Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of sintering method on the read range of brush-painted silver nanoparticle UHF RFID tags on wood and polyimide substrates

Nowadays there is an increasing interest to add functionality in various products and versatile electronics manufacturing methods are needed for different applications. In this study, brush-painting of silver nanoparticle UHF RFID tag antennas directly on wood veneer and polyimide film substrates was examined. Two sintering methods, heat and photonic sintering were tested for these antennas. According to our results, the tag antennas brushed on polyimide film showed read ranges of over 8 meters with both sintering methods. The tag antenna brushed on wood veneer did not respond after heat sintering, but showed a read range of about 2 meters after photonic sintering. The novel manufacturing process is presented and the achieved results are discussed in this paper.
Assessment of supercapacitor based on carbon material synthesized from neem tree leaves

Supercapacitor also known as electrochemical double layer capacitor is a unique energy storage device, which bridges the gap between conventional battery and capacitor. It emerges as an alternative to batteries due to its high power density, wide thermal operating range, long cycle life and low cost. Thus, allowing its applications for various energy and power requirement. Lots of research is going on for the development of activated carbon as an electrode material for supercapacitor. To get better capacitance, it is suggested that carbon material should possess large specific surface area and small pore size. A recent method of synthesizing activated carbon is by burning dead leaves of trees at a high temperature in an inert atmosphere. Trails for development of activated carbon from dead neem tree leaves at low temperature to high temperature are presented in this paper. Such method of obtaining activated carbon can be cost effective and environmental friendly.
Enhanced Face Recognition System Combining PCA, LDA, ICA with Wavelet Packets and Curvelets

Face recognition is one of the most frequently used biometrics both in commercial and law enforcement applications. The individuality of facial recognition from other biometric techniques is that it can be used for surveillance purposes; as in searching for wanted criminals, suspected terrorists, and missing children. The steps in a face recognition steps are preprocessing (image enhancement), feature extraction and finally recognition. This paper identifies techniques in each step of the recognition process to improve the overall performance of face recognition. The proposed face recognition model combines enhanced 2DPCA algorithm, LDA, ICA with wavelet packets and curvelets and experimental results proves that the combination of these techniques increases the efficiency of the recognition process and improves the existing systems.
Learning Oncogenetic Networks by Reducing to Mixed Integer Linear Programming

Cancer can be a result of accumulation of different types of genetic mutations such as copy number aberrations. The data from tumors are cross-sectional and do not contain the temporal order of the genetic events. Finding the order in which the genetic events have occurred and progression pathways are of vital importance in understanding the disease. In order to model cancer progression, we propose Progression Networks, a special case of Bayesian networks, that are tailored to model disease progression. Progression networks have similarities with Conjunctive Bayesian Networks (CBNs) [1],a variation of Bayesian networks also proposed for modeling disease progression. We also describe a learning algorithm for learning Bayesian networks in general and progression networks in particular. We reduce the hard problem of learning the Bayesian and progression networks to Mixed Integer Linear Programming (MILP). MILP is a Non-deterministic Polynomial-time complete (NP-complete) problem for which very good heuristics exists. We tested our algorithm on synthetic and real cytogenetic data from renal cell carcinoma. We also compared our learned progression networks with the networks proposed in earlier publications. The software is available on the website https://bitbucket.org/farahani/diprog.
Nonlinearity in small planar inductors: experimental characterization using multivariate response surface

This paper presents a procedure to develop a multivariate model of the behavior of planar inductors when subject to high flux density levels using Design of Experiments (DoE) methodology. A Central Composite Design (CCD) is employed to obtain a multivariate equation that shows the interaction between the number of turns, the air gap and the volt-seconds applied to the inductor. A simple and efficient method to determine the value of the effective inductance using a boost converter is also presented. Experimental results using a planar ER18/3.2/10 core are provided, confirming the accuracy of the obtained model.
A New Method to Measure and Map the Gas Scale Height of Disk Galaxies

We propose a new method to measure and map the gas scale height of nearby disk galaxies. This method is applied successfully to the Australia Telescope Compact Array interferometric H I survey of the Large Magellanic Cloud (LMC); it could also be applied to a significant number of nearby disk galaxies, thanks to the next generation of interferometric facilities, such as the expanded Very Large Array and the Combined Array for Research in Millimeter-wave Astronomy. The method consists of computing the spectral correlation function (SCF) for a spectral line map of a face-on galaxy. The SCF quantifies the correlation between spectra at different map positions as a function of their separation and is sensitive to the properties of both the gas mass distribution and the gas velocity field. It is likely that the spatial correlation properties of the gas density and velocity fields in a galactic disk are sensitive to the value of the scale height of the gas disk. A scale-free turbulent cascade is unlikely to extend to scales much larger than the disk scale height since the disk dynamics on those larger scales should be dominated by two-dimensional motions. We find a clear feature in the SCF of the LMC H I disk, on the scale of ≈180 pc, which we identify as the disk scale height. We are also tentatively able to map variations of the scale height over the disk.
Circular Polarization of Transmitted Light by Sapphirinidae Copepods

Circularly polarized light, rare in the animal kingdom, has thus far been documented in only a handful of animals. Using a rotating circular polarization (CP) analyzer we detected CP in linearly polarized light transmitted through epipelagic free living Sapphirina metallina copepods. Both left and right handedness of CP was detected, generated from specific organs of the animal's body, especially on the dorsal cephalosome and prosome. Such CP transmittance may be generated by phase retardance either in the muscle fibers or in the multilayer membrane structure found underneath the cuticle. Although the role, if any, played by circularly polarized light in Sapphirinidae has yet to be clarified, in other animals it was suggested to take part in mate choice, species recognition, and other forms of communication. Highlights Planktonic Sapphirinidae copepods were found to circularly polarize the light passing through them. Circular polarization may be created by unique, multilayered features of the membrane structure found under their cuticle or by organized muscle fibers.
The Awareness-Privacy Tradeoff in Video Supported Informal Awareness: A Study of Image-Filtering Based Techniques

Media space applications that promote informal awarene ss in an organization confront an inevitable paradox: the shared video connections between offices and rooms t hat promote informal awareness also can rob individuals of privacy. An important open problem in this area is how to foster awareness of colleagues without the accompanying loss of privacy. One proposal put forward is to filter t he communicated video streams rather than broadcasting clear video. In this article, we describe several image-filte ring techniques that may provide awareness in informal group communication applications while blurring the details of an individual’s activities, thus potentially preserving mor e privacy. We describe experiments to quantitatively and qualitatively assess the degrees of awareness and accur acy that these filtering techniques provide.
Defensive Effect of Lansoprazole in Dementia of AD Type in Mice Exposed to Streptozotocin and Cholesterol Enriched Diet

The present study investigates the potential of lansoprazole (a proton pump inhibitor and agonist of liver x receptors) in experimental dementia of AD type. Streptozotocin [STZ, 3 mg/kg, injected intracerebroventricular (i.c.v), and high fat diet (HFD, administered for 90 days)] were used to induce dementia in separate groups of Swiss mice. Morris water maze (MWM) test was performed to assess learning and memory of the animals. A battery of biochemical and histopathological studies were also performed. Extent of oxidative stress was measured by estimating the levels of brain reduced glutathione (GSH) and thiobarbituric acid reactive species (TBARS). Brain acetylcholinestrase (AChE) activity and serum cholesterol levels were also estimated. The brain level of myeloperoxidase (MPO) was measured as a marker of inflammation. STZ and HFD produced a marked decline in MWM performance of the animals, reflecting impairment of learning and memory. STZ/HFD treated mice exhibited a marked accentuation of AChE activity, TBARS and MPO levels along with a fall in GSH levels. Further, the stained micrographs of STZ/HFD treated mice indicated pathological changes, severe neutrophilic infiltration and amyloid deposition. Lansoprazole treatment significantly attenuated STZ and HFD -induced memory deficits, biochemical and histopathological alterations. It also prevented HFD-induced rise in the cholesterol level. Therefore, the findings demonstrate potential of lansoprazole in memory dysfunctions which may probably be attributed to its anti-cholinesterase, anti-oxidative and anti-inflammatory effects. Moreover, both cholesterol-dependent as well as cholesterol-independent effects of lansoprazole appear to play a role. In addition study indicates the role of liver x receptors in dementia.
Signature Wood Modifications Reveal Decomposer Community History

Correlating plant litter decay rates with initial tissue traits (e.g. C, N contents) is common practice, but in woody litter, predictive relationships are often weak. Variability in predicting wood decomposition is partially due to territorial competition among fungal decomposers that, in turn, have a range of nutritional strategies (rot types) and consequences on residues. Given this biotic influence, researchers are increasingly using culture-independent tools in an attempt to link variability more directly to decomposer groups. Our goal was to complement these tools by using certain wood modifications as ‘signatures’ that provide more functional information about decomposer dominance than density loss. Specifically, we used dilute alkali solubility (DAS; higher for brown rot) and lignin:density loss (L:D; higher for white rot) to infer rot type (binary) and fungal nutritional mode (gradient), respectively. We first determined strength of pattern among 29 fungi of known rot type by correlating DAS and L:D with mass loss in birch and pine. Having shown robust relationships for both techniques above a density loss threshold, we then demonstrated and resolved two issues relevant to species consortia and field trials, 1) spatial patchiness creating gravimetric bias (density bias), and 2) brown rot imprints prior or subsequent to white rot replacement (legacy effects). Finally, we field-tested our methods in a New Zealand Pinus radiata plantation in a paired-plot comparison. Overall, results validate these low-cost techniques that measure the collective histories of decomposer dominance in wood. The L:D measure also showed clear potential in classifying ‘rot type’ along a spectrum rather than as a traditional binary type (brown versus white rot), as it places the nutritional strategies of wood-degrading fungi on a scale (L:D=0-5, in this case). These information-rich measures of consequence can provide insight into their biological causes, strengthening the links between traits, structure, and function during wood decomposition.
WOLFE: Strength Reduction and Approximate Programming for Probabilistic Programming

Existing modeling languages lack the expressiveness or efficiency to support many modern and successful machine learning (ML) models such as structured prediction or matrix factorization. We present WOLFE, a probabilistic programming language that enables practitioners to develop such models. Most ML approaches can be formulated in terms of scalar objectives or scoring functions (such as distributions) and a small set of mathematical operations such as maximization and summation. In WOLFE, the user works within a functional host language to declare scalar functions and invoke mathematical operators. The WOLFE compiler then replaces the operators with equivalent, but more efficient (strength reduction) and/or approximate (approximate programming) versions to generate low-level inference or learning code. This approach can yield very concise programs, high expressiveness and efficient execution.
Complementary self-biased scheme for the robust design of CMOS/SET hybrid multi-valued logic

We propose a new technique to enhance the characteristics of CMOS/SET hybrid multi-valued logic (MVL) circuits in terms of their stability and performance. A complementary self-biasing method enables the SET/CMOS logic to operate perfectly well at high temperature in which the peak-to-valley current ratio of Coulomb oscillation severely decreases. The suggested scheme is evaluated by SPICE simulation with an analytical SET model, and it is confirmed that even SETs with a large Si island can be utilized efficiently in the multi-valued logic. We demonstrate a quantizer implemented by SETs with a 90-nm-long Si island on the basis of measured device characteristics and SPICE simulation, which shows high resolution and small linearity error characteristics.
Learning to rank domain experts in microblogging by combining text and non-text features

Currently microblog search engines have the function to find related users according to input topic keywords. Traditional approaches rank users by their authentication information or their self descriptions (introductions or labels). However, many users may not publish the posts closely related to their certification profile. In this paper, we study the problem of identifying domain-dependent influential users (or topic experts). We propose to fuse of non-text features and text features to analysis the influence of the users. In addition we compare three kinds of sorting methods, i.e., order-based rank aggregation, greedy selection based rank aggregation, SVM Rank method. Our experimental results show that the highest precision is achieved by SVM rank method.
Student Use of the Internet: an extension of TAM in technical and vocational institutions in Brunei Darussalam

In the educational setting, the Internet is profiteering among academics and students. In fact, this has changed the functioning of the educational establishments. However, the success of use of the Internet is largely dependent upon the user’s behaviour that in turn affects their attitudes. Even when the formidable opportunities exist for the deployment of technology, adverse attitude can inhibit the use. Keeping this in view, survey of 266 students of four technical and vocational colleges was conducted to study the attitudes of the students toward the use of the Internet. The present study develop a normative model by using Davis’s Technology Acceptance Model (TAM) and validates two specific attitudinal variables; perceived usefulness and perceived ease of use that are hypothesized to be fundamental determinants of the attitude that in turn predict the students’ use of the Internet. By adding external variables such as; demographics, task characteristics, computer exposure, and institutional support further test the parsimony of the model. A Structural Equation Modelling technique is used to validate the model. The study has confirmed that majority of the students (85%) are using the Internet. In the final model the external variables do contribute towards both of belief parts: perceived ease of use and perceived usefulness that further affects the attitude of the students in predicting the Internet usage.
A two-dimensional robust algorithm based on Mel-bank log-spectrum

In this paper, a novel technique named two-dimensional robust algorithm based on Mel-bank log-spectrum is presented which combines sub-band average log-spectrum enhancement algorithm and peak isolation algorithm in log-spectrum domain. Information on the long envelopes of time trajectories of log-spectral energies and robustness of the log-spectral peaks in frequency bands are used together to achieve more excellent performance in this algorithm. Comparative experiments on the Aurora 2 databases are performed between these techniques and the proposed two-dimensional algorithm. It is shown that this combined two-dimensional algorithm results in better recognition performance than each technique. Compared to the Aurora 2.0 baseline system, the word error rate reduction was 21.23% in the clean training case for this combined algorithm.
Optimal placement of spots in distributed proximity sensors for safe human-robot interaction

Industrial robots are today separated from human workers by means of safety barriers, that protect humans from the risk of collisions. This separation has a clear negative influence on diffusion of robotic technology in shopfloors. On the other hand the removal of protective barriers gives rise to safety issues, that can be addressed with a combination of approaches, including sensor based reactive control. In this paper a distributed proximity sensor, to be mounted on the links of the manipulator, is presented. The optimal placement of the spots of such sensor is discussed, taking into account detection capabilities and safety enhancement. Experiments developed on an ABB IRB 140 robot using off-the-shelf infrared distance sensors as spots are presented.
Improving performance through concept formation and conceptual clustering

Research from June 1989 through October 1992 focussed on concept formation, clustering, and supervised learning for purposes of improving the efficiency of problem-solving, planning, and diagnosis. These projects resulted in two dissertations on clustering, explanation-based learning, and means-ends planning, and publications in conferences and workshops, several book chapters, and journals; a complete Bibliography of NASA Ames supported publications is included. The following topics are studied: clustering of explanations and problem-solving experiences; clustering and means-end planning; and diagnosis of space shuttle and space station operating modes.
A Top-down Presentation of Purdom’s Sentence- Generation Algorithm

In this paper we present a reformulation of a sentencegeneration algorithm for context free grammars, originally due to Purdom. We restructure the original algorithm so that it resembles the familiar pattern of a top-down parsing algorithm. We express the algorithm in a declarative style that significantly facilitates its comprehension and implementation. Purdom’s sentence generation algorithm takes a contextfree grammar, and generates sentences from the language represented by that grammar [2]. Run to completion, the algorithm will generate a set of sentences such that each rule in the grammar has been used at least once. The original formulation of the algorithm, published in 1972, can be somewhat difficult to understand, since the steps involved are described in an imperative style. In previous work we have reformulated the algorithm in a more procedural style, and implemented it in C ++ [1]. This paper takes that process a step further by expressing the algorithm in a more declarative style, yielding a significantly simpler version. In what follows we describe our reformulation of Purdom’s algorithm. The reader is encouraged to consult [1] for more background information. Section 1 presents an overview of our approach, drawing an analogy between the sentence-generation problem and the standard approach to top-down parsing. Section 2 informally describes the crux of the algorithm, which involves choosing the correct rule for a non-terminal at a given stage in the generation process. Section 3 describes short and prev the main algorithms in this process, in detail.
Grey Relational Analysis Based k Nearest Neighbor Missing Data Imputation for Software Quality Datasets

Software quality estimation is important yet difficult in software engineering studies. Historical quality datasets are used to build classification models for estimating fault-proneness. However, the missing values in the datasets severely affect the estimation ability and therefore, cause inconclusive decision-making. Among the single imputation approaches, k nearest neighbor (kNN) imputation is popular in empirical studies due to the relatively high accuracy. However, researchers are still calling for the optimal parameter setting of kNN imputation. In this study, a novel grey relational analysis based incomplete-instance kNN imputation is built for software quality data. An evaluation is conducted on four quality datasets with different simulated missingness scenarios to analyze the performance of the proposed imputation. The empirical results show that the proposed approach is superior to traditional kNN imputation and mean imputation in most cases. Moreover, the classification accuracy can be maintained or even improved by using this approach in classification tasks.
Study on multi-sensor data fusion for the wheeled mobile robot

The usual method for estimating the posture of a wheeled mobile robot is dead-reckoning algorithm. However, it has the problem of gradual error accumulation due to slippage of wheels and measurement noise. To enhance the positioning precision for mobile robots, the information fusion method using Extended Kalman Filter algorithm is investigated in which multi-sensor data are provided by internal sensors such as odometers and external sensor such as laser scanner. Practical path-tracking experiment shows that the estimated posture by this system is precise to be useful.
Underwater Visual 3D SLAM Using a Bio-inspired System

Considering the various challenges in robotics, one of the most important concerns to a mobile robot build a map and estimate its location in an environment. The SLAM techniques build a map and simultaneously maintain the current location of a robot in the same time. Nowadays, probabilistic approaches dominate the field, anyway the last decade have witnessed important bio-inspired studies based on biological structures related to spatial navigation. Past and recent studies found place, head direction and grid cells, among others, related to the tasks of mapping and location in mammals. Continuous Attractor Neural Networks (CANN) are being proposed to simulate the SLAM performed by this brain structures. This work present a bio-inspired SLAM system to map a sub-aquatic 3D environment, through a simulation of an underwater robot equipped with a camera, using visual references to mimic the behavior of location tasks performed by mammals.