Monaural sound localization : mechanisms and processes

A review of the literature established that localization acuity measured during monaural listening conditions was directly related to various methodological considerations. These included method of attenuation, segment of auditory space where monaural localization was measured, and the presence or absence of head movements. An extensive measurement of monaural localization was made with due consideration of these factors, allowing a more comprehensive evaluation of monaural acuity and the underlying processes that were involved. Establishing a monaural condition is dependent both on the attenuation level of the occluded ear and the signal level, both of which are clearly inter-related since the attenuation level of the occluded ear sets the maximum level of die stimulus. In a series of experiments it was established that there was a minimum signal level for accurate localization. Testing on both sides of the head revealed that there were three regions of monaural localization acuity. The first was about the interaural axis on the ipsilateral ear where monaural localization was relatively accurate, the second a region either side of the MSP where there was some loss of localization, and a third about the interaural axis on the ipsilateral side where virtually no monaural localization ability existed. In the final series of experiments it was established that head-movements allowed subjects to extend the accuracy of the first region by minimizing the distance between the sound and the ipsilateral interaural axis, thus compensating for the loss of localization ability in the second and third regions. This was determined from changes recorded in the error data, and also the extent and direction of measured head-movements. The results of this series of experiments demonstrated the relationship between spectral cues and monaural localization. Firstly, monaural localization was not possible in the absence of accurate spectral information. Thus large errors were observed in the third region where there was blockage of the high-frequencies by the head, and in all regions during the presentation of low signal levels where the high-frequencies fell below threshold. Secondly, the inaccuracy of the second region due to the loss of information from the second pinna suggested that there was a binaural component with relation to pinna cues. It seems that for sounds in this region the spectral modifications from both pinnae are processed to determine a sound's location in space.

Secure localization with attack detection in wireless sensor networks

Rapid technological advances have enabled the development of low-cost sensor networks for various monitoring tasks, where it is important to estimate the positions of a number of regular sensor nodes whose locations cannot be known apriori. We address the problem of localizing the regular nodes with range-based location references obtained from certain anchor nodes referred to as beacons, particularly in an adverse environment where some of the beacons may be compromised. We propose an innovative modular solution featuring two lightweight modules that are for dedicated functionalities, respectively, but can also be closely integrated. First, we harness simple geometric triangular rules and an efficient voting technique to enable the attack detection module, which identifies and filters out malicious location references. We then develop a secure localization module that computes and clusters certain reference points, and the position of the concerned regular node is estimated with the centroid of the most valuable reference points identified. Extensive simulations show that our attack detection module can detect compromised beacons effectively, and the secure localization module can subsequently provide a dependable localization service in terms of bounded estimation error. The integrated system turns out to be tolerant of malicious attacks even in highly challenging scenarios.

A robust solution to the stereo-vision-based simultaneous localization and mapping problem with steady and moving landmarks

The problem of visual simultaneous localization and mapping (SLAM) is examined in this paper using recently developed ideas and algorithms from modern robust control and estimation theory. A nonlinear model for a stereo-vision-based sensor is derived that leads to nonlinear measurements of the landmark coordinates along with optical flow-based measurements of the relative robot-landmark velocity. Using a novel analytical measurement transformation, the nonlinear SLAM problem is converted into the linear domain and solved using a robust linear filter. Actually, the linear filter is guaranteed stable and the SLAM state estimation error is bounded within an ellipsoidal set. A mathematically rigorous stability proof is given that holds true even when the landmarks move in accordance with an unknown control input. No similar results are available for the commonly employed extended Kalman filter, which is known to exhibit divergence and inconsistency characteristics in practice. A number of illustrative examples are given using both simulated and real vision data that further validate the proposed method.

3D Intervertebral disc localization and segmentation from MR images by data-driven regression and classification

In this paper we propose a new fully-automatic method for localizing and segmenting 3D intervertebral discs from MR images, where the two problems are solved in a unified data-driven regression and classification framework. We estimate the output (image displacements for localization, or fg/bg labels for segmentation) of image points by exploiting both training data and geometric constraints simultaneously. The problem is formulated in a unified objective function which is then solved globally and efficiently. We validate our method on MR images of 25 patients. Taking manually labeled data as the ground truth, our method achieves a mean localization error of 1.3 mm, a mean Dice metric of 87%, and a mean surface distance of 1.3 mm. Our method can be applied to other localization and segmentation tasks.

Localization and degmentation of 3D intervertebral discs in MR Images by data driven estimation

This paper addresses the problem of fully-automatic localization and segmentation of 3D intervertebral discs (IVDs) from MR images. Our method contains two steps, where we first localize the center of each IVD, and then segment IVDs by classifying image pixels around each disc center as foreground (disc) or background. The disc localization is done by estimating the image displacements from a set of randomly sampled 3D image patches to the disc center. The image displacements are estimated by jointly optimizing the training and test displacement values in a data-driven way, where we take into consideration both the training data and the geometric constraint on the test image. After the disc centers are localized, we segment the discs by classifying image pixels around disc centers as background or foreground. The classification is done in a similar data-driven approach as we used for localization, but in this segmentation case we are aiming to estimate the foreground/background probability of each pixel instead of the image displacements. In addition, an extra neighborhood smooth constraint is introduced to enforce the local smoothness of the label field. Our method is validated on 3D T2-weighted turbo spin echo MR images of 35 patients from two different studies. Experiments show that compared to state of the art, our method achieves better or comparable results. Specifically, we achieve for localization a mean error of 1.6-2.0 mm, and for segmentation a mean Dice metric of 85%-88% and a mean surface distance of 1.3-1.4 mm.

Fully automatic localization and segmentation of 3D vertebral bodies from CT/MR images via a learning-based method

In this paper, we address the problems of fully automatic localization and segmentation of 3D vertebral bodies from CT/MR images. We propose a learning-based, unified random forest regression and classification framework to tackle these two problems. More specifically, in the first stage, the localization of 3D vertebral bodies is solved with random forest regression where we aggregate the votes from a set of randomly sampled image patches to get a probability map of the center of a target vertebral body in a given image. The resultant probability map is then further regularized by Hidden Markov Model (HMM) to eliminate potential ambiguity caused by the neighboring vertebral bodies. The output from the first stage allows us to define a region of interest (ROI) for the segmentation step, where we use random forest classification to estimate the likelihood of a voxel in the ROI being foreground or background. The estimated likelihood is combined with the prior probability, which is learned from a set of training data, to get the posterior probability of the voxel. The segmentation of the target vertebral body is then done by a binary thresholding of the estimated probability. We evaluated the present approach on two openly available datasets: 1) 3D T2-weighted spine MR images from 23 patients and 2) 3D spine CT images from 10 patients. Taking manual segmentation as the ground truth (each MR image contains at least 7 vertebral bodies from T11 to L5 and each CT image contains 5 vertebral bodies from L1 to L5), we evaluated the present approach with leave-one-out experiments. Specifically, for the T2-weighted MR images, we achieved for localization a mean error of 1.6 mm, and for segmentation a mean Dice metric of 88.7% and a mean surface distance of 1.5 mm, respectively. For the CT images we achieved for localization a mean error of 1.9 mm, and for segmentation a mean Dice metric of 91.0% and a mean surface distance of 0.9 mm, respectively.

Creatine transporter protein content, localization, and gene expression in rat skeletal muscle

The present study examined the gene expression and cellular localization of the creatine transporter (CreaT) protein in rat skeletal muscle. Soleus (SOL) and red (RG) and white gastrocnemius (WG) muscles were analyzed for CreaT mRNA, CreaT protein, and total creatine (TCr) content. Cellular location of the CreaT protein was visualized with immunohistochemical analysis of muscle cross sections. TCr was higher (P <= 0.05) in WG than in both RG and SOL, and was higher in RG than in SOL. Total CreaT protein content was greater (P <= 0.05) in SOL and RG than in WG. Two bands (55 and 70 kDa) of the CreaT protein were found in all muscle types. Both the 55-kDa (CreaT-55) and the 70-kDa (CreaT-70) bands were present in greater (P <= 0.05) amounts in SOL and RG than in WG. SOL and RG had a greater amount (P <= 0.05) of CreaT-55 than CreaT-70. Immunohistochemical analysis revealed that the CreaT was mainly associated with the sarcolemmal membrane in all muscle types. CreaT mRNA expression per microgram of total RNA was similar across the three muscle types. These data indicate that rat SOL and RG have an enhanced potential to transport Cr compared with WG, despite a higher TCr in the latter.

Effect of the toxic milk mutation (tx) on the function and intracellular localization of Wnd, the murine homologue of the Wilson copperATPase

Wilson disease is an autosomal recessive copper transport disorder resulting from defective biliary excretion of copper and subsequent hepatic copper accumulation and liver failure if not treated. The disease is caused by mutations in the ATP7B (WND) gene, which is expressed predominantly in the liver and encodes a copper-transporting P-type ATPase that is structurally and functionally similar to the Menkes protein (MNK), which is defective in the X-linked copper transport disorder Menkes disease. The toxic milk (tx) mouse has a clinical phenotype similar to Wilson disease patients and, recently, the tx mutation within the murine WND homologue (Wnd) of this mouse was identified, establishing it as an animal model for Wilson disease. In this study, cDNA constructs encoding the wild-type (Wnd-wt) and mutant (Wnd-tx) Wilson proteins (Wnd) were generated and expressed in Chinese hamster ovary (CHO) cells. The tx mutation disrupted the copper-induced relocalization of Wnd in CHO cells and abrogated Wnd-mediated copper resistance of transfected CHO cells. In addition, co-localization experiments demonstrated that while Wnd and MNK are located in the trans-Golgi network in basal copper conditions, with elevated copper, these proteins are sorted to different destinations within the same cell. Ultrastructural studies showed that with elevated copper levels, Wnd accumulated in large multi-vesicular structures resembling late endosomes that may represent a novel compartment for copper transport. The data presented provide further support for a relationship between copper transport activity and the copper-induced relocalization response of mammalian copper ATPases, and an explanation at a molecular level for the observed phenotype of tx mice

Some unusual applications of the `Error-bar` feature in Excel spreadsheets

This paper demonstrates how the "error-bar" feature can be used to extend the utility of "worldware" spreadsheet packages in producing high-quality graphs for university teaching and learning, and for research. To further utilize the advantages of spreadsheets in university education, this paper seeks to overcome some of the earlier reservations about the lack of scientific plotting capabilities of spreadsheet applications. Specific examples of educational material in the areas of enzyme kinetics, vibrational spectroscopy, vibronic spectroscopy, and mass spectrometry are discussed. It is argued that, where practical, university educators should use "worldware" packages to prepare teaching aids, since these would better prepare their students for future employment. The use of software features for purposes that were not envisioned by the programmers has additional educational benefits in fostering flexibility and innovation. Other graphing packages can also use the "error-bar" feature in a manner similar to that described here for Excel.

In situ localization associates biologically active plant natriuretic peptide immuno-analogues with conductive tissue and stomata

Plant natriuretic peptide immuno-analogues (irPNP) have previously been shown to affect a number of biological processes including stomatal guard cell movements, ion fluxes and osmoticum-dependent water transport. Tissue printing and immunofluorescent labelling techniques have been used here to study the tissue and cellular localization of irPNP in ivy (Hedera helix L.) and potato (Solanum tuberosum L.). Polyclonal antibodies active against human atrial natriuretic peptide (anti-hANP) and antibodies against irPNP from potato (anti-StPNP) were used for immunolabelling. Tissue prints revealed that immunoreactants are concentrated in vascular tissues of leaves, petioles and stems. Phloem-associated cells, xylem cells and parenchymatic xylem cells showed the strongest immunoreaction. Immunofluorescent microscopy with fluorescein isothiocyanate (FITC)-conjugated goat anti-rabbit IgG supported this finding and, furthermore, revealed strong labelling to stomatal guard cells and the adjacent apoplastic space as well. Biologically active immunoreactants were also detected in xylem exudates of a soft South African perennial forest sage (Plectranthus ciliatus E. Mey ex Benth.) thus strengthening the evidence for a systemic role of the protein. In summary, in situ cellular localization is consistent with physiological responses elicited by irPNPs reported previously and is indicative of a systemic role in plant homeostasis.

A shape error metric for sheet metal forming and its application to springback

There are many variations within sheet metal forming, some of which are manifest in the final geometry of the formed component. It is important that this geometric variation be quantified and measured for use in a process or quality control system. The contribution of this paper is to propose a novel way of measuring the geometric difference between the desired shape and an actual formed "U" channel. The metric is based upon measuring errors in terms of the significant manufacturing variations. The metric accords with the manually measured errors of the channel set. The shape error metric is then extended to develop a simple empirical, whole-component, springback error measure. The springback error measure combines into one value all the angle springback and side wall curl geometric errors for a single channel. Two trends were observed: combined springback decreases when the blank holder force is increased; and the combined springback marginally decreases when the die radii is increased.

Prediction error property of the Lasso Estimator and its generalization

The lasso procedure is an estimator-shrinkage and variable selection method. This paper shows that there always exists an interval of tuning parameter values such that the corresponding mean squared prediction error for the lasso estimator is smaller than for the ordinary least squares estimator. For an estimator satisfying some condition such as unbiasedness, the paper defines the corresponding generalized lasso estimator. Its mean squared prediction error is shown to be smaller than that of the estimator for values of the tuning parameter in some interval. This implies that all unbiased estimators are not admissible. Simulation results for five models support the theoretical results.

Effect of exercise on protein kinase C activity and localization in human skeletal muscle

To investigate the effect of exercise on protein kinase C (PKC) activity and localization in human skeletal muscle, eight healthy men performed cycle  ergometer exercise for 40 min at 76±1% the peak pulmonary O₂ uptake (V_O2peak), with muscle samples obtained at rest and after 5 and 40 min of exercise. PKC expression, phosphorylation and activities were examined by immunoblotting and in vitro kinase assays of fractionated and whole tissue preparations. In response to exercise, total PKC activity was slightly higher at 40 min in an enriched membrane fraction, and using a pSer-PKC-substrate motif antibody it was revealed that exercise increased the serine phosphorylation of a ∼50 kDa protein. There were no changes in conventional PKC (cPKC) or PKCθ activities; however, atypical PKC (aPKC) activity was ∼70% higher at 5 and 40 min, and aPKC expression and Thr^410/403 phosphorylation were unaltered by exercise. There were no effects of exercise on the abundance of PKCα, PKCδ, PKCθ and aPKC within cytosolic or enriched membrane fractions of skeletal muscle. These data indicate that aPKC, but not cPKC or PKCθ, are activated by exercise in contracting muscle suggesting a potential role for aPKC in the regulation of skeletal muscle function and metabolism during exercise in humans.

Node localization using mobile robots in delay-tolerant sensor networks

We present a novel scheme for node localization in a Delay-Tolerant Sensor Network (DTN). In a DTN, sensor devices are often organized in network clusters that may be mutually disconnected. Some mobile robots may be used to collect data from the network clusters. The key idea in our scheme is to use this robot to perform location estimation for the sensor nodes it passes based on the signal strength of the radio messages received from them. Thus, we eliminate the processing constraints of static sensor nodes and the need for static reference beacons. Our mathematical contribution is the use of a Robust Extended Kalman Filter (REKF)-based state estimator to solve the localization. Compared to the standard extended Kalman filter, REKF is computationally efficient and also more robust. Finally, we have implemented our localization scheme on a hybrid sensor network test bed and show that it can achieve node localization accuracy within 1m in a large indoor setting.