Miracle-dz/UniFAR-FTUs huggingface dataset:
Santa Clara University Design and Implementation of 60 GHz Cavity Backed Patch Antennas By :

As the lower range of radio frequency spectrum become more cluttered and the demand for faster data rates and greater bandwidth grows, next generation wireless technology is pushed to innovate and provide needed resources. Major technology companies such as Samsung, intel, Qualcomm Inc. and Google have been investing at millimeter-wave band frequencies to address this imminent need. The fifth generation of telecommunication technology could be perceived as a paradigm shift in the way we see and think about wireless communication. This next generation technology is intended to enable other up and coming industries and services such as internet of things (IOT), telemedicine and wearable devices. Antennas are the critical component in realizing any of the wireless systems in these emerging applications. This thesis investigates design and simulation of 60 GHz microstrip patch antennas, in single and array arrangements. A few single patch antennas and 2x2 patch arrays were designed to operate at 60 GHz. The needed feed network for the studied antennas were also developed using microwave design techniques and transmission line theory followed by fullwave simulations to achieve matching at the excitation ports. First, a cavity-backed patch antenna is designed and optimized to maintain and improve the operational characteristics of a single radiating element. Next, via-fence strategy used for implementation of cavity was employed in array design. Extensive fullwave simulations and design trials revealed that at 60GHz, due to comparable dimensions of the microstrip feed network with those of the radiating elements, the overall antenna system radiation performance is impacted. Hence, a second feed strategy using coaxial probe is utilized for new design iterations of single microstrip patch and 2x2 patch array. For these final designs the feed is via the vertical mount coaxial cable with center pin extended through the antenna substrate. This eliminated the need for a lengthy microstrip transmission line feed and reduced the amount of impedance fluctuations. Cavity backing strategy and via fencing were also included and optimized for these designs to optimize the antenna performance. It was concluded that cavity backing is the most practical approach for antenna system design at 60GHz which was able to improve the front to back lobe ratio of the patch antenna. This feature is very important in applications requiring confinement of radiation in only half space and directing it away from the user such as in health monitoring devices. Finally, the fabrication process is discussed in detail which involves exporting the design layout from an electromagnetic field solver to another CAD tool to generate fabrication files and define the different components and layers. The connectors and launch pins needed for prototyping have been identified for future fabrication and testing.
Multi-Sensor Integrated Navigation in Urban and Indoor Environments: GNSS, Inertial, and Range Sensors Integration

The art of multi-sensor processing, or “sensor-fusion,” is the ability to optimally infer state information from multiple noisy streams of data. One major application area where sensor fusion is commonly used is navigation technology. While global navigation satellite systems (GNSS) can provide centimeter-level location accuracy worldwide, they suffer from signal availability problems in dense urban environment and they hardly work indoors. While several alternative backups have been proposed, so far, no single sensor or technology can provide the desirable precise localization in such environments under reasonable costs and affordable infrastructures. Therefore, to navigate through these complex areas, combining sensors is beneficial. Common sensors used to augment/replace GNSS in complex environments include inertial measurement unit (IMU), range sensors, and vision sensors. This chapter discusses the design and implementation of tightly coupled sensor fusion of GNSS, IMU, and light detection and ranging (LiDAR) measurements to navigate in complex urban and indoor environments.
A comparative study on the algorithms for data privacy and security in cloud computing

Cloud computing is a technology expected to redefine the advances in information technology. In data privacy protection and retrieval- control is one of the most challenging research works in cloud computing. Cloud computing offers an innovative business model for organizations with minimal investment. Security is one of the major issues which affect the growth of cloud. An important problem in this environment is to protect the user privacy while querying the data from the cloud; to address this researcher have developed many techniques, but the problem with those techniques is the heavy computational and bandwidth costs which are unacceptable to the users. This paper gives an overview of some schemes which address the problem of bandwidth and computational costs.
3D Model Annotation based on Semi-Supervised Learning

Summary The purpose of annotation for 3D model is that it can automatically list the best suitable labels to describe the 3D models; it is an important part of the text-based 3D model retrieval. The existence of the semantic gap makes the result based on the similarity matching techniques needs to be improved. In order to improve the 3D model annotation performance using a large number of unlabeled samples, we propose a semi-supervised measure learning method to realize the 3D models multiple semantic annotation. A graph-based semi-supervised learning is firstly used to expand the training set, and the semantic words confidence of the models in the extension set is proposed. An improved relevant component analysis method is proposed in this paper to learn a distance measure based on the extended training set. Our approach is introduced to complete multiple semantic annotation task based on the learned distance measure. The test result on the PSB data set have shown that the method making use of the unlabeled samples has achieved a better annotation result when a small amount of labels were given.
Big Data Mining and Analytics

Data mining and analytics aims to analyze valuable data—such as shopper market basket data—and extract implicit, previously unknown, and potentially useful information from the data. Due to advances in technology, high volumes of valuable data—such as streams of banking, financial, and marketing data—are generated in various real-life business applications in modern organizations and society. This leads us into the new era of Big Data (Madden, 2012; Mishne, Dalton, Li, Sharma, & Lin, 2013; Suchanek & Weikum, 2013). Intuitively, Big Data are interesting high-velocity, high-value, and/or high-variety data with volumes beyond the ability of commonly-used software to capture, manage, and process within a tolerable elapsed time. Hence, new forms of processing data are needed to enable enhanced decision making, insight, knowledge discovery, and process optimization. This drives and motivates research and practices in business analytics and optimization, which require techniques like Big Data mining and analytics, business process optimization, applied business statistics, as well as business intelligence solutions and information systems. Having developed systematic or quantitative processes to mine and analyze Big Data allows us to continuously or iteratively explore, investigate, and understand the past business performance so as to gain new insight and drive business planning. Over the past few years, several algorithms have been proposed that use the MapReduce model—which mines the search space with distributed or parallel computing—for different Big Data mining and analytics tasks (Luo, Ding, & Huang, 2012; Shi, 2012; Shim, 2012; Condie, Mineiro, Polyzotis, & Weimer, 2013; Kumar, Niu, & Ré, 2013). One such task is frequent pattern mining, which discovers interesting knowledge in the forms of frequently occurring sets of merchandise items or events. In this chapter, we focus mainly on frequent pattern mining from Big Data with MapReduce.
Usability in the computer industry: what contribution can longitudinal field studies make?

The author examines field studies in general and longitudinal field studies in particular, their uses in technical communication and human-computer interaction, a plan for industry-university research partnerships, and the contribution that longitudinal field studies can make to usability research. It is concluded that longitudinal field studies make it possible to see the gross patterns of change in the way people use complex computer programs to do their work. Such studies make it possible to ask people similar questions about attitudes, satisfaction, and knowledge, and also to ask them far enough apart so that changes in those attitudes, satisfactions, and knowledge become visible. Longitudinal field studies anchor field studies in the idea of comparison and thus are in some ways easier to complete. Longitudinal field studies hold promise for unlocking long-term usability questions. <<ETX>>
A stability criterion for discrete nonlinear systems with time delayed feedback

A new criterion on the non-Lyapunov method of stability analysis is developed for a large class of discrete time nonlinear systems. The amenable class is broad and includes systems mainly encountered in practice, for example, all plants described by functions of Lipschitz class, linear, bilinear, and polynomial functions, etc. Unlike other publications on the stability of discrete nonlinear systems, the authors consider the output feedback with time delays, and the control law from a broader class of functions. The derived sufficient criterion is simple and easily applicable. >
Feature selection for quality assessment of indoor mobile mapping point clouds

Owing to complexity of indoor environment, such as close range, multi-angle, occlusion, uneven lighting conditions and lack of absolute positioning information, quality assessment of indoor mobile mapping point clouds is a tough and challenging task. It is meaningful to evaluate the features extracted from indoor point clouds prior to further quality assessment. In this paper, we mainly focus on feature extraction depend upon indoor RGB-D camera for the quality assessment of point cloud data, which is proposed for selecting and screening local features, using random forest algorithm to find the optimum feature for the next step’s quality assessment. First, we collect indoor point clouds data and classify them into classes of complete or incomplete. Then, we extract high dimensional features from the input point clouds data. Afterwards, we select discriminative features through random forest. Experimental results on different classes demonstrate the effective and promising performance of the presented method for point clouds quality assessment.
Survey on Clustering Over Categorical Streaming Data

: Clustering can be done for static as well as dynamic dataset. Clustering is performed on the basis of underlying data structure and data distribution. The data distribution varies with respect to time. Hence there need to be a smooth change in underlying cluster model with respect to time. The clustering techniques of numerical dataset are different than Categorical dataset. This works aim to study various clustering techniques for streaming data with respect to the numerical and Categorical dataset.
Using Information Features in Computer Vision for 3d Pose Estimation in Space

S.V. MELNYCHUK, PhD (Engineering), Researcher of Dynamic Systems Control Department e-mail: sergvik@ukr.net V.F. GUBAREV, Dr (Engineering), Professor, Corresponding Member of NAS of Ukraine, Head of Dynamic Systems Control Department e-mail: v.f. gubarev@gmail.com N.N. SALNIKOV, PhD (Engineering), Senior Researcher of Dynamic Systems Control Department e-mail: salnikov.nikolai@gmail.com Space Research Institute of the National Academy of Sciences of Ukraine and State Space Agency of Ukraine, Acad. Glushkov av. 40, 4/1, 03187, Kyiv 187, Ukraine
Modeling over Short Texts

Short texts are popular on today’s Web, especially with the emergence of social media. Inferring topics from large scale short texts becomes a critical but challenging task for many content analysis tasks. Conventional topic models such as latent Dirichlet allocation (LDA) and probabilistic latent semantic analysis (PLSA) learn topics from document-level word cooccurrences by modeling each document as a mixture of topics, whose inference suffers from the sparsity of word co-occurrence patterns in short texts. In this paper, we propose a novel way for short text topic modeling, referred as biterm topic model (BTM). BTM learns topics by directly modeling the generation of word co-occurrence patterns (i.e., biterms) in the corpus, making the inference effective with the rich corpus-level information. To cope with large scale short text data, we further introduce two online algorithms for BTM for efficient topic learning. Experiments on real-word short text collections show that BTM can discover more prominent and coherent topics, and significantly outperform the state-of-the-art baselines. We also demonstrate the appealing performance of the two online BTM algorithms on both time efficiency and topic learning.
Optimized design and performance of a shared pump single clad 2 μm TDFA

We report the design, experimental performance, and simulation of a single stage, co- and counter-pumped Tmdoped fiber amplifier (TDFA) in the 2 μm signal wavelength band with an optimized 1567 nm shared pump source. We investigate the dependence of output power, gain, and efficiency on pump coupling ratio and signal wavelength. Small signal gains of >50 dB, an output power of 2 W, and small signal noise figures of <3.5 dB are demonstrated. Simulations of TDFA performance agree well with the experimental data. We also discuss performance tradeoffs with respect to amplifier topology for this simple and efficient TDFA.
Analysis of NAT64 Port Allocation Methods for Shared IPv4 Addresses

This document enumerates methods of port assignment in Carrier Grade NATs (CGNs), focused particularly on NAT64 environments. A theoretical framework of different NAT port allocation methods is described. The memo is intended to clarify and focus the port allocation discussion and propose an integrated view of the considerations for selection of the port allocation mechanism in a given deployment.
CONTINUOUS HIDDEN MARKOV MODELING FOR SPEAKER-INDEPENDENT

We present a word spotting system using Gaussian hidden Markov models. Several aspects of this problem are investigated. Specifically, we present results on the use of various signal processing and feature transformation techniques. We have observed that performance can be greatly affected by choice of features used, the covariance structure of the Gaussian models, as well as transformations based on energy and feature distributions. Also, due to the open-set nature of the problem, the specific techniques for modeling out-of-vocabulary speech and the choice of scoring metric can have a significant effect on performance. In this paper, we present a word spotting system using Gaussian likelihood, hidden Markov models (HMM). In the next section, the hasic HMM model is presented as well as a discussion of novel aspects of the HMM techniques that deal with the open-set nature of word spotting. In the following section, we describe the various feature transformations considered. The experimental results are then presented, followed by an overall discussion.
Using genetic algorithm to optimize autonomous and cooperative behaviors of multiple mobile robots system

To exploit the advantages of a multiple mobile robots system, the robots must have some degree of autonomy and cooperation. However, autonomy and cooperation are contradictory to each other, which makes it difficult to optimize the control parameters of robot's behaviors manually. Genetic algorithms are used in the paper to solve this problem. By altering the weightings of the autonomy and cooperation measures, the robots can exhibit different "personalities", which is testified by a simulation experiment.
Complexity as the defining feature of the 21st Century

The 21st century is often labelled as the Century of Complexity because complexity of the socio-technological environment in which we live and work has reached the level that no longer can be ignored – complexity affects now all aspects of our lives. The paper defines complexity, identifies its sources and explains the basics of the new science and the art of managing complexity. The importance of complexity is discussed in the context of social, business and engineering systems.
A Visualization Technique for Multiagent Systems

In this dissertation we consider the problem of monitoring and visualizing the performance of multi-agent systems, i.e. systems of adaptive agents that change their behaviour as a result of learning and experiencing. Instead of relying on global performance measures, as have been done by current visualization techniques, we use a different technique which is a combination of dimensionality reduction and social network measures. The advantage of this technique, we claim, is that it does not only capture the performance of the multiagent system on its macro level, as is the case with the global performance metrics methods, but it can also capture the performance on the micro level, i.e. by being sensitive to the performance of individual agents. To test our technique, we first conduct several experiments to compare different combinations of dimensionality reduction techniques and network measures. Then we apply one such combination on networks of adaptive agents that play games (we use the term ”game” as in the Game theory). Our findings confirm that using dimensionality-reduced weighted network measures in visualizing the performance of multi-agent systems is informative in the sense that they proved to be sensitive to changes in the global as well as local system dynamics (for example the global network structure and the local learning of individual agents).
Timelines, temporal resolution, temporal zoom and time geography

The timeline is introduced as main interaction device, and as the binding element to explore the different 'levels' of time related to spatio-temporal data. The timeline is always linked to the map and potentially other graphics. The objective is to improve access and provide exploratory functions to get a better understanding of the temporal patterns in the data. The different timeline-function such as temporal zoom and the linking different types of time together are discussed. The cultural heritage world is taken as a sample environment to demonstrate the useful possibilities of the timeline. Here the space-time-cube is introduced as a medium to present and explore search results. Finally some other applications are discussed.
Design strategies and decoupling techniques for reducing the effects of electrical interference in mixed-mode IC's

A key issue in the successful integration of analog circuits is a stable analog power supply. Traditional on-chip decoupling methods exhibit transients in the supply or voltage drops and power losses. This paper introduces the RLC decoupling method that features an enhanced transient response while being especially suited for low-power, low voltage applications. Both a theoretical and a practical approach are presented together with measurement results. As the benefits of a stable local power supply can be lost by the inadequate connection of two subcircuits with relative variations on the local grounds, a differential approach of signal transfer is proposed. Furthermore, the effect of a good local decoupling can be deteriorated by substrate noise, so some attention is given to this problem too.
Image segmentation using multilayer neural network

An image segmentation method based on multilayer neural networks is introduced. This method was applied to both the aerial and texture images. The performance of this method on a synthetic image is compared to a model based image segmentation technique. Simulation results on both aerial and texture images are provided. A method to extract important features of the image before the training is proposed. <<ETX>>
Fault Diagnosis of Power Network Based on GIS Platform and Bayesian Networks

In order to determine the location of the fault components of the power network quickly and give troubleshooting solutions, this paper obtains a simplify structure of relay protection and circuit-breaker as key equipment by analyzing the power network topology of GIS platform and uses the Bayesian networks fault diagnosis algorithm and finally designs the power network fault diagnosis module based on GIS platform. Fault diagnosis algorithm based on Bayesian networks is a new method for power network fault diagnosis which deals with the power network fault diagnosis with incomplete alarm signals caused by the protection device’s and the circuit breaker’s malfunction or refusal to move, device failure of communication and other reasons in the use of Bayesian networks method. This method establishes the transmission line fault diagnosis model by using Noisy-Or, Noisy-And node model and similar BP neural network back propagation algorithm, and obtains the fault trust degree of each component by using the formula, and finally determines the fault according to the fault trust degree. The practical engineering application shows that the search speed and accuracy of fault diagnosis are improved by applying the fault diagnosis module based on GIS platform and Bayesian network.
Open Research Online Modeling multiple interactions with a Markov random ﬁeld in query expansion for session search

How to automatically understand and answer users’ questions (e.g., queries issued to a search engine) ex- pressed with natural language has become an important yet difﬁcult problem across the research ﬁelds of Information Retrieval (IR) and Artiﬁcial Intelligence (AI). In a typical interactive Web search scenario, namely Session Search. To obtain relevant information, user usually interacts with the search engine for several rounds in the forms of, e.g., query reformulations, clicks and skips. These interactions are usually mixed and interwined with each other in a complex way. For the ideal goal, an intelligent search engine can be seen as an AI agent that is able to infer the user’s information need from these interactions. However, there still exists a big gap between the current state of the art and this goal. In this paper, in order to bridge the gap, we propose a Markov Random Field based approach to capture dependencies among interactions, queries and clicked documents for automatic query expansion (as a way of inferring the user’s information need). Extensive empirical evaluation is conducted on large scale web search datasets, and the results demonstrate the effectiveness of our proposed models.
Layered black-box, behavioral interconnection perspective and applications to problems in communications, Part I: i.i.d. sources

In this paper, the problem of communication over an essentially unknown channel, which is known to be able to communicate a source to a destination to within a certain distortion level, is considered from a behavioral, interconnection view-point. Rates of reliable communication are derived and source-channel separation for communication with ﬁdelity criteria is proved. The results are then generalized to the unicast multi-user setting under certain assumptions. Other applications of this problem problem which follow from this perspective are discussed.