Miracle-dz/UniFAR-FTUs huggingface dataset:
Time Series Phase Unwrapping Based on Graph Theory and Compressed Sensing

Time Series SAR interferometry (InSAR) (TS-InSAR) has been widely applied to monitor the crustal deformation with centimeter- to millimeter-level accuracy. Phase unwrapping (PU) errors have proven to be one of the main sources of bias that hinder achieving such high accuracy. In this article, a new time series PU approach is developed to improve the unwrapping accuracy. The rationale behind the proposed method is to first improve the sparse unwrapping by mitigating the phase gradients in a 2-D network and then correcting the unwrapping errors in time, based on the triplet phase closure. Rather than the commonly used Delaunay network, we employ the all-pairs-shortest-path (APSP) algorithm from graph theory to maximize the temporal coherence of all edges and to approach the phase continuity assumption in the 2-D spatial domain. Next, we formulate the PU error correction in the 1-D temporal domain as compressed sensing (CS) problem, according to the sparsity of the remaining phase ambiguity cycles. We finally estimate phase ambiguity cycles by means of integer linear programming (ILP). The comprehensive comparisons using synthetic and real Sentinel-1 data covering Lost Hills, California, confirm the validity of the proposed 2-D + 1-D unwrapping approach and its superior performance compared to previous methods.
Near Interference-Free Space-Time User Scheduling for mmWave Cellular Network

The highly directional beams applied in millimeter wave (mmWave) cellular networks make it possible to achieve near interference-free (NIF) transmission under judiciously designed space-time user scheduling, where the power of intra-/ inter-cell interference between any two users is below a predefined threshold. In this paper, we investigate two aspects of the NIF space-time user scheduling in a multi-cell mmWave network with multi-RF-chain base stations. Firstly, given that each user has a requirement on the number of space-time resource elements, we study the NIF user scheduling problem to minimize the unfulfilled user requirements, so that the space-time resources can be utilized most efficiently and meanwhile all strong interferences are avoided. A near-optimal scheduling algorithm is proposed with performance close to the lower bound of unfulfilled requirements. Furthermore, we study the joint NIF user scheduling and power allocation problem to minimize the total transmit power under the constraint of rate requirements. Based on our proposed NIF scheduling, an energy-efficient joint scheduling and power allocation scheme is designed with limited channel state information, which outperforms the existing independent set based schemes, and has near-optimal performance as well.
Leveraging Structural Information to Improve Point Line Visual-Inertial Odometry

Leveraging line features to improve the accuracy of the SLAM system has been studied in many works. However, making full use of the characteristics of different line features (parallel, non-parallel) to improve the SLAM system is rarely mentioned. In this paper, we designed a VIO system based on points and straight lines, which divides straight lines into structural straight lines (that is, straight lines parallel to each other) and non-structural straight lines. In addition, in order to optimize the line features effectively, we only used two parameters to minimize the representation of the structural straight line and the non-structural straight line. Furthermore, we designed a straight line matching strategy based on 2D- 2D and 2D-3D matching methods to improve the success rate of straight line matching. We conducted ablation experiments on synthetic data and public datasets, as well as compared our algorithm with state-of-the-art algorithms. The experiments verified the combination of different line features can improve the accuracy of the VIO system, and also demonstrated the effectiveness of our system.
Device pre‐conditioning and steady‐state temperature dependence of CH3NH3PbI3 perovskite solar cells

The recent rise in power conversion efficiencies reported for perovskite solar cells has been a remarkable development in photovoltaics research. It is now pressing that the technology transitions from a research phenomenon to a real‐world deployable device: this will require both robust methods for efficiency measurement, and accurate models for performance variation at different conditions. However, the generally slow response of perovskite solar cells to changes in voltage bias and irradiance, and the susceptibility of these cells to degradation, presents significant challenges. In this paper, we investigate current and voltage stabilisation of planar CH3NH3PbI3 perovskite solar cells and observe remarkably large variations in stabilisation time depending on exposure history. To address this, we demonstrate a dynamic approach that continues device pre‐conditioning until pre‐determined stability criteria are met. This approach is then employed to obtain measurements of short‐circuit current and open‐circuit voltage temperature coefficients under quasi‐steady‐state conditions for perovskite devices and a control monocrystalline silicon cell. The obtained open‐circuit voltage temperature coefficient for the perovskite is −2700 ppm/°C, which interestingly, is similar to typically reported values for crystalline silicon devices. It is shown that the implemented approach can successfully differentiate between transient responses to the onset of illumination and true temperature related changes. We also find new manifestations of the complex transient processes that occur in perovskite devices. These observations highlight the importance of sophisticated characterisation approaches for correct characterisation of the performance of perovskite solar cells. Copyright © 2016 John Wiley & Sons, Ltd.
RBNN: Memory-Efficient Reconfigurable Deep Binary Neural Network With IP Protection for Internet of Things

Currently, a high demand for on-device deep neural network (DNN) model deployment is limited by the large model size, computing-intensive floating-point operations (FLOPS), and intellectual property (IP) infringements (i.e., easy access to model duplication for the avoidance of license payments). One appealing solution to addressing the first two concerns is model quantization, which reduces the model size and uses integer operations commonly supported by microcontrollers (MCUs usually do not support FLOPS). To this end, a 1-bit quantized DNN model or deep binary neural network (BNN) significantly improves the memory efficiency, where each parameter in a BNN model has only 1 bit. However, BNN cannot directly provide IP protection (in particular, the functionality of the model is locked unless there is a license payment). In this article, we propose a reconfigurable BNN (RBNN) to further amplify the memory efficiency for resource-constrained Internet of Things (IoT) devices while naturally protecting the model IP. Generally, RBNN can be reconfigured on demand to achieve any one of <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$M>1$ </tex-math></inline-formula>) distinct tasks with the same parameter set, thus only a single task determines the memory requirements. In other words, the memory utilization is improved by a factor of <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula>. Our extensive experiments corroborate that up to seven commonly used tasks (<inline-formula> <tex-math notation="LaTeX">$M=7$ </tex-math></inline-formula>, six of these tasks are image related and the last one is audio) can co-exist (the value of <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> can be larger). These tasks with a varying number of classes have no or negligible accuracy drop-off (i.e., within 1%) on three binarized popular DNN architectures, including VGG, ResNet, and ReActNet. The tasks span across different domains, e.g., computer vision and audio domains validated herein, with the prerequisite that the model architecture can serve those cross-domain tasks. To fulfill the IP protection of an RBNN model, the reconfiguration can be controlled by both a user key and a device-unique root key generated by the intrinsic hardware fingerprint (e.g., SRAM memory power-up pattern). By doing so, an RBNN model can only be used per paid user per authorized device, thus benefiting both the user and the model provider. The source code is released at <uri>https://github.com/LearningMaker/RBNN</uri>.
A Robust Fully Arctangent Adaptive Interpolated Volterra Filtering Algorithm Against Impulsive Noise

The applicability of adaptive Volterra filter is greatly limited by the high computational complexity and notable performance degradation when encountering the impulsive noise. Although the existing interpolated adaptive Volterra filter has reduced the computational complexity, its performance is still significantly degraded while the impulsive noise occurs. This brief proposes a robust fully arctangent adaptive fully interpolated Volterra (RFAAFIV) filtering algorithm by using the novel arctangent function. The proposed algorithm can effectively improve the robustness against impulsive noise while requiring the low computational cost. Moreover, the computational complexity and the steady-state mean square performance of the proposed RFAAFIV algorithm are analyzed. Simulations on nonlinear system identification further evaluate the effectiveness of the RFAAFIV algorithm against impulsive noise and verify the theoretical analysis.
A Novel Hermetic Sealing Method for the Metal Package Based on Electric-Current-Assisted Sintering of Silver Paste

Based on electric-current-assisted sintering (ECAS) of silver paste, we propose a novel hermetic sealing method for the metal package. The monitoring of the temperature field during the bonding process shows that hermetic packaging based on ECAS of silver paste is a local heating method which has little impact on the component. During the process of ECAS, the heating rate of sintered joint dropped a lot with the removal of organics, and the reliable sintered joint is gradually formed after the removal of organics. Increasing the current gets higher peak temperature of the sintered joint, which is beneficial to form a denser and robust joint. The hermeticity of the sealed specimen and the shear strength of the sintered joint are improved with the increase of peak temperature. When the peak-temperature reaches above 400 °C, the joint will be dense enough to ensure the leakage rate of specimen below the reject limit.
Multiview Variational Deep Learning With Application to Practical Indoor Localization

Radio channel state information (CSI) measured with many receivers is a good resource for localizing a transmitter device with machine learning with a discriminative model. However, CSI localization is nontrivial when the radio map is complicated, such as in building corridors. This article introduces a view-selective deep learning (VSDL) system for indoor localization using CSI of WiFi. The multiview training with CSI obtained from multiple groups of access points (APs) generates latent features on a supervised variational deep network. This information is then applied to an additional network for dominant view classification to minimize the regression loss of localization. As noninformative latent features from multiple views are rejected, we can achieve a localization accuracy of 1.28 m, which outperforms by 30% the best known accuracy in practical applications in a complex building environment. To the best of our knowledge, this is the first approach to apply variational inference and to construct a practical system for radio localization. Furthermore, our work investigates a methodology for supervised learning with multiview data where informative and noninformative views coexist.
Switchable Dual-Band Dual-Sense Circularly Polarized Patch Antenna Implemented by Dual-Band Phase Shifter of ±90°

A switchable dual-band dual-sense (DBDS) circularly polarized (CP) patch antenna is reported in this communication, which is implemented by dual-band phase shifter of ±90°. The proposed antenna is comprised by two annular-ring radiators, four H-shape slots, and two phase-controlled feeding networks. Four H-shape slots offer desired isolation between two paths of each feeding network, and also improve the impedance matching performance in two bands. Each dual-feed network is shared by two operating radiators, supporting simultaneous dual-band functionality. Meanwhile, the dual-band phase shifter is enabled by the folded shorted stubs that provide broadband dual-CP radiation performance. Thus, for each feeding network, −90° and 90° phase differences between two orthogonal signals can be achieved in each band, respectively, so as to meet the requirements of opposite polarizations. Two pairs of single pole single throws (SPSTs) are added in the feeding networks to acquire switchable CP characteristic, meanwhile improving the isolation between two feeding networks. As a proof-of-concept, a switchable DBDS CP antenna prototype operating at 2.45 GHz/5.8 GHz industrial scientific medical (ISM) bands is prototyped and tested with a low profile of $0.036~\lambda _{0}$ . By controlling two pairs of SPSTs, the proposed patch antenna can switch from the right-handed circular polarization (RHCP)/left-handed circular polarization (LHCP) state to the LHCP/RHCP state.
Definition of physical-dynamic parameters in circular kick in Muay Thai through latest generation inertial sensors with a critical review of the literature

In recent years, the study of movement analysis with the application of dedicated advanced technologies, such as inertial technologies, has also become essential in scientific research of sports disciplines such as martial arts and combat sports, to better define the real dynamics of technical components in order to adapt the training design. A great number of studies in the literature have focused on the kinematics of circular kick in combat sport and martial arts but a relatively small number have focused on the kinetic and kinematic analysis of Muay Thai and especially its circular kick technique. This study aims to highlight instrumentally the quality of circular kick gesture and through which segmental motor dynamics it achieves its greatest effectiveness, evaluating the accelerations and execution times of the different body segments involved in technical execution itself obviously in a performative sense. It is important to note that the inertial data shows that both knee and ankle have an increasing linear velocity greater than the hip in the preparation and execution phase, while for the angular velocity in the execution phase it is the hip that has higher acceleration than the knee and ankle. These results, therefore, highlight the basic importance of intra-articular and intramuscular coordination processes that allow or not linearity and fluidity of movements that are the basis to obtain maximum intersegmental performance in order to deliver maximum acceleration in the apical point of the shot and thus have the maximum effectiveness of hit and therefore the optimal performance.
G-Tran: Making Distributed Graph Transactions Fast

Graph transaction processing raises many unique challenges such as random data access due to the irregularity of graph structures, low throughput and high abort rate due to the relatively large read/write sets in graph transactions. To address these challenges, we present G-Tran -- an RDMA-enabled distributed in-memory graph database with serializable and snapshot isolation support. First, we propose a graph-native data store to achieve good data locality and fast data access for transactional updates and queries. Second, G-Tran adopts a fully decentralized architecture that leverages RDMA to process distributed transactions with the MPP model, which can achieve high performance by utilizing all computing resources. In addition, we propose a new MV-OCC implementation with two optimizations to address the issue of large read/write sets in graph transactions. Extensive experiments show that G-Tran achieves competitive performance compared with other popular graph databases on benchmark workloads.
Unsupervised Human Pose Estimation through Transforming Shape Templates

Human pose estimation is a major computer vision problem with applications ranging from augmented reality and video capture to surveillance and movement tracking. In the medical context, the latter may be an important biomarker for neurological impairments in infants. Whilst many methods exist, their application has been limited by the need for well annotated large datasets and the inability to gen-eralize to humans of different shapes and body compositions, e.g. children and infants. In this paper we present a novel method for learning pose estimators for human adults and infants in an unsupervised fashion. We approach this as a learnable template matching problem facilitated by deep feature extractors. Human-interpretable landmarks are estimated by transforming a template consisting of predefined body parts that are characterized by 2D Gaussian distributions. Enforcing a connectivity prior guides our model to meaningful human shape representations. We demonstrate the effectiveness of our approach on two different datasets including adults and infants. Project page: infantmotion.github. io
A Bregman Learning Framework for Sparse Neural Networks

We propose a learning framework based on stochastic Bregman iterations, also known as mirror descent, to train sparse neural networks with an inverse scale space approach. We derive a baseline algorithm called LinBreg, an accelerated version using momentum, and AdaBreg, which is a Bregmanized generalization of the Adam algorithm. In contrast to established methods for sparse training the proposed family of algorithms constitutes a regrowth strategy for neural networks that is solely optimization-based without additional heuristics. Our Bregman learning framework starts the training with very few initial parameters, successively adding only significant ones to obtain a sparse and expressive network. The proposed approach is extremely easy and efficient, yet supported by the rich mathematical theory of inverse scale space methods. We derive a statistically profound sparse parameter initialization strategy and provide a rigorous stochastic convergence analysis of the loss decay and additional convergence proofs in the convex regime. Using only 3.4% of the parameters of ResNet-18 we achieve 90.2% test accuracy on CIFAR-10, compared to 93.6% using the dense network. Our algorithm also unveils an autoencoder architecture for a denoising task. The proposed framework also has a huge potential for integrating sparse backpropagation and resource-friendly training.
Scalable, Decentralized Multi-Agent Reinforcement Learning Methods Inspired by Stigmergy and Ant Colonies

Bolstering multi-agent learning algorithms to tackle complex coordination and control tasks has been a long-standing challenge of on-going research. Numerous methods have been proposed to help reduce the effects of non-stationarity and unscalability. In this work, we investigate a novel approach to decentralized multi-agent learning and planning that attempts to address these two challenges. In particular, this method is inspired by the cohesion, coordination, and behavior of ant colonies. As a result, these algorithms are designed to be naturally scalable to systems with numerous agents. While no optimality is guaranteed, the method is intended to work well in practice and scale better in efficacy with the number of agents present than others. The approach combines single-agent RL and an ant-colony-inspired decentralized, stigmergic algorithm for multi-agent path planning and environment modification. Specifically, we apply this algorithm in a setting where agents must navigate to a goal location, learning to push rectangular boxes into holes to yield new traversable pathways. It is shown that while the approach yields promising success in this particular environment, it may not be as easily generalized to others. The algorithm designed is notably scalable to numerous agents but is limited in its performance due to its relatively simplistic, rule-based approach. Furthermore, the composability of RL-trained policies is called into question, where, while policies are successful in their training environments, applying trained policies to a larger-scale, multi-agent framework results in unpredictable behavior.
Microfluidics for core–shell drug carrier particles – a review

Core–shell drug-carrier particles are known for their unique features. Due to the combination of superior properties not exhibited by the individual components, core–shell particles have gained a lot of interest. The structures could integrate core and shell characteristics and properties. These particles were designed for controlled drug release in the desired location. Therefore, the side effects would be minimized. So, these particles' advantages have led to the introduction of new methods and ideas for their fabrication. In the past few years, the generation of drug carrier core–shell particles in microfluidic chips has attracted much attention. This method makes it possible to produce particles at nanometer and micrometer levels of the same shape and size; it usually costs less than other methods. The other advantages of using microfluidic techniques compared to conventional bulk methods are integration capability, reproducibility, and higher efficiency. These advantages have created a positive outlook on this approach. This review gives an overview of the various fluidic concepts that are used to generate microparticles or nanoparticles. Also, an overview of traditional and more recent microfluidic devices and their design and structure for the generation of core–shell particles is given. The unique benefits of the microfluidic technique for core–shell drug carrier particle generation are demonstrated.
Development and validation of high performance liquid chromatographic method for the determination of esomeprazole in tablets

A simple, selective and accurate high performance liquid chromatographic (HPLC) method was developed and validated for the analysis of esomeprazole magnesium trihydrate (ES) in tablets. Chromatographic separation was achieved isocratically on a C18 column utilizing a mobile phase of acetonitrile/phosphate buffer (60:40, v/v, pH 7) at a flow rate of 1.0 mL/min with UV detection at 205 nm. Lansoprazole was used as an internal standard (IS). The calibration curve of ES was linear in the range of 100~1000 ng/mL (r = 0.9992, n = 4). The RSD values for intra- and inter-day precision were 0.66~0.86% and 0.84~1.11%, respectively. The proposed method was successfully applied to the determination of ES in tablets. The mean recovery for ES from the tablets ranged between 97.82~98.22%. ES was subjected to neutral, acid and alkali hydrolysis as well as oxidation, dry heat treatment and photodegradation. Being simple, accurate and selective, the method can be used for routine quality control analysis.