Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptation of Commercial Current-controlled Inverters for Operation with Virtual Oscillator Control

Virtual oscillator control (VOC) is a decentralized time-domain control technique for ac microgrids where inverters are regulated to emulate the dynamics of weakly nonlinear oscillators. VOC enables the design of modular and scalable systems where inverters can synchronize and share power without communication and in near real-time. In this paper, we show how off-the-shelf commercial inverters with current control can be reprogrammed to behave as voltage sources with virtual oscillator dynamics for deployment in islanded settings. We focus on commercial grid-tied inverters that have an inner current-control loop and show how the outer-loop controls can be straightforwardly modified to enable voltage-control-mode operation. To illustrate the practicality and ease of our approach, the proposed strategy was implemented on a 3.2 kVA experimental test bed composed of 10 SunPower-brand micro-inverters with special firmware for VOC implementation. Results from the experiments not only demonstrate feasibility of the proposed dual-loop VOC architecture on a hardware setup but also show improved voltage regulation due to the additional voltage control loop.
Multi-Level Memory Comprising Only Amorphous Oxide Thin Film Transistors

A multi-level memory circuit with only amorphous oxide thin-film transistors (Ox-TFTs) is proposed. The proposed circuit comprises two Ox-TFTs and a capacitor. The proposed multi-level memory can modulate threshold voltage freely depending on programming voltages. Low leakage current of Ox-TFT enables the proposed circuit to operate as a memory. We measured transfer characteristics of the proposed circuit and investigated the threshold voltage shift by a simple capacitance model. We found that the proposed memory cell can be used as a multi-level memory.
Mitigating Distribution Power Losses of Standalone AC Microgrids Using Particle-Swarm-Optimization Control for Distributed Battery Systems

Distributed battery systems (DBS) are widely used in microgrids to compensate imbalanced active and reactive power flows, and for stabilizing bus voltages of microgrids with a high penetration of renewable energy sources (RES). In this paper, a particle-swarm-optimization (PSO) method is adopted as the secondary control of DBS in a standalone AC microgrid. The fitness function of the PSO algorithm contains the parameters of the bus voltage deviations and the power losses on the distribution lines. Through iteration, the bus voltage deviations and the power losses on the distribution lines are reduced, simultaneously. Importantly, the State-of-Charges (SOC) of the battery packs are also being taken into consideration and the battery packs are controlled by local controllers to prevent deep-discharge and over-charge. Both results from Matlab simulation and Real-Time Digital Simulator (RTDS) validate the effectiveness of the proposed control scheme in concurrently reducing the distribution power losses and meeting bus voltage regulations in AC microgrids.
Towards a Prototypical Approach to Tool-Use Improvisation

When a robot is operating in a dynamic environment, it cannot be assumed that a tool required to solve a given task will always be available. In case of a missing tool, an ideal response would be to find a substitute to complete the task. In this paper, we present a proof of concept of a grounded knowledge-based approach to tool substitution where knowledge is generated in an unsupervised manner from robot's sensory data about objects. Such robot-centric grounded knowledge is then used to identify a substitute from the available objects in the environment.
Simplifying Soft Robots Through Adhesive-backed Fabrics

The proliferation of soft pneumatic actuators (SPAs) has brought developments in the materials for soft actuator fabrication. However, current manufacturing methods require complicated molding processes, impose size limitations due to equipment dimensions, or involve time consuming sealing steps. This work introduces and explores a novel material for simpler SPA prototyping: the adhesive-backed fabric (ABF). Fabrication methods are described, and performance is analyzed using a horizontal and vertical grip test. Three types of ABF are used to construct soft grippers and compared with a previously validated textile material. ABF-SPAs are found to perform adequately similar to the previously validated textile in grip tests. A method for determining the failure limit of adhesive-backed material is also described based on the tensile strength and the shear strength of the adhesive, and the method is validated via pressurization tests. The method is found to reasonably predict the failure mode and failure point of ABF material. To further demonstrate capabilities of ABF, one of the ABFs is chosen for construction of a linearly expanding ABF-SPA. The actuator is able to extend by over 3400% of its original length.
An Improved Power-Decoupling Scheme with Grid Inductor Phase-Shift Modification for Single-Phase Converter

Active power decoupling (APD) techniques are widely used in single-phase PWM rectifiers under 10 kilowatts, which prove to be efficient in cutting passive component size (usually against bulky electrolytic capacitors at dc side). However, it is found that the reactive power caused by boost inductor cannot be ignored especially in low input voltage, high power situation with large input inductance. Such reactive power could bring an obvious phase-shifted angle ϕ to dc side current, which contributes to the steady state error in decoupling reference for APD circuit. This paper presents a strategy by modifying ϕ to achieve the ac-side inductor reactive power compensation (IRPC) without adding any hardware, which could further reduce dc-link capacitance. Compared with the traditional control method that usually neglects reactive power stored by inductor, a more effective power-decoupling IRPC scheme is validated experimentally with a 1kW prototype.
Nomadic Knowledge Sharing Practices and Challenges: Findings From a Long-Term Case Study

In this paper, we explore a specialized type of knowledge, namely, nomadic knowledge, to understand its implications in knowledge sharing. Nomadic knowledge is enacted in a discontinuous pattern with a changing set of actors and further flows on a defined trajectory. This knowledge is quite important but is required sporadically, so it has a varying level of importance for stakeholders at different instances of time. The limited interest of knowledge holders after the creation of this type of knowledge makes its sharing process complex. Furthermore, new sets of actors overloaded with tasks often ignore the knowledge sharing aspect of nomadic knowledge due to the urgency of the tasks at hand. Using a long-term field study, we illustrate practices concerning the sharing of nomadic knowledge, which, we argue, are to date not yet well supported technologically. The objective of this contribution is, therefore, to outline the concept of nomadic knowledge and associated implications, so that respective knowledge sharing practices can be supported by the knowledge management community with appropriate technology. The underlying complexity of knowledge sharing practices highlighted in this paper stresses the need for appropriate technological and social processes to facilitate the sharing of nomadic knowledge.
Two-level Secure Colored Image Transmission Using Novel Chaotic Map

The implementation of a robust secure system needs to generate new discrete dynamical systems. This paper introduces a two level secure colored image transmission system based on a recently proposed chaotic system, called Nahrain. In the first level, the chaotic system is used as a key for encrypting the secure color image. In the second level, the chaotic system is used as radio frequency carrier signal for modulating the digital data. The paper also proposes two new chaotic modulation methods, each method is designed with different testing types. The first method has seven types and based on single bit modulation. The second method has three types and based on two bits modulation. The nonlinear control laws are used to verify the synchronization of master-slave parts of the Nahrain Chaotic Map (NCM). The performance of the proposed methods is measured using Peak Signal to Noise Ratio (PSNR) and Bit Error Rate (BER) over Additive White Gaussian Noise (AWGN) channel. In comparison with Henon and Logistics chaotic systems, NCM showed better results when using Chaotic Shift Keying (CSK) Modulation. In this simulation, NCM achieved BER of $1.2787\times 10^{-6}$ at SNR of 21dB, while the Henon and Logistic achieved 0.0124, and 0.0313 respectively. Furthermore, the results showed that the performance of the proposed single bit modulation methods over perform CSK where most of the testing types were error free at SNR equals 10 dB. Alternatively, the proposed two bit modulation methods can duplicate the transmission rate with few degradation in SNR does not exceed 2 dB at BER equals $10^{-4}$ as compared with single bit modulation methods.
Crystal Identification in Dual-Layer-Offset DOI-PET Detectors Using Stratified Peak Tracking Based on SVD and Mean-Shift Algorithm

An Anger-logic based pixelated PET detector block requires a crystal position map (CPM) to assign the position of each detected event to a most probable crystal index. Accurate assignments are crucial to PET imaging performance. In this paper, we present a novel automatic approach to generate the CPMs for dual-layer offset (DLO) PET detectors using a stratified peak tracking method. In which, the top and bottom layers are distinguished by their intensity difference and the peaks of the top and bottom layers are tracked based on a singular value decomposition (SVD) and mean-shift algorithm in succession. The CPM is created by classifying each pixel to its nearest peak and assigning the pixel with the crystal index of that peak. A Matlab-based graphical user interface program was developed including the automatic algorithm and a manual interaction procedure. The algorithm was tested for three DLO PET detector blocks. Results show that the proposed method exhibits good performance as well as robustness for all the three blocks. Compared to the existing methods, our approach can directly distinguish the layer and crystal indices using the information of intensity and offset grid pattern.
Biomimetic Soft Airflow Sensor with Printed Ionogel Conductor

This work demonstrates a soft airflow sensor that is able to detect the direction and the magnitude of airflow, taking advantage of additive manufacturing for fast prototyping. The sensor is comprised of a printed conductive ionogel array with four channels embedded in a elastomeric film: three channels for individual sensor signals and the other for common ground. The film consists of two different types of highly compliant elastomers to maximize the deflection of the artificial hair. the design of the artificial hair was proposed to maximize the moments at the lower end of the hair resulting from the airflow. A compact readout circuit to simultaneously acquire data from the impedances of the three channels was designed and its functionality was experimentally verified. The experimental results from unidirectional static and cyclic loads show a linear and repeatable response of the sensors and a clear relationship between the response and applied airflow.
Active Voltage-Ripple Compensation in an Integrated Generator-Rectifier System

A series-stacked ac-to-dc converter integrated into a multi-port permanent-magnet-synchronous generator has been shown to be beneficial in achieving high conversion efficiency while reducing the size and rating requirements for the active rectifier [1]. The major drawback of this architecture is the bulky filter capacitors used at the passive rectifiers. These filters increase system size, weight and cost, reduce the overall reliability and cause a low power factor at the passive rectifier ac-port. This paper presents an alternative approach: using the active rectifier to compensate the capacitor-less passive rectifier voltage ripple. Simulation on a 6.25 kV, 3 MW dc bus system shows that by active compensation, the dc-bus voltage ripple is reduced to 2.4% of the dc-bus voltage. In addition, the passive rectifier ac-source power factor is improved to 0.95 compared to 0.8 using the conventional method with filter capacitors. Experimental results show the effectiveness of the proposed method for voltage ripple compensation.
Adaptive Multi-agent System for Situated Task Allocation

Multi-agent scheduling has received significant attention in tackling the problem of load balancing and task allocation in distributed systems. Apart from dividing the work through decentralization, we consider dynamicity because allocation of tasks must be concurrent with their execution, and adaptation because tasks must be reallocated when a disruptive event is performed. We will assume that agents are fully distributed and cooperative in order to optimize the global runtime, i.e a system-centric metric rather than user-centric metrics. We will also assume that a task can be performed by any single agent without preemption and precedence order. Moreover, tasks have no deadlines, are indivisible and not shareable. We follow a market-based approach to tackle the multi-agent situated task allocation problem. In order to improve load balancing, agents adopt a locality-based strategy in concurrent one-to-many negotiations for task delegations. The task reallocation is dynamic since the negotiation process is iterated and concurrent with the tasks processing. Moreover, the system is adaptive to disruptive events, e.g. task consumptions. As a practical application, we consider the distributed deployment of the MapReduce design pattern for processing large datasets. Our preliminary empirical results show that, for such an application, the locality-based strategy improves the runtime.
Low Power Speaker Identification using Look Up-free Gaussian Mixture Model in CMOS

This work discusses a CMOS-based implementation of low power speaker identification (SI) using Gaussian mixture models (GMMs). Conventionally, GMM-based SI relies on repeated access to log-add look-up table (LUT). With increasing dimensional of speaker models $\mathbf{and}/\mathbf{or}$ number of speakers in the database, accesses to the LUT dominate the overall energy expense for SI. In this work, we discuss piece-wise linear approximations to GMM model that eliminate LUT accesses, thereby limiting model parameter storage in the register files alone, while incurring a minimal accuracy drop. We evaluate our scheme on TIMIT corpus where for text-independent SI and with a two-second test speech, our scheme achieves more than $90\%$ accuracy across test-sets. We discuss the detailed architecture of the control unit, datapath, and key modules in our scheme. Compared to an equivalent design that requires LUT accesses in $\mathrm{off}$ -chip memories, our scheme limits power dissipation for SI to $\sim 600\ \mu \mathbf{W}$ and consumes $4\times$ less energy,
Efficient Neural Network Based Principal Component Analysis Algorithm

Principal Component Analysis (PCA) is a very important Statistical analysis tool and therefore many researchers are working to improve the algorithm for better performance and better data interpretation. To improve PCA algorithm in this paper we propose to deploy a multilayer neural network with linear artificial neurons to calculate eigenvalues and corresponding eigenvectors using back-propagation learning algorithm. This approach enables us to find all the eigenvalues and its corresponding eigenvectors simultaneously by training the network. Conventional approach to calculate eigen pairs using singular value decomposition (SVD) is time consuming for large datasets because it is suitable to find one particular eigen pair during one run. The second approach that we propose for improving PCA is to decide the best eigenvectors or the principal components that best represent our original input data. This is done by extracting important features by a neural network of desired dimension from our original data by training all the features of that data.
Buckling Elements for Elastomer Deformation

Buckling instabilities can be the cause for dramatic breakdown in a variety of systems. However, this paper seeks to exploit the addition of buckling elements within soft robotic systems, to create a passive binary switch from a stiffened system to an unstiffened system via a quick, predictable response. This binary switch essentially operates as a high pass filter for pressure, restraining motion at low pressures and allowing full control at high pressures. To test, the proposed elements were attached to fluidic elastomer actuators in three configurations, the first with no element, the second with an element that extended across all internal pneumatic chambers and a third that offset the element, allowing the first two pneumatic chambers to actuate prior to buckling. The actuators were then inflated and their pressure and curvature responses were recorded with respect to volume. Buckled elements are shown to suppress effective curvature at low pressures, prior to buckling. Post buckling, the rate of effective curvature increases. The snap angle of buckling for each of the constrained systems is shown to be 19.6° for the fully constrained pneu-net and 28.6° for the pneu-net with offset buckling element (averaged across five trials). This paper proposes the use of such buckling elements in responsive gripping technologies and as a method for morphological change.
Characterization and separation based on WEEE polymer particle shape

The new European targets (from 2012) for Waste from Electronic and Electric Equipment (WEEE) and the fact that up to 40% of plastics from WEEE end up in the landfill are strong reasons to pursue investigation within plastics separation and apply it to WEEE. In this context, it was proposed to investigate whether the shape of identical densities plastics fragmented particles is a differentiating factor between materials. Images of ABS, HIPS and PS from WEEE treatment facilities were analyzed according to size, type of plastic and type of fragmentation (usage off different equipment). Three morphological indicators were used to describe the behavior of the particle regarding their convexity of silhouette, circularity and elongation. It was proposed as well to perform a set of tests with exploratory character to investigate whether the difference in particle shape can lead to separation of different plastics. The separation tests were conducted in gravity concentration equipment Recglass, where particle shape would be the determining factor in the separation. In these experiments a mixture of PS and ABS was used with different particle shape and color per plastic to provide quick and easy identification. In these experiments were used binary mixtures of PS and ABS with various compositions and size classes. Main results in particle shape analysis provide evidence that fragmented plastic particles is a function of size class, fragmentation equipment used and plastic type. Separation results indicate a moderate separation of both plastics, but further investigation is needed to better understand the separation potential of similar density WEEE plastics with different particle shape. Introduction Context The management of Waste from Electronic and Electric Equipment (WEEE) already implemented and working in Portugal will be confronted with more demanding challenges originated from the growing diversity and quantity of WEEE has well the change of legal context for more ambitious collection and recycling targets [1]. In terms of separation WEEE is a heterogeneous flow [2], and for the small dimension WEEE, many materials can only be separated for recycling after a fragmentation process [3]. Plastics are, in weight, the second main component of WEEE [4], [5], with ABS, PS, HIPS and PP being the most common in
A Tensor Decomposition-Based Anomaly Detection Algorithm for Hyperspectral Image

Anomalies usually refer to targets with a spot of pixels (even subpixels) that stand out from their neighboring background clutter pixels in hyperspectral imagery (HSI). Compared to backgrounds, anomalies have two main characteristics. One is the spectral anomaly, i.e., their spectral signatures are different from those associated to their surrounding backgrounds; another is the spatial anomaly, i.e., anomalies occur as few pixels (even subpixels) embedded in the local homogeneous backgrounds. However, most of the existing anomaly detection algorithms for HSI only employed the spectral anomaly. If the two characteristics are exploited in a detection method simultaneously, better performance may be achieved. The third-order (two modes for space and one mode for spectra) tensor representation of HSI has been proved to be an effective tool to describe the spatial and spectral information equivalently; therefore, tensor representation is convenient for exhibiting the two characteristics of anomalies simultaneously. In this paper, a new anomaly detection method based on tensor decomposition is proposed and divided into three steps. Three factor matrices and a core tensor are first estimated from the third-order tensor that is constructed from the HSI data cube by using the Tucker decomposition, and their major and minor principal components (PCs) are more likely to correspond to the spectral signatures of the backgrounds and the anomalies, respectively. In the second step, a reconstruction-error-based method is presented to find the first largest PCs along each mode to eliminate the spectral signatures of the backgrounds as much as possible, and thus, the remaining data may be modeled as the spectral signatures of the anomalies with a Gaussian noise. Finally, a CFAR test is implemented to detect the anomalies from the remaining data. Experiments with simulated, synthetic, and real HSI data sets reveal that the proposed method outperforms those spectral-anomaly-based methods with better detection probability and less false alarm rate.
Energy Harvesting, Hybrid, Cooperative Wireless System Modeling and Outage Performance Analysis

We consider radio frequency-energy harvesting (RF-EH) non-cooperative system, and, an RF-EH relay-assisted, two hop, hybrid cooperative communication system that comprises of a digital link, and an analog link that uses phase modulation. For the two system models, we analytically characterize outage probability when the node transmissions undergo small scale fading as well as path loss. In the analysis, we develop simple yet insightful closed-form expressions for outage probability. We validate our analytical results with Monte-Carlo simulations. Our numerical results show that the outage can be significantly improved by using pre-emphasis and de-emphasis (PE-DE).
A Reconfigurable Crawling Robot Driven by Electroactive Artificial Muscle

In nature, inchworms can move freely on uneven terrains where conventional wheeled or tracked robots cannot. Emerging soft actuation technologies such as dielectric elastomer actuators (DEAs) offer a new approach for inchworm-inspired robot designs thanks to their large actuation strain and inherent compliance. In this work, we present a reconfigurable inchworm-inspired crawling robot driven by DEAs that demonstrates two different crawling modes: vibrational crawling and two-anchor crawling. This modular design combines the advantages of fast speed through the vibrational crawling motion and payload transportation capability of the two-anchor crawling motion. A single vibrating module shows a fast locomotion speed of 0.9 body length / second. When two of the robot modules are configured into a two-anchor crawling mode, this new configuration can carry a payload of up to 35% its body weight at a slower speed.