Miracle-dz/UniFAR-FTUs huggingface dataset:
Do Alzheimer’s Patients Appear Younger than Their Ageƒ A Study with Automatic Facial Age Estimation

Facial age estimation from images is a challenging task, especially if the subjects are older, since idiosyncratic variations increase with age, and lifestyle factors have an impact on the appearance. In this paper, we test the hypothesis that the Alzheimer’s patients appear younger than their real age. To do this objectively, and to be able to analyze the factors in case the hypothesis holds true, we use automatic age estimation methods for this task. We first propose training and normalization regimes to improve deep learning based facial age estimation. We fine-tune a pre-trained ImageNet model using first the APPA-REAL database and then the UTKFACE database. The experimental results show that the proposed approach predicts older faces more accurately compared to other studies, and improves the mean absolute error for the FG-NET database to 8.14 for the age group 60-69. We then run our approach on a special database collected from Alzheimer’s patients and healthy controls, to test our main hypothesis. The database we collected for this purpose contains video recordings of 96 subjects with an age range between 64 and 87. Our findings show that automatic age estimation indeed underestimates the age of AD patients significantly more than the healthy subjects.
A Study of High-Temperature Effects on an Asymmetrically Doped Vertical Pillar-Type Field-Effect Transistor

The effects of high temperature on an asymmetrically doped vertical pillar-type metal-oxide-semiconductor field-effect transistor (MOSFET) were investigated. An asymmetrically doped source and drain (S/D) can be easily formed in a vertical pillar-type FET due to the unique pillar structure. When high temperature is applied to the asymmetric S/D of a vertical pillar-typed silicon nanowire, it affects mobility and carrier injection differently. It decreases mobility by phonon scattering for heavily doped S and intermediately doped D. In contrast, it enhances carrier injection for intermediately doped S and heavily doped D. Thus the ON-state current (ION) shows opposite dependencies at high temperature. This tendency was verified by electrical measurements and supporting simulations.
Analytic Magnetic Fields and Semi-Analytic Forces and Torques Due to General Polyhedral Permanent Magnets

This article outlines an algorithm which analytically calculates the magnetic field produced by a general polyhedral permanent magnet with any number of faces and arbitrary face orientations, then uses the algorithm to semi-analytically calculate the force and torque on a second general polyhedral magnet. The algorithm is validated against both the literature and finite element simulations using cuboids and dodecahedra. It is then used to model a basic two-magnet repulsive system, where it is shown that frustum magnets can produce a larger force per unit volume than cuboidal magnets. The shape of the frustums is optimized to maximize the force between them at a given separation distance, showing a considerable increase in force when compared with cuboidal magnets with the same volume. This article shows that there is scope to improve the performance of magnetic systems by using novel magnet shapes and presents an algorithm which can be used for this optimization process.
Low-cost Soil Moisture Sensors Based on Inductive Coils Tested on Different Sorts of Soils

The use of precision agriculture and the Internet of Things has improved the efficiency of many cultures. Nevertheless, there are a few low-cost options to monitor soil moisture. Moreover, those options depend on the specific characteristics of the soil. In this paper, we attempt to find a sensor, based on mutual inductance, that could be used for more than one sort of soil. We study three prototypes, one of them with casing. The sensors are powered with a voltage of 10 peak to peak volts. One of the soils has a high content of organic matter and sand while the other is rich in sand and silt. The best prototype for the soil with high levels of organic matter has 10 turns on the powered coil and 5 on the induced coil. The best frequency for this sensor is 1340 kHz. For the soil with a significant quantity of silt, the best prototype has 80 turns on the powered coil and 40 on the induced coil. The frequency at which this sensor works best is 229 kHz, which happens to be its peak frequency. With those characteristics regressions lines with R2 values higher than 0.75 can be modeled.
3-D Topology Optimization of Claw-Pole Alternator Using Gaussian-Basis Function With Global and Local Searches

This article proposes a two-step topology optimization method based on the normalized Gaussian functions. The proposed method is shown to be effective for design of a claw-pole alternator. In this method, the global search using the micro-genetic algorithm (<inline-formula> <tex-math notation="LaTeX">$\boldsymbol {\mu }$ </tex-math></inline-formula>GA) is followed by the local search using the sensitivity analysis based on the adjoint variable method. The 3-D structure of the rotor is optimized using the proposed method under low- and high-speed conditions.
Comparison of MARG Sensor Results for Different Mounting Positions and Physiques for Accurate Knee Joint Motion Measurement

This study aims to validate the accuracy of sensor-based pose estimation techniques by comparison with the orientation obtained with an optical motion capture system as a reference. We used two magnetic, angular rate and gravity (MARG) sensors and attached them to the thigh and shank. MARG sensors were attached with three different position patterns (all lateral, all anterior, and lateral on the thigh and anterior on the shank), and reflective markers were attached based on point cluster technique. Two participants with different physiques performed three separate motions (knee flexion/extension, walking, and knee flexion/valgus/external rotation). We compared knee angle data acquired from two methods and evaluated the RMSE of the knee angle between different mounting positions. As a result, we found that lateral mounting was able to achieve high-accuracy measurement of lower-extremity motion with MARG sensors.
Sequential Three-Way Decision of Tolerance- Based Multi-Granularity Fuzzy-Rough Sets

The existing three-way decision-making methods are classified into two classes-a single one-step three-way decision-making and a sequential, multi-step three-way decision-making, and this paper studies the latter. In multi-granularity model, sequential thresholds are used to represent its multi-level granularity. Firstly, the concept of similarity is introduced and tolerance-based fuzzy decision theory rough set is proposed to obtain the upper and lower approximation of decision class and the three-way decisions of the whole decision class. Secondly, the tolerance-based sequential three-way decision is extended to the case of multi-granularity, and the concepts of upper and lower approximation and its three-way decision in optimistic and pessimistic situations are proposed, and some related properties are verified. By adopting the aggressive and conservative strategy, we put forward the concepts of upper and lower approximation and related properties under pessimistic-optimistic conditions as well as sequential three-way decision, the validity of these methods is verified by an example. In optimistic-pessimistic situations, a counterexample is given to prove that it is unable to make decisions. On this basis, uncertainty measures, precision and roughness, are introduced, and some properties of them are studied. Finally, the paper analyses and proves the relationship between the above three models.
A Compact wide band Multi-Stacked patch antenna for UWB Applications

A compact Mutli-stacked microstrip patch antenna has been proposed for UWB application. It consists of a single driven patch and three parasitic patches which are stacked one over another. These parasitic patches provide wideband impedance matching. By introducing the T-shaped stub and semi-circular slots in the driven patch cross polarization is reduced. The main patch is fed by co-axial feed. The overall dimension of the configuration is 0.6 $0.6 \lambda 0 \times 0.6 \lambda 0 \times 0.078 \lambda 0$ (ground plane volume). This radiating system has peak gain of 9dBi at 6.5GHz and an average gain of 7dBi is observed at 4.5-7.5GHz. An unidirectional radiation pattern with good front and back ratio can be achieved. The proposed system finds the applications in Radar target scanning and satellite communication. It is also observed that the proposed antenna has better radiation pattern, improved gain with wide bandwidth.
Visible to Near-Infrared Responsive PbS and Ag2S Quantum Dots as Efficient Sensitizers for Solar Cells

Inorganic quantum dots (QDs) with narrow bandgaps are promising for the design of highly efficient visible to near-infrared responsive solar cells. As two important optoelectronic materials, PbS and Ag2S have attracted considerable attention for photovoltaic application. In this article, PbS and Ag2S QDs have been explored to serve as efficient sensitizers for QD-sensitized solar cells (QDSCs). The successive ionic layer adsorption and reaction processed QDs are successfully deposited onto TiO2 films. Benefitted from the superior light harvesting capability of PbS and Ag2S, the resultant QDSCs delivered significantly enhanced photocurrents. When CdS capping layers are introduced, the synergistic combination of CdS and PbS (or Ag2S) led to effective surface passivation for retarded charge recombination. As a result, the PbS and Ag2S-based QDSCs resorting to cosensitization with CdS yields considerably enhance power conversion efficiencies of 2.66% and 1.80%, respectively. This article highlights the great prospect of panchromatic responsive QDs for constructing highly efficient solar cells.
Pulmonary Nodules Detection Based on CNN Multi-scale Feature Fusion

Early diagnosis of lung cancer can effectively reduce the mortality, lung nodules is an early manifestation of lung cancer, so the use of computer aided detection of lung nodules has been rapidly developed, which quickly and accurately locate the exact location of lung nodules is a challenging work. In this paper, a two-dimensional convolutional neural network (CNN) based pulmonary nodules detection framework is proposed. The detection framework consists of two parts: region proposal generation and false positive reduction. First, take the first 10 layers of VGG16 as the basic feature extraction network, and select three feature layers in the last several layers of VGG16 network to generate candidate anchors. Secondly, deconvolution is added between feature layers to fuse higher-order feature information and further generate object categories and precise object locations and sizes, and the final result was selected through non-maximum suppression to reduce false positives. Experiments on LUNA16 dataset show that the average sensitivity of nodule detection is 82.62%, which indicates the effectiveness of the proposed algorithm.
DSOD: DSO in Dynamic Environments

Recently, visual simultaneous localization and mapping (SLAM) has been widely used in robotics and autonomous vehicles. It performs well in static environments. However, real-world environments are often dynamic scenarios. Because it is difficult for SLAM to deal with moving objects such as pedestrians and moving cars, SLAM does not meet the actual needs of robots and autonomous vehicles in real-world scenarios. Visual odometry (VO) is a key component of SLAM systems. In this paper, to extend SLAM to dynamic scenarios, we propose a monocular VO based on direct sparse odometry (DSO) to solve the problems arising in a dynamic environment. The proposed method, called DSO-Dynamic (DSOD), combines a semantic segmentation network with a depth prediction network to provide prior depth and semantic information. Experiments were conducted on the KITTI and Cityscapes datasets, and the results show our method achieves good performance compared with the baseline algorithm, DSO.
ANN Optimization Using Ant Colony Algorithm for Predicting the Valsartan Sustained Release from Polyelectrolyte Complexes Matrix Tablets

The aim of this work is to combined an artificial neural network (ANN) with the Ant Colony Optimization (ACO) to model the prolonged release profile of Valsartan (VAL), an antihypertensive drug formed in matrix tablets based on a mixture blend of carboxymethyl-kappa-carrageenan (CMKC) and chitosan (CTS) forming polyelectrolyte complexes (PECs). CMKCs derivatives were obtained by etherification of KC with various degrees of substitution (DS = 0.8, 1.0 and 1.2) and different formulations were prepared by direct compression method and compared to CTS/KC tablets. The CMKC has taken different concentrations in formulated tablets (60, 80 and 100mg/tablet). The in vitro dissolution test was conducted under simulated gastric and intestinal conditions to achieve drug release for more than 12 h. The ANN-ACO model has been developed by considered the DS, the CMKC amount and the time as inputs of the system, while the VAL release amount has been considered as the output. The results showed that the CTS/CMKCs matrices ensured a prolonged release of VAL as compared to CTS/KC based formulations. Meanwhile, increasing the DS of CMKC derivatives resulted in a slower drug release rates kinetics. ANN-ACO modeling has accurately predicted the kinetic release of VAL from each formulation. This performance was demonstrated by the obtained R2=0.999 and RMSE=4,85×10-4. The experimental results indicated that CTS/CMKC has a potential as a hydrophilic matrix that can be employed to prepare prolonged drug release dosage forms.
Weighting Factors Optimization of Model Predictive Torque Control of Induction Motor Using NSGA-II With TOPSIS Decision Making

Model predictive control (MPC) is the result of the latest advances in power electronics and modem control. It is regarded as one of the best techniques when it comes to handling of nonlinearities in the intrinsic model of induction motor (IM). Conventional MPC utilizes weighting factors in the objective function that are tuned after rigorous experimental work which can be improved by utilizing the more mature intelligent optimization techniques like NSGA-II etc. In this study, the weighting factor optimization for the conventional MPC control of IM based on NSGA-II with TOPSIS decision-making criteria is studied. A control algorithm is designed, and an experimental test setup is made to obtain the results of this intelligent MPC which are compared with conventional MPC based on some performance indices like torque and flux ripple, switching frequency loss etc.
Performance Analysis of Perimeter Surveillance Unmanned Aerial Vehicles

Unmanned Aerial Vehicle (UAV) has seen exceptional growth over the past decade and become easily accessible to everyone. One of the key features that makes UAV attractive is the ability to provide the aerial perspective. This is particular the case for security and military purposes, i.e., security patrol and aerial surveillance. This paper offers the performance analysis of perimeter surveillance system comprising of multiple UAVs. These UAVs perform a surveillance task along the predefined perimeter and are capable of communicating. The key metrics under investigations are packet delivery rate, average packet delay, average network throughput, and average hop count. Through a simulation study, we demonstrate how numbers of UAVs and their mobility profile have an effect on key network metrics as well as determine the condition of optimality.
NPPs with VVER Reactors: System Efficiency of Variable Frequency Drives in the Main Circulation Pumps

Today the problem of maneuverability of nuclear power plants primarily related to those power systems where their market share is growing steadily, as in the center of the European part of the Russian Federation. One of the most effective technologies to fundamentally solve the problem of maneuverability of the water-cooled reactor is modernization of the main circulation pumps by installing variable frequency drives. These drives are used to increase the capacity of a nuclear power plant to a super-nominal, to control the frequency in the power system and to eliminate the system accidents. A technique for calculating the efficiency of variable frequency drives in the main circulation pumps of nuclear power plants with VVER reactors is proposed. The most important advantage achieved with the variable frequency drive of the main circulation pump is a possibility for an additional increase in the power up to 107 – 110% (at NPPs with VVER-1000 an increase up to 104% was achieved) without reducing the safety factor prior to the heat exchange crisis by increasing the coolant velocity. It is shown that an increase in the power, maneuverability, and acceleration capability of VVER nuclear power plants through installation of variable frequency drives in the main circulation pumps leads to an increase in the efficiency of NPPs.
Gentle Introduction to Artificial Intelligence for High-School Students Using Scratch

The importance of educating the next generations in the understanding of the fundamentals of the upcoming scientific and technological innovations that will force a broad social and economical paradigm change can not be overstressed. One such breakthrough technologies is Artificial Intelligence (AI), specifically machine learning algorithms. Nowadays, the public has little understanding of the workings and implications of AI techniques that are already entering their lives in many ways. We aim to achieve widespread public understanding of these issues in an experiential learning framework. Following a design based research approach, we propose to implement program coding scaffoldings to teach and experiment some basic mechanisms of AI systems. Such experiments would be shedding new light into AI potentials and limitations. In this paper we focus on innovative ways to introduce high school students to the fundamentals and operation of two of the most popular AI algorithms. We describe the elements of a workshop where we provide an academic use-create-modify scaffolding where students work on the Scratch partial coding of the algorithms so they can explore the behavior of the algorithm, gaining understanding of the underlying computational thinking of AI processes. The extent of the impact on the students of this experience is measured through questionnaires filled before and after participation in the workshop. Preliminary experiments offer encouraging results, showing that the workshop has differential impact on the way students understand AI.
Comparative Study on the Transients Induced by Single Event Effect and Space Electrostatic Discharge

Space radiation-induced Single Event Effect (SEE) and Space Electrostatic Discharge (SESD) are two significant phenomena which cause spacecraft anomalies. However, it is difficult to differentiate between them when trying to identify the root causes of the on-orbit anomalies. This paper pioneers the study of similarities and difference between transients induced by SEE and those induced by SESD. The work is done with the LM124, a well-known operational amplifier. The physical mechanisms between the transients induced by SEE and SESD are studied using H-SPICE simulations. The characteristics of the transient waveforms and the relationship with an external stimulus between SEE and SESD are further investigated with a pulsed laser SEE facility and SESD test system, respectively. The results show that similar transient waveforms can be generated at the device output by both SEE and SESD, while bipolar transients are found only in SEE testing. The different output transient signatures could be used to identify the root causes of spacecraft anomalies.
Instagram Sentiment Analysis with Naive Bayes and KNN: Exploring Customer Satisfaction of Digital Payment Services in Indonesia

Internet penetration and trends of social media have generated a load of data and many researchers have done sentiment analysis to know user’s customer satisfaction of a particular brand through textual data form. This research was conducted to determine the customer satisfaction of digital payment services (Go-Pay, Ovo, and LinkAja) in Indonesia, analyzed from the result of Instagram sentiment analysis, in which the textual data form used is the Instagram comment. The research model was built with sentiment analysis classification algorithm: Naïve Bayes and K-Nearest Neighbors (KNN), and customer satisfaction theory. The result of this research with 3800 training data and 200 test data by using 20 k-fold cross validation shows that GO-PAY has all kinds of customers who satisfied, not satisfied and both. As for OVO and LinkAja have a more neutral sentiment. The most accurate classification algorithm implemented in this research is the KNN classifier algorithm, up to 90%. The final sentiment analysis result of this research contributed to the current state of customer satisfaction towards Go-Pay, Ovo, and LinkAja as a digital payment service provider in Indonesia and maybe an input to the relevant stakeholder.
Control Strategy for a PV-Wind based Standalone DC Microgrid with Hybrid Energy Storage System

This paper presents a control strategy for a PV-Wind based standalone DC Microgrid with a hybrid energy storage system. A control algorithm for power management has been developed for the better utilisation of renewable sources. The proposed system helps in reducing the voltage variation in the DC bus and the current stress in the battery due to intermittency of both solar and wind energy. The PV array and the wind system operating in MPPT are interfaced to the DC load using boost converters. The control algorithm during reduced power mode uses droop control strategy for keeping bus voltage constant under excess power condition. The controllers for battery and supercapacitor designed by frequency domain analysis are also presented. The system is tested for sudden change in load, renewable energies, reduced power mode and during load shedding using MATLAB-Simulink platform. The effectiveness of the proposed strategy is validated by simulation results.
Network Data Flow Clustering based on Unsupervised Learning

Network communication data analysis is crucial in order to provide an adequate security level in computer infrastructures. As the volume of data and the number of features rise, the difficulties associated with its study also increase. A possible approach to this problem based on Artificial Intelligence techniques is to perform clustering, creating groups of similar characteristics. Self-Organized Maps are an artificial neural networks which perform dimensionality reduction as well as clustering over numerical data. In this paper, we propose a mixed numerical-categorical version of Self-Organized Maps algorithm applied to network communication data. Moreover a study of its enhancement with the inclusion of time dependent features is performed. The technique is tested with a well known dataset for Intrusion Detection Systems and it provides a technique to create different clusters or groups of different types of traffic by means of network information.