Bi-Manual Sensory Discrimination: A Kinesthetic Study.

The ability of humans to perceive and differentiate kinesthetic sensory information significantly influences our daily activities and motor control. This study examines the impact of asynchronous bi-manual discrimination tasks compared to uni-manual discrimination tasks on kinesthetic perception. Our study aims to reveal the relationship between kinesthetic perception of haptic signals by examining perceptual thresholds in three different scenarios using (i) the dominant hand, (ii) the non-dominant hand, and (iii) both hands simultaneously to differentiate between two successive signals. Subjects exposed to force signals in these three situations conveyed their perceptions of alterations in signal magnitude after each trial. Subsequently, we applied psychometric functions to the collected responses to determine perceptual thresholds. Our results indicate a substantial difference in threshold values between bi-manual and uni-manual scenarios, with the bi-manual scenario exhibiting higher thresholds, indicating inferior perceptual ability when both hands are simultaneously utilized in two separate discrimination tasks. Furthermore, our investigation reveals distinct perception thresholds between the dominant and non-dominant hands, owing to differences in the perceptual capability of the two hands. These findings provide substantial insight into how the nature of tasks may alter kinesthetic perception, offering implications for the development of haptic interfaces in practical applications.

Theoretical Analysis of Null Foley-Sammon Transform and its Implications.

Null Foley-Sammon Transform (NFST) has received increasing attention in the machine learning and pattern recognition literature. NFST finds a discriminative nullspace where all samples of the same class get mapped into a single point. It has a closed form solution and is free of parameters to tune. NFST has been leveraged in many areas including novelty detection, person or vehicle re-identification and achieved state-of-the-art results. Motivated from its attractive properties and its effectiveness in wide range of applications, in this paper we focus on the theoretical analysis of NFST. In previous literature, NFST was shown to exist in small sample size (SSS) case. We first prove that NFST can exist in non-SSS case also, under certain conditions. Thereby, we extend the domain of applicability of NFST to a more general case. Secondly, we perform analysis of the singular points of NFST, revealing important insights on their identities and existence. Thirdly, we show the theoretical relation between NFST of SSS data and NFST of the non-SSS data obtained by PCA. Fourthly, we show that this theoretical relation can be exploited to obtain an efficient algorithm for computing NFST on high dimensional SSS data. Finally, we perform extensive experiments to validate our theoretical analysis.

Novel speed-up strategies for non-local means denoising with patch and edge patch based dictionaries.

In this paper, a novel technique to speed-up a nonlocal means (NLM) filter is proposed. In the original NLM filter, most of its computational time is spent on finding distances for all the patches in the search window. Here, we build a dictionary in which patches with similar photometric structures are clustered together. Dictionary is built only once with high resolution images belonging to different scenes. Since the dictionary is well organized in terms of indexing its entries, it is used to search similar patches very quickly for efficient NLM denoising. We achieve a substantial reduction in computational cost compared with the original NLM method, especially when the search window of NLM is large, without much affecting the PSNR. Second, we show that by building a dictionary for edge patches as opposed to intensity patches, it is possible to reduce the dictionary size; thus, further improving the computational speed and memory requirement. The proposed method preclassifies similar patches with the same distance measure as used by NLM method. The proposed algorithm is shown to outperform other prefiltering based fast NLM algorithms computationally as well as qualitatively.

A 21st century approach to tackling dengue: Crowdsourced surveillance, predictive mapping and tailored communication.

This paper describes a social media system to prevent dengue in Sri Lanka and potentially in the rest of the South and Southeast Asia regions. The system integrates three concepts of public health prevention that have thus far been implemented only in silos. First, the predictive surveillance component uses a computer simulation to forewarn health authorities and the general public about impending disease outbreaks. The civic engagement component allows the general public to use social media tools to interact and engage with health authorities by aiding them in surveillance efforts by reporting symptoms, mosquito bites and breeding sites using smartphone technologies. The health communication component utilizes citizen data gathered from the first two components to disseminate customized health awareness messages to enhance knowledge and increase preventive behaviors among citizens. The system, known as "Mo-Buzz," will be made available on a host of digital platforms like simple mobile phones, smart phones and a website. We present challenges and lessons learnt including content validation, stakeholder collaborations and applied trans-disciplinary research.

Laplacian based non-local means denoising of MR images with Rician noise.

Magnetic Resonance (MR) image is often corrupted with a complex white Gaussian noise (Rician noise) which is signal dependent. Considering the special characteristics of Rician noise, we carry out nonlocal means denoising on squared magnitude images and compensate the introduced bias. In this paper, we propose an algorithm which not only preserves the edges and fine structures but also performs efficient denoising. For this purpose we have used a Laplacian of Gaussian (LoG) filter in conjunction with a nonlocal means filter (NLM). Further, to enhance the edges and to accelerate the filtering process, only a few similar patches have been preselected on the basis of closeness in edge and inverted mean values. Experiments have been conducted on both simulated and clinical data sets. The qualitative and quantitative measures demonstrate the efficacy of the proposed method.

Design and implementation of an automated secondary cooling system for the continuous casting of billets.

This paper describes a heat transfer model based automatic secondary cooling system for a billet caster. The model aims to minimize the variation in surface temperature and excessive reheating of the billet strands. It is also used to avoid the low ductility trough of the solidifying steel, which aggravates the tendency of steel to crack. The system has been designed and implemented in an integrated steel plant. A Programmable Logic Controller (PLC) based automation system has been developed to control the water flow in the secondary cooling zones of the strand. The results obtained through field trials have shown complete elimination of internal and off-corner cracks for the fifty billet samples that were monitored.

Stabilization of parametric active contours using a tangential redistribution term.

Depending on implementation, active contours have been classified as geometric or parametric active contours. Parametric contours, irrespective of representation, are known to suffer from the problem of irregular bunching and spacing out of curve points during the curve evolution. In a spline-based implementation of active contours, this leads to occasional formation of loops locally, and subsequently the curve blows up due to instabilities. In this paper, we analyze the reason for this problem and propose a solution to alleviate the same. We propose an ordinary differential equation (ODE) for controlling the curve parametrization during evolution by including a tangential force. We show that the solution of the proposed ODE is bounded. We demonstrate the effectiveness of the proposed method for segmentation and tracking tasks on closed as well as open contours.

Impact of ambulation in wearable-ECG.

Ambulatory ElectroCardioGram (ECG) analysis is adversely affected by motion artifacts induced due to body movements. Knowledge of the extent of motion artifacts could facilitate better ECG analysis. In this paper, our purpose is to determine the impact of body movement kinematics on the extent of ECG motion artifact by defining a notion called impact signal. Two approaches have been adopted in this paper to validate our experiments. One of them involves measuring local acceleration using motion sensors at appropriate body positions, in conjunction with the ECG, while performing routine activities at different intensity levels. The other method consists of ECG acquisition during Treadmill testing at controlled speeds and fixed duration. Data has been acquired from both healthy subjects as well as patients with suspected cardio-vascular disorders. In case of patients, the treadmill tests were carried out under the supervision of a cardiologist. We demonstrate that the impact signal shows a proportional increase with the increasing activity levels. The measured accelerations obtained are also found to be well correlated with the impact signal. The impact analysis thus indicates the suitability of the proposed method for quantification of body movement kinematics from the ECG signal itself, even in the absence of any accelerometer sensors. Such quantification would also help in automatic documentation of patient activity levels, which could aid in better interpretation of ambulatory ECG.

Impact analysis of body movement in ambulatory ECG.

Ambulatory ECG analysis is adversely affected by motion artifacts induced due to body movements. Knowledge of the extent of motion artifacts facilitates better ECG analysis. In [1], an unsupervised method using recursive principal component analysis (RPCA) was used to detect transitions between body movements. In this paper, we endeavour to quantify the impact of various types of body movements on the extent of ECG motion artifact using the RPCA error signal. For this purpose, acceleration data from different body parts i.e. arm(s), leg and waist, have been obtained using commercially available motion sensors, in conjunction with ECG signal, while carrying out routine body movement activities like climbing stairs, walking, twisting, and arm movements, at three different intensity levels: slow, medium and fast. The acceleration magnitudes and the RPCA error sequence are found to be well correlated, thus validating the body movement impact analysis, and also indicating the suitability of the method for quantification of body movement kinematics from the ECG signal itself in the absence of any accelerometer sensors.

Transition detection in body movement activities for wearable ECG.

It has been shown by Pawar et al. (2007) that the motion artifacts induced by body movement activity (BMA) in a single-lead wearable electrocardiogram (ECG) signal recorder, while monitoring an ambulatory patient, can be detected and removed by using a principal component analysis (PCA)-based classification technique. However, this requires the ECG signal to be temporally segmented so that each segment comprises of artifacts due to a single type of body movement activity. In this paper, we propose a simple, recursively updated PCA-based technique to detect transitions wherever the type of body movement is changed.

Body movement activity recognition for ambulatory cardiac monitoring.

Wearable electrocardiogram (W-ECG) recorders are increasingly in use by people suffering from cardiac abnormalities who also choose to lead an active lifestyle. The challenge presently is that the ECG signal is influenced by motion artifacts induced by body movement activity (BMA) of the wearer. The usual practice is to develop effective filtering algorithms which will eliminate artifacts. Instead, our goal is to detect the motion artifacts and classify the type of BMA from the ECG signal itself. We have recorded the ECG signals during specified BMAs, e.g., sitting still, walking, movements of arms and climbing stairs, etc. with a single-lead system. The collected ECG signal during BMA is presumed to be an additive mix of signals due to cardiac activities, motion artifacts and sensor noise. A particular class of BMA is characterized by applying eigen decomposition on the corresponding ECG data. The classification accuracies range from 70% to 98% for various class combinations of BMAs depending on their uniqueness based on this technique. The above classification is also useful for analysis of P and T waves in the presence of BMA.

Qualitative visual environment retrieval.

A system for retrieval of an unstructured environment under static and dynamic scenarios is proposed. The use of cylindrical mosaics or omnidirectional images is exploited for providing a rich description about the surrounding environment spanning 360 degrees. The environment description is based on defining the attributes of the nodes of a graph derived from the angular partitions of the captured images. Content-based image retrieval for each of these partitions is performed on an exemplar image database to annotate the nodes of the graph. The complete environment description is recovered by collating the retrieval results over all the partitions based on a simple voting scheme. This offers a qualitative description of the location in a totally natural and unstructured surrounding. The experiments yield quite promising results.

An object-based coding scheme for frontal surface of defective fluted ingot.

A novel image-based defect identification and coding technique has been proposed for fluted ingots, which are used for the production of locomotive wheels. The edge density map has been used for defect identification and an object-based coding approach has been applied for the storage of defective ingots. The complete scheme has been implemented in one of the integrated steel plants of India.

Analysis of ambulatory ECG signal.

Ambulatory electrocardiogram (ECG) recorders are increasingly in use by people suffering from cardiac abnormalities. However, the ECG signal acquired by the ambulatory recorder is influenced by motion artifacts induced by any body movement activity (BMA). The goal of the paper is to demonstrate that it is possible to determine the BMA from the motion artifacts in the ECG signal itself. The ECG signal during a specific BMA is presumed to be an additive mix of signals due to cardiac activities, motion artifacts induced due to the BMA and sensor noise. We propose to characterize and determine the BMA from the corresponding motion artifact data in the ECG signal itself. The proposed technique is useful for removal of motion artifacts from the ECG signals for ambulatory cardiac monitoring.

Defocus morphing in real aperture images.

A new concept called defocus morphing in real aperture images is introduced. View morphing is an existing example of shape-preserving image morphing based on the motion cue. It is proved that images can also be morphed based on the depth-related defocus cue. This illustrates that the morphing operation is not necessarily a geometric process alone; one can also perform a photometry-based morphing wherein the shape information is implicitly buried in the image intensity field. A theoretical understanding of the defocus morphing process is presented. It is shown mathematically that, given two observations of a three-dimensional scene for different camera parameter settings, we can obtain a virtual observation for any camera parameter setting through a simple nonlinear combination of these observations.

A learning-based method for image super-resolution from zoomed observations.

We propose a technique for super-resolution imaging of a scene from observations at different camera zooms. Given a sequence of images with different zoom factors of a static scene, we obtain a picture of the entire scene at a resolution corresponding to the most zoomed observation. The high-resolution image is modeled through appropriate parameterization, and the parameters are learned from the most zoomed observation. Assuming a homogeneity of the high-resolution field, the learned model is used as a prior while super-resolving the scene. We suggest the use of either a Markov random field (MRF) or an simultaneous autoregressive (SAR) model to parameterize the field based on the computation one can afford. We substantiate the suitability of the proposed method through a large number of experimentations on both simulated and real data.

Joint blind restoration and surface recovery in photometric stereo.

We address the problem of simultaneous estimation of scene structure and restoration of images from blurred photometric measurements. In photometric stereo, the structure of an object is determined by using a particular reflectance model (the image irradiance equation) without considering the blurring effect. What we show is that, given arbitrarily blurred observations of a static scene captured with a stationary camera under different illuminant directions, we still can obtain the structure represented by the surface gradients and the albedo and also perform a blind image restoration. The surface gradients and the albedo are modeled as separate Markov random fields, and a suitable regularization scheme is used to estimate the different fields as well as the blur parameter. The results of the experimentations are illustrated with real as well as synthetic images.

A novel shape-based coding-decoding technique for an industrial visual inspection system.

This paper describes a unique single camera-based dimension storage method for image-based measurement. The system has been designed and implemented in one of the integrated steel plants of India. The purpose of the system is to encode the frontal cross-sectional area of an ingot. The encoded data will be stored in a database to facilitate the future manufacturing diagnostic process. The compression efficiency and reconstruction error of the lossy encoding technique have been reported and found to be quite encouraging.

A distortion corrected single camera-based weight estimation technique for industrial objects.

This paper describes a unique single camera-based dimensional measurement with a self-calibration method of image-based measurement. The system has been designed and implemented in one of the integrated steel plants in India. The purpose of the system is to obtain the frontal cross-sectional area of an ingot irrespective of its distance from the camera head. Automatic calibration is achieved by attaching a magnetic template of known area. This self-calibrating system is further refined to correct for the various distortions arising out of lens characteristics. The results obtained through field trials have been reported and found to be quite encouraging.