We have proposed integrated waveguide structure suitable for mid- and near- infrared light propagation using Si and CaF2 heterostructures on Si substrate. Using a fabrication process based on etching, lithography and crystal growth techniques, we have formed a slab-waveguide structure with a current injection mechanism on a SOI substrate, which would be a key component for Si/CaF2 quantum cascade lasers and other optical integrated systems. The propagation of light at a wavelength of 1.55 µm through a Si/CaF2 waveguide structure have been demonstrated for the first time using a structure with a Si/CaF2 multilayered core with 610-nm-thick, waveguide width of 970 nm, which satisfies single-mode condition in the horizontal direction within a tolerance of fabrication accuracy. The waveguide loss for transverse magnetic (TM) mode has been evaluated to be 51.4 cm-1. The cause of the loss was discussed by estimating the edge roughness scattering and free carrier absorption, which suggests further reduction of the loss would be possible.

Statical causal discovery is an approach to infer the causal relationship between observed variables whose causalities are not revealed. LiNGAM (Linear Non-Gaussian Acyclic Model), an algorithm for causal discovery, can calculate the causal relationship uniquely if the independent components of variables are assumed to be non-Gaussian. However, use-cases of LiNGAM are limited because of its O(d3x) computational complexity, where dx is the number of variables. This paper shows two approaches to accelerate LiNGAM causal discovery: SIMD utilization for LiNGAM's mathematical matrixes operations and MPI parallelization. We evaluate the implementation with the supercomputer Fugaku. Using 96 nodes of Fugaku, our improved version can achieve 17,531 times faster than the original OSS implementation (completed in 17.7 hours).

SiC-MOSFETs are being increasingly implemented in power electronics systems as low-loss, fast switching devices. Despite the advantages of an SiC-MOSFET, its large dv/dt or di/dt has fear of electromagnetic interference (EMI) noise. This paper proposes and demonstrates a simple and robust gate driver that can suppress ringing oscillation and surge voltage induced by the turn-off of the SiC-MOSFET body diode. The proposed gate driver utilizes the channel leakage current methodology (CLC) to enhance the damping effect by elevating the gate-source voltage (VGS) and inducing the channel leakage current in the device. The gate driver can self-adjust the timing of initiating CLC operation, which avoids an increase in switching loss. Additionally, the output voltage of the VGS elevation circuit does not need to be actively controlled in accordance with the operating conditions. Thus, the circuit topology is simple, and ringing oscillation can be easily attenuated with fixed circuit parameters regardless of operating conditions, minimizing the increase in switching loss. The effectiveness and versatility of proposed gate driver were experimentally validated for a wide range of operating conditions by double and single pulse switching tests.

In this letter, a path dynamics assessment asynchronous advantage actor-critic scheduling algorithm (PDAA3C) is proposed to solve the MPTCP scheduling problem by using deep reinforcement learning Actor-Critic framework. The algorithm picks out the optimal transmitting path faster by multi-core asynchronous updating and also guarantee the network fairness. Compared with the existing algorithms, the proposed algorithm achieves 8.6% throughput gain over RLDS algorithm, and approaches the theoretic upper bound in the NS3 simulation.

With the development of blockchain technology, the automatic generation of smart contract has become a hot research topic. The existing smart contract automatic generation technology still has improvement spaces in complex process, third-party specialized tools required, specific the compatibility of code and running environment. In this paper, we propose an automatic smart contract generation method, which is domain-oriented and configuration-based. It is designed and implemented with the application scenarios of government service. The process of configuration, public state database definition, code generation and formal verification are included. In the Hyperledger Fabric environment, the applicability of the generated smart contract code is verified. Furthermore, its quality and security are formally verified with the help of third-party testing tools. The experimental results show that the quality and security of the generated smart contract code meet the expect standards. The automatic smart contract generation will “elegantly” be applied on the work of anti-disclosure, privacy protection, and prophecy processing in government service. To effectively enable develop “programmable government”.

Deep Neural Network (DNN) is widely used for computer vision tasks, such as image classification, object detection, and segmentation. DNN accelerator on FPGA and especially Convolutional Neural Network (CNN) is a hot topic. More research and education should be conducted to boost this field. A starting point is required to make it easy for new entrants to join this field. We believe that FPGA-based Autonomous Driving (AD) motor cars are suitable for this because DNN accelerators can be used for image processing with low latency. In this paper, we propose an FPGA-based simple and open-source mini motor car system named RVCar with a RISC-V soft processor and a CNN accelerator. RVCar is suitable for the new entrants who want to learn the implementation of a CNN accelerator and the surrounding system. The motor car consists of Xilinx Nexys A7 board and simple parts. All modules except the CNN accelerator are implemented in Verilog HDL and SystemVerilog. The CNN accelerator is converted from a PyTorch model by our tool. The accelerator is written in C++, synthesizable by Vitis HLS, and an easy-to-customize baseline for the new entrants. FreeRTOS is used to implement AD algorithms and executed on the RISC-V soft processor. It helps the users to develop the AD algorithms efficiently. We conduct a case study of the simple AD task we define. Although the task is simple, it is difficult to achieve without image recognition. We confirm that RVCar can recognize objects and make correct decisions based on the results.

Wireless sensor networks provide long-term monitoring of the environment, but sensors are powered by small batteries. Using a mobile charger (MC) to replenish energy of sensors is one promising solution to prolong their usage time. Many approaches have been developed to find the MC's moving path, and they assume that sensors have a fixed sensing rate (SR) and prefer to fully charge sensors. In practice, sensors can adaptively adjust their SRs to meet application demands or save energy. Besides, due to the fully charging policy, some sensors with low energy may take long to wait for the MC's service. Thus, the paper formulates a path and charge (P&C) problem, which asks how to dispatch the MC to visit sensors with adaptive SRs and decide their charging time, such that both survivability and throughput of sensors can be maximized. Then, we propose an efficient P&C scheduling (EPCS) algorithm, which builds the shortest path to visit each sensor. To make the MC fast move to charge the sensors near death, some sensors with enough energy are excluded from the path. Moreover, EPCS adopts a floating charging mechanism based on the ratio of workable sensors and their energy depletion. Simulation results verify that EPCS can significantly improve the survivability and throughput of sensors.

This paper proposes a Software-Defined Network (SDN)-based Moving Target Defense (MTD) to protect the network from potential scans in a compromised network. As a unique feature, contrary to traditional MTDs, the proposed MTD can work alongside other tools and countermeasures already deployed in the network (e.g., Intrusion Protection and Detection Systems) without affecting its behavior. Through extensive evaluation, we showed the effectiveness of the proposed mechanism compared to existing solutions in preventing scans of different rates without affecting the network and controller performance.

This paper proposes an efficient scheduling algorithm for the layered decoding of block low-density parity-check (LDPC) codes. To efficiently configure check node-based scheduling groups, the proposed algorithm utilizes the base matrix of the block LDPC code for a block-by-block scheduling group configuration; i.e., the proposed algorithm generates a scheduling group of check nodes, satisfying the weight condition of the layered decoding, which is performed in block units (including several check nodes). Therefore, unlike the conventional scheduling algorithms performed in node units, the proposed algorithm can efficiently generate scheduling groups for layered decoding at low computational complexity and memory requirements. In addition, to accelerate the decoding convergence speed, check nodes are allocated in each scheduling group such that messages from check nodes up to the current group are delivered as evenly as possible to bit nodes. Simulation results confirm that the proposed algorithm can accelerate decoding convergence compared to other block-based scheduling algorithms for layered decoding of block LDPC codes.

In this paper, a reference-voltage self-selected pseudo-differential sensing scheme suitable for single-ended SRAM is proposed. The proposed sensing scheme can select different reference voltage according to the offset direction. With the employment of the new sensing scheme, the swing of the read bit-line in the read operation is reduced by 74.6% and 45.5% compared to the conventional domino and the pseudo-differential sense amplifier sensing scheme, respectively. Therefore, the delay and power consumption of the read operation are significantly improved. Simulation results based on a standard 55nm CMOS show that compared with the conventional domino and pseudo-differential sensing schemes, the sensing delay is improved by 66.4% and 47.7%, and the power consumption is improved by 31.4% and 22.5%, respectively. Although the area of the sensing scheme is increased by 50.8% compared with the pseudo-differential sense amplifier sensing scheme, it has little effect on the entire SRAM area.

Aggregate signature schemes enable us to aggregate multiple signatures into a single short signature. One of its typical applications is sensor networks, where a large number of users and devices measure their environments, create signatures to ensure the integrity of the measurements, and transmit their signed data. However, if an invalid signature is mixed into aggregation, the aggregate signature becomes invalid, thus if an aggregate signature is invalid, it is necessary to identify the invalid signature. Furthermore, we need to deal with a situation where an invalid sensor generates invalid signatures probabilistically. In this paper, we introduce a model of aggregate signature schemes with interactive tracing functionality that captures such a situation, and define its functional and security requirements and propose aggregate signature schemes that can identify all rogue sensors. More concretely, based on the idea of Dynamic Traitor Tracing, we can trace rogue sensors dynamically and incrementally, and eventually identify all rogue sensors of generating invalid signatures even if the rogue sensors adaptively collude. In addition, the efficiency of our proposed method is also sufficiently practical.

New services can use fog nodes to distribute Internet of Things (IoT) data. To distribute IoT data, we apply the publish/subscribe messaging model to a fog computing system. A service provider assigns a unique identifier, called a Tag ID, to a player who owes data. A Tag ID matches multiple IDs and resolves the naming rule for data acquisition. However, when users configure their fog node and distribute IoT data to multiple players, the distributed data may contain private information. We propose a table-based access control list (ACL) to manage data transmission permissions to address this issue. It is possible to avoid unnecessary transmission of private data by using a table-based ACL. Furthermore, because there are fewer data transmissions, table-based ACL reduces traffic. Consequently, the overall system's average processing delay time can be reduced. The proposed method's performance was confirmed by simulation results. Table-based ACL, particularly, could reduce processing delay time by approximately 25% under certain conditions. We also concentrated on system security. The proposed method was used, and a qualitative evaluation was performed to demonstrate that security is guaranteed.

The hybrid implicit-explicit single-field finite-difference time-domain (HIE-SF-FDTD) method based on the wave equation of electric field is reformulated in a concise matrix-vector form. The global approximation error of the scheme is discussed theoretically. The second-order convergence of the HIE-SF-FDTD is numerically verified.

Image quality assessment (IQA) is a fundamental metric for image processing tasks (e.g., compression). With full-reference IQAs, traditional IQAs, such as PSNR and SSIM, have been used. Recently, IQAs based on deep neural networks (deep IQAs), such as LPIPS and DISTS, have also been used. It is known that image scaling is inconsistent among deep IQAs, as some perform down-scaling as pre-processing, whereas others instead use the original image size. In this paper, we show that the image scale is an influential factor that affects deep IQA performance. We comprehensively evaluate four deep IQAs on the same five datasets, and the experimental results show that image scale significantly influences IQA performance. We found that the most appropriate image scale is often neither the default nor the original size, and the choice differs depending on the methods and datasets used. We visualized the stability and found that PieAPP is the most stable among the four deep IQAs.

A power-scalable wideband distributed amplifier is proposed. For reducing the power consumption of this power-hungry amplifier, it is efficient to lower the supply voltage. However, there is a hurdle owing to the transistor threshold voltage. In this work, a CMOS deeply depleted channel process is employed to overcome the hurdle.

In order to obtain a feasible schedule of a hard real-time system, heuristic based techniques are the solution of choice. In the last few years, optimization solvers have gained attention from research communities due to their capability of handling large number of constraints. Recently, some works have used integer linear programming (ILP) for solving mono processor scheduling of real-time systems. In fact, ILP is commonly used for static scheduling of multiprocessor systems. However, two main solvers are used to solve the problem indistinctly. But, which one is the best for obtaining a schedulable system for hard real-time systems? This paper makes a comparison of two well-known optimization software packages (CPLEX and GUROBI) for the problem of finding a feasible schedule on monoprocessor hard real-time systems.

S-parameters of InGaAs/InAlAs triple-barrier resonant tunneling diodes (TBRTDs) were measured up to 67 GHz with various mesa areas and various bias voltages. Admittance data of bare TBRTDs are deembedded and evaluated by getting rid of parasitic components with help of electromagnetic simulations for particular fabricated device structures. Admittance spectroscopy up to 67 GHz is applied for bare TBRTDs for the first time and a Kramers-Kronig relation with Lorentzian function is found to be a consistent model for the admittance especially in cases of low bias conditions. Relaxation time included in the Lorentzian function are tentatively evaluated as the order of several pico second.

5G service has been launched in various countries, and research for the beyond 5G is already underway actively around the world. In beyond 5G, it is expected to expand the various capabilities of communication technologies to cover further wide use cases from 5G. As a candidate elemental technology, cell free massive MIMO has been widely researched and shown its potential to enhance the capabilities from various aspects. However, for deploying this technology in reality, there are still many technical issues such as a cost of distributing antenna and installing fronthaul, and also the scalability aspects. This paper surveys research trends of cell free massive MIMO, especially focusing on the deployment challenges with an introduction to our specific related research activities including some numerical examples.

We proposed a new method for evaluating the deterioration of messenger wires by using terahertz waves. We use terahertz time-domain spectroscopy to measure several twisted wire samples with different levels of deterioration. We find that each twisted wire sample had a different distribution of reflection intensity which was due to the wires' twist structure. We show that it is possible to assess the degradation from the straight lines present in the reflection intensity distribution image. Furthermore, it was confirmed that our method can be applied to wire covered with resin.

In this paper, we propose a scheme to strengthen network-based moving target defense with disposable identifiers. The main idea is to change disposable identifiers for each packet to maximize unpredictability with large hopping space and substantially high hopping frequency. It allows network-based moving target defense to defeat active scanning, passive scanning, and passive host profiling attacks. Experimental results show that the proposed scheme changes disposable identifiers for each packet while requiring low overhead.