Streaming services that use TCP have increased; however, throughput is unstable due to congestion control caused by packet loss when TCP is used. Thus, TCP control to secure a required transmission rate for streaming communication using Forward Error Correction (FEC) technology (TCP-AFEC) has been proposed. TCP-AFEC can control the appropriate transmission rate according to network conditions using a combination of TCP congestion control and FEC. However, TCP-AFEC was not developed for wireless Local Area Network (LAN) environments; thus, it requires a certain time to set the appropriate redundancy and cannot obtain the required throughput. In this paper, we demonstrate the drawbacks of TCP-AFEC in wireless LAN environments. Then, we propose a redundancy setting method that can secure the required throughput for FEC, i.e., TCP-TFEC. Finally, we show that TCP-TFEC can secure more stable throughput than TCP-AFEC.

In this paper, a study on the design and implementation of uniform 4-level quantizers for soft-decision decodings for binary linear codes is shown. Simulation results on quantized Viterbi decoding with a 4-level quantizer for the (64,42,8) Reed-Muller code show that the optimum stepsize, which is derived from the cutoff rate, gives an almost optimum error performance. In addition, the simulation results show that the case where the number of optimum codewords is larger than the one for a received sequence causes non-negligible degradation on error performance at high SN ratios of Eb/N0.

Symbol-pair read channels output overlapping pairs of symbols in storage applications. Pair distance and pair error are used in the channels. In this paper, we discuss error-trapping decoding for cyclic codes over symbol-pair read channels. By putting some restrictions on the correctable pair error patterns, we propose a novel error-trapping decoding algorithm over the channels and show a circuitry for implementing the decoding algorithm. In addition, we discuss how to modify the restrictions on the correctable pair error patterns.

Sudoku is a pencil puzzle. The aim of the solver is to complete the 9×9 grid by filling in a digit in every cell according to a certain rule. In this study, we regard the process of solving Sudoku as a process of decoding a codeword from a received word, and show the expected decoding error probability for erasure channels obtained by experiments.

This paper analyzes the time domain Reed Solomon Decoder with FPGA implementation. Data throughput and area is carefully evaluated compared with typical frequency domain Reed Solomon Decoder. In this analysis, three hardware architecture to enhance the data throughput, namely, the pipelined architecture, the parallel architecture, and the truncated arrays, is evaluated, too. The evaluation reveals that the number of the consumed resources of RS(255, 239) is about 20% smaller than those of the frequency domain decoder although data throughput is less than 10% of the frequency domain decoder. The number of the consumed resources of the pipelined architecture is 28% smaller than that of the parallel architecture when data throughput is same. It is because the pipeline architecture requires less extra logics than the parallel architecture. To get higher data throughput, the pipelined architecture is better than the parallel architecture from the viewpoint of consumed resources.

We show a simple example of a secret sharing scheme encoding classical secret to quantum shares that can realize an access structure impossible by classical information processing with limitation on the size of each share. The example is based on quantum stabilizer codes.

In an IoT system, neural networks have the potential to perform advanced information processing in various environments. To clarify this, the robustness of a restricted Boltzmann machine (RBM) used for deep neural networks, such as a deep belief network (DBN), was studied in this paper. Even if memory or logic errors occurred in the circuit operating in the RBM while pre-training the DBN, they did not affect the identification rate of the DBN, showing the robustness of the RBM. In addition, robustness against soft errors was evaluated. The soft errors had almost no influence on the RBM unless they were as large as 1012 times or more in the 50-nm CMOS process.

This paper studies the zero error capacity of the Nearest Neighbor Error (NNE) channels with a multilevel alphabet. In the NNE channels, a transmitted symbol is a d-tuple of elements in {0,1,2,...,l-1}. It is assumed that only one element error to a nearest neighbor element in a transmitted symbol can occur. The NNE channels can be considered as a special type of limited magnitude error channels, and it is closely related to error models for flash memories. In this paper, we derive a lower bound of the zero error capacity of the NNE channels based on a result of the perfect Lee codes. An upper bound of the zero error capacity of the NNE channels is also derived from a feasible solution of a linear programming problem defined based on the confusion graphs of the NNE channels. As a result, a concise formula of the zero error capacity is obtained using the lower and upper bounds.

This study presents the design of a phase correlator for a digital frequency discriminator (DFD) that operates in the 2.0-6.0GHz frequency range. The accuracy of frequency discrimination as determined by the isolation of the correlator mixer was analyzed, and LO-RF isolation was found to have a significant effect on the frequency discrimination error by deriving various analytic equations related to the LO-RF isolation and phase performance. We propose a novel technique (phase sector compensation) to improve the accuracy of frequency discrimination. The phase sector compensation technique improved phase error by canceling the DC offset of the I and Q signals for only the frequency bands where the mixer's LO-RF isolation was below a specified limit. In the 2.0-6.0GHz range, the phase error of the designed phase correlator was decreased from 4.57° to 4.23° (RMS), and the frequency accuracy was improved from 1.02MHz to 0.95MHz (RMS). In the 4.8-6.0GHz range, the RMS phase error was improved from 5.59° to 4.12°, the frequency accuracy was improved from 1.24MHz to 0.92MHz, and the performance of the DFD correlator was improved by 26.3% in the frequency sector where LO-RF isolation was poor. Overall, the DFD correlator performance was improved by LO leakage compensation.

In this letter, a switching detection scheme based on a channel condition number for the MIMO-OFDM system is proposed. The switching algorithm operates by selecting one of three detection schemes of QRD-M, LR-aided MMSE-DFE, and LR-aided MMSE. The switching detection uses the threshold based on the switching algorithm according to the channel condition number. From the simulation results, the proposed detection scheme shows error detection performance and computational complexity in accordance with the threshold for switching detection.

High-resolution image and video communication in home networks is highly expected to proliferate with the spread of Wi-Fi devices and the introduction of multiple-input multiple-output (MIMO) systems. This paper proposes a joint transmission and coding scheme for broadcasting high-resolution video streams over multiuser MIMO systems with an eigenbeam-space division multiplexing (E-SDM) technique. Scalable video coding makes it possible to produce the code stream comprised of multiple layers having unequal contribution to image quality. The proposed scheme jointly assigns the data of scalable code streams to subcarriers and spatial streams based on their signal-to-noise ratio (SNR) values in order to transmit visually important data with high reliability. Simulation results show that the proposed scheme surpasses the conventional unequal power allocation (UPA) approach in terms of both peak signal-to-noise ratio (PSNR) of received images and correct decoding probability. PSNR performance of the proposed scheme exceeds 35dB with the probability of over 95% when received SNR is higher than 6dB. The improvement in average PSNR by the proposed scheme compared to the conventional UPA comes up to approx. 20dB at received SNR of 6dB. Furthermore, correct decoding probability reaches 95% when received SNR is greater than 4dB.

With the increasing demand of Internet-of-Things applicability in various devices and location-based services (LBSs) with positioning capabilities, we proposed simple and effective post-processing techniques to reduce positioning error and provide more precise navigation to users in a pedestrian environment in this letter. The proposed positioning error reduction techniques (Technique 1-minimum range securement and bounce elimination, Technique 2-direction vector-based error correction) were studied considering low complexity and wide applicability to various types of positioning systems, e.g., global positioning system (GPS). Through the real field tests in urban areas, we have verified that an average positioning error of the proposed techniques is significantly decreased compared to that of a GPS-only environment.

In the present paper, we propose a novel high-resolution analog-to-digital converter (ADC) for low-power biomedical analog front-ends, which we call the successive stochastic approximation ADC. The proposed ADC uses a stochastic flash ADC (SF-ADC) to realize a digitally controlled variable-threshold comparator in a successive-approximation-register ADC (SAR-ADC), which can correct errors originating from the internal digital-to-analog converter in the SAR-ADC. For the residual error after SAR-ADC operation, which can be smaller than thermal noise, the SF-ADC uses the statistical characteristics of noise to achieve high resolution. The SF-ADC output for the residual signal is combined with the SAR-ADC output to obtain high-precision output data using the supervised machine learning method.

Aiming at detecting pronunciation errors produced by second language learners and providing corrective feedbacks related with articulation, we address effective articulatory models based on deep neural network (DNN). Articulatory attributes are defined for manner and place of articulation. In order to efficiently train these models of non-native speech without such data, which is difficult to collect in a large scale, several transfer learning based modeling methods are explored. We first investigate three closely-related secondary tasks which aim at effective learning of DNN articulatory models. We also propose to exploit large speech corpora of native and target language to model inter-language phenomena. This kind of transfer learning can provide a better feature representation of non-native speech. Related task transfer and language transfer learning are further combined on the network level. Compared with the conventional DNN which is used as the baseline, all proposed methods improved the performance. In the native attribute recognition task, the network-level combination method reduced the recognition error rate by more than 10% relative for all articulatory attributes. The method was also applied to pronunciation error detection in Mandarin Chinese pronunciation learning by Japanese native speakers, and achieved the relative improvement up to 17.0% for detection accuracy and up to 19.9% for F-score, which is also better than the lattice-based combination.

Software-based error detection techniques, which includes error detection mechanism (EDM) transformation, are used for error localization in post-silicon validation. This paper evaluates the performance of EDM for timing error localization with a noise-aware logic simulator and 65-nm test chips assuming the following two EDM usage scenarios; (1) localizing a timing error occurred in the original program, and (2) localizing as many potential timing errors as possible. Simulation results show that the EDM transformation customized for quick error detection cannot locate electrical timing errors in the original program in the first scenario, but it detects 86% of non-masked errors potential bugs in the second scenario, which mean the EDM performance of detecting electrical timing errors affecting execution results is high. Hardware measurement results show that the EDM detects 25% of original timing errors and 56% of non-masked errors. Here, these hardware measurement results are not consistent with the simulation results. To investigate the reason, we focus on the following two differences between hardware and simulation; (1) design of power distribution network, and (2) definition of timing error occurrence frequency. We update the simulation setup for filling the difference and re-execute the simulation. We confirm that the simulation and the chip measurement results are consistent.

This letter proposes a heuristic algorithm to select check variables, which are points of comparison for error detection, for soft-error tolerant datapaths. Our soft-error tolerance scheme is based on check-and-retry computation and an efficient resource management named speculative resource sharing (SRS). Starting with the smallest set of check variables, the proposed algorithm repeats to add new check variable one by one incrementally and find the minimum latency solution among the series of generated solutions. During the process, each new check variable is selected so that the opportunity of SRS is enlarged. Experimental results show that improvements in latency are achieved compared with the choice of the smallest set of check variables.

This paper presents a novel framework called error case frames for correcting preposition errors. They are case frames specially designed for describing and correcting preposition errors. Their most distinct advantage is that they can correct errors with feedback messages explaining why the preposition is erroneous. This paper proposes a method for automatically generating them by comparing learner and native corpora. Experiments show (i) automatically generated error case frames achieve a performance comparable to previous methods; (ii) error case frames are intuitively interpretable and manually modifiable to improve them; (iii) feedback messages provided by error case frames are effective in language learning assistance. Considering these advantages and the fact that it has been difficult to provide feedback messages using automatically generated rules, error case frames will likely be one of the major approaches for preposition error correction.

This paper provides a new method to correct annotation errors in a treebank. The previous error correction method constructs a pseudo parallel corpus where incorrect partial parse trees are paired with correct ones, and extracts error correction rules from the parallel corpus. By applying these rules to a treebank, the method corrects errors. However, this method does not achieve wide coverage of error correction. To achieve wide coverage, our method adopts a different approach. In our method, we consider that if an infrequent pattern can be transformed to a frequent one, then it is an annotation error pattern. Based on a tree mining technique, our method seeks such infrequent tree patterns, and constructs error correction rules each of which consists of an infrequent pattern and a corresponding frequent pattern. We conducted an experiment using the Penn Treebank. We obtained 1,987 rules which are not constructed by the previous method, and the rules achieved good precision.

The biometrical identification system, introduced by Willems et al., is a system to identify individuals based on their measurable physical characteristics. Willems et al. characterized the identification capacity of a discrete memoryless biometrical identification system from information theoretic perspectives. Recently, Mori et al. have extended this scenario to list-decoding whose list size is an exponential function of the data length. However, as the data length increases, how the maximum identification error probability (IEP) behaves for a given rate has not yet been characterized for list-decoding. In this letter, we investigate the reliability function of the system under fixed-size list-decoding, which is the optimal exponential behavior of the maximum IEP. We then use Arimoto's argument to analyze a lower bound on the maximum IEP with list-decoding when the rate exceeds the capacity, which leads to the strong converse theorem. All results are derived under the condition that an unknown individual need not be uniformly distributed and the identification process is done without the knowledge of the prior distribution.

In this paper, the performance of orthogonal space-time block codes (OSTBC) for distributed multiple-input multiple-output (MIMO) systems employing adaptive M-QAM transmission is investigated over independent but not necessarily identically distributed (i.n.i.d.) generalized-K fading channels with arbitrary positive integer-valued k(inversely reflects the shadowing severity) and m (inversely reflects the fading severity). Before this, i.n.i.d. generalized-K fading channel has never been considered for distributed OSTBC-MIMO systems. Especially, the effects of the shape parameter k on the distributed OSTBC-MIMO system performance are unknown. Thus, we investigate mainly the significance of the shape parameter k on the distributed OSTBC-MIMO system performance, in terms of the average symbol error probability (SEP), outage probability, and spectral efficiency (SE). By establishing the system model, the approximated probability density function (PDF) of the equivalent signal to noise ratio (SNR) is derived and thereafter the approximated closed-form expressions of the above performance metrics are obtained successively. Finally, the derived expressions are validated via a set of Monte-Carlo simulations and the implications of the shape parameter k on the overall performance are highlighted.