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Recycled paper is extensively used worldwide. In the last decades its market has expanded considerably. The increasing use of recycled paper in papermaking has led to the production of paper containing several types of impurities. Consequently, wastepaper mills are forced to implement quality control schemes for evaluating the incoming wastepaper stock, thus guarantying the specifications of the final product. The main objective of this work is to present a fast and reliable system for identifying different paper types. Therefore, undesirable paper types can be refused, improving the performance of the paper machine and the final quality of the paper manufactured. For this purpose two fast techniques, i.e., Fourier transform mid-infrared (FTIR) and reflectance near-infrared (*IR) were applied to acquire the infrared spectra of the paper samples. *ext, four processing multivariate methods, i.e., principal component analysis (PCA), canonical variate analysis (CVA), extended canonical variate analysis (ECVA) and support vector machines (SVM) were employed in the feature extraction –or dimension reduction– stage. Afterwards, the k nearest neighbors algorithm (k**) was used in the classification phase. Experimental results show the usefulness of the proposed methodology and the potential of both FTIR and *IR spectroscopic methods. Using the FTIR spectrum in association with SVM and k** the system achieved maximum classification accuracy of 100%, whereas using the *IR spectrum in association with ECVA or SVM and k** the system achieved maximum classification accuracy of 96.4%

Recently there has been a lot of success in using the deterministic approach to provide approximate characterization of Gaussian network capacity. In this paper, we take a deterministic view and revisit the problem of wiretap channel with side information. A precise characterization of the secrecy capacity is obtained for a linear deterministic model, which naturally suggests a coding scheme which we show to achieve the secrecy capacity of the degraded Gaussian model (dubbed as "secret writing on dirty paper") to within half a bit., Removed the section on writing on fading paper; improved the gap result from $(1/2)\log3$ bits to half a bit; added a section on secret-key agreement via dirty-paper coding; revised version submitted to the IEEE Transaction on Information Theory

We design a coding scheme for Costa's dirty paper coding (DPC) (M.H.M. Costa, 1983) using a channel and a shaping code. We show that by transforming the channel noise distribution the DPC channel can be converted into the binary erasure channel (BEC) with binary interference with memory. Furthermore, the messages exchanged during the iterative decoding between the channel and shaping codes become one dimensional under the new model. We analyze the iterative decoding and find good shaping and channel code pairs using some closed-form extrinsic information transfer (EXIT) curves. We verify that our dirty paper codes designed using this method are also good for the original DPC channel with the additive white Gaussian noise (AWGN) and arbitrary interference. Our implementation of DPC uses short block codes such as repetition codes for shaping codes. Although the shaping gains of such codes are not very high, they may provide a better complexity-performance trade-off for simple practical implementations of DPC. Furthermore, we show accurate theoretical analysis is possible for such codes under our channel model.

This paper describes a way of implementing DPC in a Gaussian MIMO broadcast channel. The outer encoder is based on a vector TCQ designed to possess certain "good properties". Simpler schemes such as the Tomlinson Harashima or Scalar Costa's scheme are also considered by a way of comparison. The inner encoder is implemented through a vector version of the ZF-DPC and the MMSE-DPC. The BER performance of the DPC schemes is evaluated and compared to that of conventional interference cancellers (pre-ZF, pre-MMSE). From simulation results the choices of the inner encoder, the outer encoder (THS/SCS/TCQ) and the interference cancellation technique (conventional or DPC) are discussed.

Costa's “writing on dirty paper” result establishes that full state pre-cancellation can be attained in the Gel'fand-Pinsker problem with additive state and additive white Gaussian noise. This result holds under the assumptions that full channel knowledge is available at both the transmitter and the receiver. In this work we consider the scenario in which the state is multiplied by an ergodic fading process which is not known at the encoder. We study both the case in which the receiver has knowledge of the fading and the case in which it does not: for both models we derive inner and outer bounds to capacity and determine the distance between the two bounds when possible. For the channel without fading knowledge at either the transmitter or the receiver, the gap between inner and outer bounds is finite for a class of fading distributions which includes a number of canonical fading models. In the capacity approaching strategy for this class, the transmitter performs Costa's pre-coding against the mean value of the fading times the state while the receiver treats the remaining signal as noise. For the case in which only the receiver has knowledge of the fading, we determine a finite gap between inner and outer bounds for two classes of discrete fading distribution. The first class of distributions is the one in which there exists a probability mass larger than one half while the second class is the one in which the fading is uniformly distributed over values that are exponentially spaced apart. Unfortunately, the capacity in the case of a continuous fading distribution remains very hard to characterize.

Dirty paper trellis codes have been introduced as an alternative to lattice codes to implement watermarking as communications with side information. Their key feature is robustness against value-metric scaling in comparison with lattice codes. Despite the strong academic recognition, parametrization issues remain unclear. For instance, the impact of the trellis configuration on performance is still not well understood. In this paper, experiments on synthetic signals are reported to investigate how the trellis configuration influences the bit error rate and the computational complexity

We consider a state-dependent parallel Gaussian channel with independent states and a common cognitive helper, in which two transmitters wish to send independent information to their corresponding receivers over two parallel subchannels. Each channel is corrupted by independent additive Gaussian state. The states are not known to the transmitters nor to the receivers, but known to a helper in a noncausal manner. The helper's goal is to assist a reliable communication by mitigating the state. Outer and inner bounds are derived and segments of the capacity region is characterized for various channel parameters.

A state-dependent Gaussian Z-interference channel model is investigated in the regime of high state power, in which transmitters 1 and 2 communicate with receivers 1 and 2, and only receiver 2 is interfered by transmitter 1's signal and a random state sequence. The state sequence is known noncausally only to transmitter 1, not to the corresponding transmitter 2. A layered coding scheme is designed for transmitter 1 to help interference cancelation at receiver 2 (using a cognitive dirty paper coding) and to transmit its own message to receiver 1. Inner and outer bounds are derived, and are further analyzed to characterize the boundary of the capacity region either fully or partially for all Gaussian channel parameters. Our results imply that the capacity region of such a channel with mismatched transmitter-side state cognition and receiver-side state interference is strictly less than that of the corresponding channel without state, which is in contrast to Costa type of dirty channels, for which dirty paper coding achieves the capacity of the corresponding channels without state.

A novel actuator has been fabricated using graphene and graphene oxide composite paper as building blocks. The two materials have nearly equal Young's modulus, however a large displacement was observed during the electromechanically driven operation, which was attributed to great difference in coefficient of thermal expansion of graphene paper and graphene oxide paper.

As bees and crystals (and people selling oranges in the market) know it for many years, lattices provide efficient structures for packing, covering, quantization and channel coding. In the recent years, interesting links were found between lattices and coding schemes for multi-terminal networks. This tutorial paper covers close to 20 years of my research in the area; of enjoying the beauty of lattice codes, and discovering their power in dithered quantization, dirty paper coding, Wyner-Ziv DPCM, modulo-lattice modulation, distributed interference cancelation, and more.

We consider a state-dependent full-duplex relay channel with the state of the channel non-causally available at only the relay. In the framework of cooperative wireless networks, some specific terminals can be equipped with cognition capabilities, i.e, the relay in our model. In the discrete memoryless (DM) case, we derive lower and upper bounds on channel capacity. The lower bound is obtained by a coding scheme at the relay that consists in a combination of codeword splitting, Gel'fand-Pinsker binning, and a decode-and-forward scheme. The upper bound is better than that obtained by assuming that the channel state is available at the source and the destination as well. For the Gaussian case, we also derive lower and upper bounds on channel capacity. The lower bound is obtained by a coding scheme which is based on a combination of codeword splitting and Generalized dirty paper coding. The upper bound is also better than that obtained by assuming that the channel state is available at the source, the relay, and the destination. The two bounds meet, and so give the capacity, in some special cases for the degraded Gaussian case., Corrected typos and added references w.r.t. the first version. Paper also published in proc. of IEEE Information Theory Workshop 2008 (6 pages, 3 figures)

Wyner-Ziv coding (WZC) is a compression technique which uses decoder side-information to help reconstruction. The vector WZC is a key element to solve the vector Gaussian CEO problem for sensor networks. Compared with the scalar CEO problem, in the vector CEO problem sensor node array is used by each agent and a vector source is to be estimated. In this paper, we present a nested-lattice based vector WZC structure, which is a generalization of the scalar WZC proposed by Zamir et al. The key is to explore the duality between the WZC problem and the dirty paper coding (DPC) problem. This coding structure is shown to be able to achieve the Berger-Tung sum rate, which is the best known compression rate for the CEO problem.

In this paper we consider the effect of channel estimation error on the capacity region of MIMO Gaussian broadcast channels. It is assumed that the receivers and the transmitter have (the same) estimates of the channel coefficients (i.e., the feedback channel is noiseless). We obtain an achievable rate region based on the dirty paper coding scheme. We show that this region is given by the capacity region of a dual multi-access channel with a noise covariance that depends on the transmit power. We explore this duality to give the asymptotic behavior of the sum-rate for a system with a large number of user, i.e., n rarr infin. It is shown that as long as the estimation error is of fixed (w.r.t n) variance, the sum-capacity is of order M log log n, where M is the number of antennas deployed at the transmitter. We further obtain the sum-rate loss due to the estimation error. Finally, we consider a training-based scheme for block fading MISO Gaussian broadcast channels. We find the optimum length of the training interval as well as the optimum power used for training in order to maximize the achievable sum-rate.

The paper provides a proof of the converse for the capacity region of the Gaussian MIMO broadcast channel under total average transmit power constraint. The proof uses several ideas from earlier works on the problem including the recent converse proof by Weingarten, Steinberg and Shamai. First the duality between Gaussian multiple access and broadcast channels is employed to show that every point on the boundary of the dirty paper coding region can be represented as the optimal solution to a convex optimization problem. Using the optimality conditions for this convex problem, a degraded broadcast channel is constructed for each point. It is then shown that the capacity region for this degraded broadcast channel contains the capacity region of the original channel. Moreover, the same point lies on the boundary of the dirty paper coding region for this degraded channel. Finally, the standard entropy power inequality is used to show that this point lies on the boundary of the capacity region of the degraded channel as well and consequently it is on the boundary of the capacity region of the original channel.

This paper proposes a new modulation method for an uncoded cognitive transmission (secondary user transmission) in presence of a Primary User (PU) for the AWGN channel. Interference of the PU is assumed to be known at the transmitter of Cognitive User (CU) non-causally. Based on this knowledge, for the design of the modulator and demodulator of the CU, a symbol by symbol approach is studied which can fulfill the coexistence conditions of the CU and the PU of the band. In this scheme, the modulator and demodulator of CU are designed jointly by solving an optimization problem to mitigate the interference of the PU and minimize the symbol error probability (P e ) in CU's communication link without increasing the symbol error probability (P e ) of the PU. The proposed method is a modulation approach in a single (complex-valued) dimension rather than a high dimensional coding scheme. Although this one-dimensional method is not capacity achieving, we show it still has a remarkable performance with low amount of complexity. An implementation algorithm for our modulation method is also presented and the performance of this method is evaluated by experimental results.

We study the two-user one-eavesdropper discrete memoryless compound wiretap channel, where the transmitter sends a common confidential message to both users, which needs to be kept perfectly secret from the eavesdropper. We provide a new achievable secrecy rate which is shown to be potentially better than the best known lower bound for the secrecy capacity of this compound wiretap channel. We next consider the two-user one-eavesdropper Gaussian multiple-input multiple-output (MIMO) compound wiretap channel. We obtain an achievable secrecy rate for the Gaussian MIMO compound wiretap channel by using dirty-paper coding (DPC) in the achievable scheme we provided for the discrete memoryless case. We show that the corresponding achievable secrecy rate achieves at least half of the secrecy capacity of the two-user one-eavesdropper Gaussian MIMO wiretap channel. We also obtain the secrecy capacity of the two-user one-eavesdropper Gaussian MIMO compound wiretap channel when the eavesdropper is degraded with respect to one of the two users.

We consider the problem of information embedding where the encoder modifies a white Gaussian host signal in a power-constrained manner to encode the message, and the decoder recovers both the embedded message and the modified host signal. This extends the recent work of Sumszyk and Steinberg to the continuous-alphabet Gaussian setting. We show that a dirty-paper-coding based strategy achieves the optimal rate for perfect recovery of the modified host and the message. We also provide bounds for the extension wherein the modified host signal is recovered only to within a specified distortion. When specialized to the zero-rate case, our results provide the tightest known lower bounds on the asymptotic costs for the vector version of a famous open problem in distributed control -- the Witsenhausen counterexample. Using this bound, we characterize the asymptotically optimal costs for the vector Witsenhausen problem numerically to within a factor of 1.3 for all problem parameters, improving on the earlier best known bound of 2., Comment: 19 pages, 7 figures. Presented at ITW'10. Submitted to IEEE Transactions on Information Theory

A generalization of the Gaussian dirty-paper problem to a multiple access setup is considered. There are two additive interference signals, one known to each transmitter but none to the receiver. The rates achievable using Costa's strategies (i.e. by a random binning scheme induced by Costa's auxiliary random variables) vanish in the limit when the interference signals are strong. In contrast, it is shown that lattice strategies ("lattice precoding") can achieve positive rates independent of the interferences, and in fact in some cases - which depend on the noise variance and power constraints - they are optimal. In particular, lattice strategies are optimal in the limit of high SNR. It is also shown that the gap between the achievable rate region and the capacity region is at most 0.167 bit. Thus, the dirty MAC is another instance of a network setup, like the Korner-Marton modulo-two sum problem, where linear coding is potentially better than random binning. Lattice transmission schemes and conditions for optimality for the asymmetric case, where there is only one interference which is known to one of the users (who serves as a "helper" to the other user), and for the "common interference" case are also derived. In the former case the gap between the helper achievable rate and its capacity is at most 0.085 bit., 42 pages, 14 figures

The presented study concerns the application of the pulse-echo ultrasound measurement technique in determining the elastic properties of fibres suspended in water. The two kinds of fibre materials are investigated, nylon 6/6 fibres and softwood pulp fibres. The fibre mass fraction was 0.5 % for nylon and ranges from 0 % up to 1 % for softwood pulp. The ultrasonic measurements are performed in the frequency range of 2 -11 MHz. It is shown that the velocity dispersion of the ultrasound is small for each suspension sample. In obtaining the fibres longitudinal Young's modulus two methods are used, one based on phase velocity and one based on acoustic attenuation. It is found that both methods gives reasonable estimates of the longitudinal Young's modulus for nylon 6/6. For pulp fibres the determined Young's modulus is overestimated in comparison with earlier findings.

This paper introduces Chance Constrained Gaussian Process-Motion Planning (CCGP-MP), a motion planning algorithm for robotic systems under motion and state estimate uncertainties. The paper's key idea is to capture the variations in the distance-to-collision measurements caused by the uncertainty in state estimation techniques using a Gaussian Process (GP) model. We formulate the planning problem as a chance constraint problem and propose a deterministic constraint that uses the modeled distance function to verify the chance-constraints. We apply Simplicial Homology Global Optimization (SHGO) approach to find the global minimum of the deterministic constraint function along the trajectory and use the minimum value to verify the chance-constraints. Under this formulation, we can show that the optimization function is smooth under certain conditions and that SHGO converges to the global minimum. Therefore, CCGP-MP will always guarantee that all points on a planned trajectory satisfy the given chance-constraints. The experiments in this paper show that CCGP-MP can generate paths that reduce collisions and meet optimality criteria under motion and state uncertainties. The implementation of our robot models and path planning algorithm can be found on GitHub11https://github.com/jacobjj/gp_prob_planning.

The bilateral filter (BF) is widely used in image processing because it can perform denoising while preserving edges. It has disadvantages in that it is nonlinear, and its computational complexity and hardware resources are directly proportional to its window size. Thus far, several approximation methods and hardware implementations have been proposed to solve these problems. However, processing large-scale and high-resolution images in real time under severe hardware resource constraints remains a challenge. This paper proposes a real-time image denoising system that uses an FPGA based on the bilateral grid (BG). In the BG, a 2D image consisting of x- and y-axes is projected onto a 3D space called a "grid," which consists of axes that correlate to the x-component, y-component, and intensity value of the input image. This grid is then blurred using the Gaussian filter, and the output image is generated by interpolating the grid. Although it is possible to change the window size in the BF, it is impossible to change it on the input image in the BG. This makes it difficult to associate the BG with the BF and to obtain the property of suppressing the increase in hardware resources when the window radius is enlarged. This study demonstrates that a BG with a variable-sized window can be realized by introducing the window radius parameter wherein the window radius on the grid is always 1. We then implement this BG on an FPGA in a fully pipelined manner. Further, we verify that our design suppresses the increase in hardware resources even when the window size is enlarged and outperforms the existing designs in terms of computation speed and hardware resources., Comment: 7 pages, 12 figures, 2 tables, FPL 2021 (Full paper), Program and Abstract: https://whova.com/embedded/session/kScht29Q8lAG98UvGiEh7UVYTNeJssTMevISW%407S-oU%3D/1837318/, Slides: https://speakerdeck.com/hashi0203/an-fpga-based-fully-pipelined-bilateral-grid-for-real-time-image-denoising-fpl-2021, Movie: https://youtu.be/q5lxi7N-uX8, Profile: https://n-hassy.info

Takagi-Sugeno-Kang (TSK) fuzzy system with Gaussian membership functions (MFs) is one of the most widely used fuzzy systems in machine learning. However, it usually has difficulty handling high-dimensional datasets. This paper explores why TSK fuzzy systems with Gaussian MFs may fail on high-dimensional inputs. After transforming defuzzification to an equivalent form of softmax function, we find that the poor performance is due to the saturation of softmax. We show that two defuzzification operations, LogTSK and HTSK, the latter of which is first proposed in this paper, can avoid the saturation. Experimental results on datasets with various dimensionalities validated our analysis and demonstrated the effectiveness of LogTSK and HTSK., Comment: Int'l Joint Conf. on Neural Networks (IJCNN), Shenzhen, China, July 2021

This paper is concerned with the problem of recovering a structured signal from a relatively small number of corrupted random measurements. Sharp phase transitions have been numerically observed in practice when different convex programming procedures are used to solve this problem. This paper is devoted to presenting theoretical explanations for these phenomenons by employing some basic tools from Gaussian process theory. Specifically, we identify the precise locations of the phase transitions for both constrained and penalized recovery procedures. Our theoretical results show that these phase transitions are determined by some geometric measures of structure, e.g., the spherical Gaussian width of a tangent cone and the Gaussian (squared) distance to a scaled subdifferential. By utilizing the established phase transition theory, we further investigate the relationship between these two kinds of recovery procedures, which also reveals an optimal strategy (in the sense of Lagrange theory) for choosing the tradeoff parameter in the penalized recovery procedure. Numerical experiments are provided to verify our theoretical results., 34 pages, 6 figures

In this paper we analyze the necessary number of samples to estimate the gradient of any multidimensional smooth (possibly non-convex) function in a zero-order stochastic oracle model. In this model, an estimator has access to noisy values of the function, in order to produce the estimate of the gradient. We also provide an analysis on the sufficient number of samples for the finite difference method, a classical technique in numerical linear algebra. For $T$ samples and d dimensions, our information-theoretic lower bound is Ω(√d/T). We show that the finite difference method for a bounded-variance oracle has rate O(d4/3 / √T) for functions with zero third and higher order derivatives. These rates are tight for Gaussian oracles. Thus, the finite difference method is not minimax optimal, and therefore there is space for the development of better gradient estimation methods. A full version of this paper is accessible at: https://arxiv.org/pdf/2003.13881.pdf

In this paper we pursue the use of the Fourier transform for a general analysis of combinatorial optimization problems. While combinatorial optimization problems are defined by means of different notions like weights in a graph, set of numbers, distance between cities, etc., the Fourier transform allows to put all of them in the same framework, the Fourier coefficients. This permits its comparison looking for similarities and differences. Particularly, the Walsh transform has recently been used over pseudo-boolean functions in order to design new surrogate models in the black-box scenario and to generate new algorithms for the linkage discovery problem, among others, presenting very promising results. In this paper we focus on binary problems and the Walsh transform. After presenting the Walsh decomposition and some main properties, we compute the transform of the Unconstrained Binary Quadratic Problem and several particular cases of this problem such as the Max-Cut Problem and the Number Partitioning Problem. The obtained Walsh coefficients not only reinforce the similarities and differences among the problems which are known in the literature, but given a set of Walsh coefficients we can say whether or not they are produced by any of the problems analyzed. Finally, a geometrical interpretation of the space of Walsh coefficients with maximum order 2 and the subspace of each analyzed problem is presented., by Spanish Ministry of Science and Innovation through the projects PID2019-104966GB-I00/AEI/10.13039/501100011033, PID2019-104933GBI00/ AEI/10.13039/501100011033, PID2019-106453GA-I00/AEI/10.13039/501100011033 and BCAM Severo Ochoa accreditation SEV-2017-0718; and by the Basque Government through the program BERC 2018-2021 and the projects IT1244-19 and IT-1252-19; and by UPV/EHU through the project GIU20/054. Imanol holds a grant from the Department of Education of the Basque Government (PRE_2020_2_0166).

International audience; The paper describes how chipless RFID tags in motion can be read at higher distances than what is currently done without taking into account displacement. The movement results in the appearance of a tag backscattered field containing frequencies different from those emitted by the reader according to the Doppler effect. The use of these frequencies not generated by the reader considerably increases the reading distance of the tag, especially in a real environment. This paper is focused on a rotational movement of the tag. The proof of concept is carried out on elementary chipless tags which will be read up to 10 m.

The new concept named reconfigurable intelligent surfaces (RIS) is becoming an appealing enabler due to its uniqueness with having low hardware complexity and low power consumption advantages simultaneously. In this paper, an RIS-aided vehicular Adhoc network (VANET) is considered, where the beacon vehicle is enabled with a passive RIS, the communication links between the beacon vehicle and client vehicle caused due to the multipath fading effects, are modeled with Fox’s H-function distribution. This paper first models the inter-vehicle links for the given system setup and then investigates the outage probability and effective rate as performance metrics. More specifically, the unsupervised expectation-maximization (EM) algorithm is consequently used to characterize the distribution of the received signal-to-noise ratio (SNR) at the client vehicle, which is modeled as the mixture of Gaussian (MoG) distribution. The accuracy of our approach is further validated with the Kolmogorov-Smirnov (KS) goodness of fit test. The MoG-based approach successfully tackles the RIS-enabled inter-vehicle communication with an easy, accurate, and tractable solution compared to the widely used central limit theorem (CLT) method. It leads to the closed-form outage probability and effective rate expressions.

Virtualized Radio Access Network (vRAN) is one of the key enablers of future wireless networks as it brings the agility to the radio access network (RAN) architecture and offers degrees of design freedom. Yet, it also creates a challenging problem on how to design the functional split configuration. In this paper, a deep reinforcement learning approach is proposed to optimize function splitting in vRAN. A learning paradigm is developed that optimizes the location of functions in the RAN. These functions can be placed either at a central/cloud unit (CU) or a distributed unit (DU). This problem is formulated as constrained neural combinatorial reinforcement learning to minimize the total network cost. In this solution, a policy gradient method with Lagrangian relaxation is applied that uses a stacked long short-term memory (LSTM) neural network architecture to approximate the policy. Then, a sampling technique with a temperature hyperparameter is applied for the inference process. The results show that our proposed solution can learn the optimal function split decision and solve the problem with a $0.4\%$ optimality gap. Moreover, our method can reduce the cost by up to $320\%$ compared to a distributed-RAN (D-RAN). We also conclude that altering the traffic load and routing cost does not significantly degrade the optimality performance., This paper has been accepted in IEEE International Conference on Communications Workshops (ICC Workshops) 2021

This paper presents a model-free reinforcement learning (RL) algorithm to synthesize a control policy that maximizes the satisfaction probability of linear temporal logic (LTL) specifications. Due to the consideration of environment and motion uncertainties, we model the robot motion as a probabilistic labeled Markov decision process with unknown transition probabilities and unknown probabilistic label functions. The LTL task specification is converted to a limit deterministic generalized B\"uchi automaton (LDGBA) with several accepting sets to maintain dense rewards during learning. The novelty of applying LDGBA is to construct an embedded LDGBA (E-LDGBA) by designing a synchronous tracking-frontier function, which enables the record of non-visited accepting sets without increasing dimensional and computational complexity. With appropriate dependent reward and discount functions, rigorous analysis shows that any method that optimizes the expected discount return of the RL-based approach is guaranteed to find the optimal policy that maximizes the satisfaction probability of the LTL specifications. A model-free RL-based motion planning strategy is developed to generate the optimal policy in this paper. The effectiveness of the RL-based control synthesis is demonstrated via simulation and experimental results., Comment: arXiv admin note: text overlap with arXiv:2109.02791

This paper investigates the classical modulo two sum problem in source coding, but with a common observation: a transmitter observes $(X,Z)$, the other transmitter observes $(Y,Z)$, and the receiver wants to compute $X \oplus Y$ without error. Through a coupling argument, this paper establishes a new lower bound on the sum-rate when $X-Z-Y$ forms a Markov chain., Comment: Accepted for presentation at IEEE ITW 2020

Previously, dynamic-assignment Blahut-Arimoto (DAB) was used to find capacity-achieving probability mass functions (PMFs) for binomial channels and molecular channels. As it turns out, DAB can efficiently identify capacity-achieving PMFs for a wide variety of channels. This paper applies DAB to power-constrained (PC) additive white Gaussian Noise (AWGN) Channels and amplitude-constrained (AC) AWGN Channels.This paper modifies DAB to include a power constraint and finds low-cardinality PMFs that approach capacity on PC-AWGN Channels. While a continuous Gaussian PDF is well-known to be capacity-achieving on the PC-AWGN channel, DAB identifies low-cardinality PMFs within 0.01 bits of the mutual information provided by a Gaussian PDF. Recall the results of Ozarow and Wyner requiring a constellation cardinality of ⌈2C+1⌉ to approach capacity C to within the asymptotic shaping loss of 1.53 dB at high SNR. PMF’s found by DAB approach capacity with essentially no shaping loss with cardinality less than 2C+1.2. As expected, DAB’s numerical approach identifies PMFs with better mutual information vs. SNR performance than the analytical approaches to finite-support constellations examined by Wu and Verdu.This paper also uses DAB to find capacity-achieving PMFs with small cardinality support sets for AC-AWGN Channels. The resulting evolution of capacity-achieving PMFs as a function of SNR is consistent with the approximate cardinality transition points of Sharma and Shamai.

In this paper, we address a statistical model extension of multichannel nonnegative matrix factorization (MNMF) for blind source separation, and we propose a new parameter update algorithm used in the sub-Gaussian model. MNMF employs full-rank spatial covariance matrices and can simulate situations in which the reverberation is strong and the sources are not point sources. In conventional MNMF, spectrograms of observed signals are assumed to follow a multivariate Gaussian distribution. In this paper, first, to extend the MNMF model, we introduce the multivariate generalized Gaussian distribution as the multivariate sub-Gaussian distribution. Since the cost function of MNMF based on this multivariate sub-Gaussian model is difficult to minimize, we additionally introduce the joint-diagonalizability constraint in spatial covariance matrices to MNMF similarly to FastMNMF, and transform the cost function to the form to which we can apply the auxiliary functions to derive the valid parameter update rules. Finally, from blind source separation experiments, we show that the proposed method outperforms the conventional methods in source-separation accuracy., 5 pages, 3 figures, To appear in the Proceedings of the 28th European Signal Processing Conference (EUSIPCO 2020). arXiv admin note: text overlap with arXiv:2002.00579

Massive MIMO (Multiple Input Multiple Output) has demonstrated as a potential candidate for 5G-and-beyond wireless networks. Instead of using Gaussian signals as most of the previous works, this paper makes a novel contribution by investigating the transmission quality of image data by utilizing the Massive MIMO technology. We first construct a framework to decode the image signal from the noisy received data in the uplink Massive MIMO transmission by utilizing the alternating direction method of multipliers (ADMM) approach. Then, a low-pass filter is exploited to enhance the efficiency of the remaining noise and artifacts reduction in the recovered image. Numerical results demonstrate the necessity of a post-filtering process in enhancing the quality of image recovery., 6 pages, 2 figures. The paper was presented at ICCE 2020

The problem of real-time control and optimization of components' routing in discrete manufacturing plants, where distinct items must undergo a sequence of jobs, is considered. This problem features a large number of discrete control inputs and the presence of temporal-logic constraints. A new approach is proposed, adopting a shift of perspective with respect to previous contributions, from a Eulerian system model that tracks the state of plant nodes, to a Lagrangian model that tracks the state of each part being processed. The approach features a hierarchical structure. At a higher level, a predictive receding horizon strategy allocates a path across the plant to each part in order to minimize a chosen cost criterion. At a lower level, a path following logic computes the control inputs in order to follow the assigned path, while satisfying all constraints. The approach is tested here in simulations, reporting extremely good performance as measured by closed-loop cost function values and computational efficiency, also with very large prediction horizon values. These features pave the way to a number of subsequent research steps, which will culminate with the experimental testing on a pilot plant., Comment: Extended version of paper in print in the Proceedings of the 59th IEEE Conference on Decision and Control, Jeju Island, Republic of Korea - December 14th-18th 2020. Copyright of original conference paper owned by IEEE

Within a business context, anomalies can be viewed as indicators for inefficiencies or fraud, which impact upon product quality and customer satisfaction. The development of approaches to monitor, detect and predict anomalous business processes remains an important research topic. In this paper, we propose a method, combining Discrete-time Markov chains (DTMCs) and hitting probabilities (HP), for detecting anomalies occurring in the execution of business processes. Our method extends standard DTMCs to be able to estimate the probability of occurring for a process instance even though it is partially recorded (i.e., the initial executions are missing). The proposed method, denoted as HPDTMC, does not rely on prior knowledge about anomalies and the business process and can be trained on datasets already consisting of anomalies. A Sidak correction is applied to balance the probability of instances of varying length since naturally, process instances with more executions have lower sequence probability and more likely to be detected as anomalies by using DTMCs. We demonstrate the effectiveness of the method by evaluating it on two artificial datasets and one real-life dataset against seven classic anomaly detection methods. In the experiments, our approach reached an F 1 score of 0.904 on average. Moreover, the proposed method outperforms competitors under noisy conditions. The main contribution of this paper is the proposed noise-robust method which is able to detect fully or partially recorded process instances of varying lengths.

Designing and developing automated systems to detect and grade Diabetic Retinopathy (DR) is one of the recent research areas in the world of medical image applications since it is considered one of the main causes of total blindness for people who have diabetes in the mid-age. In this paper, a complete pipeline for retinal fundus images processing and analysis has been described, implemented and evaluated. This pipeline has three main stages: (i) image pre-processing, (ii) features extraction and (iii) classification. In the first stage, the image has been pre-processed using different transformations to standardize the images and to enhance the images quality. It has been proven that Gaussian filtering is quite effective in this context to enhance the images contrast. In the second and third stage, the convolution neural network (CNN), one of the best neural network architecture for image analysis applications, has been used. The concept of transfer learning and fine tuning have been advocated in this paper and applied for ResNet18 using the publicly available Kaggle dataset. The problem of DR diagnosis has been handled as a multi-class classification problem where there are five levels of the disease severity (0 – No DR, 1 – Mild, 2 – Moderate, 3 – Severe, 4 – Proliferative DR). The final model has achieved accuracy of 70 %, recall of 50% and specificity of 88% outperforming other models built from scratch with less training time and proving the efficiency of transfer learning in this context. The training process has considered the problem of imbalanced dataset using two different ways and it has been discovered that using imbalanced dataset sampler is a very efficient solution. The final model developed in this research could be used as the main unit for a computer aided system to be hosted online for DR detection and diagnosis.

It has recently been shown that periodicity in discrete-time data can be analyzed using Ramanujan sums and associated dictionaries. This paper explores the role of dictionary learning methods in the context of period estimation and periodic signal representation using dictionaries. It is shown that a well-known dictionary learning algorithm, namely K-SVD, is able to learn Ramanujan and Farey periodicity dictionaries from the noisy, sparse coefficient data generated from them without imposing any periodicity structure in the learning stage. This similarity between the learned dictionary and the underlying original periodicity dictionary reaffirms the power of the K-SVD in predicting the right dictionary from data without explicit application-specific constraints. The paper also examines how the choice of different parameter values affect the similarity of the learned dictionary to the underlying dictionary. Two versions of K-SVD along with different initializations are analyzed for their effect on representation and denoising error for the data.

Coupling single-carrier networks into multi-carrier energy systems (MESs) has recently become more important. Various formulations of the single-carrier load flow problem (LFP) are used. Moreover, different coupling models lead to different integrated systems of equations for the LFP of MESs. Both could affect the convergence of the Newton-Raphson method (NR) used to solve the nonlinear system of equations. This paper considers the steady-state LFP for example MESs of varying size, with various coupling models and topologies, and various formulations in the single-carrier parts. Based on numerical experiments, this paper compares the convergence behavior of NR for the various single- and multi-carrier systems. For these examples, NR of the steady-state LFP of the MESs is independent of the size of the network and of the coupling, and NR requires at most as many iterations as the slowest single-carrier network.

There are numerous papers analyzing the theoretical background of compressive sensing (CS), but a few practical implementations exist due to the realization complexity. In this paper, a realization of a system for CS of analog one-dimensional signals in a shift- invariant subspace is proposed. We use statistical compressive sensing that allows efficient sampling of signals that follow some statistical distribution. In fact, principal component analysis is used to obtain a small number of principal components in which the majority of signal’s energy is compacted. The signal is demodulated using the obtained principal components, which provides optimal measurement procedure in the mean-square error sense. Orthonormality allows for a linear reconstruction which is perfectly suited for a real-time embedded system implementation. Due to hardware realization constraints, the adaptive demodulating signal is quantized. We provide qualitative and quantitative validations based on simulations of the proposed CS system realization. Furthermore, we validate performance of the system when additive white Gaussian noise is present in the measurements.

In this paper, the algorithm for finding a Hamiltonian cycle in an intuitionistic fuzzy graph (IFG) is proposed, based on the theories of intuitionistic fuzzy sets (IFSs) and of index matrices (IMs). The aim of the paper is to extend the algorithm to find a fuzzy Hamiltonian cycle (FHC) in an IFG to the intuitionistic fuzzy (IFHC) using the IFSs and IMs concepts. An intuitionistic fuzzy graph example about network of Wizz air airlines is modeled by the extended IM to illustrate the proposed algorithm. In the paper also are introduced for the first time three index-type operations over IMs.

The paper summarizes the theoretical background of the accelerated aging test method of electrical insulating materials and its reliability by using the Arrhenius approach. Arrhenius equation presents generally accepted practice for assessing reliability, life-time, and degree of decomposition of a material. By demonstrating a typical aging experiment in electrical engineering, the paper explains the test design, selection method, and proof of the testing concept for measuring activation energy in practice. On the base of measured activation energy, the paper further deals with the evaluation of obtained data and their suitability for use in the Arrhenius equation. Moreover, the paper also observes the temperature dependence of different interaction rates among chemical reactions and its influence on the material during the thermal decomposition.

With the advent of real-time PMU data acquisition technology, the possibility of solutions to several instability problems in power system has increased. However, PMUs may undergo different data quality issues like recording bad data or missing data. Some paper mentions about 5-10% of missing samples in some historical PMU’s dataset. This paper assumes 0-10% of missing phasor samples by randomly deleting measurements and explores imputation methods of handling missing data in real time. The simulation is carried out in a DT-based stability prediction and one-shot control scheme of WECC’s 176-bus model. Several control performances are evaluated to decide a useful method of missing data recovery for the response based one shot control scheme. A PMU data quality issue is not limited to missing samples only but also interference with noises. Later part of this paper performs simulation considering noisy phasor measurements. A 45 dB of Gaussian distributed noise is deliberately added to phasor samples and simulation is performed with different DT indices and thresholds for real time stability prediction and control actuation.

Rapid and sudden fluctuations of phase and amplitude in Global Navigation Satellite System (GNSS) signals due to diffraction of the ionosphere phase components when signals passing through small-scale irregularities (less than hundreds meters) are commonly so-called ionospheric scintillation. The aim of the paper is to analyze the implementation and compare the performance of different phase detrending algorithms to improve scintillation monitoring. Three different phase detrending methods, namely, three cascaded second-order high pass filters, six order Butterworth filter conducted by cascading six first-order high pass Butterworth filters, and Fast Iterative Filter (FIF) are considered in this paper. The study exploits real GNSS signals (GPS L1, Galileo E1b) affected by significant phase scintillation effects, collected in early September 2017 at Brazilian Centro de Radioastronomia e Astrofisica Mackenzie (CRAAM) monitoring station and at Adventdalen (Svalbard, Norway) research station. In this study, a softwaredefined radio (SDR) based GNSS receiver is used to process GNSS signals and to implement the aforementioned detrending algorithms.

This paper presents a sensitivity-based approach for the placement of distributed energy resources (DERs) in power systems. The approach is based on the fact that most planning studies utilize some form of optimization, and solutions to these optimization problems provide insights into the sensitivity of many system variables to operating conditions and constraints. However, most of the existing sensitivity-based planning criteria do not capture ranges of effectiveness of these solutions (i.e., ranges of the effectiveness of Lagrange multipliers). The proposed method detects the ranges of the effectiveness of Lagrange multipliers and uses them to determine optimal solution alternatives. Profiles for existing generation and loads, and transmission constraints are taken into consideration. The proposed method is used to determine the impacts of DERs at different locations, in the presence of a stochastic element (load variability). This method consists of sequentially calculating Lagrange multipliers of the dual solution of the optimization problem for various load buses for all load scenarios. Optimal sizes and sites of resources are jointly determined in a sequential manner based on the validity of active constraints. The effectiveness of the proposed method is demonstrated through several case studies on various test systems including the IEEE reliability test system (IEEE RTS), the IEEE 14, and 30 bus systems. In comparison with conventional sensitivity-based approaches (i.e., without considering ranges of validity of Lagrange multipliers), the proposed approach provides more accurate results for active constraints., Comment: This paper has been accepted and presented in PMAPS 2020 conference

The rapid growth of mobile devices has spurred the development of crowd-learning applications, which rely on users to collect, report and share real-time information. A critical factor of crowd-learning is information freshness, which can be measured by a metric called age-of-information (AoI). Moreover, recent advances in machine learning and abundance of historical data have enabled crowd-learning service providers to make precise predictions on user arrivals, data trends and other predictable information. These developments lead to a fundamental question: Can we improve information freshness with predictions in mobile crowd-learning? In this paper, we show that the answer is affirmative. Specifically, motivated by the age-optimal Round-Robin policy, we propose the so-called “periodic equal spreading” (PES) policy. Under the PES policy, we first reveal a counter-intuitive insight that the frequency of prediction should not be too often in terms of AoI improvement. Further, we analyze the AoI performances of the proposed PES policy and derive upper bounds for the average age under i.i.d. and Markovian arrivals, respectively. In order to evaluate the AoI performance gain of the PES policy, we also derive two closed form expressions for the average age under uncontrolled i.i.d. and Markovian arrivals, which could be of independent interest. Our results in this paper serve as a first building block towards understanding the role of predictions in mobile crowd-learning.

The application of multiple target tracking algorithms has exponentially increased during the last two decades. Recently, model-free approaches, such as Gaussian process regression and convolutional neural networks, have been developed for target tracking. This paper presents a simulation-based study on the practical aspects of a very promising and recently proposed Gaussian process method, namely the Gaussian process motion tracker [1]. The paper also provides design guidelines on the various aspects of the above-mentioned tracking method.

Článek se zabývá odhadem stavu nelineárních stochastických systémů s aditivními gaussovskými šumy pomocí gaussovských filtrů založených na numerických integračních pravidlech. Filtry byly odvozeny za předpokladu sdružené gaussovosti predikcí stavu a měření, který je však v případě nelinearity systému porušen. Takovéto porušení lze stěží monitorovat standardními gaussovskými filtry, které přegenerovávají sigma body pro každou použítou numerickou integraci za účelem zvýšení jejich variance s cílem respektovat působení aditivních šumů. Článek navrhuje algoritmus filtru se stochastickou integrací, který místo jejich opětovného vygenerování sigma body modifikuje a tudíž umožňuje opakované využití bodů v dalších časových krocích. Rozložení bodů tak může uchovávat více informace než pouze první dva momenty, jak to je běžné u standardních gaussovských filtrů. Nově získaná informace se pak následně využije ze účelem monitorování splnění gaussovských předpokladů. V okamžiku, kdy je předpoklad porušen, může filtr změnit aktivně své chování. Vedlejším důsledkem opakovaného použití bodů je podstatné snížení výpočetních nároků v porovnáním se standardním filtrem se stochastickou integrací. The paper deals with the state estimation of nonlinear stochastic systems with additive Gaussian noises by means of the Gaussian filters leveraging numerical integration rules. The filters were derived under the assumption of the joint state and measurement predictive density being Gaussian, which is violated by the system nonlinearity. Such violation can hardly be monitored by the standard Gaussian filters, which re-generate a new set of points for each involved numerical integration to accommodate their variance increase due to the additive noises. The paper proposes a stochastic integration filter algorithm that modifies the points instead of their resampling and thus admits reusing the points in the next time steps. The distribution of the points can thus bear more information than just the first two moments in case of the standard Gaussian filters. The acquired information is then utilized for the Gaussian assumption monitoring purposes. In the event of the assumption violation, the filter may change its behavior. As a by-product of reusing the points, the computational costs of the proposed filter are significantly reduced compared to the standard stochastic integration filter.

Consider a channel ${\bf Y}={\bf X}+ {\bf N}$ where ${\bf X}$ is an $n$-dimensional random vector, and ${\bf N}$ is a Gaussian vector with a covariance matrix ${\bf \mathsf{K}}_{\bf N}$. The object under consideration in this paper is the conditional mean of ${\bf X}$ given ${\bf Y}={\bf y}$, that is ${\bf y} \to E[{\bf X}|{\bf Y}={\bf y}]$. Several identities in the literature connect $E[{\bf X}|{\bf Y}={\bf y}]$ to other quantities such as the conditional variance, score functions, and higher-order conditional moments. The objective of this paper is to provide a unifying view of these identities. In the first part of the paper, a general derivative identity for the conditional mean is derived. Specifically, for the Markov chain ${\bf U} \leftrightarrow {\bf X} \leftrightarrow {\bf Y}$, it is shown that the Jacobian of $E[{\bf U}|{\bf Y}={\bf y}]$ is given by ${\bf \mathsf{K}}_{{\bf N}}^{-1} {\bf Cov} ( {\bf X}, {\bf U} | {\bf Y}={\bf y})$. In the second part of the paper, via various choices of ${\bf U}$, the new identity is used to generalize many of the known identities and derive some new ones. First, a simple proof of the Hatsel and Nolte identity for the conditional variance is shown. Second, a simple proof of the recursive identity due to Jaffer is provided. Third, a new connection between the conditional cumulants and the conditional expectation is shown. In particular, it is shown that the $k$-th derivative of $E[X|Y=y]$ is the $(k+1)$-th conditional cumulant. The third part of the paper considers some applications. In a first application, the power series and the compositional inverse of $E[X|Y=y]$ are derived. In a second application, the distribution of the estimator error $(X-E[X|Y])$ is derived. In a third application, we construct consistent estimators (empirical Bayes estimators) of the conditional cumulants from an i.i.d. sequence $Y_1,...,Y_n$.

Extensive empirical evidence reveals that, for a wide range of different learning methods and data sets, the risk curve exhibits a double-descent (DD) trend as a function of the model size. In our recent coauthored paper [Deng et al., ’19], we proposed simple binary linear classification models and showed that the test error of gradient descent (GD) with logistic loss undergoes a DD. In this paper, we complement these results by extending them to GD with square loss. We show that the DD phenomenon persists, but we also identify several differences compared to logistic loss. This emphasizes that crucial features of DD curves (such as their transition threshold and global minima) depend both on the training data and on the learning algorithm. We further study the dependence of DD curves on the size of the training set. Similar to [Deng et al., ’19] our results are analytic: we plot the DD curves by first deriving sharp asymptotics for the test error under Gaussian features. Albeit simple, the models permit a principled study, the outcomes of which theoretically corroborate related empirical findings occurring in more complex learning tasks.

In this paper, we consider the problem of recovering a binary phase shift keying (BPSK) modulated signal in a massive multiple-input-multiple-output (MIMU) system. The recovery process is done using the box-relaxation method, in which the discrete set {±I} n is relaxed to the convex set [-I,+I] n and solved by a convex optimization program followed by hard thresholding. We assume that the system has a Gaussian channel matrix with one sided left correlation. The entries of the noise vector are assumed to be independent and identically distributed (iid) zero-mean Gaussian. In this work, we precisely characterize the mean squared error (MSE) and the bit error rate (BER) of the box-relaxation decoder in the asymptotic regime where both dimensions grow simultaneously large at a fixed ratio. Numerical simulations validate the theoretical expressions derived in this paper.