Miracle-dz/UniFAR-FTUs huggingface dataset:
Hardware-in-the-loop Implementation of Residential Intelligent Microgrid

The residential-level intelligent microgrid is generally considered as a small-scale electricity system that relies mostly on local solutions to reliable and economical operation for supply-demand balance while being conducive to its connected main grid. This paper aims to conceptualize the intelligent residential microgrid from the perspectives of system-level control and component-level reformation. In particular, a newly developed hardware-in-the-loop microgrid platform is presented, through which a recently proposed online scheduling algorithm namely CHASE (Competitive Heurist Algorithm Scheduling Algorithm for Energy-scheduling) has been confirmed to be feasible and effective. Towards component level, several implementations related to low-voltage-ride-through capability on the local generation side and frequency responsive control on the demand side are also presented. Experimental results exhibit the practical feasibility of the proposed intelligent microgrid concept.
A New Robot-Based System for In-Pipe Ultrasonic Inspection of Pressure Pipelines

As pressure pipelines are usually mounted overhead or buried underground, the in-pipe inspection equipment that can run through inside pipelines is strongly demanded. Currently, the application of robotic systems for pressure pipelines in-pipe inspection is considered as one of the most attractive solutions available. Therefore, a new robot-based system for pressure pipelines in-pipe inspection was developed. A new defect detection method for pressure pipes was presented based on ultrasonic phased array technique. The developed system mainly included three parts: a tracked mobile robot for providing the driving force of the system movement, a camera used to photograph the inner surface videos or photos inside pipelines and an ultrasonic phased array module for defects detection. The structure characteristics of water/steel interface was investigated, as well as the focusing performance of convex phased array. The detection parameters such as elements size and dynamic apertures were studied and optimized with CIVA simulations. The in-pipe inspection experiments in pressure pipelines with an outer diameter of 219 mm were also carried out. The results show that the robot-based system could successfully go through inside the pressure pipelines and the ultrasonic phased array technique could implement inner surface scanning and defect detecting, which indicates that the new defect detection method can improve the pressure pipelines inspection effect.
A Scientific Autobiography

This revealing memoir by Aldo Rossi (1937--1997), one of the most visible and controversial figures ever on the international architecture scene, intermingles discussions of Rossi's architectural projects -- including the major literary and artistic influences on his work -- with his personal history. Drawn from notebooks Rossi kept beginning in 1971, these ruminations and reflections range from his obsession with theater to his concept of architecture as ritual. The book originally appeared as one of the landmark titles in the MIT Press's Oppositions Books series, but has been out of print for many years. This newly issued paperback reprint includes illustrations -- photographs, evocative images, and a set of drawings of Rossi's major architectural projects prepared particularly for this publication -- selected by the author himself to augment the text.
Dynamic Network Slicing for LoRaWAN

One of the most important novelty in 5G is network slicing proposed as a collection of logical network functions for services running on a common physical device. In an Internet of Things (IoT) context, network resources need to be efficiently reserved and assigned for IoT devices in an isolated manner to handle and support specific Quality of Service (QoS) requirements for each slice. The focus of this paper is to investigate network slicing in LoRa network and propose a dynamic inter-slicing algorithm based on a maximum likelihood estimation that avoids resource starvation and prioritizes a slice over another depending on its QoS requirements. Moreover, we place the emphasis on a novel intra-slicing strategy that maximizes resource allocation efficiency of LoRa slices with regard to their delay requirements. After integrating an energy module for LoRa in NS3, simulation results performed in realistic LoRa scenarios highlight the utility of our dynamic network slicing proposition in providing isolation between slices with specific QoS guarantees.
Automatic Recognition of Range Profiles and ISAR Images of Targets Flying in Formation

This paper proposes a new method to automatically recognize range profiles (RPs) and inverse synthetic radar (ISAR) images of targets flying in formation, despite overlapping of the RPs and ISARs in a single radar beam. On contrary to the existing methods to recognize by separating each target, the proposed method utilizes the combined RP and the combined ISAR image obtained by fusing the RPs and the ISAR images in a training database based on the flight direction of the targets. Experimental results using the measured data of five scale models prove the validity of the proposed method.
TDDB Mechanism in a-Si/TiO2 Nonfilamentary RRAM Device

Mechanisms of time-dependent dielectric breakdown (TDDB) in nonfilamentary a-Si/TiO2 RRAM cell [a-vacancy modulated conductive oxide (VMCO)] have been examined in this paper, including defects generation in the grain boundary, defects clustering, and different defects generation rates in a-Si and TiO2 layers. The unique feature of a bimodal Weibull distribution at low-resistance state (LRS) and a single shallow slope distribution at high-resistance state (HRS) cannot be explained by the above mechanisms. By using a combination of constant voltage stress, time-to-breakdown Weibull distribution, and random telegraph noise (RTN)-based defect profiling in devices of various sizes, layer thicknesses, and processes, it is revealed that the defect profile is modulated when switching between HRS and LRS and the correlation of defect profile modulation with local defect generation rate can explain the difference in Weibull distributions at HRS and LRS. The transition from bimodal distribution at LRS to a single-steep slope with thinner a-Si layer, and the good area scaling of Weibull distribution at HRS but not at LRS, can also be explained. The critical layers affecting the TDDB in a-VMCO are identified, providing useful guidance for device performance improvement.
VARC – A Cost-Effective Ultrafast DC Circuit Breaker Concept

A concept for a dc circuit-breaker is proposed. A vacuum interrupter is used together with a resonant circuit excited by a power electronic converter to create a zero-crossing in the interrupter current. A low amount of semiconductor devices is needed because they perform repeated switching operations during current excitation. Tests have been performed with a prototype interrupting 10 kA against 9 kV in 3 ms.
Generic Dynamic Models for Modeling Wind Power Plants and other Renewable Technologies in Large Scale Power System Studies

With the tremendous growth of wind power worldwide in the past decade, there has been an equally great demand for simplified, standard and publicly available models for simulating wind power generators in commercially available power system simulation tools for stability analysis. Several efforts have been on the way to meet this need. The Western Electricity Coordinating Council’s Renewable Energy Modeling Task Force has successfully achieved this goal and more recently has been working on expanding these models to include the ability to model complex plants, energy storage and frequency response capabilities. This paper presents an account of these latest developments.
A Low-Power Low-Noise Biomedical Instrumentation Amplifier Using Novel Ripple-Reduction Technique

This paper presents a low-power low-noise capacitively-coupled chopper instrumentation amplifier (CCIA), which is suitable for biomedical applications such as EEG, ECG and neural recoding. A novel ripple-reduction technique combined with ping-pong auto-zeroing is employed to suppress the ripple at the output of the instrumentation amplifier (IA) by the up-modulated amplifier offset and flicker noise. By using a positive feedback loop in the IA, the IA's input impedance is increased. The complete CCIA is simulated in a standard 0.18 μm CMOS process. The simulated result shows the IA consumes several µA current at 1.8 V supply. The equivalent input noise power spectrum density (PSD) is 54 nV/√Hz and the noise efficiency factor (NEF) achieves 4.05 within 1 kHz, while the equivalent input noise PSD is 55.4 nV/√Hz and NEF is 4.15 within 10 kHz. And the input impedance is about 100MΩ.
Multiple Kernel Clustering With Global and Local Structure Alignment

Multiple kernel clustering (MKC) based on global structure alignment (GSA) has unified many existing MKC algorithms, and shown outstanding clustering performance. However, we observe that most of existing GSA-based MKC algorithms only maximally align global structure of data with an ideal similarity matrix, while ignoring the local geometrical structure hidden in data, which is regarded to be important in improving the clustering performance. To address this issue, we propose a global and local structure alignment framework for MKC (GLSAMKC) which well considers both the alignment between the global structure and local structure of data with the same ideal similarity matrix. To illustrate the effectiveness of the proposed framework, we instantiate two specific GLSAMKC-based algorithms by exploiting the local structure with local linear embedding and locality preserving projection, respectively. A two-step alternate iterative and convergent optimization algorithm is developed to implement the resultant optimization problem. Extensive experimental results on five benchmark data sets demonstrate the superiority of proposed algorithms compared with the many state-of-the-art MKC algorithms, indicating the effectiveness of the proposed framework.
Enhancing Efficiency for ABE Through Homomorphic Encryption in Cryptography

In today's computer networking technology, most of people stored their confidential data on trusted third-party server. But along with this, data privacy also arises as number of users becomes large. Naturally people want their data only accessible to authorized users according to their access policies. But when using these services, servers may get compromised by an attack like Denial-of-service, chosen cipher-text attack or Denial of service attack. Hence it is still most difficult job in cryptosystem to provide the leveled trustworthy servers and efficient data privacy systems to keep users data under the safe zone. To solve this problem advanced cryptographic methods are needed to be apply to increase efficiency for data encryption as well as to maintain the improved privacy preserving environment on web to share data. Considering these all aspects about privacy in communication, we need to look after the successful implementation also hence we have presented this paper to put forward the ideas of homomorphism into ABE which will truly build an improved as well as trustworthy privacy preserving possible outcomes using homomorphic encryption in the field of cryptography.
Compact Model of Carrier Transport in Monolayer Transition Metal Dichalcogenide Transistors

Monolayer 2-D transition metal dichalcogenide (TMDC) field-effect transistors (FETs) have excellent scaling properties due to an extremely thin body and reduced quantum-mechanical tunneling. High-field and ballistic carrier transport can play an important role in aggressively scaled TMDC FETs. A compact model is developed to describe carrier transport in 2-D monolayer FETs based on a TMDC material. The model has the following features: 1) it treats high-field transport and velocity saturation; 2) it can be applied from ballistic to quasi-ballistic to diffusive transport regimes; and 3) it incorporates analytical expressions for carrier mobility values based on examining physical carrier scattering mechanisms in TMDC materials. Monte Carlo simulation, which solves the Boltzmann transport equation stochastically, is used to validate the model. The carrier transport model is further integrated with a virtual-source model to calculate the <inline-formula> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${V}$ </tex-math></inline-formula> characteristics of 2-D monolayer FETs. Based on the model, the effects of phonon scattering and ionized impurity scattering on the average carrier injection velocity and transistor ballisticity are discussed.
Reproducing Popularity Dynamics of YouTube Videos

To provide video streaming of user-generated contents (UGCs) with high quality and at low cost by maximizing the effect of CDN, CDN providers are required to adequately design CDN cache servers by accurately estimating the UGC view-count distribution. To achieve this goal in a practical time frame, we need to construct a simple time-series model that captures the transition of UGC popularity. Therefore, in this paper, we first analyze the daily view count (DVC) of YouTube videos over nine months and find that the DVC of YouTube videos obeys a lognormal distribution. As a simple time-series model of the DVC of each YouTube video, we propose the grouped MPP (gMPP), extending the multiplicative process (MPP) which is widely known as a simple time-series model generating a lognormal distribution. We also propose reproducing the DVC distribution of YouTube videos by using a superposed gMPP (SgMPP) that aggregating multiple gMPPs. The SgMPP can accurately reproduce the DVC distribution of YouTube videos with a low computational overhead, so we can expect to use the SgMPP as the input for computer simulations for designing various network components that require the popularity distribution of UGC, e.g., cache capacities.
Decentralized Nonlinear Control for Power Systems using Normal Forms and Detailed Models

This paper proposes a decentralized method for nonlinear control of oscillatory dynamics in power systems. The method is applicable for ensuring both transient stability as well as small-signal stability. The method uses an optimal control law which has been derived in the general framework of nonlinear control using normal forms. The model used to derive the control law is the detailed subtransient model of synchronous machines as recommended by IEEE. Minimal approximations have been made in either the derivation or the application of the control law. The developed method also requires the application of dynamic state estimation technique. As the employed control and estimation schemes only need local measurements, the method remains completely decentralized. The method has been demonstrated as an effective tool to prevent blackouts by simulating a major disturbance in a benchmark power system model and its subsequent control using the proposed method.
Symbolic Factorization Re-utilization for Contingency Analysis

As the power system evolves and is operated closer to its operational limits, accurate and real-time contignency analysis becomes more critical. The speed of power flow calculation is a limiting factor of contingency analysis real-time applications. In this work, graph theory and NICSLU, a state-of-the-art sparse and parallel circuit solver, are used to analyze how post-contingency power flows can be solved more quickly. Graph theory is used to show that the base case symbolic factorization can be reused to solve contingency cases. Statistical analysis demonstrates that this method is preferable to recomputing or updating the symbolic factorization for contingencies involving few branch elements. Experimental results on Sichuan and MP10790 systems show that symbolic factorization reuse greatly reduces computation time.
Adversarial Examples Detection Using No-Reference Image Quality Features

Recently, it has been discovered that Deep Neural Networks (DNNs) are highly vulnerable to deliberate perturbations, which, when added to the input sample, can mislead the DNNs based systems. The corresponding samples with deliberate perturbations are called adversarial examples (AEs). The challenge of AEs is very critical in security and safety systems, which if fooled or misled can yield serious consequences. Therefore, it is essential to devise methods to enhance the robustness of DNNs against adversarial attacks. Quintessential mechanism is adversarial examples detection. An adversarial attack detection method aims at disambiguating clean samples from AEs. More recently, few techniques have been proposed in the literature, nonetheless majority of them are very complex or not able to attain low enough error rates. In this paper, we present a novel technique to improve the security of DNNs by detecting different types of AEs. The proposed framework presents a very low degree of complexity and utilizes ten nonintrusive image quality features to distinguish between legitimate and adversarial attack samples. Experimental analysis on the standard MNIST and CIFAR10 datasets shows promising results not only for different adversarial examples generation methods but also various additive perturbations.
Machine Learning for Real-Time Data-Driven Security Practices

The risk of cyber-attacks exploiting vulnerable organisations has increased significantly over the past several years. These attacks may combine to exploit a vulnerability breach within a system’s protection strategy, which has the potential for loss, damage or destruction of assets. Consequently, every vulnerability has an accompanying risk, which is defined as the "intersection of assets, threats, and vulnerabilities" [1]. This research project aims to experimentally compare the similarity-based ranking of cyber security information utilising a recommendation environment. The Memory-Based Collaborative Filtering technique was employed, specifically the User-Based and Item-Based approaches. These systems utilised information from the National Vulnerability Database, specifically for the identification and similarity-based ranking of cyber-security vulnerability information, relating to hardware and software applications. Experiments were performed using the Item-Based technique, to identify the optimum system parameters, evaluated through the AUC evaluation metric. Once identified, the Item-Based technique was compared with the User-Based technique which utilised the parameters identified from the previous experiments. During these experiments, the Pearson’s Correlation Coefficient and the Cosine similarity measure was used. From these experiments, it was identified that utilised the Item-Based technique which employed the Cosine similarity measure, an AUC evaluation metric of 0.80225 was achieved.
An Impulse Radio PWM-Based Wireless Data Acquisition Sensor Interface

A sensor interface circuit based on impulse radio pulse width modulation (IR-PWM) is presented for low power and high throughput wireless data acquisition systems (wDAQ) with extreme size and power constraints. Two triple-slope analog-to-time converters (ATC) convert two analog signals, each up to 5 MHz in bandwidth, into PWM signals, and an IR transmitter with an all-digital power amplifier combines them while preserving the timing information by transmitting impulses at the PWM rising and falling edges. On the receiver side, an RF-LNA followed by an envelope detector recovers the incoming impulses, and a T-flipflop reverts the impulse sequence back to PWM to be digitized by a time-to-digital converter (TDC). Detailed analysis and design guideline on ATC was introduced, and a proof-of-concept prototype was fabricated for a capacitive micromachined ultrasound transducer imaging system in a 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> HV CMOS process, occupying 0.18 mm<sup>2</sup> active area and consuming 3.94 mW from a 1.8 V supply. The proposed TDC in this prototype yielded 7-bit resolution, while the entire wDAQ achieved 5.8 effective number of bits at <inline-formula> <tex-math notation="LaTeX">$2 \times 10$ </tex-math></inline-formula> MS/s.
Attacking a Smartphone Biometric Fingerprint System: A Novice's Approach

Biometric systems on mobile devices are an increasingly ubiquitous method for identity verification. The majority of contemporary devices have an embedded fingerprint sensor which may be used for a variety of transactions including unlock a device or sanction a payment. In this study we explore how easy it is to successfully attack a fingerprint system using a fake finger manufactured from commonly available materials. Importantly our attackers were novices to producing the fingers and were also constrained by time. Our study shows the relative ease that modern devices can be attacked and the material combinations that lead to these attacks.
Highly Maneuverable Ground Reconnaissance Robot Based on Machine Learning

This article mainly introduces the application of a reconnaissance robot’s mechanical design, motion control algorithm and image processing program. In the mechanical structure, it adopts a motor system that drives a Mecanum wheel with double wishbone independent suspension to ensure that the robot has high speed and high mobility. The robot is equipped with a two-degree-of-freedom pan/tilt for installing a wireless video camera, which can acquire images with free viewing angles to facilitate remote control of operators and obtain on-site intelligence. The image processing program uses the machine learning method of the HOG feature and the SVM classifier to identify target objects such as enemy forces or certain types of explosive objects in disaster scenarios to reduce the pressure on operators and increase the reaction speed.
Supporting a MOOC through a Conversational Agent. Design of a First Prototype

This paper presents an initial design of a conversational agent for educational purposes built for Google Assistant and its first prototype. Recent studies suggest that people will get more and more attached to voice assistant because they can easily use technology without being forced to learn it. Speech recognition might facilitate a more efficient work environment without being overly rigid and overly domineering. Modern frameworks allow harnessing natural language understanding as well a machine learning tools, thereby making it easy to build conversational agents. In this paper, we present first design decisions and a prototype for building an agent for learning Java that complements a MOOC for programming with Java.
Blockchain-based Message Dissemination in VANET

With the evolution of vehicle technology, VANET plays an important role in saving life and property of the drivers by disseminating critical event information. However, the traditional VANET faces several security issues. We propose a new type of blockchain to resolve critical message dissemination issues in VANET. We create a local blockchain for real world event messages exchanged between the vehicles within the scope of the countries. In this paper, we discuss a blockchain suitable for VANET. We present a public blockchain that stores the node trustworthiness and message trustworthiness in a distributed ledger that is appropriate for secure message dissemination.