Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning From Demonstrations Using Signal Temporal Logic in Stochastic and Continuous Domains

Learning control policies that are safe, robust and interpretable are prominent challenges in developing robotic systems. Learning-from-demonstrations with formal logic is an arising paradigm in reinforcement learning to estimate rewards and extract robot control policies that seek to overcome these challenges. In this approach, we assume that mission-level specifications for the robotic system are expressed in a suitable temporal logic such as Signal Temporal Logic (STL). The main idea is to automatically infer rewards from user demonstrations (that could be suboptimal or incomplete) by evaluating and ranking them w.r. t. the given STL specifications. In contrast to existing work that focuses on deterministic environments and discrete state spaces, in this letter, we propose significant extensions that tackle stochastic environments and continuous state spaces.
An Equivalent Circuit Model of a Commercial LED With an ESD Protection Component for VLC

An equivalent circuit model for a typical commercial light-emitting diode (LED) used in visible light communication (VLC) applications is proposed. The parameters of the elements in the model are extracted from the measured impedance using a vector network analyser. The model shows high accuracy in fitting the measured results and shows the effect of bonding/packaging parasitics and the electrostatic discharge (ESD) component on the LED’s impedance and bandwidth. This study aims to raise the designer’s awareness when designing and fabricating high-speed LED/ $\mu $ LED devices to achieve the optimal modulation bandwidth in the emerging VLC applications.
Artificial Noise Assisted Secure Transmission for Uplink of Massive MIMO Systems

Secure downlink transmission in massive multiple-input multiple-output (MIMO) systems has sparked great research enthusiasm. However, the secrecy of uplink transmission is equally important although relatively less attention has been dedicated to. In this paper, we show that a multi-antenna eavesdropper could potentially reduce the secrecy rate of legitimate communications severely through channel estimation in the training phase and coherent detection in the payload data transmission phase. To safeguard uplink transmission, an artificial noise (AN)-assisted scheme is proposed. Specifically, the optimization problem that aims to optimize the power allocation between AN and data symbols in the sense of maximizing the secrecy rate is formulated. We investigate optimal power allocation strategies for two distinct cases. That is, the base station and the eavesdropper know the channel state information to the users, and the opposite case. In each case, we further consider whether the accurate position of the eavesdropper is known to the users. Due to the complexity of the cost function, a closed-form solution is intractable. As a result, the bisection method is employed to obtain the numerical results. The impacts of the non-idealities, including the channel estimation error and the uncertainty of eavesdropper's position, on the power allocation strategy are discussed. Finally, extensive simulations are carried out to validate our proposed algorithms.
Self-Body Image Acquisition and Posture Generation With Redundancy Using Musculoskeletal Humanoid Shoulder Complex for Object Manipulation

We proposed a method for learning the actual body image of a musculoskeletal humanoid for posture generation and object manipulation using inverse kinematics with redundancy in the shoulder complex. The effectiveness of this method was confirmed by realizing automobile steering wheel operation. The shoulder complex has a scapula that glides over the rib cage and an open spherical joint, and is supported by numerous muscle groups, enabling a wide range of motion. As a development of the human mimetic shoulder complex, we have increased the muscle redundancy by implementing deep muscles and stabilize the joint drive. As a posture generation method to utilize the joint redundancy of the shoulder complex, we consider inverse kinematics based on the scapular drive strategy suggested by the scapulohumeral rhythm of the human body. In order to control a complex robot imitating a human body, it is essential to learn its own body image, but it is difficult to know its own state accurately due to its deformation which is difficult to measure. To solve this problem, we developed a method to acquire a self-body image that can be updated appropriately by recognizing the hand position relative to an object for the purpose of object manipulation. We apply the above methods to a full-body musculoskeletal humanoid, Kengoro, and confirm its effectiveness by conducting an experiment to operate a car steering wheel, which requires the appropriate use of both arms.
ESA-VLAD: A Lightweight Network Based on Second-Order Attention and NetVLAD for Loop Closure Detection

Loop closure detection (LCD) is an important portion of Simultaneous Localization and Mapping (SLAM) because of its ability to reduce accumulated position errors. In this letter, we propose a novel loop closure detection algorithm named ESA-VLAD. The crucial part of ESA-VLAD is a redesigned network with EfficientNetB0 as backbone for extracting global features, which integrates a second-order attention module in order to effectively learn the correlations between features within the feature map. A trainable Vector of Local Aggregated Descriptors (NetVLAD) is integrated in the last layer of the network to generate a compact and fixed-length global feature. Knowledge distillation strategy is adopted in training of the proposed network to accelerate the training process. For the global features, Hierarchical Navigable Small World (HNSW) is employed to retrieve the loop closure candidate images. In addition, an efficient geometrical consistency check based on local difference binary (LDB) descriptors is designed to verify loop closure matches. Experiments on several public datasets demonstrate that ESA-VLAD can obtain higher recall rates under 100% precision and less processing time per frame compared to other typical and state-of-the-art methods.
A Dual-Band Ambient Energy Harvesting Rectenna Design for Wireless Power Communications

In this paper, a long-range dual-band rectenna for harvesting ambient radio frequency (RF) energy from GSM/900 and GSM/1800 is presented. Theoretical analysis of the proposed dual-band impedance matching network (IMN) is conducted using a modified $\Pi $ –section matching network (MN). The RF-rectifier is integrated with a dual-band inverted-F monopole antenna. The rectenna circuit complexity is minimized by introducing a dual-band IMN, which plays a significant role in improving the harvester RF-to-dc power conversion efficiency (PCE). Measurement results of the proposed design achieved a peak RF-to-dc PCE of 12.93% and 8.0% for an input power of −30 dBm at 0.9 GHz, and 1.8 GHz, respectively. The RF harvester ambiance measurement attained an output dc voltage of 0.374 V. The circuit generates 0.747 V using a low-powered bq25504-674 evaluation module (EVM). Thus, adequate energy management of the proposed rectenna can be used to power many low-powered devices from the harvested ambient RF energy.
Collision Resolution With FM0 Signal Separation for Short-Range Random Multi-Access Wireless Network

In internet of things, a central node to multiple sensor nodes is usually a short-range communication network, which generally uses random multiple access to resolve collision at a media access control (MAC) layer. In this case, the communication efficiency is not optimal. On the other hand, its efficiency can be improved in separated collision resolution, where collision signals can be separated and then decoded. For the resolution, good coding techniques can improve the performance of collision resolution. However, the conventional coding techniques usually need the signal fading coefficient estimated or be known in advance. The condition is not always satisfied in the short-range network. This paper proposes a finite-symbol separation Viterbi decoder (FSVD) which does not need to know the fading coefficient. Through the estimation of a dictionary matrix, the uncertainty of signal separation can be eliminated, and thus obtain more accurate likelihood distances and improve the decoding accuracy. In experiments, we establish two ultra-high frequency (UHF) short-range wireless networks based on an FM0 code, using a simulation and a software radio platform, respectively. The experimental results show that the throughput of FSVD reaches about 0.61, which is about 0.25 higher than that of a traditional ALOHA network.
Optical Force/Tactile Sensors for Robotic Applications

Nowadays, robotic systems use tactile sensing as a key enabling technology to implement complex tasks. For example, manipulation and grasping problems strongly depend on the physical and geometrical characteristics of the objects; in fact, objects may be deformable or change their shape when in contact with the robot or the environment. For this reason, often, robots end effectors are equipped with sensorized fingers which can estimate the objects' features, forces, and contact locations. This is useful in a safe and efficient physical Human-Robot Interaction (pHRI) to perform cooperation and co-manipulation tasks while limiting damage from accidental impacts.
A New Coupled-Inductor-Based High-Gain Interleaved DC-DC Converter With Sustained Soft Switching

This paper presents a new soft switching high voltage gain interleaved dc-dc converter with a three-windings coupled inductor for a fuel cell vehicle. Two interleaved and intercoupled boost cells are connected by the input parallel and output series (IPOS) configuration, realizing low input current ripple and high voltage gain simultaneously. All power switches can operate under soft switching conditions by only adding a simple auxiliary circuit composed of one auxiliary inductor and one power switch. Besides, the auxiliary inductor is coupled with the main inductors, which results in a rapid zero convergence of the auxiliary current and a small conduction loss of the auxiliary circuit. The operating principle, steady state characteristic, comparison with other converters, and parameter design are analyzed in this paper. Finally, the main characteristics of the proposed converter are verified by a 1-kW laboratory prototype.
Low-Density Parity-Check Coded Direct Sequence Spread Spectrum Receiver Based on Analog Probabilistic Processing

Forward error correction (FEC) coding is an indispensable technique in the direct sequence spread spectrum (DS-SS) systems for satellite communication applications. Both the FEC and DS-SS can be regarded as specific cases of probabilistic computing based on analog circuits, which is expected to be a promising solution for power-limited scenarios. The combination of FEC and DS-SS techniques can provide sufficient link margin and robustness for communication systems. In this paper, a probabilistic receiver chain for the Low-Density Parity-Check (LDPC) coded DS-SS system is proposed. Generically, an <inline-formula><tex-math notation="LaTeX">$m$</tex-math></inline-formula>-sequence can be regarded as a codeword of cyclic linear codes. Similar to the decoding procedure of LDPC codes, the joint detection and decoding process of <inline-formula><tex-math notation="LaTeX">$m$</tex-math></inline-formula>-sequences can be performed by factor graph-based iterative message-passing algorithms (iMPAs). In terms of the iterative signal processing, we first present an improved approach of iterative stopping criterion which can reduce the average number of iteration by 90% for the LDPC decoding approach. Furthermore, a joint detection and decoding method is developed to provide quick synchronization of the <inline-formula><tex-math notation="LaTeX">$m$</tex-math></inline-formula>-sequence. Meanwhile, stopping criterion-based iMPAs are especially suitable for analog implementation with low complexity. Finally, cascading to the analog LDPC decoder, the implementation of the <inline-formula><tex-math notation="LaTeX">$m$</tex-math></inline-formula>-sequence detector is designed. The prototyping chip is fully integrated into a 0.35-<inline-formula><tex-math notation="LaTeX">$\mu\text{m}$</tex-math></inline-formula> CMOS technology, which can achieve higher throughput than 3 <inline-formula><tex-math notation="LaTeX">${\textbf {Gcps}}$</tex-math></inline-formula> with a core chip area of 2.79 <inline-formula><tex-math notation="LaTeX">${\textbf {mm}}^2$</tex-math></inline-formula> and power consumption of 6.99 <inline-formula><tex-math notation="LaTeX">${\textbf {mW}}$</tex-math></inline-formula> for its core circuit. Experimental results demonstrate the effectiveness of our proposed receiver mechanism.
Event-Triggering Virtual Inertia Control of PV Systems With Power Reserve

Grid frequency support, e.g., by the virtual inertia control (VIC), of photovoltaic (PV) systems, is more demanded than ever before. To achieve the full-range frequency support (i.e., to tackle the under- or overfrequency issues), the power reserve is necessary for PV systems. Accordingly, a power reserve control (PRC) method based on the maximum power point tracking (MPPT) is adopted in this article, referring to as MPPT-PRC. It measures the real-time maximum available power (MAP) periodically, being independent of physical sensors and burdensome computation. However, the MAP measurement loop inevitably couples with the power reserve loop, which makes the realization of the VIC challenging. Aiming at this issue, an event-triggering strategy is elaborately designed to switch the PV system in-between different operating modes. The strategy is realized by a set of control signals. They are responsible for the detection of frequency incidents, the activation of MPPT, the measurement of MAP, respectively. Consequently, the VIC can be achieved based on the MPPT-PRC without any conflicts or instability. Experimental tests are performed on StarSim real-time hardware-in-the-loop system to validate the proposed MPPT-PRC-based VIC, as well as the coordination of the control loops.
Dynamic Voltage Restorer based on a New Step-up Switched Capacitor Multilevel Inverter for Fault Ride-Through of Wind Generators

Providing capability to keep wind and renewable sources connecting to the grid by Dynamic voltage restores (DVRs) during transient low-voltage and over-voltage conditions is a recommended solution. But, increasing the grid nominal voltage, the power switch stresses increase significantly. Also, the generated voltage by DVRs may contain high total harmonic components. In this regard, this research work aimed to introduce a new structure for step-up switched capacitor converter (SCC) for voltage regulation by DVRs. Among the advantages of the proposed structure, increasing the voltage amplitude, providing more voltage levels and using fewer components compared to the conventional structures can be mentioned. In the proposed structure, a 13 level inverter is presented which has two independent DC voltage source. The capabilities of the suggested 13-level structure, it is used to feed resistive and resistive-inductive loads. The capabilities of the proposed structure are tested in MATLAB/Simulink. The feature of the proposed inverter in a DVR-quipped wind unit is verified through simulations results.
Evaluation of Energy Harvesting for Smart Cane Application

Walking cane is a device used by disabled people to navigate themselves. In recent days, it is equipped with various sensors and functions and it is known as a smart cane. It is already available in the market. However, the smart cane is depending on the battery supply that has to be charged every now and then. In relation to that, this paper is focusing on evaluating energy harvesting for powering up a smart cane. This project evaluates possible energy harvesting considering the nature of usage which are outside, gripping motion, tapping motion of the cane itself. Here, an energy harvesting system was designed and attached to a walking stick and the energy generated was observed. The system consists of a combination of solar modules and piezoelectric modules. The solar modules were attached to the handle, whilst the piezoelectric modules were attached to the bottom of the cane. From there, an experimental evaluation was done by testing the cane in indoor and outdoor environment by subjects with different body profile. The result shows that higher power is generated during outdoor usage, and this shows that the solar modules contribute the most to the generated energy. On the other hand, the piezoelectric modules only contribute a small portion of the generated energy. However, this contribution is significantly important during the usage of the cane in indoor environment, where less sunlight is available.
MITRE ICS Attack Simulation and Detection on EtherCAT Based Drinking Water System

Industrial control systems (ICSs) are complex systems due to the technology and protocol diversity they contain. Operational Technology (OT), an ICS operating structure, has different performance and security requirements than the standard IT infrastructure. ICS systems consist of field devices where operational processes take place and control systems that provide management of these devices. Attackers are involved in the whole process after gaining access from the control layer. As a result, critical infrastructure systems are threatened by cyber-attacks. Therefore, continuous monitoring and security audits are also necessary processes for critical infrastructures. In this study, studies on the cyberattack and detection system were carried out on the critical infrastructures of the water management process. On the EtherCAT-based water management process, six different attack vectors for field devices were developed by the techniques in the MITRE ICS ATT&CK matrix, and these attacks were separated by data obtained from network traffic and determined by the SVM algorithm. Attack scenarios were created by selecting seven different MITRE ICS ATT&CK matrix techniques for attacks on the SCADA system in the control center via the engineering computer on the same process. Wazuh HIDS was used for the intrusion detection system for the SCADA system. Visualization of both attacks was done on ELK.
Analysis on the Application of Artificial Intelligence in the Medical Field

Artificial intelligence is an interdisciplinary subject with very high comprehensiveness. It is a new and emerging discipline that integrates new technologies, new theories and new ideas. Currently artificial intelligence has been widely applied in many fields, including the field of medical health. This paper expounds the application of artificial intelligence in the field of medical health, analyzes existing problems, and from the perspective of the overall development of the industry, puts forward some suggestions on rational application development of artificial intelligence in the medical field.
Formative Quizz : An Informatic Platform and a Pedagogic Experiment

The closure of schools caused by the pandemic situation and the consequent transition to distance learning during the first confinement starting in March 2020 and now during the second term of 2020/2021, exposed the schools, students and families to enormous challenges and countless difficulties. This scenario motivated the implementation, by schools, of numerous strategies to mitigate the negative impacts of this confinement in terms of student learning and their involvement with the school.In this context, an innovative pedagogical experience is presented in this article, in terms of the assessment of students' learning, which has been running since January 2019 in a school on the 2nd and 3rd cycles and secondary in the Porto area. This experience implies the frequent use of formative assessment quizz with automatic correction and immediate feedback to students, parents and teachers. The intensive use of this pedagogical tool during periods of distance learning, in this educational context, had a positive impact in terms of students' commitment as well as their involvement and interconnection with the school and teachers, mitigating the negative effects of distance learning. at these school ages and levels. In this article, this pedagogical tool (micro-evaluations) is described in more detail, giving an account of its main characteristics, functionalities and the database architecture that supports it. The main figures for the implementation of this experiment and some of the preliminary conclusions that can be drawn from this study are also presented. Being a pedagogical tool with proven effect in terms of students' learning and its functionality and web connection, it becomes particularly useful in the school context, both in terms of distance and face-to-face teaching, leading to the discussion of the future challenges of using tools online in school context.
Developing Predictive Oil Well Diagnostics Based on Intelligent Algorithms

The current competitive market condition in the oil industry is so concentrated on companies' budgets that they require methods to extract oil from wells at the lowest possible cost. All pumping units, new or old, require regular preventive maintenance and constant inspection, and with 30 percent of total oil production coming from sucker rod pumps, the cost of diagnostics must be reduced accordingly.This article focuses on the intelligent diagnostics of the rod and borehole pump for preventive maintenance and monitoring during the life cycle of the well. The use of artificial intelligence methods for predictive diagnostics of equipment condition solves problems without production stoppages and without additional interventions from outside.
A Neuron for Velocity Detection Based on Inhibitory Mechanism in Retina Ganglion

For mammals, motion direction detection is of great significance. There are specific ganglion cells in the retina to process movement information. In the retina system, cells of the different types perform different computation in different places of the visual field. Very few parts of them are figured out. For example, the processing mechanism of velocity information is unclear. Developing a good modeling of velocity neurons is very important to many different research fields. In this paper, we propose a neuron for velocity detection based on inhibitory mechanism in retina ganglion. We show that there are velocity detective neurons in the nervous system. Different speed information is processed by different speed detection neurons which has been confirmed to exist. We use them to detect different levels of velocity in one dimension. To verify the validity of the proposed mechanism, we conduct a series of experiments which show that this model can successfully detect the movement speed of objects in one-dimension.
A Method for Detecting DC Bias in Transformer of Dual Active Bridge DC-DC Converter

A potential concern with the isolation transformer in a dual active bridge DC-DC converter (DAB) unit is the DC bias of magnetization. With the influence of DC bias, the loss of the transformer increases, and the lifespan decreases. However, it is difficult to accurately measure the small DC component mixed in a large (>10A peak), high-frequency AC current, which makes it difficult to eliminate DC bias. A novel sensor is proposed to solve this problem. The proposed sensor is based on the principle of single-core fluxgate and current transformer. Compared with an ordinary Hall effect sensor, the proposed sensor significantly improves the DC current measurement accuracy and sensitivity. This paper presents the working principle and basic structure of the sensor, and the experimental result that measuring the small DC component mixed in a large, high-frequency AC current is also be demonstrated.