In this paper, negative bias temperature instability (NBTI) reliability of pFETs is analyzed under the post-fabrication SRAM self-improvement scheme that we have developed recently, where cell stability is self-improved by simply applying high stress voltage to supply voltage terminal (VDD) of SRAM cells. It is newly found that there is no significant difference in both threshold voltage and drain current degradation by NBTI stress between fresh PFETs and PFETs after self-improvement scheme application, indicating that the self-improvement scheme has no critical reliability problem.

A predictive spatio-temporal interval join finds all pairs of moving objects satisfying a join condition on future time interval and space. In this paper, we propose a method called PTJoin. PTJoin partitions the inner index into small sub-trees and performs the join process for each sub-tree to reduce the number of disk page accesses for each window search. Furthermore, to reduce the number of pages accessed by consecutive window searches, we partition the index so that overlapping index pages do not belong to the same partition. Our experiments show that PTJoin reduces the number of page accesses by up to an order of magnitude compared to Interval_STJoin [9], which is the state-of-the-art solution, when the buffer size is small.

Parametric images can help investigate disease mechanisms and vital functions. To estimate parametric images, it is necessary to obtain the tissue time activity curves (tTACs), which express temporal changes of tracer activity in human tissue. In general, the tTACs are calculated from each voxel's value of the time sequential PET images estimated from dynamic PET data. Recently, spatio-temporal PET reconstruction methods have been proposed in order to take into account the temporal correlation within each tTAC. Such spatio-temporal algorithms are generally quite computationally intensive. On the other hand, typical algorithms such as the preconditioned conjugate gradient (PCG) method still does not provide good accuracy in estimation. To overcome these problems, we propose a new spatio-temporal reconstruction method based on the dynamic row-action maximum-likelihood algorithm (DRAMA). As the original algorithm does, the proposed method takes into account the noise propagation, but it achieves much faster convergence. Performance of the method is evaluated with digital phantom simulations and it is shown that the proposed method requires only a few reconstruction processes, thereby remarkably reducing the computational cost required to estimate the tTACs. The results also show that the tTACs and parametric images from the proposed method have better accuracy.

The GPS trajectory databases serve as bases for many intelligent applications that need to extract some trajectories for future processing or mining. When doing such tasks, spatio-temporal range queries based methods, which find all sub-trajectories within the given spatial extent and time interval, are commonly used. However, the history trajectory indexes of such methods suffer from two problems. First, temporal and spatial factors are not considered simutaneously, resulting in low performance when processing spatio-temporal queries. Second, the efficiency of indexes is sensitive to query size. The query performance changes dramatically as the query size changed. This paper proposes workload-aware Adaptive OcTree based Trajectory clustering Index (ATTI) aiming at optimizing trajectory storage and index performance. The contributions are three-folds. First, the distribution and time delay of the trajectory storage are introduced into the cost model of spatio-temporal range query; Second, the distribution of spatial division is dynamically adjusted based on GPS update workload; Third, the query workload adaptive mechanism is proposed based on virtual OcTree forest. A wide range of experiments are carried out over Microsoft GeoLife project dataset, and the results show that query delay of ATTI could be about 50% shorter than that of the nested index.

We outline a number of high temperature superconducting Josephson junction-based devices including superconducting quantum interference devices (SQUIDs) developed for a wide range of applications including geophysical exploration, magnetic anomaly detection, terahertz (THz) imaging and microwave communications. All these devices are based on our patented technology for fabricating YBCO step-edge junction on MgO substrates. A key feature to the successful application of devices based on this technology is good stability, long term reliability, low noise and inherent flexibility of locating junctions anywhere on a substrate.

The author developed a wideband precise I/Q modulator using GaAs pHEMT technology. In this technology, pHEMT has 0.22 µm metallurgical gate length and ft=51 GHz at Vds=5V. With the careful design of the wideband phase shifter, this IQ modulator achieved a large wideband frequency range of 250 MHz to 8 GHz and good EVM performance after calibration. For overall frequency range, low distortion performance is obtained, where third order intermodulation is less than -42 dBc. Also the ACPR at 2.2 GHz for W-CDMA application is less than -74 dBc.

This paper introduces new FPGA design of user-monitoring system for power management of PC display. From the camera readings the system detects whether the user looks at the screen or not and produces signals to control the display backlight. The system provides over 88% eye detection accuracy at 8f/s image processing rate. We describe new eye-tracking algorithm and hardware and present the results of its experimental evaluation in prototype display power management system.

Link prediction is the task of inferring the existence or absence of certain relationships among data objects such as identity, interaction, and collaboration. Link prediction is found in various applications in the fields of information integration, recommender systems, bioinformatics, and social network analysis. The increasing interest in dynamically changing networks has led to growing interest in a more general link prediction problem called temporal link prediction in the data mining and machine learning communities. However, only links among nodes at the same time point are considered in temporal link prediction. We propose a new link prediction problem called cross-temporal link prediction in which the links among nodes at different time points are inferred. A typical example of cross-temporal link prediction is cross-temporal entity resolution to determine the identity of real entities represented by data objects observed in different time periods. In dynamic environments, the features of data change over time, making it difficult to identify cross-temporal links by directly comparing observed data. Other examples of cross-temporal links are asynchronous communications in social networks such as Facebook and Twitter, where a message is posted in reply to a previous message. We adopt a dimension reduction approach to cross-temporal link prediction; that is, data objects in different time frames are mapped into a common low-dimensional latent feature space, and the links are identified on the basis of the distance between the data objects. The proposed method uses different low-dimensional feature projections in different time frames, enabling it to adapt to changes in the latent features over time. Using multi-task learning, it jointly learns a set of feature projection matrices from the training data, given the assumption of temporal smoothness of the projections. The optimal solutions are obtained by solving a single generalized eigenvalue problem. Experiments using a real-world set of bibliographic data for cross-temporal entity resolution and a real-world set of emails for unobserved asynchronous communication inference showed that introducing time-dependent feature projections improved the accuracy of link prediction.

Vision based human action recognition has been an active research field in recent years. Exemplar matching is an important and popular methodology in this field, however, most previous works perform exemplar matching on the whole input video clip for recognition. Such a strategy is computationally expensive and limits its practical usage. In this paper, we present a martingale framework for selection of characteristic frames from an input video clip without requiring any prior knowledge. Action recognition is operated on these selected characteristic frames. Experiments on 10 studied actions from WEIZMANN dataset demonstrate a significant improvement in computational efficiency (54% reduction) while achieving the same recognition precision.

This paper customizes Goal/Question/Metric (GQM) project monitoring models for various projects and organizations to take advantage of the data from the software tool EPM and to allow the tailoring of the interpretation models based upon the context and success criteria for each project and organization. The basic idea is to build less concrete models that do not include explicit baseline values to interpret metrics values. Instead, we add hypothesis and interpretation layers to the models to help people of different projects make decisions in their own context. We applied the models to two industrial projects, and found that our less concrete models could successfully identify typical problems in software projects.

A method for spatio-temporal resolution enhancement of video sequences based on super-resolution reconstruction is proposed. A new observation model is defined for accurate resolution enhancement, which enables subpixel motion in intermediate frames to be obtained. A modified optimization formula for obtaining a high-resolution sequence is also adopted.

Non-negative matrix factorization (NMF) is widely used for music transcription because of its efficiency. However, the conventional NMF-based music transcription algorithm often causes harmonic confusion errors or time split-up errors, because the NMF decomposes the time-frequency data according to the activated frequency in its time. To solve these problems, we proposed an NMF with temporal continuity and harmonicity constraints. The temporal continuity constraint prevented the time split-up of the continuous time components, and the harmonicity constraint helped to bind the fundamental with harmonic frequencies by reducing the additional octave errors. The transcription performance of the proposed algorithm was compared with that of the conventional algorithms, which showed that the proposed method helped to reduce additional false errors and increased the overall transcription performance.

Template matching for image sequences captured with a moving camera is very important for several applications such as Robot Vision, SLAM, ITS, and video surveillance systems. However, it is difficult to realize accurate template matching using only visual feature information such as HSV histograms, edge histograms, HOG histograms, and SIFT features, because it is affected by several phenomena such as illumination change, viewpoint change, size change, and noise. In order to realize robust tracking, structure information such as the relative position of each part of the object should be considered. In this paper, we propose a method that considers both visual feature information and structure information. Experiments show that the proposed method realizes robust tracking and determine the relationships between object parts in the scenes and those in the template.

This paper describes high-temperature electron transport properties of AlGaN-channel HEMT fabricated on a free-standing AlN substrate, estimated at temperatures between 25 and 300. The AlGaN-channel HEMT exhibited significantly reduced temperature dependence in DC and RF device characteristics, as compared to those for the conventional AlGaN/GaN HEMT, resulting in larger values in both saturated drain current and current gain cutoff frequency at 300. Delay time analyses suggested that the temperature dependence of the AlGaN-channel HEMT was primarily dominated by the effective electron velocity in the AlGaN channel. These results indicate that an AlGaN-channel HEMT fabricated on an AlN substrate is promising for high-performance device applications at high temperatures.

High output power AlGaN/GaN metal-insulator-semiconductor (MIS) hetero-junction field effect transistor (HFET) on Si substrate for millimeter-wave application has developed. High temperature chemical vapor deposition (HT-CVD) grown SiN as a gate insulator improves the breakdown characteristics which enables the operation at high drain voltage of 55 V. The device exhibits high drain current of 1.1 A/mm free from the current collapse and high RF gain of 10.4 dB. The amplifier module developed AlGaN/GaN MIS-HFET with the gate width of 5.4 mm exhibits an output power of 10.7 W and a linear gain of 4 dB at 26.5 GHz. The resultant high output power is very promising for long-distance communication at millimeter-wave in the future which would enable high speed and high density data transmission.

In this paper, a fast and automated method of counting pedestrians in crowded areas is proposed along with three contributions. We firstly propose Local Empirical Templates (LET), which are able to outline the foregrounds, typically made by single pedestrians in a scene. LET are extracted by clustering foregrounds of single pedestrians with similar features in silhouettes. This process is done automatically for unknown scenes. Secondly, comparing the size of group foreground made by a group of pedestrians to that of appropriate LET captured in the same image patch with the group foreground produces the density ratio. Because of the local scale normalization between sizes, the density ratio appears to have a bound closely related to the number of pedestrians who induce the group foreground. Finally, to extract the bounds of density ratios for groups of different number of pedestrians, we propose a 3D human models based simulation in which camera viewpoints and pedestrians' proximity are easily manipulated. We collect hundreds of typical occluded-people patterns with distinct degrees of human proximity and under a variety of camera viewpoints. Distributions of density ratios with respect to the number of pedestrians are built based on the computed density ratios of these patterns for extracting density ratio bounds. The simulation is performed in the offline learning phase to extract the bounds from the distributions, which are used to count pedestrians in online settings. We reveal that the bounds seem to be invariant to camera viewpoints and humans' proximity. The performance of our proposed method is evaluated with our collected videos and PETS 2009's datasets. For our collected videos with the resolution of 320 × 240, our method runs in real-time with good accuracy and frame rate of around 30 fps, and consumes a small amount of computing resources. For PETS 2009's datasets, our proposed method achieves competitive results with other methods tested on the same datasets [1],[2].

In this letter, we describe a low power current to time converter for wireless sensor networks. The proposed circuit has some advantages of high linearity and wide measurement range. From the evaluation using HSPICE with 0.18 µm CMOS device parameters, the output differential error for the input current variation is approximately 0.1 µs/nA under the condition that the current is varied from 100 nA to 500 nA. The idle power consumption is approximately zero.

Mel Frequency Cepstral Coefficients (MFCC) are the most popular acoustic features used in automatic speech recognition (ASR), mainly because the coefficients capture the most useful information of the speech and fit well with the assumptions used in hidden Markov models. As is well known, MFCCs already employ several principles which have known counterparts in the peripheral properties of human hearing: decoupling across frequency, mel-warping of the frequency axis, log-compression of energy, etc. It is natural to introduce more mechanisms in the auditory periphery to improve the noise robustness of MFCC. In this paper, a k-nearest neighbors based frequency masking filter is proposed to reduce the audibility of spectra valleys which are sensitive to noise. Besides, Moore and Glasberg's critical band equivalent rectangular bandwidth (ERB) expression is utilized to determine the filter bandwidth. Furthermore, a new bandpass infinite impulse response (IIR) filter is proposed to imitate the temporal masking phenomenon of the human auditory system. These three auditory perceptual mechanisms are combined with the standard MFCC algorithm in order to investigate their effects on ASR performance, and a revised MFCC extraction scheme is presented. Recognition performances with the standard MFCC, RASTA perceptual linear prediction (RASTA-PLP) and the proposed feature extraction scheme are evaluated on a medium-vocabulary isolated-word recognition task and a more complex large vocabulary continuous speech recognition (LVCSR) task. Experimental results show that consistent robustness against background noise is achieved on these two tasks, and the proposed method outperforms both the standard MFCC and RASTA-PLP.

In this letter, we address the issue of estimating the temporal dependence characteristic of link loss by using network tomography. We use a k-th order Markov chain (k > 1) to model the packet loss process, and estimate the state transition probabilities of the link loss model using a constrained optimization-based method. Analytical and simulation results indicate that our method yields more accurate packet loss probability estimates than existing loss inference methods.

In this letter, we propose a novel method for change detection in multitemporal optical satellite images. Unlike the tradition methods, the proposed method is able to detect changed region even from unregistered images. In order to obtain the change detection map from the unregistered images, we first compute the sum of the color difference (SCD) of a pixel to all pixels in an input image. Then we calculate the SCD of this pixel to all pixels in the other input image. Finally, we use the difference of the two SCDs to represent the change detection map. Experiments on the multitemporal images demonstrates the good performance of the proposed method on the unregistered images.