5153 research outputs found
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City of Bridgeport: Seeding Southend Commercial Infrastructure
No full textThe city of Bridgeport has suffered economically throughout the last 20-30 years. Much of this struggle can be traced to the loss of 19th and 20th century manufacturers coupled with poor fiscal management. In addition to Federal job development programs the State of Connecticut has actively pursued and offered a variety of tax-focused incentive programs to attract business to the state as well as it’s major cities. Despite these programs the City of Bridgeport remains reluctant to pursue these option or to create city specific programs. As a result other similar Connecticut cities are benefiting. The study identified a need for the city to implement a tax relief program for companies with less than 50 employees to encourage location into Bridgeport’s Enterprise Zone
Immigration and Economy President Trump Policy Safe?
No full textEconomic growth can not come without appropriate population composition, because we can produce products using robots but robots do not consume product. Using more robots can increase production but not consumption. If there is less consumption, then there will be less production i.e. GDP. So population is an important factor for economic growth. President Trump’s recent remarks about US immigration policy cast a doubt about US economic growth, and this research suggests a change in immigration policy for economic growth. To achieve economic growth goal, US needs higher birth rate than now. Under the current socio-economic condition, high birth rate is not expected. So, healthy population composition through appropriate immigration policy is the only solution to achieve GDP growth goal
The Effectiveness of Current Urinalysis Reflex Criteria on Urine Cultures
No full textVeronica Celone's poster on the effectiveness of current urinalysis reflex criteria on urine cultures
A Framework For Enhancing Speaker Age And Gender Classification By Using A New Feature Set And Deep Neural Network Architectures
No full textSpeaker age and gender classification is one of the most challenging problems in speech processing. Recently with developing technologies, identifying a speaker age and gender has become a necessity for speaker verification and identification systems such as identifying suspects in criminal cases, improving human-machine interaction, and adapting music for awaiting people queue. Although many studies have been carried out focusing on feature extraction and classifier design for improvement, classification accuracies are still not satisfactory. The key issue in identifying speaker’s age and gender is to generate robust features and to design an in-depth classifier. Age and gender information is concealed in speaker’s speech, which is liable for many factors such as, background noise, speech contents, and phonetic divergences.
In this work, different methods are proposed to enhance the speaker age and gender classification based on the deep neural networks (DNNs) as a feature extractor and classifier. First, a model for generating new features from a DNN is proposed. The proposed method uses the Hidden Markov Model toolkit (HTK) tool to find tied-state triphones for all utterances, which are used as labels for the output layer in the DNN. The DNN with a bottleneck layer is trained in an unsupervised manner for calculating the initial weights between layers, then it is trained and tuned in a supervised manner to generate transformed mel-frequency cepstral coefficients (T-MFCCs). Second, the shared class labels method is introduced among misclassified classes to regularize the weights in DNN. Third, DNN-based speakers models using the SDC feature set is proposed. The speakers-aware model can capture the characteristics of the speaker age and gender more effectively than a model that represents a group of speakers. In addition, AGender-Tune system is proposed to classify the speaker age and gender by jointly fine-tuning two DNN models; the first model is pre-trained to classify the speaker age, and second model is pre-trained to classify the speaker gender. Moreover, the new T-MFCCs feature set is used as the input of a fusion model of two systems. The first system is the DNN-based class model and the second system is the DNN-based speaker model. Utilizing the T-MFCCs as input and fusing the final score with the score of a DNN-based class model enhanced the classification accuracies. Finally, the DNN-based speaker models are embedded into an AGender-Tune system to exploit the advantages of each method for a better speaker age and gender classification. The experimental results on a public challenging database showed the effectiveness of the proposed methods for enhancing the speaker age and gender classification and achieved the state of the art on this database
Modelling and Mitigation of Switching Transients from Inductive Load
No full textIn medium and high voltage power system networks, electromagnetic transients are inevitable when switching inductive loads such as free-running transformers, induction motors or power compensation coils. The potential occurrence of switching oscillatory overvoltages defines the insulation co-ordination of cable-networks. Harm can be caused to this insulations by either the amplitude or rise time of transients. In this work, the basic phenomena of switching transients are defined and shown with simple power system models. This work also outlines several mitigating methods
UB Knightlines Summer/Fall 2017
No full textThe UB Knightlines newsletter for summer and fall of 2017. This issue contains articles discussing alumnus Tochukwu Mbiamnozie’s new footwear business, UB student innovations, UB alumna Maria Pesce Stasaitis creating a KidBlog program for student critical literacy skills, UB faculty and students working with international entrepreneurship in Costa Rica farming communities, President Salonen stepping down in 2018, UB faculty and students awarded $40,000 from the Connecticut Space Grant, UB collaboration with NASA to live-stream total solar eclipse, UB’s inaugural Giving Day fundraiser, four UB students won U.S. Department of State Critical Language Scholarships, UB hosting a free 5 week training course entitled Internet of Things for Teenagers, Faculty Research Day drew 350 attendees, UB hosting an inter-faith dialogue, commencement live-stream serves 16,000 people, faculty news, alumni news, books published by alums and faculty, and other campus and sports news
Demystifying McCarthy’s 4 P’s Of The Marketing Mix; To Be Or Not To Be
No full textGrace Beke and Christian Bach's poster on marketing mix and McCarthy's schemata
A Framework for Designing the Architectures of Deep Convolutional Neural Networks
No full textRecent advances in Convolutional Neural Networks (CNNs) have obtained promising results in difficult deep learning tasks. However, the success of a CNN depends on finding an architecture to fit a given problem. A hand-crafted architecture is a challenging, time-consuming process that requires expert knowledge and effort, due to a large number of architectural design choices. In this article, we present an efficient framework that automatically designs a high-performing CNN architecture for a given problem. In this framework, we introduce a new optimization objective function that combines the error rate and the information learnt by a set of feature maps using deconvolutional networks (deconvnet). The new objective function allows the hyperparameters of the CNN architecture to be optimized in a way that enhances the performance by guiding the CNN through better visualization of learnt features via deconvnet. The actual optimization of the objective function is carried out via the Nelder-Mead Method (NMM). Further, our new objective function results in much faster convergence towards a better architecture. The proposed framework has the ability to explore a CNN architecture’s numerous design choices in an efficient way and also allows effective, distributed execution and synchronization via web services. Empirically, we demonstrate that the CNN architecture designed with our approach outperforms several existing approaches in terms of its error rate. Our results are also competitive with state-of-the-art results on the MNIST dataset and perform reasonably against the state-of-the-art results on CIFAR-10 and CIFAR-100 datasets. Our approach has a significant role in increasing the depth, reducing the size of strides, and constraining some convolutional layers not followed by pooling layers in order to find a CNN architecture that produces a high recognition performance.https://doi.org/10.3390/e1906024
Design and Installation of a Direct Exchange Ground Source Heat Pump System
No full textGround source heat pump (GSHP) systems are highly efficient, renewable and clean energy technology systems suitable for heating and cooling of residential and commercial buildings. This poster presented a design and an installation of a geothermal system using direct exchange ground source heat pump (DX-GSHP) for a cooling load of 1 ton (3.5 KW). The direct heat exchange system exchange heat with the ground through R22 refrigerant circulating in copper tubes buried at a depth of 5 m in the earth. The direct heat exchange system works on a vapor compression cycle necessitating the use of refrigerant, compressor and throttling devices. The results show that such a system is 70 to 80% more efficient than the conventional cooling systems. The coefficient of performance for this system was 4.5. The effect of using the soil of different thermal conductivities on efficiency of the system has also been investigated as a part of this research work