Miracle-dz/UniFAR-FTUs huggingface dataset:
Reset-Free Memoryless Delta–Sigma Analog-to-Digital Conversion

This paper presents new techniques to obtain sample-by-sample analog-to-digital conversion using a delta–sigma modulator (DSM) without resetting the modulator or the decimation filter. It is shown that memoryless A/D conversion without reset can be realized using a discrete-time DSM with a signal transfer function (STF) of unity and a Nyquist $M$ -band decimation filter running at the oversampled rate. It is also shown that a delta–sigma ADC preceded by a sample-and-hold at the Nyquist rate is a linear, time-invariant system at the Nyquist rate. This relaxes the constraint on the STF and allows using a multi-rate decimation filter and an equalizer at the Nyquist rate to significantly lower the power in the digital filters. Crosstalk suppression, which is limited by analog imperfections when a fixed-coefficient equalizer is used, is shown to be substantially improved using an adaptive equalizer at the Nyquist rate. A 180-nm prototype operating at 32 MHz and an OSR of 32 demonstrates two-channel operation with crosstalk below 89 dB. It consumes 18.2 mA from a 1.8-V supply, occupies 3.86 mm2 and has DR/SNR/SNDR of 84.2/82.5/80.1 dB.
Kernel Density Estimation for Dynamical Systems

We study the density estimation problem with observations generated by certain dynamical systems that admit a unique underlying invariant Lebesgue density. Observations drawn from dynamical systems are not independent and moreover, usual mixing concepts may not be appropriate for measuring the dependence among these observations. By employing the $\mathcal{C}$-mixing concept to measure the dependence, we conduct statistical analysis on the consistency and convergence of the kernel density estimator. Our main results are as follows: First, we show that with properly chosen bandwidth, the kernel density estimator is universally consistent under $L_1$-norm; Second, we establish convergence rates for the estimator with respect to several classes of dynamical systems under $L_1$-norm. In the analysis, the density function $f$ is only assumed to be Holder continuous which is a weak assumption in the literature of nonparametric density estimation and also more realistic in the dynamical system context. Last but not least, we prove that the same convergence rates of the estimator under $L_\infty$-norm and $L_1$-norm can be achieved when the density function is Holder continuous, compactly supported and bounded. The bandwidth selection problem of the kernel density estimator for dynamical system is also discussed in our study via numerical simulations.
Probability of Task Completion and Energy Consumption in Cooperative Pervasive Mobile Computing

It is challenging for multiple smartphones to complete a given task in large-scale pervasive sensing systems cooperatively. Sensing paradigms such as opportunistic sensing, participatory sensing, and hybrid sensing have been used for smartphones to work together seamlessly under different contexts. However, these existing paradigms do not incorporate the energy problem and sharing sensory resources of applications. In this paper, we revisit sensing paradigms regarding the probability of task completion and energy consumption for smartphones to cooperatively complete a sensing task. In addition, we propose a symbiotic sensing paradigm that can significantly save smartphone batteries while maintaining equivalent performance to existing paradigms, provided that the smartphones allow applications to share sensing resources. We also quantitatively evaluate our probabilistic models with a realistic case study. This work is a useful aid to designing and evaluating large-scale smartphone-based sensing systems before deployment, which saves money and effort.
Human and Artificial Intelligence Driven Incentive-Operation Model and Algorithms for a Multi-Purpose Integrated Crowdsensing-Crowdsourcing Scalable System

The future sensing systems seem to need more performant crowdsensing, using highest technologies as artificial intelligence, but being also more complex by volunteer participation and progressively changing from crowdsensing to crowdsourcing. Our work main idea is to use human/artificial intelligence in order to provide highest incentives arguments and commitments for participants and users, transforming data into information and eventually in knowledge. The human/artificial intelligence support is used first to find the most desired/used tasks/targets/questions/issues, then to control the crowdsensing/crowdsourcing operation with learned artificial intelligence rules, based on two algorithms, first for implementing an optimal efficiency tasks covering strategy as reference and second for attracting participants to enlarge/improve accuracy of service by extending crowdsensing-crowdsourcing with correlation incentive/operation added features.
Visual Designs for Binned Aggregation of Multi-Class Scatterplots

Point sets in 2D with multiple classes are a common type of data. A canonical visualization design for them are scatterplots, which do not scale to large collections of points. For these larger data sets, binned aggregation (or binning) is often used to summarize the data, with many possible design alternatives for creating effective visual representations of these summaries. There are a wide range of designs to show summaries of 2D multi-class point data, each capable of supporting different analysis tasks. In this paper, we explore the space of visual designs for such data, and provide design guidelines for different analysis scenarios. To support these guidelines, we compile a set of abstract tasks and ground them in concrete examples using multiple sample datasets. We then assess designs, and survey a range of design decisions, considering their appropriateness to the tasks. In addition, we provide a web-based implementation to experiment with design choices, supporting the validation of designs based on task needs.
SVO-PL: Stereo Visual Odometry with Fusion of Points and Line Segments

Motion estimation of prevailing (point feature based) visual odometry performs poorly in low-textured scenes due to difficulties to find sufficient amount of reliable point features in these scenes. In view of that, this paper develops a stereo visual odometry that combines points with line segments (termed SVO-PL). The proposed Line Segment Detector algorithm joins the disconnected line segments caused by occlusion and other factors. Feature tracking (to find corresponding features in different frames) also gets improved in this paper through combining sparse optical flow algorithm with feature descriptor. Camera pose is retrieved through a nonlinear optimization method by minimizing re-projection errors of points and line segments. The proposed method has been tested in real world datasets with comparison to the state of the art method ORB-SLAM2 to verify its accuracy and robustness.
From NEO to LEO optical observations and back: Sensors features and observing strategies

Since 1970s and during the 1980s, optical telescopes were aimed to a not traditional distant observational targets: Searching and tracking satellites (SST) and close asteroids (NEO). The Cold War and the Shoemaker-Levi 9 Jupiter impacts biased among other reasons towards these new concerns related to the space vicinity of the Earth. At these early stages, most telescopes, which were initially designed for astrophysics, were not appropriate for these new tasks. The observing strategies, the detecting pipelines and the required immediacy of the data were limited and they needed to evolve together with optical correctors and the new digital detectors. Some experience came from SST to the NEO field, other took the inverse way. However, once again, the sensors requirements and the observing strategies on both populations turned not exactly the same, mainly due to the huge difference on the angular speeds of those close to Earth objects, 100 times faster for a GEO satellite compared to a typical NEO (10 arcsec/min). Even within the SST domain, a LEO optical sensor shall detect and measure 100 times faster objects than those in GEO regime. Precise time tag down to millisecond of UTC, recurrent sensor calibrations, trailing, crowding and many other aspects also from the hardware side, become critical and they can be applicable for improving the quality of the NEO observations. Mounts and optical designs, detector architectures, FoV, read-out, scale pixel resolutions and developed detection pipelines according to defined observing strategies require of very optimized solutions for every angular speed range, which, together with the limiting magnitude turn on the driving parameters of the sensors, instead of the nature or the orbital populations and sizes of the objects to be detected or tracked. At DeSS (Deimos Sky Survey), four sensors are mainly devoted to SST activities from GEO to LEO and a large experience on NEO observations has also been gathered. In this paper, a comparison of some of the required sensors features, constraints, differences, observing strategies from the NEO until LEO, from both, the software and hardware sides are discussed, after the practical nightly experience at DeSS.
A Case Study on Fintech in Japan Based on Keystone Strategy

Japanese Digital Consumer Electronics Manufacturer declined and collapsed. It is said that the cause is the Modularization of Technology in Architecture Theory. There is a Japanese industry that is similarly undergoing Modularization, which is the Financial Industry. This paper clarified that FinTech modularizes financial technology. For that purpose, we analyzed the function and structure of FinTech. Also, although it is possible that FinTech could change the existing financial industry structure in Japan, the objective was to propose from the case consideration that the management strategy in that environment is Keystone Strategy. As a conclusion, FinTech was found to be modularized because it has increased its added value after fragmenting financial services technology and breaking it apart. In addition, even if it continues to stay in the integral part, its comprehensive financial services part is inferior to FinTech, so it was expected that it cannot stay in the integral.
A General Approach to Achieving Stability and Safe Behavior in Distributed Robotic Architectures

This paper proposes a unified energy-based modeling and energy-aware control paradigm for robotic systems. The paradigm is inspired by the layered and distributed control system of organisms, and uses the fundamental notion of energy in a system and the energy exchange between systems during interaction. A universal framework that models actuated and interacting robotic systems is proposed, which is used as the basis for energy-based and energy-limited control. The proposed controllers act on certain energy budgets to accomplish a desired task, and decrease performance if a budget has been depleted. These budgets ensure that a maximum amount of energy can be used, to ensure passivity and stability of the system. Experiments show the validity of the approach.
Multicontact Postures Computation on Manifolds

We propose a framework to generate static robot configurations satisfying a set of physical and geometrical constraints. This is done by formulating nonlinear constrained optimization problems over non-Euclidean manifolds and solving them. To do so, we present a new sequential quadratic programming solver working natively on general manifolds and propose an interface to easily formulate the problems, with the tedious and error-prone work automated for the user. We also introduce several new types of constraints for having more complex contacts or working on forces/torques. Our approach allows an elegant mathematical description of the constraints and we exemplify it through formulation and computation examples in complex scenarios with humanoid robots.
Personalized Channel Recommendation Deep Learning From a Switch Sequence

Internet protocol TV (IPTV) services could enhance personalized viewing experience in a more interactive way than traditional broadcast TV systems, but it is still difficult for subscribers to quickly find interesting channels to watch from a huge selection. This paper focuses on a framework for personalized live channel recommending via deep learning from a historical switching sequence with a long short-term memory (LSTM) neural network. Using real-world IPTV watching logs, we first obtained insights into user behaviors when watching live channels, and then proposed a learning scheme on how to dynamically generate a recommended channel list for each user with an independent LSTM net trained using the channel watching history during a slide window. For designing a good data architecture and representation scheme for a dynamically learning framework, we then studied the performance of the proposed recommendation method by varying the width of the slide window for training, the length of input sequence for prediction, and the mode to process input and label space. We finally developed a separate learning method to fairly recommend for popular (hot) or unpopular (cold) channels, respectively, based on channel popularity in the training set with an extra price of a possible hit lag after recommendation, in order to alleviate the Matthew effect arising from the conventional recommendation based on historical information. The experimental results show LSTM succeeds in learning from a historical channel switching sequence, outperforms several baseline recommendation methods, especially for hot channels, and the classified recommendation by separate learning brings an overall performance gain.
Diameter Scaling in III-V Gate-All-Around Transistor for Different Cross-Sections

We explore the impact of channel area scaling in InGaAs gate-all-around transistor for circular (CNW), square (SNW) and triangular (TNW) cross-sections using coupled selfconsistent Schrodinger-Poisson solver. We find that, among all three cross-sections, TNW shows the strongest confinement effect for all the considered dimensions. The confinement effects are significant at 10 nm for TNW and at 5 nm for SNW and CNW. We further analyzed the performance figure of merits (threshold voltage, current ON/OFF ratio, sub-threshold slope, ON current and intrinsic delay with cross-section area scaling) for all three cross-sections. Based on the scaling trend we find that CNW with 10 nm diameter is the most optimum among all three crosssections.
Distributed Finite-time Error Constrained Tracking Control for Multiple Euler-Lagrange Systems

In this paper, distributed finite-time error constrained tracking control for multiple Euler-Lagrange systems is investigated under directed topology. It is considered that the information of the leader is available to only a subset of the followers. A tan-type barrier Lyapunov function (BLF) is utilized to demonstrate that the tracking errors would not exceed the assigned bound. Using backstepping design method, a novel distributed finite-time error constrained tracking protocol is proposed. It is proved that the tracking error of each follower is ultimately bounded in finite time by graph theory, Lyapunov theory, and finite-time stability theory. Numerical simulations demonstrate the effectiveness of the control law.
Fault Estimation for Continuous Time Linear Systems with Time-varying Delay Based on Intermediate Estimator over Fading Channels

The problem of the fault estimation for continuous time linear systems with time-varying delay based on the intermediate estimator method is considered over fading channels. The fault estimation filter parameters are designed using the Lyapunov function method, a reciprocal convex combination approach, Wirtinger integral inequality and LMI formulation for the analysis and synthesis of the resultant systems. The desired filter parameter gains are obtained such that the resulting filter error system is exponentially stable. Then, the congruence transform method is used to remove the design constraints to obtain the feasibility result. A numerical example illustrates the effectiveness of the proposed method.
Tunable Input-Quasi-Reflectionless Multiplexers

A class of frequency-tunable planar multiplexer with quasi-reflectionless behavior at its input port for modern RF wireless-communications transceivers is presented. To attain the input quasi-absorptive characteristic in a Kth-order N-channel realization, an additional Kth-order N-band multi-stopband channel terminated in a reference-impedance resistor is added. Each Kth-order stopband in this N-band bandstop channel exhibits a nearly-complementary frequency profile with regard to the one of a counterpart bandpass channel. Thus, the input-signal energy not transmitted by the bandpass channels in their stopband regions is dissipated in the resistor of the nearly-complementary multi-band bandstop branch. The theoretical operational principles and coupling-matrix-based design equations for the first-order input-quasi-reflectionless multiplexer stage are detailed. Furthermore, for experimental-validation purposes, a first-order microstrip duplexer prototype that can be tuned in the 0.85–1.15-GHz range is manufactured and characterized.
Influencing Factors on Social Acceptance of Autonomous Vehicles and Policy Implications

Autonomous vehicles are of profound worldwide interest and automotive and IT industries related to autonomous vehicles have focused on them more intensely to commercialize. Experts predict that autonomous vehicles will bring about widespread changes in our lives, society, economy and culture. However, South Korea is insufficiently prepared for these changes. Another problem is that policies pertaining to autonomous vehicles in South Korea seem to be biased towards suppliers. Therefore, it is necessary to analyze Korean consumers' acceptance of autonomous vehicles. This study aims to examine what factors of autonomous vehicles affect consumers and whether they recognize the differences in levels of autonomous driving technology. The data were collected from 459 South Koreans over 20 years of age. According to the results of multiple regression analysis, general acceptance of autonomous vehicles was affected by usefulness, reliability and legality. PAV (Partial Autonomous Vehicle) acceptance was influenced by safety, anxiety, ease of driving, driver convenience, driving education and driver's carelessness. In contrast, FAV (Full Autonomous Vehicle) acceptance was influenced by safety, user convenience, and extra expenses. These results show that factors directly related to drivers influence PAV acceptance while external environmental factors affect FAV acceptance. This study has some implications for governments and the automobile industry pertaining to autonomous vehicles. Usefulness, reliability, and legality should be considered in order to increase acceptance of autonomous vehicles. Secondly, safety and driver/user convenience are important factors for acceptance of both of PAVs and FAVs. Finally, due to conflicting aspects, the strategies should be different between PAVs and FAVs.
Dynamic Feedback Linearizer of RF CMOS Power Amplifier

A dynamic feedback linearizer for an RF CMOS power amplifier (PA) is presented, which consists of a negative feedback network and a dynamic feedback control circuit to inject a reshaped envelope signal to the network. The linearizer compensates for the gain compression of the RF CMOS PA in high-power region by reducing the feedback dynamically, and it also reduces the amplitude-to-phase modulation (AM-PM) distortions. The PA including the linearizer was fabricated using a 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> RF CMOS process, which has an output transmission-line transformer on a printed circuit board. It delivers an output power of 28.1 dBm at 1.7 GHz with power added efficiency of 40.9%, and adjacent channel leakage ratio (ACLR<inline-formula> <tex-math notation="LaTeX">$_{\mathrm {E-UTRA}}$ </tex-math></inline-formula>) of under −30 dBc for a 10-MHz 16-QAM long-term evolution signal without digital predistortions.
Empty Substrate Integrated Waveguide Slot Antenna Array for 5G Applications

This paper introduces a linear slot antenna array based on an empty substrate integrated waveguide at 28 GHz. The proposed antenna offers the advantages of reduced weight and fabrication cost as well as facilitates relatively easier integration with planar electronic components as compared to conventional slotted waveguide antenna arrays. A high gain and efficiency and low losses are major considerations which can be achieved by the proposed design when compared to the substrate integrated waveguide slot antenna array. The proposed antenna geometry suggests an operating bandwidth of 10.4% (i.e. 26.5–29.4 GHz) suitable to fulfil 5G demands with high gain performance of 11.6 dBi at 28GHz.
V-D D3QN: the Variant of Double Deep Q-Learning Network with Dueling Architecture

The fashionable DQN algorithm suffers from substantial overestimations of action-state value in reinforcement learning problem, such as games in the Atari 2600 domain and path planning domain. To reduce the overestimations of action values during learning, we present a novel combination of double Q-learning and dueling DQN algorithm, and design an algorithm called Variant of Double dueling DQN (V-D D3QN). We focus on the idea behind V-D D3QN algorithm and propose the feasible idea of using two dueling DQN networks to reduce the overestimations of action values during training, and the specific approach is to randomly select one dueling DQN network at each time step to update its parameters, by exploiting the remaining dueling DQN network to determine the update targets. And then we do our experiments in the customized virtual environment-gridmap. Our experiments demonstrate that our proposed algorithm not only reduces the overestimations more efficiently than Double DQN(DDQN) algorithm, but also leads to much better performance on route planning domain with great generalization ability of the new and rapidly changing environments.
A Study of Determination of Energy Performance Indicator for Applying Energy Management System in Industrial Sector

Establishing an energy management system (EnMS) is a useful approach for improving energy efficiency. The International Energy Agency(IEA) recommended that the EnMS is a key policy for achieving energy performance, particularly in the industrial sector, which consumes large amounts of fossil fuels. Previous studies suggested numerous energy performance indicators (EnPIs), which are based mainly on two models: 1) energy intensity (i.e., kWh per ton) and 2) the statistical model (i.e., linear and nonlinear regression). The selection of appropriate EnPIs is context specific, with various factors involving organizational, industry, and energy source related characteristics affecting it. Although having the right EnPIs is essential for achieving effective energy management, few researchers have attempted to provide guidelines for their application. Therefore, the aim of this study was to present a framework for investigating alternative EnPIs and to choose the best one based on the application context. The Delphi method and the Analytic Hierarch Process were adopted to integrate relevant expert knowledge in a systematic manner. The research findings indicated that six factors data collection, the consideration of relative variables, the choice of analytic methodology, reliability, an understanding of the analysis results, and practice and training—are regarded as significant selection, criteria for EnPIs, and the two approaches of linear regression and energy intensity were preferred regardless of the context.