Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated layout-inclusive synthesis of analog circuits using symbolic performance models

In this dissertation a new synthesis methodology, which uses parameterized layout generators and symbolic performance models (SPMs) inside the synthesis loop, has been developed to overcome the deficiencies of the previous circuit sizing method. This layout-inclusive (layout-in-loop) approach uses efficient parameterized procedural layout generators, obtained using the module-specification language (MSL) system, for speedy layout instantiation. Fast performance estimation is achieved by using pre-compiled SPMs, which are symbolic representation of circuit performances, obtained using symbolic analysis. The transfer functions of SPMs are stored as efficient symbolic graphs called element-coefficient diagrams (ECDs). Techniques to include layout geometry effects in the SPMs have also been developed. This method is used for the synthesis of opamps and filters. The method proposed above for analog circuits is then applied to the synthesis of an RF low-noise amplifier (LNA). This method also uses symbolic performance models (SPMs), and parameterized layout generator along with high-frequency extraction techniques in the synthesis loop. SPMs for noise figure and distortion parameters are developed using repetitive and weakly nonlinear symbolic analysis and are stored as pre-compiled ECDs. Full parasitic extraction is done by using multiple extractors. Quasi-static extraction is used to obtain the critical parasitic effects of interconnects and on-chip inductors. Further in the dissertation, efforts are made to overcome the shortcomings of the proposed method. The first limitation is the size of circuits that can be synthesized. It arises because of the limit on the size of ECD-code that can be compiled by a standard GNU C++ compiler. To overcome this bottleneck, a new comprehensive method and framework for exact symbolic analysis of large analog circuits is developed. The method is based on the concepts of hierarchical circuit decomposition, subcircuit symbolic analysis and transfer function synthesis. Node tearing methods have been used for decomposition and element-coefficient diagrams (ECD) based method is used for symbolic analysis of subcircuits. One of the key contributions of this work is a generalized methodology for transfer function synthesis, encompassing all interconnection templates for any two subcircuits. The method leads to the development of an easily automatable and efficient algorithm for generation of symbolic transfer function of large circuits. (Abstract shortened by UMI.)
On the design of die-sets

• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
[Assessment of the new CKD-EPI equation to estimate the glomerular filtration rate].

INTRODUCTION A recent report by the CKD-EPI Chronic Kidney Disease Epidemiology Collaboration) group describes a new equation to estimate the glomerular filtration rate (GFR). This equation has been developed from a population of 8,254 subjects who had the GFR measured by iothalamate clearance (mean 68 mL/min/1.73 m2, SD 40 mL/min/1.73 m2). It includes variables such as serum creatinine, age, sex and race with different formula according to race, sex and creatinine value. The CKD-EPI equation improved the accuracy and precision results of the current first-choice MDRD-IDMS (Modification of Diet in Renal Disease-Isotopic Dilution Mass Spectrometry) formula, specially for GFR > 60 mL/min/1.73 m2 in a group of 3,896 subjects. METHODS The goal of our study was to compare the estimated GFR by using the new equation CKD-EPI with MDRD-IDMS in a wide cohort of 14,427 patients (5,234 women and 9,193 men), and to analyze the impact of the new CKD-EPI formula on the staging of patients with CKD. RESULTS Mean estimated GFR was 0.6 mL/min/1.73 m2 higher with CKD-EPI as compared to MDRD-IDMS for the whole group, 1.9 mL/min/1.73 m2 higher for women and 0.2 mL/min/1.73 m2 lower for men. The percentage of CKD staging concordancy between equations varied from 79.4 % for stage 3A and 98.6% for stage 5. For those patients younger than 70 years, 18.9 % and 24 % MDRD-IDMS stages 3B and 3A were reclassified as CKD 3A and 2 by CKD-EPI, respectively. For the same stages in the group younger than 70 years, the percentage of reclassified patients increased up to 34.4% and 33.4%, respectively. CONCLUSION The new CKD-EPI equation to estimate the GFR reclassifies an important number of patients to higher CKD stages (higher GFR), specially younger women, classified as CKD stage 3 by MDRD-IDMS.
An Efficient Data Aggregation Scheme and Cluster Optimization in Wireless Sensor Networks.

Wireless Sensor Networks is a collection of sensor nodes with sensing, computation and communication capabilities. The sensor nodes are capable of receiving data from the monitoring environment. The sensed data should be gathered efficiently from the sensor nodes and is routed to base station through fixed path. The QoS metrics such as Bandwidth, Latency, Data accuracy, Throughput can also affect the performance of the network lifetime. The effective solution to address this challenge is to utilize the sink node’s mobility to facilitate the data gathering . This technique can reduce energy consumption of the sensor nodes. The usage of mobile sink presents additional challenges such as determining the sink node’s trajectory and cluster formation prior to data collection. Network lifetime is an important issue in an energy constrained sensor networks. The most widely used Expectation maximization (EM) algorithm is used for clustering process and to compute the optimal number of clusters in the network. One of the main problem is the implementation of flooding diffusion which is used to forward interest and discover the routing map that reduces network lifetime and data latency through high energy consumption by the use of AODV(Ad hoc on demand distance vector )routing .
Dynamic WFQ scheduling for real-time traffic in wireless ATM links

This paper proposes a dynamic scheduling scheme to provide quality of service (QoS) guarantees in wireless multiple access control (MAC). In order to achieve high bandwidth utilization and QoS guarantee ratio, token models of sources are constructed in the base station (BS) scheduler. A token model weighted fair queuing (WFQ) algorithm is developed to combine loss and delay requirements for uplink scheduling of real-time traffic. Dynamic priorities are implemented in a real-time traffic token model. In addition, an emergency token buffer is used to accommodate the bursty characteristics of rt-VBR (real-time variable bit rate). The backlogged token buffer compensates the backlogged flow suffering from channel error. Simulations are done to demonstrate that our DWFQ scheme can greatly improve both QoS performance and bandwidth allocation efficiency.
Quantitative Data Analysis with SPSS Release 8 for Windows: For Social Scientists

From the Publisher: The latest edition of this best-selling introduction to Quantitative Data Analysis through the use of a computer package has been completely updated to accommodate the needs of users of SPSS Release 8 for Windows. Like its predecessor, it provides a non-technical approach to quantitative data analysis and a user-friendly introduction to the widely used SPSS for Windows. It assumes no previous familiarity with either statistics or computing but takes the reader step-by-step through the techniques, reinforced by exercises for further practice. Techniques explained include: correlation, simple and multiple regression, multivariate analysis of variance and covariance, and factor analysis. The book also covers issues such as sampling, statistical significance, conceptualization and measurement and the selection of appropriate tests.
Preference Location-based Routing in Delay Tolerant Networks

Routing in Delay Tolerant Networks (DTNs) is a challenge due to the fact that the network graph is intermittently connected. However, routing benefits considerably if one can take advantage of knowledge concerning nodes mobility, recent studies have shown that human have location visiting preferences in their daily mobility traces, considering this is a common and stable property of human, we utilize it in the design of routing algorithm for social-oriented DTNs. In this paper, we propose a preference location-based routing strategy (PLBR). We focus on three aspects, according to the longterm behavior, we first provide the approach of acquiring one’s preference locations, then the closeness metric which is used to measure the degree of proximity of any two nodes is proposed, on the basis of that, the data forwarding algorithm is presented. Simulations based on real mobility traces show that compared to other two relevant routing protocols in DTNs, PLBR strategy achieves better tradeoff between the data delivery ratio/delay and the delivery overhead.
Statistical Techniques Utilized in Analyzing TIMSS Databases in Science Education from 1996 to 2012: A Methodological Review

A methodological review of science education articles using the TIMSS databases published by journals indexed in the SSCI database from 1996 to 2012 was conducted. The identified thirty-four articles are analyzed in terms of the statistical techniques used to analyze the TIMSS databases. The results indicate that the weights and design effects, essential adjustments for analyzing the TIMSS databases, were used in more than half of the studies. The study also summarized the most commonly used quantitative methods for analyzing TIMSS in these articles. Suggestions regarding the use of statistical techniques and reporting are made for researchers who utilize the TIMSS databases in their research.
A Novel Nonintrusive User Authentication Method Based on Touch Gestures for Smartphones

Novel nonintrusive authentication mechanisms for smartphones have recently been investigated to complement existing protection mechanisms. A major reason is that a nonintrusive approach can continually authenticate users without interrupting their operations. In this paper, a novel approach to nonintrusive authentication of smartphone users involving flick gestures is proposed. Sixteen histogram-based features of flick gestures, including five novel features related to the habitual touch positions of a user, were used for authentication. Empirical results based on 51 participants indicated that the proposed approach was feasible. The equal error rate of the proposed approach was approximately 4.37% ~ 5.13% when the histogram-based features were calculated based on 30 flicks and decreased to approximately 2.35% ~ 2.99% for features based on 60 flicks. In addition, the experimental results revealed that the five novel features proposed are effective. Possible applications and limitations of the proposed approach are discussed.
Scaling issues in chemical and biological sensors

When a system is reduced isomorphically in size (i.e., scaled down with all dimensions of the system decreased uniformly, or isomorphic scale reduction), the changes in length, area, and volume ratios alter the relative influence of various physical effects that determine the overall operation - often in unexpected ways. As objects shrink, the ratio of surface area to volume increases, rendering surface forces more important. More generally, as the size of an object decreases, forces scaling with a lower power of the linear dimension dominate over the ones scaling with a higher power (e.g., surface tension gains over gravity, electrostatics over magnetics, etc.) [see M. J. Madou, Fundamentals of Microfabrication, 2nd ed. (Boca Raton, FL: CRC, 2002)]. In this paper, we are investigating the influence of miniaturization on various aspects of chemical and biological sensors; we review scaling issues faced in sensor construction, the importance of sample size and the effect of sensor size on detection sensitivity in some of the most popular sensing approaches.
Probabilistic, fuzzy, and quantum methods for retrieving biological information

Advances in measurement technology and sequencing of genomes, have led to the emergence of DNA microarray technology in the 90's, allowing overall measurement of gene expression. This type of experience is said "high throughput" because of the volume of data they generate requiring automatic processing for results interpretation. In this context, many approaches have been developed and can be divided into two families: supervised and unsupervised classification methods. We present here "semantic distillation" a novel unsupervised classification approach, based on a formalism inspired by the physical measurement in quantum mechanics, for the analysis of results from DNA chips. This method provides the user with an ordered list of specific genes for each biological sample of the experience, and describing each cellular context and the influence of each gene in these contexts. Semantic distillation was tested on two data sets: a "tissue-specific" set for which our method has correctly characterised specific genes for each tissue, and clinical data sets of patients with liver fibrosis at various stages for which semantic distillation helped to find signatures in metabolic pathways and biological processes associated with specific genes of each stage of the disease.
Sum-Rate Optimization Problem for Multiuser OFDM Systems

A multiuser Orthogonal Frequency Division Multi-plexing (OFDM) system with perfect channel knowledge and limited power at base station is studied to maximize the average system throughput. Data rate and bit error rate (BER) can be adaptively changed with the channel variation. An eﬃcient and low complexity algorithm for subcarrier and power allocation in OFDM systems is proposed in this paper. We consider an OFDM downlink system where each subcarrier is used by only one user. Since our resource allocation problem is non-convex, it is not easy to ﬁnd an optimal solution. Our approach is to consider a user iterative water ﬁlling algorithm (IWF), which is concave, and ﬁnd a numerical method to approximate the optimal solution for our non convex problem. Our results show that each subcarrier is allocated to a user by taking into account channel gains and interference caused by other users.
A Study of Human Instruction Follower Drone

Robots are important now days because they make work easy and quick with perfection . Robots which are on ground has certain limitation like they can’t reach certain place and can do limited work. It is observed tha an unmanned aerial vehicle (UAV) instead of robots running on ground can perform far better than these robots. This paper gives a brief description about the quadcopter which is also known as quad-rotor. So the aim of this paper is to prepare such a drone (unmanned aerial vehicle) which can help a visitor who is unknown to certain place and follow visitors instruction to reach his destination . The project aims at doing autonomous navigation using GPS (Global positioning system) by giving waypoints to quadcopter and quadcopter will follow waypoint to reach its destination and will come back again to its original position. Latitude and longitude of the destination will be given as input to drone. Keywords— Quad-Copter, PID, GY-86,GPS, Pitch,Roll, Yaw, aero quad configurator
A Method for the Fault Prediction of Printing Press Based on Statistical Process Control of Registration Accuracy

Printing precision is an important evaluation standard for the printing quality. Considering that the information of equipment fault hidden in the registration accuracy has not been fully excavated and studied, this study gives a new method for the fault prediction of printing press based on statistical process control of printing precision. A special mark based on image processing is designed for the detection of registration accuracy and the printing precision. The distribution law of printing precision is analyzed through distribution test and then the prediction of printing press by SPC is realized. This research has an important significant for the automation degree of printing presses, the improvement of the debug efficiency and reduction of labor costs.
A language and simulator for hybrid systems

• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
Approaches to abnormality detection with constraints

A common problem in data analysis is that of discriminating between modes of normal behavior and modes of abnormal behavior. Of particular interest are techniques that can automatically detect abnormal activity in data. This is important since abnormal data may be indicative of measurement error in scientific data, or malicious activity in security audit data. There are two basic approaches to the problem of automatically finding abnormalities. The first is known as signature detection, which involves finding known patterns of abnormality in a database. However, it has the drawback of not being able to detect abnormalities for which there is no prior information. The second approach is known as anomaly detection, which involves building a model of normal data and then searching for patterns that do not fit this model. Unlike the signature detection approach, it is able to detect abnormalities for which there is no prior information, but has the drawback that the anomalies it does detect may not be (significantly) abnormal. The most successful approaches will use both signature detection and anomaly detection techniques to utilize their combined strengths. Much of the previous research in this area has focused on more general approaches to anomaly and signature detection. However, this work is focused on carrying out anomaly and signature detection under various constraints. For example, the data may contain heterogeneous attribute types, or have missing values. The data may also be distributed across several computers or streaming in at a high rate of speed, or there may be limitations on the resources available to analyze the data. In this work, we develop novel solutions to the abnormality detection problem with constraints, and empirically test them on various real and synthetic data sets.
Methodologies in Information Technology Research : A Comparative Study

Today, IT and its related systems and services include every facet of the business and society. As a result, researchers in the IT field often consider the historical perspective regarding the issue at hand. Obtaining historical empirical research is usually the first step toward solving a problem. Additionally, the researcher usually needs or wants to know if any other organization has experienced a similar problem or situation. This is where case studies, interviews, and experiments become the preferred primary source of information. The growth and advances in Information Technology (IT) have been stimulated by intense innovation and research efforts. Taking into consideration other research efforts in other industries, IT has shown to have one of the most funded research investments by organization and governments alike (Benbasat, 1996). This is due to the fact that IT and its services have become a critical resource for many organizations today driving profitability and boosting productivity. This paper will attempt to analyze and discuss the types of research methodologies that have been used to conduct studies in the area of IT Project Management. This paper will also attempt to compare and contrast these methodologies and analyze the issues of bias and limitations of these methodologies.
Long-range and local circuits for top-down modulation of visual cortex processing

You only see what you want to see We often focus on a particular item out of a thousand objects in a visual scene. This ability is called selective attention. Selective attention enhances the responses of sensory nerve cells to whatever is being observed and dampens responses to any distractions. Zhang et al. identified a region of the mouse forebrain that modulates responses in the visual cortex. This modulation improved the mouse's performance in a visual task. Science, this issue p. 660 Projections from the frontal cortex control stimulus processing in the visual system in mice. Top-down modulation of sensory processing allows the animal to select inputs most relevant to current tasks. We found that the cingulate (Cg) region of the mouse frontal cortex powerfully influences sensory processing in the primary visual cortex (V1) through long-range projections that activate local γ-aminobutyric acid–ergic (GABAergic) circuits. Optogenetic activation of Cg neurons enhanced V1 neuron responses and improved visual discrimination. Focal activation of Cg axons in V1 caused a response increase at the activation site but a decrease at nearby locations (center-surround modulation). Whereas somatostatin-positive GABAergic interneurons contributed preferentially to surround suppression, vasoactive intestinal peptide-positive interneurons were crucial for center facilitation. Long-range corticocortical projections thus act through local microcircuits to exert spatially specific top-down modulation of sensory processing.
SX10: A Language for Parallel Programming with Information Security

Many developers of concurrent programs would like to have strong security guarantees about their programs. One such guarantee is noninterference, the notion that high-security inputs to a program can have no influence on public outputs. Noninterference is commonly guaranteed through type systems which track information flow through a program. Unfortunately, many type systems for noninterference in concurrent programs require strict conditions, such as complete observational determinism, or track the security level of information at a fine granularity, making it difficult for programmers to reason about security policies. In this thesis, we introduce SX10, a practical parallel language based on X10. X10 contains the concurrency abstraction of places, which allow computation and data to be separated across computation nodes. SX10 uses places as a security abstraction as well, with security policies specified at the granularity of places. Flows of information between places correspond to flows of information between security levels, making it easier for the type system to locate potential information leaks and determine what parts of a program must meet strong conditions. We suspect these features will also allow programmers to more easily specify security policies and write secure code. We describe a prototype implementation and two case studies which demonstrate uses of the language.