In view of the different spatial and temporal resolutions of observed multi-source heterogeneous carbon dioxide data and the uncertain quality of observations, a data fusion prediction model for observed multi-scale carbon dioxide concentration data is studied. First, a wireless carbon sensor network is created, the gross error data in the original dataset are eliminated, and remaining valid data are combined with kriging method to generate a series of continuous surfaces for expressing specific features and providing unified spatio-temporally normalized data for subsequent prediction models. Then, the long short-term memory network is used to process these continuous time- and space-normalized data to obtain the carbon dioxide concentration prediction model at any scales. Finally, the experimental results illustrate that the proposed method with spatio-temporal features is more accurate than the single sensor monitoring method without spatio-temporal features.

With the advent of 5G and external pressure to reduce greenhouse gas emissions, wireless transceivers with low power consumption are strongly desired for future cellular systems. At the same time, increased modulation order due to the evolution of cellular systems will force power amplifiers to operate at much larger output power back-off to prevent EVM degradation. This paper begins with an analysis of load modulation and asymmetrical Doherty amplifiers. Measurement results will show an apparent 60% efficiency plateau for modulated signals with a large peak-to-average power ratio (PAPR). To exceed this efficiency limitation, the second part of this paper focuses on a new amplification topology based on the amalgamation between Doherty and outphasing. Measurement results of the proposed Doherty-outphasing power amplifier (DOPA) will confirm the feasibility of the approach with a modulated efficiency greater than 70% measured at 10 dB output power back-off.

In this letter, we will prove that chaotic binary sequences generated by the tent map and Walsh functions are i.i.d. (independent and identically distributed) and orthogonal to each other.

In this letter, we present a construction of bent functions which generalizes a work of Zhang et al. in 2016. Based on that, we obtain a cubic bent function in 10 variables and prove that, it has no affine derivative and does not belong to the completed Maiorana-McFarland class, which is opposite to all 6/8-variable cubic bent functions as they are inside the completed Maiorana-McFarland class. This is the first time a theoretical proof is given to show that the cubic bent functions in 10 variables can be outside the completed Maiorana-McFarland class. Before that, only a sporadic example with such properties was known by computer search. We also show that our function is EA-inequivalent to that sporadic one.

Permutation polynomials and their compositional inverses are crucial for construction of Maiorana-McFarland bent functions and their dual functions, which have the optimal nonlinearity for resisting against the linear attack on block ciphers and on stream ciphers. In this letter, we give the explicit compositional inverse of the permutation binomial $f(z)=z^{2^{r}+2}+alpha zinmathbb{F}_{2^{2r}}[z]$. Based on that, we obtain the dual of monomial bent function $f(x)={ m Tr}_1^{4r}(x^{2^{2r}+2^{r+1}+1})$. Our result suggests that the dual of f is not a monomial any more, and it is not always EA-equivalent to f.

Recently the data-driven learning of dynamic systems has become a promising approach because no physical knowledge is needed. Pure machine learning approaches such as Gaussian process regression (GPR) learns a dynamic model from data, with all physical knowledge about the system discarded. This goes from one extreme, namely methods based on optimizing parametric physical models derived from physical laws, to the other. GPR has high flexibility and is able to model any dynamics as long as they are locally smooth, but can not generalize well to unexplored areas with little or no training data. The analytic physical model derived under assumptions is an abstract approximation of the true system, but has global generalization ability. Hence the optimal learning strategy is to combine GPR with the analytic physical model. This paper proposes a method to learn dynamic systems using GPR with analytic ordinary differential equations (ODEs) as prior information. The one-time-step integration of analytic ODEs is used as the mean function of the Gaussian process prior. The total parameters to be trained include physical parameters of analytic ODEs and parameters of GPR. A novel method is proposed to simultaneously learn all parameters, which is realized by the fully Bayesian GPR and more promising to learn an optimal model. The standard Gaussian process regression, the ODE method and the existing method in the literature are chosen as baselines to verify the benefit of the proposed method. The predictive performance is evaluated by both one-time-step prediction and long-term prediction. By simulation of the cart-pole system, it is demonstrated that the proposed method has better predictive performances.

Multiple-valued bent functions are functions with highest nonlinearity which makes them interesting for multiple-valued cryptography. Since the general structure of bent functions is still unknown, methods for construction of bent functions are often based on some deterministic criteria. For practical applications, it is often necessary to be able to construct a bent function that does not belong to any specific class of functions. Thus, the criteria for constructions are combined with exhaustive search over all possible functions which can be very CPU time consuming. A solution is to restrict the search space by some conditions that should be satisfied by the produced bent functions. In this paper, we proposed the construction method based on spectral subsets of multiple-valued bent functions satisfying certain appropriately formulated restrictions in Galois field (GF) and Reed-Muller-Fourier (RMF) domains. Experimental results show that the proposed method efficiently constructs ternary and quaternary bent functions by using these restrictions.

In this paper, we focus on developing efficient multi-configuration selection mechanisms by exploiting the spatial degrees of freedom (DoF), and leveraging the simple design benefits of spatial modulation (SM). Notably, the SM technique, as well as its variants, faces the following critical challenges: (i) the performance degradation and difficulty in improving the system performance for higher-level QAM constellations, and (ii) the vast complexity cost in precoder designs particularly for the increasing system dimension and amplitude-phase modulation (APM) constellation dimension. Given this situation, we first investigate two independent modulation domains, i.e., the original signal- and spatial-constellations. By exploiting the analog shift weighting and the virtual spatial signature technologies, we introduce the signature spatial modulation (SSM) concept, which is capable of guaranteing superior trade-offs among spectral- and cost-efficiencies, and system bit error rate (BER) performance. Besides, we develop an analog beamforming for SSM by solving the introduced unconstrained Lagrange dual function minimization problem. Numerical results manifest the performance gain brought by our developed analog beamforming for SSM.

Binary bent functions have a strictly specified number of non-zero values. In the same way, ternary bent functions satisfy certain requirements on the elements of their value vectors. These requirements can be used to specify six classes of ternary bent functions. Classes are mutually related by encoding of function values. Given a basic ternary bent function, other functions in the same class can be constructed by permutation matrices having a block structure similar to that of the factor matrices appearing in the Good-Thomas decomposition of Cooley-Tukey Fast Fourier transform and related algorithms.

This paper reports the results of a new test on what types of failure cause falls in the stock prices of telecommunication service providers. This analysis of stock price is complementary to our previous one on market share. A clear result of our new test is that the type of failure causing falls in stock price is different from the type causing decline in market share. Specifically, the previous study identified frequent failures as causes of decline in market share, while the current study indicates large failures affecting many users as causes of falls in stock price. Together, these analyses give important information for reliability designs of telecommunications networks.

We propose a video magnification method for magnifying subtle color and motion changes under the presence of non-meaningful background motions. We use frequency variability to design a filter that passes only meaningful subtle changes and removes non-meaningful ones; our method obtains more impressive magnification results without artifacts than compared methods.

In this paper, we propose an active calibration algorithm to tackle both gain-phase errors and position perturbations. Unlike many other active calibration methods, which fix the array while changing the location of the source, our approach rotates the array but does not change the location of the source, and knowledge of the direction-of-arrival (DOA) of the far-field calibration source is not required. The superiority of the proposed method lies in the fact that measurement of the direction of a far-field calibration source is not easy to carry out, while measurement of the rotation angle via the proposed calibration strategy is convenient and accurate. To obtain the receiving data from different directions, the sensor array is rotated to three different positions with known rotation angles. Based on the eigen-decomposition of the data covariance matrices, we can use the direction of the auxiliary source to represent the gain-phase errors and position perturbations. After that, we estimate the DOA of the calibration source by a one-dimensional search. Finally, the sensor gain-phase errors and position perturbations are calculated by using the estimated direction of the calibration source. Simulations verify the effectiveness and performance of the algorithm.

The performance of video action recognition has improved significantly in recent decades. Current recognition approaches mainly utilize convolutional neural networks to acquire video feature representations. In addition to the spatial information of video frames, temporal information such as motions and changes is important for recognizing videos. Therefore, the use of convolutions in a spatiotemporal three-dimensional (3D) space for representing spatiotemporal features has garnered significant attention. Herein, we introduce recent advances in 3D convolutions for video action recognition.

Coherent transceivers are now regarded as promising candidates for upgrading the current 400Gigabit Ethernet (400GbE) transceivers to 800G. However, due to the complicated structure of a dual-polarization IQ modulator (DP-IQM) with its bulky polarization-beam splitter/comber (PBS/PBC), the increase in the transmitter size and cost is inevitable. In this paper, we propose a compact PBS/PBC-free transmitter structure with a straight-line configuration. By using the concept of polarization differential modulation, the proposed transmitter is capable of generating a DP phase-shift-keyed (DP-PSK) signal, which makes it directly applicable to the current coherent systems. A detailed analysis of the system performance reveals that the imperfect equalization and the bandwidth limitation at the receiver are the dominant penalty factors. Although such a penalty is usually unacceptable in long-haul applications, the proposed transmitter can be attractive due to its significant simplicity and compactness for short-reach applications, where the cost and the footprint are the primary concerns.

Recently, researchers paid more attention on designing optical routers, since they are essential building blocks of all photonic interconnection architectures. Thus, improving them could lead to a spontaneous improvement in the overall performance of the network. Optical routers suffer from the dilemma of increased insertion loss and crosstalk, which upraises the power consumed as the network scales. In this paper, we propose a new 7×7 non-blocking optical router based on the Dimension Order Routing (DOR) algorithm. Moreover, we develop a method that can ensure the least number of MicroRing Resonators (MRRs) in an optical router. Therefore, by reducing these optical devices, the optical router proposed can decrease the crosstalk and insertion loss of the network. This optical router is evaluated and compared to Ye's router and the optimized crossbar for 3D Mesh network that uses XYZ routing algorithm. Unlike many other proposed routers, this paper evaluates optical routers not only from router level prospective yet also consider the overall network level condition. The appraisals show that our optical router can reduce the worst-case network insertion loss by almost 8.7%, 46.39%, 39.3%, and 41.4% compared to Ye's router, optimized crossbar, optimized universal OR, and Optimized VOTEX, respectively. Moreover, it decreases the Optical Signal-to-Noise Ratio (OSNR) worst-case by almost 27.92%, 88%, 77%, and 69.6% compared to Ye's router, optimized crossbar, optimized universal OR, and Optimized VOTEX, respectively. It also reduces the power consumption by 3.22%, 23.99%, 19.12%, and 20.18% compared to Ye's router, optimized crossbar, optimized universal OR, and Optimized VOTEX, respectively.

This paper proposes statistical analysis of phase-only correlation (POC) functions under the phase fluctuation of signals due to additive Gaussian noise. We derive probability density function of phase-spectrum differences between original signal and its noise-corrupted signal with additive Gaussian noise. Furthermore, we evaluate the expectation and variance of the POC functions between these two signals. As the variance of Gaussian noise increases, the expectation of the peak of the POC function monotonically decreases and variance of the POC function monotonically increases. These results mathematically guarantee the validity of the POC functions used for similarity measure in matching techniques.

We have developed a direct spectrum division transmission (DSDT) technique that can divide a single-carrier signal into multiple sub-spectra and assign them to dispersed frequency resources of the satellite transponder to improve the spectrum efficiency of the whole system. This paper summarizes the satellite experiments on DSDT over a single and/or multiple satellite transponders, while changing various parameters such as modulation schemes, roll-off ratios, and symbol rates. In addition, by considering practical use conditions, we present an evaluation of the performance when the spectral density of each sub-spectrum differed across transponders. The satellite experiments demonstrate that applying the proposal does not degrade the bit error rate (BER) performance. Thus, the DSDT technique is a practical approach to use the scattered unused frequency resources over not only a single transponder but also multiple ones.

We propose a compact magnetic tunnel junction (MTJ) model for circuit simulation by de-facto standard SPICE in this paper. It is implemented by Verilog-A language which makes it easy to simulate MTJs with other standard devices. Based on the switching probability, we smoothly connect the adiabatic precessional model and the thermal activation model by using an interpolation technique based on the cubic spline method. We can predict the switching time after a current is applied. Meanwhile, we use appropriate physical models to describe other MTJ characteristics. Simulation results validate that the model is consistent with experimental data and effective for MTJ/CMOS hybrid circuit simulation.

Many approaches enable teachers to digitalize students' answers and mark them on the computer. However, they are still limited for supporting marking descriptive mathematical answers that can best evaluate learners' understanding. This paper presents clustering of offline handwritten mathematical expressions (HMEs) to help teachers efficiently mark answers in the form of HMEs. In this work, we investigate a method of combining feature types from low-level directional features and multiple levels of recognition: bag-of-symbols, bag-of-relations, and bag-of-positions. Moreover, we propose a marking cost function to measure the marking effort. To show the effectiveness of our method, we used two datasets and another sampled from CROHME 2016 with synthesized patterns to prepare correct answers and incorrect answers for each question. In experiments, we employed the k-means++ algorithm for each level of features and considered their combination to produce better performance. The experiments show that the best combination of all the feature types can reduce the marking cost to about 0.6 by setting the number of answer clusters appropriately compared with the manual one-by-one marking.

Identifying the infection sources in a network, including the sponsor of a network rumor, the servers that inject computer virus into a computer network, or the zero-patient in an infectious disease network, plays a critical role in limiting the damage caused by the infection. A two-source estimator is firstly constructed on basis of partitions of infection regions in this paper. Meanwhile, the two-source estimation problem is transformed into calculating the expectation of permitted permutations count which can be simplified to a single-source estimation problem under determined infection region. A heuristic algorithm is also proposed to promote the estimator to general graphs in a Breadth-First-Search (BFS) fashion. Experimental results are provided to verify the performance of our method and illustrate variations of error detection in different networks.