Since the owner's data might be leaked from the centralized server storage, the distributed storage schemes with the server storage have been investigated. To ensure the owner's data in those schemes, they use Reed Solomon code. However, those schemes occur the burden of data capacity since the parity data are increased by how much the disconnected data can be restored. Moreover, the calculation time for the restoration will be higher since many parity data are needed to restore the disconnected data. In order to reduce the burden of data capacity and the calculation time, we proposed the server-based distributed storage using Secret Sharing with AES-256 for lightweight safety restoration. Although we use Secret Sharing, the owner's data will be safely kept in the distributed storage since all of the divided data are divided into two pieces with the AES-256 and stored in the peer storage and the server storage. Even though the server storage keeps the divided data, the server and the peer storages might know the pair of divided data via Secret Sharing, the owner's data are secure in the proposed scheme from the inner attack of Secret Sharing. Furthermore, the owner's data can be restored by a few parity data. The evaluations show that our proposed scheme is improved for lightweight, stability, and safety.

In this paper, a novel approach to speech reinforcement in a low-bit-rate speech coder under ambient noise environments is proposed. The excitation vector of ambient noise is efficiently obtained at the near-end and then combined with the excitation signal of the far-end for a suitable reinforcement gain within the G.729 CS-ACELP Annex. B framework. For this reason, this can be clearly different from previous approaches in that the present approach does not require an additional arithmetic step such as the discrete Fourier transform (DFT). Experimental results indicate that the proposed method shows better performance than or at least comparable to conventional approaches with a lower computational burden.

In this letter, we propose a simple but effective technique that improves statistical model-based voice activity detection (VAD) by both reducing computational complexity and increasing detection accuracy. The improvements are made by applying Taylor series approximations to the exponential and logarithmic functions in the VAD algorithm based on an in-depth analysis of the algorithm. Experiments performed on a smartphone as well as on a desktop computer with various background noises confirm the effectiveness of the proposed technique.

In this paper, we present a speech enhancement technique based on the ambient noise classification that incorporates the Gaussian mixture model (GMM). The principal parameters of the statistical model-based speech enhancement algorithm such as the weighting parameter in the decision-directed (DD) method and the long-term smoothing parameter of the noise estimation, are set according to the classified context to ensure best performance under each noise. For real-time context awareness, the noise classification is performed on a frame-by-frame basis using the GMM with the soft decision framework. The speech absence probability (SAP) is used in detecting the speech absence periods and updating the likelihood of the GMM.

In this letter, we propose a speech reinforcement technique based on soft decision under both the far-end and near-end noise environments. We amplify the estimated clean speech signal at the far-end based on the estimated ambient noise spectrum at the near-end, as opposed to reinforcing the noisy far-end speech signal, so that it can be heard more intelligibly in far-end noisy environments. To obtain an effective reinforcement technique, we adopt the soft decision scheme incorporating a speech absence probability (SAP) in the frequency dependent signal-to-noise ratio (SNR) recovery method where the clean speech spectrum is estimated and the reinforcement gain is inherently derived and modified within the unified framework. Performance of the proposed method is evaluated by a subjective testing under various noisy environments. This is an improvement over previous approaches.

An inductive buffer peaking technique is proposed and demonstrated to extend the bandwidth of a 10-Gbit/s transimpedance amplifier (TIA) for optical communications. A TIA using this peaking technique is fabricated based on InGaP/GaAs HBT technology. The advantage of the proposed technique is verified by comparisons based on simulations and experiments. For these comparisons, three different types of TIAs using a basic gain stage, a shunt peaking gain stage and the proposed gain stage, respectively, are fabricated and measured. The measured performance of the proposed TIA shows that this bandwidth extension technique using inductive buffer peaking can be applied to circuit designs which demand wideband frequency response with low power consumption.

A mixed-mode high-speed binary phase-shift keying (BPSK) demodulator for IEEE802.15.3c mm-wave wireless personal area network (WPAN) application is realized with 0.18-µm CMOS process. The proposed demodulator scheme does not require any analog-to-digital converters (ADC) and, consequently, can have advantages over the conventional schemes for high-data-rate demodulation. The demodulator core consumes 53.8 mW from 2.5-V power supply while the chip area is 380500 µm2. The fabricated chip is verified by 60-GHz wireless link tests with 1.6-Gb/s data.

Affine projection algorithms perform well for acoustic echo cancellation and adaptive equalization. Although these algorithms typically provide fast convergence, they are unduly complex when updating the weights of the associated adaptive filter. In this paper, we propose a new subband affine projection (SAP) algorithm and a facile method for its implementation. The SAP algorithm is derived by combining the affine projection algorithm and the subband adaptive structure with the maximal decimation. In the proposed SAP algorithm, the derived weight-updating formula for the subband adaptive filter has a simple form as compared with the normalized least mean square (NLMS) algorithm. The algorithm gives improved convergence and reduced computational complexity. The efficiency of the proposed algorithm for a colored input signal is evaluated experimentally.

In this paper, we propose a spectral difference approach for noise power estimation in speech enhancement. The noise power estimate is given by recursively averaging past spectral power values using a smoothing parameter based on the current observation. The smoothing parameter in time and frequency is adjusted by the spectral difference between consecutive frames that can efficiently characterize noise variation. Specifically, we propose an effective technique based on a sigmoid-type function in order to adaptively determine the smoothing parameter based on the spectral difference. Compared to a conventional method, the proposed noise estimate is computationally efficient and able to effectively follow noise changes under various noise conditions.

The flooding DDoS attack is a serious problem these days. In order to detect the flooding DDoS attack, the survival approaches and the mitigation approaches have been investigated. Since the survival approach occurs the burden on the victims, the mitigation approach is mainly studied. As for the mitigation approaches, to detect the flooding DDoS attack, the conventional schemes using the bloom filter, machine learning, and pattern analyzation have been investigated. However, those schemes are not effective to ensure the high accuracy (ACC), the high true positive rate (TPR), and the low false positive rate (FPR). In addition, the data size and calculation time are high. Moreover, the performance is not effective from the fluctuant attack packet per second (pps). In order to effectively detect the flooding DDoS attack, we propose the lightweight detection using bloom filter against flooding DDoS attack. To detect the flooding DDoS attack and ensure the high accuracy, the high true positive rate, and the low false positive rate, the dec-all (decrement-all) operation and the checkpoint are flexibly changed from the fluctuant pps in the bloom filter. Since we only consider the IP address, all kinds of flooding attacks can be detected without the blacklist and whitelist. Moreover, there is no complexity to recognize the attack. By the computer simulation with the datasets, we show our scheme achieves an accuracy of 97.5%. True positive rate and false positive rate show 97.8% and 6.3%, respectively. The data size for processing is much small as 280bytes. Furthermore, our scheme can detect the flooding DDoS attack at once in 11.1sec calculation time.