Miracle-dz/UniFAR-FTUs huggingface dataset:
A 0.014mm2 10kHz-BW Zoom-Incremental-Counting ADC Achieving 103dB SNDR and 100dB Full-Scale CMRR

High-resolution ($>$ 100dB SNDR), kHz-BW ADCs are required by emerging Io $\top$ and smart sensing applications. These ADCs are desired for their high efficiency, but low cost and ease of integration are also required, especially to be compatible with the advanced CMOS processes that the loT processor prefers. The state-of-the-art solutions in this scenario, such as the zoom ADC [1], the DT or CT-DSM [2]–[3], and the SAR ADC [4] and its noise-shaping variants [5], have already achieved great energy efficiency with >180dB FOMs. However, most of them are large in area, and rely heavily on the analog performance of old CMOS technologies with high supply voltages. In this work, we propose a new architecture that combines the counting ADC and CT-incremental-DSM (CT-IDSM) in a zoom ADC framework. The proposed architecture is not only power efficient, but also compact in area, highly digital, and friendly to process down-scaling. It operates at Nyquist sampling, supporting single-shot conversion and channel multiplexing. Besides this, it provides a high-impedance input with full-scale common-mode rejection, allowing direct driving by many signal sources. Fabricated in 28nm CMOS, the prototype zoom-incremental-counting (ZIC) ADC is measured to have 103dB SNDR at 20kSa/s, consuming 475 $\mu$ W from a 0.9V supply. The resulting 176dB FOMs is comparable to the state-of-the-art designs. It occupies only 0.014mm2, which is a magnitude or two smaller than most reported ADCs with $>$ 90dB SNR.
Ringdown analysis for Low-Frequency Oscillation Identification

In this paper, three different Rind Down analysis methods are used to pre-process an online wide-area damping controller. A modified Linear Prediction criteria (LPC) method is proposed to handle latency delay and data dropout in the communication channels, Prony and MC-Brid methods are used to estimate the eigenvalues of the low-frequency oscillation modes, and adaptive buffer size for Prony and MC-Bride methods is proposed to decrease the necessity to the offline damping controller. Then, the transfer function of the oscillation mode is extracted from these methods, and a simple order reduction technique is applied (zero-pole cancellation) to obtain a low order transfer function for the wide-area controller.
A 16Gb 9.5Gb/S/pin LPDDR5X SDRAM With Low-Power Schemes Exploiting Dynamic Voltage-Frequency Scaling and Offset-Calibrated Readout Sense Amplifiers in a Fourth Generation 10nm DRAM Process

Mobile systems for 5G communications and emerging technologies, such as advanced driver assistance system (ADAS), augmented reality (AR), and artificial intelligence (AI), demand high-density, high-speed, and low-power DRAM.LPDDR5 has played an important role in such systems, by offering continuous improvements in memory density (8, 12, and 16Gb) and speed (up to 8.5Gb/s) [1, 2, 3]. LPDDR5X has new high-speed enabling features: per-pin DFE training, pre-emphasis for DQ driver, receiver offset calibration training, and a read duty-cycle adjuster. By employing these speed enhancement techniques, we have successfully implemented a 9.5Gb/s/pin 16Gb LPDDR5X using a fourth generation 10nm DRAM fabrication technology. This paper also presents a power-efficient LPDDR5X using (1) a non-stacking bank architecture, (2) an offset-calibrated sense amplifier for the global data lines, (3) an extended dynamic voltage and frequency scaling core (DVFSC), (4) a stacked output driver, and (5) a low-power data aligner.
Asymmetric S-Curve Generation and Tracking Under Motion Constraints: An Experimental Validation

S-curve is one of the most applied approaches for trajectory generation for the point-to-point movement in industrial drives. In this note, the asymmetric S-curve structure is addressed to develop a single-axis motion structure, and an algorithm is introduced to generate the desired motion. Furthermore, a cascade P/PI position controller structure with a feed-forward path is suggested and the determination of the controller gains is discussed. Aiming to improve the smoothness of the desired trajectory and to reduce the tracking error, the proposed scheme containing both trajectory generation and controller structure is implemented experimentally in a ball screw linear motion system. The mechanical model parameters in that experimental setup, and accordingly the controller gains, are obtained based on an estimation algorithm. The acquired results show that the proposed controller structure with the asymmetric S-curve generation algorithm is convenient for practical applications.
Abrasive Waterjet Cutting of Stainless Steel – An Experimental Investigation

Among the non-traditional cutting processes, abrasive waterjet cutting is superior and capable of cutting hard materials with a high velocity of abrasive particles by impinging them on the surface of the material. The abrasive waterjet cut the metal by erosion rather than friction or shearing and the edge quality is better than the other traditional and non-traditional machining processes. An important cutting performance parameter, depth of cut, is described in this paper on the performance measure of abrasive waterjet machining while cutting stainless steel. Experiments were conducted and results are studied and investigated in varying standoff distance, abrasive mass flow rate, water pressure, and nozzle traverse speed for cutting stainless steel plates through abrasive waterjet machining process. The effect of the variables on depth of cut has been examined based on the investigational results.
Comparative Study Investigating Compressed Natural Gas, Diesel, and Gasoline as Fuel for the Transportation Sector: A Case of UAE

The continuous population increase in many developing and developed countries leads to an increase in CO2 and SO2 emissions and oil consumption at an alarming rate in many sectors, mainly the transportation sector. As a result, natural gas is rapidly gaining interest within the transportation sector due to its considerably lower cost than oil and other fossil fuels. This study investigates the challenges of compressed natural gas (CNG) and examines the well-to-wheel emissions of CNG, diesel, and gasoline for the United Arab Emirates (UAE). The GREET 2020 model was used to simulate the GHG emissions of various vehicle and fuel technologies. Specifically, this paper assesses the feasibility of CNG, diesel, and gasoline as operational fuels in transit. This study shows that the CO2 equivalent emissions from well-to-pump for CNG, diesel, and gasoline are 12.56, 15.21, 15.02, respectively. Another finding of this study is that CNG in transit buses and school buses produces respectively 2.4% and 1.7% more operational CO2 equivalent emissions than diesel fuel. This study shows that diesel produces the highest amount of CO2 emission during its diesel extraction process from the well.
2-Tx Digital Envelope-Tracking Supply Modulator Achieving 200MHz Channel Bandwidth and 93.6% Efficiency for 2G/3G/LTE/NR RF Power Amplifiers

Envelope tracking (ET) has become essential technology to save power consumption of RF power amplifiers (PAs) in mobile handsets. 5G NR standard continuously increases their maximum channel bandwidth (CH BW) for intra-band contiguous carrier aggregation (CA) to improve data rate. 3GPP TS 38.101 (Release 16) introduces maximum aggregated BW cases of 200MHz (CA_n77C, CA_n78C, CA_n79C), 300M Hz (CA_n77D, CA_n78D, CA_n79D) and even 320M Hz (CA_n46E) [1]. However, the CH BWs that can be supported by conventional analog ET supply modulators, composed of a switching amplifier and an analog linear amplifier, almost reach their limit due to large PA decoupling capacitor (few hundreds of pF) and high-voltage process (>5V). The supply modulator (SM) in [3] isolates decoupling capacitors of unused PAs by a switch, and the linear amplifier in [5] partially employs low-voltage devices to achieve wider BW, however, still 130MHz is the widest envelope-tracking BW among supply modulators with high output voltage (4 to 5V).
Thermal and Chemical Analysis of Electrospun Polyacrylonitrile Nanofibers Embedded with Nanomaterial

Polyacrylonitrile (PAN) was electrospun from PAN and dimethylformamide (DMF) polymeric solution containing graphene nanoplatelets in varying weight proportions to make nanofiber composites. The diameter of fibers and surface morphology was determined by scanning electron morphology (SEM). Differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) were employed to conduct thermal analysis. While chemical analysis was done by means of Fourier-transform infrared spectroscopy (FTIR). No significant difference was observed in glass transition temperature in all samples with and without graphene nanoplatelets. TGA analysis exhibited the stages of thermal breakdown and weight loss in each stage. FTIR spectroscopy was used to determine the chemical bonds and molecular structure.
An Automatic Diagnosis Method of Thyroid Ultrasound Image Using Feature Fusion Network

Nowadays, diagnosis of thyroid nodules is mainly based on clinical methods, which requires a lot of manpower and medical resources. Therefore, this work proposes an automated thyroid ultrasound nodule diagnosis method that combines convolutional neural networks and image texture features. The main steps include: Firstly, ultrasound thyroid nodule dataset is established by collecting positive and negative samples, standardizing of images and segmentation of nodule area. Secondly, through texture features extraction, feature selection and data dimensionality reduction, texture features model is obtained; Thirdly, by transfer learning, deep neural network is used to obtain feature model of the nodule in images; Then, texture features model and convolutional neural network feature model are combined to form a new nodule feature model called Feature Fusion Network; Finally, Feature Fusion Network is applied to train and improve performance than single network, and a deep neural network diagnosis model that can adapt to the characteristics of thyroid nodules is built. In order to test this method, 1874 groups of clinical ultrasound thyroid nodules are collected. Harmonic average F-score based on Precision and Recall is used as an evaluation indicator. Experimental results show that Feature Fusion Network can distinguish between benign and malignant thyroid nodules with an F-score of 92.52%. Compared with traditional machine learning methods and convolutional neural networks, performance of this work is better.
Investigating Visual Imagery as a BCI Control Strategy: A Pilot Study

Brain-Computer Interface (BCI) technology may provide individuals with motor impairments or even the general population a new way to interact with the world around them. However, current BCI systems using electroencephalography (EEG) can be unreliable and produce large variations in performance. Most studies seek to improve performance by focusing on signal processing and classification techniques. However, it may also be beneficial to investigate different control strategies. For this reason, the main objective of this pilot study was to investigate the use of visual imagery, a control paradigm that has not been much tested for EEG BCI applications. Visual imagery may provide a more intuitive control strategy with a greater number of available classes than other popular imagery-based methods such as motor imagery. Using this paradigm, we have demonstrated above chance binary classification accuracy (59.9%, p < 0.05) during offline decoding of face and scene visual imagery. Furthermore, the participant in this study achieved significantly above chance performance during a three-class, closed-loop BCI interaction (47.2%, p = 0.05). The initial results of this pilot study demonstrate the feasibility of using visual imagery as an alternative EEG BCI control paradigm.
Hybrid Hierarchical Learning for Adaptive Persuasion in Human-Robot Interaction

Adaptive learning is critical to helping robots personalize their interactions with people, particularly when considering skills needed by socially assistive robots, such as persuasion. In this letter, we propose a novel, hybrid hierarchical learning architecture for use in social human-robot interaction (HRI) to adapt robot persuasive behaviors to both the static (e.g., need for cognition) and dynamic (e.g., affect) considerations of a user. A learning hierarchy is introduced that uses a contextual bandit approach in the top level to optimize for a static cognition bias and Q-Learning in the lower level to optimize selection of a robot persuasive strategy to deploy that aligns with a user's affect. We compare the performance of our system with a non-hierarchical learning method in simulated experiments for the task of persuading people to do daily exercises. The results show that our hybrid hierarchical architecture outperforms a non-hierarchical benchmark in learning speed and robustness to both longitudinal user change and noisy observations. Our architecture is the first to: 1) persuasively adapt to different users during social HRI considering both static and dynamic user change, and 2) use user state decomposition in persuasive HRI.
Evaluating methiozolin programs for golf putting greens and investigating potential modes of action

Annual bluegrass is a winter annual grass that is problematic on golf putting greens due to its light green color, prolific seedhead production and intolerance to stress. On creeping bentgrass putting greens, herbicides for annual bluegrass control are limited. A new herbicide, methiozolin, developed by Moghu Research Center, LLC, in Daejeon, South Korea, safely and selectively controls annual bluegrass in creeping bentgrass and several other turfgrass species. Methiozolin typically controls annual bluegrass over several weeks, allowing desirable turfgrass time to grow into areas previously infested by annual bluegrass with little surface disruption. The mode of action of methiozolin is unknown, but has been proposed to act as either a cell wall biosynthesis inhibitor (CBI) or an inhibitor of tyrosine aminotransferase (TAT). Field studies were conducted at Virginia Tech to investigate strategies promoting surface recovery on putting greens following atypically rapid annual bluegrass loss resulting from methiozolin application, intensive core-cultivation as well as potential interactions with plant growth regulators (PGR’s), like ethephon. In the rapid annual bluegrass removal study, all treatments receiving additional fertility via synthetic fertilizer with or without trinexapac-ethyl or biostimulant recovered 1 to 3 weeks more quickly than treatments that did not include additional fertility. Addition of the PGR trinexapac-ethyl inconsistently regulated speed of canopy recovery, both increasing and decreasing recovery speed. Under normal maintenance conditions, methiozolin does not negatively influence putting green recovery, however, if the putting green is exposed to droughty
Impact–contact dynamics in a disc–ball system

This paper concerns a disc–ball system, in which a moving ball collides against a disc resting on a rough, fixed horizontal surface. The complexity in such a simple object is due to the presence of the line contact between the disc and the fixed plate, which significantly influences the impact-generated state of the disc. We deal with this problem in a uniform framework that encapsulates different structures of the mathematical model, including contacts, impacts, stick–slip in friction, as well as the transitions among different states of the variable-structure dynamics. We design specific experiments that provide useful information to help determine the macroscopic parameters in impact and friction. Other complicated cases concerned with the couplings between impacts and friction are theoretically and experimentally investigated. Excellent agreements between numerical and experimental results support our theoretical developments.
Rationale for Need of In-Orbit Servicing Capabilities for GEO Spacecraft

At both NASA and DARPA, robotic programs are being executed which will serve as catalysts for changing how the commercial world thinks about satellites and satellite life. Technology is being developed today by both governments and private companies which will allow towing, servicing and lifetime extension of spacecraft already in orbit. These same technologies can be leveraged to come to the rescue of future satellite missions which suffer launch performance shortfalls or deployment anomalies. The capability of these robotic vehicles could even include the potential to collect orbital debris.
D-KODE: Mechanism to Generate and Maintain a Billion Keys

This work considers two prominent key management problems in the blockchain space: (i) allowing a (distributed) blockchain system to securely airdrop/send some tokens to a potential client Bob, who is yet to set up the required cryptographic key for the system, and (ii) creating a (distributed) cross-chain bridge that allows interoperability at scale by allowing a (changing) set of nodes in a blockchain to perform transactions on the other blockchain. The existing solutions for the first problem need Bob to either generate and maintain private keys locally for the first time in his life—a usability bottleneck—or place trust in third-party custodial services—a privacy and censorship nightmare. Towards solving both problems in a distributed setting against a threshold-bounded adversary, distributed key generation (DKG) based solutions are actively employed; here, a set of servers generate the transaction keys in a distributed manner and link them to clients’ ids. Never-theless, these solutions introduce computation and communication overhead that is linear in the number of keys and do not scale well even for a million keys, especially for proactive security against a mobile adversary. This work presents a Keys-On-Demand ( D-KODE ) 1 distributed protocol suite that lets the blockchain system securely generate the public key of any Bob against a mobile, threshold adversary. Multiple servers, here, compute discrete-log private/public keys on the fly through distributed pseudo-random function evaluations on the queried public string. D-KODE also introduces a proactive security mechanism for the employed black-box secret-sharing based DKG to maintain the system’s longitudinal security. The proposed protocol scales well for a very high number of keys as its communication and computation complexity is independent of the number of keys. Our experimental analysis demonstrates that, for a 20-node network with 2 / 3 honest majority, D-KODE starts to outperform the state of the art as the number of keys reaches 94K. D-KODE is practical as it takes less than 100msec to generate a secret key for a single-threaded server in a 20-node setup.
REVIEW OF SURFACINGS FOR LOW-VOLUME ROADS ON THE BASIS OF EXPERIENCE IN SOUTH AFRICA

Local and overseas specifications and design methods of single, double, slurry, Cape, sand, primer, and Otta seals were compared to investigate potential surfacings for low-volume roads. The following conclusions were drawn: (a) aggregate is rigorously specified for single, double, slurry and Cape seals and little or no relaxation has been considered, whereas the specifications for sand, primer and Otta seals are not as rigorous; (b) binder is rigorously specified by relevant South African Bureau of Standards specifications for all seal types; and (c) formal design methods exist for single and double seals, but the designs of the other seal types are mostly empirical. A calculation of life cycle costs to road authorities over a 20-year period indicated that single and double seals are the most cost-effective over a wide range of traffic volumes. Low unit construction prices do not necessarily mean high cost-effectiveness, as the expected lifetime of the seal is an important consideration.
Modeling Function Relation for Automatic Code Comment Generation

Comments are essential for software mainte-001 nance and comprehension. However, com-002 ments are often missing, mismatched or out-003 dated in software projects. This paper presents 004 a novel approach to automatically generate de-005 scriptive comments for methods and functions. 006 Our work targets a practical problem where 007 hand-written comments are only available for 008 a few methods in a source file – a common 009 problem seen in real-world software develop-010 ment. We develop a novel learning framework 011 to model the code relation among methods 012 based on graph neural networks. Our model 013 learns to utilize the partially contextual infor-014 mation extracted from the existing comments 015 to generate missing comments for all methods 016 in a source file. We evaluate our approach by 017 applying it to Java programs. Experimental re-018 sults show that our approach outperforms prior 019 methods by a large margin by generating com-020 ments that are judged to be helpful by human 021 evaluators and of a higher quality measured by 022 quantified metrics. 023
Proposal of a Method to Predict Subjective Rating on Drowsiness Using Physiological and Behavioral Measures

OCCUPATIONAL APPLICATIONS Subjective drowsiness was predicted during a simulated driving task with an accuracy of more than 90%. This was done using a multinomial logistic regression model, using physiological and behavioral measures as predictors. The actual and/or potential applications of these results include the development of a system for predicting drowsiness and presenting drivers a warning. These results can contribute to the enhancement of transportation safety by decreasing the risk of crashes or traffic accidents caused by drowsy driving. TECHNICAL ABSTRACT Background: From the viewpoint of automotive safety, it is useful to detect a decrease in arousal level and to warn drivers of the risk of a traffic accident. Although many measures of drowsy states have been developed, effective methods for predicting drowsy driving states and to warn drivers of these states have not been established. Purpose: The aim of this study was to explore the effectiveness of physiological and behavioral evaluation measures for predicting a drivers' subjective drowsiness using a regression model. Methods: Eight participants completed the study, which involved simulated driving. They were required to steer and maintain their vehicle at the centerline and to maintain the distance between their own car and a preceding car. Physiological measures were obtained (electroencephalography, heart rate variability and blink frequency), along with behavioral measures (neck bending angle, back pressure, foot pressure, and tracking error), and participants reported subjective drowsiness once every minute. Drowsy states were predicted via three multinomial logistic regression models consisting of different independent variables—Model A: both physiological and behavioral measures, Model B: only behavioral measures, and Model C: only physiological measures. For each model, prediction accuracies were examined, and the length of the data window used for predicting drowsiness was explored. Results: When both physiological and behavioral measures were used, prediction accuracy was 96.8%. The interval used for attaining the highest prediction accuracy was 100 seconds (from 120 to 20 seconds before the prediction). When only physiological measures were used, prediction accuracy was 90.2%, and accuracy was 94.9% using only behavioral measures. Conclusions: The proposed multinomial model could attain higher prediction accuracy when both physiological and behavioral measures are used and is potentially useful for the development of drowsiness warning systems.