A method to generate color palettes from images is proposed. Here, deep neural networks (DNN) are utilized in order to consider human perception. Two aspects of human perception are considered; one is attention to image, and the other is human preference for colors. This method first extracts N regions with dominant color categories from the image considering human attention. Here, N is the number of colors in a color palette. Then, the representative color is obtained from each region considering the human preference for color. Two deep neural-net systems are adopted here, one is for estimating the image area which attracts human attention, and the other is for estimating human preferable colors from image regions to obtain representative colors. The former is trained with target images obtained by an eye tracker, and the latter is trained with dataset of color selection by human. Objective and subjective evaluation is performed to show high performance of the proposed system compared with conventional methods.

In the upcoming Internet of Things era, reducing energy consumption of embedded processors is highly desired. Minimum Energy Point Tracking (MEPT) is one of the most efficient methods to reduce both dynamic and static energy consumption of a processor. Previous works proposed a variety of MEPT methods over the past years. However, none of them incorporate their algorithms with practical real-time operating systems, although edge computing applications often require low energy task execution with guaranteeing real-time properties. The difficulty comes from the time complexity for identifying an MEP and changing voltages, which often prevents real-time task scheduling. The conventional Dynamic Voltage and Frequency Scaling (DVFS) only scales the supply voltage. On the other hand, MEPT needs to adjust the body bias voltage in addition. This additional tuning knob makes MEPT much more complicated. This paper proposes an approximate MEPT algorithm, which reduces the complexity of identifying an MEP down to that of DVFS. The key idea is to linearly approximate the relationship between the processor frequency, supply voltage, and body bias voltage. Thanks to the approximation, optimal voltages for a specified clock frequency can be derived immediately. We also propose a task scheduling algorithm, which adjusts processor performance to the workload and then provides a soft real-time capability to the system. The operating system stochastically adjusts the average response time of the processor to be equal to a specified deadline. MEPT will be performed as a general task, and its overhead is considered in the calculation of the frequency. The experiments using a fabricated test chip and on-chip sensors show that the proposed algorithm is a maximum of 16 times more energy-efficient than DVFS. Also, the energy loss induced by the approximation is only 3% at most, and the algorithm does not sacrifice the fundamental real-time properties.

A method for runtime energy optimization based on the supply voltage (Vdd) and the threshold voltage (Vth) scaling is proposed. This paper refers to the optimal voltage pair, which minimizes the energy consumption of LSI circuits under a target delay constraint, as a Minimum Energy Point (MEP). The MEP dynamically fluctuates depending on the operating conditions determined by a target delay constraint, an activity factor and a chip temperature. In order to track the MEP, this paper proposes a closed-form continuous function that determines the MEP over a wide operating performance region ranging from the above-threshold region down to the sub-threshold region. Based on the MEP determination formula, an MEP tracking algorithm is also proposed. The MEP tracking algorithm estimates the MEP even though the operating conditions widely change. Measurement results based on a 32-bit RISC processor fabricated in a 65-nm Silicon On Thin Buried oxide (SOTB) process technology show that the proposed method estimates the MEP within a 5% energy loss in comparison with the actual MEP operation.

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 proposes an area- and energy-efficient DLL-based body bias generator (BBG) for minimum energy operation that controls p-well and n-well bias independently. The BBG can minimize total energy consumption of target circuits under a skewed process condition between nMOSFETs and pMOSFETs. The proposed BBG is composed of digital cells compatible with cell-based design, which enables energy- and area-efficient implementation without additional supply voltages. A test circuit is implemented in a 65-nm FDSOI process. Measurement results using a 32-bit RISC processor on the same chip show that the proposed BBG can reduce energy consumption close to a minimum within a 3% energy loss. In this condition, energy and area overheads of the BBG are 0.2% and 0.12%, respectively.

The Fifth-Generation new radio (5G NR) services that started in 2020 in Japan use a higher peak-to-average power ratio (PAPR) of a modulated signal with a maximum bandwidth of up to 100MHz and support multi-input/multi-output (MIMO) systems even in mobile handsets, compared to the Third-Generation (3G) and/or Fourth-Generation (4G) handsets. The 5G NR requires wideband operation for power amplifiers (PAs) used in handsets under a high PAPR signal condition. The 5G NR also requires a number of operating bands for the handsets. These requirements often cause significand degradation of the PA efficiency, consequently. The degradation is due to wideband and/or high PAPR operation as well as additional front-end loss between a PA and an antenna. Thus, the use of an efficiency enhancement technique is indispensable to 5G NR handset PAs. An envelope tracking (ET) is one of the most effective ways to improve the PA efficiency in the handsets. This paper gives recent progress in ET power amplifiers (ETPAs) followed by a brief introduction of ET techniques. The introduction describes a basic operation for an ET modulator that is a key component in the ET techniques and then gives a description of some kinds of ET modulators. In addition, as an example of a 5G NR ETPA, the latest experimental results for a 5G ETPA prototype are demonstrated while comparing overall efficiency of the ET modulator and PA in the ET mode with that in the average power tracking (APT) mode.

In daily reality, people often pay attention to several objects that change positions while being observed. In the laboratory, this process is investigated by a phenomenon known as multiple object tracking (MOT) which is a task that evaluates attentive tracking performance. Recent findings suggest that the attentional set for multiple moving objects whose depth changes in three dimensions from one plane to another is influenced by the initial configuration of the objects. When tracking objects, it is difficult for people to expand their attentional set to multiple-depth planes once attention has been focused on a single plane. However, less is known about people contracting their attentional set from multiple-depth planes to a single-depth plane. In two experiments, we examined tracking accuracy when four targets or four distractors, which were initially distributed on two planes, come together on one of the planes during an MOT task. The results from this study suggest that people have difficulty changing the depth range of their attention during attentive tracking, and attentive tracking performance depends on the initial attentional set based on the configuration prior to attentive tracking.

An eye model expressed by a revolution about the optical axis of the eye is one of the most accurate models for use in a 3D gaze estimation method. The measurement range of the previous gaze estimation method that uses two cameras based on the eye model is limited by the larger of the two angles between the gaze and the optical axes of two cameras. The previous method cannot calculate the gaze when exceeding a certain limit of the rotation angle of the eye. In this paper, we show the characteristics of reflections on the surface of the eye from two light sources, when the eye rotates. Then, we propose a method that extends the rotation angle of the eye for a 3D gaze estimation based on this model. The proposed method uses reflections that were not used in the previous method. We developed an experimental gaze tracking system for a wide projector screen and experimentally validated the proposed method with 20 participants. The result shows that the proposed method can measure the gaze of more number of people with increased accuracy compared with the previous method.

Recently, visual trackers based on the framework of kernelized correlation filter (KCF) achieve the robustness and accuracy results. These trackers need to learn information on the object from each frame, thus the state change of the object affects the tracking performances. In order to deal with the state change, we propose a novel KCF tracker using the filter response map, namely a confidence map, and adaptive model. This method firstly takes a skipped scale pool method which utilizes variable window size at every two frames. Secondly, the location of the object is estimated using the combination of the filter response and the similarity of the luminance histogram at multiple points in the confidence map. Moreover, we use the re-detection of the multiple peaks of the confidence map to prevent the target drift and reduce the influence of illumination. Thirdly, the learning rate to obtain the model of the object is adjusted, using the filter response and the similarity of the luminance histogram, considering the state of the object. Experimentally, the proposed tracker (CFCA) achieves outstanding performance for the challenging benchmark sequence (OTB2013 and OTB2015).

We combine the siamese network and the recurrent regression network, proposing a two-stage tracking framework termed as SiamReg. Our method solves the problem that the classic siamese network can not judge the target size precisely and simplifies the procedures of regression in the training and testing process. We perform experiments on three challenging tracking datasets: VOT2016, OTB100, and VOT2018. The results indicate that, after offline trained, SiamReg can obtain a higher expected average overlap measure.

Multiple human tracking is widely used in various fields such as marketing and surveillance. The typical approach associates human detection results between consecutive frames using the features and bounding boxes (position+size) of detected humans. Some methods use an omnidirectional camera to cover a wider area, but ID switch often occurs in association with detections due to following two factors: i) The feature is adversely affected because the bounding box includes many background regions when a human is captured from an oblique angle. ii) The position and size change dramatically between consecutive frames because the distance metric is non-uniform in an omnidirectional image. In this paper, we propose a novel method that accurately tracks humans with an association metric for omnidirectional images. The proposed method has two key points: i) For feature extraction, we introduce local rectification, which reduces the effect of background regions in the bounding box. ii) For distance calculation, we describe the positions in a world coordinate system where the distance metric is uniform. In the experiments, we confirmed that the Multiple Object Tracking Accuracy (MOTA) improved 3.3 in the LargeRoom dataset and improved 2.3 in the SmallRoom dataset.

Deep neural networks have achieved great success in visual tracking by learning a generic representation and leveraging large amounts of training data to improve performance. Most generic object trackers are trained from scratch online and do not benefit from a large number of videos available for offline training. We present a real-time generic object tracker capable of incorporating temporal information into its model, learning from many examples offline and quickly updating online. During the training process, the pre-trained weight of convolution layer is updated lagging behind, and the input video sequence length is gradually increased for fast convergence. Furthermore, only the hidden states in recurrent network are updated to guarantee the real-time tracking speed. The experimental results show that the proposed tracking method is capable of tracking objects at 150 fps with higher predicting overlap rate, and achieves more robustness in multiple benchmarks than state-of-the-art performance.

This paper proposes a generative moment matching network (GMMN)-based post-filtering method for providing inter-utterance pitch variation to singing voices and discusses its application to our developed mixing method called neural double-tracking (NDT). When a human singer sings and records the same song twice, there is a difference between the two recordings. The difference, which is called inter-utterance variation, enriches the performer's musical expression and the audience's experience. For example, it makes every concert special because it never recurs in exactly the same manner. Inter-utterance variation enables a mixing method called double-tracking (DT). With DT, the same phrase is recorded twice, then the two recordings are mixed to give richness to singing voices. However, in synthesized singing voices, which are commonly used to create music, there is no inter-utterance variation because the synthesis process is deterministic. There is also no inter-utterance variation when only one voice is recorded. Although there is a signal processing-based method called artificial DT (ADT) to layer singing voices, the signal processing results in unnatural sound artifacts. To solve these problems, we propose a post-filtering method for randomly modulating synthesized or natural singing voices as if the singer sang again. The post-filter built with our method models the inter-utterance pitch variation of human singing voices using a conditional GMMN. Evaluation results indicate that 1) the proposed method provides perceptible and natural inter-utterance variation to synthesized singing voices and that 2) our NDT exhibits higher double-trackedness than ADT when applied to both synthesized and natural singing voices.

Real-time 3D players tracking plays an important role in sports analysis, especially for the live services of sports broadcasting, which have a strict limitation on processing time. For these kinds of applications, 3D trajectories of players contribute to high-level game analysis such as tactic analysis and commercial applications such as TV contents. Thus real-time implementation for 3D players tracking is expected. In order to achieve real-time for 60fps videos with high accuracy, (that means the processing time should be less than 16.67ms per frame), the factors that limit the processing time of target algorithm include: 1) Large image area of each player. 2) Repeated processing of multiple players in multiple views. 3) Complex calculation of observation algorithm. To deal with the above challenges, this paper proposes a representative spatial selection and temporal combination based real-time implementation for multi-view volleyball players tracking on the GPU device. First, the representative spatial pixel selection, which detects the pixels that mostly represent one image region to scale down the image spatially, reduces the number of processing pixels. Second, the representative temporal likelihood combination shares observation calculation by using the temporal correlation between images so that the times of complex calculation is reduced. The experiments are based on videos of the Final and Semi-Final Game of 2014 Japan Inter High School Games of Men's Volleyball in Tokyo Metropolitan Gymnasium. On the GPU device GeForce GTX 1080Ti, the tracking system achieves real-time on 60fps videos and keeps the tracking accuracy higher than 97%.

A multi-camera setup for a surveillance system enables a larger coverage area, especially when a single camera has limited monitoring capability due to certain obstacles. Therefore, for large-scale coverage, multiple cameras are the best option. In this paper, we present a method for detecting multiple objects using several cameras with large overlapping views as this allows synchronization of object identification from a number of views. The proposed method uses a graph structure that is robust enough to represent any detected moving objects by defining their vertices and edges to determine their relationships. By evaluating these object features, represented as a set of attributes in a graph, we can perform lightweight multiple object detection using several cameras, as well as performing object tracking within each camera's field of view and between two cameras. By evaluating each vertex hierarchically as a subgraph, we can further observe the features of the detected object and perform automatic separation of occluding objects. Experimental results show that the proposed method would improve the accuracy of object tracking by reducing the occurrences of incorrect identification compared to individual camera-based tracking.

Although correlation filter-based trackers have demonstrated excellent performance for visual object tracking, there remain several challenges to be addressed. In this work, we propose a novel tracker based on the correlation filter framework. Traditional trackers face difficulty in accurately adapting to changes in the scale of the target when the target moves quickly. To address this, we suggest a scale adaptive scheme based on prediction scales. We also incorporate a speed-based adaptive model update method to further improve overall tracking performance. Experiments with samples from the OTB100 and KITTI datasets demonstrate that our method outperforms existing state-of-the-art tracking algorithms in fast motion scenes.

Envelope tracking (ET) technology provides the potential for achieving high efficiency in power amplifiers (PAs) with high peak-to-average ratio (PAR) signals. Envelope amplifiers with high fidelity, high efficiency, and wide bandwidth are critical components for the widespread application of envelope tracking. This paper presents the design of a linear-assisted switching buck converter for use in an envelope amplifier. To effectively leverage the high efficiency of buck converters and the wide bandwidth capabilities of linear amplifiers, a parallel combination of these two devices is employed in this work. A novel current-sense constant-on-time (COT) controller is proposed to coordinate this hybrid power supply. The combination mainly enables the switching converter to provide the average power required by the PA with high efficiency, while the wideband linear amplifier provides a wide range of dynamic voltages. The technique improves the efficiency of the envelope amplifier, especially for applications requiring high PAR with wider bandwidth signals. Measurement of the envelope amplifier showed an efficiency of approximately 77% with 10 W output power using LTE downlink signals. The overall ET system was demonstrated by using a GaN PA. The measured average power-added efficiency of the amplifier reached above 45% for an LTE modulated signal with 20 MHz bandwidth and PAR of 8.0 dB, at an average output power of 5 W and gain of 10.1 dB. The measured normalized RMS error is below 2.1% with adjacent channel leakage ratio of -48 dBc at an offset frequency of 20 MHz.

We research device-free (DF) multi-target tracking scheme in this paper. The existing localization and tracking algorithms are always pay attention to the single target and need to collect a large amount of localization information. In this paper, we exploit the sparse property of multiple target locations to achieve target trace accurately with much less sampling both in the wireless links and the time slots. The proposed approach mainly includes the target localization part and target trace recovery part. In target localization part, by exploiting the inherent sparsity of the target number, Compressive Sensing (CS) is utilized to reduce the wireless links distributed. In the target trace recovery part, we exploit the compressive property of target trace, as well as designing the measurement matrix and the sparse matrix, to reduce the samplings in time domain. Additionally, Kronecker Compressive Sensing (KCS) theory is used to simultaneously recover the multiple traces both of the X label and the Y Label. Finally, simulations show that the proposed approach holds an effective recovery performance.

In order to solve the influence of scale change on target tracking using the drone, a multi-scale target tracking algorithm is proposed which based on the color feature tracking algorithm. The algorithm realized adaptive scale tracking by training position and scale correlation filters. It can first obtain the target center position of next frame by computing the maximum of the response, where the position correlation filter is learned by the least squares classifier and the dimensionality reduction for color features is analyzed by principal component analysis. The scale correlation filter is obtained by color characteristics at 33 rectangular areas which is set by the scale factor around the central location and is reduced dimensions by orthogonal triangle decomposition. Finally, the location and size of the target are updated by the maximum of the response. By testing 13 challenging video sequences taken by the drone, the results show that the algorithm has adaptability to the changes in the target scale and its robustness along with many other performance indicators are both better than the most state-of-the-art methods in illumination Variation, fast motion, motion blur and other complex situations.

Various systems that share remote co-worker's awareness information have been proposed for realizing efficient collaborative work among distributed offices. In this study, we implemented an interruptibility sharing system in a university laboratory and assessed the observation behavior for the displayed information. Observation behavior for each target member was detected using an eye tracker to discuss the usage and effect of the system in a quantitative manner, along with the considerations of workers' job positions and relationships. The results suggested that participants observed interruptibility information approximately once an hour while at their desks. Observations were frequent during break-times rather than when the participants wanted to communicate with others. The most frequently observed targets were the participants themselves. The participants gazed the laboratory members not only in a close work relationship but also in a weak relationship. Results suggested that sharing of interruptibility information assists worker's self-reflection and contributes to the establishment of horizontal connection in an organization including members in weak work relationship.