Miracle-dz/UniFAR-FTUs huggingface dataset:
Memory Feasibility Analysis of Parallel Tasks Running on Scratchpad-Based Architectures

This work proposes solutions for bounding the worst-case memory space requirement for parallel tasks running on multicore platforms with scratchpad memories. It introduces a feasibility test that verifies whether memories are large enough to contain the maximum memory backlog that may be generated by the system. Both closed-form bounds and more accurate algorithmic techniques are proposed. It is shown how one can use max-plus algebra and solutions to the max-flow cut problem to efficiently solve the memory feasibility problem. Experimental results are presented to evaluate the efficiency of the proposed feasibility analysis techniques on synthetic workload and state-of-the-art benchmarks.
Predicting Objective Function Change in Pose-Graph Optimization

Robust online incremental SLAM applications require metrics to evaluate the impact of current measurements. Despite its prevalence in graph pruning, information-theoretic metrics solely are insufficient to detect outliers. The optimal value of the objective function is a better choice to detect outliers but cannot be computed unless the problem is solved. In this paper, we show how the objective function change can be predicted in an incremental pose-graph optimization scheme, without actually solving the problem. The predicted objective function change can be used to guide online decisions or detect outliers. Experiments validate the accuracy of the predicted objective function, and an application to outlier detection is also provided, showing its advantages over M-estimators.
Micromagnetic Study of Probabilistic Switching Behavior in Sub 20 nm-CoFeB/MgO Magnetic Tunnel Junction

The fast probabilistic switching in the sub 20 nm CoFeB/MgO-Magnetic tunnel junction (MTJ) with stepped- and pillar structure were studied using micromagnetic simulation. It was observed that back-hopping phenomenon was occurred for AP to P switching in the pillar structure, while both P to AP and AP to P error-free switching were accomplished within 2 ns in the stepped structure. It was found that this back-hopping was triggered by the reference layer flipping, and driven by both the stray filed from the pinned layer and voltage control of magnetic anisotropy (VCMA) effects. Furthermore, it was found that this back-hopping can be reduced by increasing the interface anisotropy of the reference layer and decreasing VCMA effects at the CoFeB-reference/MgO-barrier interface.
A Deployable Quasi-Yagi Monopole Antenna Using Three Origami Magic Spiral Cubes

We propose a deployable quasi-Yagi monopole antenna using origami magic spiral cubes, constructed from low-cost paper substrate. The proposed quasi-Yagi antenna consists of an L-shaped driven monopole, an L-shaped reflector, and two L-shaped directors. These driven monopole, reflector, and directors are designed in L-shaped geometries to achieve maximum gain in a limited cube size. The driven element and reflector are realized on the origami magic cube #1, while the two directors are designed on the magic cubes #2 and #3, respectively. The cubes are stacked together using a thin adhesive film. The presented geometry of the origami magic spiral cube antenna can be easily folded and unfolded. The dimensions in the unfolded state are 44 mm × 44 mm × 132 mm while the dimensions in the folded state are 44 mm × 44 mm × 8 mm. Therefore, in the folded state, the antenna occupies only 6% of the unfolded volume. We have numerically analyzed the antenna performance and verified the outcomes experimentally. The L-shaped reflector has increased the monopole antenna gain from 1.9 to 5.7 dBi. With the addition of two directors, the gain is further increased to 7.3 dBi at 1.9 GHz.
Design of Boosted Supply Modulator With Reverse Current Protection for Wide Battery Range in Envelope Tracking Operation

This paper presents a hybrid supply modulator (SM) including a buck-boost (BB) converter, which has wide battery operating range for cellular envelope tracking (ET) applications. The proposed SM supports both ET and average power tracking (APT) mode depending on the power amplifier (PA) output power level. The output voltage swing of SM is boosted up to 4.5 V by the integrated BB converter as a supply of a linear amplifier. The dynamic-controlled BB output increases ET operating range, whose efficiency is higher than that of APT, by scaling down the supply voltage of the linear amplifier according to the PA output power level. A selective supply voltage of a switching amplifier and a reverse current sensing circuit are employed to prevent the reverse current through the power inductor of the switching amplifier. The proposed automatic current ratio controller between the linear amplifier and the switching amplifier provides the robust performance against the battery voltage variation of 3.2–4.6 V. A programmable bias current of the class-AB output buffer and a programmable hysteretic comparator are employed to achieve the optimum efficiency and bandwidth depending on the operating signal bandwidth. Optimal control for low signal bandwidth applications such as voice over Long-Term Evolution (LTE) with the selective notch filter provides the maximum efficiency of 88.2% with −135 dBm/Hz output noise. The proposed SM supports the LTE 20-MHz envelope signal with 78% efficiency at 800-mW output. Adapting the designed SM to frequency-division duplex Band3 radio frequency PA, the implemented ET-PA achieves the power added efficiency of 41.6% at 27-dBm PA output power while achieving −40 dBc of E-UTRA ACLR and −137 dBm/Hz of receiver band noise. The chip is implemented in the 0.13- $\mu \text{m}$ CMOS process, and the die size is 5 mm2.
Soft LEGO: Bottom-Up Design Platform for Soft Robotics

This paper introduces soft LEGO for bottom-up design platform of soft robotics that can be used for various purposes, ranging from research and fast prototyping of soft robots to toys and entertainment. We integrated the interlocking mechanism of LEGO into a modular soft robot. With this design, soft robots could be built by a simple and play-like assembling process. Three kinds of components were proposed to make soft robotics compatible with LEGO: pneumatically inflatable soft brick, flexible bending brick, and channel brick. The soft brick has an air chamber and can generate motions when inflated. The bending brick has flexure and is bendable for generating motion when the assembled soft bricks are pneumatically actuated. The air channel brick has an air channel inside and works as an interface between air hoses and soft LEGO bricks. Detailed design parameters of the soft brick were optimized based on the Taguchi method with finite-element analysis to improve robustness. Design of the bending brick was selected based on experimental results to enhance the robustness of the flexure. Thanks to the multi-material 3-dimensional printer, the soft LEGO bricks could be fabricated with a single printing process. To see the feasibility of soft LEGO as a bottom-up design platform, a simple toy robot for children and a gripper that had a hybrid mechanism of hard and soft materials were built and tested. We hope this soft LEGO could lower the hurdle of soft robotics for children, researchers from other fields, and the public interest in robotics.
Inspection System for Automatic Measurement of Level Differences in Belt Conveyors Using Inertial Measurement Unit

Belt conveyors are transport systems composed by a plurality of belt lines. When those systems are used to transport fragile materials or liquid containers, it is necessary to minimize the oscillations of the transported objects in order to avoid damage, spillage and particle degradation. Those oscillations are regularly caused by steps (i.e. differences in level) at the joints of the belt lines, and for that reason, it is necessary to inspect those steps during installation and maintenance. Regular inspections involve the visual verification of the steps, which stops production, takes significant time, occasionally requires system disassembly and is subjected to human errors. In this paper, a novel belt conveyor inspection system which is able to detect and measure the steps at the joints of the belt lines is presented. This system consists in the acquirement of data of the belt conveyor with an inertial measurement unit (IMU), and the processing of this data with original algorithms for zero offset filtering, sensor progressing direction detection, step event detection and step height calculation. The presented system had successfully detected and measured the steps of a complex belt conveyor with an accuracy of $\pm 0.3\ \text{mm}$. It is demonstrated that this cost-effective and ready to use system enables an automatic and prompt inspection of the whole belt conveyor system at once, thus reducing the workload, time and errors of the belt conveyor inspection.
A Novel Positive Transfer Learning Approach for Telemonitoring of Parkinson’s Disease

Telemonitoring is the use of electronic devices to remotely monitor patients. Taking the Parkinson’s disease (PD) as an example, the use of at-home testing device (AHTD) enables remote, internet-based measurement of PD vocal symptoms. Translating AHTD measurement into a unified PD rating scale (UPDRS) through predictive analytics enables cost-effective, convenient, and close tracking of PD progression. Building a predictive model between AHTD measurement and UPDRS is not straightforward because PD patients are highly heterogeneous, which requires patient-specific models. Learning a patient-specific model faces the challenge of limited data. Transfer learning (TL) tackles this challenge by leveraging other patients’ information to make up the data shortage when modeling a target patient. Among different TL methods, the category of parameter transfer methods is more appropriate for the telemonitoring application because it transfers patient-specific model parameters but not patients’ data. However, existing parameter transfer methods fall short because not every other patient’s information is helpful and blind transfer causes the problem of negative transfer. To tackle this limitation, we propose a positive TL (PTL) method. We provide an in-depth theoretical study on the risk and condition for negative transfer to happen, which further drive the development of novel PTL algorithms that are robust to negative transfer. We apply PTL to predict UPDRS of 42 PD patients using their AHTD vocal measurement. PTL achieves significantly better accuracy compared with single learning and one-model-fits-all approaches. Note to Practitioners—This paper was motivated by the growing use of telemonitoring devices to facilitate remote, close tracking of disease progression. These devices are usually paired up with predictive analytics to translate the measurement signals into a clinical indicator of disease progression. This is a challenging task because the predictive model needs to be patient specific in order to account for patient heterogeneity. This paper proposes a TL method called PTL that can leverage other patients’ information when building a predictive model for a target patient. The unique feature of PTL is that it can intelligently select which patients to transfer from, and thus preventing negative transfer. PTL is a just-in-time development for the emerging healthcare paradigm of telemonitoring and telemedicine by providing predictive analytics models and algorithms to address the unique challenges and contributing to the use of these remote devices to improve patient care.
Magnetic Resonance Coupling Modelling for Electric Vehicles Wireless Charging

Due to the fast-growing market for the electric vehicle, it is necessary that the drawbacks involved in it should be overcome, therefore introducing wireless charging technique which is more convenient as battery cost, recharge time and weight has been removed. Different wireless charging techniques for electric vehicles are discussed. The feasibility of wireless power transfer for Electric Vehicles by electromagnetic resonance coupling is modelled in this paper. Wireless power transfer (WPT) for Electric Vehicles by magnetic resonance coupling is of high priority due to its efficiency, high power transmission and larger charging distance. Also, we demonstrated with simulations how energy can be transferred efficiently between two magnetically coupled resonating coils. Also, the effect of parameters such as the inductor, capacitor, load, and coupling coefficient on efficiency are also discussed.
A microstrip resonator based sensor for GHz characterization of in vitro cell culture

This contribution describes a sensor for GHz characterization of in vitro cell culture, that overcomes the limitation in high frequency of classical bio-impedance spectroscopy. The sensor we developed enables to monitor a thin layer of approximatively 30μm of cells in their liquid culture medium for a relative permittivity in the range of 5 to 55. Here, we demonstrate the measurement principle using a Finite Element model, which has been validated with experimental response. Our sensor has a minimal sensitivity of 0.3 MHz. We also used a multiphysics model to demonstrate that this technology does not affect tissue safety for input power level compatible with a sufficient measurement signal on noise ratio. This technology will be used in experiments aiming at the exploration of biomarkers for post-implantation inflammatory reactions of active devices.
Online Self-body Image Acquisition Considering Changes in Muscle Routes Caused by Softness of Body Tissue for Tendon-driven Musculoskeletal Humanoids

Tendon-driven musculoskeletal humanoids have many benefits in terms of the flexible spine, multiple degrees of freedom, and variable stiffness. At the same time, because of its body complexity, there are problems in controllability. First, due to the large difference between the actual robot and its geometric model, it cannot move as intended and large internal muscle tension may emerge. Second, movements which do not appear as changes in muscle lengths may emerge, because of the muscle route changes caused by softness of body tissue. To solve these problems, we construct two models: ideal joint-muscle model and muscle-route change model, using a neural network. We initialize these models by a man-made geometric model and update them online using the sensor information of the actual robot. We validate that the tendon-driven musculoskeletal humanoid Kengoro is able to obtain a correct self-body image through several experiments.
Analysis of Radiation Effects for Monitoring Circuit Based on Deep Belief Network and Support Vector Method

The monitoring circuit is widely applied in radiation environment and it is of significance to study the circuit reliability with the radiation effects. In this paper, an intelligent analysis method based on Deep Belief Network (DBN) and Support Vector Method is proposed according to the radiation experiments analysis of the monitoring circuit. The Total Ionizing Dose (TID) of the monitoring circuit is used to identify the circuit degradation trend. Firstly, the output waveforms of the monitoring circuit are obtained by radiating with the different TID. Subsequently, the Deep Belief Network Model is trained to extract the features of the circuit signal. Finally, the Support Vector Machine (SVM) and Support Vector Regression (SVR) are applied to classify and predict the remaining useful life (RUL) of the monitoring circuit. According to the experimental results, the performance of DBN-SVM exceeds DBN method for feature extraction and classification, and SVR is effective for predicting the degradation
Wearable Pediatric Gait Exoskeleton - A Feasibility Study

This study reports the initial testing of a gait exoskeleton for Spinal Muscular Atrophy (SMA) patients having variable muscle strength with no balance or ambulation capabilities. To improve the quality of life of such patients, a pediatric gait exoskeleton was developed. The ATLAS exoskeleton has 8 active degrees of freedom (DOF): 2 at the hip (adduction/abduction and flexion/extension), 1 at the knee and ankle joint for flexion and extension. A feasibility test was performed to gauge the initial response of the patients. This study demonstrates that the exoskeleton was able to provide gait assistance and sit-to-stand movements effectively to the subjects. This kind of wearable exoskeleton will play a key role in the rehabilitation of SMA patients and delay further metabolic degeneration in the future.
Hybrid Analog and Digital Control of a High Current Converter Based on an EDLC Bank for Rapidly Decreasing Input Voltage

This paper proposes unconventional control techniques for a portable power supply consisting of electric double-layer capacitors (EDLCs), a dc–dc converter, and a low-impedance load such a plasma load. Between the EDLCs and the plasma load consuming high power, a dc–dc converter is required to control the output voltage from the rapidly decreasing voltage of EDLCs. In addition, the dc–dc converter should maintain a set point by responding to arcs occurred in plasma process. This paper proposed hybrid analog and digital controls for a nonideal boost converter which was setup by theoretical analysis. The proposed system is verified with experiment and simulation. As a result, the proposed control method can maintain the constant output voltage longer than a traditional analog pulsewidth modulation control.
Analysis, Calibration, and Performance Evaluation of a Generalized  $N$ -Phase Quadrature Phase Shift Frequency Selective Receiver

In this paper, a new low-power and blocker-tolerant receiver architecture, the generalized N-phase quadrature phase shift frequency selective (QPS-FS), is proposed and analyzed that is suitable for radio frequency (RF) energy harvesting networks. A complete comparative analysis of the N-phase QPS-FS receiver with conventional N-path passive mixer (P-M) receiver is also provided. The QPS-FS receiver utilizes the impedance translation concept to improve the receiver selectivity but eliminates the need of an active multiphase clock generation circuit used in the conventional N-path P-M receivers. It uses a single clock signal source at the desired signal carrier frequency and a phase shift network in the RF path of the receiver for frequency downconversion and quadrature (I/Q) demodulation. The QPS-FS receiver requires a slower clock signal source, the frequency of which is equal to the desired band RF signal carrier frequency ( $f_{c})$ as opposed to at least N/2 times $f_{c}$ for the conventional N-path P-M receivers. Consequently, the lower frequency clock source requirement extends the frequency coverage of the receiver by a factor of at least N/2 for a given clock source with a fixed maximum frequency. Reduction of the operating frequency and elimination of the active multiphase clock generation circuit would also eventually reduce the overall power consumption for the whole receiver system as confirmed through measurements. A wideband receiver calibration approach provides less than 2% of error vector magnitudes between the transmitted and received constellation points for various modulated signals having bandwidths up to 4 MHz.
KnowRobSIM — Game Engine-Enabled Knowledge Processing Towards Cognition-Enabled Robot Control

AI knowledge representation and reasoning methods consider actions to be blackboxes that abstract away from how they are executed. This abstract view does not suffice for the decision making capabilities required by robotic agents that are to accomplish manipulation tasks. Such robots have to reason about how to pour without spilling, where to grasp a pot, how to open different containers, and so on. To enable such reasoning it is necessary to consider how objects are perceived, how motions can be executed and parameterized, and how motion parameterization affects the physical effects of actions. To this end, we propose to complement and extend symbolic reasoning methods with KnowRobSIM, an additional reasoning infrastructure based on modern game engine technology, including the subsymbolic world modeling through data structures, action simulation based on physics engine, and world scene rendering. We demonstrate how KnowRobSIM can perform powerful reasoning, prediction, and learning tasks that are required for informed decision making in object manipulation.
Walking on a Steep Slope Using a Rope by a Life-Size Humanoid Robot

In this paper, we propose methods for walking on a steep slope using a rope by a humanoid robot. There are two difficulties for walking on a steep slope without a rope. First, range of motion of ankle joints get limited. Second, feet of a robot slip on a steep slope. For these problems, using a rope is effective solution because the robot can receive enough friction force from the slope and walk on a steep slope by pulling a rope with proper tension. In addition, the robot pulling a rope on a slope can relax limitations of ankle joints. Therefore, we propose methods to determine tension of a grasped rope by solving a linear least-square problem considering deformability of a rope. With these methods, a life-size humanoid robot HRP-2 could walk on a steep slope which angle is 40 degree.
Optimization of an Autonomous Hybrid Renewable Energy System Using Reformed Electric System Cascade Analysis

This paper presents reformed electric system cascade analysis (RESCA) technique for optimization of hybrid renewable energy system (HRES) comprising of wind energy conversion system (WECS), photovoltaic (PV) system, battery energy storage system, and nonintermittent source (NIS). Optimization constraints of final excess energy, energy generation ratio, cost of energy, net present cost (NPC), and renewable energy fraction are considered for the optimization of the system. This optimization is realized for an isolated load comprising of the ten households in Malaysia with a daily consumption of 84.5 kWh. On successful implementation of RESCA with different constraints, it is found that the optimum size of energy sources for the HRES varies depending on the chosen constraints. Also, the NPC of the system can be considerably reduced on introduction of NIS to the HRES. RESCA provides the user with better insight into the influence of variation of PV and WECS power generation while identifying the optimal size of energy storage unit. The optimized results obtained are also compared with benchmark HOMER software to validate the methodology.
Stereo Camera Localization in 3D LiDAR Maps

As simultaneous localization and mapping (SLAM) techniques have flourished with the advent of 3D Light Detection and Ranging (LiDAR) sensors, accurate 3D maps are readily available. Many researchers turn their attention to localization in a previously acquired 3D map. In this paper, we propose a novel and lightweight camera-only visual positioning algorithm that involves localization within prior 3D LiDAR maps. We aim to achieve the consumer level global positioning system (GPS) accuracy using vision within the urban environment, where GPS signal is unreliable. Via exploiting a stereo camera, depth from the stereo disparity map is matched with 3D LiDAR maps. A full six degree of freedom (DOF) camera pose is estimated via minimizing depth residual. Powered by visual tracking that provides a good initial guess for the localization, the proposed depth residual is successfully applied for camera pose estimation. Our method runs online, as the average localization error is comparable to ones resulting from state-of-the-art approaches. We validate the proposed method as a stand-alone localizer using KITTI dataset and as a module in the SLAM framework using our own dataset.