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Driven by strong market demand, various research approaches for in-cell touch display implementation have been undertaken in recent years, with several being successfully delivered to the industry and market. As a result, in-cell touch-display solutions that offer low-profile formfactor, low module cost, and simplified supply chain have extensively replaced conventional add-on touch panels. Among these, in-cell touch liquid-crystal display (LCD) using segmented-VCOM electrodes, also known as advanced in-cell touch (AIT) LCD [1] running on touch-display-driver IC (TDD), has emerged as one of the best-performing solutions in the market.

As interest in autonomous driving system has increased recently, variety of ADAS technologies that are responsible for safety of passengers are receiving attention. Realtime analysis and robust vehicle detection are essential for ADAS technology to cope with rapidly changing road conditions. In this paper, we propose a real-time vehicle detection system based on correlation filter.

In this paper, we devise methods for the multiobjective control of humanoid robots, a.k.a. prioritized whole-body controllers, that achieve efficiency and robustness in the algorithmic computations. We use a form of whole-body controllers that is very general via incorporating centroidal momentum dynamics, operational task priorities, contact reaction forces, and internal force constraints. First, we achieve efficiency by solving a quadratic program that only involves the floating base dynamics and the reaction forces. Second, we achieve computational robustness by relaxing task accelerations such that they comply with friction cone constraints. Finally, we incorporate methods for smooth contact transitions to enhance the control of dynamic locomotion behaviors. The proposed methods are demonstrated both in simulation and in real experiments using a passive-ankle bipedal robot.

The distributed shared memory (DSM)-based computation offloading scheme allows collaborative multiple threads to dynamically migrate and execute across a mobile device and computing nodes. Despite this strong advantage, it misses a significant portion of the potential performance gain because the traditional DSM model is suboptimal for computation offloading. This paper proposes an enhanced DSM model that aims to enable multiple computing nodes to efficiently and reliably offload concurrent multiple threads from a mobile device. To achieve this design goal, we propose the following novel schemes: a) selective object tracking minimizes the set of objects to be monitored by the DSM layer; b) lock-thread repartitioning dynamically relocates threads and locks in order to reduce remote lock acquisitions and inter-node synchronizations; and c) thread-state checkpointing protects the data and context upon unexpected system failures. We implemented METEOR, which is a prototype based on the proposed schemes, and evaluated it with diverse applications. The evaluation showed that METEOR, with four computing nodes, improved the performance by up to 109% and reduced energy consumption by up to 52% in comparison with the previous DSM-based offloading scheme.

Recently, supply voltage has been reduced for low power applications, and near threshold computing (NTC) is considered as a promising solution for optimal energy efficiency. However, NTC suffers a significant performance degradation, which is prone to timing errors. Thus, in order to improve the reliability of NTC operations, error-resilient techniques are indispensable, though they cause area and power overheads. In this paper, we propose a design methodology which provides an optimal implementation of error-resilient circuits. A modified Quine-McCluskey (Q-M) algorithm is exploited to earn the minimum set of error-resilient circuits without any loss of detection ability. From the proposed design flow, benchmark results show that optimal design reduces up to 72% of required flip-flops to be changed to error-resilient circuits without compromising an error detection ability.

This paper presents a real-time framework for tele-manipulation by using sEMG signals to estimate both human motion and force intention. Our previous study showed that the ability to detect discrete force levels was not applicable to complex tasks such as grasping, holding, and manipulating various objects with variable force. Consequently, we identified the need to simultaneously track the arm and hand configurations and estimate the grasping force. However, it is difficult to continuously estimate the grasping force because of the time-varying nature of surface Electromyogram (sEMG) signals, even if a force remains constant. To solve such a problem, this study proposes a new regression strategy to enable continuous and proportional measurements and transmission of the grasping force by using sEMG signals in transient and steady-states. A 7-DOF robot arm with a robotic hand was able to remotely imitate a subject via an easily-wearable sEMG and inertia measurement units sensor interface. The experimental results verified that the motion and force capturing system successfully enabled interaction tasks, such as grasping, holding, and releasing motions with objects, with reliable and continuous force estimation.

In this paper, we analyze on- and off-current characteristic of the 22-nm Bi-level FinFET in which two different fin widths are formed. From the 3D Technology CAD (TCAD) simulations, we find out that the narrower the fin width the lower the leakage current. However, narrower fin width results in the reduced driving current for the triple-gate FinFET structure. We propose the optimal shape parameters of Bi-level FinFET such as the fin width and height of the narrower fin for providing better leakage current while keeping the required driving current of the nominal FinFET. Simulation results show that up to 4% speed up with 33% leakage current reduction by the optimal Bi-level FinFET compared to the nominal one.

As power-assisted robots such as exoskele-ton robot have been widely used in eclectic applications, the robot becomes more interactive than industrial robots. More specifically, the power-assisted robot for rehabilitation requires to enhance power with respect to the intended motion. To do that, the power-assisted robot should recognize which part of interaction is based on human-intention. In this paper, a new classifier, which consists of force information measured by F/T sensor on the robot and sEMG signals from muscle activation, is proposed to extract human-intention under interaction including external force. The proposed classifier can be applied to estimate the external force level generated due to the interaction. Based on the proposed classifier, a simple control method to enhance power to assist the intention-based motion is developed to validate the proposed approach. For the simplicity and clarity of the approach, 1DOF testbed robot is used to demonstrate the proposed approach.

As the technology node has scaled down below 32 nm, the supply voltage has decreased to 1V and the current demands for active devices have increased. Therefore, the supply noise due to the IR drop in the power delivery network (PDN) has become a critical problem for robust circuit operation. Huge decoupling capacitors are introduced to overcome the supply voltage fluctuations. In this study, we investigate a 32-nm double-gate FinFET device for a decoupling capacitor in the PDN. The circuit designers can independently control both the gates in the double-gate FinFET. We compare the supply and ground noise reduction in the conventional planar CMOS and in various FinFET structures in a PEEC-based practical PDN and propose the best decoupling capacitor design strategy for double-gate FinFETs. The simulation results show that we can achieve an increased reduction in the supply voltage noise up to 50% by shorting the front gate and back gate together.

Myo-electric signals have been widely used in human-machine interfaces because these biosignal directly reflect human intentions to robots. The major difficulty of applying these biosignal in a pattern recognition system in real time is that they are unstable and vary in time. This instability occurs outside of the steady state of the signal, at the beginning and the ending of the motions. For real-time application users, the errors at the beginning of motion can lower the credibility of a pattern recognition system. In this sense, precise classification is the most significant factor for the system; thus the classification accuracy has higher priority compared to other factors. Generally, a trade-off relationship between the time delay of control commands and the classification accuracy has been known for sEMG users. Since parameters for signal processing can alter the sensitivity(time delay and accuracy) of the system, this study investigates limitations of a pattern recognition system due to transient-state errors. In particular, the performance of the system is analysed with respect to the analysis window size and the voting size of classification. Through an off-line simulation, we propose useful guidelines for the analysis window size and voting size in myoelectric signals for real-time applications.

This paper proposes an integrated control method including learning with an ellipsoid function and impedance controller for rehabilitation using power-assisted robotic de- vices. The proposed controller consists of two parts of a primary algorithm, which are ellipsoid regression method for re-designing trajectory and impedance controller with pseudo mass/inertia. The ellipsoid regression method generates refer- ence impedance profiles though acquiring motion and force trajectories during rehabilitation tasks assisted by therapists. The assisted force is controlled by impedance controller during execution of rehabilitation task using a concept of pseudo mass/inertia. The proposed method offers the power-assisted rehabilitation as guided by therapist, without consistent help from the therapist or other assisters. The proposed control method is validated by experiments throughout a 2-DOF reha- bilitation robot, KULEX-2DOF(KIST Upper Limb Exoskeleton - 2DOF). I. INTRODUCTION The importance of rehabilitation robots, one of the repre- sentative applications of interactive robots, has been growing. After stroke or injuring upper limbs, the patients requires a consistent rehabilitation for recovering functions of the upper limb. Especially, approximately 50% of stroke suffers requires special care and rehabilitation as soon and regular as possible after the stroke (1). Additionally, repetitive training or implementing remedial motion are helpful to improve the performance of rehabilitation in motor system of suffers (2). As conventional researches have reported that the repetitive practice of movements and specific functional activities with external assisting force can be effective for stroke suffers (3), robot-assisted rehabilitation becomes more important and remarkable. In this sense, the robotic system can contribute to reha- bilitation for not only stroke suffers but also elderly and disabled by assisting behavior. As shown in Fig. 1. (a), the therapist or the physiatrist are demanded to guide a remedial exercise of suffers with proper therapeutic program considering physical condition of them. It means that a self-rehabilitation can be impossible or inefficient when the therapist is absent. However, the rehabilitation robot can help rehabilitation while the therapist is absent as Fig. 1. (b). For safe and effective rehabilitation, it is an interesting issue to learn and to reproduce the therapeutic behavior with training motion and assisting power for rehabilitation, simultaneously. The power-assisted exoskeleton robot is able to be used to improve performance of daily tasks for the old, infirm and

Impact Wrench is one of power tools which have sophisticated structure to generate impact motion by using electrical motor or hydraulic actuators. In the system, an impacting module, which consists of impactor, spindle, connect balls, locker and spring, has an important role to generate impacting motion. In this paper, two strategies are investigated for improving performance to generate high level torque at an anvil. The motion equation of the system during impacting motion is derived and the control methodologies are proposed to manipulate contact force, sequentially. One of the proposed control methods is controlling the spring length by compressing on the opposite side of impactor. The other method is to accelerate or decelerate the rotational behavior by modifying velocity profile. Besides, the simulation results of two strategies are compared in magnitude of contact force and impacting frequency.

As more sophisticated robots are being developed, robots are increasingly expected to execute many types of task sets sequentially or simultaneously. When these multiple task sets are executed, the controllers of the robots should be able to deal dynamically with task changes. Especially during the task transitions, there will be discontinuous behaviors of robots in the absence of careful consideration of control. In this paper, a task transition approach is proposed to provide continuous task transitions and to ensure stable behavior of robots within the operational space control framework. In the proposed approach, the control law is not modified but the control command is composed using intermediate desired values. In this paper, a continuous task transition algorithm is applied for singularity avoidance and for joint limit avoidance purposes during the control of a 6-DOF manipulator to demonstrate its performance.

We have investigated pad open failure of data line. To verify the mechanism of DPO failure, we designed the new method included in CCD camera and driving circuit and successfully achieved the failure images. The DPO procedure was divided into three steps. First, side wall of Cu film was exposed to Cl plasma and then CuClx compounds formed on surface of Cu film. Subsequently, when it was rinsed by DI water, CuClx was decomposed into Cu and HCl. As a result, etch taper was changed to be abnormal because of re-etching of Cu surface due to HCl. Second, passivation layer was deposited on the data line with poor step coverage. Finally, the panel with poor step coverage was introduced to cleaning bath before module processing and residual water still remained on the data pad area by final operating inspection. Under these conditions, when the operating voltage was applied to the panel, migration of Cu was easily occurred. As a result, we successfully verified the mechanism of DPO in real time.

Capacitive touch screen sensor, though it enables multi-touch recognition, has problems rejecting distributed noise such as LCD, RF and EMI interferences coupled to transparent ITO electrodes. This paper presents a capacitive sensor design for multiple channels at the X-Y crossing of ITO patterns on the LCD panel. Capacitance-to-Voltage converter (C-VC) with moving average and time interleaved scheme is proposed to reject noises and achieve high data-rate. Calibration algorithm is used to compensate the delay of each channel due to series RC of ITO pattern. The measured signal-to-noise ratio of the sensor is 24dB at 65Hz scanning frequency for 320 channels. The prototype IC is implemented in a 0.18 CMOS technology, while consuming 13mW from 3.3V and 6mW from 1.8V supply.