Recently, the performances of discriminative correlation filter (CF) trackers are getting better and better in visual tracking. In this paper, we propose spatial-temporal regularization with precise state estimation based on discriminative correlation filter (STPSE) in order to achieve more significant tracking performance. First, we consider the continuous change of the object state, using the information from the previous two filters for training the correlation filter model. Here, we train the correlation filter model with the hand-crafted features. Second, we introduce update control in which average peak-to-correlation energy (APCE) and the distance between the object locations obtained by HOG features and hand-crafted features are utilized to detect abnormality of the state around the object. APCE and the distance indicate the reliability of the filter response, thus if abnormality is detected, the proposed method does not update the scale and the object location estimated by the filter response. In the experiment, our tracker (STPSE) achieves significant and real-time performance with only CPU for the challenging benchmark sequence (OTB2013, OTB2015, and TC128).

In this paper, we propose a new training framework named the INmfCA algorithm for nonparallel voice conversion (VC) systems. To train conversion models, traditional VC frameworks require parallel corpora, in which source and target speakers utter the same linguistic contents. Although the frameworks have achieved high-quality VC, they are not applicable in situations where parallel corpora are unavailable. To acquire conversion models without parallel corpora, nonparallel methods are widely studied. Although the frameworks achieve VC under nonparallel conditions, they tend to require huge background knowledge or many training utterances. This is because of difficulty in disentangling linguistic and speaker information without a large amount of data. In this work, we tackle this problem by exploiting NMF, which can factorize acoustic features into time-variant and time-invariant components in an unsupervised manner. The method acquires alignment between the acoustic features of a source speaker's utterances and a target dictionary and uses the obtained alignment as activation of NMF to train the source speaker's dictionary without parallel corpora. The acquisition method is based on the INCA algorithm, which obtains the alignment of nonparallel corpora. In contrast to the INCA algorithm, the alignment is not restricted to observed samples, and thus the proposed method can efficiently utilize small nonparallel corpora. The results of subjective experiments show that the combination of the proposed algorithm and the INCA algorithm outperformed not only an INCA-based nonparallel framework but also CycleGAN-VC, which performs nonparallel VC without any additional training data. The results also indicate that a one-shot VC framework, which does not need to train source speakers, can be constructed on the basis of the proposed method.

Previous studies on anomaly detection in videos have trained detectors in which reconstruction and prediction tasks are performed on normal data so that frames on which their task performance is low will be detected as anomalies during testing. This paper proposes a new approach that involves sorting video clips, by using a generative network structure. Our approach learns spatial contexts from appearances and temporal contexts from the order relationship of the frames. Experiments were conducted on four datasets, and we categorized the anomalous sequences by appearance and motion. Evaluations were conducted not only on each total dataset but also on each of the categories. Our method improved detection performance on both anomalies with different appearance and different motion from normality. Moreover, combining our approach with a prediction method produced improvements in precision at a high recall.

This paper proposes a low-complexity variational Bayesian inference (VBI)-based method for massive multiple-input multiple-output (MIMO) downlink channel estimation. The temporal correlation at the mobile user side is jointly exploited to enhance the channel estimation performance. The key to the success of the proposed method is the column-independent factorization imposed in the VBI framework. Since we separate the Bayesian inference for each column vector of signal-of-interest, the computational complexity of the proposed method is significantly reduced. Moreover, the temporal correlation is automatically uncoupled to facilitate the updating rule derivation for the temporal correlation itself. Simulation results illustrate the substantial performance improvement achieved by the proposed method.

This paper presents a novel method for optimal control of timed Petri nets, introducing a novel temporal logic based constraint called a generalized mutual exclusion temporal constraint (GMETC). The GMETC is described by a metric temporal logic (MTL) formula where each atomic proposition represents a generalized mutual exclusion constraint (GMEC). We formulate an optimal control problem of the timed Petri nets under a given GMETC and solve the problem by transforming it into an integer linear programming problem where the MTL formula is encoded by linear inequalities. We show the effectiveness of the proposed approach by a numerical simulation.

High frame rate and ultra-low delay vision system, which can finish reading and processing of 1000fps sequence within 1ms/frame, draws increasing attention in the field of robotics that requires immediate feedback from image process core. Meanwhile, tracking task plays an important role in many computer vision applications. Among various tracking algorithms, Lucas Kanade (LK)-based template tracking, which tracks targets with high accuracy over the sub-pixel level, is one of the keys for robotic applications, such as factory automation (FA). However, the substantial spatial iterative processing and complex computation in the LK algorithm, make it difficult to achieve a high frame rate and ultra-low delay tracking with limited resources. Aiming at an LK-based template tracking system that reads and processes 1000fps sequences within 1ms/frame with small resource costs, this paper proposes: 1) High temporal resolution-based temporal iterative tracking, which maps the spatial iterations into the temporal domain, efficiently reduces resource cost and delay caused by spatial iterative processing. 2) Label scanner-based multi-stream spatial processing, which maps the local spatial processing into the labeled input pixel stream and aggregates them with a label scanner, makes the local spatial processing in the LK algorithm possible be implemented with a small resource cost. Algorithm evaluation shows that the proposed temporal iterative tracking performs dynamic tracking, which tracks object with coarse accuracy when it's moving fast and achieves higher accuracy when it slows down. Hardware evaluation shows that the proposed label scanner-based multi-stream architecture makes the system implemented on FPGA (zcu102) with resource cost less than 20%, and the designed tracking system supports to read and process 1000fps sequence within 1ms/frame.

Bitcoin is currently a hot issue worldwide, and it is expected to become a new legal tender that replaces the current currency started with El Salvador. Due to the nature of cryptocurrency, however, difficulties in tracking led to the arising of misuses and abuses. Consequently, the pain of innocent victims by exploiting these bitcoins abuse is also increasing. We propose a way to detect new signatures by applying two-fold NLP-based clustering techniques to text data of Bitcoin abuse reports received from actual victims. By clustering the reports of text data, we were able to cluster the message templates as the same campaigns. The new approach using the abuse massage template representing clustering as a signature for identifying abusers is much efficacious.

Computing the Lempel-Ziv Factorization (LZ77) of a string is one of the most important problems in computer science. Nowadays, it has been widely used in many applications such as data compression, text indexing and pattern discovery, and already become the heart of many file compressors like gzip and 7zip. In this paper, we show a linear time algorithm called Xone for computing the LZ77, which has the same space requirement with the previous best space requirement for linear time LZ77 factorization called BGone. Xone greatly improves the efficiency of BGone. Experiments show that the two versions of Xone: XoneT and XoneSA are about 27% and 31% faster than BGoneT and BGoneSA, respectively.

Siamese visual tracking, viewed as a problem of max-similarity matching to the target template, has absorbed increasing attention in computer vision. However, it is a challenge for current Siamese trackers that the demands of balance between accuracy in real-time tracking and robustness in long-time tracking are hard to meet. This work proposes a new Siamese based tracker with a dual-pipeline correlated fusion network (named as ADF-SiamRPN), which consists of one initial template for robust correlation, and the other transient template with the ability of adaptive feature optimal selection for accurate correlation. By the promotion from the learnable correlation-response fusion network afterwards, we are in pursuit of the synthetical improvement of tracking performance. To compare the performance of ADF-SiamRPN with state-of-the-art trackers, we conduct lots of experiments on benchmarks like OTB100, UAV123, VOT2016, VOT2018, GOT-10k, LaSOT and TrackingNet. The experimental results of tracking demonstrate that ADF-SiamRPN outperforms all the compared trackers and achieves the best balance between accuracy and robustness.

This paper reviews the evolutionary process that reduced the transmission loss of silica optical fibers from the report of 20dB/km by Corning in 1970 to the current record-low loss. At an early stage, the main effort was to remove impurities especially hydroxy groups for fibers with GeO2-SiO2 core, resulting in the loss of 0.20dB/km in 1980. In order to suppress Rayleigh scattering due to composition fluctuation, pure-silica-core fibers were developed, and the loss of 0.154dB/km was achieved in 1986. As the residual main factor of the loss, Rayleigh scattering due to density fluctuation was actively investigated by utilizing IR and Raman spectroscopy in the 1990s and early 2000s. Now, ultra-low-loss fibers with the loss of 0.150dB/km are commercially available in trans-oceanic submarine cable systems.

Predictive spatial-monitoring, which predicts spatial information such as road traffic, has attracted much attention in the context of smart cities. Machine learning enables predictive spatial-monitoring by using a large amount of aggregated sensor data. Since the capacity of mobile networks is strictly limited, serious transmission delays occur when loads of communication traffic are heavy. If some of the data used for predictive spatial-monitoring do not arrive on time, prediction accuracy degrades because the prediction has to be done using only the received data, which implies that data for prediction are ‘delay-sensitive’. A utility-based allocation technique has suggested modeling of temporal characteristics of such delay-sensitive data for prioritized transmission. However, no study has addressed temporal model for prioritized transmission in predictive spatial-monitoring. Therefore, this paper proposes a scheme that enables the creation of a temporal model for predictive spatial-monitoring. The scheme is roughly composed of two steps: the first involves creating training data from original time-series data and a machine learning model that can use the data, while the second step involves modeling a temporal model using feature selection in the learning model. Feature selection enables the estimation of the importance of data in terms of how much the data contribute to prediction accuracy from the machine learning model. This paper considers road-traffic prediction as a scenario and shows that the temporal models created with the proposed scheme can handle real spatial datasets. A numerical study demonstrated how our temporal model works effectively in prioritized transmission for predictive spatial-monitoring in terms of prediction accuracy.

Nano crystalline zinc oxide (ZnO) is deposited by room temperature atomic layer deposition (RT-ALD) using dimethylzinc and a plasma excited humidified Ar without thermal treatments. The TEM observation indicated that the deposited ZnO films were crystallized with grain sizes of ∼20 nm on Si in the course of the RT-ALD process. The crystalline ZnO exhibited semiconducting characteristics in a thin film transistor, where the field-effect mobility was recorded at 1.29×10-3cm2/V·s. It is confirmed that the RT deposited ZnO film has an anticorrosion to hot water. The water vapor transmission rate of 8.4×10-3g·m-2·day-1 was measured from a 20 nm thick ZnO capped 40 nm thick Al2O3 on a polyethylene naphthalate film. In this paper, we discuss the crystallization of ZnO in the RT ALD process and its applicability to flexible electronics.

This paper describes a novel 4K 120fps (frames per second) real-time HEVC (High Efficiency Video Coding) encoder for high-frame-rate video encoding and transmission. Motion portrayal problems such as motion blur and jerkiness may occur in video scenes containing fast-moving objects or quick camera panning. A high-frame-rate solves such problems and provides a more immersive viewing experience that can express even the fast-moving scenes without discomfort. It can also be used in remote operation for scenes with high motion, such as VAR (Video Assistant Referee) systems in sports. Real-time encoding of high-frame-rate videos with low latency and temporal scalability is required for providing such high-frame-rate video services. The proposed encoder achieves full 4K/120fps real-time encoding, which is twice the current 4K service frame rate of 60fps, by multichip configuration with two encoder LSI. Exchange of reference picture data near a spatially divided slice boundary provides cross-chip motion estimation, and maintains the coding efficiency. The encoder supports temporal-scalable coding mode, in which it output stream with temporal scalability transmitted over one or two transmission paths. The encoder also supports the other mode, low-delay coding mode, in which it achieves 21.8msec low-latency processing through motion vector restriction. Evaluation of the proposed encoder's multichip configuration shows that the BD-bitrate (the average rate of bitrate increase), compared to simple slice division without inter-chip transfer, is -2.86% at minimum and -2.41% on average in temporal-scalable coding mode. The proposed encoder system will open the door to the next generation of high-frame-rate UHDTV (ultra-high-definition television) services.

This paper formulates minimal word-line (WL) delay time with pre-emphasis pulses to design the pulse width as a function of the overdrive voltage for large memory arrays such as 3D NAND. Circuit theory for a single RC line only with capacitance to ground and that only with coupling capacitance as well as a general case where RC lines have both grounded and coupling capacitance is discussed to provide an optimum pre-emphasis pulse width to minimize the delay time. The theory is expanded to include the cases where the resistance of the RC line driver is not negligibly small. The minimum delay time formulas of a single RC delay line and capacitive coupling RC lines was in good agreement (i.e. within 5% error) with measurement. With this research, circuit designers can estimate an optimum pre-emphasis pulse width and the delay time for an RC line in the initial design phase.

In this study, we present a novel method for removing smoke from videos based on a single image sequence. Smoke is a significant artifact in images or videos because it can reduce the visibility in disaster scenes. Our proposed method for removing smoke involves two main processes: (1) the development of a smoke imaging model and (2) smoke removal using spatio-temporal pixel compensation. First, we model the optical phenomena in natural scenes including smoke, which is called a smoke imaging model. Our smoke imaging model is developed by extending conventional haze imaging models. We then remove the smoke from a video in a frame-by-frame manner based on the smoke imaging model. Next, we refine the appearance of the smoke-free video by spatio-temporal pixel compensation, where we align the smoke-free frames using the corresponding pixels. To obtain the corresponding pixels, we use SIFT and color features with distance constraints. Finally, in order to obtain a clear video, we refine the pixel values based on the spatio-temporal weightings of the corresponding pixels in the smoke-free frames. We used simulated and actual smoke videos in our validation experiments. The experimental results demonstrated that our method can obtain effective smoke removal results from dynamic scenes. We also quantitatively assessed our method based on a temporal coherence measure.

An olfactory display is a device to present smells. Temporal characteristics of three types of olfactory displays such as one based upon high-speed switching of solenoid valves, desktop-type one based on SAW atomizer and wearable-type one based on SAW atomizer were evaluated using three odorants with different volatilities. The sensory test revealed that the olfactory displays based on SAW atomizer had the presentation speeds faster than that of solenoid valves switching. Especially, the wearable one had an excellent temporal characteristic. These results largely depend on the difference in the odor delivery method. The data obtained in this study provides basic knowledge when we make olfactory contents.

With the development and spread of Internet of Things (IoT) technology, various kinds of data are now being generated from IoT devices. Some data generated from IoT devices depend on geographical location and time, and we refer to them as spatio-temporal data (STD). Since the “locally produced and consumed” paradigm of STD use is effective for location-dependent applications, the authors have previously proposed a vehicle-based STD retention system. However, in low vehicle density environments, the data retention becomes difficult due to the decrease in the number of data transmissions in this method. In this paper, we propose a new data transmission control method for data retention in the low vehicle density environments.

Temperature-independent zero-zero-birefringence polymer (TIZZBP), which exhibits very small birefringence over the wide temperature range, is required to realize real-color images for displays, particularly vehicle-mounted displays. Previously, a TIZZBP was synthesized, but they did not put into practical use because of their too complex composition and low mechanical strength. In this paper, we propose a practical TIZZBP that has high heat resistance, high transparency and sufficient mechanical strength, using a simple binary copolymerization system. Our proposed novel polymer exhibits very low photoelastic birefringence and very low orientational birefringence. Both types of birefringence of this TIZZBP satisfy the negligible levels for displays, which are defined as follows: the absolute values of photoelastic coefficient and intrinsic birefringence are less than 1 ×10-12 Pa-1 and 1 ×10-3, respectively. In addition, temperature dependency of orientational birefringence was very low. Orientational birefringence satisfies the negligible level all over the temperature range from around -40°C to 85°C. This temperature range is important because it is the operational temperature range for vehicle-mounted display. Furthermore, our proposed novel TIZZBP showed high heat resistance, high transparency and sufficient mechanical strength. The glass transition temperature was 194°C. The total light transmittance and the haze value is more than 91% and less than 1%, respectively. The tensile strength of non-oriented films was 35 ~ 50 MPa. These results suggest our proposed novel TIZZBP has high practicality in addition to very low birefringence. Therefore, this TIZZBP film will be very useful for various displays including vehicle-mounted displays and flexible displays.

The relationships between producers and consumers have changed radically by the recent growth of sharing economy. Promoting resource sharing can contribute to finding a solution to environmental issues (e.g. reducing food waste, consuming surplus electricity, and so on). Although prosumers have both roles as consumers and suppliers, matching between suppliers and consumers should be determined when the prosumers share resources. Especially, it is important to achieve envy-freeness that is a metric indicating how the number of prosumers feeling unfairness is kept small since the capacity of prosumers to supply resources is limited. Changing resource capacity and demand will make the situation more complex. This paper proposes a resource sharing model based on a temporal network and flows to realize envy-free resource sharing among prosumers. Experimental results demonstrate the deviation of envy among prosumers can be reduced by setting appropriate weights in a flow network.

A differential on chip oscillator (OCO) is proposed in this paper for low supply voltage, high frequency accuracy and fast startup. The differential architecture helps the OCO achieve a good power supply rejection ratio (PSRR) without using a regulator so as to make the OCO suitable for a low power supply voltage of 1.38V. A reference voltage generator is also developed to generate two output voltages lower than Vbe for low supply voltage operation. The output frequency is locked to 48MHz by a frequency-locked loop (FLL) and a 3.3-ppm/°C temperature coefficient of frequency is realized by the differential voltage ratio adjusting (differential VRA) technique. The startup time is only 1.47μs because the differential OCO is not necessary to charge a big capacitor for ripple reduction.