This paper surveys development of quantum error correction. With the familiarity with conventional coding theory and tensor product in multi-linear algebra, this paper can be read in a self-contained manner.

A Silent Speech Interface (SSI) is a sensor-based, Artificial Intelligence (AI) enabled system in which articulation is performed without the use of the vocal chords, resulting in a voice interface that conserves the ambient audio environment, protects private data, and also functions in noisy environments. Though portable SSIs based on ultrasound imaging of the tongue have obtained Word Error Rates rivaling that of acoustic speech recognition, SSIs remain relegated to the laboratory due to stability issues. Indeed, reliable extraction of acoustic features from ultrasound tongue images in real-life situations has proven elusive. Recently, Representation Learning has shown considerable success in learning underlying structure in noisy, high-dimensional raw data. In its unsupervised form, Representation Learning is able to reveal structure in unlabeled data, thus greatly simplifying the data preparation task. In the present article, a 3D Convolutional Neural Network architecture is applied to unlabeled ultrasound images, and is shown to reliably predict future tongue configurations. By comparing the 3DCNN to a simple previous-frame predictor, it is possible to recognize tongue trajectories comprising transitions between regions of stability that correlate with formant trajectories in a spectrogram of the signal. Prospects for using the underlying structural representation to provide features for subsequent speech processing tasks are presented.

This paper presents a novel method for weakly supervised semantic segmentation of 3D point clouds using a novel graph and edge convolutional neural network (GECNN) towards 1% and 10% point cloud with labels. Our general framework facilitates semantic segmentation by encoding both global and local scale features via a parallel graph and edge aggregation scheme. More specifically, global scale graph structure cues of point clouds are captured by a graph convolutional neural network, which is propagated from pairwise affinity representation over the whole graph established in a d-dimensional feature embedding space. We integrate local scale features derived from a dynamic edge feature aggregation convolutional neural networks that allows us to fusion both global and local cues of 3D point clouds. The proposed GECNN model is trained by using a comprehensive objective which consists of incomplete, inexact, self-supervision and smoothness constraints based on partially labeled points. The proposed approach enforces global and local consistency constraints directly on the objective losses. It inherently handles the challenges of segmenting sparse 3D point clouds with limited annotations in a large scale point cloud space. Our experiments on the ShapeNet and S3DIS benchmarks demonstrate the effectiveness of the proposed approach for efficient (within 20 epochs) learning of large scale point cloud semantics despite very limited labels.

The Volterra filter is one of the digital filters that can describe nonlinearity. In this paper, we analyze the dynamic behaviors of an adaptive signal-processing system including the Volterra filter by a statistical-mechanical method. On the basis of the self-averaging property that holds when the tapped delay line is assumed to be infinitely long, we derive simultaneous differential equations in a deterministic and closed form, which describe the behaviors of macroscopic variables. We obtain the exact solution by solving the equations analytically. In addition, the validity of the theory derived is confirmed by comparison with numerical simulations.

We propose a self-stabilizing leader election protocol on directed rings in the model of population protocols. Given an upper bound N on the population size n, the proposed protocol elects a unique leader within O(nN) expected steps starting from any configuration and uses O(N) states. This convergence time is optimal if a given upper bound N is asymptotically tight, i.e., N=O(n).

We propose an efficient network upgrade and expansion method that can make the most of the next generation channel resources to accommodate further increases in traffic. Semi-flexible grid configuration and two cost metrics are introduced to establish a regularity in frequency assignment and minimize disturbance in the upgrade process; both reduce the fragmentation in frequency assignment and the number of fibers necessary. Various investigations of different configurations elucidate that the number of fibers necessary is reduced about 10-15% for any combination of upgrade scenario, channel frequency bandwidth, and topology adopted.

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.

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.

Computing the Lempel-Ziv Factorization (LZ77) of a string is one of the most important problems in computer science. Nowadays, it has been widely used in many applications such as data compression, text indexing and pattern discovery, and already become the heart of many file compressors like gzip and 7zip. In this paper, we show a linear time algorithm called Xone for computing the LZ77, which has the same space requirement with the previous best space requirement for linear time LZ77 factorization called BGone. Xone greatly improves the efficiency of BGone. Experiments show that the two versions of Xone: XoneT and XoneSA are about 27% and 31% faster than BGoneT and BGoneSA, respectively.

Reboot-based recovery is a simple but powerful method to recover applications from failures and unstable states. Reboot-based recovery faces a challenge to apply it to a new type of applications, in-memory databases (DBs). Unlike legacy applications, since rebooting in-memory DBs loses memory objects including key-value pairs and DB blocks, it is required to restore them, causing severe performance degradation after the reboot. This paper presents an approach that allows us to perform reboot-based recovery of in-memory DBs with lower performance degradation. Our key insight is to decouple data content objects from all the memory objects. Our approach treats data items as data content objects, preserves data content objects on memory across reboots, and enforces restarted in-memory DBs to attach them. To show the effectiveness of our approach, we elaborate the idea into two real-world DBs, MyRocks and memcached. The prototypes successfully mitigate performance degradation after their reboot-based recovery.

Real-time machine speech translation systems mimic human interpreters and translate incoming speech from a source language to the target language in real-time. Such systems can be achieved by performing low-latency processing in ASR (automatic speech recognition) module before passing the output to MT (machine translation) and TTS (text-to-speech synthesis) modules. Although several studies recently proposed sequence mechanisms for neural incremental ASR (ISR), these frameworks have a more complicated training mechanism than the standard attention-based ASR because they have to decide the incremental step and learn the alignment between speech and text. In this paper, we propose attention-transfer ISR (AT-ISR) that learns the knowledge from attention-based non-incremental ASR for a low delay end-to-end speech recognition. ISR comes with a trade-off between delay and performance, so we investigate how to reduce AT-ISR delay without a significant performance drop. Our experiment shows that AT-ISR achieves a comparable performance to the non-incremental ASR when the incremental recognition begins after the speech utterance reaches 25% of the complete utterance length. Additional experiments to investigate the effect of ISR on translation tasks are also performed. The focus is to find the optimum granularity of the output unit. The results reveal that our end-to-end subword-level ISR resulted in the best translation quality with the lowest WER and the lowest uncovered-word rate.

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.

Multi-FPGA systems have gained attention because of their high performance and power efficiency. A multi-FPGA system called Flow-in-Cloud (FiC) is currently being developed as an accelerator of multi-access edge computing (MEC). FiC consists of multiple mid-range FPGAs tightly connected by high-speed serial links. Since time-critical jobs are assumed in MEC, a circuit-switched network with static time-division multiplexing (STDM) switches has been implemented on FiC. This paper investigates techniques of enhancing the interconnection performance of FiC. Unlike switching fabrics for Network on Chips or parallel machines, economical multi-FPGA systems, such as FiC, use Xilinx Aurora IP and FireFly cables with multiple lanes. We adopted the link aggregation and the slot distribution for using multiple lanes. To mitigate the bottleneck between an STDM switch and user logic, we also propose a multi-ejection STDM switch. We evaluated various combinations of our techniques by using three practical applications on an FiC prototype with 24 boards. When the number of slots is large and transferred data size is small, the slot distribution was sometimes more effective, while the link aggregation was superior for other most cases. Our multi-ejection STDM switch mitigated the bottleneck in ejection ports and successfully reduced the number of time slots. As a result, by combining the link aggregation and multi-ejection STDM switch, communication performance improved up to 7.50 times with few additional resources. Although the performance of the fast Fourier transform with the highest communication ratio could not be enhanced by using multiple boards when a lane was used, 1.99 times performance improvement was achieved by using 8 boards with four lanes and our multi-ejection switch compared with a board.

Ceph is an object-based parallel distributed file system that provides excellent performance, reliability, and scalability. Additionally, Ceph provides its Cephx authentication system to authenticate users, so that it can identify users and realize authentication. In this paper, we first model the basic architecture of Ceph using process algebra CSP (Communicating Sequential Processes). With the help of the model checker PAT (Process Analysis Toolkit), we feed the constructed model to PAT and then verify several related properties, including Deadlock Freedom, Data Reachability, Data Write Integrity, Data Consistency and Authentication. The verification results show that the original model cannot cater to the Authentication property. Therefore, we formalize a new model of Ceph where Cephx is adopted. In the light of the new verification results, it can be found that Cephx satisfies all these properties.

In this letter, we propose a new color quantization method suppressing saturation decrease. In the proposed method, saturation-based weight and intensity-based weight are used so that vivid colors are selected as the representative colors preferentially. Experiments show that the proposed method tends to select vivid colors even if they occupy only a small area in the image.

Hybrid storage techniques are useful methods to improve the cost performance for input-output (IO) intensive workloads. These techniques choose areas of concentrated IO accesses and migrate them to an upper tier to extract as much performance as possible through greater use of upper tier areas. Automated tiered storage with fast memory and slow flash storage (ATSMF) is a hybrid storage system situated between non-volatile memories (NVMs) and solid-state drives (SSDs). ATSMF aims to reduce the average response time for IO accesses by migrating areas of concentrated IO access from an SSD to an NVM. When a concentrated IO access finishes, the system migrates these areas from the NVM back to the SSD. Unfortunately, the published ATSMF implementation temporarily consumes much NVM capacity upon migrating concentrated IO access areas to NVM, because its algorithm executes NVM migration with high priority. As a result, it often delays evicting areas in which IO concentrations have ended to the SSD. Therefore, to reduce the consumption of NVM while maintaining the average response time, we developed new techniques for making ATSMF more practical. The first is a queue handling technique based on the number of IO accesses for NVM migration and eviction. The second is an eviction method that selects only write-accessed partial regions in finished areas. The third is a technique for variable eviction timing to balance the NVM consumption and average response time. Experimental results indicate that the average response times of the proposed ATSMF are almost the same as those of the published ATSMF, while the NVM consumption is three times lower in best case.

Bitcoin is one of popular cryptocurrencies widely used over the world, and its blockchain technology has attracted considerable attention. In Bitcoin system, it has been reported that transactions are prioritized according to transaction fees, and that transactions with high priorities are likely to be confirmed faster than those with low priorities. In this paper, we consider performance modeling of Bitcoin-blockchain system in order to characterize the transaction-confirmation time. We first introduce the Bitcoin system, focusing on proof-of-work, the consensus mechanism of Bitcoin blockchain. Then, we show some queueing models and its analytical results, discussing the implications and insights obtained from the queueing models.

In unlicensed spectrum, wireless communications employing carrier sense multiple access with collision avoidance (CSMA/CA) suffer from longer transmission delay time as the number of user terminals (UTs) increases, because packet collisions are more likely to occur. To cope with this problem, this paper proposes a new multiuser detection (MUD) scheme that uses both request-to-send (RTS) and enhanced clear-to-send (eCTS) for high-reliable and low-latency wireless communications. As in conventional MUD scheme, the metric-combining MUD (MC-MUD) calculates log likelihood functions called metrics and accumulates the metrics for the maximum likelihood detection (MLD). To avoid increasing the number of states for MLD, MC-MUD forces the relevant UTs to retransmit their packets until all the collided packets are correctly detected, which requires a kind of central control and reduces the system throughput. To overcome these drawbacks, the proposed scheme, which is referred to as cancelling MC-MUD (CMC-MUD), deletes replicas of some of the collided packets from the received signals, once the packets are correctly detected during the retransmission. This cancellation enables new UTs to transmit their packets and then performs MLD without increasing the number of states, which improves the system throughput without increasing the complexity. In addition, the proposed scheme adopts RTS and eCTS. One UT that suffers from packet collision transmits RTS before the retransmission. Then, the corresponding access point (AP) transmits eCTS including addresses of the other UTs, which have experienced the same packet collision. To reproduce the same packet collision, these other UTs transmit their packets once they receive the eCTS. Computer simulations under one AP conditions evaluate an average carrier-to-interference ratio (CIR) range in which the proposed scheme is effective, and clarify that the transmission delay time of the proposed scheme is shorter than that of the conventional schemes. In two APs environments that can cause the hidden terminal problem, it is demonstrated that the proposed scheme achieves shorter transmission delay times than the conventional scheme with RTS and conventional CTS.

We propose a new framework for estimating depth information from a single image. Our framework is relatively small and straightforward by employing a two-stage architecture: a residual network and a simple decoder network. Our residual network in this paper is a remodeled of the original ResNet-50 architecture, which consists of only thirty-eight convolution layers in the residual block following by pair of two up-sampling and layers. While the simple decoder network, stack of five convolution layers, accepts the initial depth to be refined as the final output depth. During training, we monitor the loss behavior and adjust the learning rate hyperparameter in order to improve the performance. Furthermore, instead of using a single common pixel-wise loss, we also compute loss based on gradient-direction, and their structure similarity. This setting in our network can significantly reduce the number of network parameters, and simultaneously get a more accurate image depth map. The performance of our approach has been evaluated by conducting both quantitative and qualitative comparisons with several prior related methods on the publicly NYU and KITTI datasets.

This study has developed a system that performs data communications using high frequency bands of sound signals. Unlike radio communication systems using advanced wireless devices, it only requires the legacy devices such as microphones and speakers employed in ordinary telephony communication systems. In this study, we have investigated the possibility of a machine learning approach to improve the recognition accuracy identifying binary symbols exchanged through sound media. This paper describes some experimental results evaluating the performance of our proposed technique employing a neural network as its classifier of binary symbols. The experimental results indicate that the proposed technique may have a certain appropriateness for designing an optimal classifier for the symbol identification task.