Knowledge about the current state of charge (SoC) of the vehicles battery is of use beyond remaining usage estimation. Next to the electric engine, all security relevant devices also rely on sufficient remaining energy in order to function. For hybrid and fully electric vehicles (EV s), lithium ion batteries have proven their potential and are widely adopted. Due to a specialty in this cell chemistry, the open circuit voltage (OCV) can not be used for SoC estimation. As an alternative, impedance based methods can be applied. This paper evaluates the NASCAR method to calculate the synchrophasor. The focus relies on superimposed signal scenarios with missing data, as they can occur on bus systems. Bus transmission systems, like the controller area network (CAN) bus, are widely used in EV s. The investigations in this paper show a significant root mean square error (RMSE) reduction in regular operation scenarios in case of data losses.

Knowing the current State of Charge (SoC) of a battery is a critical task in electric mobility. Precise knowledge can improve range predictions, help to derive plans for best remaining usage, and assure the availability of security measures. Lithium ion batteries have proved their potential within the last years and are prospects to be a feasible solution for energy storage in automotives. Due to their characteristics, alternatives to open circuit voltage (OCV) based methods are required to estimate the SoC. This paper presents a new method to calculate a dynamic synchrophasor, in the case of superimposed signals. The method has been designed for impedance calculation in vehicles, as it accounts for operando superimposed disturbances in the on-board power supply nets by approximating them with a truncated Taylor series in order to suppress their influence on the impedance estimation results. Furthermore, the time vector is not required to feature uniform sampling frequency. In the given usecases, the accuracy of SoC estimation was increased by 20%, and the effect of non-uniform sampling frequency was significantly reduced.

The performance of the sparse matrix-vector multiplication (SMVM) on a parallel system is strongly affected by the distribution of data among its components. Two costs arise as a result of the used data mapping method: arithmetic and communication. The communication cost often dominates the arithmetic cost, and the gap between these costs tends to increase. Therefore, finding a mapping method that reduces the communication cost is of high importance. On the other hand, the load distribution among the processing units must not be sacrificed. In this paper, a data mapping method is proposed for SMVM on Network-on-Chip which achieves balanced working load and reduces the communication cost. Afterwards, an FPGA-based architecture is introduced which is designed to fit with the proposed data mapping method.

For indoor localization systems, Radio Frequency Identification (RFID) is an often chosen technique. A passive UHF RFID label can be localized via Received Signal Strength Indicator (RSSI) values using a Constrained Unscented Kalman Filter (CUKF). A camera-based localization technique which employs back projection method and movement estimation is combined with the RFID-based localization. This camera-assisted localization technique leads to an increase in localization accuracy by a factor of two compared to the Constrained Unscented Kalman Filter without camera assistance which already performes twice as good as the Unscented Kalman Filter (UKF).

A new syndrome trellis based decoding approach for Turbo coded data is described in this paper. Based on estimating error symbols instead of code symbols, it inherently features options for reducing the computational complexity of the decoding process. After deriving required transition metrics, numerical results in terms of block error rate and required equivalent iterations are presented to demonstrate the efficiency of the approach.

Generalized minimum mean squared error (GMMSE) detector has a bit error rate (BER) performance that is almost the same as minimum mean squared error (MMSE) detector and it has the advantage that it does not require the knowledge of the noise power. However, the computational complexity of GMMSE detector is higher than that of MMSE detector. In this paper, the computational complexity of the GMMSE detector is reduced by using the hidden convexity combined with the idea of using the circular approximation of the banded Toeplitz channel matrix. In this case, the solutions are obtained by computing the EVD of the circular matrix using FFT/IFFT such that, the required EVD implies no additional effort.

In this paper the effect of unreliable buffer memory on Turbo equalization is investigated. Under the assumption that the resulting bit errors are uniformly and independently distributed on receive and LLR buffer output, the effect of unreliable memory on the channel capacity is analyzed. It is further shown, that extrinsic information transfer and bit error rate (BER) performance of a conventional Turbo Equalizer are heavily degraded. Based on these observations a fault tolerant (FT) Turbo Equalizer is derived, which considers the memory error characteristics by using a modified transition metric for the involved MAP equalizer and decoder. It is demonstrated, that the FT Turbo Equalizer can effectively compensate memory errors and thus yields a significantly improved BER performance.

Mobile phone sensors have the ability to provide significant information about environmental conditions as well as about different activities of persons. This paper deals with the acceleration sensor of a commercial mobile phone that makes it possible to identify and classify the user's means of transportation. The needed pre-processing of the sensor data is described as well as the used classification algorithms, i.e. Naive Bayes classifier and Support Vector Machine (SVM). Their performance for solving the classification problem in combination with the different pre-processings is analyzed. It is shown that both classifiers are able to solve the classification task with high accuracy given a proper preprocessing. Support Vector Machines outperform Naive Bayes classifiers and achieve a classification accuracy of over 97% on an unknown test data set.

In this paper syndrome former trellis construction for punctured convolutional codes (PCCs) is discussed. The class of (np-1)/np-rate PCCs derived from 1/n-rate mother codes is considered. It is shown that a syndrome former trellis of the PCC with identical complexity as the encoder trellis of the mother code can always be constructed. An explicit construction of the syndrome former trellis can be beneficial, when realizing adaptive complexity reduced decoding, or decoding with reduced state transitions, based on the syndrome decoding approach.

In order to implement watermarking scheme for embedded systems, we present an energy efficient and robust watermarking mechanism based on DS-CDMA. The DS-CDMA based watermarking can obtain the capacity according to the orthogonal spreading codes. The high capacity can improve the robustness efficiently. For power saving, we propose a low complexity frequency domain transform for the DS-CDMA embedding method. The experimental results show that our approach not only reduces 82.19% energy consumption, but can survive from various signal distortions such as JPEG compression, sharpening, blurring, intensity adjustment, and cropping.

In this paper a low-complexity and highly-integrated IP Core for image/video transformations is presented. It is possible to perform quantized 8×8 DCT and quantized 8×8/4×4 integer transforms on the presented reconfigurable architecture using only shift and add operations. The XVID experimental and FPGA synthesis results show that the proposed architecture not only achieves multiplierless video transformations efficiently, but also retains good transformation quality. It is worth noticing that the proposed IP Core is very suitable for low-complexity and multi-purpose Video CODECs in SoC designs.

Guard periods are widely used to avoid inter block interference in wireless communication systems. The independent processing of each data block enables computational efficient data estimation, however, the necessary insertion of guard periods produces a transmission overhead and reduces the throughput of the respective transmission system. By using overlapping techniques a block-wise data estimation can be realized without using guard periods. With this modification, either the throughput of the system can be increased or the code rate of the forward error correction code can be reduced. Based on the latter option we will propose two optimized channel coding schemes for the overlapping based system. The first uses a reduced code rate while the second exploits the characteristic error distribution of the overlapping based data estimator. The bit error performance of the resulting transmission systems is finally compared to a common cyclic prefix based transmission system.

Least square and minimum mean square error data estimation based on rectangular and circulant channel submatrices is compared in terms of estimation quality, storage requirements and the number of multiplications. The circulant case is applied in OFDM based transmission systems. When applying an approximated Cholesky decomposition, the computation of the estimator in the rectangular case may require even less computations than in the circulant case, while yielding better bit error rates in a time dispersive wireless transmission scenario. The considered approximations may improve the condition of the estimator and thus may even yield lower bit error rates than the exact estimator. The actual computation of the estimates is usually more efficient in the circulant case than in the rectangular.

In this article the computation of FIR filter weights in adaptive channel equalization tasks in quasi stationary environments is considered. The problem is formulated as a system of equations. It can be solved via direct matrix inversion (DMI) or iteratively via the LMS or RLS algorithm. Thereby suitable criteria such as least squares (LS) or mean square error (MSE) are minimized. By using these techniques the filter weights are estimated. Another technique is to estimate the channel impulse response (CIR) by exploiting the eigenvalue decomposition (EVD) of cyclic matrices as done in orthogonal frequency domain multiplex (OFDM) systems and computing the FIR filter weights from the CIR via solving the zero forcing matrix equation. Different techniques to solve this equation are presented: One uses a cyclic prefix (CP) based approach, another a QR decomposition. After describing the different techniques they are assessed in terms of ensemble-averaged square error and computational complexity. The EVD based techniques can render the lowest square error and require significantly fewer multiplications than iterative methods or the DMI technique.

Currently most of the terminals for UMTS-FDD implement a simple Rake receiver. This paper shows how the Rake, including despreading and descrambling, could be replaced in a least squares receiver that can be implemented on a Cordic based hardware architecture. The performance in conjunction with the computational requirements of the receiver is widely adjustable. Simulation results show that the performance of this concept can be significantly better than that of the Rake.

To enable efficient detection strategies in wireless communications a guard period is frequently inserted into the transmitted data symbols. This is the case in OFDM systems and in single-carrier systems with frequency domain equalization. Especially for transmissions over wireless channels with long delay spreads the required guard period is quite long. Here we present efficient data detection algorithms that are applicable if a guard period is not inserted after every symbol or if it is omitted completely. The increase of the computational requirements in the base station is moderate and can be implemented via parallel processing. Finally, an OFDM system without guard periods is presented as an example.

Third-generation mobile radio systems use time division-code division multiple access (TD-CDMA) in their time division duplex (TDD) mode. Due to the time division multiple access (TDMA) component of TD-CDMA, joint (or multi-user) detection techniques can be implemented with a reasonable complexity. Therefore, joint-detection will already be implemented in the first phase of the system deployment to eliminate the intracell interference. In a TD-CDMA mobile radio system, joint-detection is performed by solving a least squares problem, where the system matrix has a block-Sylvester structure. We present and compare several techniques that reduce the computational complexity of the joint-detection task even further by exploiting this block-Sylvester structure and by incorporating different approximations. These techniques are based on the Cholesky factorization, the Levinson algorithm, the Schur algorithm, and on Fourier techniques, respectively. The focus of this paper is on Fourier techniques since they have the smallest computational complexity and achieve the same performance as the joint-detection algorithm that does not use any approximations. Similar to the well-known implementation of fast convolutions, the resulting Fourier-based joint-detection scheme also uses a sequence of fast Fourier transforms (FFTs) and overlapping. It is well suited for the implementation on parallel hardware architectures.

In this paper we present practical techniques for implementing Givens rotations based on the well-known CORDIC algorithm. Rotations are the basic operation in many high performance adaptive filtering schemes as well as numerous other advanced signal processing algorithms relying on matrix decompositions. To improve the efficiency of these methods, we propose to use "approximate rotations", whereby only a few (i.e. r/spl Lt/b, where b is the operand word length) elementary angles of the original CORDIC sequence are applied, so as to reduce the total number of required shift add operations. This seamingly rather ad hoc and heuristic procedure constitutes a representative example of a very useful design concept termed "approximate signal processing" recently introduced and formally exposed by S.H. Nawab et al. (1997), concerning the trade-off between system performance and implementation complexity, i.e. between accuracy and resources. This is a subject of increasing importance with respect to the efficient realization of demanding signal processing tasks. We present the application of the described rotation schemes to QRD-RLS filtering in wireless communications, specifically high speed channel equalization and beamforming, i.e. for intersymbol and co-channel/interuser interference suppression, respectively. It is shown via computer simulations that the convergence behavior of the scheme using approximate Givens rotations is insensitive to the value of r, and that the misadjustment error decreases as r is increased, opening zip possibilities for "incremental refinement" strategies.

A method is presented that parameterizes orthogonal wavelet transforms with respect to their properties (i.e. compact support, vanishing moments, regularity, symmetry) and also takes into consideration a simple implementation of the transform. The parameter space is given by the rotation angles of the orthogonal 2/spl times/2 rotations used in the lattice filters that realize the different stages of the wavelet transform. The different properties of an orthogonal wavelet transform can be expressed in this parameter space. Then, restricting the parameter space to the rotation angles of simple CORDIC-based approximate rotations leads to a reduced parameter space. The wavelet transforms in this reduced parameter space are amenable to a very simple implementation (only a small number of shift and add operations).

This paper presents efficient Schur-type algorithms for estimating the column space (signal subspace) of a low rank data matrix corrupted by additive noise. Its computational structure and complexity are similar to that of an LQ-decomposition, except for the fact that plane and hyperbolic rotations are used. Therefore, they are well suited for a parallel (systolic) implementation. The required rank decision, i.e., an estimate of the number of signals, is automatic, and updating as well as downdating are straightforward. The new scheme computes a matrix of minimal rank which is /spl gamma/-close to the data matrix in the matrix 2-norm, where /spl gamma/ is a threshold that can be determined from the noise level. Since the resulting approximation error is not minimized, critical scenarios lead to a certain loss of accuracy compared to SVD-based methods. This loss of accuracy is compensated by using unitary ESPRIT in conjunction with the Schur-type subspace estimation scheme. Unitary ESPRIT represents a simple way to constrain the estimated phase factors to the unit circle and provides a new reliability test. Due to the special algebraic structure of the problem, all required factorizations can be transformed into decompositions of real-valued matrices of the same size. The advantages of unitary ESPRIT dramatically improve the resulting subspace estimates, such that the performance of unitary Schur ESPRIT is comparable to that of SVD-based methods, at a fraction of the computational cost. Compared to the original Schur method, unitary Schur ESPRIT yields improved subspace estimates with a reduced computational load, since it is formulated in terms of real-valued computations throughout.

The Jacobi algorithm for eigenvalue calculation of symmetric matrices can be performed with a CORDIC algorithm as its basic module. Recently, a simplified Jacobi algorithm, by employing approximate rotations based on CORDIC rotations, was proposed. It fully exploits the binary data structure and reduces the overall computational cost significantly. In this paper an error analysis of the approximate CORDIC-based Jacobi algorithm and the conventional CORDIC-based Jacobi algorithm is performed. The new algorithm behaves numerically better than the conventional CORDIC-based Jacobi algorithm for fixed as well as floating point arithmetic.>

Since the stage of diagonalization of Jacobi-type methods is difficult to monitor in a parallel environment, it is usually proposed to execute a predetermined number of sweeps (iterations) on a parallel processor array. A possibility for monitoring the stage of diagonalization is essential in order to avoid the execution of a significant number of unnecessary sweeps. Based on a Lemma used for a generalized proof of the quadratic convergence of the Jacobi EVD and SVD methods a new criteria for monitoring the stage of diagonalization is derived. Using this criteria it can easily be monitored when the stage of quadratic convergence is reached (only one bit yields this information). Therefore, only the (small) number of quadratically convergent sweeps must be predetermined. A further similar criteria particularly useful for Jacobi-type methods using CORDIC-based approximate rotations is also given.>

An algebraic approach to the design of different kinds of discrete wavelet transforms (orthogonal and biorthogonal single-/multiwavelet transforms, multiwavelet-like transforms) is taken. The different transforms are analysed with respect to computational efforts, approximation properties and symmetry. The design of the orthogonal and biorthogonal single-/multiwavelets requires the solution of a system of linear and nonlinear equations. Only the biorthogonal case enables symmetric coefficients. The basis matrix of the multiwavelet-like transform is easy to compute, orthogonal and ultimately symmetric. Modifications of this multiwavelet-like transform are given with respect to practical applications.>

A very fast Jacobi-like algorithm for the parallel solution of symmetric eigenvalue problems is proposed. It becomes possible by not focusing on the realization of the Jacobi rotation with a CORDIC processor, but by applying approximate rotations and adjusting them to single steps of the CORDIC algorithm, i.e., only one angle of the CORDIC angle sequence defines the Jacobi rotation in each step. This angle can be determined by some shift, add and compare operations. Although only linear convergence is obtained for the most simple version of the new algorithm, the overall operation count (shifts and adds) decreases dramatically. A slow increase of the number of involved CORDIC angles during the runtime retains quadratic convergence.>

An algorithm is presented that requires only multiplications, additions, and a single division for the orthogonal solution of a system of linear equations. For that purpose the QR-decomposition of an extended system matrix, called the orthogonal Faddeeva algorithm, is computed by a square-root- and division-free Givens rotation, called scaled standard Givens rotation (SSGR). A special kind of number description, which is tailored to the standard Givens rotation, allows the execution of the SSGR solely by application of multiplications and additions. Therefore, the SSGR is highly suited for VLSI implementation. The roundoff error of the SSGR is as stable as the roundoff error of any available square-root-free Givens rotation, and its deviation factor is better.>

A systolic algorithm is presented which allows the parallel execution of iterative methods for solving systems of linear equations on a processor array. These methods are based on a repeated matrix-vector multiplication. In order to achieve an efficient realization on VLSI circuits special regard is given to the sparse property of the system matrix which is found in many applications. The arising transportation problem is solved by a two-dimensional systolic sorting procedure which determines the array structure and the time complexity of one matrix-vector multiplication. Therefore, the solution of a linear system with n equations requires n times the time complexity of the sorting algorithm and an area complexity of O(e) where e denotes the number of the nonzero elements in the system matrix.>