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This paper proposes an adaptive sliding mode control (ASMC) with a modified reduced-order proportional integral observer (MROPIO) to provide speed control for a permanent magnet synchronous motor against unknown disturbances, such as system parameter uncertainties and external load torque variation. The ASMC method, based on a new sliding mode reaching law, improves chattering in the control signal and reduces the time required for the system trajectory to reach the sliding mode surface. The MROPIO estimates unknown disturbances and provides feedforward compensation for the ASMC, thus overcoming incorrect estimations of unknown disturbances during speed jumps can cause overshoots in the speed response. All the proposed methods were implemented on an industrial motor drive. The experimental results confirm the validity of the proposed method for practical applications.

This paper proposes a robot calibration method using an extended Kalman filter (EKF) and an artificial neural network (ANN) based on a butterfly and flower pollination algorithm (ANN-BFPA) to improve the robot's absolute pose (position and orientation) accuracy. After establishing a geometric error model, the EKF, a robust optimization algorithm for a nonlinear system with Gaussian noise, was used to estimate geometric parameter errors and compensate for geometric errors. However, nongeometric errors caused by joint clearance, gear backlash, and link deflection could still affect the pose accuracy and interfere with the correctness of the model. Therefore, the ANN-BFPA was proposed to compensate for these errors. The ANN model was used to establish the complex relationship between joint lengths and pose error. In addition, BFPA was used to optimize weights and bias of the neural network. The efficiency of the proposed calibration method was evaluated using a Stewart platform. Experimental results demonstrated that the proposed method significantly improved the robot's pose accuracy and showed better performance than previous techniques.

Rectification is a standard process in every system that requires multiviews. Existing algorithms largely work on similar field of view (FoV) cases where the two views are mostly identical. Dissimilarities between different FoVs can generate unexpected errors during the optimization process, resulting in a large amount of rectification errors and unwanted geometric distortion. In this study, we present a full pipeline to rectify uncalibrated images captured by cameras that have dissimilar FoVs under the constraints of geometric distortion. The proposed method contains two main parts: Field of View Neutralization and Rectification with Adaptive Geometric Constraints. The Field of View Neutralization module estimates the transformation matrix to compensate for the imbalance between different views. In addition, this module improves the overall quality of correspondences by removing misleading feature matching pairs. Finally, by applying an adaptive optimization process with the geometric constraints involved, our method addresses the overdistortion issue while maintaining small rectification errors. Extensive experiments are conducted to demonstrate the robust performance of the proposed method. Besides the existing datasets, we provide our dissimilar FoVs dataset with multiple baselines to examine the performance. Our method outperforms the existing algorithms in terms of both rectification errors and geometric distortion rates.

Stereo vision systems in autonomous vehicles can come with camera sensors with different models and lenses to detect objects at long and short ranges. The intrinsic/extrinsic parameters of the camera sensors, such as the focal length and distortion parameters used for calibration, are not provided. In this article, we present a novel rectification method for uncalibrated stereo cameras based on their corresponding feature points. To obtain reliable key/feature points and descriptors from stereo images, we propose a deep neural network, called a multilevel feature pooling network. The model processes images individually to learn both local and semiglobal features, from which the location and descriptor of each pixel in the image are estimated. We also introduce a method to balance the zoom effect caused by the different focal lengths of the camera lenses and rectify the images by estimating homography matrices for stereo images based on their refined corresponding feature points.

This article proposes a method to improve the accuracy of a vernier absolute magnetic encoder. The encoder consists of a master and a nonius multipolar magnetic track. Sinusoidal signals from the master and nonius tracks are used to infer the absolute information. Unfortunately, these signals are contaminated by nonideal factors such as different amplitudes, dc-offsets, phase shifts, and random noise. Moreover, harmonics existing in the encoder signals distort the vernier principle and significantly affect the accuracy of the encoder. To address these problems, the present article proposes an efficient method with three main parts. The first is an observer phase-locked loop (OPLL), which is used to estimate the phase and eliminate the nonideal factors. The second is nonlinear phase compensation, which is used to correct the vernier principle that deviated due to the existing harmonics. Finally, a pole pitch compensation method is introduced to modulate the master phase angle from the OPLL to eliminate the harmonic distortion. The proposed method can eliminate the nonideal factors, harmonic distortion and improve the accuracy of the encoder. All the proposed methods were implemented on an ARM STM32F407ZG. The experimental results confirm the validity of the proposed method for practical applications.

This article presents a portable smartphone-based laser measurement system (SLM) for measuring gaps and flushes on a car body. The system is designed to replace conventional gauges that are used by human operators. The developed device consists of a smartphone and an off-the-shelf laser-line projector held together by a three-dimensional–printed structure. It captures images of a laser line scattered on target surfaces, identifies the extreme points on the laser profiles, and obtains gap and flush measurements with minimal uncertainty. Since the smartphone is used as the operational unit, the measurement data can be stored and allows the capability for data transfer with other storage locations. Experiments were performed on the specimens and real cars to validate the application of the developed system. The measurement uncertainty on a real car was reported as $\pm$ 0.201 mm for gap and $\pm$ 0.154 mm for flush.

In recent DRAM processes, obtaining the maximum capacitor presents a difficult challenge. Also, the aspect ratio continually increases during the process. To address these issues, the thickness of the top and bottom layers must be constant. However, the thickness is not constant due to the time difference between the bottom and top processes, and because the source changes in each step. To solve this problem, the same reaction should occur at all times while the density of the gas at the bottom and top is gradually changed. In this study, a new vaporizer is developed and evaluated to ensure a constant rate of density change and a rapid vaporization response of the liquid source. To develop a vaporizer that responds quickly, we experimentally evaluated the structures of the vaporizer's showerhead and vaporizing part. To keesp the density of the liquid source for evaporation constant, several holes were made in the showerhead from which the evaporation source is discharged. Also, to ensure a constant flow of liquid to the chamber where the source droplet meets the carrier gas, a helical structure was proposed and evaluated. In addition, using an orifice, the pressure was decreased to increase the vaporization efficiency. As a result, the settling time decreased from 10 to 3 s, overshooting decreased from 25 to 1%, and flow fluctuation decreased from 2.6 to 1.8%. Therefore, this new vaporizer is well-adapted to the process of new devices and provides greater capacitance design margins.

This article presents a self-referencing lookup table (LUT) algorithm to enhance the multiturn accuracy of a battery-free multiturn absolute magnetic encoder. The encoder consists of a single-turn angle for the measurement of the degree of rotation within $2\pi $ using a multipolar magnet to achieve high resolution and a multiturn counter to monitor the total number of revolutions made by the encoder’s shaft through the different angles of the four bipolar magnets (BPMs) based on four-shaft gears. However, the signals from the BPMs are affected by factors, such as amplitude mismatch, dc offsets, random noises, and harmonic components that are as a result of electrical and mechanical distortions. To solve this problem, this article suggested a method derived from the single-turn angle and the raw signals from the BPMs to generate the LUTs for multiturn angles. These angles eliminate the effects of the disturbances. Additionally, a multiturn compensation algorithm is suggested to compensate for the number of revolutions made by the encoder. The proposed methods are simulated in MATLAB software and implemented in ARM STM32F407ZG. The effectiveness of the proposed method is demonstrated by the obtained results.

To improve the accuracy of permanent magnet (PM) rotor position estimation for a hybrid stepper motor (HSM), the authors propose a sensorless control design based on the back electromagnetic force (back-EMF) caused by magnetic induction and harmonic rejection via an orthogonal third-order phase-locked loop (PLL 3 rd) and an integral harmonic filter (IHF). The accuracy of estimation is analysed considering PM rotor position estimation errors. The harmonics of the back-EMF signal are eliminated by the IHF before entering the PLL 3 rd, which synchronises and decreases position estimation error. This technique is a simple, reliable, and effective method for implementing sensorless control of an HSM. An industrial test-bench was used to implement the proposed scheme. The experimental results are presented to validate the effectiveness of the proposed estimation method.

Traditional deep learning-based vehicle detection methods are often designed using a pyramid of filters with multiple scales and sizes; therefore, the processing time is slow due to the large number of scales used and because the classifier runs at all scales. Recently, a deep learning-based region proposal network was introduced to detect vehicles that only employ the network one time regardless of the size of the input image. In object detection, deep learning-based region proposal networks have achieved state-of-the-art performance in terms of accuracy. These systems achieve a very high accuracy under normal driving conditions; however, their performance decreases under difficult driving conditions such as in snow, rain, or fog. In addition, the current state-of-the-art system-based region proposal networks still fail to satisfy the real-time requirements of the driving assistant systems. More recently, the identification of local patterns has been shown to improve the performance of the traditional deep-learning systems; hence, this paper investigates local patterns in region proposal networks to improve their accuracy. Depth information is also investigated to improve the processing time of current region proposal networks. Our experimental results show that the proposed system obtains better performance than the state-of-the-art object region detection systems in terms of both accuracy and processing time.

In this paper, we propose an advanced approach to improve the accuracy of stepper motors based on an effective phase compensator. The proposed approach includes an adaptive current controller and an adaptive position controller. The proposed adaptive feed-forward proportional-resonant (AFPR) current control technique with a neural network (NN) is an improvement over the conventional proportional-integral algorithm. In which, the NN algorithm is applied to obtain the optimal parameters for the AFPR controller. This results in an enhancement of the stepper motor current tracking. In addition, the advanced position controller is a combination of a damping and a phase-compensated (D&PC) method to reduce steady-state error and variations during low-speed operation. The position tracking performance is further appreciated by applying an advanced proportional integral derivative (PID) controller during high-speed operation. A bandpass filter is designed to switch the motor between low- and high-speed operational regimes. The proposed approach does not require any changes in the structure of the hardware. Therefore, it can be widely applied to a diverse set of industrial applications. The performance of the proposed approach was validated by conducting experiments under practical conditions.

This paper proposes a method to improve the accuracy of an absolute magnetic encoder by using harmonic rejection (HR) and a dual-phase-locked loop (DPLL). The encoder consists of two permanent magnets: an edge-located multipolar magnet (MPM) and a center-located bipolar magnet, in which the signal-processing accuracy of the MPM is crucial for achieving high accuracy of the entire encoder. However, the MPM signals are disturbed by nonidealities such as dc offsets, amplitude mismatch, low- and high-order harmonics, and random noises. In this paper, the HR approach investigates the characteristics of nonidealities of the phase detector and rejects them by using gradient descent. In addition, the DPLL remains robust by maintaining a zero steady-state error during a phase jump, a constant frequency, and a ramp frequency. The proposed method is simulated with MATLAB software and implemented in ARM STM32F407ZG. The obtained results demonstrate efficient performance. This method can be applied to any use of quadrature sinusoidal signals, such as in power grids and in permanent-magnet synchronous motor phase detection.

Absolute magnetic encoders (AMEs) use two magnets: a ring multipolar magnet (MPM) generating high-resolution and improving the accuracy for the encoder, and a bipolar magnet in the center calculating the number cycle of MPM signals. The phase outputs of these AMEs are tracked from the sinusoidal signals of the MPM. However, these sine/cosine signals are disturbed by amplitude differences, offsets, phase-shift, harmonic components, and random noise. In order to solve this problem, this paper presents an adaptive linear neuron based on a third-order phase-locked loop (ALN-PLL) to improve the accuracy of AMEs. The proposed approach consists of two main parts: The first part is an ALN algorithm that uses the phase feedback of the third-order PLL in order to build the mathematical model of input signals, and then reject the disturbances. The second part is a third-order PLL that is designed based on a dominant pole approximation algorithm. The proposed PLL can reduce noise and eliminate dc-error during the phase step, frequency step, and frequency ramp. The simulation and experimental results demonstrate the effectiveness of the proposed approach.

Change/motion detection is a challenging problem in video analysis and surveillance system. Recently, the state-of-the-art methods using the sample-based background model have demonstrated astonishing results with this problem. However, they are ineffective in the dynamic scenes that contain complex motion patterns. In this paper, we introduce a novel data-driven approach that combines the sample-based background model with a feature extractor obtained by training a triplet network. We construct the network by three identical convolutional neural networks, each of which is called a motion feature network. Our network can automatically learn motion patterns from small image patches and transform input images of any size into feature embeddings for high-level representations. The sample-based background model of each pixel is then employed by using the color information and the extracted feature embeddings. We also propose an approach to generate triplet examples from CDNet 2014 for training our network model from scratch. The offline trained network can be used on the fly without re-training on any video sequence before each execution. Therefore, it is feasible for real-time surveillance systems. In this paper, we show that our method outperforms the other state-of-the-art methods on CDNet 2014 and other benchmarks (BMC and Wallflower).

This paper presents an effective closed-loop control approach for a stepper motor. The proposed approach consists of motor parameter identification, closed-loop current control, closed-loop position control, and damping control. The presented implementation achieves accurate position, high torque, and smooth speed. By using an improved proportional-integral current control algorithm, this approach not only guarantees the current tracking performance but also increases the bandwidth during the speed response. The advanced position controller achieves both high torque and the synchronous condition of the motor after a loss of synchronism occurs. In addition, this method applies an effective speed damping method to remove resonance at low speed and reduce vibration at high speed. All the proposed algorithms were implemented on an industrial test-bench. The experimental results demonstrate the validity of the proposed approach for practical applications.

Modern in-vehicle networks are comprised of heterogeneous systems that use various communication protocols with different source clocks. Moreover, gateway systems are designed with additional features that ensure converted/forwarded data meet real-time constraints. In this paper, we propose a synchronization mechanism for FlexRay and Ethernet audio video bridging (AVB) network that guarantees a high quality-of-service. We also designed the corresponding embedded system based-gateway system. Moreover, we built an in-vehicle network environment that consists of FlexRay and Ethernet AVB networks using an embedded system, which is integrated and synchronized by the gateway. We evaluate the synchronization performance of the proposed method using the developed gateway by conducting experiments as well as a theoretical analysis. The results of the experiment show that the synchronization mechanism provides timing guarantees for the FlexRay network that are similar to those of the Ethernet AVB network.

Stereo matching is a challenging task because stereo images are affected by many factors, such as radiometric distortion, sun and rain flare, flying snow, occlusion, textureless and noisy image regions, and object boundaries. However, most of the existing methods for stereo matching aim to solve only one specific problem. As a result, their performance is degraded significantly when operating with stereo images captured under a variety of scenes and conditions. In this paper, we propose a novel matching cost function based on adaptive normalized cross-correlation (ANCC). We demonstrate several weaknesses of ANCC and propose techniques to resolve them. In addition, we employ available information, such as intensity mean, intensity variance, and support window radius, to estimate the parameters of the proposed matching cost function. Compared with ANCC, the proposed matching cost function reduces the error rates from 24.1% to 17.8% in the Middlebury data set and from 64.1% to 26.4% in the KITTI data set. In addition, for noisy stereo pairs, the proposed function reduces the error rate from 73.6% to 37.3%. The qualitative and quantitative experimental results based on stereo images in different data sets under various conditions show that our proposed matching cost function outperforms state-of-the-art matching cost functions in indoor and outdoor stereo images having various radiometric distortions.

Object proposals have recently emerged as an essential cornerstone for object detection. The current state-of-the-art object detectors employ object proposals to detect objects within a modest set of candidate bounding box proposals instead of exhaustively searching across an image using the sliding window approach. However, achieving high recall and good localization with few proposals is still a challenging problem. The challenge becomes even more difficult in the context of autonomous driving, in which small objects, occlusion, shadows, and reflections usually occur. In this paper, we present a robust object proposals re-ranking algorithm that effectivity re-ranks candidates generated from a customized class-independent 3DOP (3D Object Proposals) method using a two-stream convolutional neural network (CNN). The goal is to ensure that those proposals that accurately cover the desired objects are amongst the few top-ranked candidates. The proposed algorithm, which we call DeepStereoOP, exploits not only RGB images as in the conventional CNN architecture, but also depth features including disparity map and distance to the ground. Experiments show that the proposed algorithm outperforms all existing object proposal algorithms on the challenging KITTI benchmark in terms of both recall and localization. Furthermore, the combination of DeepStereoOP and Fast R-CNN achieves one of the best detection results of all three KITTI object classes. HighlightsWe present a robust object proposals re-ranking algorithm for object detection in autonomous driving.Both RGB images and depth features are included in the proposed two-stream CNN architecture called DeepStereoOP.Initial object proposals are generated from a customized class-independent 3DOP method.Experiments show that the proposed algorithm outperforms all existing object proposals algorithms.The combination of DeepStereoOP and Fast R-CNN achieves one of the best detection results on KITTI benchmark.

The performance of stereo matching algorithms strongly depends on the quality of the stereo data/matching cost. Most state-of-the-art data costs require expert knowledge for the design of a transformation function, such as census for handling gray-level changes monotonically, adaptive normalized cross correlation for handling Lambertian cases, guided filtering for preserving edge information, and local density encoding for handling illumination differences. However, it is difficult to design a complex transformation function to handle unknown factors that often occur in driving conditions such as snow, rain, and sun. Therefore, this paper has investigated the deep learning strategy to develop a novel stereo matching cost model without using much expert knowledge. Experimental results show that the proposed deep learning model obtains better results than the state-of-the-art stereo matching cost as judged by the standard KITTI benchmark, Middlebury, and HCI datasets.

Stereo correspondence is a challenging task because stereo images are affected by many factors such as radiometric distortion, sun and rain flares, flying snow, occlusions and object boundaries. However, most of the existing stereo correspondence methods use simple matching cost functions. As a result, their performance is degraded significantly when operating with real-world stereo images whose intensities of corresponding pixels can be arbitrarily transformed. In this study, the authors propose a novel matching cost function based on the order relations between pixel pairs that can operate accurately under various conditions of transformed intensities between stereo images. The proposed matching cost function is an improvement of the soft rank transform (SRT) and can tolerate local, monotonically non-linear changes in intensities between the left and right images. The proposed function significantly reduces the error rate from 24.7 to 12.7% in the Middlebury dataset, and from 19.8 to 7.1% in the KITTI dataset as compared with the SRT. The qualitative and quantitative experimental results obtained using stereo images in different datasets under various conditions show that the proposed matching cost function outperforms the state-of-the-art matching cost functions in indoor and outdoor stereo images.

This paper proposes a gateway framework for in-vehicle networks (IVNs) based on the controller area network (CAN), FlexRay, and Ethernet. The proposed gateway framework is designed to be easy to reuse and verify to reduce development costs and time. The gateway framework can be configured, and its verification environment is automatically generated by a program with a dedicated graphical user interface (GUI). The gateway framework provides state-of-the-art functionalities that include parallel reprogramming, diagnostic routing, network management (NM), dynamic routing update, multiple routing configuration, and security. The proposed gateway framework was developed, and its performance was analyzed and evaluated.

In this study, an advanced variational model is presented for problem modelling in computer vision and image processing. The proposed model allows for the definition of multiple constraints in data fidelity, which has not been considered in previous state-of-the-art methods. With this definition, the model is more robust and flexible with regard to problem modelling. Two algorithms are introduced to solve the optimisation problems: one for the vector domain and the other for the frequency domain. The issue of multiple L 1-norms in the data fidelity term is resolved with these algorithms; this remained unsolved in previous research because of the difficulty with optimisation. The proposed model is demonstrated through two problems in image processing: image denoising and image deblurring. The results indicate that, compared to previous methods, images of high visual quality were both produced and recovered when using the proposed model. In addition, good and stable results in real-world images were yielded by the proposed model, which indicates vast potential for practical uses.

This paper proposes a method for seamless interaction between students and their professor using Twitter, one of the typical social network service (SNS) platforms, in large lectures. During the lecture, the professor poses surprise questions in the form of a quiz on an overhead screen at unexpected moments, and students submit their answers through a smartphone response system using Twitter. Since points are awarded on a first-come–first-served basis to only a limited number of the students who submit correct answers, students are required to concentrate on the lecture if they want to score. The proposed method solves both the problem of decreased concentration arising from frequent use of smartphones for nonlearning purposes in lectures and of inefficient interaction that occurs in large university classrooms. Evaluation through analysis of exam results and a post-course survey confirm that the proposed solution produces better results in terms of both students' academic achievement and their concentration during lectures.

Enhancing the sharpness and reducing the noise of blurred, noisy images are crucial functions of image processing. Widely used unsharp masking filter-based approaches suffer from halo-artefacts and/or noise amplification, while noise- and halo-free adaptive bilateral filtering (ABF) is computationally intractable. In this study, the authors present an efficient sharpening algorithm inspired by guided image filtering (GF). The author's proposed adaptive GF (AGF) integrates the shift-variant technique, a part of ABF, into a guided filter to render crisp and sharpened outputs. Experiments showed the superiority of their proposed algorithm to existing algorithms. The proposed AGF sharply enhances edges and textures without causing halo-artefacts or noise amplification, and it is efficiently implemented using a fast linear-time algorithm.

Stereo correspondence is challenging under realistic conditions due to uncontrolled factors that affect input images, including illumination inconsistencies and radiometric variations. Many local and global models have been suggested to address these problems; however, their performance is often degraded due to the assumption of color consistency between the left and right images. Therefore, we present a new local pattern, local density encoding, for stereo matching measurements to improve the performance of existing stereo methods. Our experimental results indicate that the proposed method is less sensitive to illumination changes and radiometric variations. Moreover, in the cases with normal and severe illumination changes, the proposed method is more robust than state-of-the-art data costs.

The local binary pattern (LBP) and its variants have been widely investigated in many image processing and computer vision applications due to their robust ability to capture local image structures and their computational simplicity. The existing LBPs extract local structure information by establishing a relationship between the central pixel and its adjacent pixels. However, most LBPs miss the relationship among all of the pixels in the local region. Therefore, this paper proposes a novel model to establish this relationship by introducing a support LBP. The proposed model improves the performance of the existing LBP methods and results in lower sensitivity to illumination changes and radiometric variations. Moreover, the proposed model has been successfully investigated in two applications: disparity map generation and texture classification. For disparity map generation, the proposed model reduces the root mean square (RMS) error by 23.6% (in Baby1 dataset, Middlebury), and 16.58% (in Aloe dataset, Middlebury) as compared with the standard LBP under radiometric variation conditions. Moreover, the proposed model reduces the RMS by 28.11% as compared with the standard LBP under the Gaussian noise condition in the ESATS dataset. For texture classification applications, the proposed model improves the classification results from 96.26% to 98.13% on the Outext database, from 88.03% to 91.41% on the Xu database, and from 94.00% to 96.67% on the KTH-TIPS database as compared with the completed LBP.

The evolutionary algorithm (EA) is an effective method for solving various problems because it can search through very large search spaces and can quickly come to nearly optimal solutions. However, existing EA-based methods for vehicle detection cannot achieve high performance because their fitness functions depend on sensitive information, such as edge or color information on the preceding vehicle. This paper focuses on improving the performance of existing evolutionary-based methods for vehicle detection by introducing an effective fitness function that can more accurately capture a vehicle's information by combining a disparity map, edge information, and the position and motion of the preceding vehicle. The proposed method can detect multiple vehicles by using a turn-back genetic algorithm (GA) and can prevent false detection by using motion detection. Our fitness function is designed in a typical manner along with the fitness parameters. These parameters are usually selected using heuristic methods, making the choice of optimal parameters difficult. Therefore, this paper proposes a new approach to estimating optimal fitness parameters using EA and the least squares method. Robustness testing showed that the proposed method provides detection rate (DR) results close to those obtained using a state-of-the-art system and outperforms other dominant vehicle-detection-based EAs.

Binocular stereo matching is one of the most important algorithms in the field of computer vision. Adaptive support-weight approaches, the current state-of-the-art local methods, produce results comparable to those generated by global methods. However, excessive time consumption is the main problem of these algorithms since the computational complexity is proportionally related to the support window size. In this paper, we present a novel cost aggregation method inspired by domain transformation, a recently proposed dimensionality reduction technique. This transformation enables the aggregation of 2-D cost data to be performed using a sequence of 1-D filters, which lowers computation and memory costs compared to conventional 2-D filters. Experiments show that the proposed method outperforms the state-of-the-art local methods in terms of computational performance, since its computational complexity is independent of the input parameters. Furthermore, according to the experimental results with the Middlebury dataset and real-world images, our algorithm is currently one of the most accurate and efficient local algorithms.

In this study, the authors propose an adaptive scheme to improve motion estimation of a variational model based on image features and flow quality measurements. Using image features, the authors introduce adaptive functions and inject them into the energy function to fine-tune the estimation process. They propose a hybrid scheme to deal with large motions and improve the accuracy of the flow field. They introduce a trusted-map based on constraints to measure flow quality. They use this map as a reference for the proposed occlusion-aware filter. The proposed filter and hybrid scheme are integrated to correct the flow field iteratively, thus significantly improving the estimation results. The filter also enhances the flow field in occlusion areas. The authors experimental results demonstrate that their method provides sharp flow fields and significantly improved estimation accuracy.

As gesture recognition technology has improved, it has been applied to various consumer appliances to provide natural interfaces. Television is a representative system for this kind of interface because of its complex requirements for various functions. Icon-based interfaces are the general method used. However, according to the performed survey, various functions were not required for watching TV. This work found most viewers mainly tend to use some specific functions such as channel or volume. These functions should be controlled easily and quickly since they are frequently used. However, the iconbased interface has difficulty fulfilling this requirement since it is affected by Fitts' law. Hence, this paper proposes a gesturebased method for easy and quick control. User hand gestures are recognized by capturing the motion path when the user draws different symbols in the air. These gestures are used to interact with the TV. It is implemented using a single-camera dedicated hardware system. This system is based on a real time parallel processing structure. The analysis shows that subjects enjoy this TV control system.

This article describes a reliable gateway for in-vehicle networks. Such networks include local interconnect networks, controller area networks, and FlexRay. There is some latency when transferring a message from one node (source) to another node (destination). A high probability of error exists due to different protocol specifications such as baud-rate, and message frame format. Therefore, deploying a reliable gateway is a challenge to the automotive industry. We propose a reliable gateway based on the OSEK/VDX components for in-vehicle networks. We also examine the gateway system developed, and then we evaluate the performance of our proposed system.

This paper presents design and implementation of a pipelined datapath for real-time face detection using cascades of boosted classifiers. We propose following methods: symmetric image downscaling, classifier sharing, and cascade merging, to achieve the desired processing speed and area efficiency. First, an image pyramid with 16 levels is generated from the input image to simultaneously detect faces with different scales. The downscaled images are then transferred to the first stage of the cascade that is shared between the corresponding image pairs based on the pixel validity of the symmetric image pyramid. The last method exploits the different hit ratios of the cascade stages. We use a tree-structured cascade of classifiers since most of the nonface elements are eliminated during the early stages of the classifier. The use of a synthesis tool confirms that the proposed design reduces resource utilization by one-eighth without accuracy loss, compared to the fully parallelized implementation of the same algorithm. We implemented the proposed hardware architecture on a Xilinx Virtex-5 LX330 FPGA. The indicative throughput is 307 frames/s irrespective of the number of faces in the scene for standard VGA (640 × 480) images with an operating frequency of 125.59 MHz. We may ensure that face detection results are generated at each clock cycle after the initial pipeline delay, using this fully pipelined datapath for tree-structured cascade classifiers.

A magnetic encoder (ME) is a kind of sinusoidal encoder using magnetic effects that is currently utilized in many industrial control systems because it has many advantageous characteristics: low cost, simple structure, works in harsh environments, high reliability, and so on. The signals generated by an ME are always disturbed by noises; therefore, these signals are not ideal. The challenge is to achieve the highest resolution and to get the maximum operating speed as well as to use the most cost-effective hardware. To solve this problem, this paper humbly proposes an effective approach, which contains two parts: The main part is “compensating the noisy signals of MEs” by applying a new proposed method called the advanced adaptive digital phase-locked loop (AADPLL), while the second is a pulse interpolator which generates high-resolution quadrature pulses. The AADPLL algorithm provides a robust filtering characteristic to eliminate the noises and improve the accuracy of the ME's input signals. It also takes advantage of tracking high-speed input signals without time lag, unlike the traditional filters. Additionally, the computation burden is significantly reduced in this algorithm to allow it to be easily implemented in a low-cost processor. The pulse interpolator is based on an existing idea that extracts high-order sinusoids from the original ME signals. However, a new scheme is presented to achieve higher resolution per period with smaller noises affecting the output pulses. Both parts are mainly implemented in a unique hardware platform using a low-cost digital signal processor, such as the TMS320F2812, combined with a small-size field-programmable gate array. This method has already been applied to control a linear motor without using an expensive optical linear encoder. Practical results are provided to demonstrate the effectiveness of the proposed method.

Vision-based vehicle speed measurement (VSM) is one of the most convenient methods available in intelligent transportation systems. Existing methods use an uncalibrated camera to measure vehicle speed, but they do not consider the possibility of camera vibration that leads to poor measurement results. This paper considers the issue when the camera is tilted downward and mounted at a fixed location on a bridge crossing the target street. The camera may vibrate due to wind or bridge movement. A vision-based speed measurement system is described in this paper, along with the vertical-and-horizontal-histogram-based method, which is used to compensate the background of an incoming image. This novel method is utilized to eliminate noise coming from the displacement between an incoming image and a background image that is caused by camera vibration over time. Moreover, a method is presented to automatically detect the vanishing point based on the Hough transform and quadtree. Experimental comparisons of the system to those of the vehicle's own speedometer show that the proposed approach yields a satisfactory estimate of vehicle speed.

We present a low-speed-damping controller for a stepper motor using artificial neural networks (ANNs). This controller is designed to remove nonlinear disturbance at low speeds. The proposed controller improves the stepper motor performance at less than the resonance speed of the stepper motor system. Due to its ability to learn, the proposed controller can adapt to different resonant speed ranges without any identification process for system parameters. Conversely, we also introduce the implementation of an ANN-based controller, online backpropagation learning, and a microstep driver on a single field-programmable gate array. An implementation and experimental results are conducted to verify the feasibility and the effectiveness of the proposed controller.