Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Adaptive Pulse Compression in Cluttered Radar Cross Section Measurements

This article discusses a post-processing method to achieve highly accurate radar cross section (RCS) measurements in challenging test environments. In the ideal RCS measurement setup, the only received signal is the reflection from the object under test (OUT). However, this scenario is often not realizable in practice as outside measurements suffer from a multitude of scattering objects beyond the OUT, and even anechoic chambers have nonnegligible multipath clutter. Time-gating has been shown to be a powerful post-processing clutter suppression tool by temporally filtering any undesired return signals. However, this technique is only sufficient when there is ample spacing between the OUT and undesired return signals or when the sidelobes of one scatterer do not mask the return of a smaller nearby scatterer in the time domain. In the latter scenario, traditional time-gating techniques cannot separate the sidelobes from a nearby dominant scatterer overlapping with the main lobe of the OUT, and the RCS of the OUT cannot be extracted accurately. By applying the proposed stepped frequency continuous wave (SFCW) adaptive pulse compression (APC) algorithm, a range-dependent filter is created to iteratively estimate the range profile and suppress sidelobes allowing for temporal separation of the scatterers and accurate RCS estimation. To validate the concept, the RCS of a 15.2 cm sphere is extracted in two measurement configurations (with and without a large nearby dominating scatterer). The traditional matched filter data processing accuracy is compared to the proposed APC technique. Implementation of APC in post-processing yields a 71.2% improvement in average RCS error compared to traditional processing in the sphere-only measurement and an 88.2% improvement in the presence of a metal plate.
Load Balancing for Microservice Service Meshes

Microservices break down monolithic applications into smaller components and have become a popular model for application deployment. In this model, load balancing has moved from the server to the request side where a load balancer resides with each upstream service.Least Connection, a derivative of the Join the Shortest Queue (JSQ), is a popular algorithm used in the microservice architecture. Despite JSQ being proven to be nearly optimal in certain scenarios, Least Connection significantly underperforms in microservices and edge environments.My thesis aims to adapt load balancing to the microservices environment such that they can autonomously choose from a collection of approaches to mitigate widening of response time distributions.
3-Dimensional Integration with High Interconnection Density

A conventional 2-dimensional scaling down seems to reach its fundamental limits. Further increasing areal integration density required a significant investment of time and money as the dimension of the devices becomes close to those of molecules. On the other hand, the systems that has been requested lately should require more functions to be incorporated in a constrained space. Therefore, 3-dimensional stacking of device layers has attracted attention. To take over 2D scaling of system-on-chip approach, 3D integration should have high density of interconnection density. In this talk, 3D integration for high interconnect density technologies would be addressed. Monolithic 3D is a sequential 3D integration technique to stack multiple device that was proposed to increase integration density and decrease the signal delay and power consumption by reducing interconnection length. Hybrid bonding is a parallel 3D integration technique having a dielectric bond with embedded metal to form interconnections. Higher connectivity can be accomplished because solder bumps on dies are not required to make connections. However, to adopt these technologies, it is necessary to develop novel process techniques and study several technical issues.
Shared Control of Upper Limb Prosthesis for Improved Robustness and Usability

Feed-forward control paradigms currently domi-nate upper limb prosthesis research. Electromyographic (EMG) signals measured from muscles in a user's residual limb are filtered and processed before becoming the input to a machine learning algorithm. The output of this algorithm is sent directly as a command to a prosthetic. Despite advances in feed-forward methods, upper limb prostheses remain difficult for amputee users to control, and abandonment rates are high [1]. In this paper, we present a novel shared control paradigm that uses a hybrid gaze/EMG interface to control prosthetic hand and wrist movements. Six subjects used a virtual prosthesis to perform a pick-and-place task in an augmented reality (AR) environment using both a semi-autonomous (SA) controller and a feedforward (FF) controller representative of the current state of the art. Results show a 75% increase in average successful task completion rate when using the SA controller instead of the FF controller. The SA controller was found to be more robust against variation in object type and orientation, scored significantly higher on subjective usability metrics (p ≤ 0.05), and resulted in a dramatic decrease in user frustration during the task (p <. 01).
Comparison of PPG and BCG Features for Camera-based Blood Pressure Estimation by Ice Water Stimulation

Non-invasive Blood Pressure (BP) measurement is highly demanded for pervasive healthcare with the development of Internet of Things, sensors and mobile technology. Camera-based Photoplethysmography (camera-PPG) has been applied for non-contact BP estimation. Most camera-PPG based approaches calculate the Pulse Transmission Time between different peripheral sites like face and palm for BP calibration, which require more than one body part to be simultaneously measured and thus introduce inconvenience to real applications. In this study, we investigate the feasibility of measuring BP from a single body site using either the forehead PPG signals or neck ballistocardiographic (BCG) motion signals. Two morphological features (K-value and Augmentation Index) that have clinical meanings for BP monitoring have been compared. The study was conducted in the ice water stimulation experiment involving 16 healthy subjects. The results show that the neck can be an attractive site for BP estimation as the neck-BCG signals show more distinct features (e.g. dicrotic wave) that have stronger correlations with BP than the forehead-PPG signals, and it eliminates the privacy issue of imaging a face. Both the K-value and Augmentation Index can well track the changes of BP. The conclusions drawn from this study inspire the selection of physiological site and features for non-contact BP estimation.
An Adaptive Update Algorithm for Fingerprint Database Based on Validity Detection

Maintaining the accuracy of fingerprint-based indoor positioning requires ensuring the validity of the fingerprint database. The main task of fingerprint database maintenance is to detect and update invalid data in a timely manner. In this paper, we address the problem of invalid fingerprint data with the fingerprint matching effectiveness evaluation method and the adaptive update algorithm of fingerprint database. The experimental design and field data collection and analysis were conducted in two typical experimental scenarios: an indoor basketball court and an indoor school building. Experiments with real measurements show that the proposed algorithm improves the performance of updating the fingerprint database in real time and robustness.
WAFLASH: Taming Unaligned Writes in Solid-State Disks

Ahstract-NAND-flash based solid-state disks (SSDs) are replacing the hard-disk drives (HDDs) in various storage systems from high-end servers in data centers to mobile computers on the edges of cloud computing. Due to the architectural nature of SSDs, performance-sensitive applications may generate a large number of unaligned writes on SSDs, which can cause many issues including chip congestion, sub-request blocking, chip load imbalance, and write space amplification. This paper presents a Write-Aligned FLASH drive (WAFLASH) that comprehensively and significantly alleviates the side-effects of unaligned writes in solid-state disks. We utilize three key techniques: (1) prioritizing eviction of fully-filled pages over partially-filled pages in write buffers, (2) storing multi-version data requested by unaligned writes/overwrites in flash memory, and (3) compacting multiple small partially-filled pages in a physical page to circumvent write amplification and reduce the number of additional reads in the critical I/O path. We implement WAFLASH in VSSIM and Cosmos+ OpenSSD. The results show WAFLASH increases I/O throughput by up to 125% with the Filebench benchmark.
Inertial migration in dilute and semi-dilute suspensions of rigid particles in laminar square duct flow

We study the inertial migration of finite-size neutrally buoyant spherical particles in dilute and semi-dilute suspensions in laminar square duct flow. We perform several direct numerical simulations using an immersed boundary method to investigate the effects of the bulk Reynolds number $Re_b$, particle Reynolds number $Re_p$ and duct to particle size ratio $h/a$ at different solid volume fractions $\phi$, from very dilute conditions to $20\%$. We show that the bulk Reynolds number $Re_b$ is the key parameter in inertial migration of particles in dilute suspensions. At low solid volume fraction ($\phi=0.4\%$) and low bulk Reynolds number $(Re_b= 144)$, particles accumulate at the center of the duct walls. As $Re_b$ is increased, the focusing position moves progressively towards the corners of the duct. At higher volume fractions, $\phi=5, 10$ and $20\%$, and in wider ducts with $Re_b=550$, particles are found to migrate away from the duct core towards the walls. In particular, for $\phi=5$ and $10\%$, particles accumulate preferentially at the corners. At the highest volume fraction considered, $\phi=20\%$, particles sample all the volume of the duct, with a lower concentration at the duct core. The presence of particles induces secondary cross-stream motions in the duct cross-section, for all $\phi$. The intensity of these secondary flows depends strongly on particle rotation rate, on the maximum concentration of particles in focusing positions, and on the solid volume fraction. We find that the secondary flow intensity increases with the volume fraction up to $\phi=5\%$. However, beyond $\phi=5\%$ excluded volume effects lead to a strong reduction of cross-stream velocities.
TCAD-Based Investigation of the Electrical Characteristics of Normally off p-GaN Passivated GaN HEMTs

This paper presents the influence of the energy level and concentration of acceptor or donor traps which could be introduced in the plasma passivation process on the saturation current of etch-free HEMTs. Acceptor traps in passivated pGaN region, and donor traps in SiNx/passivated pGaN and passivated pGaN/AlGaN interface are included in discussion. By the variation of the concentration and energy level of the trap, some general phenomena are discovered. In SiNx/passivated pGaN interface, the saturation current is proportional to the concentration and energy of donor traps. In passivated pGaN region, the saturation current shows negligible variations to the increase of concentration under 1×1018 cm-3 and the energy of acceptor traps. In passivated pGaN/AlGaN interface, the trend is the same as previous: the saturation current is proportional to the concentration and energy of donor traps. However, the saturation current is more sensitive to the change of donor trap concentration in this interface.
LCS: A Collaborative Optimization Framework of Vector Extraction and Semantic Segmentation for Building Extraction

In the field of building extraction, many convolutional neural network (CNN)-based methods have been developed to solve the problem of the irregular boundaries in their predictions. The prevailing approach is to build an additional edge segmentation branch or obtain accurate vector components of buildings. However, pixel-based methods still cannot obtain an accurate location of the boundary, while vector extraction will bring the problem of sample imbalance and missing detection. In this work, we utilize the complementarity of the two types of methods and propose the line segment collaborate segmentation (LCS) framework. In the proposed LCS framework, semantic segmentation provides location guidance for vector extraction, while vector extraction provides precise positioning for semantic segmentation. In this way, the two tasks can leverage their respective strengths. The results on three datasets show that the performance of vector extraction and semantic segmentation is improved simultaneously using the LCS framework, which proves the effectiveness of our method. At the same time, our framework is flexible and can be embedded in other vector extraction methods to improve performance.
Experimental Investigation and Model Analysis on a GaN Electrostatic Discharge Clamp

This work carried out the experimental investigation and model analysis on a GaN electrostatic discharge (ESD) clamp, which features a discharge high electron mobility transistor (HEMT) and a voltage divider formed by a lateral field effect rectifier (L-FER) chain and a resistor in series. Firstly, it is found through the experimental investigation, the turn-on voltage (<inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {T}}$ </tex-math></inline-formula>) of GaN ESD clamp’s static current is increased with the increase in L-FER’s number and the decrease in resistor’s value. Then, an analytical model is proposed to analyze the dependence of <inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {T}}$ </tex-math></inline-formula> on L-FER’s number and resistor’s value. And the analytical model exhibits a good consistency with the experimentally measured results, which confirms the validity of the presented analytical model. Lastly, the dependence of GaN ESD clamp’s transmission line pulsing characteristics on the L-FER’s number and resistor’s value is also analyzed. Similarly, GaN ESD clamp’s triggering voltage is also increased with the increase in L-FER’s number and the decrease in resistor’s value. The experimental investigation and model analysis of this work can be used to direct the design of GaN ESD clamp, and help to save a lot of manpower, material resources and time costs.
An Adaptive K-Nearest-Neighbor Approach for Predicting Chemical Composition Content in Soil

Land quality is evaluated according to the chemical composition content in soil. The geochemical evaluation of land quality can help users to determine how to use the land, e.g., dynamically manage land resources and adjust the pattern of farming. However, some chemical composition contents are missing in practice. It is necessary to predict the missing chemical composition content for the geochemical evaluation. This paper proposes an adaptive k-nearest-neighbor approach for predicting the chemical composition content in soil. The approach can adaptively determine the similarity between soil samples based on the characteristics of geological background, soil type, land use type and geographical position. According to the similarity, the proposed approach selects the k nearest neighbors of a sample and predicts the missing chemical composition content. The experimental results show that the proposed approach has better accuracy and stability than its competitors.
Study on the Specific Absorption Rate of Implantable Medical Devices in Multilayer Tissues

The exposure of the human body to electromagnetic radiation levels is exacerbated when medical devices are implanted. With the wide application of implantable medical devices, the electromagnetic radiation safety of such devices cannot be ignored. This paper studies the effect of tissue type, implantation depth, frequency and deflection angle on the specific absorption rate of human tissue in vivo with the radiation source. This study takes the multi-layer tissue model as the main research object, and uses CST Microwave Studio to establish a radiation model with a planar inverted-F antenna implanted in human tissue as the radiation source. Calculate the specific absorption rate under various conditions, and judge the influence of various factors on the specific absorption rate. The results show that tissue type and frequency have significant effects on the specific absorption rate. When the frequency is increased from 2.45GHz to 5.8GHz, the specific absorption rate is also increased from 58.2111W/kg to 118.095W/kg. Different tissue types have different specific absorption rates due to differences in relative permittivity and electrical conductivity. However, the effect of implant depth and deflection angle on the specific absorption rate is not obvious.
Enhancing Gait Assistance Control Robustness of a Hip Exosuit by means of Machine Learning

Optimally synchronising the assistance provided by wearable devices with the human voluntary motion is still an open challenge in robotics. In order to provide accurate and robust assistance, this paper presents a novel approach that combines a layered implementation of a controller for an underactuated exosuit assisting hip flexion during human locomotion: the first layer is based on Adaptive Oscillators (AOs layer), while the second one uses Machine Learning (ML layer). The latter has been introduced to enhance the robustness of the AOs-based controller in abrupt changes of the gait frequency, with the final goal to achieve higher synchronisation and symbiosis between the user and assistive devices in presence of variable and unpredictable locomotion patterns. The effectiveness of the layered controller has been tested on six healthy subjects. Preliminary results suggested that the additional ML layer provided improvement to the overall performances during overground walking. In addition, we found a reduction of metabolic rates when receiving assistance from the device: 7.4% on average on treadmill evaluations and 10% overground including the extra ML layer, without alteration of the physiological human motion.
An Intrusion Detection Algorithm Based on Hybrid Autoencoder and Decision Tree

Intrusion detection can monitor network transmis-sion in real-time. It is an active security protection technology, which plays a great role in network security. In this paper, a method based on a hybrid autoencoder and decision tree is proposed to conduct intrusion detection. The autoencoder is trained through positive sample data to make its parameters fit the normal flow. The gap between normal samples and abnormal samples is distinguished by calculating the loss value, and the gap is standardized as a newly generated feature. This method can not only avoid the information loss caused by dimensionality reduction of high-dimensional data but also ensure speed and accuracy. The intrusion detection algorithm with hybrid auto encoder and decision tree obtained by the method proposed in this paper is stronger than using decision tree alone and many common machine learning methods. For example, compare the decision tree method 1.74 % better in accuracy, 2.16% better in precision, 1.47% better in recall, 1.81 % better in fscore.
Modeling Optically Thick Molecular Emission Spectra of Comets Using Asymmetric Spherical Coupled Escape Probability

Title of Dissertation: Modeling Optically Thick Molecular Emission Spectra of Comets Using Asymmetric Spherical Coupled Escape Probability Alan Michael Gersch, Doctor of Philosophy, 2014 Dissertation directed by: Professor Michael F. A’Hearn Department of Astronomy Comets are frozen remnants from the formation of the Solar System. As such, their chemical composition is of great significance to understanding the origin of the planets and the distribution of important molecules, including water and other volatiles, throughout the Solar System. Recent observations, in particular those of the Deep Impact and EPOXI Missions, have provided better spectra of a cometary coma than were previously available. These observations include spectra with high spatial resolution very near to the nucleus. The purpose of this research is to better understand the abundances, distributions and creation mechanisms of various volatiles observed in cometary comae, in particular those of comet 9P/Tempel 1, the target of the Deep Impact Mission, and 103P/Hartley 2, the subject of the EPOXI mission. In order to do so, I have built a computer model of the spectrum of the comet’s coma which includes the difficult and often ignored problem of accurately including radiative transfer to account for the potentially optically thick coma (or regions of the coma) near the nucleus. I have adapted Coupled Escape Probability, a new exact method of solving radiative transfer problems, from its original plane-parallel formulation for use in asymmetrical spherical situations. My model is designed specifically for use in modeling optically thick cometary comae, although not limited to such use. By providing for asymmetric geometry in the coma, the model is able to include the morphology of the near nucleus coma, as observed by the Deep Impact spacecraft for Tempel 1 and Hartley 2, and include this in the modeling of radiative transfer. This method enables the accurate modeling of comets’ spectra even in the potentially optically thick regions nearest the nucleus, such as those seen in Deep Impact observations of 9P/Tempel 1 and EPOXI observations of 103P/Hartley 2. This model will facilitate analyzing the actual spectral data from the Deep Impact and EPOXI missions to better determine abundances of key volatile species, including CO, CO2 and H2O, as well as remote sensing data on active comets. Modeling Optically Thick Molecular Emission Spectra of Comets Using Asymmetric Spherical Coupled Escape Probability
Learning Whole-body Effects for Biomechanics Analysis from Partial IMU Sensing

In this paper, we propose a framework of biome-chanics analysis by estimating unknown dynamics from partial sensing information, exploring the minimalism of motion cap-ture sensing. A preliminary experiment reveals that Centroidal Dynamics in a cyclic motion such as running can be extracted as a simple time-series profile. Based on this result, we apply an LSTM network to the estimation of unknown upper-body Centroidal Dynamics from measured lower-body information. Estimated result allows us to compute the inverse dynamics of the human musculoskeletal model. We report an experimental validation for an outdoor running motion using an IMU motion capture suit.