The nearest neighbor method is a simple and flexible scheme for the classification of data points in a vector space. It predicts a class label of an unseen data point using a majority rule for the labels of known data points inside a neighborhood of the unseen data point. Because it sometimes achieves good performance even for complicated problems, several derivatives of it have been studied. Among them, the discriminant adaptive nearest neighbor method is particularly worth revisiting to demonstrate its application. The main idea of this method is to adjust the neighbor metric of an unseen data point to the set of known data points before label prediction. It often improves the prediction, provided the neighbor metric is adjusted well. For statistical shape analysis, shape classification attracts attention because it is a vital topic in shape analysis. However, because a shape is generally expressed as a matrix, it is non-trivial to apply the discriminant adaptive nearest neighbor method to shape classification. Thus, in this study, we develop the discriminant adaptive nearest neighbor method to make it slightly more useful in shape classification. To achieve this development, a mixture model and optimization algorithm for shape clustering are incorporated into the method. Furthermore, we describe several helpful techniques for the initial guess of the model parameters in the optimization algorithm. Using several shape datasets, we demonstrated that our method is successful for shape classification.

A robust adaptive beamforming algorithm is proposed based on the precise interference-plus-noise covariance matrix reconstruction and steering vector estimation of the desired signal, even existing large gain-phase errors. Firstly, the model of array mismatches is proposed with the first-order Taylor series expansion. Then, an iterative method is designed to jointly estimate calibration coefficients and steering vectors of the desired signal and interferences. Next, the powers of interferences and noise are estimated by solving a quadratic optimization question with the derived closed-form solution. At last, the actual interference-plus-noise covariance matrix can be reconstructed as a weighted sum of the steering vectors and the corresponding powers. Simulation results demonstrate the effectiveness and advancement of the proposed method.

This paper presents a weighted spatial filter (WSF) design method based on direction of arrival (DOA) estimates for a novel array configuration called a sum and difference composite co-array. A sum and difference composite co-array is basically a combination of sum and difference co-arrays. Our configuration can realize higher degrees of freedom (DOF) with the sum co-array part at a calculation cost lower than those of the other sparse arrays. To further enhance the robustness of our proposed sum and difference composite co-array we design an optimal beam pattern by WSF based on the information of estimated DOAs. Performance of the proposed system and the DOA estimation accuracy of close-impinging waves are evaluated through computer simulations.

Speech enhancement has been proposed to reduce the impulsive noise whose frequency characteristic is wideband. On the other hand, it is challenging to reduce the ringing sound, which is narrowband in impulsive noise. Therefore, we propose the modeling of the ringing sound and its estimation by a linear predictor (LP). However, it is difficult to estimate the ringing sound only in noisy speech due to the auto-correlation property of speech. The proposed system adopts the 4th order moment-based adaptive algorithm by noticing the difference between the 4th order statistics of speech and impulsive noise. The brief analysis and simulation results show that the proposed system has the potential to reduce ringing sound while keeping the quality of enhanced speech.

With increasing technology scaling and the use of lower voltages, more research interest is being shown in variability-tolerant analog front end design. In this paper, we describe an adaptive amplitude control transmitter that is operated using differential signaling to reduce the temperature variability effect. It enables low power, low voltage operation by synergy between adaptive amplitude control and Vth temperature variation control. It is suitable for high-speed interface applications, particularly cable interfaces. By installing an aggressor circuit to estimate transmitter jitter and changing its frequency and activation rate, we were able to analyze the effects of the interface block on the input buffer and thence on the entire system. We also report a detailed estimation of the receiver clock-data recovery (CDR) operation for transmitter jitter estimation. These investigations provide suggestions for widening the eye opening of the transmitter.

This paper demonstrates how to achieve simulation-based strong attribute hiding against adaptive adversaries for predicate encryption (PE) schemes supporting expressive predicate families under standard computational assumptions in bilinear groups. Our main result is a simulation-based adaptively strongly partially-hiding PE (PHPE) scheme for predicates computing arithmetic branching programs (ABP) on public attributes, followed by an inner-product predicate on private attributes. This simultaneously generalizes attribute-based encryption (ABE) for boolean formulas and ABP's as well as strongly attribute-hiding PE schemes for inner products. The proposed scheme is proven secure for any a priori bounded number of ciphertexts and an unbounded (polynomial) number of decryption keys, which is the best possible in the simulation-based adaptive security framework. This directly implies that our construction also achieves indistinguishability-based strongly partially-hiding security against adversaries requesting an unbounded (polynomial) number of ciphertexts and decryption keys. The security of the proposed scheme is derived under (asymmetric version of) the well-studied decisional linear (DLIN) assumption. Our work resolves an open problem posed by Wee in TCC 2017, where his result was limited to the semi-adaptive setting. Moreover, our result advances the current state of the art in both the fields of simulation-based and indistinguishability-based strongly attribute-hiding PE schemes. Our main technical contribution lies in extending the strong attribute hiding methodology of Okamoto and Takashima [EUROCRYPT 2012, ASIACRYPT 2012] to the framework of simulation-based security and beyond inner products.

End-to-end (E2E) multilingual automatic speech recognition (ASR) systems aim to recognize multilingual speeches in a unified framework. In the current E2E multilingual ASR framework, the output prediction for a specific language lacks constraints on the output scope of modeling units. In this paper, a language supervision training strategy is proposed with language masks to constrain the neural network output distribution. To simulate the multilingual ASR scenario with unknown language identity information, a language identification (LID) classifier is applied to estimate the language masks. On four Babel corpora, the proposed E2E multilingual ASR system achieved an average absolute word error rate (WER) reduction of 2.6% compared with the multilingual baseline system.

The carrier-phase multipath effect can seriously affect the accuracy of GPS-based positioning in static short baseline applications. Although several kinds of methods based on time domain and spatial domain techniques have been proposed to mitigate this error, they are still limited by the accuracy of the multipath model and the effectiveness of the correction strategy. After analyzing the existing methods, a new method based on adaptive thresholding wavelet packet transform (AW) and time domain bootstrap spatial domain search strategy (TB) is presented (AWTB). Taking advantage of adaptive thresholding wavelet packet transform, we enhance the precision of the correction model and the efficiency of the extraction method. In addition, by adopting the proposed time domain bootstrap spatial domain strategy, the accuracy and efficiency of subsequent multipath correction are improved significantly. Specifically, after applying the adaptive thresholding wavelet packet method, the mean improvement rate in the RMS values of the single-difference L1 residuals is about 27.93% compared with the original results. Furthermore, after applying the proposed AWTB method, experiments show that the 3D positioning precision is improved by about 38.51% compared with the original results. Even compared with the method based on stationary wavelet transform (SWT), and the method based on wavelet packets denoising (WPD), the 3D precision is improved by about 26.94% over the SWT method and about 22.96% over the WPD method, respectively. It is worth noting that, although the mean time consumption of the proposed algorithm is larger than the original method, the increased time consumption is not a serious burden for overall performance.

This paper presents a 28-GHz-band highly linear stacked-FET power amplifier (PA) IC. A 4-stacked-FET structure is employed for high output power considering the low breakdown voltage of scaled MOSFET transistors. A novel adaptive bias circuit is proposed to dynamically control the gate-to-source bias voltage for amplification MOSFETs. The novel adaptive bias allows the PA to attain high linearity with high back-off efficiency. In addition, the third-order intermodulation distortion (IM3) is improved by a multi-cascode structure. The PA IC is designed, fabricated and fully tested in 56-nm SOI CMOS technology. At a supply voltage of 4 V, the PA IC has achieved an output power of 20.0 dBm with a PAE as high as 38.1% at the 1-dB gain compression point (P1dB). Moreover, PAEs at 3-dB and 6-dB back-off from P1dB are 36.2% and 28.7%, respectively. The PA IC exhibits an output third-order intercept point (OIP3) of 25.0 dBm.

This paper proposes an adaptive call-occurrence probability (COP) setting method for a slotted-ALOHA based consensus problem. Individual agents in the focused consensus problem control themselves in a distributed manner based on the partial information of overall control system which can be received only from the neighbor agents. In order to realize a reliable consensus problem based on wireless communications, we have to consider several constraints caused by the natures of wireless communications such as communication error, coverage, capacity, multi-user interference, half-duplex and so on. This work first investigates the impacts of wireless communication constraints, especially communication coverage, half-duplex, and multiple-access interference constraints, on the quality of control. To mitigate the impact of multiple-access constraint, we propose an adaptive COP setting method that changes the COP corresponding to the states of communication and control. The proposed adaptive COP based slotted-ALOHA needs the information about the number of neighbor agents at its own and neighbor agents, but can still work in a distributed manner. Computer simulations show that the proposed system can achieve better convergence performance compared to the case with the fixed COP based system.

Near real-time periodic tasks, which are popular in multimedia streaming applications, have deadline periods that are longer than the input intervals thanks to buffering. For such applications, the conventional frame-based schedulings cannot realize the optimal scheduling due to their shortsighted deadline assumptions. To realize globally energy-efficient executions of these applications, we propose a novel task scheduling algorithm, which takes advantage of the long deadline period. We confirm our approach can take advantage of the longer deadline period and reduce the average power consumption by up to 18%.

In this paper, we propose an adaptive fusion successive cancellation list decoder (ADF-SCL) for polar codes with single cyclic redundancy check. The proposed ADF-SCL decoder reasonably avoids unnecessary calculations by selecting the successive cancellation (SC) decoder or the adaptive successive cancellation list (AD-SCL) decoder depending on a log-likelihood ratio (LLR) threshold in the decoding process. Simulation results show that compared to the AD-SCL decoder, the proposed decoder can achieve significant reduction of the average complexity in the low signal-to-noise ratio (SNR) region without degradation of the performance. When Lmax=32 and Eb/N0=0.5dB, the average complexity of the proposed decoder is 14.23% lower than that of the AD-SCL decoder.

High frame rate and ultra-low delay matching system plays an important role in various human-machine interactive applications, which demands better performance in matching deformable and out-of-plane rotating objects. Although many algorithms have been proposed for deformation tracking and matching, few of them are suitable for hardware implementation due to complicated operations and large time consumption. This paper proposes a hardware-oriented template update and recovery method for high frame rate and ultra-low delay deformation matching system. In the proposed method, the new template is generated in real time by partially updating the template descriptor and adding new keypoints simultaneously with the matching process in pixels (proposal #1), which avoids the large inter-frame delay. The size and shape of region of interest (ROI) are made flexible and the Hamming threshold used for brute-force matching is adjusted according to pixel position and the flexible ROI (proposal #2), which solves the problem of template drift. The template is recovered by the previous one with a relative center-shifting vector when it is judged as lost via region-wise difference check (proposal #3). Evaluation results indicate that the proposed method successfully achieves the real-time processing of 784fps at the resolution of 640×480 on field-programmable gate array (FPGA), with a delay of 0.808ms/frame, as well as achieves satisfactory deformation matching results in comparison with other general methods.

Linear Prediction (LP) analysis is commonly used in speech processing. LP is based on Auto-Regressive (AR) model and it estimates the AR model parameter from signals with l2-norm optimization. Recently, sparse estimation is paid attention since it can extract significant features from big data. The sparse estimation is realized by l1 or l0-norm optimization or regularization. Sparse LP analysis methods based on l1-norm optimization have been proposed. Since excitation of speech is not white Gaussian, a sparse LP estimation can estimate more accurate parameter than the conventional l2-norm based LP. These are time-invariant and real-valued analysis. We have been studied Time-Varying Complex AR (TV-CAR) analysis for an analytic signal and have evaluated the performance on speech processing. The TV-CAR methods are l2-norm methods. In this paper, we propose the sparse TV-CAR analysis based on adaptive LASSO (Least absolute shrinkage and selection operator) that is l1-norm regularization and evaluate the performance on F0 estimation of speech using IRAPT (Instantaneous RAPT). The experimental results show that the sparse TV-CAR methods perform better for a high level of additive Pink noise.

In this paper the quality of adaptive bit rate video streaming is investigated and two state-of-the-art models, i.e., the NTT audiovisual quality-estimation and ITU-T P.1203 models, are considered. This paper shows how these models can be applied to new conditions, e.g., 4K ultra high definition (4K-UHD) videos encoded using H.265, considering that they were originally designed and trained for HD videos encoded with H.264. Six subjective evaluations involving up to 192 participants and a large variety of test conditions, e.g., durations from 10sec to 3min, coding-quality variation, and stalling events, were conducted on both TV and mobile devices. Using the subjective data, this paper addresses how models and coefficients can be transferred to new conditions. A comparison between state-of-the-art models is conducted, showing the performance of transferred and retrained models. It is found that other video-quality estimation models, such as VMAF, can be used as input of the NTT and ITU-T P.1203 long-term pooling modules, allowing these other video-quality-estimation models to support the specificities of adaptive bit-rate-streaming scenarios. Finally, all retrained coefficients are detailed in this paper allowing future work to directly reuse the results of this study.

Adaptive Volterra filters (AVFs) are usually used to identify nonlinear systems, such as loudspeaker systems, and ordinary adaptive algorithms can be used to update the filter coefficients of AVFs. However, AVFs require huge computational complexity even if the order of the AVF is constrained to the second order. Improving calculation efficiency is therefore an important issue for the real-time implementation of AVFs. In this paper, we propose a novel sub-band AVF with high calculation efficiency for second-order AVFs. The proposed sub-band AVF consists of four parts: input signal transformation for a single sub-band AVF, tap length determination to improve calculation efficiency, switching the number of sub-bands while maintaining the estimation accuracy, and an automatic search for an appropriate number of sub-bands. The proposed sub-band AVF can improve calculation efficiency for which the dominant nonlinear components are concentrated in any frequency band, such as loudspeakers. A simulation result demonstrates that the proposed sub-band AVF can realize higher estimation accuracy than conventional efficient AVFs.

The least mean squares (LMS) algorithm has been widely used for adaptive filtering because of easily implementing at a computational complexity of O(2N) where N is the number of taps. The drawback of the LMS algorithm is that its performance is sensitive to the scaling of the input. The normalized LMS (NLMS) algorithm solves this problem on the LMS algorithm by normalizing with the sliding-window power of the input; however, this normalization increases the computational cost to O(3N) per iteration. In this work, we derive a new formula to strictly perform the NLMS algorithm at a computational complexity of O(2N), that is referred to as the C-NLMS algorithm. The derivation of the C-NLMS algorithm uses the H∞ framework presented previously by one of the authors for creating a unified view of adaptive filtering algorithms. The validity of the C-NLMS algorithm is verified using simulations.

An adaptive bit allocation scheme for zero-forcing (ZF) Tomlinson-Harashima precoding (THP) is proposed. The ZF-THP enables us to achieve feasible bit error rate (BER) performance when appropriate substream permutations are installed at the transmitter. In this study, the number of bits in each substream is adaptively allocated to minimize the average BER in fading environments. Numerical examples are provided to compare the proposed method with eigenbeam space division multiplexing (E-SDM) method.

Due to its simplicity and efficiency, differential evolution (DE) has gained the interest of researchers from various fields for solving global optimization problems. However, it is prone to premature convergence at local minima. To overcome this drawback, a novel hybrid dragonfly algorithm with differential evolution (Hybrid DA-DE) for solving global optimization problems is proposed. Firstly, a novel mutation operator is introduced based on the dragonfly algorithm (DA). Secondly, the scaling factor (F) is adjusted in a self-adaptive and individual-dependent way without extra parameters. The proposed algorithm combines the exploitation capability of DE and exploration capability of DA to achieve optimal global solutions. The effectiveness of this algorithm is evaluated using 30 classical benchmark functions with sixteen state-of-the-art meta-heuristic algorithms. A series of experimental results show that Hybrid DA-DE outperforms other algorithms significantly. Meanwhile, Hybrid DA-DE has the best adaptability to high-dimensional problems.

This study proposes a Peak-to-Average Power Ratio (PAPR) reduction method using an adaptive Finite Impulse Response (FIR) filter in Orthogonal Frequency Division Multiplexing systems. At the transmitter, an iterative algorithm that minimizes the p-norm of a transmitted signal vector is used to update the weight coefficients of the FIR filter to reduce PAPR. At the receiver, the FIR filter used at the transmitter is estimated using pilot symbols, and its effect can be compensated for by using an equalizer for proper demodulation. Simulation results show that the proposed method is superior to conventional methods in terms of the PAPR reduction and computational complexity. It also shows that the proposed method has a trade-off between PAPR reduction and bit error rate performance.