Miracle-dz/UniFAR-FTUs huggingface dataset:
Hopping Robot Using Variable Structured Elastic Actuators

In this paper, we focus on the validity of using a Variable Structured Elastic Actuator (VSEA) for a hopping robot. A VSEA is proposed by Miura and Katsura. The VSEA is composed of an electromagnetic clutch, springs, a geared motor, and a load. The clutch connects the output of the geared motor and the load. The spring is installed between the load and motor housing. By changing the clutch current, the clutch system can mechanically separate the geared motor and the output. A VSEA has a structure similar to a Parallel Elastic Actuator (PEA) in which a spring and gear motor are connected in parallel when a clutch is connected. By disconnecting the clutch, gear friction can be ignored and high power and high-speed motion on the output side are possible. High power and high-speed operation are important factors in hopping motion, and the spring energy was efficiently extracted by using the clutch, and continuous hopping was realized by adjusting the timing of enabling/disabling the clutch. We performed three experiments. The first experiment showed that a VSEA enables a robot to hop higher than a robot including a PEA. The second experiment examined energy efficiency and showed the effectiveness of the robot including the VSEA. The third experiment showed that continuous hopping was possible using the proposed hopping control. From the above, the proposed VSEA robot is effective for efficient hopping motion.
A Framework for Risk Assessment in Augmented Reality-Equipped Socio-Technical Systems

New technologies, such as augmented reality (AR) are used to enhance human capabilities and extend human functioning; nevertheless they may cause distraction and incorrect human functioning. Systems including socio entities (such as human) and technical entities (such as augmented reality) are called socio-technical systems. In order to do risk assessment in such systems, considering new dependability threats caused by augmented reality is essential, for example failure of an extended human function is a new type of dependability threat introduced to the system because of new technologies. In particular, it is required to identify these new dependability threats and extend modeling and analyzing techniques to be able to uncover their potential impacts. This research aims at providing a framework for risk assessment in AR-equipped socio-technical systems by identifying AR-extended human failures and AR-caused faults leading to human failures. Our work also extends modeling elements in an existing metamodel for modeling socio-technical systems, to enable AR-relevant dependability threats modeling. This extended metamodel is expected to be used for extending analysis techniques to analyze AR-equipped socio-technical systems.
Magnetic Inductor Arrays for Intel® Fully Integrated Voltage Regulator (FIVR) on 10th generation Intel® Core™ SoCs

Circuit area scaling driven by Moore’s law coupled with the push to thinner packages for mobile microprocessors has reduced the volume available for Air Core Inductors (ACI). This translates to a reduction in the conversion efficiency of Fully Integrated Voltage Regulator (FIVR). Magnetic Inductor Array (MIA) modules are used for the first time on the 10th generation Intel Core™ microprocessor in mobile segments to improve FIVR efficiency. In addition to improving the peak efficiency, it is also possible to realize large light load efficiency gains and minimize voltage ripple through the use of magnetic inductors. This paper covers the magnetic inductor array (MIA) design considerations for improving performance as well as meeting high volume manufacturability and reliability requirements.
Adaptive Sliding Mode Control Design for Nonlinear Unmanned Surface Vessel With Fuzzy Logic System and Disturbance-Observer

As a kind of marine electrical and mechanical equipment, Unmanned surface vessel(USV) has been widely used in various fields with its features of safety and workability. However, due to the existence of nonlinearities, modeling uncertainties and external disturbance, the good tracking performance of USV is difficult to be achieved. Nowadays, the most existing control design has been proposed based on the linearation of USV’s dynamic model, which is only suitable for the specific trajectory at the equilibrium point. Moreover, the overall effect of nonlinearities, modeling uncertainties and external disturbance are not been well considered. Therefore, an adaptive sliding mode control design for nonlinear USV with fuzzy logic system and disturbanceObserver is designed in this paper, where a disturbance-observer is designed to estimate the external disturbance caused by wind, wave and current, a fuzzy logic system is designed to estimate the modeling uncertainties cased by modeling error and parameter variations. Finally, the stability and effectiveness of proposed control design is guaranteed with the lyaponov stability theory and comparative simulation.
Irregularity Analysis of Sympathetic and Parasympathetic Activity Indices from HRV: A Pilot Study on Postural Changes

Complexity analysis of heartbeat dynamics has been widely proven effective for the characterization of cardiac autonomic control in health and disease. Due to methodological limitations, underlying physiological correlates of heartbeat complexity are not well defined yet. In this preliminary study we perform a Sample Entropy (SampEn) analysis on time series of Sympathetic and Parasympathetic Activity Indices (SAI and PAI), which allow a reliable measurement of autonomic peripheral outflow from HRV series. Irregularity of SAI and PAI time series was investigated during postural changes in 10 healthy subjects. Results show a significant SampEn increase after active stand-up with respect to resting state associated with SAI, PAI, and their ratio (SAI/PAI), suggesting that a decrease in heartbeat complexity may be sustained by increased complexity in underpinning sympathetic and vagal dynamics.
Topology and Geometry optimization for Design of a 3D Printed Compliant Constant-Force Mechanism

A compliant constant-force mechanism is a soft and passive force regulation mechanism that can provide a nearly constant output force over a range of input displacements. This study presents a topology and geometry optimization procedure to design a fully compliant constant-force mechanism. A topology optimization method is used to synthesize the optimal layout of a compliant constant-force mechanism from an initial design domain with pre-specified boundary conditions, and a geometry optimization method is further used to identify detailed geometric parameters for the design. The optimized compliant constant-force mechanism is prototyped by 3D printing using thermoplastic elastomer. Experimental results show that the proposed design can generate a nearly constant output force at output port within the desired input displacement range. The presented design is expected to be used for various applications in the emerging fields such as soft robots and flexible mechatronics.
Array Antenna System for Wireless Power Transfer (WPT) System at 6.78 MHz

This paper proposes an array antenna system for wireless power transfer (WPT) at 6.78GHz. The development of new techniques to increase power transmission and covered area is proposed. The power transmitter with a power output of 22-60 W is designed. Using the principle of magnetic resonance technique, the transmitting array antenna consists of 4 antennas and arranged in the same plane. The in-phase signal feeding technique is selected. Therefore, the electric field spreads around the array transmitting antenna. Measurement results found that the maximum received power of 9.4W. The average value is 3.5W which is sufficiently used to charge the batteries. Charging time depends on the position and received power.
Analysis and Control of a Body-Attached Spring-Mass Runner Based on Central Pivot Point Approach

The Spring-Loaded Inverted Pendulum (SLIP) template and its extensions have long been used as benchmark models for analyzing the dynamics of legged systems in biology and robotics. The fundamental SLIP model is composed of single point mass attached to the ground (during stance phase) via an ideal lossless spring. Many researchers introduced various extensions to this fundamental model, such as damping & torque actuation, to handle critical physical phenomena that are unavoidable in real systems. Another crucial missing concept in SLIP template is the effect of the upper body in humans and humanoid robotic systems on the closed-loop system dynamics. Even though the SLIP template can effectively capture COM behavior, it cannot provide a framework for describing fullbody stabilization and control. In this paper, we present a new control policy called the Central Pivot Point (CPP) for the body attached spring-mass runners. In the stance phase, CPP directs ground reaction forces through the center of mass and cancels the torque created by these forces on the body. In this way, the CPP model makes it possible to develop different controllers for both the body’s rotational and euclidean dynamics. Firstly, we analyze the characteristics and stability of the periodic solutions of the CPP model. Then, we develop a PD controller for pitch dynamics and an LQR (Linear Quadratic Regulator) for gait level apex to apex discrete dynamics to stabilize the system’s periodic solutions. We compute the basin of attraction of the proposed control scheme and show examples of how the model behaves under disturbances. The results show that the purposed model and associated control policy could be beneficial in the design and control of humanoid robotic systems.
A Fully Tunable Low-power Low-noise and High Swing EMG Amplifier with 8.26 PEF

In this paper, a low-noise low-power bio-potential amplifier for electromyogram (EMG) signals have designed and simulated in a commercially available 0.18 μm CMOS technology. In the first stage, a tunable band low-noise amplifier (TBLNA) is designed. In the second stage, a programmable gain amplifier (PGA) is utilized. Inside the TBLNA, by utilizing current splitting technique and current scaling technique in a current mirror operational transconductance amplifier (CM-OTA), a very good trade-off between power and noise is achieved. In addition, the second stage is designed to meet the maximum output swing. After post-layout simulation, the proposed amplifier has a variable gain between 40.2 dB to 57 dB while its high-pass and low-pass cutoff frequencies are 5-16 Hz and 1-1.75 kHz, respectively. Total input referred noise in the total bandwidth is 2.28 μVrms and the total current consumption is 1.35 μA at a 1.4 V supply voltage. Therefore, the noise efficiency factor (NEF) is 2.43. By utilizing a rail-to-rail structure and a noise efficient design, the power efficiency factor (PEF) of the proposed structure is 8.26 which is relatively lower than state-of-the-art EMG amplifiers while its output swing is 1.2 V. The minimum value of the common mode rejection ratio (CMRR) and power supply rejection ratio (PSRR) are 70 dB and 73 dB, respectively. Finally, the total area consumption without pads is 0.07 mm2.
ZTE-Predictor: Disk Failure Prediction System Based on LSTM

Disk failure prediction technology has become a hotspot in both academia and industry, which is of great significance to improve the reliability of data center. This paper studies ZTE's disk SMART (Self-Monitoring Analysis and Reporting Technology) data set, trying to predict whether the disk will fail within 5-7 days. In the model training stage, the disk state is classified as normal and failure within 5 days. Then the positive and negative samples are balanced by both over-sampling and under-sampling. Finally, the data set is trained by LSTM (Long Short-Term Memory) and the disk failure prediction model is obtained. In the experiment of ZTE historical data set, the best FDR (Fault Detection Rate) is 97.4% and FAR (False Alarm Rate) is 0.3%. After launching in ZTE data center for 7 months, the best FDR is 94.5%, and the FAR is 0.7%.
Signal to Image to Classification: Transfer Learning for ECG

Transfer learning offers a potentially valuable tool for detecting a pathological electrocardiogram (ECG) trace. Here, four separate methods of generating an image from a single ECG trace are presented. Transfer learning is used to train three deep neural networks for classification of these images into healthy or pathological categories. The performance of the three pretrained networks is compared.
A Zeroth-Order ADMM Algorithm for Stochastic Optimization over Distributed Processing Networks

In this paper, we address the problem of stochastic optimization over distributed processing networks, which is motivated by machine learning applications performed in data centers. In this problem, each of a total n nodes in a network receives stochastic realizations of a private function fi(x) and aims to reach a common value that minimizes $\sum\nolimits_{i = 1}^n {{f_i}} (x)$ via local updates and communication with its neighbors. We focus on zeroth-order methods where only function values of stochastic realizations can be used. Such kind of methods, which are also called derivative-free, are especially important in solving realworld problems where either the (sub)gradients of loss functions are inaccessible or inefficient to be evaluated. To this end, we propose a method called Distributed Stochastic Alternating Direction Method of Multipliers (DS-ADMM) which can choose to use two kinds of gradient estimators for different assumptions. The convergence rates of DS-ADMM are $O(n\sqrt {k\log (2k)/T} )$ for general convex loss functions and O(nk log(2kT)/T) for strongly convex functions in terms of optimality gap, where k is the dimension of domain and T is the time horizon of the algorithm. The rates can be improved to $O(n/\sqrt T )$ and O(nlogT/T) if objective functions have Lipschitz gradients. All these results are better than previous distributed zerothorder methods. Lastly, we demonstrate the performance of DSADMM via experiments of two examples called distributed online least square and distributed support vector machine arising in estimation and classification tasks.
A Possibilistic Portfolio Model with Fuzzy Liquidity Constraint

Investors are concerned about the reliability and safety of their capital, especially its liquidity, when investing. This paper sets up a possibilistic portfolio selection model with liquidity constraint. In this model, the asset return and liquidity are fuzzy variables which follow the normal possibility distributions. Liquidity is measured as the turnover rate of the asset. On the basis of possibility theory, we transform the model into a quadratic programming problem to obtain its solution. We illustrate that, in the process of investment, investors can make better use of capital by choosing their investment portfolios according to their expected return and asset liquidity.
Tunable Voltage-Mode Subthreshold CMOS Neuron

To address the ever-increasing computational demands of machine learning applications, neuromorphic computing has emerged as a possible solution. The goal is to design a platform able to mimic the processing strategies of the brain. A neuromorphic system is composed by artificial neurons and synapses implemented in hardware with high level of integration. Such implementations entail challenges including power-efficiency, compactness and biophysical resemblance. This work proposes a new implementation of a neuron circuit, initially introduced by Wijekoon and Dudek. We show that the proposed neuron, designed in a standard 0. 18µm CMOS process, consumes 58.5fJ/spike at 0.2V supply voltage. The area covered by the circuit is 16.8% of the area of the state-of-the-art implementation. This result was achieved by lowering the membrane capacitance and the number of transistors. In addition, spiking activity unfolds on a biological time scale - rather than accelerated. The circuit preserves the possibility of being adjusted by external biases to attain different firing patterns.
Personality-based emotion recognition for learning environments

In this paper we present the creation of a video personality recognition model using convolutional neural networks. We also propose the implementation of these models towards the creation of datasets for learning centered emotions with the use of personality as a mean of partitioning them for accuracy enhancement. The model when compared with other state of the art models, shown an improvement of roughly 3% over the best state of the art model when tested.
Edge Inference for UWB Ranging Error Correction Using Autoencoders

Indoor localization knows many applications, such as industry 4.0, warehouses, healthcare, drones, etc., where high accuracy becomes more critical than ever. Recent advances in ultra-wideband localization systems allow high accuracies for multiple active users in line-of-sight environments, while they still introduce errors above 300 mm in non-line-of-sight environments due to multi-path effects. Current work tries to improve the localization accuracy of ultra-wideband through offline error correction approaches using popular machine learning techniques. However, these techniques are still limited to simple environments with few multi-path effects and focus on offline correction. With the upcoming demand for high accuracy and low latency indoor localization systems, there is a need to deploy (online) efficient error correction techniques with fast response times in dynamic and complex environments. To address this, we propose (i) a novel semi-supervised autoencoder-based machine learning approach for improving ranging accuracy of ultra-wideband localization beyond the limitations of current improvements while aiming for performance improvements and a small memory footprint and (ii) an edge inference architecture for online UWB ranging error correction. As such, this paper allows the design of accurate localization systems by using machine learning for low-cost edge devices. Compared to a deep neural network (as state-of-the-art, with a baseline error of 75 mm) the proposed autoencoder achieves a 29% higher accuracy. The proposed approach leverages robust and accurate ultra-wideband localization, which reduces the errors from 214 mm without correction to 58 mm with correction. Validation of edge inference using the proposed autoencoder on a NVIDIA Jetson Nano demonstrates significant uplink bandwidth savings and allows up to 20 rapidly ranging anchors per edge GPU.
Identification and Genetic Effect of Haplotypes in the Distal Promoter Region of Chicken CCT6A Gene Associated with Egg Production Traits

The chaperonin containing TCP-1 complex protein 1 subunit zeta (CCT6A) is the only cytosolic chaperonin in eukaryotes assisting in the folding of cytoplasmic proteins. Previous study revealed that the mRNA expression of chicken CCT6A gene was remarkably elevated in the sexually mature ovaries. However, the mechanism underlying chicken CCT6A expression changes remains largely unknown. In this study, haplotypes caused by two single nucleotide polymorphisms (SNPs) of chicken CCT6A gene promoter (g.−2215 T>C and g.−1959 T>C) were identified and their associations with egg production traits as well as effects on gene expression were analyzed. Altogether four haplotypes including A (C−2215-T−1959), B (C−2215-C−1959), C (T−2215-T−1959) and D (T−2215-C−1959) were detected in all of the five chicken populations. Diplotypes AA, AD and DD were predominant in Xinyang brown hens, among which diplotype AD was associated with higher egg number at the age of 28 weeks old (E28) (P<0.05). In addition, diplotype AD was also predominant in Xinyang brown and Hy-line brown chicken populations with high egg production; whereas in Wenchang and Shouguang chicken populations which are Chinese indigenous chicken breeds and relatively lower in egg production, diplotype AA was predominant. Compared with diplotypes AA and DD, the mRNA expression of CCT6A in diplotype AD birds is the highest in F1, F5, and POF1 follicles of Hy-line brown hens (P<0.05). These results suggest that the two SNPs in chicken CCT6A promoter region are potential DNA marker for improving egg production trait.
A new strategy for the selection of communication technologies in VANETs with fully controllable vehicles

Vehicular communications are laying the foundations for new research areas. Embedded systems within vehicles allow the management of information in movement situations. This article focuses on the choice of the communication technology used by the vehicles with controllable trajectories. The objective is to maximize the throughput, in the centralized way, provided by these vehicles. The study area is a map divided into zones. The choice of communication technologies depends on the crossing zone of the map. In the studied scenario, the vehicle paths and their communication technologies are defined according to this environment. The number of communication technologies usable in the same zone, c, may be restricted. The problem is first formulated as an optimization problem. The complexity of this problem is then proven as $\mathcal{N}\mathcal{P}-$ hard and there is no possibility of constant factor approximation algorithm generally. Assuming $c\geq k$, with k the number of controllable vehicles, the problem remains $\mathcal{N}\mathcal{P}-$ hard, but a new polynomial time $\alpha$ -approximation algorithm is analyzed. The variable $\alpha$ is equal to $\left(1-\frac{1}{e}\right)$ with e being the base of the natural logarithm. This ratio is the best possible, unless $\mathcal{N}\mathcal{P}\subseteq\mathcal{D}\mathcal{T}\mathcal{I}\mathcal{M}\mathcal{E}(n^{\log\log(n)})$
Molded Interconnect Substrate (MIS) Technology for Semiconductor Packages

This paper provides a comprehensive treatment on the performance characteristics of Molded Interconnect Substrate (MIS) technology, which has been deemed a highly effective solution to small and reliable semiconductor packages tailored for applications including but not limited to Power Management, RF Frontend, Edge-AI/Computing, and Automotive. This paper is organized as follows: Upon Introduction, a practical treatment on key performance characteristics of MIS technology is provided. The respective processes of implementing one-layer and multiple-layer MIS package designs are described, and the corresponding challenges and performance/cost implications are presented. After that, production-proven package examples implemented with MIS technology are reported, demonstrating the versatility of this semiconductor technology. The last section of this paper explores potential usages of MIS technology for, e.g. Package-on-Package.
Yield Estimation of a Memristive Sensor Array

This paper proposes a method to calculate the yield of a memristor based sensor array considered as the probability that the chip provides acceptable sensing results when the array is affected by manufacturing defects. The modeling is based on a Markov Chain approach, in which each state represents an operating chip configuration and the state transitions take into account manufacturing defects. The proposed method is applicable to evaluate the yield with different fault models to achieve the comparative yield obtained by several redundancy allocations.