As a result of progressive use of BIM in effective collaboration and sophisticated data management, the amount of diverse project information is rapidly increased, thus its appearance directly led to ...revolutionary change in intelligent engineering and production mode. However, it has not been quite advanced in supporting road engineering, especially in conducting a structural calculation for asphalt pavement. Relying on the capabilities of BIM and finite element modeling software ABAQUS, this paper proposed a data conversion interface that can not only present three-dimensional visual modeling but also conduct structural calculation for asphalt pavement. A 3D visualization modeling of asphalt pavement was firstly carried out through Revit modeling software. Afterward, the generated model file (.RVT) was imported into the transferring software YJK, and the model was transformed into a file named INP by the transferring software. Thus, the generated model file (.INP) was analyzed by the software ABAQUS. This study effectively strengthened the BIM with structural analysis capability by developing a conversion interface based on BIM-ABAQUS. The framework provided a supporting platform for the integration of BIM-based highway route design and pavement structural analysis in pavement engineering.
•3D visualization modeling of asphalt pavement was carried out through BIM software.•A data conversion interface based on BIM-ABAQUS was constructed.•The data conversion interface can effectively transform data to BIM model.•Interface solves the information isolated island phenomenon during design process.
A 12-bit 10-GS/s interleaved (IL) pipeline analog-to-digital converter (ADC) is described in this paper. The ADC achieves a signal to noise and distortion ratio (SNDR) of 55 dB and a spurious free ...dynamic range (SFDR) of 66 dB with a 4-GHz input signal, is fabricated in the 28-nm CMOS technology, and dissipates 2.9 W. Eight pipeline sub-ADCs are interleaved to achieve 10-GS/s sample rate, and mismatches between sub-ADCs are calibrated in the background. The pipeline sub-ADCs employ a variety of techniques to lower power, like avoiding a dedicated sample-and-hold amplifier (SHA-less), residue scaling, flash background calibration, dithering and inter-stage gain error background calibration. A push-pull input buffer optimized for high-frequency linearity drives the interleaved sub-ADCs to enable >7-GHz bandwidth. A fast turn-ON bootstrapped switch enables 100-ps sampling. The ADC also includes the ability to randomize the sub-ADC selection pattern to further reduce residual interleaving spurs.
In this brief, a novel charge-domain 4T2C eDRAM-CIM macro is proposed that has three key features: 1) a novel 4T2C eDRAM cell with an enhanced PVT variation tolerance that resolves the limitations of ...previous eDRAM-CIM macros such as current domain operation, large bit-cell size, cell leakage, and low energy- and area-efficiency, resulting in high linearity (R2 = 0.9998) and 76.8% reduction in <inline-formula> <tex-math notation="LaTeX">3\sigma </tex-math></inline-formula> PVT variation, 2) a quarter-ADC-reduction scheme with an offset-calibration comparator that reduces the number of ADC by 73% while improving the accuracy drop by 7.82%, and 3) an array-embedded DAC that reduces the area overhead by 64.2% compared to current-based DAC. The proposed 4T2C eDRAM-CIM macro is fabricated in 65nm LP technology and achieves 43.02- to 49.20-TOPS/W and 2.4-TOPS/mm 2 when 4b<inline-formula> <tex-math notation="LaTeX">\times 4\text{b} </tex-math></inline-formula> MAC operation is performed with 250MHz. In addition, an 90.03% accuracy at the CIFAR-10 dataset with the ResNet-20 network is achieved.
This paper presents a microphone readout chip incorporating a VCO-based ADC that can be directly connected to a capacitive MEMS sensor without requiring a voltage buffer. The ADC uses an open-loop ...pseudo differential architecture with two ring oscillators followed by a coarse-fine frequency-to-digital converter. The proposed coarse-fine architecture optimizes power consumption thanks to a new algorithm. The MEMS is connected to the ring oscillators with a source follower circuit that can be programmed to interchange SNR by power consumption in small steps. This feature enables always-on operation in voice recognition applications. The microphone is compatible with standard PDM audio interfaces. Total ADC power consumption ranges between 245μW and 438μW for peak SNDRs between 74dB-A and 80.3dB-A, including the ADC and MEMS coupling circuitry. The Dynamic Range achieves 108dB at full performance with a THD of 1.5% at the Acoustic Overload Point of 128dBSPL. The ADC occupies an area of 0.14mm 2 implemented in 0.13μm CMOS.
Deep learning (DL) is a new machine learning (ML) methodology that has found successful implementations in many application domains. However, its usage in communications systems has not been well ...explored. This paper investigates the use of the DL in modulation classification, which is a major task in many communications systems. The DL relies on a massive amount of data and, for research and applications, this can be easily available in communications systems. Furthermore, unlike the ML, the DL has the advantage of not requiring manual feature selections, which significantly reduces the task complexity in modulation classification. In this paper, we use two convolutional neural network (CNN)-based DL models, AlexNet and GoogLeNet. Specifically, we develop several methods to represent modulated signals in data formats with gridlike topologies for the CNN. The impacts of representation on classification performance are also analyzed. In addition, comparisons with traditional cumulant and ML-based algorithms are presented. Experimental results demonstrate the significant performance advantage and application feasibility of the DL-based approach for modulation classification.
Recent advances in proteome informatics have led to an explosion in tools to analyze mass spectrometry data. These tools operate across the analysis pipeline doing everything from assessing quality ...control to matching peptides to spectra to quantification. Unfortunately, the vast majority of these tools are not able to operate directly on the proprietary formats generated by the diverse mass spectrometers. Consequently, the first step in many protocols is the conversion of data from vendor-specific binary files to open-format files. This protocol details the use of ProteoWizard's msConvert and msConvertGUI software for this conversion, taking format features, coding options, and vendor particularities into account. We specifically describe the various options available when doing conversions and the implications of each option.
An ultra-small-area, low-power analog front-end (AFE) for high-density neural recording is presented in this brief. It features an 11-bit incremental delta-sigma analog-to-digital converter ...(<inline-formula> <tex-math notation="LaTeX">\Delta \Sigma </tex-math></inline-formula> ADC) enhanced with an offset-rejecting event-driven input biasing network. This network avoids saturation of the ADC input caused by leakage of the input-coupling capacitor implemented in an advanced technology node. Combining AC-coupling with direct data conversion, the proposed AFE can tolerate a rail-to-rail electrode offset and achieves a good trade-off between power, noise, bandwidth, input impedance, and area. Fabricated in a 22-nm fully-depleted silicon on insulator (FDSOI) process, the design occupies an active area of <0.001mm 2 , the smallest obtained to this date for a neural AFE, and consumes <inline-formula> <tex-math notation="LaTeX">\mathbf { < }3~\mu \text{W} </tex-math></inline-formula> from a 0.8-V supply. It achieves an input-referred noise of <inline-formula> <tex-math notation="LaTeX">11.3~\mu \text{V}_{\mathrm{ rms}} </tex-math></inline-formula> in the action potential band (300 Hz - 10 kHz) and 10 <inline-formula> <tex-math notation="LaTeX">\mu \text{V}_{\mathrm{ rms}} </tex-math></inline-formula> in the local field potential band (1 Hz - 300 Hz).
Real-time personalized motion monitoring and analysis are important for human health. Thus, to satisfy the needs in this area and the ever-increasing demand for wearable electronics, we design and ...develop a wireless piezoelectric device consisting of a piezoelectric pressure sensor based on electrospun PVDF/BaTiO3 nanowire (NW) nanocomposite fibers and a wireless circuit system integrated with a data conversion control module, a signal acquisition and amplification module, and a Bluetooth module. Finally, real-time piezoelectric signals of human motion can be displayed by an App on an Android mobile phone for wireless monitoring and analysis. This wireless piezoelectric device is proven to be sensitive to human motion such as squatting up and down, walking, and running. The results indicate that our wireless piezoelectric device has potential applications in wearable medical electronics, particularly in the fields of rehabilitation and sports medicine.
Rough set theories are utilized in class-specific feature selection to improve the classification performance of continuous data while handling data uncertainty. However, most of those approaches are ...converting continuous data into discrete or fuzzy data before applying rough set theories. These data conversions can reduce or change the meaning of data, as well as introduce unnecessary complexity to the feature domain. Therefore, in this study, we use neighborhood rough sets in class-specific feature selection to improve the classification performance without data conversions. As the standard classification algorithms are capable of handling a single feature set, we propose a novel classification algorithm based on the K-Nearest Neighbour algorithm to use class-specific feature subsets. Experimental evaluations prove that the proposed approach outperforms most of the baselines, and the selected feature sets are more effective than using the full feature sets in classification. The approach highly reduces the number of selected features and hence, can be used for effective data analysis of continuous data with high performance.
•Class-specific feature selection to improve continuous data classification.•Feature selection in continuous data while handling data uncertainty.•Control information losses that can occur through data conversions.•Neighborhood rough sets in class-specific feature selection without data conversions.•Improve K-nearest neighbor classification algorithm to use multiple feature subsets.
Reverse-time migration (RTM) has shown its advantages over other conventional migration algorithms for ground-penetrating radar (GPR) imaging. RTM is preferred to be implemented in the ...computationally attractive 2-D domain, whereas a real measurement can only be conducted in a 3-D domain. Thus, we propose an asymptotic 3-D-to-2-D data conversion filter in the frequency domain for preprocessing of the recorded data for 2-D RTM. The accuracy of the data conversion filter is verified by two numerical tests on a homogeneous and a layered model. Then, we evaluate the effectiveness of the data conversion filter on the imaging result of 2-D RTM, which is applied to simulated multioffset GPR data from a buried pipe model. With the filter, subsurface image by the 2-D RTM matches better with the 3-D RTM result especially in the aspect of phase congruency. Therefore, we conclude that this data conversion filter is necessary for 2-D RTM. We also conducted a laboratory experiment on a volcanic ash pit using a multiinput-multioutput GPR system, which is adopted on the Chang-E 5 lunar exploration lander and works in a stationary mode. The 3-D-to-2-D data conversion filter is applied to the measured multioffset GPR data before the 2-D RTM. The imaging results demonstrate that three marble slabs buried at different depths up to 2 m are clearly imaged.
PDF
