The increasing demand for millimeter-wave (mmWave) frequencies with wider signal bandwidths, such as 5G NR, requires large investments on test equipment. This work presents a 5G mmWave up/down-converter with a 40 GHz LO, fabricated in custom PCBs with off-the-shelf components. The mmWave converter has broad IF and RF bandwidths of 1∼5 GHz and 21∼45 GHz, and the built-in LO generates 20∼29.5 GHz and 33.5∼40 GHz of output. To achieve high linearity of the converter simultaneously, the LO must produce low-phase-noise and be capable of high harmonics/spur rejection, and design techniques related to these features are demonstrated. Additionally, a reconfigurable IF amplifier for bi-directional conversion is included and demonstrates low gain variation to maintain the linearity of the wideband modulation signals. The final designed converter is tested with 5G OFDM 64-QAM 100 MHz 1-CC (4-CC) signals and shows RF/IF output power of -3/8 dBm with a linear range of 35 (30)/38 (33) dB at an EVM of 25 dB.

We propose a novel silicon diplexer integrated with filters for frequency-division multiplexing in the 300-GHz band. The diplexer consists of a directional coupler formed of unclad silicon wires, a photonic bandgap-based low-pass filter, and a high-pass filter based on frequency-dependent bending loss. These integrated filters are capable of suppressing crosstalk and providing >15dB isolation over 40GHz, which is highly beneficial for terahertz-range wireless communications applications. We have used this diplexer in a simultaneous error-free wireless transmission of 300-GHz and 335-GHz channels at the aggregate data rate of 36Gbit/s.

In this paper, we describe a wavelength-division multiplexing visible-light communication (VLC) system using two colored light-emitting diodes (LEDs) with similar emission wavelengths. A multi-input multi-output signal-separation method using a neural network is proposed to cancel the optical cross chatter caused by the spectral overlap of LEDs. The experimental results demonstrate that signal separation using neural networks can be achieved in wavelength-multiplexed VLC systems with a bit error rate of less than 3.8×10-3 (forward error correction limit). Furthermore, the simulation results reveal that the carrier-to-noise ratio (CNR) is improved by 2dB for the successive interference canceller (SIC) compared to the zero-forcing method.

Indoor free space optical (FSO) communication technology that provides high-speed connectivity to edge users is expected to be introduced in the near future mobile communication system, where the silicon photonics solid-state beam scanning device is a promising tool because of its low cost, long-term reliability, and other beneficial properties. However, the current two-dimensional beam scanning devices using grating coupler arrays have difficulty in increasing the transmission capacity because of bandwidth regulation. To solve the problem, we have introduced a broadband surface optical coupler, “elephant coupler,” which has great potential for combining wavelength and spatial division multiplexing technologies into the beam scanning device, as an alternative to grating couplers. The prototype port-selective silicon beam scanning device fabricated using a 300 mm CMOS pilot line achieved broadband optical beam emission with a 1 dB-loss bandwidth of 40 nm and demonstrated beam scanning using an imaging lens. The device has also exhibited free-space signal transmission of non-return-to-zero on-off-keying signals at 10 Gbps over a wide wavelength range of 60 nm. In this paper, we present an overview of the developed beam scanning device. Furthermore, the theoretical design guidelines for indoor mobile FSO communication are discussed.

The optical phased array (OPA) is an emerging non-mechanical device that enables high-speed beam steering by emitting precisely phase-controlled lightwaves from numerous optical antennas. In practice, however, it is challenging to drive all phase shifters on an OPA in a deterministic manner due to the inevitable fabrication-induced phase errors and crosstalk between the phase shifters. In this work, we fabricate a 16-element silicon photonic non-redundant OPA chip with integrated phase monitors and experimentally demonstrate accurate monitoring of the relative phases of light from each optical antenna. Under the beam steering condition, the optical phase retrieved from the on-chip phase monitors varies linearly with the steering angle, as theoretically expected.

One of cost-effective ways to increase the transmission capacity of current standard wavelength division multiplexing (WDM) transmission systems is to use a wavelength band other than the C-band to transmit in multi-band. We proposed the concept of multi-band system using wavelength conversion, which can simultaneously process signals over a wide wavelength range. All-optical wavelength conversion could be used to convert C-band WDM signals into other bands in a highly nonlinear fiber (HNLF) by four-wave mixing and allow to simultaneously transmit multiple WDM signals including other than the C-band, with only C-band transceivers. Wavelength conversion has been reported for various nonlinear waveguide materials other than HNLF. In such nonlinear materials, we noticed the possibility of wideband transmission by dispersion-tailored silicon-on-insulator (SOI) waveguides. Based on the CMOS process has high accuracy, it is expected that the chromatic dispersion fluctuation could be reduced in mass production. As a first step in the investigation of the broadness of wavelength conversion using SOI-based waveguides, we designed and fabricated dispersion-tailored 12 strip waveguides provided with an edge coupler at both ends. Each of the 12 waveguides having different widths and lengths and is connected to fibers via lensed fibers or by lenses. In order to characterize each waveguide, the pump-probe experimental setup was constructed using a tunable light source as pump and an unmodulated 96-ch C-band WDM test signal. Using this setup, we evaluate insertion loss, input power dependence, conversion bandwidth and conversion efficiency. We confirmed C-band test signal was converted to the S-band and the L-band using the same silicon waveguide with 3dB conversion bandwidth over 100-nm. Furthermore, an increased design tolerance of at least 90nm was confirmed for C-to-S conversion by shortening the waveguide length. It is confirmed that the wavelength converters using the nonlinear waveguide has sufficiently wide conversion bandwidth to enhance the multi-band WDM transmission system.

We propose a novel mode-multiplexed light source using angularly-multiplexed volume holograms. Mode division multiplexing beams can be generated from a simple transmitter that is made of a laser array, single lens, and volume holograms. Hologram media has low recording sensitivity; hence, using holograms in the communication band is difficult. However, a dual wavelength method that uses different wavelengths for recording and reading holograms can realize the volume holograms for the infrared region. The volume holograms for three spatial mode multiplexing are formed using a compact Michelson interferometer type recording setup; simultaneous generations of three modes were demonstrated using a fiber array or vertical cavity surface emitting laser array with the volume holograms. A low loss coupling of three modes to few-mode-fiber can be achieved through the precise design and recording of volume holograms. The simple and low-cost mode-multiplexed light source using the volume holograms has the potential to broaden the application of MDM.

In this paper, a hybrid electromagnetic simulation method of two-dimensional FDTD and ray-tracing methods suitable for an airport surface was proposed. The power variation due to ground reflection, refraction and creeping is calculated by two-dimensional FDTD method and ray-tracing method is used to calculate the reflecting and diffracted powers from buildings. The proposed approach was validated by measurement using a 1/50 scale-model of an airport model with a building model in various positions at 5 GHz. The proposed method allowed measured power distributions to correlate with simulated figures to within 4.8 dB and their null positions were also estimated to an error tolerance of within 0.01 m.

The maximal independent set (MIS) problem is one of the most fundamental problems in the field of distributed computing. This paper focuses on the MIS problem with unreliable communication between processes in the system. We propose a relaxed notion of MIS, named almost MIS (ALMIS), and show that the loosely-stabilizing algorithm proposed in our previous work can achieve exponentially long holding time with logarithmic convergence time and space complexity regarding ALMIS, which cannot be achieved at the same time regarding MIS in our previous work.

FPGA clusters that consist of multiple FPGA boards have been gaining interest in recent times. Massively parallel processing with a stand-alone heterogeneous FPGA cluster with SoC- style FPGAs and mid-scale FPGAs is promising with cost-performance benefit. Here, we propose such a heterogeneous FPGA cluster with FiC and M-KUBOS cluster. FiC consists of multiple boards, mounting middle scale Xilinx's FPGAs and DRAMs, which are tightly coupled with high-speed serial links. In addition, M-KUBOS boards are connected to FiC for ensuring high IO data transfer bandwidth. As an example of massively parallel processing, here we implement genomic pattern search. Next-generation sequencing (NGS) technology has revolutionized biological system related research by its high-speed, scalable and massive throughput. To analyze the genomic data, short read mapping technique is used where short Deoxyribonucleic acid (DNA) sequences are mapped relative to a known reference sequence. Although several pattern matching techniques are available, FM-index based pattern search is perfectly suitable for this task due to the fastest mapping from known indices. Since matching can be done in parallel for different data, the massively parallel computing which distributes data, executes in parallel and gathers the results can be applied. We also implement a data compression method where about 10 times reduction in data size is achieved. We found that a M-KUBOS board matches four FiC boards, and a system with six M-KUBOS boards and 24 FiC boards achieved 30 times faster than the software based implementation.

Existing simple routing protocols (e.g., OSPF, RIP) have some disadvantages of being inflexible and prone to congestion due to the concentration of packets on particular routers. To address these issues, packet routing methods using machine learning have been proposed recently. Compared to these algorithms, machine learning based methods can choose a routing path intelligently by learning efficient routes. However, machine learning based methods have a disadvantage of training time overhead. We thus focus on a lightweight machine learning algorithm, OS-ELM (Online Sequential Extreme Learning Machine), to reduce the training time. Although previous work on reinforcement learning using OS-ELM exists, it has a problem of low learning accuracy. In this paper, we propose OS-ELM QN (Q-Network) with a prioritized experience replay buffer to improve the learning performance. It is compared to a deep reinforcement learning based packet routing method using a network simulator. Experimental results show that introducing the experience replay buffer improves the learning performance. OS-ELM QN achieves a 2.33 times speedup than a DQN (Deep Q-Network) in terms of learning speed. Regarding the packet transfer latency, OS-ELM QN is comparable or slightly inferior to the DQN while they are better than OSPF in most cases since they can distribute congestions.

An interconnection network is an inevitable component for constructing parallel computers. It connects computation nodes so that the nodes can communicate with each other. As a parallel computation essentially requires inter-node communication according to a parallel algorithm, the interconnection network plays an important role in terms of communication performance. This paper focuses on the collective communication that is frequently performed in parallel computation and this paper addresses the Cup-Stacking method that is proposed in our preceding work. The key issues of the method are splitting a large packet into slices, re-shaping the slice, and stacking the slices, in a genetic algorithm (GA) manner. This paper discusses extending the Cup-Stacking method by introducing additional items (genes) and proposes the extended Cup-Stacking method. Furthermore, this paper places comprehensive discussions on the drawbacks and further optimization of the method. Evaluation results reveal the effectiveness of the extended method, where the proposed method achieves at most seven percent improvement in duration time over the former Cup-Stacking method.

A multifunctional radar (MFR) with varying pulse sequences can change its signal characteristics and/or pattern, based on the presence of targets and to avoid being jammed. To take a countermeasure against an MFR, it is crucial for an electronic warfare (EW) system to be able to identify and separate a MFR's modes via analyzing intercepted radar signals, without a priori knowledge. In this article, two correlation-based methods, one taking the signal's order into account and another one ignoring the signal's order, are proposed and investigated for this task. The results demonstrate their great potential.

We assess how well the recently created MRI reconstruction technique, Multi-Resolution Convolutional Neural Network (MRCNN), performs in the core medical vision field (classification). The primary goal of MRCNN is to identify the best k-space undersampling patterns to accelerate the MRI. In this study, we use the Figshare brain tumor dataset for MRI classification with 3064 T1-weighted contrast-enhanced MRI (CE-MRI) over three categories: meningioma, glioma, and pituitary tumors. We apply MRCNN to the dataset, which is a method to reconstruct high-quality images from under-sampled k-space signals. Next, we employ the pre-trained VGG16 model, which is a Deep Neural Network (DNN) based image classifier to the MRCNN restored MRIs to classify the brain tumors. Our experiments showed that in the case of MRCNN restored data, the proposed brain tumor classifier achieved 92.79% classification accuracy for a 10% sampling rate, which is slightly higher than that of SRCNN, MoDL, and Zero-filling methods have 91.89%, 91.89%, and 90.98% respectively. Note that our classifier was trained using the dataset consisting of the images with full sampling and their labels, which can be regarded as a model of the usual human diagnostician. Hence our results would suggest MRCNN is useful for human diagnosis. In conclusion, MRCNN significantly enhances the accuracy of the brain tumor classification system based on the tumor location using under-sampled k-space signals.

Learning analytics (LA) has emerged as a technique for educational quality improvement in many learning contexts, including blended learning (BL) courses. Numerous studies show that students' academic performance is significantly impacted by their ability to engage in self-regulated learning (SRL). In this study, learning behaviors indicating SRL and motivation are elucidated during a BL course on second language learning. Online trace data of a mobile language learning application (m-learning app) is used as a part of BL implementation. The observed motivation were of two categories: high-level motivation (study in time, study again, and early learning) and low-level motivation (cramming and catch up). As a result, students who perform well tend to engage in high-level motivation. While low performance students tend to engage in clow-level motivation. Those findings are supported by regression models showing that study in time followed by early learning significantly influences the academic performance of BL courses, both in the spring and fall semesters. Using limited resource of m-learning app log data, this BL study could explain the overall BL performance.

We present an effective system for integrating generative zero-shot classification modules into a YOLO-like dense detector to detect novel objects. Most double-stage-based novel object detection methods are achieved by refining the classification output branch but cannot be applied to a dense detector. Our system utilizes two paths to inject knowledge of novel objects into a dense detector. One involves injecting the class confidence for novel classes from a classifier trained on data synthesized via a dual-step generator. This generator learns a mapping function between two feature spaces, resulting in better classification performance. The second path involves re-training the detector head with feature maps synthesized on different intensity levels. This approach significantly increases the predicted objectness for novel objects, which is a major challenge for a dense detector. We also introduce a stop-and-reload mechanism during re-training for optimizing across head layers to better learn synthesized features. Our method relaxes the constraint on the detector head architecture in the previous method and has markedly enhanced performance on the MSCOCO dataset.

Driver fatigue detection has become crucial in vehicle safety technology. Achieving high accuracy and real-time performance in detecting driver fatigue is paramount. In this paper, we propose a novel driver fatigue detection algorithm based on dynamic tracking of Facial Eyes and Yawning using YOLOv7, named FEY-YOLOv7. The Coordinate Attention module is inserted into YOLOv7 to enhance its dynamic tracking accuracy by focusing on coordinate information. Additionally, a small target detection head is incorporated into the network architecture to promote the feature extraction ability of small facial targets such as eyes and mouth. In terms of compution, the YOLOv7 network architecture is significantly simplified to achieve high detection speed. Using the proposed PERYAWN algorithm, driver status is labeled and detected by four classes: open_eye, closed_eye, open_mouth, and closed_mouth. Furthermore, the Guided Image Filtering algorithm is employed to enhance image details. The proposed FEY-YOLOv7 is trained and validated on RGB-infrared datasets. The results show that FEY-YOLOv7 has achieved mAP of 0.983 and FPS of 101. This indicates that FEY-YOLOv7 is superior to state-of-the-art methods in accuracy and speed, providing an effective and practical solution for image-based driver fatigue detection.

In this study, we present a spherical style deformation algorithm to be applied on single component models that can deform the models with spherical style, while preserving the local details of the original models. Because 3D models have complex skeleton structures that consist of many components, the deformation around connections between each single component is complicated, especially preventing mesh self-intersections. To the best of our knowledge, there does not exist not only methods to achieve a spherical style in a 3D model consisting of multiple components but also methods suited to a single component. In this study, we focus on spherical style deformation of single component models. Accordingly, we propose a deformation method that transforms the input model with the spherical style, while preserving the local details of the input model. Specifically, we define an energy function that combines the as-rigid-as-possible (ARAP) method and spherical features. The spherical term is defined as l2-regularization on a linear feature; accordingly, the corresponding optimization can be solved efficiently. We also observed that the results of our deformation are dependent on the quality of the input mesh. For instance, when the input mesh consists of many obtuse triangles, the spherical style deformation method fails. To address this problem, we propose an optional deformation method based on convex hull proxy model as the complementary deformation method. Our proxy method constructs a proxy model of the input model and applies our deformation method to the proxy model to deform the input model by projection and interpolation. We have applied our proposed method to simple and complex shapes, compared our experimental results with the 3D geometric stylization method of normal-driven spherical shape analogies, and confirmed that our method successfully deforms models that are smooth, round, and curved. We also discuss the limitations and problems of our algorithm based on the experimental results.

Until today, digital image watermarking has not been large-scale used in the industry. The first reason is that the watermarking efficiency is low and the real-time performance cannot be satisfied. The second reason is that the watermarking scheme cannot cope with various attacks. To solve above problems, this paper presents a multi-domain based digital image watermarking scheme, where a fast DFT (Discrete Fourier Transform) based watermarking method is proposed for synchronization correction and an IWT-DCT (Integer Wavelet Transform-Discrete Cosine Transform) based watermarking method is proposed for information embedding. The proposed scheme has high efficiency during embedding and extraction. Compared with five existing schemes, the robustness of our scheme is very strong and our scheme can cope with many common attacks and compound attacks, and thus can be used in wide application scenarios.

Modern distributed storage requires microsecond-scale tail latency, but the current coordinator-based quorum coordination causes a burdensome latency overhead. This paper presents Archon, a new quorum coordination architecture that supports low tail latency for microsecond-scale replicated storage. The key idea of Archon is to perform the quorum coordination in the network switch by leveraging the flexibility and capability of emerging programmable switch ASICs. Our in-network quorum coordination is based on the observation that the modern programmable switch provides nanosecond-scale processing delay and high flexibility simultaneously. To realize the idea, we design a custom switch data plane. We implement a Archon prototype on an Intel Tofino switch and conduct a series of testbed experiments. Our experimental results show that Archon can provide lower tail latency than the coordinator-based solution.