Miracle-dz/UniFAR-FTUs huggingface dataset:
Film Families: The Portrayal of the Family in Teen Films from 1980 to 2007

FILM FAMILIES: THE PORTRAYAL OF THE FAMILY IN TEEN FILMS FROM 1980 TO 2007 Caroline Clayton Clark Department of Communications Master of Arts American adolescents watch an average of 3.5 hours of television and movies everyday; many attend more than one movie a month. Adolescents as a group watch more movies than any other group of the population, yet little research has been done on what is shown in teen movies. Adolescence is a time when values, beliefs, and opinions are formed and the media has been found to be a place that adolescents find information that can influence the construction of these identities. While there has been a vast amount of research looking at the family as portrayed on television shows, there has been little research done on film families. More specifically, there has not been an examination of the family as seen in movies targeted towards the teen audience. Through the use of a content analysis, this thesis reviews three decades of families as depicted in teen films, focusing specifically on five areas: family structure, ethnicity, occupation and children, socio-economic status, and parental depictions. This thesis includes a sample of the 90 top-grossing teen movies made during the 1980s, 1990s, and the 2000s (2000-2007) and includes a total of 139 different families. Results indicate that the typical family as depicted in a teen movie, is a middle-class, Caucasian, dual or single-parent family with one or two children; dad is a working professional and mom stays at home. The parents are adequate in their parenting skills and are authoritative in their parenting style. The results of this thesis are compared to findings of past studies regarding television families and against U.S. census data. The implications of the results of this thesis are discussed through the lens of cultivation theory. ACKNOWLEDGMENTS Writing a thesis never goes as quickly or smoothly as you plan or hope, especially when you throw away the first attempt and start over. This thesis was no exception. But with the support and patience of many, I was able to finally complete my goal. I want to thank my husband Brad, and my parents John and Susan Rather for supporting me, encouraging me, and helping me get through the process of writing a thesis. I thank my mom for reminding me that it doesn’t matter how long it takes, just so long as you finish. And I want to give a special thank you to Brad who reminded me that I could finish this task when I didn’t know that I could. I would also like to thank my entire committee, Dr. Callister, Dr. Baker, and Dr. Robinson for taking the time to review this thesis and for giving me suggestions as to how to make it better. Specifically, I want to thank Dr. Mark Callister for being an excellent thesis chair. He was very patient and helpful and he took the time to answer my many questions. It has been much appreciated.
Global Loop Closure Detection of Multi-Robot Based on Particle Filter

A loop closure detection method based on the combination of CSM (correlative scan matching) and particle filter is proposed for the uncertainty of initial relative pose of the robot during the multi-robot SLAM(simultaneous localization and mapping). This method applies the concept of particle filter location to multi-robot loop closure detection, and use the matching of sub-maps to calculate the similarity score, which can quickly and effectively detect the loop closure state between multiple robots, thus it can providing a basis for fast and consistentjoint mapping of multi-robot.
Flex Force Smart Glove Prototype for Physical Therapy Rehabilitation

A nonintrusive and noninvasive Flex Force Smart Glove (FFSG) design is presented that allows for acquisition and processing of sensorimotor information obtained from the human hand. The novel FFSG design is powered by the Intel FPGA system on chip and incorporates all the sensors needed to measure the force and rotation of the human wrist and fingers. Quaternion-based Kalman filters are used to fuse the raw sensor data from five finger joints and one wrist joint to provide detailed orientation information. In addition, feed forward neural network filters are used to classify possible hand exercises that can be further used facilitate rehabilitation through exercise sessions. The novel design will allow for a unified way to quantify the effectiveness of both conventional and robotic-assisted rehabilitation.
Multi-view Clustering: A Brief Review

With the rapid development of information technology, data exhibit multi-view characteristics, and particularly single-view data cannot comprehensively describe the information of all examples. It is very significant to sufficiently make good use of the information from different views. A good multi-view learning strategy may lead to performance improvements. Multi-view clustering is one of the important branches in multi-view learning. The key problem is how to use information effectively from multiple different views, so as to discover the underlying structure of data more accurately. In general, it uses the complementary information available to improve clustering performance in multiple views. Meanwhile, it needs to ensure that the clustering results are consistent. In this paper, we review a number of representative multi-view clustering approaches in the different fields, which can be classified into four groups: cooperative style approaches, graph-based style approaches, multiple kernel learning-based approaches, and subspace learning-based approaches. Therefore, we try to summarize and analyze the development of multi-view clustering. Finally, we point out some specific challenges that are expected to improve further research in this rapidly developing field.
A Semantic Model for Service Description in the Internet of Things

Internet of Things (IoT) applications are seen as complex eco-systems regarding their features such as the high diversity of entities and the open, heterogeneous, and dynamic nature of IoT environments. As a consequence, data interpretation and processing capacity, scalability, reuse, and autonomous decision-making are strongly restricted and compromised for the top level systems. To tackle these issues, Semantic Technologies are considered a solution, due to their provided tools and techniques, to address this diversity. In this work, we present an abstract and complete semantic model for IoT service-based applications. It takes into account the high diversity of IoT systems, the specific IoT environment characteristics, and the interactions of users with whole IoT platforms. A modular and generic knowledge model, that allows semantic definition and representation of applications, entities (spaces and/or objects), IoT devices, IoT services, domain ontologies, user requests, etc., is put forward. This ontological model improves semantic searching and querying capabilities by hiding the heterogeneity of entities and their produced data. It allows designers to choose without any restriction the appropriate public and/or private ontologies. The use-case scenarios test shows clearly the interest, feasibility, and suitability of the proposed knowledge model for the IoT applications.
Distributed Approach for Optimizing Multiple Distributed Generators in a Distribution Network

The ever-increasing demand for more reliable and flexible energy supply for customers has pushed up the distributed generation technology to the center stage in the development of distribution networks. If managed and coordinated efficiently, the distributed generators (DGs) can provide distinct benefits to the suppliers as well as energy consumers. In this paper, a distributed optimization method is proposed to coordinate the multiple DGs in the distribution network (DN) as well as to achieve the optimal operation among them. To optimize the power generation DGs in the DN is actually equivalent to solve the AC optimal power flow (OPF) problem. By defining new variables, the OPF problem is first transformed to a convex second-order cone program (SOCP) problem. By applying alternating direction method of multipliers (ADMM), the SOCP problem is decomposed to the bus level, and it is then solved in a distributed manner. The proposed control enables the autonomous operation of DGs in the distribution network while still achieves the optimality. Simulation studies further demonstrate the effectiveness of the proposed approach.
Parameter Space and Rotor Angle Stability Control of Virtual Synchronous Machine

The virtual synchronous machine technology is able to control the inverter to mimic the behaviors of the traditional synchronous generator. In this paper, the rotor angle stability is thoroughly analyzed under the classical nonlinear dynamic model of the synchronous generator in a single machine (inverter) infinite bus system. Possible situations covering the global parameter space are presented, analyzed and verified through simulations. Furthermore, the idea of global bifurcation structure shaping control is proposed, which can eliminate the attractor that causes the transient instability under emergency and thus guarantee the stability.
The Optimization of a Lateral Flow Immunoassay for Detection of Aflatoxin B1 in Potable Water Samples

Aflatoxin B<sub>1</sub> (AFB<sub>1</sub>) is a highly toxic and carcinogenic metabolite produced by fungi of genus Aspergillus. While AFB<sub>1</sub> contamination in food and crops is well known, little attention has been paid to potable water which is another potential source of AFB<sub>1</sub> contamination that can be ingested by humans. In order to develop a robust, rapidly manufactured sensing device that can monitor the AFB<sub>1</sub> contamination in water, a lateral flow immunostrip based on gold nanoparticles (AuNPs) has been optimized for determination of AFB<sub>1</sub> in potable water samples. A visual detection limit (LOD) of 0.5 ppb was achieved for determination of AFB<sub>1</sub> for the majority of water samples tested. The strip remained functional after being stored at 4 °C for 3 months.
Training Restricted Boltzmann Machines Using Modified Objective Function Based on Limiting the Free Energy Value

A restricted Boltzmann machine (RBM) is a generative model that can capture the probability distribution of data relevant to the problem domain and is usually deployed as the fundamental building block to form more complex deep architectures, such as deep belief network, deep Boltzmann machine, and deep stacked auto-encoder. In addition, the RBM itself can be used as a feature extractor to learn features from raw data. In addition, an RBM is a special type of energy-based model. This paper proposes a modified loss function as an example of an energy-based model defined by restricting the free energy value of the training data. This restriction punishes very low free energy value to reduce the model complexity, which is helpful to the training procedure of the RBM. We validate our method using the MNIST and MNIST-ROTATION datasets. Experiments reveal that the modified loss function behaves better in learning discriminative features as well as in providing better parameters when used to initialize deep feed-forward neural networks (DNN). The convergence speed of the DNN can improve by 44% on both datasets.
Dynamic Analysis and Parameters Design of the Load Virtual Synchronous Machine

This paper introduces the load virtual synchronous machine (LVSM) topology and corresponding control strategy at first. The control strategy mimics operation properties of a synchronous machine, which also has the function of actively participating in grid frequency adjustment (GFA) and grid voltage adjustment (GVA). Then dynamic models of the LVSM participating in GFA and GVA are analyzed. The parameters design method is proposed to realize desired dynamic responses. Finally, the theoretical analysis and the parameters design method are verified through hardware in loop experiments.
Don't smile until Christmas : accounts of the first year of teaching

This handbook outlines a therapy for the treatment of stuttering based on the Organismic Method. It is geared to the level of the preschool and early elementary school child, grades kindergarten through 3A. The therapy developed by the late George A. Kopp of Wayne State University is based on the theory that, regardless of causes, stuttering can be treated successfully by retraining individuals to use normal speech patterns. The exercises concentrate on areas of respiration, phonation, resonation, articulation, and cerebration, require no complex equipment, and are applicable to individual or group sessions. The result is an easy to follow guide which will be useful to speech therapists, teachers, and parents. Elwood G. Anderson is supervising teacher in speech therapy with the Detroit Public Schools.
A Multilevel Dual Converter fed Open end Transformer Configuration for Hybrid AC-DC Microgrid

Recent trends in renewable source integration and advancements in electric loads led to the emerging of local DC microgrids. However, due to majority of AC loads hybrid AC-DC microgrids are formed, incorporating feasible power converters as medium of power flow. Instead of conventional bidirectional AC-DC and DC-DC converters as interlinking converters in hybrid AC-DC microgrid, an open-end transformer based multilevel converter configuration is proposed as interlinking converter in this paper. The converter configuration includes two converters concatenated with primary of transformer, is a unique substitution for conventional multiple converters utilized. The salient features of the configuration include reduced control complexity, inherent isolation from AC grid, and able to provide inter-grid bidirectional power flow. A modified control scheme is proposed for precise power flow control in the system. Real time simulation of the proposed configuration of hybrid AC-DC microgrid is implemented in RSCAD/RTDS and experimented on a scaled hardware prototype. The outcomes depict the potency of control scheme in inter-grid power flow control.
Detection of Breathing and Heart Rates in UWB Radar Sensor Data Using FVPIEF-Based Two-Layer EEMD

Ultra-wideband (UWB) radar is an important remote sensing tool of life detection or a non-contact monitor of the vital signals. By processing the received UWB pulse echoes reflected from the body, different signals corresponding to heart activity and breathing, corrupted by body motion and the environment noise, are wanted to be separated clearly. However, the heartbeat signal is so tiny that it is covered by breathing harmonics and clutters. At the same time, since the frequencies of the vital signals are very close, usually around 1 Hz, it is difficult to apply an ordinary frequency filter to separate them apart. This problem induces that the vital signal detection method, usually, only detects the large breath signal, not the heartbeat signal. To solve this problem, a novel method is provided, in this paper, to extract the heartbeat and the breath information simultaneously. The method uses the feature time index with the first valley peak of the energy function of intrinsic mode functions (FVPIEF) calculated by pseudo bi-dimension ensemble empirical mode decomposition method and extracts the vital signals by the ensemble empirical mode decomposition (EEMD). Both simulation and experiment results evidently show that the proposed FVPIEF based two-layer EEMD method is effective for separating the small heartbeat signal from the large breath signal and significantly improves the evaluation of heart and breathing rates in both hold-breathing and breathing conditions.
Thermal and Wirelength Optimization With TSV Assignment for 3-D-IC

3-D-IC is an emerging technology that overcomes the drawbacks of 2-D-IC and a vital structure called through-silicon via (TSV) is used to connect the adjacent layers vertically. In this paper, based on graph theories, we formulate the TSV assignment as an integer multicommodity min-cost (IMCMC) problem, and using multilevel algorithm, we get optimal solution. During the assignment, the capacity of the grid could be insufficient to contain the flows so that conditional grid extension effectively relieves the capacity limits and accelerate running speed. Thermal resistive model is introduced to simulate the heat dissipation of chip and estimate temperature distribution each layer. By inserting the thermal TSV, the temperature of superheated region is reduced. Moreover, considering about a more complicated situation, we propose a method to assign multipins in a net. The experimental results demonstrate our model in the IMCMC network using multilevel algorithm achieve optimized congestion, less wirelength, higher running speed, and reduced temperature.
Highly Efficient Wearable CPW Antenna Enabled by EBG-FSS Structure for Medical Body Area Network Applications

A wearable fabric CPW antenna is presented for medical body area network (MBAN) applications at 2.4 GHz based on an electromagnetic bandgap design and frequency selective surface (EBG-FSS). Without EBG-FSS, the basic antenna has an omnidirectional radiation pattern, and when operated close to human tissue, the performance and efficiency degrade, and there is a high specific absorption rate. To overcome this problem, the antenna incorporates EBG-FSS, which reduces the backward radiation, with SAR reduced by 95%. The gain is improved to 6.55 dBi and the front-to-back ratio is enhanced by 13 dB compared to the basic antenna. The overall dimensions of the integrated design are $60\times 60\times2.4$ mm3. Simulation and experimental studies reveal that the antenna integrated with EBG-FSS can tolerate loading by human tissue as well as bending. Thus, the design is a good candidate for MBAN applications.
MCTOPE Ensemble Machine Learning Framework: A Case Study of Routing Protocol Prediction

Many crucial applications of wireless sensor networks rely radically on routing protocols for an efficient data delivery. This paper presents a case study of scrutinizing the use-fulness of hybridization of machine learning classifiers in order to develop a Multi-Criteria Topsis based Ensemble (MCTOPE) framework. Technique for Order of preferences by similarity to Ideal Solution (TOPSIS), a multi-criteria assessment algorithm is employed to optimize the built ensemble learner for the prediction of an optimal reactive routing protocol for a wireless sensor network (WSN). The performance of the framework is first validated using six different machine learning datasets, and then the proposed method is implemented as a web application using R script and Python Django web framework. After experimenting with more than thousand combinations of training samples and ten base classifiers for the routing protocol prediction problem, MCTOPE framework builds an ensemble of support vector machine and neural network classifiers with an accuracy of 99.6%, which is far better, when it is compared with the performance of state-of-the-art classifiers. With the appearance of tremendous growth of machine learning classifiers in plenty of applications, an automatic ensemble building machine learning technique helps in minimizing the risk of obtaining poor results from a single classifier system, and will play a big part for efficient predictions in the future.
Identifying and Exploiting Ineffectual Computations to Enable Hardware Acceleration of Deep Learning

This article summarizes somde of our work on hardware accelerators for inference with Deep Learning Neural Networks (DNNs). Early success in hardware acceleration for DNNs exploited the computation structure and the significant reuse in their access stream. Our approach to further boost benefits has been to first identify properties in the value stream of DNNs which we can exploit at the hardware level to improve execution time, reduce off- and on-chip communication and storage, resulting in higher energy efficiency and execution time reduction. We have been focusing on properties that are difficult or impossible to discern in advance. These properties include values that are zero or near zero and that prove ineffectual, values that have reduced precision needs, or even the bit-level content of values that lead to ineffectual computations. The presented designs cover a spectrum of choices in terms of area cost, energy efficiency, and relative execution time performance and target a variety of hardware devices from embedded systems to server class machines. A key characteristic of these designs is that they reward but do not requires advances in model design that increase the aforementioned properties (such as reduced precision or sparsity) and thus provide a safe path to innovation.
Automatic Grasp Planning Using Depth Image for Robotic Manipulator

In this paper, we present a new solution for automatic grasp, which uses the depth image to reconstruct the real world at first. Then our system will recognize the object automatically by using a variant of HFC algorithm. Since the segmentation algorithm is based on primitive shape, we can use it to decide the grasp pose. In the end, our system will implement the grasp process automatically. A set of primitive shape, such as cylinder, plane, sphere, will be given. And we can generate the grasp rules for each shape in the set of primitive shape. In the end of this paper, experimental result will be given. The innovation of this paper is that we use the depth sensor to reconstruct the real world in high-precision, and because of the precision of the virtual scene we can implement the grasp process automatically. We believe that automatic grasp planning system using depth image will be an excellence solution for automatic grasp.
A Charge Balanced Neural Stimulator with 3.3 V to 49 V Supply Compliance and Arbitrary Programmable Current Pulse Shapes

A power-efficient six channel prototype of a neural stimulator, with arbitrary programmable current pulse shapes, and a high voltage compliance of up to 49 V is presented. Rectangular, sinusoidal and exponential shapes are possible, supporting e.g. selective stimulation via anodal and HF block. It features high flexibility in current amplitudes from 2 µA to 10 mA with 9-bit resolution per biphasic current DAC, current settling time constant of 0.8 µs, and low voltage headroom. Each channel is equipped with passive charge balancing by a high voltage switch with adequate on-resistance of 2 kΩ. Two channels are additionally equipped with active charge balancing by a PI-controlled offset compensation. The smallest achievable overall static power consumption is 20.8 µW per channel and 41 µW if active charge balancing (CB) is enabled.
Distance Metric Approach for Nearest Neighbour Recall of Neonatal EEG

Clinical neurophysiologists often find it difficult to recall rare EEG patterns despite the fact that this information could be diagnostic and help with treatment intervention. Having the neurophysiologist physically searching through previous neonatal EEG recordings is a cumbersome and time consuming task. This paper examines the performance of a brute force distance metric approach to locate similar neonatal EEG patterns. This preliminary work is to set a baseline for neonatal EEG nearest neighbour pattern recall. A fixed point distance metric and an elastic distance metric are evaluated in this paper on the time series data and on the features extracted from data. The system was tested on six different neonatal EEG pattern types with 430 events in total and the results are presented.