This paper focuses on the coalescing operator applied to the processing of continuous queries with temporal functions and predicates over windowed data streams. Coalescing is a key operation enabling the evaluation of interval predicates and functions on temporal tuples. Applying this operation for temporal query processing on windowed streams brings the challenge of coalescing tuples in a window extent each time the window slides over the data stream. This coalescing becomes even more involving when some tuples arrive out of order. This paper distinguishes between eager coalescing and lazy coalescing, the two known coalescing schemes. The former coalesces tuples during window extent update and the latter does it during window extent scan. With these two schemes, the paper first presents algorithms for updating a window extent for both tuple-based and time-based windows. Then, the problem of optimally selecting between eager and lazy coalescing for concurrent queries is formulated as a 0-1 integer programming problem. Through extensive performance study, the two schemes are compared and the optimal selection is demonstrated.

The non-reference method is widely useful for picture quality estimation on the decoder side. In other work, we discussed pure non-reference estimation using only the decoded picture, and we proposed quantitative estimation methods for mosquito noise and block artifacts. In this paper, we discuss the estimation method as it applies to the degradation of the temporal domain. In the proposed method, motion compensated inter-picture differences and motion vector activity are the basic parameters of temporal degradation. To obtain these parameters, accurate but unstable motion estimation is used with a 1/16 reduction of processing power. Similar values of the parameters in the pictures can be seen in the stable original picture, but temporal degradation caused by the coding increases them. For intra-coded pictures, the values increase significantly. However, for inter-coded pictures, the values are the same or decrease. Therefore, by taking the ratio of the peak frame and other frames, the absolute value of the temporal degradation can be estimated. In this case, the peak frame may be intra-coded. Finally, we evaluate the proposed method using coded pictures with different quantization.

Biometric authentication has attracted attention because of its high security and convenience. However, biometric feature such as fingerprint can not be revoked like passwords. Thus once the biometric data of a user stored in the system has been compromised, it can not be used for authentication securely for his/her whole life long. To address this issue, an authentication scheme called cancelable biometrics has been studied. However, there remains a major challenge to achieve both strong security and practical accuracy. In this paper, we propose a novel and fundamental algorithm for cancelable biometrics called correlation-invariant random filtering (CIRF) with provable security. Then we construct a method for generating cancelable fingerprint templates based on the chip matching algorithm and the CIRF. Experimental evaluation shows that our method has almost the same accuracy as the conventional fingerprint verification based on the chip matching algorithm.

In this paper we first demonstrate that effective selection functions in power analysis attacks change depending on circuit architectures of a block cipher. We then conclude that the most resistant architecture on its own, in the case of the loop architecture, has two data registers have separate roles: one for storing the plaintext and ciphertext, and the other for storing intermediate values. There, the pre-whitening operation is placed at the output of the former register. The architecture allows the narrowest range of selection functions and thereby has resistance against ordinary CPA. Thus, we can easily defend against attacks by ordinary CPA at the architectural level, whereas we cannot against DPA. Secondly, we propose a new technique called "self-templates" in order to raise the accuracy of evaluation of DPA-based attacks. Self-templates enable to differentiate meaningful selection functions for DPA-based attacks without any strong assumption as in the template attack. We also present the results of attacks to an AES co-processor on an ASIC and demonstrate the effectiveness of the proposed technique.

Spectrum sensing is a fundamental function for cognitive radio network to protect transmission of primary system. Cooperative spectrum sensing, which can help increasing sensing performance, is regarded as one of the most promising methods in realizing a reliable cognitive network. In such cooperation system, however the communication resources such as sensing time delay, control channel bandwidth and consumption energy for reporting the cognitive radio node's sensing results to the fusion center may become extremely huge when the number of cognitive users is large. In this paper, we propose an ordered sequential cooperative spectrum sensing scheme in which the local sensing data will be sent according to its reliability order to the fusion center. In proposed scheme, the sequential fusion process is sequentially conducted based on Dempster Shafer theory of evidence's combination of the reported sensing results. Above all, the proposed scheme is highly feasible due to the proposed two ordered sequential reporting methods. From simulation results, it is shown that the proposed technique not only keeps the same sensing performance of non-sequential fusion scheme but also extremely reduces the reporting resource requirements.

This paper investigates the relationship between averaged SAR (Specific Absorption Rate) over 10 g mass and temperature elevation in Japanese numerical anatomical models when devices are mounted on the body. Simplifying the radiation source as a half-wavelength dipole, the generated electrical field and SAR are calculated using the FDTD (Finite-Difference Time-Domain) method. Then the bio-heat equation is solved to obtain the temperature elevation due to the SAR derived using the FDTD method as heat source. Frequencies used in the study are 900 MHz and 1950 MHz, which are used for mobile phones. In addition, 3500 MHz is considered because this frequency is reserved for IMT-Advanced (International Mobile Telecommunication-Advanced System). Computational results obtained herein show that the 10 g-average SAR and the temperature elevation are not proportional to frequency. In addition, it is clear that those at 3500 MHz are lower than that at 1950 MHz even though the frequency is higher. It is the point to be stressed here is that good correlation between the 10 g-average SAR and the temperature elevation is observed even for the body-worn device.

Due to the superlinear dependence of leakage power consumption on temperature, and spatial variations in on-chip thermal profiles, methods of leakage power estimation that are known to be accurate require detailed knowledge of thermal profiles. Leakage power depends on the integrated circuit (IC) thermal profile and circuit design style. Here, we show that piecewise linear models can be used to permit accurate leakage estimation over the operating temperature ranges of the ICs. We then show that for typical IC packages and cooling structures, a given amount of heat introduced at any position in the active layer will have a similar impact on the average temperature of the layer. These two observations support the proof that, for wide ranges of design styles and operating temperatures, extremely fast, coarse-grained thermal models, combined with piecewise linear leakage power consumption models, enable the estimation of chip-wide leakage power consumption. These results are further confirmed through comparisons with leakage estimates based on detailed, time-consuming thermal analysis techniques. Experimental results indicate that, when compared with a leakage analysis technique that relies on accurate spatial temperature estimation, the proposed technique yields a 59,259 to 1,790,000 speedup in estimating leakage power consumption, while maintaining accuracy.

In the field of software reengineering, many component identification approaches have been proposed for evolving legacy systems into component-based systems. Understanding the behaviors of various component identification approaches is the first important step to meaningfully employ them for legacy systems evolution, therefore we performed an empirical study on component identification technology with considerations of their similarity measures, clustering approaches and stopping criteria. We proposed a set of evaluation criteria and developed the tool CIETool to automate the process of component identification and evaluation. The experimental results revealed that many components of poor quality were produced by the employed component identification approaches; that is, many of the identified components were tightly coupled, weakly cohesive, or had inappropriate numbers of implementation classes and interface operations. Finally, we presented an analysis on the component identification approaches according to the proposed evaluation criteria, which suggested that the weaknesses of these clustering approaches were the major reasons that caused components of poor-quality.

This paper proposes two algorithms to balance energy consumption among sensor nodes by distributing the workload of image compression tasks within a cluster on wireless sensor networks. The main point of the proposed algorithms is to adopt the energy threshold, which is used when we implement the exchange and/or assignment of tasks among sensor nodes. The threshold is well adaptive to the residual energy of sensor nodes, input image, compressed output, and network parameters. We apply the lapped transform technique, an extended version of the discrete cosine transform, and run length encoding before Lempel-Ziv-Welch coding to the proposed algorithms to improve both quality and compression rate in image compression scheme. We extensively conduct computational experiments to verify the our methods and find that the proposed algorithms achieve not only balancing the total energy consumption among sensor nodes and, thus, increasing the overall network lifetime, but also reducing block noise in image compression.

We proposed a novel representation of the thermal noise for scaled MOSFETs by applying an extended van der Ziel's model. A comparison between the proposed representation and Pospieszalski's model is also performed. We confirmed that the representation of drain noise temperature, Td corresponds to the electron temperature in a gradual channel region.

In this paper, a human detection method is developed. An appearance based detector and a motion based detector are proposed respectively. A multi scale block histogram of template feature (MB-HOT) is used to detect human by the appearance. It integrates the gray value information and the gradient value information, and represents the relationship of three blocks. Experiment on INRIA dataset shows that this feature is more discriminative than other features, such as histogram of orientation gradient (HOG). A motion based feature is also proposed to capture the relative motion of human body. This feature is calculated in optical flow domain and experimental result in our dataset shows that this feature outperforms other motion based features. The detection responses obtained by two features are combined to reduce the false detection. Graphic process unit (GPU) based implementation is proposed to accelerate the calculation of two features, and make it suitable for real time applications.

The spatio-temporal multiple view geometry can represent the geometry of multiple images in the case where non-rigid arbitrary motions are viewed from multiple translational cameras. However, it requires many corresponding points and is sensitive to the image noise. In this paper, we investigate mutual projections of cameras in four-dimensional space and show that it enables us to reduce the number of corresponding points required for computing the spatio-temporal multiple view geometry. Surprisingly, take three views for instance, we no longer need any corresponding point to calculate the spatio-temporal multiple view geometry, if all the cameras are projected to the other cameras mutually for two time intervals. We also show that the stability of the computation of spatio-temporal multiple view geometry is drastically improved by considering the mutual projections of cameras.

Since a radio frequency identification (RFID) transponder (tag) generates both location and time information when it enters and leaves a reader, the trajectory of a moving, tagged object can be traced. Due to the time intervals between entries to successive readers, during which tags are not tracked, accurate tracing of complete trajectories can be difficult. To overcome this problem, we propose a tag trajectory indexing scheme called TR-tree (R-tree-based tag trajectory index) that can trace tags by combining the local trajectories at each reader. In experiments, this scheme showed superior performance compared with other indices.

Similarities and differences of the thermal analysis issues between the intelligent and general AC contactors are analyzed. Heat source model of the magnet system is established according to the unique control mode of the intelligent AC contactor. Linking with the features common of the two kinds of contactors, the extension of the thermal analysis method of the general AC contactor to the intelligent AC contactor is demonstrated. Consequently, a comprehensive thermal analysis model considering heat sources of both main circuit and magnet system is constructed for the intelligent AC contactor. With this model, the steady-state temperature rise of the intelligent AC contactor is calculated and compared with the measurements of an actual intelligent AC contactor.

This paper presents a Pulse-Width Modulation (PWM) pre-emphasis technique which utilizes time-domain information processing to increase the data rate for a given bandwidth of interconnection. The PWM pre-emphasis method does not change the pulse amplitude as for conventional FIR pre-emphasis, but instead exploits timing resolution. This fits well with recent CMOS technology trends toward higher switching speeds and lower supply voltage. We discuss multiple-valued data transmission based on time-domain pre-emphasis techniques in consideration of higher-order channel effects. Also, a new data-dependent adaptive time-domain pre-emphasis technique is proposed to compensate for the data-dependent jitter.

In this paper, we propose a novel feature named histogram of template (HOT) for human detection in still images. For every pixel of an image, various templates are defined, each of which contains the pixel itself and two of its neighboring pixels. If the texture and gradient values of the three pixels satisfy a pre-defined formula, the central pixel is regarded to meet the corresponding template for this formula. Histograms of pixels meeting various templates are calculated for a set of formulas, and combined to be the feature for detection. Compared to the other features, the proposed feature takes texture as well as the gradient information into consideration. Besides, it reflects the relationship between 3 pixels, instead of focusing on only one. Experiments for human detection are performed on INRIA dataset, which shows the proposed HOT feature is more discriminative than histogram of orientated gradient (HOG) feature, under the same training method.

We propose a randomness test based on the T-complexity of a sequence, which can be calculated using a parsing algorithm called T-decomposition. Recently, the Lempel-Ziv (LZ) randomness test based on LZ-complexity using the LZ78 incremental parsing was officially excluded from the NIST test suite in NIST SP 800-22. This is caused from the problem that the distribution of P-values for random sequences of length 106 is strictly discrete for the LZ-complexity. Our proposed test can overcome this problem because T-complexity has almost ideal continuous distribution of P-values for random sequences of length 106. We also devise a new sequential T-decomposition algorithm using forward parsing, while the original T-decomposition is an off-line algorithm using backward parsing. Our proposed test can become a supplement to NIST SP 800-22 because it can detect several undesirable pseudo-random numbers that the NIST test suite almost fails to detect.

Cancelable approaches for biometric person authentication have been studied to protect enrolled biometric data, and several algorithms have been proposed. One drawback of cancelable approaches is that the performance is inferior to that of non-cancelable approaches. In this paper, we propose a scheme to improve the performance of a cancelable approach for online signature verification. Our scheme generates two cancelable dataset from one raw dataset and uses them for verification. Preliminary experiments were performed using a distance-based online signature verification algorithm. The experimental results show that our proposed scheme is promising.

A 2.4 GHz 0.13 µm CMOS transceiver LSI, supporting Bluetooth V2.1+enhanced data rate (EDR) standard, has achieved a high reception sensitivity and high-quality transmission signals between -40 and +90. A low-IF receiver and direct-conversion transmitter architecture are employed. A temperature compensated receiver chain including a low-noise amplifier accomplishes a sensitivity of -90 dBm at frequency shift keying modulation even in the worst environmental condition. Design optimization of phase noise in a local oscillator and linearity of a power amplifier improves transmission signals and enables them to meet Bluetooth radio specifications. Fabrication in scaled 0.13 µm CMOS and operation at a low supply voltage of 1.5 V result in small area and low power consumption.

We propose a dynamic voltage scaling algorithm to exploit the temporal locality called TLDVS (Temporal Locality DVS) that can achieve significant energy savings while simultaneously preserving timeliness guarantees made by real-time scheduling. Traditionally hard real-time scheduling algorithms assume that the actual computation requirement of tasks would be varied continuously from time to time, but most real-time tasks have a limited number of operational modes changing with temporal locality. Such temporal locality can be exploited for energy savings by scaling down the operating frequency and the supply voltage accordingly. The proposed algorithm does not assume task periodicity, and requires only previous execution time among a priori information on the task set to schedule. Simulation results show that TLDVS achieves up to 25% energy savings compared with OLDVS, and up to 42% over the non-DVS scheduling.