Miracle-dz/UniFAR-FTUs huggingface dataset:
Vidyut

Global synchronization across time and frequency domains significantly benefits wireless communications. Multi-Cell (Network) MIMO, interference alignment solutions, opportunistic routing techniques in ad-hoc networks, OFDMA etc. all necessitate synchronization in either time or frequency domain or both. This paper presents sysname, a system that exploits the easily accessible and ubiquitous power line infrastructure to achieve synchronization in time and frequency domains across nodes distributed beyond a single-collision domain. sysname uses the power lines to transmit a reference frequency tone to which each node locks its frequency. sysname exploits the steady periodicity of delivered power signal itself to synchronize distributed nodes in time. We validate the extent of sysname's synchronization and evaluate its effectiveness. We verify sysname's suitability for wireless applications such as OFDMA and multi-cell MIMO by validating the benefits of global synchronization in an enterprise wireless network. Our experiments show a throughput gain of 8.2x over MegaMIMO, 7x over NemoX and 2.5x over OFDMA systems. Enterprise wireless networks are supported by an Ethernet backbone. Researchers have been exploring techniques over the backbone to enable and assist in improving the performance of the wireless networks. Recent work also showed the benefit of sharing information in the air between the nodes to enable higher performance of the network. Even sharing information as little as synchronization information has been demonstrated to open new avenues (such as MU-MIMO, Physical Network Coding, Opportunistic routing, etc.) for the wireless networks to enhance its performance. However, another medium shared by majority of the nodes in the enterprise network - the power line infrastructure - has been largely left untapped to assist the wireless network. While the power lines are noisy and have a frequency selective transmission characteristic, its uniqueness is that its range can extend beyond that over air while it is unburdened by switching and other responsibilities of the Ethernet backbone. This paper poses the following question: How best to exploit the opportunity presented by the power lines to further enhance enterprise wireless networks? The key contributions of this paper are the following: Identify and demonstrate the feasibility of utilizing power lines as a medium to achieve synchronization (in time and frequency domains) between nodes in the network; Demonstrate the scalability of this technique by achieving synchronization between nodes beyond the transmission range of any of the individual nodes. The paper presents empirical results pertaining to the accuracy of synchronization and the benefit of the proposed synchronization method to existing distributed wireless techniques by virtue of extension across multiple collision domains.
User-Centric Transactions for Ambient Intelligence Environments

In this paper we investigate the concept of designing user-centric transaction protocols towards achieving dependable coordination in AmI environments. As a proof-of-concept, we propose a protocol that takes into account the schedules of roaming users that move from one AmI environment to another, to avoid abnormal terminations of transactions when the users leave an environment for short, only to return later. We compare the proposed schedule-aware protocol against a schedule-agnostic one. Our findings show that the use of user-centric information in such situations is quite beneficial.
Access-Path Abstraction: Scaling Field-Sensitive Data-Flow Analysis with Unbounded Access Paths (T)

Precise data-flow analyses frequently model field accesses through access paths with varying length. While using longer access paths increases precision, their size must be bounded to assure termination, and should anyway be small to enable a scalable analysis. We present Access-Path Abstraction, which for the first time combines efficiency with maximal precision. At control-flow merge points Access-Path Abstraction represents all those access paths that are rooted at the same base variable through this base variable only. The full access paths are reconstructed on demand where required. This makes it unnecessary to bound access paths to a fixed maximal length. Experiments with Stanford SecuriBench and the Java Class Library compare our open-source implementation against a field-based approach and against a field-sensitive approach that uses bounded access paths. The results show that the proposed approach scales as well as a field-based approach, whereas the approach using bounded access paths runs out of memory.
A PROBABILISTIC ANALYSIS OF THE COMPRESSIVE MATCHED FILTER

In this paper we study the “compressive matched filter,” a correlation-based technique for estimating the unknown delay and amplitude of a signal using only a small number of randomly chosen (and possibly noisy) frequency-domain samples of that signal. To study the performance of this estimator, we model its output as a random process and—borrowing from analytical techniques that have been used to derive state-of-the-art signal recovery bounds in the field of compressive sensing— we derive a lower bound on the number of samples needed to guarantee successful operation of the compressive matched filter. Our analysis allows the roles of time and frequency to be exchanged, and we study the particular problem of estimating the frequency of a pure sinusoidal tone from a small number of random samples in the time domain. Thus, for signals parameterized by an unknown translation in either the time or frequency domain, our theoretical bounds and experimental results confirm that random measurements provide an economical means for capturing and recovering such information. Keywords— Compressive sensing, Matched filtering, Tone estimation, Random processes
FPGA Based Efficient Implementation of Viterbi Decoder

It is well known that data transmissions over wirele ss channels are affected by attenuation, distortion, i nterference and noise, which affect the receiver's ability to receiv e correct information. Convolutional encoding with Viterbi decoding is a powerful method for forward error detection and correction. It has been widely deployed in many wireless communication systems to improve the limited capacity of the comm unication channels. In this paper, we present a Spartan XC3S400A Field- Programmable Gate Array efficient implementation of Viterbi Decoder with a constraint length of 3 and a code rat e of 1/3. The Viterbi Decoder is compatible with many common standards, such as DVB, 3GPP2, 3GPP LTE, IEEE 802.16, Hiperlan, and Intelsat IESS-308/309.
Tactile Interface for Electric Wheelchair

In the framework of an augmented multimodal electric wheelchair for people with multiple disabilities, we propose a new user interface based on an Android application on a tablet to steer the wheelchair. The proposed system aims at overcoming some of the problems encountered with the traditional joystick interface like the involuntary arm extensions due to spasticity conditions. These often lead to unwanted movement of the chair and, in many cases, to accidents. The application uses different output modalities to allow the user keep his/her attention on the navigation task without having to visually focus on the tablet screen. We conducted some preliminary tests with some people with disabilities to engage them in the design process and get their feedback on the first prototype. The general impression was positive with insight on future improvements to the system.
Novel visualisation approach for Intensive Care Unit Clinical Activity monitoring

Nursing activity can significantly influence patient outcome in the Intensive Care Unit (ICU). The ability to track a patient bedside nursing activity and to analyse the interactions between the nurses' and patient can provide clinically useful information to optimize the distribution of clinical resources, improving patient care and better manage the ICU nurse workload. In this paper, 4 potential methods that can be used to quantify the nursing activity are presented and evaluated. The corresponding advantages and disadvantages of each method are evaluated in a weighted scoring system. The highest scored method is then further developed for the application. This system, the Clinical Activity Tracking System (CATS) utilizes an infrared depth sensor to automatically quantify nursing activity at patient's bedside. This new system is tested in a simulated environment and the preliminary results on its accuracy and robustness are presented.
UNBIASED INCLINATION DISTRIBUTIONS FOR OBJECTS IN THE KUIPER BELT

Using data from the Deep Ecliptic Survey (DES), we investigate the inclination distributions of objects in the Kuiper Belt. We present a derivation for observational bias removal and use this procedure to generate unbiased inclination distributions for Kuiper Belt objects (KBOs) of different DES dynamical classes, with respect to the Kuiper Belt plane. Consistent with previous results, we find that the inclination distribution for all DES KBOs is well fit by the sum of two Gaussians, or a Gaussian plus a generalized Lorentzian, multiplied by sin i. Approximately 80% of KBOs are in the high-inclination grouping. We find that Classical object inclinations are well fit by sin i multiplied by the sum of two Gaussians, with roughly even distribution between Gaussians of widths 2.0+0.6−0.5° and 8.1+2.6−2.1°. Objects in different resonances exhibit different inclination distributions. The inclinations of Scattered objects are best matched by sin i multiplied by a single Gaussian that is centered at 19.1+3.9−3.6° with a width of 6.9+4.1−2.7°. Centaur inclinations peak just below 20°, with one exceptionally high-inclination object near 80°. The currently observed inclination distribution of the Centaurs is not dissimilar to that of the Scattered Extended KBOs and Jupiter-family comets, but is significantly different from the Classical and Resonant KBOs. While the sample sizes of some dynamical classes are still small, these results should begin to serve as a critical diagnostic for models of solar system evolution.
A Novel Cannabinoid Peripheral Cannabinoid Receptor-Selective Inverse Agonist Blocks Leukocyte Recruitment in Vivo

The expression of the cannabinoid peripheral cannabinoid receptor (CB2) receptor on peripheral immune cells suggests that compounds specific for CB2 might be effective anti-inflammatory agents. In this report, we present the initial biological profiling of a novel triaryl bis-sulfone, Sch.336 (N-[1(S)-[4-[[4-methoxy-2-[(4-methoxyphenyl)sulfonyl]phenyl]-sulfonyl]phenyl]ethyl]methanesulfonamide), which is selective for the human cannabinoid CB2 receptor (hCB2). Sch.336 is an inverse agonist at hCB2, as shown by its ability to decrease guanosine 5′-3-O-(thio)triphosphate (GTPγS) binding to membranes containing hCB2, by the ability of GTPγS to left-shift Sch.336 binding to hCB2 in these membranes, and by the compound's ability to increase forskolin-stimulated cAMP levels in CHO cells expressing hCB2. In these systems, Sch.336 displays a greater potency than that reported for the CB2-selective dihydropyrazole, SR144528 (N-[(1S)-endo-1,3,3-trimethylbicyclo [2.2. 1]heptan2-yl]-5-(4-chloro-3-methylphenyl)-1-[(4-methylphenyl)methyl]-1H-pyrazole-3-carboxamide). In vitro, Sch.336 impairs the migration of CB2-expressing recombinant cell lines to the cannabinoid agonist 2-arachidonylglycerol. In vivo, the compound impairs migration of cells to cannabinoid agonist HU210 [(6aR)-trans-3-(1,1-dimethylheptyl)-6a,7,10,10a-tetrahydro-1-hydroxy-6,6-dimethyl-6H-dibenzo [b,d] pyran-9-methanol]. Oral administration of the Sch.336 significantly inhibited leukocyte trafficking in several rodent in vivo models, induced either by specific chemokines or by antigen challenge. Finally, oral administration of Sch.336 blocked ovalbumin-induced lung eosinophilia in mice, a disease model for allergic asthma. We conclude that selective cannabinoid CB2 inverse agonists may serve as novel immunomodulatory agents in the treatment of a broad range of acute and chronic inflammatory disorders in which leukocyte recruitment is a hallmark of disease pathology.
International Journal of Advances in Electrical and Electronics Engineering Design and Implementation of Home Monitoring System Using Rf Technology

Home monitoring system uses the RF technology to monitor the status and control the electric device. The system has been developed to save energy. The system is implemented using modules which are capable of communicating with each other. In the existing method we can control the device like ON/OFF of the device remotely but we cannot know the status of the device whether the device is working or not. In the proposed method we overcome this disadvantage by providing good solution for monitoring and controlling of the LED's from the remote locations. The modules are designed using ARMLPC148 and AT89S52 microcontrollers, comparator-LM324 IC, CC2500 RF transceivers which are powered by a battery source. LED is used as device which is interfaced with AT89S52 and status of the LED can be monitored on LCD interfaced with LPC2148.This paper focuses on the software aspects of the design.
Secure Multiplex Network Coding

In the secure network coding for multicasting, there is loss of information rate due to inclusion of random bits at the source node. We show a method to eliminate that loss of information rate by using multiple statistically independent messages to be kept secret from an eavesdropper. The proposed scheme is an adaptation of Yamamoto et al.'s secure multiplex coding to the secure network coding.
ORBIT: A Platform for Smartphone-Based Data-Intensive Sensing Applications

Owing to the rich processing, multi-modal sensing, and versatile networking capabilities, smartphones are increasingly used to build data-intensive embedded sensing applications. However, various challenges must be systematically addressed before smartphones can be used as a generic embedded sensing platform, including high power consumption, lack of real-time functionality, and user-friendly embedded programming support. This paper presents ORBIT, a smartphone-based platform for data-intensive embedded sensing applications. ORBIT features a tiered architecture, in which a smartphone can interface to an energy-efficient peripheral board and/or a cloud service. ORBIT as a platform addresses the shortcomings of current smartphones while utilizing their strengths. ORBIT provides a profile-based task partitioning that allows it to intelligently dispatch the processing tasks among the tiers to minimize the system power consumption. ORBIT also provides a data processing library that includes two mechanisms namely adaptive delay-quality trade-off and data partitioning via multi-threading to optimize resource usage. Moreover, ORBIT supplies an annotation-based programming API for developers that significantly simplifies the application development and provides programming flexibility. Extensive microbenchmark evaluation and three case studies including seismic sensing, visual tracking using an ORBIT robot, and multi-camera 3D reconstruction, validate the generic design of ORBIT.
Books also Received

Gene array analysis has been widely used to identify genes induced during T cell activation. Our studies identified an immediate early gene that is strongly induced in response to IL-2 in mouse T cells which we named cysteineserine-rich nuclear protein-1 (CSRNP-1). The human ortholog was previously identified as an AXIN1 induced gene (AXUD1). The protein does not contain sequence defined domains or motifs annotated in public databases, however the gene is a member of a family of three mammalian genes that share conserved regions, including cysteineand serine-rich regions and a basic domain, they encode nuclear proteins, possess transcriptional activation domain and bind the sequence AGAGTG. Consequently we propose the nomenclature of CSRNP-1, -2 and -3 for the family. To elucidate the physiological functions of CSRNP-1, -2 and -3, we generated mice deficient for each of these genes by homologous recombination in embryonic stem cells. Although the CSRNP proteins have the hallmark of transcription factors and CSRNP-1 expression is highly induced by IL-2, deletion of the individual genes had no obvious consequences on normal mouse development, hematopoiesis or T cell functions. However, combined deficiencies cause partial neonatal lethality suggesting that the genes have redundant functions.
Hfq Is a Global Regulator That Controls the Pathogenicity of Staphylococcus aureus

The Hfq protein is reported to be an RNA chaperone, which is involved in the stress response and the virulence of several pathogens. In E. coli, Hfq can mediate the interaction between some sRNAs and their target mRNAs. But it is controversial whether Hfq plays an important role in S. aureus. In this study, we found that the deletion of hfq gene in S. aureus 8325-4 can increase the surface carotenoid pigments. The hfq mutant was more resistant to oxidative stress but the pathogenicity of the mutant was reduced. We reveal that the Hfq protein can be detected only in some S. aureus strains. Using microarray and qRT-PCR, we identified 116 genes in the hfq mutant which had differential expression from the wild type, most of which are related to the phenotype and virulence of S. aureus. Among the 116 genes, 49 mRNAs can specifically bind Hfq protein, which indicates that Hfq also acts as an RNA binding protein in S. aureus. Our data suggest that Hfq protein of S. aureus is a multifunctional regulator involved in stress and virulence.
A survey on sensing methods and feature extraction algorithms for SLAM problem

This paper is a survey work for a bigger project for designing a Visual SLAM robot to generate 3D dense map of an unknown unstructured environment. A lot of factors have to be considered while designing a SLAM robot. Sensing method of the SLAM robot should be determined by considering the kind of environment to be modeled. Similarly the type of environment determines the suitable feature extraction method. This paper goes through the sensing methods used in some recently published papers. The main objective of this survey is to conduct a comparative study among the current sensing methods and feature extraction algorithms and to extract out the best for our work.
Age-related differences in neural activities during risk taking as revealed by functional MRI

Previous research has clearly documented that risky decision making is different in young and older adults. Yet, there has been a relative dearth of research that seeks to understand such age-related changes in the neural activities associated with risk taking. To address this research issue, 21 men (12 young men, mean age 29.9 ± 6.2 years and 9 older men, mean age 65.2 ± 4.2 years) performed a risky-gains task while their brain activities were monitored by an fMRI scanner. The older adults, relative to their younger peers, presented with contralateral prefrontal activity, particularly at the orbitofrontal cortex. Furthermore, stronger activation of the right insula was observed for the older-aged participants compared to the younger-aged adults. The findings of this study are consistent with the a priori speculations established in accordance with the HAROLD model as well as previous findings. Findings of this study suggest that when making risky decisions, there may be possible neuropsychological mechanisms underlying the change in impulsive and risk-taking behaviors during the course of natural ageing.
Iterative Solution of Systems of Linear Equationsarising in the Context of Stochastic

The particular properties of systems of linear equations arising in the context of the Stochastic Finite Element Method motivate the customization of existing iterative solution algorithms. The implementation described in this paper has aimed at optimizing data management, MAT-VEC operations and preconditioning strategies. It turns out that SFEM-systems can be solved with much less eeort than their size suggests. The main idea is based on the fact that the full system matrix consists of few, relatively small submatrices with identical dimensions and sparsity pattern. This makes it very eecient to perform matrix-vector multiplications at the submatrix level and to avoid the assembly of the full coeecient matrix.
An introduction to boosting based classification

We start with an example of a simple classification problem. Suppose we have some real valued random variable, , which represents some measurable quantity, such as the height of a person, and that our goal is to find a rule for classifying people to males vs. females based on their height only (in general, we shall assume that is a vector of measurements. We shall use to denote the class labels (in this case corresponding to male or female). As data we are given the height measurements for a large randomly selected sample of women and men. Given this training data we would like to find a classification rule, (i.e. a mapping from real numbers to “male” or “female”) that is likely to be correct on new instances which might or might not have occurred in the training data. We call the problem of finding a good classification rule given training data the learning problem, it is also often called the pattern recognition problem. There are two main approaches to this problem, which we call the model based approach and the rule based approach. The model based approach, (see e.g. Duda and Hart [8]) consists of two steps. First, we estimate a statistical model for each of the classes, then we choose a decision rule that is optimal with respect to the estimated models. A popular statistical model is the normal distribution. Given this choice our task reduces to estimating the probability of each class and the mean and variance of each class. From these estimates we can compute the joint probability of the measurement and the class . From this joint distribution it is easy to derive the Bayes optimal classification rule (see Figure 1[(a)]). The rule based approach, popularized by Vapnik [19], consists of a single step, and sidesteps the problem of estimating distribution models. According to this approach we select, out of a pre-specified set of rules, which we denote by , the one rule that makes the smallest number of mistakes on the training data. (see Figure 1[(a)]). In this simple example the two approaches generate rules of the same form, however, for the same data each of the methods will generate a different rule. Which rule is likely to perform better? Well, it depends:
DISSIPATIVITY-BASED NONLINEAR DECENTRALIZED CONTROL DESIGN

This contribution summarizes a methodology, based on dissipative systems theory, for the analysis and control of interconnected nonlinear processes. The global objective of the proposed approach is to design decentralized feedback controllers in such a way that plantwide stability and performance objectives are met, for a known constant interconnection structure. At a plantwide level, extensions of classical results on the stability of large-scale interconnected systems lead to input-output constraints for each subsystems, encoded as supply rates from input ports to output ports. Then for each subsystem, a (possibly parameterized) feedback controller is designed using nonlinear dissipative inequalities to ensure that the aforementioned input-output constraint is met in closed-loop. After a review of dissipativity theory for the analysis of interconnected networks, this paper focuses on the design of nonlinear feedback controllers ensuring that each subsystems meet the interconnection constraints. In particular, this paper presents new results related to the construction of storage functions for control affine systems, as a generalization of some physically-based approaches to dissipative systems theory that appeared recently in the literature. Potential extensions of the proposed approach and areas for future research are also discussed.