Front-end Processing Research Articles

Coherent Optical Transceivers Scaling and Integration Challenges

The advancement of digital coherent technologies has dramatically increased the system capacity per single-core single-mode fiber to the point that we can now approach the Shannon limit by utilizing high-order modulation formats and high-coding gain forward error correction (FEC) codes. Because the required energy per bit increases exponentially the closer we get to the Shannon limit, extending the available optical bandwidth by using ultrawideband wavelength-division multiplexing (WDM) and/or spatial-division multiplexing (SDM) is indispensable for increasing the system capacity with high energy efficiency. However, simple extensions of wavelength resources and spatial parallelization dramatically increase the number of transceivers (TxRxs) in proportion to the wavelength/spatial multiplicity. The key to achieving cost- and energy-efficient systems is to reduce the system complexity by using high-density integration and broadband optelectronics. In this article, we overview and discuss the recent advances of coherent optical transceivers integrated with an optical front end and digital signal processing (DSP)/application-specific integrated circuit (ASIC). We then present the transponder architectures and the challenges involved in applying them for massive parallelized transmission systems.

BeNEdiCTE (Boron Neutron Capture): A Versatile Gamma-Ray Detection Module for Boron Neutron Capture Therapy

We present a gamma-ray detection module for quantifying the boron neutron capture events that occur in the boron neutron capture therapy (BNCT) and neutron capture enhanced particle therapy (NCEPT). The goal of the module is to differentiate between the background prompt gamma peaks and the 478-keV neutron capture photopeak, in order to estimate the dose delivered to the patient. It is a compact module, coupling a large array of 64 silicon photomultipliers (SiPMs) with a 2” <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 2” cylindrical LaBr3(Ce+Sr) scintillator crystal (73-ph/keV light yield, 25-ns decay time). The electronic front-end ASIC features low-noise processing of photodetector signals, while SiPMs pixellation and individual readout allow for position sensitivity in the crystal, although position estimation is not the object of this work. The module experimental characterization shows excellent energy resolution (2.7% FWHM at 662keV), that allows to discriminate the neutron capture photons at 478keV from the annihilation photons at 511keV. The module features also an anti-coincidence circuit that provides a mechanism to distinguish and reject scintillation events created within specific temporal windows, thus enhancing the signal-to-background ratio of the spectrometer.

Research on Improved Algorithm of Mobile Robot Vision SLAM Based on Depth Camera

&lt;p&gt;Aiming at the problems of low accuracy, poor real-time performance and large cumulative error in traditional depth camera visual synchronization positioning and map construction algorithms, this paper proposes an improved visual SLAM algorithm for mobile robots based on depth camera. According to the realization process of robot vision, a feature point extraction algorithm based on feature extraction and image segmentation watershed algorithm is proposed in the front-end process, which improves the real-time performance of the algorithm. Then the RE-RANSAC algorithm is used to eliminate the mismatched feature points to improve the matching accuracy, and then the accumulated error is eliminated through closed loop detection, and finally the process of robot mapping is completed. After simulation experiments, the feasibility of the improved algorithm is proved, and the robot’s mapping and trajectory estimation are completed.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

Enhancement of formant regions in magnitude spectra to develop children’s KWS system in zero resource scenario

The mismatches due to pitch, speaking rate, formant dispersion and ambient noise deteriorate the performance of an automatic keyword spotting (KWS) system. The work presented in this paper aims at reducing the aforementioned mismatches through front-end signal processing. In the proposed approach, the short-term magnitude spectra (ST-MS) are firstly computed with a smaller frameshift and then averaged over the adjacent frames to enhance the formant regions. The formant regions in the ST-MS have a higher magnitude than the nearing frequency regions. Consequently, temporal averaging of ST-MS over adjacent frames suppresses the high-frequency variation due to pitch, and formant dispersion. Furthermore, the formant peaks can be more accurately detected from the temporal averaged ST-MS when compared to detection from the original ST-MS. The Mel frequency cepstral coefficients (MFCC) computed from the temporally averaged magnitude spectra (TAS-MFCC) are pitch robust compared to the MFCC, and MFCC extracted from the reported spectral smoothing approaches employing variational mode decomposition (VMD-MFCC), pitch adaptive cepstral truncation (PACT-MFCC) and single-pole filter (SPS-MFCC). Performance of TA-MFCC feature in mismatched test condition is further improved by appending five logarithmically compressed resonant peaks at least separated by 400 Hz, here this feature is termed as TAS-MFCC-ARP. The spectral peaks mostly represent the formants in ST-MS. The performances of the deep neural network-hidden Markov model-based children’s KWS system reported in this work show that the TAS-MFCC-ARP provides a relative performance improvement of 103.83% compared to MFCC. The performance of the KWS system is further improved by data-augmented training through duration modification.

Deep-Learning-Based Signal Enhancement of Low-Resolution Accelerometer for Fall Detection Systems

In the last two decades, fall detection (FD) systems have been developed as a popular assistive technology. To support long-term FD services, various power-saving strategies have been implemented. Among them, a reduced sampling rate is a common approach for an energy-efficient system in the real world. However, the performance of FD systems is diminished owing to low-resolution (LR) accelerometer signals. To improve the detection accuracy with LR accelerometer signals, several technical challenges must be considered, including mismatch of effective features and the degradation effects. In this work, a deep-learning-based accelerometer signal enhancement (ASE) model is proposed as a front-end processor to help typical LR-FD systems achieve better detection performance. The proposed ASE model based on a deep denoising convolutional autoencoder architecture reconstructs high-resolution (HR) signals from the LR signals by learning the relationship between the LR and HR signals. The results show that the FD system using support vector machine (SVM) and the proposed ASE model at an extremely low sampling rate (sampling rate < 2 Hz) achieved 97.34% and 90.52% accuracies in the SisFall and FallAllD data sets, respectively, while those without ASE models only achieved 95.92% and 87.47% accuracies in the SisFall and FallAllD data sets, respectively. The results also demonstrate that the proposed ASE mode can be suitably combined with deep-learning-based FD systems.

Salicide Residues Defects Characterization and Reduction in Front-End Pre-Metal Cleaning Process Wafer Fabrication

Wafer fabrication for integrated circuit is one of the most complicated process in semiconductor manufacturing industry. High yield is always the ultimate goal to achieve hence a good defect management is the key to ensure the goal is met. Salicide residue is a major defect wet etching process. The defect is contributing a total of 1% loss in overall wafer fab sort yield, that is an equivalent to USD$ 5 million loss per year. The objective for this research is to identify the root cause and determine the element of the residue defect in the wafer substrate at Salicide Pre-Clean wafer fabrication process. Once this has been achieved, next is to determine a novelty solution to reduce the residue defects and finally validate the effectiveness of the novel solution in reducing the salicide residue defects. An investigation of one factor at the time has been conducted with various experiments including screening all the hardware resources available by using ANOVA studies. The result has concluded that the salicide residue consists of carbon defect is observed after Salicide Pre-Clean step when the standard diluted hydrofluoric acid (dHF) is used by the wet station equipment to clean the product wafers. This study has discovered an innovated solution to minimize the chemical contact on the wafer has resulted in reducing the post clean defects residue by 80% and eliminate 1% of the total product sort yield loss issue.

Physical Education Attendance Based on Wireless and Passive RFID Technology

Attendance information is an important data resource for physical education management in colleges and universities. Based on wireless and passive RFID technology theory, this paper designs a UHF passive RFID radio frequency identification physical education attendance model. The device includes three parts: carrier leakage canceller, radio frequency transceiver, and digital baseband signal processing. In the model hierarchy, the client sends a Web request to the server, and the request information is organized by the RFID document, including information such as the range, scale, and display attributes of the requested map. The Web server modifies the predefined map definition file by parsing the information. In the simulation process, the frequency domain model and baseband complex envelope time domain system of the RF front-end of the reader based on carrier leakage cancellation were built in ADS software and Matlab/Simulink, respectively. The reader front-end and digital baseband signal processing are comprehensively simulated, and the result verifies the function of the whole system. After testing and debugging, it has been proven that the median synchronization number of the signal is 12, the point synchronization number is 29, the input IDB compression point measured by a single tone is 9.6 dB smaller than that of IIP3, and the input IDB compression point measured by two tones is smaller than 14.4 dB, which can promote the informatization process of physical education management in colleges and universities.

Salicide Residues Defects Characterization and Reduction in Front-End Pre-Metal Cleaning Process Wafer Fabrication

Salicide Residues Defects Characterization and Reduction in Front-End Pre-Metal Cleaning Process Wafer Fabrication

Salicide Residues Defects Characterization and Reduction in Front-End Pre-Metal Cleaning Process Wafer Fabrication

Salicide Residues Defects Characterization and Reduction in Front-End Pre-Metal Cleaning Process Wafer Fabrication

Celebrating the one millionth cochlear implant

Cochlear implants have been the most successful neural prosthesis, with one million users globally. Researchers used the source-filter model and speech vocoder to design the modern multi-channel implants, allowing implantees to achieve 70%-80% correct sentence recognition in quiet, on average. Researchers also used the cochlear implant to help understand basic mechanisms underlying loudness, pitch, and cortical plasticity. While front-end processing advances improved speech recognition in noise, the unilateral implant speech recognition in quiet has plateaued since the early 1990s. This lack of progress calls for action on re-designing the cochlear stimulating interface and collaboration with the general neurotechnology community.

By the fjord: successful public and private collaboration in a neighbourhood redevelopment project in Norway

The purpose of this paper is to investigate and study the green procurement processes in the new and ongoing neighbourhood-building project named ‘Fjordbyen’ in Norway. The purpose behind this study is to look at how Fjordbyen approached these processes in order to contribute to the pool of knowledge on this topic. The Fjordbyen project is located on the shoreline in the Drammensfjord and represents in effect the development of a new neighbourhood. This new development aims to settle at least 16 000 new residents and 16 000 jobs in a zero-emission urban area that is green and future oriented. The topic is addressed by using a case study approach. The data for this paper were collected through a combination of desk research, a study of secondary data such as document studies (reports, documents from websites etc.), two citizen surveys and four interviews with the key personnel involved in the front-end planning process of the Fjordbyen project. Through a combination of urban planning and typological considerations, green procurement was conducted in the context of ensuring that not just greener products were purchased but also green supply chains were managed, as were the ‘grassroots’ development processes themselves. The finding of this study has an impact on the consideration of processes leading towards green procurement in urban and city development projects. This is particularly the case with regard to pushing even further research on the value and challenges associated with green procurement processes.

Impact of Epitaxial Defects on Device Behavior and their Correlation to the Reverse Characteristics of SiC Devices

In this work we report on the impact of various crystalline defects present in 4H-SiC epitaxial layers on the electrical blocking characteristics of SiC power devices. Dedicated test structures were fabricated and electrically characterized in reverse bias mode. SiC substrate and epitaxial crystal defects, as well defects due to front-end processing were detected and classified using commercial inspection tools. Devices with a single defect-type were studied which leads to a direct correlation of the leakage current spot position within the device and the obtained blocking characteristics. This gives a better understanding of each crystal defect impact on device ́s performance which leads to an improvement in the reliability and cost reduction of SiC power devices.

Research on Feature Fusion Speech Emotion Recognition Technology for Smart Teaching

At present, there are numerous intelligent teaching applications based on various advanced pattern recognition technologies, such as face sign-in, classroom action recognition, student facial expression recognition, and other systems which have been gradually applied to major schools. Speech emotion recognition can analyze the characteristics of current teaching emotions, so as to discover the rules and details of teachers’ grasp of emotions. In order to realize the task of emotion classification in the process of intelligent teaching through machine learning technology, a teaching speech emotion recognition method with multifeature fusion and deep learning is proposed. The proposed method front-end processes the speech spectrum features in a multifeature fusion manner and combines them with an artificial neural network classifier to form the final speech emotion recognition model. First, after speech preprocessing, three features are selected and fused features using a network structure trained by parallel subnetworks. Then, the classifier used is a hybrid neural network classifier combining a convolutional neural network and a recurrent neural network. Finally, 100 open web courses were used to train and test the model. The test results show that the hybrid neural network sound model using multifeature fusion has good teaching speech emotion recognition capability.

CMOS MEMS Design and Fabrication Platform

This article mainly describes the technology related to the CMOS MEMS process platform provided by the Taiwan Semiconductor Research Institute (TSRI), including the process flow, design verification, back-end dicing, and packaging of the CMOS MEMS integrated sensing single chip. The front-end CMOS processes can be a standard 0.35 or 0.18 µm CMOS process, or even a BCD high-voltage process. Some academic designs also utilize this platform for in-house post-CMOS process. Finally, the article also explains in detail the CMOS MEMS design flowchart and implementation method provided by TSRI.

Understanding juvenile pre-adjudicatory detention and front-end juvenile case processing: The moderating role of race

PurposeThe current study expands our knowledge regarding the effects of pre-adjudicatory detention by examining the moderating role of a juvenile's race/ethnicity on the association between pre-adjudicatory detention and juvenile court outcomes (e.g., dismissal, adjudication, or disposition outcome decisions). MethodsThe current study draws on data from the Florida Department of Juvenile Justice and employs both logistic regression and an analysis of the resulting predicted probabilities to examine the moderating effect of race/ethnicity on the association between pre-adjudicatory detention and three distinct juvenile case outcomes. ResultsResults suggest that the relationship between pre-adjudicatory detention and the likelihood of case dismissal and adjudication differs significantly among Black, White, and Hispanic youth. When the analysis was limited to youth who had been adjudicated, however, the positive association observed between time spent detained and the restrictiveness of placement following disposition was equivalent among the groups examined. ConclusionsSince the effects of pre-adjudicatory detention on some front-end juvenile case processing outcomes vary by race, it is crucial to develop interventions/policies to reduce disparities in this type of detention, especially for Black youth.

Comparison of discrete transforms for deep‐neural‐networks‐based speech enhancement

In recent studies of speech enhancement, a deep-learning model is trained to predict clean speech spectra from the known noisy spectra of speech. Rather than using the traditional discrete Fourier transform (DFT), this paper considers other well-known transforms to generate the speech spectra for deep-learning-based speech enhancement. In addition to the DFT, seven different transforms were tested: discrete Cosine transform, discrete Sine transform, discrete Haar transform, discrete Hadamard transform, discrete Tchebichef transform, discrete Krawtchouk transform, and discrete Tchebichef-Krawtchouk transform. Two deep-learning architectures were tested: convolutional neural networks (CNN) and fully connected neural networks. Experiments were performed for the NOIZEUS database, and various speech quality and intelligibility measures were adopted for performance evaluation. The quality and intelligibility scores of the enhanced speech demonstrate that discrete Sine transformation is better suited for the front-end processing with a CNN as it outperformed the DFT in this kind of application. The achieved results demonstrate that combining two or more existing transforms could improve the performance in specific conditions. The tested models suggest that we should not assume that the DFT is optimal in front-end processing with deep neural networks (DNNs). On this basis, other discrete transformations should be taken into account when designing robust DNN-based speech processing applications.

Simultaneous Production and Maintenance Scheduling for Refinery Front-End Process with Considerations of Risk Management and Resource Availability

Simultaneous Production and Maintenance Scheduling for Refinery Front-End Process with Considerations of Risk Management and Resource Availability

Effect of not vortexing nasopharyngeal and throat swabs on SARS-CoV-2 nucleic acid detection: A pilot study

The processing of swabs for respiratory virus detection involves vortexing while still in the viral transport medium (VTM). The effect of not vortexing swabs prior to analysis has not been studied extensively for SARS-CoV-2 detection, and presents an opportunity to improve pre-analytic laboratory workflow. We aimed to assess the impact of not vortexing nasopharyngeal/throat swabs submitted in VTM for SARS-CoV-2 testing. To assess the impact of not vortexing swabs, 277 swab samples were tested for SARS-CoV-2 RNA in paired vortexed and non-vortexed aliquots using eight routine nucleic acid amplification assays. We compared the qualitative (positive/negative) and semi-quantitative (cycle threshold, Ct) results. Following discordant analysis, all but one non-vortexed sample had the same qualitative result as the vortexed sample. 27.4 % of samples were SARS-CoV-2 positive. Comparison of Ct values revealed an apparent reduction in human cellular nucleic acid in the non-vortexed samples (mean Ct values of 24.0 and 26.5 for vortexed and non-vortexed samples, respectively, p < 0.0001) and increased Ct values for non-vortexed samples using a laboratory-developed SARS-CoV-2 assay (mean Ct values of 4.1 and 4.2 for vortexed and non-vortexed samples, respectively; p < 0.0001), but this was not observed for a more automated commercial SARS-CoV-2 assay (mean Ct values of 15.2 for both vortexed and non-vortexed samples, respectively; p = 0.68). While vortexing swabs appears to improve the recovery of cellular material, it does not have an appreciable impact on the qualitative sensitivity of SARS-CoV-2 nucleic acid tests, which may support omission of this step and simplification of front-end sample processing.

Integrated Remote Laser Source for 6G Advanced Antenna Systems

Future generations of mobile communications promise near unlimited access to information and data sharing with ubiquitous wireless connectivity for any kind of device and application that may benefit from being connected. This poses new design challenges and requires a renewal of the hardware platforms to meet the increased demand of capacity and reliability of the network. Next generation Radio Access Network (RAN) will evolve towards an increase of the radio bandwidth, with carrier frequency extended to the millimeter waves (mmW) range. Other technologies that will be extensively used to increase the RAN capacity will be beamforming and spatial multiplexing with advanced antenna systems (AAS) comprising hundreds of antenna elements. Optical technologies are the natural candidates to support the traffic increase, by providing high-capacity interconnection within the radio unit and towards the base band unit (BBU). However, the higher level of integration of AAS will cause an increase of the temperature to values >100C, due to power dissipation of RF power amplifiers (PA), digital front-end (DFE) and baseband processing Application Specific Circuits (ASIC). This makes the AAS an inhospitable environment for optical transceivers that include lasers, typically operating at temperatures <85C. A promising approach to solve this issue is to place the laser outside the transceiver as a remote laser source (RLS) in a less harsh environment or in a thermally controlled environment e.g. in the same cabinet of the BBU. In this article we demonstrate a fully integrated RLS enabling a reduction of power consumption and packaging cost.

Market Knowledge Impacts on Performances through Front End Process: Empirical Evidence from Manufacturing Plants across Countries

Market Knowledge Impacts on Performances through Front End Process: Empirical Evidence from Manufacturing Plants across Countries