Sectors in the airspace are units of the air traffic control. For airspace traffic data consists of the location of each aircraft with timestamp, we propose an efficient method to identify the sector where each aircraft lies.

This paper proposes a novel low complexity lattice reduction-aided iterative receiver for overloaded MIMO. Novel noise cancellation is proposed that increases an equivalent channel gain with a scalar gain introduced in this paper, which results in the improvement of the signal to noise power ratio (SNR). We theoretically analyze the performance of the proposed receiver that the lattice reduction raises the SNR of the detector output signals as the scalar gain increases, when the Lenstra-Lenstra-Lova's (LLL) algorithm is applied to implement the lattice reduction. Because the SNR improvement causes the scalar gain to increase, the performance is improved by iterating the reception process. Computer simulations confirm the performance. The proposed receiver attains a gain of about 5dB at the BER of 10-4 in a 6×2 overloaded MIMO channel. Computational complexity of the proposed receiver is about 1/50 as much as that of the maximum likelihood detection (MLD).

In this paper, new classes of binary generalized cyclotomic sequences of period 2pm+1qn+1 are constructed. These sequences are balanced. We calculate the linear complexity of the constructed sequences with a simple method. The results show that the linear complexity of such sequences attains the maximum.

A topological quantum circuit is a representation model for topological quantum computation, which attracts much attention recently as a promising fault-tolerant quantum computation model by using 3D cluster states. A topological quantum circuit can be considered as a set of “loops,” and we can transform the topology of loops without changing the functionality of the circuit if the transformation satisfies certain conditions. Thus, there have been proposed many researches to optimize topological quantum circuits by transforming the topology. There are two directions of research to optimize topological quantum circuits. The first group of research considers so-called a placement and wiring problem where we consider how to place “parts” in a 3D space which corresponds to already optimized sub-circuits. The second group of research focuses on how to optimize the structure and locations of loops in a relatively small circuit which is treated as one part in the above-mentioned first group of research. This paper proposes a new idea for the second group of research; our idea is to consider topological transformations as a placement and wiring problem for modules which we derive from the information how loops are crossed. By using such a formulation, we can use the techniques for placement and wiring problems, and successfully obtain an optimized solution. We confirm by our experiment that our method indeed can reduce the cost much more than the method by Paetznick and Fowler.

Notes on two constructions of zero-difference balanced (ZDB) functions are made in this letter. Then ZDB functions over Ze×∏ki=0 Fqi are obtained. And it shows that all the known ZDB functions using cyclotomic cosets over Zn are special cases of a generic construction. Moreover, applications of these ZDB functions are presented.

Generalized approximate message passing (GAMP) is introduced into distributed compressed sensing (DCS) to reconstruct jointly sparse signals under the mixed support-set model. A GAMP algorithm with known support-set is presented and the matching pursuit generalized approximate message passing (MPGAMP) algorithm is modified. Then, a new joint recovery algorithm, referred to as the joint MPGAMP algorithm, is proposed. It sets up the jointly shared support-set of the signal ensemble with the support exploration ability of matching pursuit and recovers the signals' amplitudes on the support-set with the good reconstruction performance of GAMP. Numerical investigation shows that the joint MPGAMP algorithm provides performance improvements in DCS reconstruction compared to joint orthogonal matching pursuit, joint look ahead orthogonal matching pursuit and regular MPGAMP.

We propose a method to find assembly models contained in another assembly model given as a query from a set of 3D CAD assembly models. A 3D CAD assembly model consists of multiple components and is constructed using a 3D CAD software. The proposed method distinguishes assembly models which consist of a subset of components constituting the query model and also whose components have the same layout as the subset of the components. We compute difference between the shapes and the layouts of the components from the sinograms which are constructed by the Radon transform of their projections from various angles. We evaluate the proposed method experimentally using the assembly models which we prepare as a benchmark. The proposed method can also be used to find the database models which contains a query model.

This paper presents a method combining array signal processing and adaptive noise cancellation to suppress unwanted ambient interferences in in situ measurement of radiated emissions of equipment. First, the signals received by the antenna array are processed to form a main data channel and an auxiliary data channel. The main channel contains the radiated emissions of the equipment under test and the attenuated ambient interferences. The auxiliary channel only contains the attenuated ambient interferences. Then, the adaptive noise cancellation technique is used to suppress the ambient interferences based on the correlation of the interferences in the main and auxiliary channels. The proposed method overcomes the problem that the ambient interferences in the two channels of the virtual chamber method are not correlated, and realizes the suppression of multi-source ambient noises in the use of fewer array elements. The results of simulation and experiment show that the proposed method can effectively extract radiated emissions of the equipment under test in complex electromagnetic environment. Finally, discussions on the effect of the beam width of the main channel and the generalization of the proposed method to three dimensionally distributed signals are addressed.

Approximate computing is a promising paradigm to realize fast, small, and low power characteristics, which are essential for modern applications, such as Internet of Things (IoT) devices. This paper proposes the Carry-Predicting Adder (CPredA), an approximate adder that is scalable relative to accuracy and power consumption. The proposed CPredA improves the accuracy of a previously studied adder by performing carry prediction. Detailed simulations reveal that, compared to the existing approximate adder, accuracy is improved by approximately 50% with comparable energy efficiency. Two application-level evaluations demonstrate that the proposed approximate adder is sufficiently accurate for practical use.

In this paper, we briefly review the concept of superconducting quantum computers and discuss their hardware architecture. We also describe the necessary technologies for the development of a medium-scale quantum computer with more than tens of thousands of quantum bits.

The Machine-to-Machine (M2M) service network platform accommodates M2M communications traffic efficiently by using tree-structured networks and the computation resources deployed on network nodes. In the M2M service network platform, program files required for controlling devices are placed on network nodes, which have different amounts of computation resources according to their position in the hierarchy. The program files must be dynamically repositioned in response to service quality requests from each device, such as computation power, link bandwidth, and latency. This paper proposes a Program File Placement (PFP) method for the M2M service network platform. First, the PFP problem is formulated in the Mixed-Integer Linear Programming (MILP) approach. We prove that the decision version of the PFP problem is NP-complete. Next, we present heuristic algorithms that attain sub-optimal but attractive solutions. Evaluations show that the heuristic algorithm based on the number of devices that share a program file reduces the total number of placed program files compared to the algorithm that moves program files based on their position.

Given two feasible solutions A and B, a reconfiguration problem asks whether there exists a reconfiguration sequence (A0=A, A1,...,Aℓ=B) such that (i) A0,...,Aℓ are feasible solutions and (ii) we can obtain Ai from Ai-1 under the prescribed rule (the reconfiguration rule) for each i ∈ {1,...,ℓ}. In this paper, we address the reconfiguration problem for induced trees, where an induced tree is a connected and acyclic induced subgraph of an input graph. We consider the following two rules as the prescribed rules: Token Jumping: removing u from an induced tree and adding v to the tree, and Token Sliding: removing u from an induced tree and adding v adjacent to u to the tree, where u and v are vertices of an input graph. As the main results, we show that (I) the reconfiguration problemis PSPACE-complete even if the input graph is of bounded maximum degree, (II) the reconfiguration problem is W[1]-hard when parameterized by both the size of induced trees and the length of the reconfiguration sequence, and (III) there exists an FPT algorithm when the problem is parameterized by both the size of induced trees and the maximum degree of an input graph under Token Jumping and Token Sliding.

This paper introduces a new noise generation algorithm for vocoder-based speech waveform generation. White noise is generally used for generating an aperiodic component. Since short-term white noise includes a zero-frequency component (ZFC) and inaudible components below 20 Hz, they are reduced in advance when synthesizing. We propose a new noise generation algorithm based on that for velvet noise to overcome the problem. The objective evaluation demonstrated that the proposed algorithm can reduce the unwanted components.

This paper presents a 6th-order quadrature bandpass delta sigma AD modulator (QBPDSM) with 2nd-order image rejection using dynamic amplifier and noise coupling (NC) SAR quantizer embedded by passive adder for the application of wireless communication system. A novel complex integrator using dynamic amplifier is proposed to improve the energy efficiency of the QBPDSM. The NC SAR quantizer can realize an additional 2nd-order noise shaping and 2nd-order image rejection by the digital domain noise coupling technique. As a result, the 6th-order QBPDSM with 2nd-order image rejection is realized by two complex integrators using dynamic amplifier and the NC SAR quantizer. The SPICE simulation results demonstrate the feasibility of the proposed QBPDSM in 90nm CMOS technology. Simulated SNDR of 76.30dB is realized while a sinusoid -3.25dBFS input is sampled at 33.3MS/s and the bandwidth of 2.083MHz (OSR=8) is achieved. The total power consumption in the modulator is 6.74mW while the supply voltage is 1.2V.

Multiuser massive multi-input multi-output (MU massive MIMO) is considered as a promising technology for the fifth generation (5G) of the wireless communication system. In this paper, we propose a low-complexity joint antenna and user selection scheme with block diagonalization (BD) precoding for MU massive MIMO downlink channel in the time division duplex (TDD) system. The base station (BS) is equipped with a large-scale transmit antenna array while each user is using the single receive antenna in the system. To reduce the hardware cost, BS will be implemented by limited number of radio frequency (RF) chains and BS must activate some selected transmit antennas in the BS side for data transmitting and some users' receive antennas in user side for data receiving. To achieve the reduction in the computation complexity in the antenna and user selection while maintaining the same or higher sum-rate in the system, the proposed scheme relies on three complexity reduction key factors. The first key factor is that finding the average channel gains for the transmit antenna in the BS side and the receive antenna in the user side to select the best channel gain antennas and users. The second key factor called the complexity control factor ξ(Xi) for the antenna set and the user set limitation is used to control the complexity of the brute force search. The third one is that using the assumption of the point-to-point deterministic MIMO channel model to avoid the singular value decomposition (SVD) computation in the brute force search. We show that the proposed scheme offers enormous reduction in the computation complexity while ensuring the acceptable performance in terms of total system sum-rate compared with optimal and other conventional schemes.

Blind nonlinear compensation for RF receivers is an important research topic in 5G mobile communication, in which higher level modulation schemes are employed more often to achieve high capacity and ultra-broadband services. Since nonlinear compensation circuits must handle intermodulation bandwidths that are more than three times the signal bandwidth, reducing the sampling frequency is essential for saving power consumption. This paper proposes a novel blind nonlinear compensation technique that employs sub-Nyquist sampling analog-to-digital conversion. Although outband distortion spectrum is folded in the proposed sub-Nyquist sampling technique, determination of compensator coefficients is still possible by using the distortion power. Proposed technique achieves almost same compensation performance in EVM as the conventional compensation scheme, while reducing sampling speed of analog to digital convertor (ADC) to less than half the normal sampling frequency. The proposed technique can be applied in concurrent dual-band communication systems and adapt to flat Rayleigh fading environments.

The recent rapid increase in the scale of superconducting quantum computing systems greatly increases the demand for qubit control by digital circuits operating at qubit temperatures. In this paper, superconducting digital circuits, such as single-flux quantum and adiabatic quantum flux parametron circuits are described, that are promising candidates for this purpose. After estimating their energy consumption and speed, a conceptual overview of the superconducting electronics for controlling a multiple-qubit system is provided, as well as some of its component circuits.

Unitary operator discrimination is a fundamental problem in quantum information theory. The basic version of this problem can be described as follows: Given a black box implementing a unitary operator U∈S:={U1, U2} under some probability distribution over S, the goal is to decide whether U=U1 or U=U2. In this paper, we consider the query complexity of this problem. We show that there exists a quantum algorithm that solves this problem with bounded error probability using $lceil{sqrt{6} heta_{ m cover}^{-1}} ceil$ queries to the black box in the worst case, i.e., under any probability distribution over S, where the parameter θcover, which is determined by the eigenvalues of $U_1^dagger {U_2}$, represents the “closeness” between U1 and U2. We also show that this upper bound is essentially tight: we prove that for every θcover > 0 there exist operators U1 and U2 such that any quantum algorithm solving this problem with bounded error probability requires at least $lceil{rac{2}{3 heta_{ m cover}}} ceil$ queries under uniform distribution over S.

Due to the inevitable data missing problem during visual data acquisition, the recovery of color images and videos from limited useful information has become an important topic, for which tensor completion has been proved to be a promising solution in previous studies. In this paper, we propose a novel completion scheme, which can effectively recover missing entries in color images and videos represented by tensors. We first employ a modified tensor train (TT) decomposition as tensor approximation scheme in the concept of TT rank to generate better-constructed and more balanced tensors which preserve only relatively significant informative data in tensors of visual data. Afterwards, we further introduce a TT rank-based weight scheme which can define the value of weights adaptively in tensor completion problem. Finally, we combine the two schemes with Simple Low Rank Tensor Completion via Tensor Train (SiLRTC-TT) to construct our completion algorithm, Low Rank Approximated Tensor Completion via Adaptive Tensor Train (LRATC-ATT). Experimental results validate that the proposed approach outperforms typical tensor completion algorithms in recovering tensors of visual data even with high missing ratios.

This paper introduces e-spill, an eager spill mechanism, which dynamically finds the optimal spill-threshold by monitoring the GC time at runtime and thereby prevent expensive GC overhead. Our e-spill adopts a slow-start model to gradually increase the spill-threshold until it reaches the optimal point without substantial GCs. We prototype e-spill as an extension to Spark and evaluate it using six workloads on three different parallel platforms. Our evaluations show that e-spill improves performance by up to 3.80× and saves the cost of cluster operation on Amazon EC2 cloud by up to 51% over the baseline system following Spark Tuning Guidelines.