Empty space skipping can be efficiently implemented with hierarchical data structures such as k-d trees and bounding volume hierarchies. This paper compares several recently published hierarchical data structures with regard to construction and rendering performance. The papers that form our prior work have primarily focused on interactively building the data structures and only showed that rendering performance is superior to using simple acceleration data structures such as uniform grids with macro cells. In the area of surface ray tracing, there exists a trade-off between construction and rendering performance of hierarchical data structures. In this paper we present performance comparisons for several empty space skipping data structures in order to determine if such a trade-off also exists for volume rendering with uniform data topologies.

In this paper, a satellite-aerial integrated computing (SAIC) architecture in disasters is proposed, where the computation tasks from two-tier users, i.e., ground/aerial user equipments, are either locally executed at the high-altitude platforms (HAPs), or offloaded to and computed by the Low Earth Orbit (LEO) satellite. With the SAIC architecture, we study the problem of joint two-tier user association and offloading decision aiming at the maximization of the sum rate. The problem is formulated as a 0-1 integer linear programming problem which is NP-complete. A weighted 3-uniform hypergraph model is obtained to solve this problem by capturing the 3D mapping relation for two-tier users, HAPs, and the LEO satellite. Then, a 3D hypergraph matching algorithm using the local search is developed to find a maximum-weight subset of vertex-disjoint hyperedges. Simulation results show that the proposed algorithm has improved the sum rate when compared with the conventional greedy algorithm.

This short paper presents a case report on detecting hijacked journals. Towards identification of a fake journal website and preventing a hijacked paper, we can use different tools including Google Scholar (an altmetric tool), Web of Science (WoS) and Scopus (both as scientometric databases) to distinguish a fake website from a legal journal website. Our evaluation shows that analysis of a doubtful website for a targeted journal based on Google Scholar is not reliable. In fact, the use of scientometric tools for tracking prior publications of the targeted journal is compulsory. Another result of this case study is that in some uncommon cases, fake websites may sometimes convince a scientometric database in order to be partially indexed along with an abstracting of their hijacked papers while these websites steal identity of the legal journals. Therefore as a results, we should check both of WoS and Scopus for verifying a fake website at the same time to obtain more reliability.

A memory state equation consistent with several experimental observations is presented and discussed within the framework of Chua's memristive systems theory. The proposed equation describes the evolution of the memory state corresponding to a bipolar resistive switching device subjected to a variety of electrical stimulus.

In this correspondence, we correct the ergodic capacity versus SNR curves of the coherent multiple-input multiple-output (MIMO) channel in independent and identically distributed (IID) Rayleigh fading in the correspondence cited in the title. More importantly, the corrected capacity results present an interesting and compelling contrast between performances of the coherent MIMO systems with and without channel state information at the transmitter; whereas this view is somewhat limited in in the correspondence cited in the title because of flaws in the capacity curves.

Corrected version of 19-12-10 (YR-Month-Day) of Part I 2D IPO’s for constant speed Lines, Curves, NURBS. Mainly corrected typos and some clearer formulations. The manuscript (44 pages) “Constant Speed Lines – Curves – NURBS Reference Pulse IPOs (Part I)” of Valere Huypens has been accepted for publication in “The International Journal of Advanced Manufacturing Technology” of Springer

Noise and decoherence are two major obstacles to the implementation of large-scale quantum computing. Because of the no-cloning theorem, which says we cannot make an exact copy of an arbitrary quantum state, simple redundancy will not work in a quantum context, and unwanted interactions with the environment can destroy coherence and thus the quantum nature of the computation. Because of the parallel and distributed nature of classical neural networks, they have long been successfully used to deal with incomplete or damaged data. In this work, we show that our model of a quantum neural network (QNN) is similarly robust to noise, and that, in addition, it is robust to decoherence. Moreover, robustness to noise and decoherence is not only maintained but improved as the size of the system is increased. Noise and decoherence may even be of advantage in training, as it helps correct for overfitting. We demonstrate the robustness using entanglement as a means for pattern storage in a qubit array. Our results provide evidence that machine learning approaches can obviate otherwise recalcitrant problems in quantum computing.

A document by Ying Han . Click on the document to view its contents.

The use of large-scale antenna arrays grants considerable benefits in energy and spectral efficiency to wireless systems due to spatial resolution and array gain techniques. By assuming a dominant line-of-sight environment in a massive MIMO scenario, we derive analytical expressions for the sum-capacity. % Then, we show that convenient simplifications on the sum-capacity expressions are possible when working at low and high SNR regimes. % Furthermore, in the case of a high SNR regime, it is demonstrated that the Gamma PDF can approximate the PDF of the instantaneous channel sum-capacity as the number of BS antennas grows. A second important demonstration presented in this work is that a Gamma PDF can also be used to approximate the PDF of the summation of the channel’s singular values as the number of devices increases. Finally, it is important to highlight that the presented framework is useful for a massive number of Internet of Things devices as we show that the transmit power of each device can be made inversely proportional to the number of BS antennas.

p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px ‘Times New Roman’; min-height: 15.0px} p.p2 {margin: 0.0px 0.0px 0.0px 0.0px; font: 9.0px ‘Times New Roman’} span.s1 {font: 12.0px ‘Times New Roman’} Mobility is a key aspect in current cellular networks, allowing users to access the provided services almost anywhere. When a user transitions from a base station’s coverage area to another cell being serviced by another station, a handoff process takes place, where resources are released in the first base station, and allocated in the second for the purpose of servicing the user. Predicting the future location of a cell phone user allows the handoff process to be optimized. This optimization allows for a better utilization of the available resources, regarding bot the transmitted power and the frequency allocation, resulting in less amount of wasted power in unwanted directions and the possibility of reusing frequencies in a single base station. To achieve this goal, Deep Learning techniques are proposed, which have proven to be efficient tools for predicting and detecting patterns. The purpose of this paper is to give an overview of the state of the art in Deep Learning techniques for making spatio-temporal predictions, which could be used to optimize the handoff process in cellular systems.

The asymmetrical double-sided two-way ranging means uneven session duration, as a result clocks errors propagate differently in the two TWR measurement sessions. The article proposes a method of the errors compensation by weighted mean of two TWR measurements and minimization of error expectation. By this method, the offset error is cancelled, the full-scale error is not diminished, the cumulated noise variance is attenuated, and the correlated noises variance is strongly diminished. The error analysis shows that the minimum lower band is reached by SDS-TWR.

Entering the 5G/6G era, the core concept of human-centric communications has intensified the search effort into analytical frameworks for integrating technological and non-technological domains. Among non-technological domains, human behavioral, psychological, and socio-economic contexts are widely considered as indispensable elements for characterizing user experience (UE). In this study, we introduce the prospect theory as a promising methodology for modeling UE and perceptual measurements for human-centric communications. As the founding pillar of behavioral economics, the prospect theory proposes the non-linear quantity and probability perception of human psychology, which extends to five fundamental behavioral attributes that have profound implications for diverse disciplines. By expatiating on the prospect theoretic framework, we aim to provide a guideline for developing human-centric communications and articulate a novel interdisciplinary research area for further investigation.

In this paper, a new hypervolume-based evolutionary multi-objective optimization algorithm (EMOA), namely R2HCA-EMOA (R2-based Hypervolume Contribution Approximation EMOA), is proposed for many-objective optimization. The core idea of the algorithm is to use an R2 indicator variant to approximate the hypervolume contribution. The basic framework of the proposed algorithm is the same as SMS- EMOA. In order to make the algorithm computationally efficient, a utility tensor structure is introduced for the calculation of the R2 indicator variant. Moreover, a normalization mechanism is incorporated into R2HCA-EMOA to enhance the performance of the algorithm. Through experimental studies, R2HCA-EMOA is compared with three hypervolume-based EMOAs and several other state-of-the-art EMOAs on 5-, 10- and 15-objective DTLZ, WFG problems and their minus versions. Our results show that R2HCA-EMOA is more efficient than the other hypervolume-based EMOAs, and is superior to all the compared state-of-the-art EMOAs.

We consider power efficient scheduling and precoding solutions for multiantenna hybrid digital-analog transmission systems that use Time-Modulated Arrays (TMAs) in the analog domain. TMAs perform beamforming with switches instead of conventional Phase Shifters (PSs). The extremely low insertion losses of switches, together with their reduced power consumption and cost make TMAs attractive in emerging technologies like massive Multiple-Input Multiple-Output (MIMO) and millimeter wave (mmWave) systems. We propose a novel analog processing network based on TMAs and provide an angular scheduling algorithm that overcomes the limitations of conventional approaches. Next, we pose a convex optimization problem to determine the analog precoder. This formulation allows us to account for the Sideband Radiation (SR) effect inherent to TMAs, and achieve remarkable power efficiencies with a very low impact on performance. Computer experiments results show that the proposed design, while presenting a significantly better power efficiency, achieves a throughput similar to that obtained with other strategies based on angular selection for conventional architectures.

We have thoroughly studied the paper of Perazzo et al., which presents a routing attack named DIO suppression attack with its impact analysis. However, the considered simulation grid of size 20mx20m does not correspond to the results presented in their paper. We believe that the incorrect simulation detail needs to be rectified further for the scientific correctness of the results. In this comment, it is shown that the suppression attack on such small sized network topology does not have any major impact on routing performance, and specific reason is discussed for such behavior.

A document by James Storey . Click on the document to view its contents.

This article considers the fusion of target estimates stemming from multiple sensors, where the spatial extent of the targets is incorporated. The target estimates are represented as ellipses parameterized with center orientation and semi-axis lengths and width. Here, the fusion faces challenges such as ambiguous parameterization and an unclear meaning of the Euclidean distance between such estimates. We introduce a novel Bayesian framework for random ellipses based on the concept of a Minimum Mean Gaussian Wasserstein (MMGW) estimator. The MMGW estimate is optimal with respect to the Gaussian Wasserstein (GW) distance, which is a suitable distance metric for ellipses. We develop practical algorithms to approximate the MMGW estimate of the fusion result. The key idea is to approximate the GW distance with a modified version of the Square Root (SR) distance. By this means, optimal estimation and fusion can be performed based on the square root of the elliptic shape matrices. We analyze different implementations using, e.g., Monte Carlo methods, and evaluate them in simulated scenarios. An extensive comparison with state-of-the-art methods highlights the benefits of estimators tailored to the Gaussian Wasserstein distances.

In this paper, we propose quantum circuits to enable dynamic assertions for classical values, entanglement, and superposition. This enables a dynamic debugging primitive, driven by a programmer’s understanding of the correct behavior of the quantum program. We show that besides generating assertion errors, the assertion logic may also force the qubits under test to be into the desired state. Besides debugging, our proposed assertion logic can also be used in noisy intermediate scale quantum (NISQ) systems to filter out erroneous results, as demonstrated on a 20-qubit IBM Q quantum computer. Our proposed assertion circuits have been implemented as functions in the open-source Qiskit tool.