Miracle-dz/UniFAR-FTUs huggingface dataset:
Xenon difluoride etching and molecular oxygen oxidation of silicon by reactive scattering

While both molecular fluorine (F₂) and xenon difluoride (XeF₂) fluorinate Si(l00)2xI surfaces at coverages up to one monolayer, fluorine is unable to cleave Si-Si bonds that ultimately leads to etching silicon at coverages above one monolayer (reaction probability 9x 10-⁴) while XeF₂ does so readily (reaction probability 0.6). Previous studies have demonstrated that both fluorine and xenon difluoride react with silicon via atom-abstraction mechanisms at low coverages, and that the XeF radicals produced by atom abstraction from xenon difluoride dissociate in the gas phase, producing a source of F radicals that may contribute to Si-Si bond cleavage by formation of multiple F-Si bonds. In addition, it has been shown that van der Waals clusters of F₂ with a Xe atom and with a Kr atom have reaction probabilities with silicon at one monolayer of fluorine coverage of roughly 0.9 and 0.04, respectively, suggesting that the effect of mass of the incident gas molecules is important in activating the cleavage of silicon-silicon bonds through a multiple collision process. A model based on a classical harmonic oscillator linked via a variable damping term to the thermal energy of the lattice is proposed to test the importance of the mass of the gas molecules to energy transfer and the duration of the vibrational excitation of the silicon-silicon lattice bonds. Computer simulations using this model suggest that the mass of the gas molecule does not affect the overall energy transferred to the silicon. However, the heavy mass and the resulting multiple collisions do extend the duration of the silicon excitation and the duration during which the gas molecule is in contact with the vibrationally excited silicon lattice. The techniques that have been used to study the reactivity of fluorine compounds with silicon are potentially of use to the study of another problem: why triplet molecular oxygen (O₂ ) is able to dissociatively chemisorb across singlet silicon-silicon dimer bonds to form the singlet Si-O product. Several mechanisms, including atom abstraction and non-adiabatic ladder climbing of (O₂ )₂ van der Waals dimers are suggested to explain this reaction and a series of experiments to test the hypothesized mechanisms are proposed. In addition, a detailed account of the ultra-high-vacuum molecular beam surface scattering apparatus used for these studies is provided, including a detailed description of its operation and maintenance procedures.
Distribution of glucosinolates in camelina seed fractions by HPLC‐ESI‐MS/MS

High glucosinolate concentrations were detected and identified in camelina (Camelina sativa L. Crantz.) seed fractions using reversed phase high performance liquid chromatography-electrospray ionization-mass spectrometry (HPLC-ESI-MS) in multiple reaction monitoring mode. Total glucosinolate quantitation was performed using proton nuclear magnetic resonance spectrometry using N,N-dimethylformamide as an internal standard. Individual glucosinolate concentrations were determined by analysis on extracted ion chromatograms generated by a MS detector. Distribution of glucosinolates in C. sativa seed fractions during pressing is reported. Practical applications: HPLC-ESI-MS and HPLC-ESI-MS/MS using monolithic HPLC columns were used to detect, quantify, and identify the three major glucosinolates in Camelina sativa L. and fractions of C. sativa produced by processing. Quantitative extracted ion MS chromatograms afforded excellent quantitation of individual glucosinolates. The use of monolithic HPLC columns enabled rapid analysis of these samples. Schematic of a proposed dehulling system for Camelina sativa seed.
Efficient Hardware Architecture of Convolutional Neural Network for ECG Classification in Wearable Healthcare Device

Nowadays, with the increasing shortage of traditional medical resources, the existing portable monitoring healthcare device is no longer satisfactory. Thus, wearable healthcare device with diagnostic capability is becoming much more desirable. However, the design of wearable healthcare device faces the challenge of limited hardware resource and high diagnostic accuracy. In this paper, an efficient hardware architecture is proposed to implement a 1-D CNN with global average pooling (GAP) specially for embedded electrocardiogram (ECG) classification. The GAP is implemented by substituting division into shifting operation without extra computing resource consumption and it can largely reduce the parameters of the network. The fully pipelined processing unit (PU) array is designed to increase computing efficiency. A sign bit based dynamic activation strategy is developed for removing redundant multiplications and resource consumption of ReLU. The proposed efficient hardware architecture is implemented on Xilinx Zynq ZC706 board and achieves an average performance of 25.7 GOP/s under 200-MHz with resource consumption of 1538 LUT, which makes resource efficiency improved by more than $3\times $ compared with non-optimized case. The averaged classification accuracy of five ECG beats classes is 99.10%. In brief, the proposed efficient hardware design is prospective for wearable healthcare device especially in ECG classification area.
A Fully-Integrated Band-Switchable CMOS Power Amplifier for Wireless Applications

This work, for the first time, proposes a fully integrated band-switchable RF power amplifier (PA) by using a novel approach towards switching the matching networks. In this approach, two matching networks which can be controlled by digital bits will provide the lower and higher operating frequency bands for the PA. In order to implement the proposed band-switchable PA, a robust R switch is presented by using resistive body floating technique and 6-terminal triple-well NMOS. The measured results show that the proposed band-switchable power amplifier can operate at 980MHz and 1900MHz as the lower and the higher frequency bands, and delivers saturated output power (Psat) of 21. 3dBm/23.0dBm, output 1-dB compression point (PldB) of 19. 5dBm/21.0dBm, and 3-dB bandwidth of 400MHz/550MHz, respectively, showing promising advancement compared to the state-of-the-art. The proposed power amplifier is designed and implemented in 180nm CMOS process with die area of 3.1mm× l.6mm.
Leaf Recognition and Disease Detection using Content based Image Retrieval

The agricultural domain in past few decades has seen a decrease in its productivity. The main cause for this was found to be an increase in plant diseases. Having diseases in plants is quite common, but due to improper care there have been serious effects on plants. But we cannot keep inspecting each and every plant present in thousands. Hence, in this work an approach is developed which provides faster and more accurate results of the detected plant leaves and its corresponding diseases. The proposed work approach uses various image processing techniques for recognising the plant leaf type and detecting disease. The system uses two different classification methods namely, Support Vector Machine (SVM) and K-Nearest Neighbours (KNN) and their performances are compared.
Antenna Research Directions for 6G : A brief overview through sampling literature

Antennas are a critical component in any wireless link. The significance of their role continues to grow, together with the rise of machine-to-machine (M2M) communications and telecommunications starting to shift from the fourth generation (4G, or Long-Term Evolution, LTE) into the fifth generation (5G) and beyond 5G (B5G), and also due to the prominence of the convenience promised by the ubiquitous wireless communications. The antennas, or more precisely their large size and appearance, also become a subject of public debate, leading to the need for better antennas, which are can do both: provide high performance and offer visually attractive or nearly invisible/transparent designs. This work reviews several key research trends in antennas to fulfill these demands for the fifth generation of communications (5G) and beyond, especially 6G, and considers novel antenna techniques and designs needed to increase the smartness of the antenna systems and to provide improved beamforming and security.
Implementation of Machine Learning Classifiers for Built-up Extraction Using Textural Features on Sentinel-2 Data

Accurate built-up extraction is a challenging job due to the heterogeneity within the same class as well as spectral confusion among classes having similar charactertics. This paper presents an approach for effective built-up extraction using textural features along with spectral features by utilizing Sentine1-2 data at 10m spatial resolution (four spectral bands i.e. Red, Green, Blue and NIR). In the proposed approach, eight Gray-Level Co-occurrence Matrix (GLCM) features have been computed corresponding to each spectral band. Therefore, a total of 32 features have been stacked with 4 spectral features to form the input dataset. Thus, two input datasets have been formed, first datasets consist of only spectral information (spectral bands), second dataset consists of spectral bands as well as textural information, which is termed as spectral-textural dataset. In order to perform classification, two popular machine learning techniques namely Random Forest (RF) and Support Vector Machine (SVM) have been implemented. Here, grid search approach has been employed to determine the optimal value of tuning parameters for both machine learning techniques. Results have shown that textural features provide additional information, which is beneficial for effective built-up extraction. It is found that after including textural features an increase of 5.18% and 4.42% in Overall Accuracy have been achieved by RF and SVM classifier respectively. Further, it has been observed that among all the considered textural features GLCM-Dissimilarity GLCM-Contrast and GLCM-Entropy are more robust predictor variables.
The Analog Behavior of Pseudo Digital Ring Oscillators Used in VCO ADCs

In this work, we focus on the three most relevant pseudo-digital ring oscillator circuits for VCO A/D conversion. First, we show that they can be accurately modeled by an equivalent 2-terminal diode-like element. Next, we study the main analog performance figures: the effective oscillation frequency (which affects the quantization noise of a VCO ADC), the input referred noise and the power efficiency both for voltage and current control. We do this both through theoretical modeling as well as through simulations, which are in good agreement with each other. Our study highlights several important independencies: e.g. most performance metrics are independent on the number of VCO stages and the external load capacitance on the VCO nodes. In contrast to these independencies, we identify several important dependencies. In particular, we show that the biasing of the ring has an enormous impact on power efficiency of the ring. This dependency is different for the case of a voltage- or current mode configuration of the ring.
Decentralized Tracking Control for Modular Reconfigurable Robots Using Data-Based Concurrent Learning

This paper proposes a decentralized tracking control (DTC) through data-based concurrent learning for modular reconfigurable robots (MRRs) with unknown dynamics. By using the local input-output data and reference trajectories of interconnected subsystems, a neural network (NN)-based local observer is established to acquire the MRR dynamics online. Based on the adaptive dynamic programming algorithm, the local Hamilton- Jacobi-Bellman equation is solved by a local critic NN, whose weight vector is tuned by a concurrent learning-based updating law. Then, the DTC policies are obtained, and the persistence of excitation condition is removed. The tracking error of the entire closed-loop MRR system is guaranteed to be uniformly ultimately bounded by the Lyapunov’s direct method. The simulation on a 2- DOF MRR system demonstrates that the proposed DTC scheme is effective.
Output Controllability of Mix-valued Logic Control Networks

This paper focuses on output controllability of a specific kind of mix-valued logic control networks (MLCNs) via semi-tensor product method. First, we introduced the definition for output controllability of a specific MLCN, of which the updating of outputs is determined by both inputs and states via logical rules. Second, propositions of the number of different control sequences steering a MLCN from a given initial state to a destination output in a given number of time steps are derived. Consequently, criteria for the output controllability are obtained by construsting the output controllability matrix. Finally, a hydrogeological example is presented to verify the obtained results.
Genome Assembly : A Review

Genome is complete set of DNA, including all of its genes. After getting reads from sequencing technologies, it’s a huge task to reconstruct the original genome because there are millions of overlapping reads which are of poor quality at ends and generate many types of errors while reconstructing. A number of techniques and technologies are already there but still there is need of tools which are fast and accurate for assembling genomes. These tools are based on various algorithms and error handling techniques. Primarily we discussed OLC and De Bruijn Graph. Also, there is discussion related to different techniques used to structure genome using above graph concepts. Also we have discussed about different tools which are already being used and their specific roles.
An Energy Efficiency Strategy Based on Bayesian Classification in SDN Data Center

The research focuses of this paper is data center energy saving. An algorithm DSSBC (the Dynamic Scheduling Strategy Based on Bayesian Classification) is proposed and it is deployed by the algorithm named SDCEES (SDN Data Center Energy Efficiency Strategy) for energy saving of SDN data center, formed the dynamic task scheduling strategy of this paper. The strategy takes full advantage of the separation of network control and forwarding in SDN architecture, and dynamically senses the network load and virtual machine load of data center in real time. Based on the normal execution of tasks, the strategy can schedule tasks dynamically, improve the utilization of virtual machines in working state, and put idle virtual machines into sleep state. Simulation results show that the performance of the proposed strategy is better than other typical energy optimization algorithms, and can be deployed and executed efficiently. The energy saving rate is 11 %.
Analysis of Tremors in Parkinson’s Disease Using Accelerometer

Parkinson’s Disease is one that affects the brain and symptoms include shaking, stiffness, and difficulty to walk. Tremor is the most identifiable symptom of this disease and it affects nearly 80% of the patients with Parkinson's Disease. This prototype was designed to observe and quantify the tremor signal from Parkinson's disease patients. The prototype is based on Arduino Uno programming and interfacing, and the ADXL335 tri-axial accelerometer is used as a sensor. The resting tremor signal was acquired in the form of acceleration using the sensor accelerometer from fingertip, wrist and forearm of the patient. The Arduino processed the data which was transferred to MATLAB for further processing. The resting tremor was observed in terms of amplitude and spectral density. For the three parts considered, the amplitude values of acceleration were ranging from 40 dB/Hz- 80 dB/Hz and spectral density were observed and compared. Hence this basic prototype could be useful and developed further to assist the Parkinson's Disease patients.
Energy-aware Multimedia Applications Scheduling on VFI-NoC-MPSoC using as an Edge-device in IoT

The reliance on Network-on-Chip (NoC) Multiprocessor Systems-on-Chip (NoC-MPSoC) is proliferating in modern embedded systems to satisfy the higher performance requirement of computationally extensive multimedia streaming applications. Recently, Voltage Frequency Island (VFI) based MPSoCs are deployed for data extensive applications due to their efficient energy management. High energy utilization of edge-devices in the Internet-of-Things (IoT) produces an increased carbon footprint. In this paper we develop a novel energy-aware scheduler considering tasks with conditional constraints using VFI based heterogeneous NoC-MPSoCs (VFINoC-HMPSoC) as an edge-device. We propose a novel non liner programming based scheduling and voltage scaling approach referred to as ALI-EBAD. This performs energy-efficiently task scheduling and voltage scaling in an integrated way. The major reason for this design is that task voltage scaling helps in steering task scheduling towards an energy-efficient solution. We performed experiment on 12 real benchmarks and the results demonstrate that ALI-EBAD outperforms CATMES-Search and CA-TMES-Quick schedulers in terms of energy-efficiency. ALI-EBAD achieves an energy-efficiency of ~ 20% over CA-TMES-Search and ~ 25% over CA-TMES-Quick.
The Safety Management System Using Q-Learning Algorithm in IoT Environment

The Industrial Internet of Things (IIoT) is the framework in which a large number of devices are connected and synchronized for handling different processes and machinery in industry, to remove the risk of human error and improve safety. Many IoT-based worker’s safety systems contain a network of different sensors and assessable mobile information center. Such as, workers wear sensors that monitor the heart rate, activity, toxic gases, and other factors that are affecting worker’s safety. The initial investment for such automation is very high and it is not affordable for small industries therefore, mostly high-level manufacturing industries install automation in the workplace. The purpose of this paper is to design a safety management architecture for workers in the small-scale candy manufacturing industry using IoT and Q-learning mechanism. Also, to provide the benefits of automation at a low-cost. The system consists of gas, temperature, humidity, and flame sensor. ADC is used to convert recorded sensor values from analog to digital form and these converted values are received by the raspberry pi board and simultaneously stored in a database. The Q-learning approach is used to identify crucial situations using database values and execute the output appliances like a fan and buzzer.
Simulation Analysis of Network Capacity for LEO Satellite

In this paper, a Low Earth Orbit satellite (LEO) network capacity model is proposed to value the influence of routing strategy and the difference between the intra-plan ISL and the inter-plan ISL on throughput capacity. The Analysis shows that for LEO constellation, Central routing algorithm presents superior traffic distribution ability in LEO network than distributed routing strategy. The performance of choosing a co-orbiting satellite is better than choosing a different-orbiting satellite when there are mutliple equal cost transmission paths. Simulation results prove that central routing algorithm combined with priority selection of satellites in the same orbital plane in LEO satellite network could reduce data packet loss rate, achieve optimization on network capacity.
APCC: Agile and Precise Congestion Control in Datacenters

Modern datacenter networks exhibit complicated and time-varying traffic patterns, from long-running flows to burst short-lived flows. Recently in-network-telemetry (INT) is employed by datacenter transports to perform precise congestion control (CC). Current INT-based CC works well for long flows, however, suffers from serious performance downgrades when BDP-level small flows burst in the first RTT, due to the reason that current INT information from the receivers (host-based INT) needs at least one RTT to respond. In this paper, we make the first attempt to propose an agile and precise congestion control, called APCC, in datacenter networks, working for traffic patterns that is a mix of BDP-level short flows and long flows. APCC explores INT information from switches (switch-based INT) to feedback the congestion information eagerly, and effectively manage BDP-level flows. APCC utilizes the switch-based INT to schedule the complicated and time-varying traffic stably and precisely, and achieve low latency, high bandwidth and network stability simultaneously. We conduct extensive experiments to evaluate the performance of APCC. The experiment results show that with data centers load containing many BDP-level flows such as Cache Follower and Web Server, switch-based INT shows huge potential of improving the long tail effect on completion time (FCT). APCC reduces tail delay by 21.7%-28.9% under normal circumstances, and can still reduce tail delay by 9.1% under more severe conditions comparing with HPCC. Moreover, APCC shows better convergence.
ToF-based Indoor Positioning for Low-power IoT Nodes

In this work, we address the challenge of using power and complexity-limited internet of things (IoT) nodes for time of flight (ToF)-based indoor localization. This requires a high signaling bandwidth, and thus the narrowband radios of many low-power, low-cost, commercial off-the-shelf (COTS) transceiver chips can not be used. However, as these kind of nodes are deployed in vast numbers, it is worth considering unorthodox approaches to facilitate wideband signal generation without changes to the hardware. We make use of an unsupported mode of operation of one typical transceiver chip to generate a frequency-stepped chirp waveform with a bandwidth of up to 80 MHz in the 2.4 GHz industrial, scientific, and medical (ISM) band.This paper evaluates a pulse-compression technique for such chirped signals, which accounts for unknown properties of nonideal transceivers. This edge computing approach allows for an immediate rate reduction of the received data and the application of efficient localization algorithms in the cloud. The performance of the phase difference estimation as the crucial step of the pulse- compression is evaluated using a Monte Carlo simulation of the introduced channel and processing chain. We compare it to the theoretical bound for an ideal waveform.
A Proposal for Virtual Mental Health Assistant

In these current times, mental health has become one of the most neglected and yet one of the most serious aspects of our overall well being. This paper proposes a system for a virtual mental health assistant owing to financial, time and space constraints and shortage of resources related to in-person therapy. Of tentimes, disturbed mental health is a snowball effect built up over time and requires continuous attention and conscious efforts to improve. This is possible with the help of a virtual mental health assistant. The proposed assistant will have a chat feature, psychological assessment, an emotion detection module and a recommendation system for improving the mood of the user. We have used Naive Bayes classifier and Neural Networks for sentiment analysis. Our system has shown comparatively higher accuracy for Naive Bayesian model.