Enhanced detection-guided NLMS estimation of sparse FIR-modeled signal channels

In various signal-channel-estimation problems, the channel being estimated may be well approximated by a discrete finite impulse response (FIR) model with sparsely separated active or nonzero taps. A common approach to estimating such channels involves a discrete normalized least-mean-square (NLMS) adaptive FIR filter, every tap of which is adapted at each sample interval. Such an approach suffers from slow convergence rates and poor tracking when the required FIR filter is "long." Recently, NLMS-based algorithms have been proposed that employ least-squares-based structural detection techniques to exploit possible sparse channel structure and subsequently provide improved estimation performance. However, these algorithms perform poorly when there is a large dynamic range amongst the active taps. In this paper, we propose two modifications to the previous algorithms, which essentially remove this limitation. The modifications also significantly improve the applicability of the detection technique to structurally time varying channels. Importantly, for sparse channels, the computational cost of the newly proposed detection-guided NLMS estimator is only marginally greater than that of the standard NLMS estimator. Simulations demonstrate the favourable performance of the newly proposed algorithm. © 2006 IEEE.

Measurement of strain and strain rate by echocardiography - Ready for prime time?

Strain and strain rate (SR) are measures of deformation that are basic descriptors of both the nature and the function of cardiac tissue. These properties may now be measured using either Doppler or two-dimensional ultrasound techniques. Although these measurements are feasible in routine clinical echocardiography, their acquisition and analysis nonetheless presents a number of technical challenges and complexities. Echocardiographic strain and SR imaging has been applied to the assessment of resting ventricular function, the assessment of myocardial viability using low-dose dobutamine infusion, and stress testing for ischemia. Resting function assessment has been applied in both the left and the fight ventricles, and may prove particularly valuable for identifying myocardial diseases and following up the treatment response. Although the evidence base is limited, SR imaging seems to be feasible and effective for the assessment of myocardial viability. The use of the technique for the detection of ischemia during stress echocardiography is technically challenging and likely to evolve further. The clinical availability of strain and SR measurement may offer a solution to the ongoing need for quantification of regional and global cardiac function. Nonetheless, these techniques are susceptible to artifact, and further technical development is necessary.

Multisensorial obstacles detection and classification for ADAS and autonomous driving

A reliable perception of the real world is a key-feature for an autonomous vehicle and the Advanced Driver Assistance Systems (ADAS). Obstacles detection (OD) is one of the main components for the correct reconstruction of the dynamic world. Historical approaches based on stereo vision and other 3D perception technologies (e.g. LIDAR) have been adapted to the ADAS first and autonomous ground vehicles, after, providing excellent results. The obstacles detection is a very broad field and this domain counts a lot of works in the last years. In academic research, it has been clearly established the essential role of these systems to realize active safety systems for accident prevention, reflecting also the innovative systems introduced by industry. These systems need to accurately assess situational criticalities and simultaneously assess awareness of these criticalities by the driver; it requires that the obstacles detection algorithms must be reliable and accurate, providing: a real-time output, a stable and robust representation of the environment and an estimation independent from lighting and weather conditions. Initial systems relied on only one exteroceptive sensor (e.g. radar or laser for ACC and camera for LDW) in addition to proprioceptive sensors such as wheel speed and yaw rate sensors. But, current systems, such as ACC operating at the entire speed range or autonomous braking for collision avoidance, require the use of multiple sensors since individually they can not meet these requirements. It has led the community to move towards the use of a combination of them in order to exploit the benefits of each one. Pedestrians and vehicles detection are ones of the major thrusts in situational criticalities assessment, still remaining an active area of research. ADASs are the most prominent use case of pedestrians and vehicles detection. Vehicles should be equipped with sensing capabilities able to detect and act on objects in dangerous situations, where the driver would not be able to avoid a collision. A full ADAS or autonomous vehicle, with regard to pedestrians and vehicles, would not only include detection but also tracking, orientation, intent analysis, and collision prediction. The system detects obstacles using a probabilistic occupancy grid built from a multi-resolution disparity map. Obstacles classification is based on an AdaBoost SoftCascade trained on Aggregate Channel Features. A final stage of tracking and fusion guarantees stability and robustness to the result.

Immunochemical detection of sequence-specific modifications to DNA induced by UV light

Sequence specificity of antibodies to UV-damaged DNA has not been described previously. The antisera investigated here were specific for UV-modified DNA and were absolutely dependent upon the presence of thymine residues. Using a series of oligonucleotides in competition ELISA, increased inhibition was observed with increasing chain length of UV-polythymidylate. A minimum of three adjacent thymines was required for effective inhibition; alone, dimers of thymine were poor antigens. Although UV-irradiated poly(dC) was not antigenic, cytosines could partially replace thymines within the smallest effective epitope (T-T-T) with a high degree of sequence specificity, not previously described. The main epitope induced by UV was formed from adjacent thymines and either a 3' or a 5' pyrimidine.

Detection of signals corrupted by the combination of Gaussian and Non-Gaussian noise

The detection of signals in the presence of noise is one of the most basic and important problems encountered by communication engineers. Although the literature abounds with analyses of communications in Gaussian noise, relatively little work has appeared dealing with communications in non-Gaussian noise. In this thesis several digital communication systems disturbed by non-Gaussian noise are analysed. The thesis is divided into two main parts. In the first part, a filtered-Poisson impulse noise model is utilized to calulate error probability characteristics of a linear receiver operating in additive impulsive noise. Firstly the effect that non-Gaussian interference has on the performance of a receiver that has been optimized for Gaussian noise is determined. The factors affecting the choice of modulation scheme so as to minimize the deterimental effects of non-Gaussian noise are then discussed. In the second part, a new theoretical model of impulsive noise that fits well with the observed statistics of noise in radio channels below 100 MHz has been developed. This empirical noise model is applied to the detection of known signals in the presence of noise to determine the optimal receiver structure. The performance of such a detector has been assessed and is found to depend on the signal shape, the time-bandwidth product, as well as the signal-to-noise ratio. The optimal signal to minimize the probability of error of; the detector is determined. Attention is then turned to the problem of threshold detection. Detector structure, large sample performance and robustness against errors in the detector parameters are examined. Finally, estimators of such parameters as. the occurrence of an impulse and the parameters in an empirical noise model are developed for the case of an adaptive system with slowly varying conditions.

Detection of bacterial DNA in cardiac vegetations by PCR after the completion of antimicrobial treatment for endorcarditis

PCR with broad-range primers for prokaryotic 16S rRNA genes was used to identify bacterial DNA in tissue from patients undergoing valve replacements following a previous episode of infective endocarditis (IF). Of eight valves investigated, bacterial DNA was detected in three from patients for whom IE had been treated by antibiotic therapy 5, 12 and 18 months previously. The demonstration of bacterial DNA within resected heart valves suggests either recurrence of infection, treatment failure or the persistence of bacterial debris within the cardiac vegetation. There may also be implications for routine use of PCR in the diagnosis of infection. © 2004 Copyright by the European Society of Clinical Microbiology and Infectious Diseases.

Spatially extensive summation of contrast-energy is revealed by contrast detection of micro-pattern textures

Vision must analyze the retinal image over both small and large areas to represent fine-scale spatial details and extensive textures. The long-range neuronal convergence that this implies might lead us to expect that contrast sensitivity should improve markedly with the contrast area of the image. But this is at odds with the orthodox view that contrast sensitivity is determined merely by probability summation over local independent detectors. To address this puzzle, I aimed to assess the summation of luminance contrast without the confounding influence of area-dependent internal noise. I measured contrast detection thresholds for novel Battenberg stimuli that had identical overall dimensions (to clamp the aggregation of noise) but were constructed from either dense or sparse arrays of micro-patterns. The results unveiled a three-stage visual hierarchy of contrast summation involving (i) spatial filtering, (ii) long-range summation of coherent textures, and (iii) pooling across orthogonal textures. Linear summation over local energy detectors was spatially extensive (as much as 16 cycles) at Stage 2, but the resulting model is also consistent with earlier classical results of contrast summation (J. G. Robson & N. Graham, 1981), where co-aggregation of internal noise has obscured these long-range interactions.

Redox signalling and detection of protein oxidation by mass spectrometry

It is now recognised that redox control of proteins plays an important role in many signalling pathways both in health and disease. Proteins can undergo a wide variety of oxidative post-translational modifications (oxPTM); while the reversible modifications are thought to be most important in physiological processes, non-reversible oxPTM may contribute to pathological situations and disease. The oxidant is also important in determining the type of oxPTM (chlorination, nitration, etc.), and the susceptibilities of residues vary depending on their structural location. The best characterized oxPTMs involved in signalling modulation are partial oxidations of cysteine to the disulfide, glutathionylated or sulfenic acid forms, but there is increasing evidence that specific oxidations of methionine and tyrosine may have some biological roles. Well understood examples of oxidative regulation include protein tyrosine phosphatases, e.g. PTP1B/C, and members of the MAPK pathways such as MEKK1 and ASK1. Transcription factors such as NFkB and Nrf-2 are also regulated by redox-active cysteines. Improved methods for analysing specific oxPTMs in biological samples are critical for understanding the physiological and pathological roles of these changes, and tandem or MS3 mass spectrometry techniques interfaced with nano-LC separation are being now used. MS3 fragmentation markers for a variety of oxidized residues including tyrosine, tryptophan and proline have been identified, and a precursor ion scanning method that allows the selective identification of these oxPTMs in complex samples has been developed. Such advances in technology offer potential for biomarker development, disease diagnosis and understanding pathology.