Miracle-dz/UniFAR-FTUs huggingface dataset:
Detailed Modelling Procedure for Lithium-ion Battery Using Thevenin Equivalent

This paper presents a comprehensive procedure for modelling of Lithium-ion batteries. The proposed modelling procedure adopts Thevenin equivalent circuit of both first and second orders in order to incorporate the dynamic characteristics of Li-ion batteries. The model parameter extraction process is done through the use of pulse discharge (PD) and pulse charge (PC) tests and the procedure is discussed in detail. The effect of self-discharging is ignored due to its very large time constant. In order to verify the accuracy of the model, comparison of simulation results with experimental data is demonstrated for three temperatures, 0°C, 25°C and 45°C. The modelling technique for the battery would specially find applications in electric vehicle based simulations where the load current is highly pulsating and the battery gets very frequently charged and discharged.
Low-Power Wireless Wearable ECG Monitoring Chestbelt Based on Ferroelectric Microprocessor

Since cadiovascular disease (CVD) posts a heavy threat to people’s health, long-term electrocardiogram (ECG) monitoring is of great value for the improvement of treatment. To realize remote long-term ECG monitoring, a low-power wireless wearable ECG monitoring device is proposed in this paper. The ECG monitoring device, abbreviated as ECGM, is designed based on ferroelectric microprocessor which provides ultra-low power consumption and contains four parts—-MCU, BLE, Sensors and Power. The MCU part means circuit of MSP430FR2433, the core of ECGM. The BLE part is the CC2640R2F module applied for wireless transmission of the collected bio-signal data. And the sensors part includes several sensors like BMD101 used for monitoring bio-signals and motion of the wearer, while the Power part consists of battery circuit, charging circuit and 3.3V/1.8V/4.4V power supply circuit. The ECGM first collects ECG signals from the fabric electrodes adhered to wearers’ chest, preprocesses the signals to eliminate the injected noise, and then transmit the output data to wearers’ hand-held mobile phones through Bluetooth low energy (BLE). The wearers are enabled to acquire ECGs and other physiological parameters on their phones as well as some corresponding suggestions. The novelty of the system lies in the combination of low-power ECG sensor chip with ferroelectric microprocessor, thus achieving ultra-low power consumption and high signal quality.
5G Antenna Challenges and Opportunities

Antenna is one of the most important single part of a wireless communications system and significantly affects the total energy consumption. Current mobile antennas can be inefficient in both converting radio energy to radiating waves and also focusing the radiated waves. The upcoming 5G introduces additional challenges to antennas but could also provide opportunities to use RF energy more efficiently. This paper discusses 5G antenna challenges and presents two potential antenna solutions for 5G mobile devices and one for mm-wave access points.
RIC Bounds for Recovery of Sparse Signals by lp(0 < p ≤ 1) Minimization

In this paper, recovery of sparse signals via $\ell_{p} (0\ \lt\ p\ \leq\ 1)$ minimization in both noiseless and noisy cases is considered. General RIP condition $\delta_{2(t-1)k} \lt \delta_{0}(t,p)(t\ \geq\ 2)$ is established, which can guarantee the exact recovery of k sparse signals in the noiseless case and the stable recovery of approximately k-sparse signals in noisy cases. The obtained results are the generalization of the existing results for the RIP conditions of sparse recovery.
Musiquence: a framework to customize music and reminiscence cognitive stimulation activities for the dementia population.

Due to the limitations of pharmaceutical related approaches for people with dementia (PwD), there has been a need to find complementary methods. Within non-pharmaceutical approaches music and reminiscence-based activities appear to bring advantages to PwD: (1) it is easy to implement, (2) does not lead to side effects, (3) PwD appear to have an intact music memory even at later stages of the disease, among other benefits. However, literature is mixed regarding the long-term benefits of music and reminiscence related approaches in PwD. Another approach that has gained much attention over the years are serious games. However, the usage of technologies and the lack of customizable content may provide an additional challenge to health professionals and family caregivers to utilize such an approach in PwD. In this article, we present a framework called “Musiquence”, which is allows customization of gamified activities in terms of technology and content, with special emphasis on music and reminiscence-based activities. Here we discuss the functionalities of the platform, the activities designed for PwD and future implementations for the platform.
Periodic Fixed-Frequency Staggered Line Leaky Wave Antenna With Wide-Range Beam Scanning Capacity

A periodic fixed-frequency staggered line leaky wave antenna (LWA) with wide-range beam scanning capacity is proposed in this paper. The reconfigurable antenna is based on a periodic staggered line LWA. The dispersion and Bloch characteristics of the periodic antenna is analyzed by the Macro Cell Method (MCM). The open-stop band (OSB) of the antenna can be suppressed effectively using the relationship between widths of oblique feeding lines and stubs. The reconfigurable characteristic is achieved by switches and microstrip lines between the edge gaps of each unit cell. A “supercell” is established by combining several reconfigurable unit cells, and the state is controlled by connecting or disconnecting the edge gaps of each unit cell using switches. Fixed-frequency beam-scanning capacity is implemented due to different propagation constants in different supercell states. The prototyped reconfigurable antenna can scan the beam between 130° and 54° at 4.5 GHz.
Complexity Reduction in Multicast Beamforming for D2D Assisted Coded Caching

A novel D2D aided beamforming scheme is proposed, where the local cache content exchange among nearby users is exploited. The transmission phase is split into two sub-phases: local D2D content exchange and downlink transmission. In the D2D sub-phase, users can autonomously share content with the adjacent users. In the downlink sub-phase, the Base station (BS) simultaneously serves some number of users to fulfill their remaining content requests by utilizing multicast beamforming. We first explain the main procedure via two simple examples and show the complexity involved in beamformer design. Then, we present the general formulation and detailed complexity analysis. We show analytically that, by exploiting the D2D exchange, the complexity of downlink multicast beamformer design can be greatly decreased. This provides significantly enhanced overall content delivery performance in terms of computational complexity and total delivery time.
Crop Diseases and Pests Detection Using Convolutional Neural Network

The Indian economy is extremely reliant on the agricultural productivity. In order to fulfill the citizen's demand and to give more benefits to farmers, increasing crop production is very crucial task nowadays. Crop diseases and pests play a key role in reducing crop production and quality. Therefore, the detection is fundamental in precision agriculture task. Manual detection of diseases takes additional time and efforts on the larger area of the farm. Deep learning approach can be used to detect the diseases and pest more accurately on leaves and other parts of the crop. The proposed method is helpful in detecting crop diseases as well as pests. In this paper, the deep learning techniques related to diseases and pest detection has been reviewed and the deep learning model for automatic diagnosis of crop diseases and pests is proposed.
Observation of Laser-Cavity Solitons in Micro-Resonators

Optical frequency combs based on micro-cavity resonators, also known as ‘micro-combs’, are ready to achieve the full capability of their bulk counterparts but on an integrated footprint [1]. They have enabled major breakthroughs in spectroscopy, communications, microwave photonics, frequency synthesis, optical ranging, quantum sources and metrology. Of particular relevance was the recent experimental implementation of temporal cavity-solitons [2,3]. Temporal cavity-solitons in micro-resonators are described by the well-known Lugiato-Lefever equation. Currently, these self-localised waves form on top of a strong background of radiation, usually containing 95% of the total power [4] and require active control of an external driving laser — a complex process which limits the choice of fundamental parameters such as the repetition-rate. Developing simple methods for controlling and generating highly efficient, self-localised pulses is one of the most compelling challenges to overcome, in anticipation of the widespread use of micro-combs outside of laboratory environments.
A Microfluidic Chip for CTC Whole Genome Sequencing

This paper developed a microfluidic chip for whole genome sequencing (WGS) of circulating tumor cells (CTC) at single-cell level. For the first time, all processing steps between collecting blood sample and feeding prepared sample to sequencer are finished in a single microfluidic chips. The microfluidic chip comprehensively performs blood filtering, CTC enrichment, CTC identification/isolation, CTC lysis and whole genome amplification (WGA) at the single cell level. The microfluidic chip was validated by clinical samples. Our results demonstrate that for the first time, the aim of “whole blood in, WGA product out” has been realized at single CTC level with clinical samples, in a microfluidic chip.
Classification of Chopped Strand Mat Defects Based on CSMNet

In view of the limitation of extracting features from traditional manual design in chopped strand mat defects detection, the convolutional neural network is used to extract the features of the defect image, and classification method based on CSMNet (Chopped Strand Mat Network) is proposed. Convolutional layers and pooling layers are used to extract and select the features of the dataset, and the feature vectors are trained with SVM classifier. The extraction ability of AlexNet and CSMNet are compared, and Softmax and SVM are used to train feature vectors. The experimental results show that CSMNet can well characterize the datasets, and the classification accuracy of SVM for each kind of the defects is more than 90%, which can effectively realize the classification of chopped strand mat and meet the needs of industrial production.
The TPACK model as the basis of a didactic proposal for the teaching-learning of Linear Programming

Linear Programming (LP) is the response to situations in which it is necessary to plan the use of limited resources; the results obtained are the quantitative support of the decisions made against models that summarize real-life problems. Given the importance of LP, it is necessary to consider the problems that students face: operative difficulties; and inappropriate use of propositions and formal language. Besides, nowadays the development of knowledge-competitions is fundamental; in doing, in being and in life. In this context, the TPACK methodology is proposed as a theoretical conceptual scheme to design proposals, based upon disciplinary, pedagogical and technological decisions, which integrate technology into the capacitation process. The proposal is structured in 3 stages: first stage evidences the research problem; second stage presents planning incorporating participative methods and techniques mediated by ICT; and third stage, using rubrics and statistical tools, embraces the students process.
An Autonomous Navigation Method for Libration Point Satellites

In recent years, the idea of establishing deep space navigation constellations around the libration points has received extensive attention. The current research is mainly to establish a navigational constellation in the orbit around libration points of the Earth-Moon system. On this basis, this paper studies the technology of autonomous navigation using the libration points of both Earth-Moon system and the Sun-Earth system. The simulation results show that the long-term and high-precision autonomous orbit determination can be reached and the navigation accuracy is better than that of only using orbit around the libration points of the Earth-moon system.
Input-Conscious Approximate Multiply-Accumulate (MAC) Unit for Energy-Efficiency

The Multiply-Accumulate Unit (MAC) is an integral computational component of all digital signal processing (DSP) architectures and thus has a significant impact on their speed and power dissipation. Due to an extraordinary explosion in the number of battery-powered “Internet of Things” (IoT) devices, the need for reducing the power consumption of DSP architectures has tremendously increased. Approximate computing (AxC) has been proposed as a potential solution for this problem targeting error-resilient applications. In this paper, we present a novel FPGA implementation for input-aware energy-efficient 8-bit approximate MAC (AxMAC) unit that reduces its power consumption by: performing multiplication operation approximately, or approximating the input operands then replacing multiplication by a simple shift operation. We propose an input-aware conditional block to bypass operands multiplication by (1) zero forwarding for zero-value operands, (2) judiciously approximating 43.8% of inputs into power-of-2 values, and (3) replacing the multiplication of power-of-2 operands by a simple shift operation. Experimental results show that these simplification techniques reduce delay, power and energy consumption with an acceptable quality degradation. We evaluate the effectiveness of the proposed AxMAC units on two image processing applications, i.e., image blending and filtering, and a logistic regression classification application. These applications demonstrate a negligible quality loss, with 66.6% energy reduction and 5% area overhead.
Development of an electromechanical testing and loading system for cell-biomaterial interaction study

The electromechanical properties of biomaterial are often essential to cell function during cell-biomaterial interaction. We describe the design and validation of an electromechanical testing and loading system for live cell study while co-cultured with biomaterial. The system is extended based on an uniaxial cell stretching device, and we use it to overcome a significant limitation of existing cell stretching devices, in which only strain are considered for cell stimulation. Combining proper mechanical design with high-precision electrical and force sensing, our system can simultaneously examine the mechanical and electrical stimulation on cell function caused by biomaterial. We validate our system by culturing mesenchymal stem cells on piezoelectric nanofiber membrane. The piezoelectric response of the nanofiber membrane is investigated under simulated in vivo stretch loading. Live cell imaging using an inverted microscope are implemented to clarify the manipulation principle of the device. Experiment results demonstrate that our system provides a useful tool for in vitro studies of electromechanical coupling effects of biomaterial on cell.
THz Super-Resolution Imaging with Parallel-Pixel Data Acquisition for Rapid Inspection Applications

A multi-frame, super-resolution imaging algorithm Low-resolution for a THz array detector with parallel-pixel data acquisition is proposed to overcome both detector resolution limit and prolonged acquisition time. A correction scheme was also implemented to counteract a fixed-pattern variation caused by non-uniform pixel responsivity observed in our experimental results. The proposed technique aims to serve the demands for THz non-destructive testing (NDT) and quality control (QC) applications, which requires a rapid high-resolution THz imaging system.
Wavelet Transformation of Functional Data for Hyperspectral Image Classification

In this paper, a novel method based on wavelet transformation of functional data for accurate classification of hyperspectral images is proposed. The motivation of the proposed method is that in the hyperspectral images, the spectral curve of each pixel can be viewed as a function, mathematically. And it can make the best of the abundant spectral information of the original hyperspectral images. Based on this, we have proposed an effective hyperspectral image classification method which mainly used functional principal component analysis (FPCA) before. However, FPCA method focuses on the global features analysis of hyperspectral image data and may not well extract local structural features. In this paper, in order to better reflect the local structural features of hyperspectral images, we make use of the time-frequency localization characteristics of wavelet transformation to decompose hyperspectral images. Then, the sparse wavelet decomposition coefficients are classified by using extreme learning machine (ELM). Experimental results on two popular hyperspectral images show that our method outperforms some state-of-the-art hyperspectral image classification methods.
Economic Model Predictive Control for Wind Turbine

For economic goals, power maximization and mechanical load minimization are quite important for variable-speed wind turbines in the modern wind energy industry. However, these turbines are generally non-linear and highly coupled non-affine systems. In this work, an economic model predictive control is proposed to guarantee the economical control operation, meanwhile, improves the closed-loop optimal performance. Based on the 5-MW NREL wind turbine reference model with considering the tower fore-aft motion, the proposed control scheme is compared with the classical MPC scheme based on stepwise wind speed variations. The simulations result indicates the proposed controller effectiveness over the classical MPC.
A3CM: Automatic Capability Annotation for Android Malware

Android malware poses serious security and privacy threats to the mobile users. Traditional malware detection and family classification technologies are becoming less effective due to the rapid evolution of the malware landscape, with the emerging of so-called zero-day-family malware families. To address this issue, our paper presents a novel research problem on automatically identifying the security/privacy-related capabilities of any detected malware, which we refer to as Malware Capability Annotation (MCA). Motivated by the observation that known and zero-day-family malware families share the security/privacy-related capabilities, MCA opens a new alternative way to effectively analyze zero-day-family malware (the malware that do not belong to any existing families) through exploring the related information and knowledge from known malware families. To address the MCA problem, we design a new MCA hunger solution, Automatic Capability Annotation for Android Malware (A3CM). A3CM works in the following four steps: 1) A3CM automatically extracts a set of semantic features such as permissions, API calls, network addresses from raw binary APKs to characterize malware samples; 2) A3CM applies a statistical embedding method to map the features into a joint feature space, so that malware samples can be represented as numerical vectors; 3) A3CM infers the malicious capabilities by using the multi-label classification model; 4) The trained multi-label model is used to annotate the malicious capabilities of the candidate malware samples. To facilitate the new research of MCA, we create a new ground truth dataset that consists of 6,899 annotated Android malware samples from 72 families. We carry out a large number of experiments based on the four representative security/privacy-related capabilities to evaluate the effectiveness of A3CM. Our results show that A3CM can achieve promising accuracy of 1.00, 0.98 and 0.63 in inferring multiple capabilities of known Android malware, small size-families’ malware and zero-day-families’ Android malware, respectively.
A Fuzzy Comprehensive Evaluation Method for Spacecraft Control Systems under Different Scheduling Strategies

The spacecraft control system is a typical cyber-physical system (CPS). Its performance includes many aspects, such as control performance, real-time performance, resource utilization, system trustworthiness, and so on. These aspects are closely related to the scheduling strategy of system tasks. To select an optimized scheduling strategy, it is necessary to evaluate the performance of the spacecraft control system considering various factors. However, there is currently no effective method in this regard. In this paper, we propose a fuzzy comprehensive evaluation method to evaluate the performance of spacecraft control systems. Control, real-time and other aspects of performance under different scheduling strategies are taken into account. We first define time models, task models and scheduling models of spacecraft control systems. Then, we discuss the evaluation metrics and their membership functions, and finally give the implementation process of the method and demonstrate the method through an example. This work can be used for comprehensive performance evaluation of spacecraft control systems and other cyber-physical systems.
Parallelized Level-Set Velocity Extension Algorithm for Nanopatterning Applications

We present a parallelized algorithm for accelerating the velocity extension calculations in a level-set method, which is essential for surface velocity based topography simulations, such as etching or deposition simulations for nanopatterning applications. The proposed algorithm improves the prevailing fast marching method by optimizing the heap data structure and efficiently reordering the calculations. We implemented the algorithm into Silvaco’s Victory Process simulator, which is utilized for evaluating our algorithm with a three-dimensional simulation of an ion beam etching process used for spin-transfer torque magnetoresistive random access memory devices. Our results show a significant serial speed-up by a factor of at least 1.4 and a total speed-up by a factor of up to 8 using 8 threads for the velocity extension.
Recent progress in the Development of French THz Schottky Diodes for Astrophysics, Planetology and Atmospheric Study

During the last 10 years the LERMA-Observatoire de Paris in close collaboration with C2N has made a great progress in the development of the French technology of THz electronic components based on Schottky diodes. By bringing together the unique knowledge and skills of both laboratories, we have developed the submillimeter devices at 300GHz, 600GHz and 1.2 THz, with state-of-the-art performances. These devices are selected today for the submillimeter wave instrument (SWI) instrument of the Jupiter Icy moon Explorer (JUICE) satellite, ESA’s first class L mission. The progress made over the last years and our future work on the device miniaturization and increasing working frequency will be discussed in this presentation.