the fixed-length strategy because it is more flexible than the latter; this strategy can avoid load imbalance due to overstealing.

In this letter, a novel imaging method to reduce the hand shake blur of a cell phone camera without using frame memory is proposed. The method improves the captured image in real time through the use of two additional preview images whose parameters can be calculated in advance and stored in a look-up table. The method does not require frame memory, and thus it can significantly reduce the chip size. The scheme is suitable for integration into a low-cost image sensor of a cell phone camera.

In this paper, we propose a new method for the bias-dependent parameter extraction of a MOSFET, which covers DC to over 100 GHz. The DC MOSFET model provided by the chip foundry is assumed to be correct, and the core DC characteristics are designed to be asymptotically recovered at low frequencies. This is carried out by representing the corrections required at high frequencies using a bias-dependent Y matrix, assuming that a parasitic nonlinear two-port matrix (Y-wrapper) is connected in parallel with the core MOSFET. The Y-wrapper can also handle the nonreciprocity of the parasitic components, that is, the asymmetry of the Y matrix. The reliability of the Y-wrapper model is confirmed through the simulation and measurement of a one-stage common-source amplifier operating at several bias points. This paper will not discuss about non-linearity.

The lateral velocity is of importance in cases like target identification and traffic management. Conventional Doppler methods are not capable of measuring lateral velocities since they quantify only the radial component. Based on the spectrogram characteristic of laterally moving targets, an algorithm based on fractional Fourier transform has been studied in the signal processing literature. The algorithm searches the peak position of the transformation, and calculates the lateral velocity from the peak position. The performance analysis of this algorithm is carried out in this paper, which shows that this algorithm approaches Cramer-Rao bound with reasonable computational complexity. Simulations are conducted at last to compare the analytical performance and the experimental result.

In this paper, a hybrid overlay/underlay cognitive radio system is modeled as an M/M/1 queue where the rate of arrival and the service capacity are subject to Poisson alternations. Each packet as a customer arriving at the queue makes a decision to join the queue or not. Upon arrival, the individual decision of each packet is optimized based on his observation about the queue length and the state of system. It is shown that the individually optimal strategy for joining the queue is characterized by a threshold of queue length. Thus, the individual optimal threshold of queue length is analyzed in detail in this work.

Although there have been enormous investments into English education all around the world, not many differences have been made to change the English instruction style. Considering the shortcomings for the current teaching-learning methodology, we have been investigating advanced computer-assisted language learning (CALL) systems. This paper aims at summarizing a set of POSTECH approaches including theories, technologies, systems, and field studies and providing relevant pointers. On top of the state-of-the-art technologies of spoken dialog system, a variety of adaptations have been applied to overcome some problems caused by numerous errors and variations naturally produced by non-native speakers. Furthermore, a number of methods have been developed for generating educational feedback that help learners develop to be proficient. Integrating these efforts resulted in intelligent educational robots – Mero and Engkey – and virtual 3D language learning games, Pomy. To verify the effects of our approaches on students' communicative abilities, we have conducted a field study at an elementary school in Korea. The results showed that our CALL approaches can be enjoyable and fruitful activities for students. Although the results of this study bring us a step closer to understanding computer-based education, more studies are needed to consolidate the findings.

As RFID technology is being more widely adopted, it is fairly common to read mobile tags using RFID systems, such as packages on conveyer belt and unit loads on pallet jack or forklift truck. In RFID systems, multiple tags use a shared medium for communicating with a reader. It is quite possible that tags will exit the reading area without being read, which results in tag leaking. In this letter, a reliable tag anti-collision algorithm for mobile tags is proposed. It reliably estimates the expectation of the number of tags arriving during a time slot when new tags continually enter the reader's reading area and no tag leaves without being read. In addition, it gives priority to tags that arrived early among read cycles and applies the expectation of the number of tags arriving during a time slot to the determination of the number of slots in the initial inventory round of the next read cycle. Simulation results show that the reliability of the proposed algorithm is close to that of DFSA algorithm when the expectation of the number of tags entering the reading area during a time slot is a given, and is better than that of DFSA algorithm when the number of time slots in the initial inventory round of next read cycle is set to 1 assuming that the number of tags arriving during a time slot follows Poisson distribution.

In this paper, we propose an optimized virtual re-convergence system especially to reduce the visual fatigue caused by binocular stereoscopy. Our unique idea to reduce visual fatigue is to utilize the virtual re-convergence based on the optimized disparity-map that contains more depth information in the negative disparity area than in the positive area. Therefore, our system facilitates a unique search-range scheme, especially for negative disparity exploration. In addition, we used a dedicated method, using a so-called Global-Shift Value (GSV), which are the total shift values of each image in stereoscopy to converge a main object that can mostly affect visual fatigue. The experimental result, which is a subjective assessment by participants, shows that the proposed method makes stereoscopy significantly comfortable and attractive to view than existing methods.

This paper presents a novel 2-D (2-dimension) receiver that adopts the reception scheme to promote OFDM-DS-CDMA (orthogonal frequency division multiplexing multi-carrier coded-division multiple-access) system performance. The system model includes spread coding and a system block diagram of the 2-D receiver shown graphically with 3-D (three dimensions) plots. The analytical calculation of system performance for an OFDM-DS-CDMA system combined with the proposed receiver equipment is investigated. To evaluate the results from the channel fading effect is considered over the correlated fading environments. The correlated-Nakagami-m statistical distribution is taken into account in the evaluation. The results show that the number of users, the number of subcarriers and the fading channel correlation generally affect OFDM-DS-CDMA systems. The system is also influenced by the Doppler shift and the signal propagation environment (fading parameter).

This study investigates the effect of frame intervals on the accuracy of the Lucas–Kanade optical flow estimates for high-frame-rate (HFR) videos, with a view to realizing accurate HFR-video-based optical flow estimation. For 512 512 pixels videos of patterned objects moving at different speeds and captured at 1000 frames per second, the averages and standard deviations of the estimated optical flows were determined as accuracy measures for frame intervals of 1–40 ms. The results showed that the accuracy was highest when the displacement between frames was around 0.6 pixel/frame. This common property indicates that accurate optical flow estimation for HFR videos can be realized by varying frame intervals according to the motion field: a small frame interval for high-speed objects and a large frame interval for low-speed objects.

Defining quality requirements completely and correctly is more difficult than defining functional requirements because stakeholders do not state most of quality requirements explicitly. We thus propose a method to measure a requirements specification for identifying the amount of quality requirements in the specification. We also propose another method to recommend quality requirements to be defined in such a specification. We expect stakeholders can identify missing and unnecessary quality requirements when measured quality requirements are different from recommended ones. We use a semi-formal language called X-JRDL to represent requirements specifications because it is suitable for analyzing quality requirements. We applied our methods to a requirements specification, and found our methods contribute to defining quality requirements more completely and correctly.