Aiming at the problem of low classification accuracy of surface defects of lithium battery pole pieces by traditional classification methods, an image classification algorithm for surface defects of lithium battery pole piece based on deep learning is proposed in this paper. Firstly, Wavelet Threshold and Histogram Equalization are used to preprocess the detect image to weaken influence of noise in non-defect regions and enhance defect features. Secondly, a VGG-InceptionV2 network with better performance is proposed by adding InceptionV2 structure to the improved VGG network structure. Then the original data set is expanded by rotating, flipping and contrast adjustment, and the optimal value of the model hyperparameters is determined by experiments. Finally, the model in this paper is compared with VGG16 and GoogLeNet to realize the recognition of defect types. The results show that the accuracy rate of the model in this paper for the surface pole piece defects of lithium batteries is 98.75%, and the model parameters is only 1.7M, which has certain significance for the classification of lithium battery surface pole piece defects in industry.

In human-computer interaction, acoustic scene classification (ASC) is one of the relevant research domains. In real life, the recorded audio may include a lot of noise and quiet clips, making it hard for earlier ASC-based research to isolate the crucial scene information in sound. Furthermore, scene information may be scattered across numerous audio frames; hence, selecting scene-related frames is crucial for ASC. In this context, an integrated convolutional neural network with a fusion attention mechanism (ICNN-FA) is proposed for ASC. Firstly, segmented mel-spectrograms as the input of ICNN can assist the model in learning the short-term time-frequency correlation information. Then, the designed ICNN model is employed to learn these segment-level features. In addition, the proposed global attention layer may gather global information by integrating these segment features. Finally, the developed fusion attention layer is utilized to fuse all segment-level features while the classifier classifies various situations. Experimental findings using ASC datasets from DCASE 2018 and 2019 indicate the efficacy of the suggested method.

With the recent commercialization of fifth-generation mobile communication systems (5G), wireless communications are being used in various fields. Accordingly, the number of situations in which sensitive information, such as personal data is handled in wireless communications is increasing, and so is the demand for confidentiality. To meet this demand, we proposed a chaos-based radio-encryption modulation that combines physical layer confidentiality and channel coding effects, and we have demonstrated its effectiveness through computer simulations. However, there are no demonstrations of performances using real signals. In this study, we constructed a transmission system using Universal Software Radio Peripheral, a type of software-defined radio, and its control software LabVIEW. We conducted wired transmission experiments for the practical use of radio-frequency encrypted modulation. The results showed that a gain of 0.45dB at a bit error rate of 10-3 was obtained for binary phase-shift keying, which has the same transmission efficiency as the proposed method under an additive white Gaussian noise channel. Similarly, a gain of 10dB was obtained under fading conditions. We also evaluated the security ability and demonstrated that chaos modulation has both information-theoretic security and computational security.

The design and adjustment of the divergence in audio applications using nonnegative matrix factorization (NMF) is still open problem. In this study, to deal with this problem, we explore a representation of the divergence using neural networks (NNs). Instead of the divergence, our approach extends the multiplicative update algorithm (MUA), which estimates the NMF parameters, using NNs. The design of the extended MUA incorporates NNs, and the new algorithm is referred to as the deep MUA (DeMUA) for NMF. While the DeMUA represents the algorithm for the NMF, interestingly, the divergence is obtained from the incorporated NN. In addition, we propose theoretical guides to design the incorporated NN such that it can be interpreted as a divergence. By appropriately designing the NN, MUAs based on existing divergences with a single hyper-parameter can be represented by the DeMUA. To train the DeMUA, we applied it to audio denoising and supervised signal separation. Our experimental results show that the proposed architecture can learn the MUA and the divergences in sparse denoising and speech separation tasks and that the MUA based on generalized divergences with multiple parameters shows favorable performances on these tasks.

Visible light communication (VLC) and visible light ranging are applicable techniques for intelligent transportation systems (ITS). They use every unique light-emitting diode (LED) on roads for data transmission and range estimation. The simultaneous VLC and ranging can be applied to improve the performance of both. It is necessary to achieve rapid data rate and high-accuracy ranging when transmitting VLC data and estimating the range simultaneously. We use the signal modulation method of pulse-width modulation (PWM) to increase the data rate. However, when using PWM for VLC data transmission, images of the LED transmitters are captured at different luminance levels and are easily saturated, and LED saturation leads to inaccurate range estimation. In this paper, we establish a novel simultaneous visible light communication and ranging system for ITS using PWM. Here, we analyze the LED saturation problems and apply bicubic interpolation to solve the LED saturation problem and thus, improve the communication and ranging performance. Simultaneous communication and ranging are enabled using a stereo camera. Communication is realized using maximal-ratio combining (MRC) while ranging is achieved using phase-only correlation (POC) and sinc function approximation. Furthermore, we measured the performance of our proposed system using a field trial experiment. The results show that error-free performance can be achieved up to a communication distance of 55 m and the range estimation errors are below 0.5m within 60m.

We have developed and evaluated a prototype micro-pump for a new form of medication that is driven by a chemical reaction. The chemical reaction between citric acid and sodium bicarbonate produces carbon dioxide, the pressure of which pushes the medication out. This micropump is smaller in size than conventional diaphragm-type micropumps and is suitable for swallowing.

In the massive machine-type communication scenario, aiming at the problems of active user detection and channel estimation in the grant-free non-orthogonal multiple access (NOMA) system, new sets of non-orthogonal spreading sequences are proposed by using the zero/low correlation zone sequence set with low correlation among multiple sets. The simulation results show that the resulting sequence set has low coherence, which presents reliable performance for channel estimation and active user detection based on compressed sensing. Compared with the traditional Zadoff-Chu (ZC) sequences, the new non-orthogonal spreading sequences have more flexible lengths, and lower peak-to-average power ratio (PAPR) and smaller alphabet size. Consequently, these sequences will effectively solve the problem of high PAPR of time domain signals and are more suitable for low-cost devices in massive machine-type communication.

In this paper, we present a novel discriminative dictionary learning (DDL) method for image classification. The local structural relationship between samples is first built by the Laplacian eigenmaps (LE), and then integrated into the basic DDL frame to suppress inter-class ambiguity in the feature space. Moreover, in order to improve the discriminative ability of the dictionary, the category label information of training samples is formulated into the objective function of dictionary learning by considering the discriminative promotion term. Thus, the data points of original samples are transformed into a new feature space, in which the points from different categories are expected to be far apart. The test results based on the real dataset indicate the effectiveness of this method.

In this paper, we propose a heuristic planning method to efficiently accommodate dynamic multilayer path (MLP) demand in multilayer networks consisting of a Time Division Multiplexing (TDM) layer and a Wavelength Division Multiplexing (WDM) layer; the goal is to achieve the flexible accommodation of increasing capacity and diversifying path demands. In addition to the grooming of links at the TDM layer and the route and frequency slots for the elastic optical path to be established, MLP requires the selection of an appropriate operational mode, consisting of a combination of modulation formats and symbol rates supported by digital coherent transceivers. Our proposed MLP planning method defines a planning policy for each of these parameters and embeds the values calculated by combining these policies in an auxiliary graph, which allows the planning parameters to be calculated for MLP demand requirements in a single step. Simulations reveal that the choice of operational mode significantly reduces the blocking probability and demonstrate that the edge weights in the auxiliary graph allow MLP planning with characteristics tailored to MLP demand and network requirements. Furthermore, we quantitatively evaluate the impact of each planning policy on the MLP planning results.

In this study, vertically aligned carbon nanotubes (VA-CNTs) were grown from filler-added LB films with accumulated AlFe2O4 nanoparticles and palmitic acid (C16) as the filler molecule after different hydrogen reduction temperatures of 500°C and 750°C, and the grown VA-CNTs were compared and evaluated. As a result, VA-CNTs were approximately doubled in length after 500°C hydrogen reduction compared to 750°C hydrogen reduction when AlFe2O4 NPs were used. On the other hand, when the catalyst area ratio was decreased by using palmitic acid, i.e., the distance between CNTs was increased, VA-CNTs rapidly shortened after 500°C hydrogen reduction, and VA-CNTs were no longer obtained even in the range where VA-CNTs were obtained in 750°C hydrogen reduction. The inner and outer diameters of VA-CNTs decreased with decreasing catalyst area ratio at 750°C hydrogen reduction and tended to increase at 500°C hydrogen reduction. The morphology of the catalyst nanoparticles after CVD was observed to change significantly depending on the hydrogen reduction temperature and catalyst area ratio. These observations indicate that the state of the catalyst nanoparticles immediately before the CNT growth process greatly affects the physical properties of the CNTs.

We investigated the photochemical stability of an electro-optic (EO) polymer under laser irradiation at 1310nm to reveal photodegradation mechanisms. It was found that one-photon absorption excitation assisted with the thermal energy at the temperature is involved in the photodegradation process, in contrast to our previous studies at a wavelength of 1550nm where two-photon absorption excitation is involved in the photodegradation process. Thus, both the excitation wavelength and the thermal energy strongly affect to the degradation mechanism. In any cases, the photodegradation of EO polymers is mainly related to the generation of exited singlet oxygen.

Incorporating a tool for administering medication, such as a syringe, is required in microneedles (MNs) for medical use. This renders it easier for non-medical personnel to administer medication. Because it is difficult to fabricate a hollow MN, we fabricated a capillary groove on an MN and its substrate to enable the administration of a higher dosage. MN grooving is difficult to accomplish via the conventional injection molding method used for polylactic acid. Therefore, biodegradable polyacid anhydride was selected as the material for the MN. Because polyacid anhydride is a low-viscosity liquid at room temperature, an MN can be grooved using a processing method similar to vacuum casting. This study investigated the performance of the capillary force of the MN and the optimum shape and size of the MN by a puncture test.

Studies on intrinsic Josephson junctions (IJJs) of cuprate superconductors are reviewed. A system consisting of a few IJJs provides phenomena to test the Josephson phase dynamics and its interaction between adjacent IJJs within a nanometer scale, which is unique to cuprate superconductors. Quasiparticle density of states, which provides direct information on the Cooper-pair formation, is also revealed in the system. In contrast, Josephson plasma emission, which is an electromagnetic wave radiation in the sub-terahertz frequency range from an IJJ stack, arises from the synchronous phase dynamics of hundreds of IJJs coupled globally. This review summarizes a wide range of physical phenomena in IJJ systems having capacitive and inductive couplings with different nanometer and micrometer length scales, respectively.

Many deep convolutional neural network (CNN) inference accelerators on the field-programmable gate array (FPGA) platform have been widely adopted due to their low power consumption and high performance. In this paper, we develop the following to improve performance and power efficiency. First, we use a high bandwidth memory (HBM) to expand the bandwidth of data transmission between the off-chip memory and the accelerator. Second, a fully-pipelined manner, which consists of pipelined inter-layer computation and a pipelined computation engine, is implemented to decrease idle time among layers. Third, a multi-core architecture with shared-dual buffers is designed to reduce off-chip memory access and maximize the throughput. We designed the proposed accelerator on the Xilinx Alveo U280 platform with in-depth Verilog HDL instead of high-level synthesis as the previous works and explored the VGG-16 model to verify the system during our experiment. With a similar accelerator architecture, the experimental results demonstrate that the memory bandwidth of HBM is 13.2× better than DDR4. Compared with other accelerators in terms of throughput, our accelerator is 1.9×/1.65×/11.9× better than FPGA+HBM2 based/low batch size (4) GPGPU/low batch size (4) CPU. Compared with the previous DDR+FPGA/DDR+GPGPU/DDR+CPU based accelerators in terms of power efficiency, our proposed system provides 1.4-1.7×/1.7-12.6×/6.6-37.1× improvement with the large-scale CNN model.

To simultaneously enhance data rate and physical layer security (PLS) for low-luminance smartphone screen to camera uplink communication, space division multiplexing using high-luminance cell-size reduction arrangement is numerically analyzed and experimentally verified. The uplink consists of a low-luminance smartphone screen and an indoor telephoto camera at a long distance of 3.5 meters. The high-luminance cell-size reduction arrangement avoids the influence of spatial inter-symbol interference (ISI) and ambient light to obtain a stable low-luminance screen. To reduce the screen luminance without decreasing the screen pixel value, the arrangement reduces only the high-luminance cell area while keeping the cell spacing. In this study, two technical issues related to high-luminance cell-size reduction arrangement are solved. First, a numerical analysis and experimental results show that the high-luminance cell-size reduction arrangement is more effective in reducing the spatial ISI at low luminance than the conventional low-luminance cell arrangement. Second, in view point of PLS enhancement at wide angles, symbol error rate should be low in front of the screen and high at wide angles. A numerical analysis and experimental results show that the high-luminance cell-size reduction arrangement is more suitable for enhancing PLS at wide angles than the conventional low-luminance cell arrangement.

This paper summarizes the modulation configurations of phase locked loops (PLLs) and their integration in semiconductor circuits, e.g., the input modulation for cellular phones, direct-modulation for low power wireless sensor networks, feedback-loop modulation for high-speed transmission, and two-point modulation for short-range radio transceivers. In this survey, basic configuration examples of integrated circuits for wired and wireless applications which are using the PLL modulation configurations are explained. It is important to select the method for simply and effectively determining the characteristics corresponding to the specific application. The paper also surveys technologies for future PLL design for digitizing of an entire PLL to reduce the phase noise due to a modulation by using a feedback loop with a precise digital phase comparison and a numerically controlled oscillator with high linearity.

This paper proposes a deep neural network named BayesianPUFNet that can achieve high prediction accuracy even with few challenge-response pairs (CRPs) available for training. Generally, modeling attacks are a vulnerability that could compromise the authenticity of physically unclonable functions (PUFs); thus, various machine learning methods including deep neural networks have been proposed to assess the vulnerability of PUFs. However, conventional modeling attacks have not considered the cost of CRP collection and analyzed attacks based on the assumption that sufficient CRPs were available for training; therefore, previous studies may have underestimated the vulnerability of PUFs. Herein, we show that the application of Bayesian deep neural networks that incorporate Bayesian statistics can provide accurate response prediction even in situations where sufficient CRPs are not available for learning. Numerical experiments show that the proposed model uses only half the CRP to achieve the same response prediction as that of the conventional methods. Our code is openly available on https://github.com/bayesian-puf-net/bayesian-puf-net.git.

Reconfigurable intelligent surface (RIS) has the capability of boosting system performance by manipulating the wireless propagation environment. This paper investigates a downlink RIS-aided non-orthogonal multiple access (NOMA) system, where a RIS is deployed to enhance physical-layer security (PLS) in the presence of an eavesdropper. In order to improve the main link's security, the RIS is deployed between the source and the users, in which a reflecting element separation scheme is developed to aid data transmission of both the cell-center and the cell-edge users. Additionally, the closed-form expressions of secrecy outage probability (SOP) are derived for the proposed RIS-aided NOMA scheme. To obtain more deep insights on the derived results, the asymptotic performance of the derived SOP is analyzed. Moreover, the secrecy diversity order is derived according to the asymptotic approximation in the high signal-to-noise ratio (SNR) and main-to-eavesdropper ratio (MER) regime. Furthermore, based on the derived results, the power allocation coefficient and number of elements are optimized to minimize the system SOP. Simulations demonstrate that the theoretical results match well with the simulation results and the SOP of the proposed scheme is clearly less than that of the conventional orthogonal multiple access (OMA) scheme obviously.

Multi-band transmission technologies promise to cost-effectively expand the capacity of optical networks by exploiting low-loss spectrum windows beyond the conventional band used in already-deployed fibers. While such technologies offer a high potential for capacity upgrades, available capacity is seriously restricted not only by the wavelength-continuity constraint but also by the signal-to-noise ratio (SNR) constraint. In fact, exploiting more bands can cause higher SNR imbalance over multiple bands, which is mainly due to stimulated Raman scattering. To relax these constraints, we propose wavelength-selective band switching-enabled networks (BSNs), where each wavelength channel can be freely switched to any band and in any direction at any optical node on the route. We also present two typical optical node configurations utilizing all-optical wavelength converters, which can realize the switching proposal. Moreover, numerical analyses clarify that our BSN can reduce the fiber resource requirements by more than 20% compared to a conventional multi-band network under realistic conditions. We also discuss the impact of physical-layer performance of band switching operations on available benefits to investigate the feasibility of BSNs. In addition, we report on a proof-of-concept demonstration of a BSN with a prototype node, where C+L-band wavelength-division-multiplexed 112-Gb/s dual-polarization quadrature phase-shift keying signals are successfully transmitted while the bands of individual channels are switched node-by-node for up to 4 cascaded nodes.

To understand the radiation mechanism of an electrically small spherical helix antenna, we develop a theory on the radiation characteristics of the antenna. An analytical model of the antenna presuming a current on the wire to be sinusoidally distributed is proposed and analyzed with the spherical wave expansion. The radiation efficiency, radiation resistance, and radiation patterns are obtained in closed-form expression. The radiation efficiency evidently varies with the surface area of the wire and the radiation resistance depends on the square of the length of the wire. The obtained result for the radiation pattern illustrates the tilt of the pattern caused by the modes asymmetric to the z-axis. The radiation efficiency formula indicates a good agreement between the simulation and measurement result. In addition, the radiation resistance of the theoretical and simulation results exhibits good agreement. Considering the effect of the feeding structure of the fabricated antenna, the radiation resistance of the analytical model can be treated as a reasonable result. The result of radiation pattern also shows good agreement between the simulation and measurement results excluding a small contribution from the feeding cable acting as a scatterer.