In this paper, we propose a hybrid crystal oscillator which achieves both quick startup and low steady-state power consumption. At startup, a large negative resistance is realized by configuring a Pierce oscillating circuit with a multi-stage inverter amplifier, resulting in high-speed startup. During steady-state oscillation, the oscillator is reconfigured as a class-C complementary Colpitts circuit for low power consumption and low phase noise. Prototype chips were fabricated in 65nm CMOS process technology. With Pierce-type configuration, the measured startup time and startup energy of the oscillator are reduced to 1/11 and 1/5, respectively, compared with the one without Pierce-type configuration. The power consumption during steady oscillation is 30 µW.

The phenomenon where a speaker mixes two or more languages within the same conversation is called code-switching (CS). Handling CS is challenging for automatic speech recognition (ASR) and text-to-speech (TTS) because it requires coping with multilingual input. Although CS text or speech may be found in social media, the datasets of CS speech and corresponding CS transcriptions are hard to obtain even though they are required for supervised training. This work adopts a deep learning-based machine speech chain to train CS ASR and CS TTS with each other with semisupervised learning. After supervised learning with monolingual data, the machine speech chain is then carried out with unsupervised learning of either the CS text or speech. The results show that the machine speech chain trains ASR and TTS together and improves performance without requiring the pair of CS speech and corresponding CS text. We also integrate language embedding and language identification into the CS machine speech chain in order to handle CS better by giving language information. We demonstrate that our proposed approach can improve the performance on both a single CS language pair and multiple CS language pairs, including the unknown CS excluded from training data.

Input devices based on direct touch have replaced traditional ones and become the mainstream interactive technology for handheld devices. Although direct touch interaction proves to be easy to use, its problems, e.g. the occlusion problem and the fat finger problem, lower user experience. Camera-based mobile interaction is one of the solutions to overcome the problems. There are two typical interaction styles to generate camera-based pointing interaction for handheld devices: move the device or move an object before the camera. In the first interaction style, there are two approaches to move a cursor's position across the handheld display: move it towards the same direction or the opposite direction which the device moves to. In this paper, the results of a comparison research, which compared the pointing performances of three camera-based pointing techniques, are presented. All pointing techniques utilized input from the rear-facing camera. The results indicate that the interaction style of moving a finger before the camera outperforms the other one in efficiency, accuracy, and throughput. The results also indicate that within the interaction style of moving the device, the cursor positioning style of moving the cursor to the opposite direction is slightly better than the other one in efficiency and throughput. Based on the findings, we suggest giving priority to the interaction style of moving a finger when deploying camera-based pointing techniques on handheld devices. Given that the interaction style of moving the device supports one-handed manipulation, it also worth deploying when one-handed interaction is needed. According to the results, the cursor positioning style of moving the cursor towards the opposite direction which the device moves to may be a better choice.

Touch screen has become the mainstream manipulation technique on handheld devices. However, its innate limitations, e.g. the occlusion problem and fat finger problem, lower user experience in many use scenarios on handheld displays. Back-of-device interaction, which makes use of input units on the rear of a device for interaction, is one of the most promising approaches to address the above problems. In this paper, we present the findings of a user study in which we explored users' pointing performances in using two types of touch input on handheld devices. The results indicate that front-of-device touch input is averagely about two times as fast as back-of-device touch input but with higher error rates especially in acquiring the narrower targets. Based on the results of our study, we argue that in the premise of keeping the functionalities and layouts of current mainstream user interfaces back-of-device touch input should be treated as a supplement to front-of-device touch input rather than a replacement.

This paper proposes a Watts and Strogatz-model based routing method for wireless sensor network along with link-exchange operation. The proposed routing achieves low data-collection delay because of hub-node existence. By applying the link exchanges, node with low remaining battery level can escape from a hub node. Therefore, the proposed routing method achieves the fair battery-power consumptions among sensor nodes. It is possible for the proposed method to prolong the network lifetime with keeping the small-world properties. Simulation results show the effectiveness of the proposed method.

The most important advantage of HMM-based TTS is its highly intelligible. However, speech synthesized by HMM-based TTS is muffled and far from natural, especially under limited data conditions, which is mainly caused by its over-smoothness. Therefore, the motivation for this paper is to improve the naturalness of HMM-based TTS trained under limited data conditions while preserving its intelligibility. To achieve this motivation, a hybrid TTS between HMM-based TTS and the modified restricted Temporal Decomposition (MRTD), named HTD in this paper, was proposed. Here, TD is an interpolation model of decomposing a spectral or prosodic sequence of speech into sparse event targets and dynamic event functions, and MRTD is one simplified version of TD. With a determination of event functions close to the concept of co-articulation in speech, MRTD can synthesize smooth speech and the smoothness in synthesized speech can be adjusted by manipulating event targets of MRTD. Previous studies have also found that event functions of MRTD can represent linguistic information of speech, which is important to perceive speech intelligibility, while sparse event targets can convey the non-linguistics information, which is important to perceive the naturalness of speech. Therefore, prosodic trajectories and MRTD event functions of the spectral trajectory generated by HMM-based TTS were kept unchanged to preserve the high and stable intelligibility of HMM-based TTS. Whereas MRTD event targets of the spectral trajectory generated by HMM-based TTS were rendered with an original speech database to enhance the naturalness of synthesized speech. Experimental results with small Vietnamese datasets revealed that the proposed HTD was equivalent to HMM-based TTS in terms of intelligibility but was superior to it in terms of naturalness. Further discussions show that HTD had a small footprint. Therefore, the proposed HTD showed its strong efficiency under limited data conditions.

A 5.6-GHz 1-V balanced LC-tank Colpitts voltage controlled oscillator is designed and implemented with a TSMC 0.18-µm CMOS process. This proposed Colpitts VCO circuit adopts two single-ended complementary LC-tank VCOs coupled by two pairs of varactors. The proposed VCO operates at low power consumption because it has the same dc current path as the np-MOSFETs. The Measured results of the proposed VCO achieve tuning range of 670 MHz from 5.23 to 5.9 GHz while the controlled voltage is tuned from 0 to 1-V, phase noise of -118.8 dBc/Hz at 1 MHz offset frequency from the carrier of 5.6 GHz and output power of -10.97 dBm at the supply voltage of 1 V. The power consumption of the core circuit is 1.79 mW and the chip area including pads is 0.451 (0.55 0.82) mm2.

In this paper, a 24 GHz frequency source for low phase noise is presented in a 0.18 µm CMOS process. The 24 GHz frequency source chip is composed of a 12 GHz voltage controlled oscillator (VCO) and a 24 GHz balanced frequency doubler with class B gate bias. Compared to a conventional complementary VCO, the proposed 12 GHz VCO has phase noise improvement by using resistor current sources and substituting the nMOS cross-coupled pair in the conventional complementary VCO for a gm-boosted nMOS differential Colpitts pair. The measured phase noise and fundamental frequency suppression are -107.17 dBc/Hz at a 1 MHz offset frequency and -20.95 dB at 23.19 GHz frequency, respectively. The measured frequency tuning range is from 23.19 GHz to 24.76 GHz drawing 2.72 mA at a supply voltage of 1.8 V not including an output buffer.

This letter presents the design and analysis of phase noise optimization of a 4-GHz differential Colpitts voltage-controlled-oscillator (VCO). A low phase noise is achieved by a Colpitts oscillator and a VCO bias optimization using an amplitude control method. The measured phase noise is -134.8 dBc/Hz at 1.25 MHz offset frequency from 4 GHz operating frequency. The VCO is implemented using 0.24 µm SiGe BiCMOS process with integrated copper inductors. The wide VCO frequency range covers both PCS and IMT bands and draws about 15.9 mA from a 2.7 V power supply.

Two-Terminal Series Parallel (TTSP, for short) graphs are used as data models in applications for electric networks and scheduling problems. We propose a TTSP term graph which is a TTSP graph having structured variables, that is, a graph pattern over a TTSP graph. Let TGTTSP be the set of all TTSP term graphs whose variable labels are mutually distinct. For a TTSP term graph g in TGTTSP, the TTSP graph language of g, denoted by L(g), is the set of all TTSP graphs obtained from g by substituting arbitrary TTSP graphs for all variables in g. Firstly, when a TTSP graph G and a TTSP term graph g are given as inputs, we present a polynomial time matching algorithm which decides whether or not L(g) contains G. The minimal language problem for the class LTTSP={L(g) | g ∈ TGTTSP} is, given a set S of TTSP graphs, to find a TTSP term graph g in TGTTSP such that L(g) is minimal among all TTSP graph languages which contain all TTSP graphs in S. Secondly, we give a polynomial time algorithm for solving the minimal language problem for LTTSP. Finally, we show that LTTSP is polynomial time inductively inferable from positive data.

This work presents a technique to enhance the performance of the conventional PMOS Colpitts VCO circuit. This technique is accomplished by adding an NMOS cross-coupled pair under the traditional differential Colpitts VCO to enhance the oscillator startup condition and its efficiency. The analytics also support this viewpoint and present a device- choosing method to optimize the output power and phase noise. This new VCO can also be applied to realize the QVCO circuit, because the coupling transistors can be placed in parallel, connecting with the transistors in the NMOS cross-coupled pair, to achieve the proper coupling between individual VCOs. To verify the proposed design concept, two prototypes, which are VCO and QVCO operated at 2.4 GHz and fabricated in CMOS 0.25-µm technology, are designed and tested. The measurement results show that the performance of VCO demonstrates a FOM of about 180 dBC/Hz, and the phase noise of QVCO is -116 dBc/Hz at the 1 MHz offset from oscillation frequency.

A complementary cross-coupled differential Colpitts voltage controlled oscillator (VCO) is reported. The combination of gm-boosting and the complementary transistors allows record low power integrated VCO implementation. The proposed VCO and the corresponding parallel quadrature VCO (P-QVCO) are implemented using 0.25-µm CMOS technology for 1.8 GHz operation. Measurements for the VCO and P-QVCO show phase noise of -116.8 and -117.7 dBc/Hz at 1 MHz offset, while dissipating only 0.4 and 1.1 mA from a 0.9-V supply, respectively.

In human-computer interaction, Fitts' law has been applied in one-dimensional pointing task evaluation for some decades, and the usage of effective target width (We) in Fitts' law has been accepted as an international standard in ISO standards 9241-9 [4]. However, the discussion on the concrete methods for calculating We has not been developed comprehensively nor have the different methods of calculation been integrated. Therefore, this paper focuses on a detailed description and a comparison of the two main We calculation methods. One method is mapping all the abscissa data in one united relative coordinate system to perform the calculation (called CC method) and the other is dividing the data into two groups and mapping them in two separate coordinate systems (called SC method). We tested the accuracy of each method and compared both methods in a highly controlled experiment. The experiments' results and data analysis show that the CC method is better than the SC method for human computer interface modeling. These results will be instrumental for future application of Fitts' law.

Fitts' law has been applied in many studies to evaluate pointing tasks. However, the quantitative effect of using color in the interfaces has not been discussed in the literature. This paper introduces research on the effects of color in pointing tasks using Fitts' law as the evaluation method. Different colors and color presentation styles are applied in the experiments which are similar in design to the paradigmatic Fitts' law pointing task. The experimental results show that when the subjects use a mouse as the input device, there is no significant difference between an interface with a colored target and one with a white target in the mean performance time. The results also reveal that color presentation styles will offer no significant difference to pointing tasks when the mouse is applied. However, when the interface of tablet PC and pen was applied, subjects without much experience in tablet personal computer usage needed more time to perform the task with colored targets than with a white target. Furthermore, when the colors are changed randomly during the selection process, the difference is even more obvious. These results are confirmed by a Checking Experiment and a Learning Effect Experiment which we performed on different groups of subjects.

This work explores generative models of handwritten digit images using natural elastic nets. The analysis aims to extract global features as well as distributed local features of handwritten digits. These features are expected to form a basis that is significant for discriminant analysis of handwritten digits and related analysis of character images or natural images.

In this study, we clarified the differences in the pointing time required when using a touch panel and a PC mouse for three age groups: young, middle-aged, and elderly. We constructed a performance model for a touch panel operation (Experiment 1). Moreover, we investigated the visual interference caused by a multi-target presentation (Experiment 2). The delay caused by visual interference for the right-hand target was longer than that for the left-hand target, and that for the upper target was longer than that for the lower target.

In this study, we developed a new performance model that uses an index of difficulty to predict the pointing time required for a pointing task when using a PC mouse. Forty-nine subjects were classified into three age groups: young, middle-aged, and elderly. Experimental factors were the target size, the target distance, and the approach angle to the target. The time for pointing and the coordination of the pointer (mouse cursor) were measured in all conditions. The pointing time was significantly longer for the elderly group than for the young and middle-aged groups. Moreover, the pointing time in each age group tended to increase linearly with an increase in target distance and to decrease as a logarithmic function with an increase in target size. Based on these results, we proposed a new model for measuring performance when operating a PC mouse. Our model received a higher rating for ease-of-use than conventional models. It was clarified that the performance model for elderly persons was different from that for other age groups using the proposed model.

We have demonstrated synchronization of chaos in a pair of one-way coupled Colpitts oscillators by both experiment and numerical simulation. We have investigated parameter regions for achieving chaos-synchronization when one of the internal parameters is mismatched between the master and slave oscillators, and clarify the tolerance of parameter regions for synchronization against parameter mismatching.

It is shown by the derivation of solution methods for an elementary optimization problem that the stochastic relaxation in image analysis, the Potts neural networks for combinatorial optimization and interior point methods for nonlinear programming have common formulation of their dynamics. This unification of these algorithms leads us to possibility for real time solution of these problems with common analog electronic circuits.