Convolutional neural network (CNN) is widely used in acoustic scene classification (ASC) tasks. In most cases, local convolution is utilized to gather time-frequency information between spectrum nodes. It is challenging to adequately express the non-local link between frequency domains in a finite convolution region. In this paper, we propose a dual-path convolutional neural network based on band interaction block (DCNN-bi) for ASC, with mel-spectrogram as the model’s input. We build two parallel CNN paths to learn the high-frequency and low-frequency components of the input feature. Additionally, we have created three band interaction blocks (bi-blocks) to explore the pertinent nodes between various frequency bands, which are connected between two paths. Combining the time-frequency information from two paths, the bi-blocks with three distinct designs acquire non-local information and send it back to the respective paths. The experimental results indicate that the utilization of the bi-block has the potential to improve the initial performance of the CNN substantially. Specifically, when applied to the DCASE 2018 and DCASE 2020 datasets, the CNN exhibited performance improvements of 1.79% and 3.06%, respectively.

In this letter, we discuss the ambiguity function of interleaved sequences. Furthermore, using the Guassian sum and choosing binary m-sequences as column sequences, we investigate the property of a binary sequence set given by Zhou, Tang, Gong (IEEE Trans. Inf. Theory, 54(9), 2008), which has low ambiguity property in a large region. Those sequences could be used in radar systems.

Almost-complementary pairs (ACPs) are sequence pairs whose autocorrelations sum up to zero at all but one non-zero time-shifts. Periodic ACPs (P-ACPs) display almost similar correlation properties to that of the periodic complementary pairs (PCPs). In this letter, we propose systematic constructions of quadriphase P-ACPs (QP-ACPs) from aperiodic (periodic) complementary pairs and almost perfect binary (quadriphase) sequences. The proposed construction gives QP-ACPs of new lengths which are not covered in the literature.

In this letter, we focus on the spectrum sharing cognitive radio system, wherein a single-input multi-output cognitive fading channel is considered. Subject to the joint average interference constraint and peak interference constraint at the primary receiver, the outage capacity of the cognitive channel involving joint beamforming and power control is analyzed. We derive the optimal beamforming and power control strategy and deduce the closed-form expression for the outage capacity under Rayleigh fading model, the functional regions of two kinds of interference constraints are discussed as well. Furthermore, considering zero-outage transmission, we investigate the delay-limited capacity and introduce a new concept called the zero-outage average interference wall. Extensive simulations corroborate our theoretical results.

A number of on-line and off-line algorithms for load balancing on multiple paths for MPLS (MultiProtocol Label Switching) traffic engineering have now been proposed, in which it is always assumed that sets of LSPs (Label Switched Path) have already been established between node pairs. While how to choose these paths is an important issue in traffic engineering, it has not been well studied yet. In this paper, we attempt to fill in this gap. As the shortest paths are always preferred in routing problems, we evaluate several k shortest path algorithms from the viewpoint of bandwidth use efficiency and the number of the found paths. Extensive simulations have been performed in different kinds of topologies to factor out effects of network characteristics on these algorithms' path calculation performances. It is found out that the performances of the evaluated algorithms are limited in some cases and the design of new algorithms for the path calculation problem is worth studying in the future.

In this letter, we propose an appealing class of nonbinary quasi-cyclic low-density parity-check (QC-LDPC) cycle codes. The parity-check matrix is carefully designed such that the corresponding generator matrix has some nice properties: 1) systematic, 2) quasi-cyclic, and 3) sparse, which allows a parallel encoding with low complexity. Simulation results show that the performance of the proposed encoding-aware LDPC codes is comparable to that of the progressive-edge-growth (PEG) constructed nonbinary LDPC cycle codes.

Network Coding-based Epidemic Routing (NCER) facilitates the reduction of data delivery delay in Delay Tolerant Networks (DTNs). The intrinsic reason lies in that the network coding paradigm avoids competitions for transmission opportunities between segmented packets of a large data file. In this paper, we focus on the impact of transmission competitions on the delay performance of NCER when multiple data files exist. We prove analytically that when competition occurs, transmitting the least propagated data file is optimal in the sense of minimizing the average data delivery delay. Based on such understanding, we propose a family of competition avoidance policies, namely the Least Propagated First (LPF) policies, which includes a centralized, a distributed, and a modified variants. Numerical results show that LPF policies can achieve at least 20% delay performance gain at different data traffic rates, compared with the policy currently available.

This paper is focused on constructing even-length binary Z-complementary pairs (EB-ZCPs) with new length. Inspired by a recent work of Adhikary et al., we give a construction of EB-ZCPs with length 8N+4 (where N=2α 10β 26γ and α, β, γ are nonnegative integers) and zero correlation zone (ZCZ) width 5N+2. The maximum aperiodic autocorrelation sums (AACS) magnitude of the proposed sequences outside the ZCZ region is 8. It turns out that the generated sequences have low PAPR.

This paper introduces an interactive expression editing system that allows users to design facial expressions easily. Currently, popular example-based methods construct face models based on the examples of target face. The shortcoming of these methods is that they cannot create expressions for novel faces: target faces not previously recorded in the database. We propose a solution to overcome this limitation. We present an interactive facial-geometric-feature animation system for generating expressions of novel faces. Our system is easy to use. By click-dragging control points on the target face, on the computer screen display, unique expressions are generated automatically. To guarantee natural animation results, our animation model employs prior knowledge based on various individuals' expressions. One model prior is learned from motion vector fields to guarantee effective facial motions. Another, different, model prior is learned from facial shape space to ensure the result has a real facial shape. Interactive animation problem is formulated in a maximum a posterior (MAP) framework to search for optimal results by combining the priors with user-defined constraints. We give an extension of the Motion Propagation (MP) algorithm to infer facial motions for novel target faces from a subset of the control points. Experimental results on different facial animations demonstrate the effectiveness of the proposed method. Moreover, one application of our system is exhibited in this paper, where users create expressions for facial sketches interactively.

Small cell networks have been promoted as an enabling solution to enhance indoor coverage and improve spectral efficiency. Users usually deploy small cells on-demand and pay no attention to global profile in residential areas or offices. The reduction of cell radius leads to dense deployment which brings intractable computation complexity for resource allocation. In this paper, we develop a semi-distributed resource allocation algorithm by dividing small cell networks into clusters with limited inter-cluster interference and selecting a reference cluster for interference estimation to reduce the coordination degree. Numerical results show that the proposed algorithm can maintain similar system performance while having low complexity and reduced information exchange overheads.

Semi-bent functions have very high nonlinearity and hence they have many applications in symmetric-key cryptography, binary sequence design for communications, and combinatorics. In this paper, we focus on studying the additive autocorrelation of semi-bent functions. We provide a lower bound on the maximum additive autocorrelation absolute value of semi-bent functions with three-level additive autocorrelation. Semi-bent functions with three-level additive autocorrelation achieving this bound with equality are said to have perfect three-level additive autocorrelation. We present two classes of balanced semi-bent functions with optimal algebraic degree and perfect three-level additive autocorrelation.

Internet traffic engineering is much important for Internet Service Providers (ISPs) today, since it can be used to fully utilize already deployed network resources. For ISPs, the requirements for traffic engineering should be simple, easy to configure, cost-effective and efficient. Based on these considerations, we propose an algorithm called Web First Adaptive Traffic Engineering (WFATE). Since World Wide Web (WWW) services dominate most of the total Internet traffic and WWW flows are not long-lived, we only apply load balancing to WWW traffic in the algorithm. It can be shown that the number of coexistent WWW flows at an ingress node is almost certainly below a bound, and thus a forward-per-flow mechanism without keeping track of the state of each flow is feasible. This mechanism can balance traffic load at fine granularity and therefore get better performance. Through simulations and performance comparison, it is shown that WFATE is quite efficient, which can improve the network throughput averagely by 26% under the "dense source" traffic pattern and 9% under the "sparse source" traffic pattern.

We investigate the problem of joint frequency and power allocation in wireless mesh networks, using a self-pricing game based solution. In traditional pricing game models, the price factor is determined from the global information of the network, which causes heavy communication overhead. To overcome this problem, we propose a self-pricing game model, in which the price factor is determined by the distributed access points processing their individual information; moreover, it is implemented in an autonomous and distributed fashion. The existence and the efficiency of Nash equilibrium (NE) of the proposed game are studied. It is shown that the proposed game based solution achieves near cooperative network throughput while it reduces the communication overhead significantly. Also, a forcing convergence algorithm is proposed to counter the vibration of channel selection. Simulation results verify the effectiveness and robustness of the proposed scheme.

In this paper, for any given prime power q, using Helleseth-Gong sequences with ideal auto-correlation property, we propose a class of perfect sequences of length (qm-1)/(q-1). As an application, a subclass of constructed perfect sequences is used to design optimal and perfect difference systems of sets.

For an integer q≥2, new sets of q-phase aperiodic complementary sequences (ACSs) are constructed by using known sets of q-phase ACSs and certain matrices. Employing the Kronecker product to two known sets of q-phase ACSs, some sets of q-phase aperiodic complementary sequences with a new length are obtained. For an even integer q, some sets of q-phase ACSs with new parameters are generated, and their equivalent matrix representations are also presented.

Most of the previous work on power optimization regarded the capacity of battery power as an ideal constant value. In fact, experiments showed that 30% of the total battery capacity was wasted by improper discharge pattern [1]. In this letter, a battery-aware task scheduling protocol which harnesses one of the typical characteristics of batteries, i.e., battery recovery, is proposed to extend usage time for smart phones. The key idea is to adjust the working schedule of the components in smart phones for more energy recovering. Experiments show that when the proposed protocol is applied in an online music application, as much as 9% lifespan extension for batteries can be obtained.