Front-end Processing Research Articles

Inequities among patient placement in emergency department hallway treatment spaces

BackgroundLimited capacity in the emergency department (ED) secondary to boarding and crowding has resulted in patients receiving care in hallways to provide access to timely evaluation and treatment. However, there are concerns raised by physicians and patients regarding a decrease in patient centered care and quality resulting from hallway care. We sought to explore social risk factors associated with hallway placement and operational outcomes. Study design/methodsObservational study between July 2017 and February 2020. Primary outcome was the adjusted odds ratio (aOR) of patient placement in a hallway treatment space adjusting for patient demographics and ED operational factors. Secondary outcomes included left without being seen (LWBS), discharge against medical advice (AMA), elopement, 72-h ED revisit, 10-day ED revisit and escalation of care during boarding. ResultsAmong 361,377 ED visits, 100,079 (27.7%) visits were assigned to hallway beds. Patient insurance coverage (Medicaid (aOR 1.04, 95% CI 1.01,1.06) and Self-pay/Other (1.08, (1.03, 1.13))) with comparison to private insurance, and patient sex (Male (1.08, (1.06, 1.10))) with comparison to female sex are associated with higher odds of hallway placement but patient age, race, and language were not. These associations are adjusted for ED census, triage assigned severity, ED staffing, boarding level, and time effect, with social factors mutually adjusted. Additionally adjusting for patients' social factors, patients placed in hallways had higher odds of elopement (1.23 (1.07,1.41)), 72-h ED revisit (1.33 (1.08, 1.64)) and 10-day ED revisit (1.23 (1.11, 1.36)) comparing with patients placed in regular ED rooms. We did not find statistically significant associations between hallway placement and LWBS, discharge AMA, or escalation of care. ConclusionWhile hallway usage is ad hoc, we find consistent differences in care delivery with those insured by Medicaid and self-pay or male sex being placed in hallway beds. Further work should examine how new front-end processes such as provider in triage or split flow may be associated with inequities in patient access to emergency and hospital care.

Innovation to Improve Critical Thinking Skills in the Generation Z using Peeragogy as a Learning Approach and Artificial Intelligence (AI) as a Tool

The current generation, known as generation Z (Gen Z), is a generation that lives and is familiar with information technology in everyday life, including but not limited to learning purposes. Gen Z is characterized by teamwork in cyberspace and solving problems. Gen Z can also adopt artificial intelligence (AI) technology as their learning tool. Meanwhile, Gen Z faces the challenge of acquiring 21st-century skills requiring higher-order thinking (HOTS). This paper will focus on learning skills, especially critical thinking (CT). For this reason, it is essential to improve the competency of CT skills in Gen Z by using a model that combines the 7E learning cycle with peeragogy learning approach and adopting the latest AI applications as a tool. This model will contribute to the theoretical and practical use of AI apps for education, particularly the role of correct utilization of AI. Additionally, it can be misused to improve learning skills, especially CT skills. The results carried out in class show AI's effectiveness in improving CT skills for Gen Z, especially the role of AI in the back-end learning process, namely to verify and validate understanding. This paper suggests that AI apps should be controlled if used in the front-end learning process, especially at the exploration stage, to improve CT skills. Without controlling the AI apps, they can reduce learners' ability to explore a topic according to their creativity and criticality.

Clustering of Feature Vectors and Recognition of Bodo Phoneme Using MLP Technique

The process through which a computer can identify spoken words is termed as speech recognition. After analysis and finding of features of the speech sound, one can go towards the recognition of the speech. The extraction of feature vector is known as the feature extraction process or the front-end process. This front-end process is considered as the 1st stage of speech recognition. Pattern matching process is the 2nd stage or final stage of speech recognition where actual search is carried out to decode the spoken utterances by matching the sequence of feature vectors against the acoustic and language models stored in the recognizer. To reduce this problem, clustering technique is used. Clustering makes it possible to look at properties of whole clusters instead of individual objects - a simplification that might be useful when handling large volume of data. Clustering is nothing but the assignment of a set of observations into subsets so that the observations in the same cluster are similar in some sense.

Multi-attribute hierarchical clustering for product family division of customized wooden doors

To improve the production system for customized wooden doors and to gain research and development efficiency, this paper proposed the feasibility of using hierarchical clustering algorithms to cluster a company’s customized wooden door products and its application to rational product family architecture. The particular use of multi-attribute feature data to locate products and the integration of image data into the database can make the original hierarchical clustering more compatible and adaptable for application to customized wooden doors. The preprocessed data was analyzed by clustering to obtain the clustering results and similarity relationships. Hierarchical clustering results were uneven and not entirely interpreted. However, the internal order structure of clusters and the clustering process could be clearly observed, and the distance hierarchical relationship between the products could be obtained, which was beneficial to the division of the product architecture. The results illustrated that processing using hierarchical clustering of multi-attribute data is feasible for optimizing customized wooden door product systems. In addition, the product architecture, product coding rules, and front-end development process were established to improve standardization and research and development efficiency. There is still great potential for developing the custom wooden door category in custom furniture companies.

Impact of Oxygen on the Generation of Slip Lines and the Electronic Properties of Si-based Substrates

We focus on slip line formation and propagation on different types of Si and SOI substrates that underwent high temperature anneals during 65nm RFSOI device fabrication. Different parameters are found to contribute to slip line formation and dislocations propagation, mainly the substrate properties (thickness, layer and oxygen in silicon handle), the temperature gradients and the support between furnace carriers and backsides of substrates. Several parameters contribute to substrate deformation, e.g. the warpage increase. The warp degradation is found to be directly linked to low oxygen concentrations in silicon handle. This understanding is very helpful to introduce and process new substrates in advanced front-end process flows for MOS device fabrication.

High-Density Pixel Imaging Sensor Readout Electronics for Space Applications: A Design Overview

With the specialization of VLSI ASICs for front-end signal processing electronics, the customization of the control back-end electronics (BEE) has become critical to fully deploy the ASIC performance. In the context of space operations, with typical constraints on power and reliability, the design and qualification of such integrated systems present significant challenges. In this paper, we review the design and performance of the BEE systems after two years of operations in low Earth orbit (LEO); these systems read out the custom ASICs inside the gas pixel detectors, which are located at the heart of the imaging X-ray polarimetry explorer (IXPE), a NASA-ASI small explorer mission designed to measure X-ray polarization in the 2–8 keV energy range.

Design and Implementation of a C++Based Hotel Management System

In this paper, we use C++ as the development language and QT to design and develop a hotel management system for the current development of the hotel industry. The system adopts the B/S model to connect the object with the database, so that it has better flexibility and reusability. Its functional modules mainly include customer service module, front-end processing module, back-end management module, data storage module and other parts, which mainly realize the hotel customer's reservation, check-in and check-out and the management of the hotel internal staff and other functions. The development cost of the system is low and the operation is simple, which reduces the learning cost of the staff, improves the management efficiency of the hotel and facilitates the maintenance and management at a later stage.

Feature-Based Scanning LiDAR-Inertial Odometry Using Factor Graph Optimization

Localization of a mobile robot in the absence of an absolute position sensor often relies on techniques such as visual or lidar-inertial odometry. While lidar has many advantages, the most capable sensors use scanning mechanisms, leading to motion-distorted scans. Previous strategies used to account for robot motion when performing state estimation and outlier rejection have drawbacks for use on highly dynamic, resource-constrained robots such as spacecraft during descent and landing. In this paper we develop a novel probabilistic factor for the inclusion of scanning lidar features, and an accompanying outlier rejection methodology. By using well-established, efficient feature tracking techniques, our image processing front end is both reliable and amenable to FPGA implementation, both of which are critical for operation on a spacecraft. We demonstrate our technique on a dataset from simulated planetary descent and landing. The results show that our system can be used to perform accurate lidar-inertial odometry, even in highly dynamic scenarios.

Design and Implementation of Multi-Channel Readout Circuits for Low-Temperature Environments

Infrared sensors and focal plane imaging arrays are among the most important types of devices in the field of aerospace applications. To effectively amplify the small signals collected by infrared sensors and focal plane imaging arrays for subsequent processing, a new multi-channel preamplifier circuit based on ultra-low temperatures was designed in this study to read the acquisition signals of such devices. The technology of an SMIC 180 nm CMOS with 1.8 V power was adopted to realize the circuit. Meanwhile, an eight-level adjustable gain switch was used to increase the selectivity of signal processing. According to the simulation’s results, the single-channel power consumption of the circuit in the 77 K ultra-low temperature environment was only 5.17 mW. The circuit could drive a large load of 200–400 pF with an open-loop gain of 131.4 dB, which showed excellent performance in driving large loads, providing high gain and consuming less power. Additionally, the circuit exhibited good aspects for front-end signal reading and processing of infrared sensors and focal plane arrays in extreme environments.

Speech Signal Processing of Industrial Speech Recognition

The noise in the industrial production environment is more complex than the application scenario in the home or office environment, and the most of the noise loudness is much larger. Automatic speech recognition(ASR) system will fail when the front-end speech signal processing SNR is too poor. At this time the front-end speech signal processing ability is particularly important. The product solution mentioned in this article involves multiple point. It includes omnidirectional Microphones Array scheme selection, selection of Mic monomer, sampling, digitization, noise reduction and echo processing, so as to improve the overall SNR and improve the speech recognition rate under noise,therefore, it has become an AR head-mounted computer for industrial intelligent speech recognition accepted by many business worldwide.

A Real-Time Stereo Visual-Inertial SLAM System Based on Point-and-Line Features

Simultaneous localization and mapping (SLAM) is widely used in various fields, such as unmanned driving, robotics, and VR. The SLAM system with multiple landmarks is also a research hotspot currently. In this study, a real-time stereo visual-inertial SLAM system based on point-and-line features is proposed; the system is studied based on VINS-fusion. It improves the front-end process of VINS fusion. First, we proposed an IMU-assisted hierarchical grid optical flow tracking method that can more accurately and quickly tracks points between frames. Second, we add line features on the basis of existing point features. To match line features in real time, we only select the best line features to participate in optimization. We combine line segments by geometric relation to reduce the line segment splitting and redundancy brought by LSD algorithm, and further predict line features by IMU-assisted optical flow tracking to achieve higher precision matching. In the back-end optimization process, we used a redundant structure to avoid the failure of stereo constraints in a highly dynamic environment. The proposed method outperformed the state-of-the-art methods (VINS-fusion and PL-VINS) on the EuRoC MAV dataset. The system also achieved good performance in a real environment.

Application of Behavioral Psychology in Clothing Design from The Perspective of Big Data

ABSTRACT In order to improve the practical effect of clothing design, this paper applies the relevant theories of behavioral psychology to the design of fashion clothing, and improves the algorithm through the intelligent psychological identification algorithm to construct a quantitative identification system of behavioral psychology to help designers design more comprehensively. At the system level, the overall structure of the entire behavioral psychological ECG signal acquisition system is planned, and the Multisim software is used to simulate and verify whether each module of the front-end processing circuit achieves the expected effect according to the calculated parameters. In addition, on the basis of verifying the reliability of quantitative identification of behavioral psychology, the construction of intelligent clothing design system is carried out. Finally, this paper combines the simulation test and evaluation test to verify the effect of the system in this paper. Through the experimental research, it is verified that the clothing design system based on behavioral psychology proposed in this paper can effectively improve the clothing design effect and user experience.

Deep learning-based speaker-adaptive postfiltering with limited adaptation data for embedded text-to-speech synthesis systems

End-to-end (e2e) speech synthesis systems have become popular with the recent introduction of text-to-spectrogram conversion systems, such as Tacotron, that use encoder–decoder-based neural architectures. Even though those sequence-to-sequence systems can produce mel-spectrograms from the letters without a text processing frontend, they require substantial amounts of well-manipulated, labeled audio data that have high SNR and minimum amounts of artifacts. These data requirements make it difficult to build end-to-end systems from scratch, especially for low-resource languages. Moreover, most of the e2e systems are not designed for devices with tiny memory and CPU resources. Here, we investigate using a traditional deep neural network (DNN) for acoustic modeling together with a postfilter that improves the speech features produced by the network. The proposed architectures were trained with the relatively noisy, multi-speaker, Wall Street Journal (WSJ) database and tested with unseen speakers. The thin postfilter layer was adapted with minimal data to the target speaker for testing. We investigated several postfilter architectures and compared them with both objective and subjective tests. Fully-connected and transformer-based architectures performed the best in subjective tests. The novel adversarial transformer-based architecture with adaptive discriminator loss performed the best in the objective tests. Moreover, it was faster than the other architectures both in training and inference. Thus, our proposed lightweight transformer-based postfilter architecture significantly improved speech quality and efficiently adapted to new speakers with few shots of data and a hundred training iterations, making it computationally efficient and suitable for scalability.

Voice cue sensitivity in children with cochlear implants and with hearing aids

Voice cues, such as fundamental frequency (F0) and vocal-tract length (VTL), allow listeners to distinguish speakers, which can facilitate speech perception in challenging listening conditions. Children with typical hearing continue to develop their sensitivity to F0 and VTL differences throughout childhood, but this is not as clear for children with hearing loss. In prelingually deaf implanted children with cochlear implants (CIs), reduced spectrotemporal details may cause a delay or even a plateau in the development of voice cue sensitivity, while neuroplasticity may compensate for these factors. For children with hearing aids, it is unclear how voice cue perception could be affected by combined factors of neuroplasticity, physiological aspects of hearing loss, and compensatory front-end processing of hearing aids. Here, we will present recent work on F0 and VTL sensitivity in children with CIs and with hearing aids (4–18 years). Our results show a large variability in both CI and hearing-aided children, with some children performing at the level of age-matched normal-hearing children, while others performing lower. Both groups show trends for development as a function of age, but these seem to differ for F0 and VTL. Potential factors that may be contributing to these results will be discussed during the presentation.

Model-based design for water-soluble crystals with anti-caking function by the feedback between caking prediction and crystallization control

Water-soluble crystals with anti-caking function have become high-end products, whereas they are prepared by trial and error due to the lack of quantitative correlation between particle size and caking tendency, as well as the absence of the models for global consideration between caking and crystallization. This work presents a coupled Caking-Size-Crystallization (CSC) model, which connects the front-end crystallization process and the post-end caking process to achieve the closed-loop design of the anti-caking function for the first time. The CSC model can directly generate the optimal cooling trajectory to crystallize the product free of caking. The introduction of the caking constant term, the mean particle size and the Mullin-Nyvlt’s theory improve the design efficiency by an order of magnitude with the precision guaranteed. This model-based approach presents an opportunity to find optimal crystallization operating profiles with diverse crystal seed loading strategies that meet both product function and manufacturability demands.

Single-electron pump in a quantum dot array for silicon quantum computers

It is necessary to load single electrons into individual quantum dots (QDs) in an array for implementing fully scalable silicon-based quantum computers. However, this single-electron loading would be impacted by the variability of the QD characteristics, and suppressing this variability is highly challenging even in the state-of-the-art silicon front-end process. Here, we used a single-electron pump (SEP) for loading single electrons into a QD array as a preparatory step to use electrons as spin qubits. We used parallel gates in the QD array as a SEP and demonstrated 100 MHz operation with an accuracy of 99% at 4 K. By controlling the timing of a subsequent gate synchronously as a shutter, we found that the jitter representing electron transfer was less than 10 ns, which would be acceptable for a typical operating speed of around 1 MHz for silicon qubits.

Mandarin Text-to-Speech Front-End with Lightweight Distilled Convolution Network

Mandarin text-to-speech (TTS) systems heavily depend on front-end processing, such as grapheme-to-phoneme conversion and prosodic boundary prediction, to produce expressive, human-like speech. Utilizing a pre-trained language model, such as the bidirectional encoder representations from Transformers (BERT), could significantly improve the TTS front-end's performance. However, the original BERT is too big for edge TTS applications with tight limits on memory costs and inference latency. Although a distilled BERT can alleviate this problem, a considerable efficiency barrier may still exist due to the self-attention module's quadratic complexity and the feed-forward module's enormous computation. To this end, we propose a lightweight distilled convolution network as an alternative to the distilled BERT. Unlike previous knowledge distillation methods, which commonly used the same self-attention network for the teacher and student models, we transfer knowledge from the self-attention network to a convolution network. Experiments on two major Mandarin TTS front-end tasks have shown that our distilled convolution model can achieve comparable results to various distilled BERT variants while drastically reducing the model size and inference latency.

Front-end design prototyping strategies during remote stakeholder engagement

Abstract Engineers must engage project stakeholders effectively if stakeholder needs are to be met, and prototypes are key tools for communicating design form and function. Quality stakeholder engagement in the front end of design processes, in particular, is critical in the success or failure of design projects. As remote stakeholder engagement has become increasingly common as industry trends toward distributed design, there is a need to develop the theory and practices behind effective remote design processes, which have not yet been as well-studied as in-person design. This study explored the prototyping strategies for remote stakeholder engagement during front-end design used by 10 engineering practitioners and 10 senior engineering students through semi-structured interviews. Prototyping strategies were found to overlap with many of the strategies described by prior literature that are not specific to remote engagement modes, though several of these strategies were adapted to the remote context, and three emergent strategies for prototyping in remote engagements were identified. Designers’ perceptions of remote versus in-person prototyping strategies for stakeholder engagement in front-end design, including perceived advantages and limitations, were also explored, and recommendations for educators to better prepare engineering students for hybrid and remote work are provided.

Design of an Intelligent Educational Evaluation System Using Deep Learning

Nowadays, online education has been a more general demand in context of COVID-19 epidemic. The intelligent educational evaluation systems assisted by intelligent techniques are in urgent demand. To deal with this issue, this paper introduces the strong information processing ability of deep learning, and proposes the design of an intelligent educational evaluation system using deep learning. Inside the algorithm part, the low-complexity offset minimal sum (OMS) is selected as the front-end processor of deep neural network, so as to reduce following computational complexity in deep neural network. And the deep neural network is adopted as the major calculation backbone. In this paper, our OMS deep neural network parameters are 23 and 57 compared with other parameters, which can save about 59.64% of the network parameters, and the training time is 11270 s and 25000 s respectively, which saves the training time 54.92%. It can be also reflected from experiments that the proposal further improves the performance of unbalanced data classification in this problem scenario.

Why MERN Stack?

Abstract: In the past, web development was primarily based on the LAMP stack (Linux, Apache, MySQL, PHP, or Perl) and Java-based applications (Java EE, Spring). However, these stacks are made up of different programming languages and can be difficult for a single developer to understand. Advances in web technology over the last few years have allowed developers to participate in both the front-end and back-end processes of web applications. The main goal of this thesis was to explore the basic components of the very popular MREN stack. The MERN stack, which stands for MongoDB, Express.js, React.js, and Node.js, allows developers to easily integrate web development in a single programming language called JavaScript.