In this work, fluctuation patterns of ReRAM current are classified automatically by proposed fluctuation pattern classifier (FPC). FPC is trained with artificially created dataset to overcome the difficulties of measured current signals, including the annotation cost and imbalanced data amount. Using FPC, fluctuation occurrence under different write conditions is analyzed for both HRS and LRS current. Based on the measurement and classification results, physical models of fluctuations are established.

An enzymatic biofuel cell (EBFC) is a device that uses an enzyme as a catalyst to convert chemical energy into electrical energy by a redox reaction to generate electricity. EBFC has the advantage that it can operate under mild conditions (normal temperature, normal pressure, and near neutral pH) and can use various energy sources such as sugar and alcohol. Hoshi et al. reported EBFC of glucose fuel using graphene-coated carbon fiber cloth (GCFC) with a large specific surface area. However, it was considered that GOD was affected by dissolved oxygen in the fuel and generated hydrogen peroxide, which hindered the reaction. In order to further increase the output, it was necessary to improve the performance of the anode with a novel enzyme that is less affected by oxygen and generates electricity from glucose. Therefore, we focused on FAD glucose dehydrogenase (FAD-GDH). It can generate electricity with glucose fuel by using it as a catalyst like GOD. Characteristic is that it is resistant to impurities such as maltose and galactose and is not easily affected by oxygen. It was thought that this would alleviate the concern about hydrogen peroxide and improve the output.

Recently, complex orthogonal variable spreading factor (OVSF) codes based on polyphase orthogonal codes have been proposed to support multi-user/multi-rate data transmission services in synchronous direct-sequence code-division multiple access (DS-CDMA) systems. This study investigates the low signal-envelope fluctuation property of the complex OVSF codes in terms of transmission signal trajectories. In addition, a new method is proposed to suppress the envelope fluctuation more strongly at the expense of reducing the number of spreading sequences of the codes.

In this paper, we proposed the phase disturbing device using randomly-fluctuated liquid crystal (LC) alignment to reduce the speckle noise generated in holographic displays. Some parameters corresponding to the alignment fluctuation of thick LC layer were quantitatively evaluated, and we clarified the effect of the LC alignment fluctuation with the parameters on speckle noise reduction.

Recently, the face hallucination method based on deep learning understands the mapping between low-resolution (LR) and high-resolution (HR) facial patterns by exploring the priors of facial structure. However, how to maintain the face structure consistency after the reconstruction of face images at different scales is still a challenging problem. In this letter, we propose a novel multi-scale structure prior learning (MSPL) for face hallucination. First, we propose a multi-scale structure prior block (MSPB). Considering the loss of high-frequency information in the LR space, we mainly process the input image in three different scale ascending dimensional spaces, and map the image to the high dimensional space to extract multi-scale structural prior information. Then the size of feature maps is recovered by downsampling, and finally the multi-scale information is fused to restore the feature channels. On this basis, we propose a local detail attention module (LDAM) to focus on the local texture information of faces. We conduct extensive face hallucination reconstruction experiments on a public face dataset (LFW) to verify the effectiveness of our method.

A new software based in-situ training (SBIST) method to achieve high accuracies is proposed for binarized neural networks inference accelerator chips in which measured offsets in sense amplifiers (activation binarizers) are transformed into biases in the training software. To expedite this individual training, the initial values for the weights are taken from results of a common forming training process which is conducted in advance by using the offset fluctuation distribution averaged over the fabrication line. SPICE simulation inference results for the accelerator predict that the accuracy recovers to higher than 90% even when the amplifier offset is as large as 40mV only after a few epochs of the individual training.

This brief proposes a solar-cell-assisted wireless biosensing system that operates using a biofuel cell (BFC). To facilitate BFC area reduction for the use of this system in area-constrained continuous glucose monitoring contact lenses, an energy harvester combined with an on-chip solar cell is introduced as a dedicated power source for the transmitter. A dual-oscillator-based supply voltage monitor is employed to convert the BFC output into digital codes. From measurements of the test chip fabricated in 65-nm CMOS technology, the proposed system can achieve 99% BFC area reduction.

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.

Biofuel cells (BFCs) using graphene-coated carbon fiber cloth electrodes and glucose gel fuel were fabricated and evaluated. A new structure using fuel gel, in which the anode was embedded in gel and the cathode was exposed to the atmosphere, was adopted. Air-exposed biofuel cells using gel have already been reported, however, adhesion between the anode and the gel was improved by the proposed structure. In addition, the enlargement of the gel area prevented its drying. These innovations improved the power density and lifetime of the BFCs. The anode was modified with a glucose oxidase (GOD) enzyme and a mediator (ferrocene) and the cathode was modified with a bilirubin oxidase enzyme. The power density of the proposed structure was 176.4 µW/cm2 at 0.19 V, which was approximately 3.8 times higher than that of BFCs using liquid fuel (45.9 µW/cm2).

This paper proposes statistical analysis of phase-only correlation (POC) functions under the phase fluctuation of signals due to additive Gaussian noise. We derive probability density function of phase-spectrum differences between original signal and its noise-corrupted signal with additive Gaussian noise. Furthermore, we evaluate the expectation and variance of the POC functions between these two signals. As the variance of Gaussian noise increases, the expectation of the peak of the POC function monotonically decreases and variance of the POC function monotonically increases. These results mathematically guarantee the validity of the POC functions used for similarity measure in matching techniques.

This paper shows design and implementation of an image-based vibration detection system on a field-programmable gate array (FPGA), aiming at application to tremor suppression for microsurgery assistance systems. The system can extract a vibration component within a user-specified frequency band from moving images in real-time. For fast and robust detection, we employ a statistical approach using dense optical flow to derive vibration component, and design a custom hardware based on the Lucas-Kanade (LK) method to compute optical flow. And for band-pass filtering without phase delay, we implement the band-limited multiple Fourier linear combiner (BMFLC), a sort of adaptive band-pass filter which can recompose an input signal as a mixture of sinusoidal signals with multiple frequencies within the specified band, with no phase delay. The whole system is implemented as a deep pipeline on a Xilinx Kintex-7 XC7K325T FPGA without using any external memory. We employ fixed-point arithmetic to reduce resource utilization while maintaining accuracy close to double-precision floating-point arithmetic. Empirical experiments reveal that the proposed system extracts a high-frequency tremor component from hand motions, with intentional low-frequency motions successfully filtered out. The system can process VGA moving images at 60fps, with a delay of less than 1 µs for the BMFLC, suggesting effectiveness of the deep pipelined architecture. In addition, we are planning to integrate a CNN-based segmentation system for improving detection accuracy, and show preliminary software evaluation results.

The authors have been investigating the deterioration process of Au-plated slip-ring and Ag-Pd brush system with lubricant to realize stable and long lifetime. Through the past tests, it can be made clear that lubricant is very important for long lifetime, and a simple model of the deterioration process was proposed. However, it is still an issue how the lubricant is deteriorated and also what the relation between lubricant deterioration and contact voltage behavior is. In this paper, the contact voltage waveforms were regularly recorded during the test, and analyzed to obtain the time change of peak voltage and standard deviation during one rotation. Based on these results, it is discussed what happens at the interface between ring and brush with the lubricant. And the following results are made clear. The fluctuation of voltage waveforms, especially peaks of pulse-like fluctuation more easily occurs for minus rings than for plus rings. Further, peak values of the pulse-like fluctuation rapidly decreases and disappear at lower rotation speed as mentioned in the previous works. In addition, each peaks of the pulse-like fluctuation is identified at each position of the ring periphery. From these results, it can be assumed that lubricant film exists between brush and ring surface and electric conduction is realized by tunnel effect. In other words, it can be made clear that the fluctuation would be caused by the lubricant layer, not only by the ring surface. Finally, an electric conduction model is proposed and the above results can be explained by this model.

In recent years, deep neural network (DNN) has achieved considerable results on many artificial intelligence tasks, e.g. natural language processing. However, the computation complexity of DNN is extremely high. Furthermore, the performance of traditional von Neumann computing architecture has been slowing down due to the memory wall problem. Processing in memory (PIM), which places computation within memory and reduces the data movement, breaks the memory wall. ReRAM PIM is thought to be a available architecture for DNN accelerators. In this work, a novel design of ReRAM neuromorphic system is proposed to process DNN fully in array efficiently. The binary ReRAM array is composed of 2T2R storage cells and current mirror sense amplifiers. A dummy BL reference scheme is proposed for reference voltage generation. A binary DNN (BDNN) model is then constructed and optimized on MNIST dataset. The model reaches a validation accuracy of 96.33% and is deployed to the ReRAM PIM system. Co-design model optimization method between hardware device and software algorithm is proposed with the idea of utilizing hardware variance information as uncertainness in optimization procedure. This method is analyzed to achieve feasible hardware design and generalizable model. Deployed with such co-design model, ReRAM array processes DNN with high robustness against fabrication fluctuation.

This work presents the lowest power consumption sub-mm2 supply-modulated OOK transmitter for self-powering a continuous glucose monitoring (CGM) contact lens. By combining the transmitter with a glucose fuel cell that functions as both the power source and a sensing transducer, a self-powered CGM contact lens was developed. The 385×385μm2 test chip implemented in 65-nm standard CMOS technology operates at 270pW with a supply voltage of 0.165V. Self-powered operation of the transmitter using a 2×2mm2 solid-state glucose fuel cell was thus demonstrated.

In this letter, we propose a novel method for face hallucination by learning a new distance metric in the low-resolution (LR) patch space (source space). Local patch-based face hallucination methods usually assume that the two manifolds formed by LR and high-resolution (HR) image patches have similar local geometry. However, this assumption does not hold well in practice. Motivated by metric learning in machine learning, we propose to learn a new distance metric in the source space, under the supervision of the true local geometry in the target space (HR patch space). The learned new metric gives more freedom to the presentation of local geometry in the source space, and thus the local geometries of source and target space turn to be more consistent. Experiments conducted on two datasets demonstrate that the proposed method is superior to the state-of-the-art face hallucination and image super-resolution (SR) methods.

Periodic interference frequently affects the measurement of small signals and causes problems in clinical diagnostics. Adaptive filters can be used as potential tools for cancelling such interference. However, when the interference has a frequency fluctuation, the ideal adaptive-filter coefficients for cancelling the interference also fluctuate. When the adaptation property of the algorithm is slow compared with the frequency fluctuation, the interference-cancelling performance is degraded. However, if the adaptation is too quick, the performance is degraded owing to the target signal. To overcome this problem, we propose an adaptive filter that suppresses the fluctuation of the ideal coefficients by utilizing a $rac{pi}{2}$ phase-delay device. This method assumes a frequency response that characterizes the transmission path from the interference source to the main input signal to be sufficiently smooth. In the numerical examples, the proposed method exhibits good performance in the presence of a frequency fluctuation when the forgetting factor is large. Moreover, we show that the proposed method reduces the calculation cost.

The purpose of this study was to identify the key variables that determine the quality of the auditory environment, for the purposes of workplace auditory design and assessment. To this end, we characterized changes in oscillatory neural activity in electroencephalographic (EEG) data recorded from subjects who performed an intellectual activity while exposed to fluctuating ambient noise. Seven healthy men participated in the study. Subjects performed a verbal and spatial task that used the 3-back task paradigm to study working memory. During the task, subjects were presented with auditory stimuli grouped by increasing high-frequency content: (1) a sound with frequencies similar to Brownian noise and no modulation; (2) an amplitude-modulated sound with frequencies similar to white noise; (3) amplitude-modulated pink noise; and (4) amplitude-modulated Brownian noise. Upon presentation, we observed a characteristic change in three EEG bands: theta (4-8Hz), alpha (8-13Hz), and beta (13-30Hz). In particular, a frequency-dependent enhancement and reduction of power was observed in the theta and beta bands, respectively.

In recent years, wireless power transfer has been attracting a great deal of attention. In order to realize efficient power transfer, it is necessary to communicate data such as a frequency, required power, or error tolerance. In the proposed system, because of the use of the same antennas for power transmission and data transmission, the frequency response of a channel for the data transmission changes owing to load fluctuation and the distance between antennas. This paper investigates data transmission performance in the wireless power transfer system with frequency response estimation at the transmitter side. The numerical results obtained through computer simulation show that the proposed scheme can estimate the frequency response of the channel and the difference between the expected bit error rate (BER) and the BER with the estimation error is less than 0.5dB at the BER of 10-3.

In this paper, the Quality of Experience (QoE) on Dynamic Adaptive Streaming based on HTTP (DASH) is researched. To study users' experience on DASH, extensive subjective tests are firstly designed and conducted, based on which, we research QoE enhancement in DASH and find that DASH ensures more fluent playback (less stall) than constant bitrate (CBR) streaming to promote users' satisfaction especially in mobile networks. Then we adopt two-way analysis of variance (ANOVA) tests in statistics to identify the effect of specific factors (segment bitrate, bitrate fluctuation pattern, and bitrate switching) that impair users' experience on DASH. The impairment functions are then derived for these influence factors based on the Primacy and Recency Effect, a psychological phenomenon that has been proved to exist in users' experience on DASH in this paper. And the final QoE evaluation model is proposed to provide high correlation assessment for QoE of DASH. The good performance of our QoE model is validated by the subjective tests. In addition, our QoE study on DASH is also applied for QoE management to propose a QoE-based bitrate adaptation strategy, which promotes users' experience on DASH more strongly than the strategy based on QoS.

Cloud computing, a novel distributed paradigm to provide powerful computing capabilities, is usually adopted by developers and researchers to execute complicated IoT applications such as complex workflows. In this scenario, it is fundamentally important to make an effective and efficient workflow application scheduling and execution by fully utilizing the advantages of the cloud (as virtualization and elastic services). However, in the current stage, there is relatively few research for workflow scheduling in cloud environment, where they usually just bring the traditional methods directly into cloud. Without considering the features of cloud, it may raise two kinds of problems: (1) The traditional methods mainly focus on static resource provision, which will cause the waste of resources; (2) They usually ignore the performance fluctuation of virtual machines on the physical machines, therefore it will lead to the estimation error of task execution time. To address these problems, a novel mechanism which can estimate the probability distribution of subtask execution time based on background VM load series over physical machines is proposed. An elastic performance fluctuations-aware stochastic scheduling algorithm is introduced in this paper. The experiments show that our proposed algorithm can outperform the existing algorithms in several metrics and can relieve the influence of performance fluctuations brought by the dynamic nature of cloud.