Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-Time Compact Identification System of Amplified Target RNA Based on a Filter-Free Multiple Wavelengths Sensor

We proposed an identification system of amplified target ribonucleic acids (RNAs) without optical filters using a filter-free multiple wavelengths sensor. To detect fluorescence from transcription reverse transcription concerted reaction (TRC) combined with intercalation activating fluorescence probe (INAF), a compact system fabricated with optimized physical parameters of light source and sensor. The excitation light and fluorescence from reagent were successfully separated using the proposed parameters based on controlled the intensity of the excitation light and the potential depth of the sensor. We succeeded in identifying each target RNAs in less than 10 minutes without using an optical filter. The Filter-free multiple wavelengths sensor can be applied in the application of biotechnology and help to realize miniaturized bio-devices.
Information Spaces for Big Data Processing: Unification and Parallelization of Sequential Information Accumulation Procedures

In large-scale research, data are usually collected on many sites, have a huge volume, and new data are constantly generated. Since it is often impossible to collect all the relevant data on a single computer, much attention is paid to the algorithms that provide sequential or parallel accumulation of information and do not need to store all the original data. As an example of information accumulation, the Bayesian updating procedure for linear experiments is analyzed. The corresponding information spaces are defined and the relations between them are studied. It is shown that processing can be unified and simplified by introducing a special canonical form of information representation and transforming all the data and the original prior information into this form. Thanks to the rich algebraic properties of the canonical information space, the sequential Bayesian procedure allows various parallelization options that are ideally suited for distributed data processing platforms, such as Hadoop MapReduce. This opens up the possibility of a flexible and efficient scaling of information accumulation in distributed data processing systems.
Design of Observer-Based SMC Controller for Three-Phase LCL-Filtered Grid-Connected Inverters with Less Sensors

Currently, Sliding Mode Control (SMC) controller based on global stability has been successfully applied to three-phase LCL-flltered Grid-Connected Inverter (GCI). However, to achieve the global stability of the system, all three state vectors are required to provide the full information, which would utilize many sensors, increasing the cost and complexity. In this paper, an observer-based SMC controller for three-phase LCL-flltered GCI is proposed. The discrete state observer is adopted to estimate the state vectors (inverter-side current and capacitor voltage) needed for the SMC controller. In addition, the detailed design of the SMC controller and the discrete state observer are introduced. The experimental results obtained from a 3kW system demonstrate the effectiveness of the proposed control strategy.
A Broadband Balun With Complex Impedance Transformation and High Isolation

The design of baluns with wide bandwidth, transformation between complex source and load impedances, and high isolation is presented in this paper. These features can be accomplished simultaneously by employing double-stub couple lines (DSCL). The differential characteristics are established by only exciting the odd-mode operation of the balun, and suppressing its even-mode operation. For the odd-mode operation, the structure of the DSCL acts as a multi-stub structure and contributes to bandwidth enhancement. An additional resistor added to the DSCL between the output ports improves their isolation and contributes to the impedance transformation. An experiment was prepared for the operations of the proposed design at 2.8 GHz with <inline-formula> <tex-math notation="LaTeX">$Z_{S} =35-j10\,\,\Omega $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$Z_{L} = 60+j10 \,\,\Omega $ </tex-math></inline-formula>. A measured wide bandwidth of 35% was obtained under the criterions of <inline-formula> <tex-math notation="LaTeX">$\vert S_{11}\vert < -10$ </tex-math></inline-formula> dB, (<inline-formula> <tex-math notation="LaTeX">$\angle S_{31}-\angle S_{21}) < 180^{\circ }\pm 5^{\circ }$ </tex-math></inline-formula>, and (<inline-formula> <tex-math notation="LaTeX">$\vert S_{31}\vert -\vert S_{21}\vert) < 1$ </tex-math></inline-formula> dB.
Detecting Thermal Face Signature Abnormalities

In this paper, we propose a novel method of applying deep learning techniques to face biometrics in infrared spectrum. It addresses detection of abnormal thermal patterns, thus identifying, in particular, indicators of insobriety. This finds its application for security and healthcare emergency detection in city shelters. We applied the deep learning approach on 16,000 usable images of 40 subjects from a publicly available Drunk-Sober database. Two Convolutional Neural Network architectures were investigated for the task of processing of two regions of interest - the forehead and the eyes. The accuracy of the neural network classifiers to predict subject's insobriety using the forehead and eye regions-of-interest reached 95.5% and 96.67%, respectively, compared to to best known results on the same data using a non-deep neural networks. To boost the accuracy of classification, both the feature-level and the score-level fusion were applied as well, thus improving the accuracy to 96.92%.
An Application of Geographical Random Forests for Population Estimation in Dakar, Senegal using Very-High-Resolution Satellite Imagery

In this paper we investigate a local implementation of Random Forest (RF), named Geographical Random Forest (GRF) to predict population density with Very-High-Resolution Remote Sensing (VHHRS) data. As an independent variable we use population density at the neighborhood level from the 2013 census of Dakar, while as explanatory features, the proportions of three different built-up types in each neighborhood derived from a VHHRS land cover classification. The results demonstrated, that by using an appropriate geographic scale to calibrate GRF, we can maximize prediction accuracy due to the incorporation of spatial heterogeneity in the estimates. Additionally, since GRF is an ensemble of local sub-models, the results can be mapped, highlighting local model performance and other interesting spatial variations. Consequently, GRF is suggested as an interesting exploratory and explanatory technique to model remotely-sensed spatially heterogeneous relationships.
Dual-Stage-Electrode-Enhanced Efficient SSHI for Rotational Electret Energy Harvester

This paper presents a dual-stage electrode design for electret-based rotational energy harvester (EH) to improve efficiency of synchronized switch harvesting on inductor (SSHI) technique. With the aid of the present parallel SSHI circuit, the output power of rotational electret EH becomes 2.6 times higher than that with the conventional circuit using a resistive load.
A Flexible Pressure Sensor with High Sensitivity and Broad Measuring Range

It is usually necessary for a pressure sensor to possess both of low detection limit and wide measuring range, covering a broad pressure range from Pa to hundred kPa, so as to meet diverse demands in different applications. This paper reports a novel flexible pressure sensor based on piezo-thermic transduction, using a porous Ag/PDMS (polydimethylsiloxane) sponge with a graded porosity. The sensor exhibits a high sensitivity, a low detection limit, and a wide measuring range, which allows monitoring different human signals from subtle wrist pulse to foot tapping pressure by a single device.
IoT Smart Home Ecosystem: Architecture and Communication Protocols

The goal of Internet of Things is to facilitate human life, by collecting information from the physical environment using sensing objects, process this information locally and transmit it to the Internet for decision making respecting an IoT architecture. IoT can be applied in different sectors of activity, in our work we focus on the Smart Home ecosystem, which use a lot of sensors and actuators inside the home to make life more secure and enjoyable for its occupant and to hold the user aware of any changes inside the home.
Isolated Voltage Sensor Using Ring Resonator for Battery Power Management

The electrostatic ring resonator is newly applied for measuring the voltage in a highly isolated manner. The principle is the resonant frequency shift caused by the electrical field. The ring resonator is for increasing the natural frequency and Q. The realized values are 100kHz and 320, they are more than 4- and 2-times increase compared to the previous translation type resonator. The measurement over 380V matches with the need.
A Sensitive Resonant Gas Sensor Based on Multimode Excitation of a Buckled Beam

We report a new gas sensing technique based on the simultaneous tracking of multiple modes of vibration of an electrothermally heated bridge resonator operated near the buckling point. The proposed technique maximizes the sensitivity of the sensor to the change in the gas concentration and enables the identification of the gas type without the need for selective coating. Under the same gas concentration value, the reported measurements demonstrate a 200% change in the frequency in contrast to 0.5% using the conventional technique based on resistance measurements. The proposed method is simple in principle and design and is promising for achieving practical low-cost gas sensors.
Spatio-Temporal Texture Features for Presentation Attack Detection in Biometric Systems

Spatio-temporal information is valuable as a discriminative cue for presentation attack detection, where the temporal texture changes and fine-grained motions (such as eye blinking) can be indicative of some types of spoofing attacks. In this paper, we propose a novel spatio-temporal feature, based on motion history, which can offer an efficient way to encapsulate temporal texture changes. Patterns of motion history are used as primary features followed by secondary feature extraction using Local Binary Patterns and Convolutional Neural Networks, and evaluated using the Replay Attack and CASIA-FASD datasets, demonstrating the effectiveness of the proposed approach.
On Fresnel-Based Single and Multi Spectral Sensors for Insects’ Wingbeat Recording

This paper reports on optoelectronic sensors and their associated recorders that are based on Fresnel lenses and record the wingbeat of insects in flight. We present a novel single-band sensor and, a low-cost multispectral sensor. This work belongs to a broader context of applications that relate to automated insect surveillance of insects of economic and hygienic importance.
A Model for the Effect of Glia on the Communication Amongst Neurons

In the course of synaptic connection, which is effective in the formation and learning of cognitive processes, the role of glia cells has been taken into consideration only in recent years. In this context, the studies conducted by the neuroscience community are mainly experimental; however, certain processes related to signal communication between glia and nerve cells cannot be investigated experimentally. In that regard, an quintessential approach to overcome this complication is to take advantage of the mathematical models, when experiments do not suffice. In this study, a mathematical model is proposed to elucidate the role of nitric oxide, which is reported to be crucial in three-part synaptic model of long-term potentiation and the simulation results obtained from this mathematical model are discussed.
Ambient Environment Recognition Algorithm Fusing Vision and LiDAR Sensors for Robust Multi-channel V2X System

Recently, 5G commercialization issues and standardization of WAVE communication have been done on V2X communication of autonomous vehicles. In this paper, we propose an algorithm for predicting the communication performance of multichannel V2X with a vision sensor. The proposed method recognizes the surrounding environment as a vision sensor and provides information to the TCU board to select optimal parameters. The sensing system integrates camera and LiDAR sensor data into a single data set. We applied the CNN-based object detection algorithm to the fusion sensor and recognized the driving environment. We defined the situation that affects the communication performance and measured the accuracy by perceiving the scenario. This algorithm can optimize the communication channel and select the communication channel suitable for the driving environment in advance to improve the communication stability.
Automated High-Throughput Hermetic Failure Monitoring System for Millimeter-Sized Wireless Implantable Medical Devices

This paper presents an automated high-throughput hermetic failure monitoring system for millimeter-sized wireless implantable biomedical devices (IMDs). To protect the electronics from the harsh body environment as well as the body from potential toxic chemicals in the package of the IMDs, a lifetime estimation for the hermetic package of the IMDs is essential as a key part of IMD design and development. However, traditional leak tests are not suitable for the small form factor of mm-sized IMDs. The presented automated hermetic failure monitoring system can enable lifetime estimations for multiple mm-sized wireless IMDs at low cost and in a user-friendly setup. Thereby, liquid/vapor ingression into the IMD package is monitored by registering the phase-dip frequency shift of a passive LC tank. Exposing 1.3×1.3 mm2 sized IMDs to an accelerated testing environment (1 atm, 100% RH, and 80 °C), a 186 kHz frequency resolution and a signal to noise ratio (SNR = 10log(△φdip/φnoise)) of 21.8 ± 0.8 dB were achieved within an operating frequency range of 50-200 MHz and at 0.4 mm interrogation distance.
A Microtas for Liquid Sample Bulk Modulus Screening

This paper presents a micro total analysis system (microTAS) integrating digital microfluidics, acoustic wave microsensors and electronic circuits for rapid sample screening based on liquid bulk modulus measurement. The microsystem is designed for in-field analysis where a number of liquid samples need to be processed in a short time. As a preliminary validation, the microsystem processed 7 glycerol-water mixture samples in less than 3 minutes (from sample loading and delivery to measurement and waste collection). In another experiment, the microsystem processed 6 samples in parallel in just 5 seconds. The experimental results demonstrate the features of fast samples-to-results, low consumption, reliable screening ability and high-throughput potentials.
Bidirectional Equalization for Long-Range Underwater Acoustic Communication in BLAC18

The bidirectional equalization is an effective method for demodulating communication signals with weak strength since it improves the signal-to-noise ratio by obtaining additional diversity as well as temporal and spatial diversity when used in combination with the time reversal technique. In this paper, we will demodulate the communication signals acquired from biomimetic long-range acoustic communication experiment 2018 (BLAC18) conducted in East Sea of Korea in October 2018 using time reversal and bidirectional equalization techniques. It is shown that the bidirectional equalization including decision direct equalizer has the best error performance.
A 10 GHz Single-Crystalline Scandium-Doped Aluminum Nitride Lamb-Wave Resonator

This work reports on the first demonstration of Lamb-wave resonators based on single-crystalline Scandium (Sc)-doped Aluminum Nitride (AlN) films operating at 8-10 GHz. Sc-AlN and AlN films are grown on Silicon substrates using molecular beam epitaxy (MBE). The electrodes are defined using electron beam lithography with sub-micron feature sizes to maximize the electromechanical coupling coefficient$(k_t^2)$. A high $k_t^2$ value of 4.8 % is reported at 9.9 GHz, with unloaded Quality factor (Qm) of 185, yielding $f \times {Q_m} \times k_t^2$ values of 74 GHz. The high $k_t^2$ is attributed to enhanced piezoelectric coefficients achieved due to single crystallinity, as well as Sc-doping. This work demonstrates higher performance resonators achieved by using single-crystalline Sc-AlN thin films compared to sputter-deposited films with sub-micron thicknesses, required for 5G filter applications.
A Hybrid Artificial Intelligence and Internet of Things Model for Generation of Renewable Resource of Energy

The world is consuming large amounts of energy in various forms like electric energy and mechanical energy. Since the electrical energy is an important factor for the development of the world, many researchers tried to generate electricity from renewable energy sources collected by sensors in order to overcome the shortage of electrical energy for household appliances and industrial areas. In this paper, we develop Internet-of-Things (IoT) based system to generate electrical energy from multiple sensors for household appliances and industrial areas. Different sensors namely piezoelectric sensor, body heat to electric converter and solar panel are utilized and connected to the power storage circuit for generation of electrical energy. Two different Artificial Intelligence (AI) models such as Artificial Neural Network (ANN), Adaptive Network based Fuzzy Inference System (ANFIS) are utilized for the total power generated from renewable energy resources. Validation is done through the statistical parameters such as Root Mean Square Error (RMSE) and R2 coefficient of correlation. Result outcome from the models shows that ANN performance is better than ANFIS.
Security Implications of Intentional Capacitive Crosstalk

With advances in shrinking process technology sizes, the parasitic effects of closely routed adjacent wires, crosstalk, still present problems in practice since they directly influence performance and functionality. Even though there is a solid understanding of parasitic effects in hardware designs, the security implications of such undesired effects have been scarcely investigated. In this paper, we leverage the physical routing effects of capacitive crosstalk to demonstrate a new parametric hardware Trojan design methodology. We show that such Trojans can be implemented by only rerouting already existing resources. Thus, our approach possesses a zero-gate area overhead which is both stealthy and challenging to detect with standard visual inspection techniques. In two case studies, we demonstrate its devastating consequences: (1) we realize an implementation attack on a third-party cryptographic AES IP core and (2) we realize a privilege escalation on a general-purpose processor capable of running any modern operating system. In these case studies, we take special care to ensure that the Trojans do not violate design rule checks, which further highlights that the capacitive crosstalk Trojans can be building blocks for malicious circuitry design. We then investigate how state-of-the-art visual inspection techniques can be enhanced to cope with parametric hardware Trojans. In particular, we develop an automated layout-level mitigation approach which exploits the characteristic wire length of capacitive crosstalk Trojans. Finally, we highlight further implementation strategies for capacitive crosstalk Trojans and pinpoint future research directions.
Utilizing Machine Learning Techniques to Reveal VAT Compliance Violations in Accounting Data

In recent years, compliance management has gained more and more interest from a practice and research point of view. The financial service industry, in general, is strongly regulated and has to follow specific laws, standards and guidelines. However, research has shown that little attention is being paid to Value Added Tax (VAT) issues, although there is a high cost and risk exposure, especially in large international companies which use large IT-Infrastructures for tax handling. In this paper, we examine a commonly applied approach for the verification of VAT regulations within Enterprise Resource Planning systems (ERP) and highlight weaknesses as well as error susceptibilities. Furthermore, we show that machine learning techniques can be utilized to minimize risks and increase VAT compliance. We use a supervised learning classifier to predict tax subjects and corresponding tax rates based on related voucher information of journal reports. By comparing the results of our model with the existing rule-based system of an ERP system, we reveal potential anomalies and compliance issues. Our approach was evaluated on a given real-world data set of a leading chemical industry company that was exported from its ERP system. The results were validated by VAT experts of the company
Internet of Things in agriculture: A survey

Increasing food consumption, asking for quality food, and environmental impacts of agriculture lead to has used information technology in the agriculture sector, which comes under the heading of precision agriculture. Internet of things (IOT) is a technology that is growing rapidly in recent years and brings numerous benefits with it for agriculture. Because of the heterogeneous and enormous amount of data collected by IoT devices, future of internet of things (IoT) agricultural applications depend on cloud computing. At the same time, microcontrollers will add new abilities to the internet of things (IoT). In this survey, the research trend, the concepts, fundamental components of IOT, the challenges, and IOT applications in agriculture are examined. Firstly, the numbers of published papers in this field reviewed. Secondly, IOT definition and IOT architecture together with its layers are introduced. Thirdly, some involved technologies in the IOT are compared; finally, the main challenges in IOT and precision agriculture (PA) are considered.
A Path Planning Approach Based on Q-learning for Robot Arm

A path planning method based on Q-learning is proposed for robot arm. As reinforcement learning, Q-learning is widely used in the field of mobile robot navigation due to its simple and well-developed theory. However, most of researchers avoid using it for solving the robot arm path planning problem because it has to take each joints motion into account. In this study, approximate regions instead of accurate measurements are used to define new state space and joint actions. Moreover, the reward function takes the distance from current position of robots’ end-effector to the goal position into account. The experimental simulation shows that the Q-learning approach is efficient and has ability to plan a collision free path for robot arm when the order of magnitude of state space is below 104. Furthermore, the experimental results indicate that the number of obstacles affects the calculation time for the same iterations. The more obstacles, the less calculation time the algorithm needs. The weight coefficient in the reward function affects the convergence speed and the quality of the solutions.