Clinical validation of an algorithm to correct the error in the keratometric estimation of corneal power for normal eyes

To validate clinically an algorithm for correcting the error in the keratometric estimation of corneal power by using a variable keratometric index of refraction (nk) in a normal healthy population.

Selective SWIFT-R. A Flexible Software-Based Technique for Soft Error Mitigation in Low-Cost Embedded Systems

Commercial off-the-shelf microprocessors are the core of low-cost embedded systems due to their programmability and cost-effectiveness. Recent advances in electronic technologies have allowed remarkable improvements in their performance. However, they have also made microprocessors more susceptible to transient faults induced by radiation. These non-destructive events (soft errors), may cause a microprocessor to produce a wrong computation result or lose control of a system with catastrophic consequences. Therefore, soft error mitigation has become a compulsory requirement for an increasing number of applications, which operate from the space to the ground level. In this context, this paper uses the concept of selective hardening, which is aimed to design reduced-overhead and flexible mitigation techniques. Following this concept, a novel flexible version of the software-based fault recovery technique known as SWIFT-R is proposed. Our approach makes possible to select different registers subsets from the microprocessor register file to be protected on software. Thus, design space is enriched with a wide spectrum of new partially protected versions, which offer more flexibility to designers. This permits to find the best trade-offs between performance, code size, and fault coverage. Three case studies have been developed to show the applicability and flexibility of the proposal.

S-SETA: Selective Software-Only Error-Detection Technique Using Assertions

Software-based techniques offer several advantages to increase the reliability of processor-based systems at very low cost, but they cause performance degradation and an increase of the code size. To meet constraints in performance and memory, we propose SETA, a new control-flow software-only technique that uses assertions to detect errors affecting the program flow. SETA is an independent technique, but it was conceived to work together with previously proposed data-flow techniques that aim at reducing performance and memory overheads. Thus, SETA is combined with such data-flow techniques and submitted to a fault injection campaign. Simulation and neutron induced SEE tests show high fault coverage at performance and memory overheads inferior to the state-of-the-art.

Basic research in crashworthiness II -- testing and evaluation of collapsing forward structure modification concept in car-to-car frontal collisions. Interim technical report.

National Highway Traffic Safety Administration, Washington, D.C.

Quantum-error correction on linear-nearest-neighbor qubit arrays

A quantum circuit implementing 5-qubit quantum-error correction on a linear-nearest-neighbor architecture is described. The canonical decomposition is used to construct fast and simple gates that incorporate the necessary swap operations allowing the circuit to achieve the same depth as the current least depth circuit. Simulations of the circuit's performance when subjected to discrete and continuous errors are presented. The relationship between the error rate of a physical qubit and that of a logical qubit is investigated with emphasis on determining the concatenated error correction threshold.

Stabilizer formalism for operator quantum error correction

Operator quantum error correction is a recently developed theory that provides a generalized and unified framework for active error correction and passive error avoiding schemes. In this Letter, we describe these codes using the stabilizer formalism. This is achieved by adding a gauge group to stabilizer codes that defines an equivalence class between encoded states. Gauge transformations leave the encoded information unchanged; their effect is absorbed by virtual gauge qubits that do not carry useful information. We illustrate the construction by identifying a gauge symmetry in Shor's 9-qubit code that allows us to remove 3 of its 8 stabilizer generators, leading to a simpler decoding procedure and a wider class of logical operations without affecting its essential properties. This opens the path to possible improvements of the error threshold of fault-tolerant quantum computing.

Analysis of the error performance of adaptive array antennas for CDMA with noncoherent M-ary orthogonal modulation in Nakagami fading

This letter presents an analytical model for evaluating the Bit Error Rate (BER) of a Direct Sequence Code Division Multiple Access (DS-CDMA) system, with M-ary orthogonal modulation and noncoherent detection, employing an array antenna operating in a Nakagami fading environment. An expression of the Signal to Interference plus Noise Ratio (SINR) at the output of the receiver is derived, which allows the BER to be evaluated using a closed form expression. The analytical model is validated by comparing the obtained results with simulation results.

Curvature-driven smoothing: a learning algorithm for feed-forward networks

The performance of feed-forward neural networks in real applications can be often be improved significantly if use is made of a-priori information. For interpolation problems this prior knowledge frequently includes smoothness requirements on the network mapping, and can be imposed by the addition to the error function of suitable regularization terms. The new error function, however, now depends on the derivatives of the network mapping, and so the standard back-propagation algorithm cannot be applied. In this paper, we derive a computationally efficient learning algorithm, for a feed-forward network of arbitrary topology, which can be used to minimize the new error function. Networks having a single hidden layer, for which the learning algorithm simplifies, are treated as a special case.

Topographic mappings and feed-forward neural networks

This thesis is a study of the generation of topographic mappings - dimension reducing transformations of data that preserve some element of geometric structure - with feed-forward neural networks. As an alternative to established methods, a transformational variant of Sammon's method is proposed, where the projection is effected by a radial basis function neural network. This approach is related to the statistical field of multidimensional scaling, and from that the concept of a 'subjective metric' is defined, which permits the exploitation of additional prior knowledge concerning the data in the mapping process. This then enables the generation of more appropriate feature spaces for the purposes of enhanced visualisation or subsequent classification. A comparison with established methods for feature extraction is given for data taken from the 1992 Research Assessment Exercise for higher educational institutions in the United Kingdom. This is a difficult high-dimensional dataset, and illustrates well the benefit of the new topographic technique. A generalisation of the proposed model is considered for implementation of the classical multidimensional scaling (¸mds}) routine. This is related to Oja's principal subspace neural network, whose learning rule is shown to descend the error surface of the proposed ¸mds model. Some of the technical issues concerning the design and training of topographic neural networks are investigated. It is shown that neural network models can be less sensitive to entrapment in the sub-optimal global minima that badly affect the standard Sammon algorithm, and tend to exhibit good generalisation as a result of implicit weight decay in the training process. It is further argued that for ideal structure retention, the network transformation should be perfectly smooth for all inter-data directions in input space. Finally, there is a critique of optimisation techniques for topographic mappings, and a new training algorithm is proposed. A convergence proof is given, and the method is shown to produce lower-error mappings more rapidly than previous algorithms.

Correcting errors in optical data transmission using neural networks

Optical data communication systems are prone to a variety of processes that modify the transmitted signal, and contribute errors in the determination of 1s from 0s. This is a difficult, and commercially important, problem to solve. Errors must be detected and corrected at high speed, and the classifier must be very accurate; ideally it should also be tunable to the characteristics of individual communication links. We show that simple single layer neural networks may be used to address these problems, and examine how different input representations affect the accuracy of bit error correction. Our results lead us to conclude that a system based on these principles can perform at least as well as an existing non-trainable error correction system, whilst being tunable to suit the individual characteristics of different communication links.

A learning feed-forward current controller for linear reciprocating vapor compressors

Direct-drive linear reciprocating compressors offer numerous advantages over conventional counterparts which are usually driven by a rotary induction motor via a crank shaft. However, to ensure efficient and reliable operation under all conditions, it is essential that motor current of a linear compressor follows a sinusoidal current command with a frequency which matches the system resonant frequency. The design of a high-performance current controller for linear compressor drive presents a challenge since the system is highly nonlinear, and an effective solution must be low cost. In this paper, a learning feed-forward current controller for the linear compressors is proposed. It comprises a conventional feedback proportional-integral controller and a feed-forward B-spline neural network (BSNN). The feed-forward BSNN is trained online and in real time in order to minimize the current tracking error. Extensive simulation and experiment results with a prototype linear compressor show that the proposed current controller exhibits high steady state and transient performance. © 2009 IEEE.

Correcting errors in optical data transmission using neural networks

Optical data communication systems are prone to a variety of processes that modify the transmitted signal, and contribute errors in the determination of 1s from 0s. This is a difficult, and commercially important, problem to solve. Errors must be detected and corrected at high speed, and the classifier must be very accurate; ideally it should also be tunable to the characteristics of individual communication links. We show that simple single layer neural networks may be used to address these problems, and examine how different input representations affect the accuracy of bit error correction. Our results lead us to conclude that a system based on these principles can perform at least as well as an existing non-trainable error correction system, whilst being tunable to suit the individual characteristics of different communication links.

An adaptive code position modulation scheme for wireless sensor networks

In this letter, an energy-efficient adaptive code position modulation scheme is proposed for wireless sensor networks to provide the relatively stable bit error ratio (BER) performance expected by the upper layers. The system is designed with focus on the adaptive control of transmission power, which is adjusted based on the measured power density of background noise. Interfaces among the modulation module, packet scheduling module and upper layer are provided for flexible adjustments to adapt to the background noise and deliver expected application quality. Simulations with Signal Processing Worksystem (SPW) validate the effectiveness of the scheme. © 2005 IEEE.

Novel BCH code design for mitigation of phase noise induced cycle slips in DQPSK systems

We show that by proper code design, phase noise induced cycle slips causing an error floor can be mitigated for 28 Gbaud DQPSK systems. Performance of BCH codes are investigated in terms of required overhead. © 2014 OSA.

Peak -to -average power ratio reduced parallel interference cancellation Multicarrier-Code Division Multiple Access system with anti -interference property

Orthogonal Frequency-Division Multiplexing (OFDM) has been proved to be a promising technology that enables the transmission of higher data rate. Multicarrier Code-Division Multiple Access (MC-CDMA) is a transmission technique which combines the advantages of both OFDM and Code-Division Multiplexing Access (CDMA), so as to allow high transmission rates over severe time-dispersive multi-path channels without the need of a complex receiver implementation. Also MC-CDMA exploits frequency diversity via the different subcarriers, and therefore allows the high code rates systems to achieve good Bit Error Rate (BER) performances. Furthermore, the spreading in the frequency domain makes the time synchronization requirement much lower than traditional direct sequence CDMA schemes. There are still some problems when we use MC-CDMA. One is the high Peak-to-Average Power Ratio (PAPR) of the transmit signal. High PAPR leads to nonlinear distortion of the amplifier and results in inter-carrier self-interference plus out-of-band radiation. On the other hand, suppressing the Multiple Access Interference (MAI) is another crucial problem in the MC-CDMA system. Imperfect cross-correlation characteristics of the spreading codes and the multipath fading destroy the orthogonality among the users, and then cause MAI, which produces serious BER degradation in the system. Moreover, in uplink system the received signals at a base station are always asynchronous. This also destroys the orthogonality among the users, and hence, generates MAI which degrades the system performance. Besides those two problems, the interference should always be considered seriously for any communication system. In this dissertation, we design a novel MC-CDMA system, which has low PAPR and mitigated MAI. The new Semi-blind channel estimation and multi-user data detection based on Parallel Interference Cancellation (PIC) have been applied in the system. The Low Density Parity Codes (LDPC) has also been introduced into the system to improve the performance. Different interference models are analyzed in multi-carrier communication systems and then the effective interference suppression for MC-CDMA systems is employed in this dissertation. The experimental results indicate that our system not only significantly reduces the PAPR and MAI but also effectively suppresses the outside interference with low complexity. Finally, we present a practical cognitive application of the proposed system over the software defined radio platform.