In this letter, an artificial message error-based code scrambling scheme is proposed for secure communications in wiretap channels with channel reciprocity. In the proposed scheme, the artificial message bit errors agreed between the legitimate transmitter and receiver are added to the scrambled message bits at the transmitter prior to the channel encoding procedure, through which the artificial errors are generated by using the reciprocal channel between the legitimate transmitter and receiver. Because of the inaccessibility to the channel state information between the legitimate transmitter and receiver, an eavesdropper would fail to compensate for the artificial errors perfectly. Thus, in addition to decoding errors, the residual artificial errors will also be spread over the descrambled message of the eavesdropper by the error spreading effect of code scrambling. Therefore, unlike the conventional code scrambling scheme, the proposed scheme can provide strong message confidentiality for non-degraded eavesdropping channels, e.g., when the eavesdropper experiences no decoding errors. Furthermore, given that the artificial errors are introduced before the channel encoding procedure, the spread residual errors in the descrambled message remain undetected after the decoding procedures of the eavesdropper. Simulation results confirm that the proposed scheme outperforms the conventional scheme and provides strong message confidentiality in wiretap channels.

IEEE P802.3cg established a new pair of Ethernet physical layer devices (PHY), one of which, the short-reach 10BASE-T1S, uses 4B/5B mapping over Differential Manchester Encoding to maintain a data rate of 10 Mb/s at MAC/PLS interface, while providing in-band signaling between transmitter and receivers. However, 10BASE-T1S does not have any error correcting capability built into it. As a response to emerging building, industrial, and transportation requirements, this paper outlines research that leads to the possibility of establishing low-complexity, backward-compatible Forward Error Correction with per-frame configurable guaranteed burst error and erasure correcting capabilities over any 10BASE-T1S Ethernet network segment. The proposed technique combines a specialized, systematic Reed-Solomon code and a novel, three-tier, technique to avoid the appearance of certain inadmissible codeword symbols at the output of the encoder. In this way, the proposed technique enables error and erasure correction, while maintaining backwards compatibility with the current version of the standard.

Multisignatures enable multiple users to sign a message interactively. Many instantiations are proposed for multisignatures, however, most of them are quantum-insecure, because these are based on the integer factoring assumption or the discrete logarithm assumption. Although there exist some constructions based on the lattice problems, which are believed to be quantum-secure, their security reductions are loose. In this paper, we aim to improve the security reduction of lattice-based multisignature schemes concerning tightness. Our basic strategy is combining the multisignature scheme proposed by El Bansarkhani and Sturm with the lattice-based signature scheme by Abdalla, Fouque, Lyubashevsky, and Tibouchi which has a tight security reduction from the Ring-LWE (Ring Learning with Errors) assumption. Our result shows that proof techniques for standard signature schemes can be applied to multisignature schemes, then we can improve the polynomial loss factor concerning the Ring-LWE assumption. Our second result is to address the problem of security proofs of existing lattice-based multisignature schemes pointed out by Damgård, Orlandi, Takahashi, and Tibouchi. We employ a new cryptographic assumption called the Rejected-Ring-LWE assumption, to complete the security proof.

There have been increasing demands for distributed operating systems to better utilize scattered resources over multiple nodes. This paper enlightens the challenges and requirements for the communication layers for distributed operating systems, and makes a case for a versatile, high-performance communication layer over InfiniBand network.

This paper proposes a method for cooperative multi-static Multiple Input Multiple Output (MIMO) radar that can estimate the number of targets. The purpose of this system is to monitor humans in an indoor environment. First, target positions within the estimation range are roughly detected by the Capon method and the mode vector corresponding to the detected positions is calculated. The mode vector is multiplied by the eigenvector to eliminate the virtual image. The spectrum of the evaluation function is calculated from the remaining positions, and the number of peaks in the spectrum is defined as the number of targets. Experiments carried out in an indoor environment confirm that the proposed method can estimate the number of targets with high accuracy.

In this study, we propose a mechanism called adaptive failsoft control to address peak traffic in mobile live streaming, using a chasing playback function. Although a cache system is avaliable to support the chasing playback function for live streaming in a base station and device-to-device communication, the request concentration by highlight scenes influences the traffic load owing to data unavailability. To avoid data unavailability, we adapted two live streaming features: (1) streaming data while switching the video quality, and (2) time variability of the number of requests. The second feature enables a fallback mechanism for the cache system by prioritizing cache eviction and terminating the transfer of cache-missed requests. This paper discusses the simulation results of the proposed mechanism, which adopts a request model appropriate for (a) avoiding peak traffic and (b) maintaining continuity of service.

A pushdown system (PDS) is known as an abstract model of recursive programs. For PDS, model checking methods have been studied and applied to various software verification such as interprocedural data flow analysis and malware detection. However, PDS cannot manipulate data values from an infinite domain. A register PDS (RPDS) is an extension of PDS by adding registers to deal with data values in a restricted way. This paper proposes algorithms for LTL model checking problems for RPDS with simple and regular valuations, which are labelings of atomic propositions to configurations with reasonable restriction. First, we introduce RPDS and related models, and then define the LTL model checking problems for RPDS. Second, we give algorithms for solving these problems and also show that the problems are EXPTIME-complete. As practical examples, we show solutions of a malware detection and an XML schema checking in the proposed framework.

Weighted Round-Robin (WRR) is often used, due to its simplicity, for scheduling packets or tasks. With WRR, a number of packets equal to the weight allocated to a flow can be served consecutively, which leads to a bursty service. Interleaved Weighted Round-Robin (IWRR) is a variant that mitigates this effect. We are interested in finding bounds on worst-case delay obtained with IWRR. To this end, we use a network calculus approach and find a strict service curve for IWRR. The result is obtained using the pseudo-inverse of a function. We show that the strict service curve is the best obtainable one, and that delay bounds derived from it are tight (i.e., worst-case) for flows of packets of constant size. Furthermore, the IWRR strict service curve dominates the strict service curve for WRR that was previously published. We provide some numerical examples to illustrate the reduction in worst-case delays caused by IWRR compared to WRR.

Leveraging on Network Function Virtualization (NFV) and Software Defined Networking (SDN), network slicing (NS) is recognized as a key technology that enables the 5G Infrastructure Provider (InP) to support diversified vertical services over a shared common physical infrastructure. 5G end-to-end (E2E) NS is a logical virtual network that spans across the 5G network. Existing works on improving the reliability of the 5G mainly focus on reliable wireless communications, on the other hand, the reliability of an NS also refers to the ability of the NS system to provide continued service. Hence, in this work, we focus on enhancing the reliability of the NS to cope with physical network node failures, and we investigate the NS deployment problem to improve the reliability of the system represented by the NS. The reliability of an NS is enhanced by two means: firstly, by considering the topology information of an NS, critical virtual nodes are backed up to allow failure recovery; secondly, the embedding of the augmented NS virtual network is optimized for failure avoidance. We formulate the embedding of the augmented virtual network (AVN) to maximize the survivability of the NS system as the survivable AVN embedding (S-AVNE) problem through an Integer Linear Program (ILP) formulation. Due to the complexity of the problem, a heuristic algorithm is introduced. Finally, we conduct intensive simulations to evaluate the performance of our algorithm with regard to improving the reliability of the NS system.

Horizon detection is useful in maritime image processing for various purposes, such as estimation of camera orientation, registration of consecutive frames, and restriction of the object search region. Existing horizon detection methods are based on edge extraction. For accuracy, they use multiple images, which are filtered with different filter sizes. However, this increases the processing time. In addition, these methods are not robust to blurting. Therefore, we developed a horizon detection method without extracting the candidates from the edge information by formulating the horizon detection problem as a global optimization problem. A horizon line in an image plane was represented by two parameters, which were optimized by an evolutionary algorithm (genetic algorithm). Thus, the local and global features of a horizon were concurrently utilized in the optimization process, which was accelerated by applying a coarse-to-fine strategy. As a result, we could detect the horizon line on high-resolution maritime images in about 50ms. The performance of the proposed method was tested on 49 videos of the Singapore marine dataset and the Buoy dataset, which contain over 16000 frames under different scenarios. Experimental results show that the proposed method can achieve higher accuracy than state-of-the-art methods.

Observed results of arc discharges generated between the brush and commutator are reported. The motion of the arc discharges was observed by a high-speed camera. The brush and commutator were installed to an experimental device that simulated the rotational motion of a real DC motor. The aim of this paper is to investigate the occurring position, dimensions, and moving characteristics of the arc discharges by means of high-speed imaging. Time evolutions of the arc voltage and current were measured, simultaneously. The arc discharges were generated when an inductive circuit was interrupted. Circuit current before interruption was 4A. The metal graphite or graphite brush and a copper commutator were used. Following results were obtained. The arc discharge was dragged on the brush surface and the arc discharge was sticking to the side surface of the commutator. The positions of the arc spots were on the end of the commutator and the center of the brush in rotational direction. The dimensions of the arc discharge were about 0.2 mm in length and about 0.3 mm in width. The averaged arc voltage during arc duration became higher and the light emission from the arc discharge became brighter, as the copper content of the cathode decreased.

In this letter, we propose a semantic guided infrared and visible image fusion method, which can train a network to fuse different semantic objects with different fusion weights according to their own characteristics. First, we design the appropriate fusion weights for each semantic object instead of the whole image. Second, we employ the semantic segmentation technology to obtain the semantic region of each object, and generate special weight maps for the infrared and visible image via pre-designed fusion weights. Third, we feed the weight maps into the loss function to guide the image fusion process. The trained fusion network can generate fused images with better visual effect and more comprehensive scene representation. Moreover, we can enhance the modal features of various semantic objects, benefiting subsequent tasks and applications. Experiment results demonstrate that our method outperforms the state-of-the-art in terms of both visual effect and quantitative metrics.

This article proposes to apply the auto-correlation function (ACF), bispectrum analysis, and convolutional neural networks (CNN) to implement radar emitter identification (REI) based on intrapulse features. In this work, we combine ACF with bispectrum for signal feature extraction. We first calculate the ACF of each emitter signal, and then the bispectrum of the ACF and obtain the spectrograms. The spectrum images are taken as the feature maps of the radar emitters and fed into the CNN classifier to realize automatic identification. We simulate signal samples of different modulation types in experiments. We also consider the feature extraction method directly using bispectrum analysis for comparison. The simulation results demonstrate that by combining ACF with bispectrum analysis, the proposed scheme can attain stronger robustness to noise, the spectrograms of our approach have more pronounced features, and our approach can achieve better identification performance at low signal-to-noise ratios.

We introduce the r-ary sequence with period 2p2 derived from Euler quotients modulo 2p (p is an odd prime) where r is an odd prime divisor of (p-1). Then based on the cyclotomic theory and the theory of trace function in finite fields, we give the trace representation of the proposed sequence by determining the corresponding defining polynomial. Our results will be help for the implementation and the pseudo-random properties analysis of the sequences.

In this paper, a special class of two-generator quasi-twisted (QT) codes with index 2 will be presented. We explore the algebraic structure of the class of QT codes and the form of their Hermitian dual codes. A sufficient condition for self-orthogonality with Hermitian inner product is derived. Using the class of Hermitian self-orthogonal QT codes, we construct two new binary quantum codes [[70, 42, 7]]2, [[78, 30, 10]]2. According to Theorem 6 of Ref.[2], we further can get 9 new binary quantum codes. So a total of 11 new binary quantum codes are obtained and there are 10 quantum codes that can break the quantum Gilbert-Varshamov (GV) bound.

Since deep learning workloads perform a large number of matrix operations on training data, GPUs (Graphics Processing Units) are efficient especially for the training phase. A cluster of computers each of which equips multiple GPUs can significantly accelerate the deep learning workloads. More specifically, a back-propagation algorithm following a gradient descent approach is used for the training. Although the gradient computation is still a major bottleneck of the training, gradient aggregation and optimization impose both communication and computation overheads, which should also be reduced for further shortening the training time. To address this issue, in this paper, multiple GPUs are interconnected with a PCI Express (PCIe) over 10Gbit Ethernet (10GbE) technology. Since these remote GPUs are interconnected with network switches, gradient aggregation and optimizers (e.g., SGD, AdaGrad, Adam, and SMORMS3) are offloaded to FPGA-based 10GbE switches between remote GPUs; thus, the gradient aggregation and parameter optimization are completed in the network. The proposed FPGA-based 10GbE switches with the four optimizers are implemented on NetFPGA-SUME board. Their resource utilizations are increased by PEs for the optimizers, and they consume up to 56% of the resources. Evaluation results using four remote GPUs connected via the proposed FPGA-based switch demonstrate that these optimizers are accelerated by up to 3.0x and 1.25x compared to CPU and GPU implementations, respectively. Also, the gradient aggregation throughput by the FPGA-based switch achieves up to 98.3% of the 10GbE line rate.

Convolutional neural networks (CNNs) are widely used for image processing tasks in both embedded systems and data centers. In data centers, high accuracy and low latency are desired for various tasks such as image processing of streaming videos. We propose an FPGA-based low-latency CNN inference for randomly wired convolutional neural networks (RWCNNs), whose layer structures are based on random graph models. Because RWCNNs have several convolution layers that have no direct dependencies between them, our architecture can process them efficiently using a pipeline method. At each layer, we need to use the calculation results of multiple layers as the input. We use an FPGA with HBM2 to enable parallel access to the input data with multiple HBM2 channels. We schedule the order of execution of the layers to improve the pipeline efficiency. We build a conflict graph using the scheduling results. Then, we allocate the calculation results of each layer to the HBM2 channels by coloring the graph. Because the pipeline execution needs to be properly controlled, we developed an automatic generation tool for hardware functions. We implemented the proposed architecture on the Alveo U50 FPGA. We investigated a trade-off between latency and recognition accuracy for the ImageNet classification task by comparing the inference performances for different input image sizes. We compared our accelerator with a conventional accelerator for ResNet-50. The results show that our accelerator reduces the latency by 2.21 times. We also obtained 12.6 and 4.93 times better efficiency than CPU and GPU, respectively. Thus, our accelerator for RWCNNs is suitable for low-latency inference.

FPGA (Field Programmable Gate Array) based accelerators are attracting significant interest in cloud computing systems. Combining multi-FPGA systems with cloud computing brings a new perspective to the reconfigurable computing research. However, the multi-tenancy of a multi-FPGA system has not been fully discussed in the previous researches. In this paper, we propose a multi-tenant resource management system, named FiC-RM, for a multi-FPGA cloud system. FiC-RM provides users with a set of FPGA resources according to their requirements and allows them to exclusively access FPGA boards and the interconnection network. To achieve this, we propose a placement algorithm which is a key to efficiently share the limited resources. We demonstrate FiC-RM controls a practical scale multi-FPGA system. Moreover, Our simulation study shows that our placement algorithm achieved 3 to 4% improvement in the average resource usage and a 20-second reduction in the response time, compared to other existing naive algorithms.

With the development of IoT devices and sensors, edge computing is leading towards new services like autonomous cars and smart cities. Low-latency data access is an essential requirement for such services, and a large-capacity cache server is needed on the edge side. However, it is not realistic to build a large capacity cache server using only DRAM because DRAM is expensive and consumes substantially large power. A hybrid main memory system is promising to address this issue, in which main memory consists of DRAM and non-volatile memory. It achieves a large capacity of main memory within the power supply capabilities of current servers. In this paper, we propose Fogcached, that is, the extension of a widely-used KVS (Key-Value Store) server program (i.e., Memcached) to exploit both DRAM and non-volatile main memory (NVMM). We used Intel Optane DCPM as NVMM for its prototype. Fogcached implements a Dual-LRU (Least Recently Used) mechanism that seamlessly extends the memory management of Memcached to hybrid main memory. Fogcached reuses the segmented LRU of Memcached to manage cached objects in DRAM, adds another segmented LRU for those in DCPM and bridges the LRUs by a mechanism to automatically replace cached objects between DRAM and DCPM. Cached objects are autonomously moved between the two memory devices according to their access frequencies. Through experiments, we confirmed that Fogcached improved the peak value of a latency distribution by about 40% compared to Memcached.

Recently, edge servers located closer than the cloud have become expected for the purpose of processing the large amount of sensor data generated by IoT devices such as robots. Research has been proposed to improve responsiveness as a cache server by applying KVS (Key-Value Store) to the edge as a method for obtaining high responsiveness. Above all, a hybrid-KVS server that uses both DRAM and NVMM (Non-Volatile Main Memory) devices is expected to achieve both responsiveness and reliability. However, its effectiveness has not been verified in actual applications, and its effectiveness is not clear in terms of its relationship with the cloud. The purpose of this study is to evaluate the effectiveness of hybrid-KVS servers using the SLAM (Simultaneous Localization and Mapping), which is a widely used application in robots and autonomous driving. It is appropriate for applying an edge server and requires responsiveness and reliability. SLAM is generally implemented on ROS (Robot Operating System) middleware and communicates with the server through ROS middleware. However, if we use hybrid-KVS on the edge with the SLAM and ROS, the communication could not be achieved since the message objects are different from the format expected by KVS. Therefore, in this research, we propose a mechanism to apply the ROS memory object to hybrid-KVS by designing and implementing the data serialization function to extend ROS. As a result of the proposed fogcached-ros and evaluation, we confirm the effectiveness of low API overhead, support for data used by SLAM, and low latency difference between the edge and cloud.