We present TouchSense, which provides additional touchscreen input vocabulary by distinguishing the areas of users' finger pads contacting the touchscreen. It requires minimal touch input area and minimal movement, making it especially ideal for wearable devices such as smart watches and smart glasses. For example, users of a calculator application on a smart watch could tap normally to enter numbers, and tap with the right side of their fingers to enter the operators (e.g. , -, =). Results from two human-factor studies showed that users could tap a touchscreen with five or more distinct areas of their finger pads. Also, they were able to tap with more distinct areas closer to their fingertips. We developed a TouchSense smart watch prototype using inertial measurement sensors, and developed two example applications: a calculator and a text editor. We also collected user feedback via an explorative study.

We present FingerPad, a nail-mounted device that turns the tip of the index finger into a touchpad, allowing private and subtle interaction while on the move. FingerPad enables touch input using magnetic tracking, by adding a Hall sensor grid on the index fingernail, and a magnet on the thumbnail. Since it permits input through the pinch gesture, FingerPad is suitable for private use because the movements of the fingers in a pinch are subtle and are naturally hidden by the hand. Functionally, FingerPad resembles a touchpad, and also allows for eyes-free use. Additionally, since the necessary devices are attached to the nails, FingerPad preserves natural haptic feedback without affecting the native function of the fingertips. Through user study, we analyze the three design factors, namely posture, commitment method and target size, to assess the design of the FingerPad. Though the results show some trade-off among the factors, generally participants achieve 93% accuracy for very small targets (1.2mm-width) in the seated condition, and 92% accuracy for 2.5mm-width targets in the walking condition.

We present a system which automatically reduces the ringtone volume as soon as users start picking up their mobile devices to answer incoming phone calls. Our system uses multiple sensors already in mobile phones (e.g. accelerometer, gyroscope, and proximity sensor) to extract 47 features, and uses supported vector machine (SVM) as the classifier to identify the act of picking up a phone. We have collected data under several typical conditions: including walking vs stationary and picking up the phone from inside a bag vs pockets. Our results show that the system can correctly identify users picking up their mobile phones 95% of the time on average.

The fabrication of a micro-machined microwave switch using the standard 0.35/spl mu/m CMOS process and the post-process by wet etching without extra mask is implemented in this work. The components of the microwave switch are coplanar waveguide (CPW) transmission lines, a suspended membrane and supported springs. Experimental results show that the driving voltage of the switch approximates to 17 V. The switch has an insertion loss of -1.5 /spl sim/ -2.5dB and an isolation of -5 /spl sim/ -15 dB between 30 /spl sim/ 50 GHz. Moreover, an equivalent circuit is proposed to analyse the performance of this switch.

In this paper, we propose a novel cross-layer decoder design that efficiently recovers more video data in the presence of transmission errors for 3G-324M video telephony over WCDMA networks. The proposed method requires collaboration of the video decoder and multiplexing protocol. In particular, it considers loss characteristics of WCDMA channels and modifies demultiplexer to provide additional information to assist video decoder to locate the error position. Then, the video decoder performs error concealment only on the lost macroblocks (MBs), instead of all the MBs in a video slice. The simulation results show that the proposed method can reduce the number of concealed MBs by 22-27% and improve video quality by 0.3-0.7 dB, depending on the video contents and block error rates.

A novel processing scheme for gray level image compression based on the human visual system (HVS) and fuzzy control is proposed. The spatial model of the threshold vision that incorporates the masking processes takes account of two major sensitivities of the human visual system, namely background illumination levels and spatial frequency sensitivities. The distortion measures use common sense fuzzy rules for image quality prediction. The human visual models have been successfully applied in image compression. By the addition of the visual model, performance of these systems has a visible improvement of subject quality depending on visual perception. The processed image performs both improved compression ratio as well as improved SNR (signal to noise ratio) in compression to the standardized still image compression technique, i.e., the JPEG.>

A combination of adaptive and repetitive control for dynamic control of robot manipulators is introduced. The repetitive controller is a plug-in controller for connection to an existing model-reference adaptive controller. The input to the repetitive controller is the same as the adaptation error signal for the adaptive controller. In this scheme, desired trajectories and external disturbance are assumed to be periodic. Global stability for this control system is achieved and disturbance is rejected successfully. Based on the characteristics of the reference model, two types of control algorithm are presented: a unity-gain and a pure-integrator reference model. Discretized versions suitable for digital implementations, and sufficient conditions for their stability in the discrete time domain are presented. The performance of the adaptive and repetitive controller is demonstrated by simulation.>

The analysis and experimental implementation of a discrete-time repetitive control scheme is presented. The repetitive control structure is designed to be easily implemented on any system without modification to the existing controller. Simulation and experimental results show that the repetitive controller in conjunction with a computed-torque control or a simple proportional-derivative control law achieves good tracking performance when the desired trajectory is periodic and the period is known.>

The model reference adaptive control of a two axis direct drive manipulator arm is presented. The two axes exhibit a significant dynamic interaction. The model reference adaptive controller is utilized in the velocity loop to adaptively decouple the dynamic interaction and to linearize the dynamics. The outer loop controller for positioning and tracking is designed based on the linear decoupled dynamics. The adaptive velocity loop controller as well as position loop controller are implemented digitally. The use of a series-parallel, as well as a parallel reference model is suggested in the adaptive velocity loop controller. The stability problems arising from the straightforward digital implementation of the continuous time algorithm are analyzed and a modification in the digital algorithm is introduced which guarantees the asymptotic stability of the system. Simulation and experimental results show a consistently superior performance of the manipulator under adaptive control.