Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-Powered Load Sensing Circuitry for Total Knee Replacement

There has been a significant increase in the number of total knee replacement (TKR) surgeries over the past few years, particularly among active young and elderly people suffering from knee pain. Continuous and optimal monitoring of the load on the knee is highly desirable for designing more reliable knee implants. This paper focuses on designing a smart knee implant consisting of a triboelectric energy harvester and a frontend electronic system to process the harvested signal for monitoring the knee load. The harvester produces an AC signal with peak voltages ranging from 10 V to 150 V at different values of knee cyclic loads. This paper demonstrates the measurement results of a PCB prototype of the frontend electronic system fabricated to verify the functionality and feasibility of the proposed approach for a small range of cycling load. The frontend electronic system consists of a voltage processing unit to attenuate high peak voltages, a rectifier and a regulator to convert the input AC signal into a stabilized DC signal. The DC voltage signal provides biasing for the delta-sigma analog-to-digital converter (ADC). Thus, the output of the triboelectric harvester acts as both the power signal that is rectified/regulated and data signal that is digitized. The power consumption of the proposed PCB design is approximately $5.35~\mu \text{W}$ . Next, the frontend sensor circuitry is improved to accommodate a wider range of cyclic load. These results demonstrate that triboelectric energy harvesting is a promising technique for self-monitoring the load inside knee implants.
Prediction-Error Negativity to Assess Singularity Avoidance Strategies in Physical Human-Robot Collaboration

In physical human-robot collaboration (pHRC), singularity avoidance strategies are often critical to obtain stable interaction dynamics. It is hypothesised a predictable singularity avoidance strategy is preferred in pHRC as humans tend to maximise predictability when using complex systems. By using an electroencephalogram (EEG), it is possible to assess the predictability of a task through a feature found in event-related potentials (ERP) and called prediction-error negativity (PEN). In this paper, two research questions are addressed. Can a complex pHRC singularity avoidance strategy generate a detectable PEN? Are PEN and human preferences related when comparing different control settings in a singularity avoidance strategy? Fourteen participants compared two different sets of parameters (modes) in a singularity avoidance strategy based on the exponentially damped least-squared (EDLS) method. ERP results are presented in terms of power spectral density (PSD). ERP results were then compared with human preferences to see whether they are related. Results show that the mode that causes PEN is also the one that participants did not like, suggesting that a lack of predictability might have an impact on human preference.
Preemptive Anomaly Prediction in IoT Components (short paper)

The Internet-of-Things (IoT) has become a very promising and fruitful area of research. The rapid development of IoT is revolutionizing our daily utilization of technology in every way. The IoT paradigm is that the devices making up an IoT system have resource constraints such as storage, computing and energy consumption. That paradigm makes possible a flexible and pervasive communication between devices that are bound to low resources. These constraints may create a state where there is anomaly occurrence on the component level that may impact the whole system. Some innovative techniques have been proposed to quantify the reliability of these devices for the aforementioned constraints. However, there is a gap between the quantification of the component reliability and the predictive and preemptive maintenance of these components. In this study, we propose an approach combining reliability quantification and reinforcement learning to build a mechanism that can achieve a predictive maintenance for the components of an IoT system such as devices and links. In the approach, a component-level mechanism is built to synthesize the reliability data, and to determine the probability of anomaly occurrence for each component. The approach is being applied to a self-adaptive IoT system for smart environment monitoring named DeltaIoT.
AGDLI: ArCo, GVP and DBpedia Linking Initiative

We present the ArCo , GVP and DBpedia Linking Initiative (AGDLI), a research activity within the project SMARTOUR: intelligent platforms for tourism , funded by the Italian Ministry of University and Research. Our initiative is aimed at linking ArCo ’s cultural entities to the well known Getty Vocabulary Program and DBpedia ontologies, with the main goal of providing a semantically rich representation of the Italian cultural heritage for tourism-related knowledge-based applications. In this paper we provide a detailed description of the initiative and describe the current research developments and outcomes.
Movement recognition and prediction using DMPs

This paper proposes an approach for (a) recognizing an observed trajectory from a library of pre-learned motions; and (b) predicting the target position of such trajectory. In our approach, motions are represented as Dynamic Movement Primitives (DMPs). We use critical points from the observed trajectory to time-align it with those in the library. To match the observed trajectory with those in the library, we compare the changes in velocity orientation between consecutive critical points. The proposed approach is computationally light and, as such, can be performed at execution time. As for the prediction, we adopt a similar approach: after matching the observed trajectory to one in the library, we use the latter to predict the target point, modulating it to match the observed trajectory. Both recognition and prediction approaches are probabilistic and, as such, provide a measure of certainty/uncertainty in the recognition/prediction process. Such a measure of uncertainty is important in tasks involving human-robot collaboration, as it allows the robot to decide when it is sufficiently certain to act conditioned on the estimated trajectory. We illustrate our approach both in simulation and in a human-robot interaction scenario involving the Baxter robot.
Simulation of mode division multiplex transmission method in shielded four-wire cable

A mode division multiplex transmission system that assigns signals to the eigenmodes of a transmission line was proposed for a cable that is uniform in the longitudinal direction. The transmission characteristics when the proposed method is used in a 1m long shielded untwisted 4-wire cable were verified by circuit simulation. It is shown that the proposed method enables four-channel simultaneous transmission. In addition, the proposed method significantly improves the crosstalk compared to the differential and single-ended transmission methods.
A Dual-Band Rat-Race Coupler for High Band Ratio Wireless Applications

This article presents a dual-band rat-race coupler for high band ratio wireless applications. In this article, a dual-band transmission line (TL) has also been proposed to design the rat-race coupler. The proposed rat-race coupler has been developed by substituting each TL segment of a single-band rat-race coupler with the dual-band TL. To validate the design method, a rat-race coupler for 0.7- and 4.9-GHz operating frequencies has been developed. A maximum of 3.3-dB insertion loss and 20-dB return loss and isolation have been attained for both the frequency bands. Compared with the existing dual-band rat-race couplers, the proposed coupler offers dual-band operation with a high band ratio. In addition, for the first time a dual-band rat-race coupler compatible with microstrip TL and having input and output on different sides has been developed. This feature makes simpler connections when connected to other circuits.
Features of Intercalation Processes in Polymer Nanocomposites Based on Oligoethylene Glycol and Organoclay

The structure features and intercalation processes in oligoethylene glycol (OEG)-based systems and organoclay were studied using X-ray diffraction analysis. It is established that after the introduction of surfactants, the interlayer distance in montmorillonite (MMT) increased. This indicates the intercalation process of surfactant molecules into the interlayer space of the MMT. After introduction to polymer matrix the increase in the interlayer distance is observed. This indicates the intercalation of two monolayers of OEG and the formation of quasibilayer structures. It is shown that the structure of OEG-based systems significantly depends on the content of the nanofiller. Partial processes of intercalation are observed in OEG-organoclay nanocomposites.
Polygeist: Raising C to Polyhedral MLIR

We present Polygeist, a new compilation flow that connects the MLIR compiler infrastructure to cutting edge polyhedral optimization tools. It consists of a C and C++ frontend capable of converting a broad range of existing codes into MLIR suitable for polyhedral transformation and a bi-directional conversion between MLIR and OpenScop exchange format. The Polygeist/MLIR intermediate representation featuring high-level (affine) loop constructs and n-D arrays embedded into a single static assignment (SSA) substrate enables an unprecedented combination of SSA-based and polyhedral optimizations. We illustrate this by proposing and implementing two extra transformations: statement splitting and reduction parallelization. Our evaluation demonstrates that Polygeist outperforms on average both an LLVM IR-level optimizer (Polly) and a source-to-source state-of-the-art polyhedral compiler (Pluto) when exercised on the Polybench/C benchmark suite in sequential (2.53x vs 1.41x, 2.34x) and parallel mode (9.47x vs 3.26x, 7.54x) thanks to the new representation and transformations.
Hybrid Bird’s-Eye Edge Based Semantic Visual SLAM for Automated Valet Parking

Vision-based localization and mapping solution is promising to be adopted in the automated valet parking task. In this paper, a semantic SLAM framework that leverages the hybrid edge information on bird’s-eye view images is presented. To extract useful edges from the synthesized bird’s-eye view image and the free-space contours for the SLAM task, different segmentation methods are designed to remove the noisy glare edges and distorted object edges caused by the inverse perspective mapping in view synthesis. Since only the free-space segmentation model needs training, our methods can dramatically reduce the labeling burden compared with previous road marking based methods. Those incorrect and incomplete edges are further cleaned and recovered by a temporal fusion of consecutive edges in a local map, respectively. Both a semantic edge point cloud map and an occupancy grid map can be built simultaneously in real time. Experiments in a parking garage demonstrate that the proposed framework can achieve higher accuracy and perform more robustly than previous point feature based methods.
GCC-PHAT with Speech-oriented Attention for Robotic Sound Source Localization

Robotic audition is a basic sense that helps robots perceive the surroundings and interact with humans. Sound Source Localization (SSL) is an essential module for a robotic system. However, the performance of most sound source localization techniques degrades in noisy and reverberant environments due to inaccurate Time Difference of Arrival (TDoA) estimation. In robotic sound source localization, we are more interested in detecting the arrival of human speech than other sound sources. Ideally, we expect an effective TDoA estimation to respond only to speech signals, while masking off other interferences. In this paper, we propose a novel technique that learns to attend to speech fundamental frequency and harmonics while suppressing noise interference and reverberation. The novel TDoA feature is referred to as Generalized Cross Correlation with Phase Transform and Speech Mask (GCC-PHAT-SM). We perform sound source localization experiments on real-world data captured from a robotic platform. Experiments show that GCC-PHAT-SM feature significantly outperforms traditional Generalized Cross Correlation (GCC) feature in noisy and reverberant acoustic environments.
Development of Mathematical Model and Subordinate Control for Nonlinear Electric Drivers of Exoskeleton

The article discusses a mathematical model of a control system for exoskeleton electric drives with nonlinear elements. The development of a exoskeleton dynamic model includes five links with two legs and a body, taking into account non-linear electric drives as a control object. Modeling mechanical systems is an important step in the practical design and application of control systems. The nonlinear approach to modeling is due to the use of a control system for the exoskeleton electric drives in various conditions, which is a multi-mass rotating in two directions with nonlinearities of the viscous and Coulomb friction, elasticity. A method of subordinate control (SC) of exoskeleton nonlinear electric drives for tracking control of the exoskeleton joints trajectory is presented. Also, the results of the experiment on the Matlab / Simulink program were obtained to illustrate the effectiveness of the proposed control strategy.
MemoMusic: A Personalized Music Recommendation Framework Based on Emotion and Memory

Music is universally recognized as an effective way for human to express emotion and regulate emotional states. But perceived music emotion is subjective and much dependent on culture, environment, and life experience. Therefore, personalized music recommendation is necessary to gain user satisfaction and navigate a listener to a more positive emotional state as well. Existing work on emotion- based music recommendation and personalized music recommendation often lack of considering the impact of past life experiences on music emotion perceiving. We argue that memories associated with music could play a vital role in determining the new emotional states after music listening. To verify our hypothesis, we propose a personalized music recommendation framework called MemoMusic, which estimates the new emotional state of a listener based on an individual’s current emotional state and possible memory associated with the music being listened to. For the preliminary experiment, a dataset of 60 piano music was collected and labelled using the Valence-Arousal model from three categories of Classical, Popular, and Yanni music. Experimental results demonstrate that memory is actually an important factor in determining perceived music emotion. And MemoMusic based on emotion and memory achieves a good performance in terms of improving a listener’s emotional states.
Enhancing Robot Perception in Grasping and Dexterous Manipulation through Crowdsourcing and Gamification

Robot grasping and manipulation planning in unstructured and dynamic environments is heavily dependent on the attributes of manipulated objects. Although deep learning approaches have delivered exceptional performance in robot perception, human perception and reasoning are still superior in processing novel object classes. Moreover, training such models requires large datasets that are generally expensive to obtain. This work combines crowdsourcing and gamification to leverage human intelligence, enhancing the object recognition and attribute estimation aspects of robot perception. The framework employs an attribute matching system that encodes visual information into an online puzzle game, utilizing the collective intelligence of players to expand an initial attribute database and react to real-time perception conflicts. The framework is deployed and evaluated in a proof-of-concept application for enhancing object recognition in autonomous robot grasping and a model for estimating the response time is proposed. The obtained results demonstrate that given enough players, the framework can offer near real-time labeling of novel objects, based purely on visual information and human experience.
Dynamic Stochastic Demand Response With Energy Storage

We consider a power system with an independent system operator (ISO), and distributed aggregators who have energy storage and purchase energy from the ISO to serve their customers. All the entities in the system are foresighted: each aggregator seeks to minimize its own long-term payments for energy purchase and operational costs of energy storage by deciding how much energy to buy from the ISO, and the ISO seeks to minimize the long-term total cost of the system (e.g., energy generation costs and the aggregators’ costs) by dispatching the energy production among the generators. The decision making of the foresighted entities is complicated for two reasons. First, the information is decentralized among the entities, namely each entity does not know the others’ states. Second, an aggregator’s current decision affects its future costs due to the coupling introduced by the energy storage. We propose a design framework in which the ISO provides each aggregator with a conjectured future price, and each aggregator distributively minimizes its own long-term cost based on its conjectured price as well as its locally-available information. We prove that the proposed framework can achieve the social optimum despite being decentralized and involving complex coupling. Simulation results show that the proposed foresighted demand side management achieves significant reduction in the total cost, compared to the optimal myopic demand side management (up to 60% reduction), and the foresighted demand side management based on the Lyapunov optimization framework (up to 30% reduction).
End-to-end Multi-Instance Robotic Reaching from Monocular Vision

Multi-instance scenes are especially challenging for end-to-end visuomotor (image-to-control) learning algorithms. "Pipeline" visual servo control algorithms use separate detection, selection and servo stages, allowing algorithms to focus on a single object instance during servo control. End-to-end systems do not have separate detection and selection stages and need to address the visual ambiguities introduced by the presence of an arbitrary number of visually identical or similar objects during servo control. However, end-to-end schemes avoid embedding errors from detection and selection stages in the servo control behaviour, are more dynamically robust to changing scenes and are algorithmically simpler. In this paper, we present a reactive real-time end-to-end visuomotor learning algorithm for multi-instance reaching. The proposed algorithm uses a monocular RGB image and the manipulator’s joint angles as the input to a light-weight fully-convolutional network (FCN) to generate control candidates. A key innovation of the proposed method is identifying the optimal control candidate by regressing a control-Lyapunov function (cLf) value. The multi-instance capability emerges naturally from the stability analysis associated with the cLf formulation. We demonstrate the proposed algorithm effectively reaching and grasping objects from different categories on a table-top amid other instances and distractors from an over-the-shoulder monocular RGB camera. The network is able to run up to ∼160 fps during inference on one GTX 1080 Ti GPU.
Supplementary Feedforward Control of DGs in a Reconfigurable Microgrid for Load Restoration

Network reconfiguration (NR) has received attention due to its potential to improve grid resilience by realizing self-healing microgrids (MGs). This paper proposes a new strategy for the real-time frequency regulation of a reconfigurable MG, wherein the feedforward control of synchronous and inverter-interfaced distributed generators (DGs) is achieved in coordination with the operations of sectionalizing and tie switches (SWs). This enables DGs to compensate more quickly, and preemptively, for a forthcoming variation in load demand due to NR-aided restoration. An analytical dynamic model of a reconfigurable MG is developed to analyze the MG frequency response to NR and determine the desired dynamics of the feedforward controllers when integrated with feedback loops for inertial response emulation and primary and secondary frequency control. A small-signal analysis is conducted to analyze the contribution of the feedforward control to the MG frequency regulation. Simulation case studies of NR-aided load restoration are also performed. The results of the small-signal analysis and case studies confirm that the proposed strategy is effective for the improvement of the MG frequency regulation under various conditions of load demand, model parameter errors, and communication time delays.
36-Month Evaluation of a Weight Management Programme in Chinese Overweight and Obese Adults

Background: Few comprehensive lifestyle intervention programmes have been investigated on overweight and obese adults in China. This study was to evaluate the effect of a 36-month weight management programme on weight loss and its maintenance among overweight and obese patients in Ningbo, China. Methods: Adults with BMI ≥24kg/m2 enrolled in this programme, including nutritional, physical activity, psychological and endocrinological counselling sessions, from July 2015 to January 2020. Adults participated in face-to-face counselling sessions and group-based education. Then, participants joined 21-day intensive programme using Bohe health APP and WeChat group to get personal advice of nutrition and lifestyle. In the end, participants were requested to join 33-month follow-ups including face-to-face counselling and personal advice on WeChat group. The main outcome was to evaluate the changes in weight at each followup from baseline weight. Results: In total, 692 adults participated in this entire weight management programme. During follow-ups, 579, 475, 299, 219, and 135 adults participated at 3, 6, 12, 24, and 36 months, respectively. All participants had a significant initial weight loss at 3 months, then maintained the weight loss during 33-month follow-ups. At 36 months, 11.0%, 6.4%, and 3.5% of all participants achieved 5%, 10%, and 15% weight loss from the baseline weight, respectively. Adjusted weight at 36 months was significantly reduced from the baseline weight in both sex (−7.2 kg). Conclusion: This weight management programme is suggested to benefit to reduce initial body weight and maintain long-term weight loss among overweight and obese adults.