We propose an auxiliary-noise power-scheduling method for a pre-inverse active noise control (PIANC) system. Conventional methods cannot reduce the power of auxiliary-noise due to the use of the filtered-x least mean square (FxLMS) algorithm. We developed our power-scheduling method for a PIANC system to solve this problem. Since a PIANC system uses a delayed input signal for a control filter, the proposed method delivers stability even if the acoustic path fluctuates. The proposed method also controls the gain of the auxiliary-noise based on the secondary-path-modeling state. The proposed method determines this state by the variation in the power of the secondary-path-modeling-error signal. Thus, the proposed method changes the power-scheduling of the auxiliary-noise. When the adaptive algorithm does not sufficiently converge, the proposed method injects auxiliary-noise. However, auxiliary-noise stops when the adaptive algorithm sufficiently converges. Therefore, the proposed method improves noise reduction performance.

This paper discusses the parallelization of boost and buck converters. Passivity-based control is applied to each converter to achieve the asymptotic stability of the system. The ripple characteristics, error characteristics, and time constants of the parallelized converters are discussed with considering the dependency on the feedback gains. The numerical results are confirmed to coincide with the results in the experiment for certain feedback gains. The stability of the system is also discussed in simulation and experiment. The results will be a step to achieve the design of parallel converters.

The decentralized energy network is one of the promising solutions as a next-generation power grid. In this system, each house has a photovoltaic (PV) panel as a renewable energy source and a battery which is an essential component to balance between generation and demand. The common objective of the battery management on such systems is to minimize only the purchased energy from a power company, but battery degradation caused by charge/discharge cycles is also a serious problem. This paper proposes a State-of-Health (SOH) aware system-level battery management methodology for the decentralized energy network. The power distribution problem is often solved with mixed integer programming (MIP), and the proposed MIP formulation takes into account the SOH model. In order to minimize the purchased energy and reduce the battery degradation simultaneously, the optimization problem is divided into two stages: 1) the purchased energy minimization, and 2) the battery aging factor reducing, and the trade-off exploration between the purchased energy and the battery degradation is available. Experimental results show that the proposed method achieves the better trade-off and reduces the battery aging cost by 14% over the baseline method while keeping the purchased energy minimum.

This paper presents new encoding and decoding methods for Berlekamp-Preparata convolutional codes (BPCCs) based on tail-biting technique. The proposed scheme can correct a single block of n bit errors relative to a guard space of m error-free blocks while no fractional rate loss is incurred. The proposed tail-biting BPCCs (TBBPCCs) can attain optimal complete burst error correction bound. Therefore, they have the optimal phased-burst-error-correcting capability for convolutional codes. Compared with the previous scheme, the proposed scheme can also improve error correcting capability.

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.

The modern radar signals are in a wide frequency space. The receiving bandwidth of the radar reconnaissance receiver should be wide enough to intercept the modern radar signals. The Nyquist folding receiver (NYFR) is a novel wideband receiving architecture and it has a high intercept probability. Chirp signals are widely used in modern radar system. Because of the wideband receiving ability, the NYFR will receive the concurrent multiple chirp signals. In this letter, we propose a novel parameter estimation algorithm for the multiple chirp signals intercepted by single channel NYFR. Compared with the composite NYFR, the proposed method can save receiving resources. In addition, the proposed approach can estimate the parameters of the chirp signals even the NYFR outputs are under frequency aliasing circumstance. Simulation results show the efficacy of the proposed method.

In this paper, we propose an image to sound mapping method. This technique treats an image as a spectrogram and maps it to a sound by taking inverse FFT of the spectrogram. Amplitude spectra of a speech signal are embedded to the spectrogram to give speech intelligibility for the mapped sound. Specifically, we hold amplitude spectra of a speech signal with strong power and embed the image brightness in other frequency bands. Holding amplitude spectra of a speech signal with strong power preserves a speech spectral envelope and improves the speech quality of the mapped sound. The amplitude spectra of the mapped sound with weak power represent the image brightness, and then the image is successfully reconstructed from the mapped sound. Simulation results show that the proposed method achieves sufficient speech quality.

The objective of this letter is to present a family of q-ary codes with parameters $[\frac{q^m-1}{q-1},\frac{q^m-1}{q-1}-2m,d]$, where m is a positive integer, q is a power of an odd prime and 4≤d≤5. The parameters are proved to be optimal or almost optimal with respect to an upper bound on linear codes.

Let m, k be positive integers with m=2k and k≥3. Let C_{(u, ν)} is a class of cyclic codes of length 2^m-1 whose parity-check polynomial is m_u(x)m_ν(x), where m_u(x) and m_ν(x) are the minimal polynomials of α^-u and α^-ν over GF(2). For the case $(u,\nu)=(1,\frac{1}{3}(2^m-1))$, the weight distributions of binary cyclic codes C_{(u, ν)} was determined in 2017. This paper determines the weight distributions of the binary cyclic codes C_{(u, ν)} for the case of (u, ν)=(3, 2^k-1+1). The application of these cyclic codes in secret sharing is also considered.

In this letter, we adopt two multi-carrier relay selections, i.e., bulk and per-subcarrier (PS), to the multi-hop decode-and-forward relaying orthogonal frequency-division multiplexing with index modulation (OFDM-IM) system. Particularly, in the form of average outage probability (AOP), the influence of joint selection and non-joint selection acting on the last two hops on the system is analyzed. The closed-form expressions of AOPs and the asymptotic AOPs expressions at high signal-to-noise ratio are given and verified by numerical simulations. The results show that both bulk and PS can achieve full diversity order and that PS can provide additional power gain compared to bulk when JS is used. The theoretical analyses in this letter provide an insight into the combination of OFDM-IM and cooperative communication.

Feature learning based on deep network has been verified as beneficial for person re-identification (Re-ID) in recent years. However, most researches use a single network as the baseline, without considering the fusion of different deep features. By analyzing the attention maps of different networks, we find that the information learned by different networks can complement each other. Therefore, a novel Dual Network Fusion (DNF) framework is proposed. DNF is designed with a trunk branch and two auxiliary branches. In the trunk branch, deep features are cascaded directly along the channel direction. One of the auxiliary branch is channel attention branch, which is used to allocate weight for different deep features. Another one is multi-loss training branch. To verify the performance of DNF, we test it on three benchmark datasets, including CUHK03NP, Market-1501 and DukeMTMC-reID. The results show that the effect of using DNF is significantly better than a single network and is comparable to most state-of-the-art methods.