Miracle-dz/UniFAR-FTUs huggingface dataset:
Optical imaging of the spatiotemporal dynamics of cerebral blood flow and oxidative metabolism in the rat barrel cortex

Oxidative metabolism and cerebral blood flow (CBF) are two of the most important measures in neuroimaging. However, results from concurrent imaging of the two with high spatial and temporal resolution have never been published. We used flavoprotein autofluorescence (AF) and laser speckle imaging (LSI) in the anaesthetized rat to map oxidative metabolism and CBF in response to single vibrissa stimulation. Autofluorescence responses reflecting oxidative metabolism demonstrated a fast increase with a delay of 0.1 s. The sign‐reversed speckle contrast reflecting CBF started to rise with a delay of 0.6 s and reached its maximum 1.4 s after the stimulation offset. The fractional signal changes were 2.0% in AF and 9.7% in LSI. Pixelwise modelling revealed that CBF maps spread over an area up to 2.5‐times larger than metabolic maps. The results provide evidence that the increase in cerebral oxidative metabolism in response to sensory stimulation is considerably faster and more localized than the CBF response. This suggests that future developments in functional imaging concentrating on the metabolic response promise an increased spatial resolution.
Isolated Malayalam digit recogntion using Support Vector Machines

Voice is the natural communication system used by all beings, human beings in particular. Understanding and recognizing human uttered voice for various applications is the core technology of “information” age. Automatic speech recognition has wide spread applications in real life situations. Here speech recognition of Malayalam isolated digit is created by using Mel Frequency Cepstral Coefficients (MFCC) and Support Vector Machines. (SVM).
A Load Balancing Approach for Silicon Retina Based Asynchronous Temporal Data Processing

In this paper we present a load balancing approach for Silicon Retina technology based computer vision applications. The Silicon Retina technology is a new kind of bio-inspired analogue sensor that is derived from the human vision system. In comparison to conventional imagers that provide frame information captured by a fixed frame-rate, Silicon Retina imagers only provide information of pixels with variations of intensity in a scene. The high amount of pixels without intensity variations need neither be transmitted nor processed. Due to these special characteristics, the imager delivers asynchronous data with data-rates up to a peak of 6M events per second (Meps) per channel and a time resolution of 10ns. A distributed embedded system consisting of a single-core processor for data acquisition and load balancing and a multi-core processor for data processing is used. We discuss both the Silicon Retina technology, the principles of the computer vision algorithms being used, and the load balancing approach.
Students Taking Social Action: Critical Literacy Practices Through School-As-Museum Learning

Author(s): Caffrey, Genevieve Erker; Rogers, Rebecca | Abstract: Inspired by critical literacy practices, sixth-grade students at Carter Elementary designed, curated, and publicly displayed a museum exhibit to expose and confront issues of social justice. Through this case study of one display within the exhibit, we analyze the ideas and stances represented in each of its artifacts and investigate how, together, the data sources create a discursive chain in regard to social action. We call on critically oriented discourse analysis (Gee, 2005; Rogers a Mosley Wetzel, 2013) to interpret the densely multimodal artifacts, considering how ideas and stances are embodied and intertextual. Our findings reveal how student-created museum learning can stimulate transformative stances toward social action and serve as powerful mediums for youth activism. The study contributes important insights to the field of literacy studies, particularly how social action can be integrated into teaching and learning processes through multimodal public exhibits.
Pose imagery and automated three-dimensional modeling of urban environments

Three-dimensional (3-D) modeling of urban environments has numerous applications, including virtual environments, urban planning, and physical simulation. Constructing 3-D models from photographs (images) is thus an important area of research in computer vision, and increasingly, computer graphics. However, despite many years of research, a system that automatically recovers realistic 3-D models remains elusive; most practical systems require significant human input. Unlike automatic algorithms, human-assisted systems are not scalable, both in terms of the number of images processed and the complexity of the generated 3-D model. This thesis describes novel techniques to automatically extract textured 3-D models of urban environments from pose imagery, i.e., images annotated with camera position and orientation in a single global coordinate system. Physical instruments (e.g., surveying, Global Positioning System (GPS), inertial sensors, etc.) are used to provide accurate initial pose estimates to the proposed algorithms. As these estimates are not perfect, I first describe two optimization techniques that refine pose estimates using information present in the images: spherical mosaicing recovers relative rotations between images taken from a single position, and mosaic registration accurately locates mosaics in a global coordinate system. Next, I describe an algorithm that extracts vertical facades from mosaics annotated with accurate pose. The algorithm employs horizontal line segments to detect likely facade orientations and locates these facades using a space-sweep technique. Textures are robustly computed for the facades by combining information from several mosaics using median statistics. I present results for a large pose image dataset (consisting of about four thousand images taken from eighty-one positions) of an urban office complex. These techniques were successful in recovering all significant vertical facades in the complex, as well as several ell as several neighboring facades. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)
Maintaining stream statistics over sliding windows: (extended abstract)

We consider the problem of maintaining aggregates and statistics over data streams, with respect to the last <i>N</i> data elements seen so far. We refer to this model as the <i>sliding window</i> model. We consider the following basic problem: Given a stream of bits, maintain a count of the number of 1's in the last <i>N</i> elements seen from the stream. We show that using <i>O</i>(1/<i>e</i> log<sup>2</sup><i>N</i>) bits of memory, we can estimate the number of 1's to within a factor of 1 + ε. We also give a matching lower bound of Ω(1/<i>e</i> log<sup>2</sup> <i>N</i>) memory bits for any deterministic or randomized algorithms. We extend our scheme to maintain the sum of the last <i>N</i> positive integers. We provide matching upper and lower bounds for this more general problem as well. We apply our techniques to obtain efficient algorithms for the <i>L<inf>p</inf></i> norms (for <i>p</i> ε [1, 2]) of vectors under the sliding window model. Using the algorithm for the basic counting problem, one can adapt many other techniques to work for the sliding window model, with a multiplicative overhead of <i>O</i>(1/εlog <i>N</i>) in memory and a 1 + ε factor loss in accuracy. These include maintaining approximate histograms, hash tables, and statistics or aggregates such as sum and averages.
On Log-Normality of RSSI in Narrowband Receivers Under Static Conditions

A growing set of environmental sensing applications use received signal strength measurements of a static wireless network for unobtrusive monitoring purposes. The success of these systems, which typically process low-amplitude signals, depend strongly on the distribution of the measurements when there are no changes in the channel. In this letter, a statistical model for signal strength measurements acquired when the environment is static is studied. As previously empirically verified, it is shown that the measurements have log-normal distribution even in idealistic environments, which cannot be explained using log-normal shadow fading arguments. Quantization and round-off errors induced by different measurement system components are also considered, and their impact are analyzed. As a result, it is shown that the logarithmic received signal strength measurements under static channel conditions are samples from stationary Gaussian process independent of the environment.
Enhancing Father Engagement in Parenting Programs: Translating Research into Practice Recommendations

Objective Given the increasing research and practice interest in father engagement, this article aimed to develop a clinical narrative integrating the extant research literature to distil key practice recommendations for enhancing father engagement in parenting interventions for child wellbeing. Method A narrative review of research on father engagement in interventions for child wellbeing was conducted, to identify and distil evidence‐based policies and practices to enhance father engagement for practitioners and organisations. Results Six broad policy and practice recommendations are provided that pertain to: engaging the parenting team, avoiding a father deficit model, increasing father awareness of parenting interventions, ensuring father‐inclusive program content and delivery, increasing organisational support for father‐inclusive practice, and increasing professional father engagement training. Conclusion This review provides practitioners with guidelines for enhancing father engagement based on the available research. It also provides recommendations for further research regarding the effectiveness of strategies to enhance father engagement.
Planning force-closure grasps on 3-D objects

Computation of grasps with form/force-closure is one of the fundamental problems in the study of multifingered grasping and dexterous manipulation. Based on the geometric condition of the closure property, this paper presents a numerical test to quantify how far a grasp is from losing form/force-closure. With the polyhedral approximation of the friction cone, the proposed numerical test can be formulated as a single linear program. An iterative algorithm for computing optimal force-closure grasps, which is implemented by minimizing the proposed numerical test in the grasp configuration space, is also developed. The algorithm is computationally efficient and generally applicable. It can be used for computing form/force-closure grasps on 3D objects with curved surfaces, and with any number of contact points. Several simulation examples are given to show the effectiveness and computational efficiency of the proposed algorithm.
Portcullis: protecting connection setup from denial-of-capability attacks

Systems using capabilities to provide preferential service to selected flows have been proposed as a defense against large-scale network denial-of-service attacks. While these systems offer strong protection for established network flows, the Denial-of-Capability (DoC) attack, which prevents new capability-setup packets from reaching the destination, limits the value of these systems. Portcullis mitigates DoC attacks by allocating scarce link bandwidth for connection establishment packets based on per-computation fairness. We prove that a legitimate sender can establish a capability with high probability regardless of an attacker's resources or strategy and that no system can improve on our guarantee. We simulate full and partial deployments of Portcullis on an Internet-scale topology to confirm our theoretical results and demonstrate the substantial benefits of using per-computation fairness.
Transform coding of arbitrarily-shaped image segments

Envisioned advanced multimedia video services include both rectangular and arbitrarily-shaped image segments. Image segments of the TV weather reporter produced by the chromo-key technique and image segments produced by video segmentation or image editing are typical examples. In this paper, we investigate efficient transform coding techniques of arbitrarily-shaped image segments. We formulate the optimal representation problem in two different domains — the full rectangular domain and the shape-projected domain. In the former, we still use the traditional rectangular transform coding method (e.g. DCT) but try to find optimal pixel values outside the segment boundary in order to make the transform spectrum as compact as possible. A simple but efficient mirror-image extension technique is proposed. In the shape-projected domain, we project the image segment and all basis functions into the subspace spanned over the image region only. Existing coding algorithms, such as orthogonal transform by Gilge [1] and iterative coding by Kaup and Aach [2], can be intuitively interpreted. To demonstrate the flexibility of the proposed formulation, we also derive a new KLT-like algorithm in the shape-projected domain. We analyze tradeoff between compression performance, computational complexity, and codec complexity for different coding schemes. Simulation results show that complicated algorithms (e.g. iterative, adaptive) can improve the quality by about 5-10 dB at some computational or hardware cost. On the other hand, the proposed simple mirror-image extension technique improves the quality by about 3-4 dB without any overheads. The contributions of this paper lie in efficient problem formulation, new transform coding techniques, and numerical tradeoff analyses. Currently, we are implementing a software program for AS image object editing and manipulation .
Weather Detection in Vehicles by Means of Camera and LIDAR Systems

We describe a method for the automatic recognition of weather conditions from a moving car. Our system consists of sensors to acquire data from cameras as well as from Light Detection and Recognition (LIDAR) instruments. We discuss how this data can be collected, analyzed and merged to assist the control systems of moving vehicles in making autonomous decisions. Laboratory based experimental results are presented for weather conditions like rain and fog, showing that the recognition scenario works with better than adequate results. This paper demonstrates that LIDAR technology, already onboard for the purpose of autonomous driving independent from auxiliary light sources, can be used to improve weather condition recognition when compared with a camera only system. We conclude that the combination of a front camera and a LIDAR laser scanner is well suited as a sensor instrument set for weather recognition that can contribute accurate data to driving assistance systems.
DPCC and QR factorization-based color medical image authentication algorithm

This paper presents a novel color medical image authentication scheme based on QR Factorization and Dual Pseudorandom Circular Chain (DPCC). The red component and the green component of the color image possess more brightness than the blue one, so QR Factorization values of the red component and the green component are used to be the initial values of logistic mapping to generate two watermarks. Then, the Least Significant Bit (LSB) plane of blue component by the watermarks is replaced according to DPCC. Experimental results show that the proposed algorithm is sensitive to malicious attacks on the watermarked image, and can localize the tampered region accurately.
Mechanical sensorless control of PMSM with on-line estimation of stator resistance

Sensorless control of PMSM is sensible to stator resistance uncertainties. In this paper, an on-line identification method of a sensorless controlled PMSM stator resistance is proposed. It is easy to implement and highly stable. A stability analysis based on the first method of Lyapunov shows the stability of the closed-loop system with the proposed estimator combined to the sensorless algorithm. This approach makes the sensorless control more robust with respect to the stator resistance variations at low speeds. The simulation and experimental results illustrate the validity of the analytical approach and the efficiency of the proposed method.
An Empirical and Formal Analysis of Decision Trees for Ranking

Decision trees are known to be good classifiers but less good rankers. A few methods have been proposed to improve their performance in terms of AUC, along with first empirical evidence showing their effectiveness. The goal of this paper is twofold. First, by replicating and extending previous empirical studies, we not only improve the understanding of earlier results but also correct implicit assumptions and conjectures. We focus on the dependence of AUC on the pruning level, the effect of Laplace correcting the class frequencies in leaf nodes, and the influence of the number of distinct scores produced by a decision tree. Second, we complement the empirical studies by a formal analysis that offers further insights and explanations. Essentially, we show that the AUC is likely to increase with the number of scores produced by a tree, at least when these scores are reasonable (better than random) approximations of the true conditional probabilities in the leaves. Simulation studies with synthetic data verify our formal analysis and show its robustness toward estimation errors. As a byproduct, our result suggests a simple method for improving the ranking performance of decision trees, and it helps to understand why some classifiers are better rankers than others.
Incorporating Contextual Audio for an Actively Anxious Smart Home

In this paper we explore an emotive, multi-modal smart house. The smart house is an instance of a monitoring application, inspired by the need to provide semi-autonomous assisted living for elderly and infirm people. A particular aspect of smart environments relevant to the care of the elderly is the detection of potential hazards. A hazardous situation represents an abnormal activity or event. Consequently, to detect abnormality we model normality, that is, the normal activities associated with a user's interaction with the environment. We use the concept of anxiety as a measure of normality modelled with a probabilistic approach. The anxiety is associated with a hazardous device using a fusion of multi-model data. The data is gathered from simple sensors, and from information derived from the audio domain indicating the presence of an activity within the environment. We present the results for the anxiety for a number of activity sequences, both normal and abnormal. The pervasive nature of the audio data enabled the detection of activity when interactions between a user and device didn't occur, successfully preventing false hazardous situations from being detected.
Existence of Credit Rationing for Sme's in the Belgian Corporate Bank Loan Market

Credit rationing is by many Keynesian economists nowadays seen as one of the most important examples of market failure in a modern capitalistic economy. Credit rationing occurs if in equilibrium the demand for loans exceeds the supply at the ruling price (interest rate). Since (Belgian) SMEs heavily rely on bank finance, this restricted access to bank finance could have a negative effect on the investment in new profitable projects and growth of the economy. Due to the lack of empirical research in this domain, we study the empirical significance of credit rationing for SMEs in a Belgian (bank based) context for the period 1993-2001. The novelty of our research consists of the distinction we make between credit rationing for long and short term bank credit. Moreover, in contrast to the numerous previous studies, pioneered by Fazzari et al. (1988), we opt for an endogenous classification into 'credit rationed' and 'non credit rationed' firms, allowing for switching between both groups throughout time. An extensive panel data set consisting of 2.698 SMEs reporting data over the period 1993-2001 is used to estimate the demand-supply disequilibrium model for long and short term bank debt. This is done by means of the instrumental variable technique 3 Stage Least Squares (3SLS). Based on these estimated models, the proportion of credit rationed SMEs is calculated. Our results suggest that, over the entire period, more than 50% of the Belgian SMEs are credit rationed for long and short term bank credit. In most years, long term bank credit is being slightly more rationed than short term bank credit, except in 2001. Moreover, in that year a spectacular increase in credit rationing of more than 10% is perceived. The results reveal that credit rationed firms for short term bank credit are smaller, faster growing firms with a low return on assets, added value, quick ratio, cash flow to assets ratio and much less accounts receivable and inventories to offer as collateral. For long term bank credit, credit rationed firms are in general smaller and younger firms with low cash flow to assets ratios, low growth rate and much less tangible assets to offer as collateral even though they have a higher added value and return on assets ratio than unconstrained firms. We can conclude from both analyses that constrained Belgian SMEs can be mainly characterized as young, small SMEs with little internal financial resources and a lack of assets to guarantee the repayment of debt.
Enhancing GNSS signal acquisition through the Gradient method

This paper describes an efficient algorithm, based on the Gradient method, for the fine estimate of the Doppler shift of Global Navigation Satellite System (GNSS) signals. The algorithm is a suitable solution for software radio implementation, as it requires a moderate computational burden. The new approach is based on a close-form expression that gives an effective way to refine the estimate of Doppler shift without using conventional Frequency Lock Loop (FLL) architectures. The algorithm has been assessed in simulation and tested with live GPS signals. Compared with the classical FLL-based architectures, a shorter transient time between the signal acquisition and the tracking phase is achieved.
Performance analysis of extreme learning machine for automatic diagnosis of electrical submersible pump conditions

This work presents a performance analysis of the Extreme Learning Machine (ELM) compared to the Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN) classifiers for automatic diagnosis of machine conditions. Tests were performed using 5,314 real examples extracted from electrical submersible pumps. The vibration signal extraction was executed in laboratory and the samples were labeled by experts. Two feature extraction models were employed, statistical features from the time and frequency domains and amplitude peaks of harmonics and subharmonics of the shaft rotation frequency. Sequential feature selection was applied to improve classifier performance and to reduce dataset dimensionality. Experimental results suggest that the ELM may be used as a classification algorithm in automatic diagnosis systems. In certain scenarios, the ELM can outperform SVM regarding the quality of results and training speed.