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Paper waste in the mockups design with regular, irregular, and amorphous patterns is a critical problem in digital printing presses. Paper waste reduction directly impacts production costs, generating business and environmental benefits. This problem can be mapped to the two-dimensional irregular bin-packing problem. In this paper, an iterated local search algorithm using a novel neighborhood structure to detect overlaps between amorphous shapes is introduced. This algorithm is used to solve the paper waste problem, modeled as one 2D irregular bin-packing problem. The experimental results show that this approach works efficiently and effectively to detect and correct the overlaps between regular, irregular, and amorphous figures.

To facilitate better interpretation of dielectric response measurements—thereby directing numerical evidence for condition assessments of oil-paper-insulated equipment in high-voltage alternating current (HVAC) transmission systems—a novel approach is presented to estimate the parameters in the extended Debye model (EDM) using wideband frequency domain spectroscopy (FDS). A syncretic algorithm that integrates a genetic algorithm (GA) and the Levenberg-Marquardt (L-M) algorithm is introduced in the present study to parameterize EDM using the FDS measurements of a real-life 126 kV oil-impregnated paper (OIP) bushing under different controlled temperatures. As for the uncertainty of the EDM structure due to variable branch quantity, Akaike’s information criterion (AIC) is employed to determine the model orders. For verification, comparative analysis of FDS reconstruction and results of FDS transformation to polarization–depolarization current (PDC)/return voltage measurement (RVM) are presented. The comparison demonstrates good agreement between the measured and reconstructed spectroscopies of complex capacitance and tan δ over the full tested frequency band (10−4 Hz–103 Hz) with goodness of fit over 0.99. Deviations between the tested and modelled PDC/RVM from FDS are then discussed. Compared with the previous studies to parameterize the model using time domain dielectric responses, the proposed method solves the problematic matching between EDM and FDS especially in a wide frequency band, and therefore assures a basis for quantitative insulation condition assessment of OIP-insulated apparatus in energy systems.

This paper studies the second-order coding rates for memoryless channels with a state sequence known non-causally at the encoder. In the case of finite alphabets, an achievability result is obtained using constant-composition random coding, and by using a small fraction of the block to transmit the empirical distribution of the state sequence. For error probabilities less than 0.5, it is shown that the second-order rate improves on an existing one based on independent and identically distributed random coding. In the Gaussian case (dirty paper coding) with an almost-sure power constraint, an achievability result is obtained using random coding over the surface of a sphere, and using a small fraction of the block to transmit a quantized description of the state power. It is shown that the second-order asymptotics are identical to the single-user Gaussian channel of the same input power without a state.

The condition of the transformer insulation has an impact on the transformer’s performance during the operation. The aging of the oil-impregnated cellulose insulation and the associated loss of mechanical strength are the important factors that limit the life of expectancy of a transformer. To determine the condition of the oil-impregnated cellulose insulation, the Degree of Polymerization (DP) parameter is commonly used. An Adaptive Neuro-Fuzzy Inference System (ANFIS) has been developed to predict the DP Value by the chemical characteristics and dissolved gas parameters (acidity, interfacial tension, CO, CO 2 , breakdown voltage, and water content of the oil). This paper generates some algorithms which are based on the input space partitioning method to generate rules (grid partition or subtractive clustering) and data is normalized or not. The estimation result has been observed and evaluated to provide that the ANFIS algorithm is suitable to estimate insulation condition on field operating transformers.

In this work, we describe a new approach that uses deep neural networks (DNN) to obtain regularization parameters for solving inverse problems. We consider a supervised learning approach, where a network is trained to approximate the mapping from observation data to regularization parameters. Once the network is trained, regularization parameters for newly obtained data can be computed by efficient forward propagation of the DNN. We show that a wide variety of regularization functionals, forward models, and noise models may be considered. The network-obtained regularization parameters can be computed more efficiently and may even lead to more accurate solutions compared to existing regularization parameter selection methods. We emphasize that the key advantage of using DNNs for learning regularization parameters, compared to previous works on learning via optimal experimental design or empirical Bayes risk minimization, is greater generalizability. That is, rather than computing one set of parameters that is optimal with respect to one particular design objective, DNN-computed regularization parameters are tailored to the specific features or properties of the newly observed data. Thus, our approach may better handle cases where the observation is not a close representation of the training set. Furthermore, we avoid the need for expensive and challenging bilevel optimization methods as utilized in other existing training approaches. Numerical results demonstrate the potential of using DNNs to learn regularization parameters., 27 pages, 16 figures

Oil paper insulation is the main insulation form of large transformers. Partial discharge(PD) in oil paper insulation will not only damage the insulation performance, but also is the precursor and manifestation of insulation deterioration. Weibull distribution parameter is an important characteristic parameter of PD. In this paper a method is proposed to extract the characteristic parameter of PD based on the three-parameter Weibull model, then gray wolf optimization algorithm and particle swarm optimization algorithm are used to calculate the parameters of the model. The result shows that the two algorithms can get the parameters quickly and accurately, and the model can effectively reflect the characteristics of the $H_{n}(q)$ spectrum of PD times.

We propose a density evolution based dirty-paper code design framework that combines trellis coded quantization with multi-level low-density parity-check (LDPC) codes. Unlike existing design techniques based on Gaussian approximation and EXIT charts, the proposed framework tracks the empirically collected log-likelihood ratio (LLR) distributions at each iteration, and employs density evolution and differential evolution algorithms to design each LDPC component code. The performance of the dirty-paper codes designed using the proposed method comes within 0.37 dB of the theoretical limit at 1 bit per sample transmission rate, achieving an 0.21 dB gain over the best known result.

In 1998 Crandall introduced a method based on coding theory to secretly embed a message in a digital support such as an image. Later, in 2005, Fridrich et al. improved this method to minimize the distortion introduced by the embedding; a process called wet paper. However, as previously emphasized in the literature, this method can fail during the embedding step. Here we find sufficient and necessary conditions to guarantee a successful embedding, by studying the dual distance of a linear code. Since these results are essentially of combinatorial nature, they can be generalized to systematic codes, a large family containing all linear codes. We also compute the exact number of embedding solutions and point out the relationship between wet paper codes and orthogonal arrays.

We propose a robust close-to-capacity dirty-paper coding (DPC) design framework in which multi-level low density parity check (LDPC) codes and trellis coded quantization (TCQ) are employed as the channel and source coding components, respectively. The proposed design framework is robust in the sense that it yields close to capacity solutions in the high-, medium-, and low-rate regimes. This is in contrast to existing practical DPC schemes that perform well only in one or two of these regimes, but not all three. We design codes for transmission rates of 0.5, 1.0, 1.5, and 2.0 bits/sample (b/s) using one, two, three, and four LDPC levels; at a block length of 2×105, the codes perform 0.95, 0.58, 0.55, and 0.54 dB from the corresponding information theoretic limits, respectively. We also propose a low-complexity decoding scheme that does not involve iterative message passing between the source and channel decoders; the low-complexity scheme performs only 1.08, 0.85, and 0.79 dB away from the theoretical limits at transmission rates of 1.0, 1.5, and 2.0 b/s, respectively.

We consider practical schemes for binary dirty-paper channels and broadcast channels (BCs) with two receivers and varying channel quality. With the BC application in mind, this paper proposes a new design for binary dirty paper coding (DPC). By exploiting the concept of coset binning, the complexity of the system is greatly reduced when compared to the existing works. Some design challenges of the coset binning approach are identified and addressed. The proposed binary DPC system achieves similar performance to the state-of-the-art, superposition-coding-based system while demonstrating significant advantages in terms of complexity and flexibility of system design. For binary BCs, achieving the capacity generally requires the superposition of a normal channel code and a carefully designed channel code with non-uniform bit distribution. The non-uniform bit distribution is chosen according to the channel conditions. Therefore, to achieve the capacity for binary BCs with varying channel quality, it is necessary to use quantization codes of different rates, which significantly increases the implementation complexity. In this paper, we also propose a broadcast scheme that generalizes the concept of binary DPC, which we term soft DPC. By combining soft DPC with time sharing, we achieve a large percentage of the capacity for a wide range of channel quality with little complexity overhead. Our scheme uses only one fixed pair of codes for users 1 and 2, and a single quantization code, which possesses many practical advantages over traditional time sharing and superposition coding solutions and provides strictly better performance.

International Journal of Soft Computing, Mathematics and Control (IJSCMC)is a Quarterly peer-reviewed and refereed open access journal that publishes articles which contribute new results in all areas of Soft Computing, Pure, Applied and Numerical Mathematics and Control. The focus of this new journal is on all theoretical and numerical methods on soft computing, mathematics and control theory with applications in science and industry. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on latest topics of soft computing, pure, applied and numerical mathematics and control engineering, and establishing new collaborations in these areas. Authors are solicited to contribute to this journal by submitting articles that illustrate new algorithms, theorems, modelling results, research results, projects, surveying works and industrial experiences that describe significant advances in Soft Computing, Mathematics and Control Engineering Learn More

A binned dirty-paper code (DPC) divides a set of codewords into a number of bins. The codeword in the bin that has the maximum correlation with the side information is selected as the transmitted dirty-paper codeword. This letter derives and verifies the analytical bit-error rate (BER) expression of binned orthogonal block code used as DPC in watermarking applications. The BER trends of such orthogonal DPC under constant code length or constant code rate constrains are analyzed. Finally, we propose a new class of DPC based on binned bi-orthogonal codes and demonstrate its BER performance gain over orthogonal DPC.

Corrugated paper is produced by gluing three types of papers of the same breadth. Given a set of orders, we first assign each order to one of the standard breadths, and then sequence those assigned to each standard breadth so that they are continuously manufactured from the three rolls of the specified standard breadth equipped in the machine called corrugator. Here we are asked to achieve multi-goals of minimizing total length of roll papers, total loss of papers caused by the differences between standard breadths and real breadths of the orders, and the number of machine stops needed during production. We use integer programming to assign orders to standard breadths, and then develop a special purpose algorithm to sequence the orders assigned to each standard breadth. This is a first attempt to handle scheduling problems of the corrugator machine.

The oil-paper compound insulation plays a vital role in the insulation structure of power transformer. To obtain the characteristics of the transformer's insulation system, it is of great importance to study the dielectric response of oil-paper compound insulation. In this study, fractional calculus is applied to model the oil-paper compound insulation. Both low-frequency and broadband high-frequency models are proposed and then they are verified by fitting the measured data of different insulation papers. Finally they are applied to two different cases: (i) the low-frequency model for fitting polarity reversal property; (ii) the broadband high-frequency model for studying the influence of the parameters variations on the frequency domain spectroscopy (FDS). The results demonstrate the advantages of the proposed models when compared with traditional ones.

We present a silent self-stabilizing distributed algorithm computing a maximal p-star decomposition of the underlying communication network. Under the unfair distributed scheduler, the most general scheduler model, the algorithm converges in at most \(12\varDelta m + \mathcal {O}(m+n)\) moves, where m is the number of edges, n is the number of nodes, and \(\varDelta \) is the maximum node degree. Regarding the move complexity, our algorithm outperforms the previously known best algorithm by a factor of \(\varDelta \). While the round complexity for the previous algorithm was unknown, we show a \(5\left\lfloor \frac{n}{p+1} \right\rfloor +5\) bound for our algorithm.

The "writing on dirty paper"-channel model offers an information-theoretic framework for precoding techniques for canceling arbitrary interference known at the transmitter. It indicates that lossless precoding is theoretically possible at any signal-to-noise ratio (SNR), and thus dirty-paper coding may serve as a basic building block in both single-user and multiuser communication systems. We design an end-to-end coding realization of a system materializing a significant portion of the promised gains. We employ multidimensional quantization based on trellis shaping at the transmitter. Coset decoding is implemented at the receiver using "virtual bits." Combined with iterative decoding of capacity-approaching codes we achieve an improvement of 2dB over the best scalar quantization scheme. Code design is done using the EXIT chart technique.

In this correspondence, we compare the achievable throughput for the optimal strategy of dirty paper coding (DPC) to that achieved with suboptimal and lower complexity linear precoding techniques (zero-forcing and block diagonalization). Both strategies utilize all available spatial dimensions and therefore have the same multiplexing gain, but an absolute difference in terms of throughput does exist. The sum rate difference between the two strategies is analytically computed at asymptotically high SNR. Furthermore, the difference is not affected by asymmetric channel behavior when each user has a different average SNR. Weighted sum rate maximization is also considered. In the process, it is shown that allocating user powers in direct proportion to user weights asymptotically maximizes weighted sum rate.

In this paper, we present a practical method to construct dirty paper coding (DPC) schemes using sum codes. Unlike the commonly used approach to DPC where the coding scheme involves concatenation of a channel code and a quantization code, the proposed method embodies a unified approach that emulates the binning method used in the proof of the DPC result. Auxiliary bits are used to create the desired number of code vectors in each bin. Sum codes are obtained when information sequences augmented with auxiliary bits are encoded using linear block codes. Sum-code-based DPC schemes can be implemented using any linear block code, and entail a relatively small increase in decoder complexity when compared to standard communication schemes. They can also lead to significant reduction in transmit power in comparison to standard schemes.

Deconvolution technique-which is based on nonlinear total least square (TLS) -is a mathematical tool that extracts the drawdown type curve from the rate and pressure history. This technique provides additional flow regime information that would not normally be seen within the specified time period of the buildup test through conventional analysis. It is an inverse process which aims to rebuild the theoretical reservoir pressure response during a single constant rate production period. This technique was adopted at different Tal Block wells in which conventional well test analysis didn't sight multiple no-flow boundaries due to small radius of investigation. A case is presented in this paper in which deconvolution was applied on a well, where reservoir pressure depletion effects were not matching during the test sequence with conventional analysis and the effect of negative superposition masked the boundary dominated flow, both of these issues were resolved using deconvolution technique. This technique have resulted in additional data/info of tested intervals which helped to resolve certain structural and reservoir uncertainties. However, application of this technique and yielded results should be considered/used keeping in view the assumptions involved in deconvolution, areas of its applicability and quality of data available. This paper presents principle, working, applications, limitations, and observations of deconvolution technique, available methods and a case study.

In this paper, the approximate capacity of the 3-receiver “writing on random dirty paper” (WRDP) channel is derived. In the M-receiver WRDP channel, the channel output is obtained as the sum of the channel input, white Gaussian noise and a channel state sequence randomly selected among a set of M independent Gaussian sequences. The transmitter has non-causal knowledge of the set of possible state sequences but does not know which one is selected to produce the channel output. In the following, we derive upper and lower bounds to the capacity of the 3-receiver WRDP channel which are to within a distance of at most 3 bits-per-channel-use (bpcu) for all channel parameters. In the achievability proof, the channel input is composed of the superposition of three codewords: the receiver opportunistically decodes a different set of codewords, depending on the variance of the channel state appearing in the channel output. Time-sharing among multiple transmission phases is employed to guarantee that transmitted message can be decoded regardless of the state realization. In the converse proof, we derive a novel outer bound which matches the pre-log coefficient arising in the achievability proof due to time-sharing. Although developed for the case of three possible state realizations, our results can be extended the general WRDP.

Upon the introduction a simplified distortion model for a Gaussian audio watermaking scenario, we derive a lower bound for the Gelfand-Pinsker capacity of the watermark channel. Then, we use the capacity bound for designing an efficient practical watermarking scheme based on dirty trellis codes. The proposed information-theoretic framework is then validated, through the use of experimentally acquired audio signals, with the use of a recently proposed frequency-domain audio watermarking. Both an “ideal” (reflection-free) Gaussian acoustic channel and a realistic multipath acoustic channel are considered.

In this paper, a practical multiple input multiple output Dirty paper coding (MIMO-DPC) scheme is designed to cancel the effect of additive interference that is known perfectly to the transmitter. The proposed system uses a trio of encoders — a LDPC code, a vector quantizer implemented as a convolutional decoder and an Orthogonal space time block code (STBC) to achieve temporal coding gain, interference cancelation and spatial diversity, respectively. First, we derive the equivalent noise seen by the receiver using an equivalent lattice based dirty paper code. Then the optimal value of the power inflation factor, which is one of the key system parameters used to minimize the equivalent noise seen by the receiver is derived. Furthermore, we analytically prove that the equivalent noise seen by the receiver tends to 0 for large number of receive antennas. Performance results in the case of various number of receiver antennas are presented and show that significant reduction in bit error probabilities can be obtained over a system that uses no interference cancelation.

We demonstrate the “biprimary” color system with dual particle electrophoretic dispersions and a 3-electrode system. Preliminary contrast ratios reach 10:1. Furthermore an electrokinetic cell is demonstrated confirming basic functionality for EKD panels. A theoretical pixel simulation confirms biprimary doubles the color performance compared to RGBW pixels.

This paper examines near-capacity dirty-paper code designs based on source-channel coding. We first point out that the performance loss in signal-to-noise ratio (SNR) in our code designs can be broken into the sum of the packing loss from channel coding and a modulo loss, which is a function of the granular loss from source coding and the target dirty-paper coding rate (or SNR). We then examine practical designs by combining trellis-coded quantization (TCQ) with both systematic and nonsystematic irregular repeat-accumulate (IRA) codes. Like previous approaches, we exploit the extrinsic information transfer (EXIT) chart technique for capacity-approaching IRA code design; but unlike previous approaches, we emphasize the role of strong source coding to achieve as much granular gain as possible using TCQ. Instead of systematic doping, we employ two relatively shifted TCQ codebooks, where the shift is optimized (via tuning the EXIT charts) to facilitate the IRA code design. Our designs synergistically combine TCQ with IRA codes so that they work together as well as they do individually. By bringing together TCQ (the best quantizer from the source coding community) and EXIT chart-based IRA code designs (the best from the channel coding community), we are able to approach the theoretical limit of dirty-paper coding. For example, at 0.25 bit per symbol (b/s), our best code design (with 2048-state TCQ) performs only 0.630 dB away from the Shannon capacity.

The vector dirty paper coding (DPC) can achieve rate region for multicell cooperation system. Conventional DPC implementation approach suffer the capacity loss due to quantization which lead to modulo loss. In this paper, we proposed a vector DPC transmission scheme with non-binary LDPC codes and low density generator matrix based quantizer for multicell processing. And the random coding based proof for the encoding and decoding transmission system is presented. It can achieve a shaping gain and significantly reduce computational complexity.

The problem of Dirty Paper Coding (DPC) over the Fading Dirty Paper Channel (FDPC) Y = H(X + S)+Z, a more general version of Costa's channel, is studied for the case in which there is partial and perfect knowledge of the fading process H at the transmitter (CSIT) and the receiver (CSIR), respectively. A key step in this problem is to determine the optimal inflation factor (under Costa's choice of auxiliary random variable) when there is only partial CSIT. Towards this end, two iterative numerical algorithms are proposed. Both of these algorithms are seen to yield a good choice for the inflation factor. Finally, the high-SNR (signal-to-noise ratio) behavior of the achievable rate over the FDPC is dealt with. It is proved that FDPC (with t transmit and r receive antennas) achieves the largest possible scaling factor of min(t,r) log SNR even with no CSIT. Furthermore, in the high SNR regime, the optimality of Costa's choice of auxiliary random variable is established even when there is partial (or no) CSIT in the special case of FDPC with t, 5 pages with 2 figures, presented at 42nd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, USA, Oct. 2008

(Invited Paper) Abstract—While considerable progress has been made in the realm of speed-enhanced electromagnetic (EM) solvers, these fast solvers generally achieve their results through methods that introduce additional error components by way of geometric type approximations, sparse-matrix type approximations, multilevel type decomposition of interactions, and assumptions regarding the stochastic nature of EM problems. This work introduces the O(N log N ) Unified-FFT grid totalizing (UFFT-GT) method, a derivative of method of moments (MoM), which achieves fast analysis with minimal to zero reduction in accuracy relative to direct MoM solution. The method uniquely combines FFT-enhanced Matrix Fill Operations (MFO) that are calculated to machine precision with FFT-enhanced Matrix Solve Operations (MSO) that are also calculated to machine precision, for an expedient solution that does not compromise accuracy.

In this paper, weighted stochastic gradient (WSG) algorithms for ARX models are proposed by modifying the standard stochastic gradient identification algorithms. In the proposed algorithms, the correction term is a weighted term of the correction terms of the standard SG algorithm in the current and last recursive steps. In addition, a latest estimation based WSG (LE-WSG) algorithm is also established. The convergence performance of the proposed LE-WSG algorithm is then analyzed. It is shown by a numerical example that both the WSG and LE-WSG algorithms can possess faster convergence speed and higher convergence precision compared with the standard SG algorithms if the weighting factor is appropriately chosen.

Existing dirty-paper coding (DPC) schemes based on binned FEC codes lack of tractable BER analysis, hence it is difficult to predict the performance behavior of DPC. In this paper, we derive closed-form BER upper bounds of binned Walsh-Hadamard codes and binned Gold codes as DPC. Their BER upper bounds are verified to be very tight under the condition that the channel contains side information only, or side information plus noise.

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It seems that the results obtained by the so-called Khater method contain computational or print errors. We look at this issue from a different point of a view, namely, from a theoretical side. We prove our claim by a formal direct approach instead of back substitution (trial and error) approach.

The "Carbon Copy onto Dirty Paper" (CCDP) channel is the compound "writing on dirty paper" channel in which the channel output is obtained as the sum of the channel input, white Gaussian noise and a Gaussian state sequence randomly selected among a set possible realizations. The transmitter has non-causal knowledge of the set of possible state sequences but does not know which sequence is selected to produce the channel output. We study the capacity of the CCDP channel for two scenarios: (i) the state sequences are independent and identically distributed, and (ii) the state sequences are scaled versions of the same sequence. In the first scenario, we show that a combination of superposition coding, time-sharing and Gel'fand-Pinsker binning is sufficient to approach the capacity to within three bits per channel use for any number of possible state realizations. In the second scenario, we derive capacity to within four bits-per-channel-use for the case of two possible state sequences. This result is extended to the CCDP channel with any number of possible state sequences under certain conditions on the scaling parameters which we denote as "strong fading" regime. We conclude by providing some remarks on the capacity of the CCDP channel in which the state sequences have any jointly Gaussian distribution.

Dirty paper coding (DPC) refers to methods for pre-subtraction of known interference at the transmitter of a multiuser communication system. There are numerous applications for DPC, including coding for broadcast channels. Recently, lattice-based coding techniques have provided several designs for DPC. In lattice-based DPC, there are two codes - a convolutional code that defines a lattice used for shaping and an error correction code used for channel coding. Several specific designs have been reported in the recent literature using convolutional and graph-based codes for capacity-approaching shaping and coding gains. In most of the reported designs, either the encoder works on a joint trellis of shaping and channel codes or the decoder requires iterations between the shaping and channel decoders. This results in high complexity of implementation. In this work, we present a lattice-based DPC scheme that provides good shaping and coding gains with moderate complexity at both the encoder and the decoder. We use a convolutional code for sign-bit shaping, and a low-density parity check (LDPC) code for channel coding. The crucial idea is the introduction of a one-codeword delay and careful parsing of the bits at the transmitter, which enable an LDPC decoder to be run first at the receiver. This provides gains without the need for iterations between the shaping and channel decoders. Simulation results confirm that at high rates the proposed DPC method performs close to capacity with moderate complexity. As an application of the proposed DPC method, we show a design for superposition coding that provides rates better than time-sharing over a Gaussian broadcast channel., 5 pages, submitted to ISIT 2010

Dirty paper coding (DPC) is a promising preceding technique for canceling arbitrary interference known only at the transmitter. The interference-free rate is thus achieved. However, this result relies on the perfectly known channel coefficients at the transmitter when being applied to the fading channel. We thus consider the fading paper problem where only the channel statistics information is available at the transmitter. In general, the optimal transmission strategies to achieve the capacity of this channel is still unknown. We confine ourselves to the linear-assignment Gel'fand-Pinsker coding which has been proved to have good, sometimes even optimal, performance in the a variety of fast and slow fading channels. However, lack of structured codebook so far limited the practical applications of this coding. In this paper, we present a lattice-based coding structure for the vector fading paper channel. It can achieve the rate performance of the linear-assignment strategies previously proved by the random Gaussian codebook. Moreover, the lattice codebook has an algebraic structure and is possible to be implemented in practice. The results can be applied to the emerging fields such as fading multiple-input multiple-output (MIMO) Gaussian broadcast channels or fading MIMO cognitive channels.

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.

The restoration of regular paper fragments can be solved by edge matching. Edge matching is based primarily on the length and location of the break in writing, however, it is possible that the edges of some regular paper fragments are blank. And so the error of restoration is the inevitable. In this case, it needs manual intervention based on the article content and then eliminate errors. Prior to the establishment of a mathematical model, there is a need for binarization of regular paper fragments with Matlab, then establish vector space model and construct edge contrast matrix, get the order after the Q cluster analysis of fragments. Finally the original picturecan be obtained through splicing and restoring in the order.

This paper deals with the study of factors which are affecting basis weight sensor measuring accuracy, the Z distance, XY dislocation, air gap temperature, dust and paper grades. The calibration techniques and solving methods of accurately measuring are given. Automatic calibration and range division are combined to improve the accuracy of basis weight sensor, and the BW measuring precision is reasonable. Static accuracy (2σ) is 0.20% and dynamic accuracy (2σ) is 0.25% at the range of 10 g/m2-1500 g/m2.

The restoration of regular paper fragments can be solved just as the Traveling Salesman Problem. Paper pretreatment and edge matching are the main steps during the restoration process, which contain binarization, screening between the left and right boundary depending the width of paper margins. For some longer paper fragments, the transverse splicing can be realized with Greed Algorithm. But the algorithm will get a non-ideal restoration for more fine paper fragments. Therefore the noise-suppressed processing before clustering from line to line through projection is essential in order to make sure that the regular English fragments can get the regularity like Chinese characters. The two experiments get an 100% correct rates when using the method combining the improved genetic algorithm for traveling salesman problem.combining the improved genetic algorithm for traveling salesman problem.

A Dirty Paper Coding (DPC) based transmission scheme for the Gaussian multiple-input multiple-output (MIMO) cognitive radio channel (CRC) is studied when there is imperfect and perfect channel knowledge at the transmitters (CSIT) and the receivers, respectively. In particular, the problem of optimizing the sum-rate of the MIMO CRC over the transmit covariance matrices is dealt with. Such an optimization, under the DPC-based transmission strategy, needs to be performed jointly with an optimization over the inflation factor. To this end, first the problem of determination of inflation factor over the MIMO channel $Y=H_1 X + H_2 S + Z$ with imperfect CSIT is investigated. For this problem, two iterative algorithms, which generalize the corresponding algorithms proposed for the channel $Y=H(X+S)+Z$, are developed. Later, the necessary conditions for maximizing the sum-rate of the MIMO CRC over the transmit covariances for a given choice of inflation factor are derived. Using these necessary conditions and the algorithms for the determination of the inflation factor, an iterative, numerical algorithm for the joint optimization is proposed. Some interesting observations are made from the numerical results obtained from the algorithm. Furthermore, the high-SNR sum-rate scaling factor achievable over the CRC with imperfect CSIT is obtained., To be presented at ISIT 2009, Seoul, S. Korea

We study a dirty paper channel model where the input is constrained to belong to a PAM constellation. In particular, we provide lower bounds on the capacity as well as explicit coding schemes for the binary-input dirty-paper channel. We examine the case of causal as well as non-causal side information.

In this paper, we improve the peak to average power ratio (PAPR) reduction scheme for multicarrier systems proposed by Collings and Clarkson by applying dirty paper coding with peak power constraint. We compare the bit error rate (BER) performance among conventional orthogonal frequency division multiplexing (OFDM), Collings and Clarkson’s method, and our proposed scheme with bit loading. From simulation we find that when channel coding is considered, Collings and Clarkson’s method is the worst independent of the number of bits loaded. The proposed method performs the best when the number of bits is large and hence is suitable for high speed transmission.

The Tomlinson-Harashima precoding is well known for dirty paper coding implementation. Despite its simplicity, THP suffers from a significant performance loss in the low SNR region due to modulo operations. In this paper, we propose new dirty paper precoding scheme by taking advantage of the known modulation structure of interference (e.g., BPSK and QPSK signals). The new method, termed structured DPC (SDPC), outperforms the regular THP with modest changes to the transmitter and receiver. For BPSK and QPSK cases investigated, the SDPC only suffers power loss, which is up to 1.25 dB compared with non-interference case, while the regular THP-based scalar dirty paper coding has a typical 4-5 dB capacity loss in the same low SNR regions

We present an image data-hiding scheme based on near-capacity dirty-paper codes. The scheme achieves high embedding rates by "hiding" information into mid-frequency DCT coefficients among each DCT block of the host image. To reduce the perceptual distortion due to data-hiding, the mid-frequency DCT coefficients are first perceptually scaled according to Watson's model. Then a rate-1/3 projection matrix in conjunction with a rate-1/5 capacity-approaching dirty-paper code is applied. We are able to embed 1500 information bits into 256×256 images, outperforming, under a Gaussian noise attack, currently the best known data-hiding scheme by 33%. Robustness tests against different attacks, such as low-pass filtering, image scaling, and lossy compression, show that our scheme is a good candidate for high-rate image data-hiding applications.

Aiming at the problem that paper smoothness cannot be measured on line, a new method for soft measurement of paper smoothness is put forward on the basis of studying paper quantization distribution. A soft measurement system is established, by which, paper quantization noise is processed by time–frequency analysis and non-linear transformation, so that online paper smoothness can be obtained through a Radial Basis Function Neural Networks (RBFNNs). The results show that this soft computing method is a feasible way with high measuring accuracy.

A dirty paper channel is considered, where the transmitter knows the interference sequence up to a constant multiplicative factor, known only to the receiver. We derive lower bounds on the achievable rate of communication by proposing a coding scheme that partially compensates for the imprecise channel knowledge.We focus on a communication scenario where the Gaussian noise is small while the interference is strong. Our approach is based on analyzing the performance achievable using extended Tomlinson-Harashima like coding schemes. When the power of the interference is finite, we show that this may be achieved by a judicious choice of the scaling parameter at the receiver. We further show that the communication rate may be improved, for finite as well as infinite interference power, by allowing randomized scaling at the transmitter.

Discussion of the paper by Johansen & Nielsen

Discussion of the Paper "Asymptotic Theory of Outlier Detection Algorithms for Linear Time Series Regression Models" by Soren Johansen and Bent Nielsen

This paper provides a corrected formulation to the mixed integer programming model proposed by Aydogan et al. (2012) 1. They proposed a genetic algorithm to learn fuzzy rules for a fuzzy rule-based classification system and developed a Mixed Integer Programming model (MIP) to prune the generated rules by selecting the best set of rules to maximize predictive accuracy. However, their proposed MIP formulation contains errors, which are described in this technical note. We develop corrections and improvements to the original formulation and test it with non-parametric statistical tests on the same data sets used to evaluate the original model. The statistical analysis shows that the results of the correction formulation are significantly different from the original model.

We present practical codes designed for the Gaussian dirty paper channel. We show that the dirty paper decoding problem can be transformed into an equivalent multiple-access problem, for which we apply superposition coding. Our approach is a generalization of the nested lattices approach of Zamir et al. (2002). We present simulation results which confirm the effectiveness of our methods.