Miracle-dz/UniFAR-FTUs huggingface dataset:
Development and Evaluation of Maternal Sensitivity Assessment Game

The present study tried to develop and validate a serious game in order to assess maternal sensitive behavior toward her child in certain situations. Based on the existing literature, maternal sensitivity assessment tools, and the serious game elements, a serious game was developed with the purpose of assessing maternal behaviors. 75 mothers were evaluated by sensitivity assessment software, in four attachment-based situations, sleep, injury, play, and feeding at three ages and the results were analyzed by Pearson correlation and discriminant analysis. Findings indicated that there was a significant difference between game components among mothers with high and low quality of caregiving, which confirm the validity of the game in assessing maternal behavior. This game not only can examine and evaluate mother behaviors in the situations which can be hardly observed naturally but also increase the ease and speed in the measurement process. In addition, by providing a pleasant stimulated condition for interacting with a child, the process of evaluation, will be performed in an implicit way.
Novel Drain-Connected Field Plate GaN HEMT Designs for Improved VBD–RON Tradeoff and RF PA Performance

TCAD studies are performed to develop physical insights into the breakdown behavior of drain-connected field plate-based GaN HEMTs. Using the developed insights, to mitigate the performance bottleneck caused by the lateral drain-connected field plate design, we have proposed novel vertical-field-plate designs. The proposed designs alleviate the channel electric field by uniformly distributing it vertically into the GaN buffer region. As a result, the proposed vertical and dual-field-plate design offer <inline-formula> <tex-math notation="LaTeX">${2} \times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${3} \times $ </tex-math></inline-formula> improvements in breakdown voltage, respectively, compared with the design without field plate. Similarly, compared with a design with a lateral field plate, a 50% improvement in the breakdown voltage was seen with dual-field-plate architecture. RF power amplifier (PA) performance extracted using load-pull simulations demonstrates an improved RF PA linearity at higher drain bias, improved output power, efficiency, and PA gain for HEMTs with dual- and vertical-field-plate designs.
Surface charging characteristics of GIL model spacers under DC stress in C4F7N/CO2 gas mixture

The C<inf>4</inf>F<inf>7</inf>N/CO<inf>2</inf> gas mixture has attracted much attention due to its excellent insulation property and eco-friendly character. In order to evaluate the feasibility of its application in DC GIL, this paper focuses on the surface charging characteristics of spacers under DC stress in the C<inf>4</inf>F<inf>7</inf>N/CO<inf>2</inf> mixture. First, the DC insulation strength was tested under uniform field. The results show that the critical dielectric strength of pure C<inf>4</inf>F<inf>7</inf>N is more than twice that of SF<inf>6</inf> and the dielectric strength of 15%∼20% C<inf>4</inf>F<inf>7</inf>N/CO<inf>2</inf> mixture is equivalent to that of SF<inf>6</inf>. Next, the surface charge measurement of spacers was performed in C<inf>4</inf>F<inf>7</inf>N/CO<inf>2</inf> mixtures, which reveals that the surface charge density decreases slightly with the increase of C<inf>4</inf>F<inf>7</inf>N content. However, the surface charging level in 25% C<inf>4</inf>F<inf>7</inf>N/CO<inf>2</inf> mixture is still much higher than that in SF<inf>6</inf>. It is found that most of the surface charge stems from partial discharges (PDs) near the flange. The PD measurement shows that in SF<inf>6</inf>, there are more PDs with low magnitude, but much less PDs with high magnitude compared to that in the 15%∼25% C<inf>4</inf>F<inf>7</inf>N/CO<inf>2</inf> mixtures. It is the different PD behaviors between C<inf>4</inf>F<inf>7</inf>N/CO<inf>2</inf> mixtures and SF<inf>6</inf> that lead to their different surface charging characteristics.
Space Charge Analysis of Epoxy-Boron Nano-Composites and the Importance of Dispersion Techniques

The present research work analyzes the space charge (S.C.) behavior and correlated dynamics in epoxy based nano-composites (NC) with hexagonal boron nitride (hBN) nanoparticles. The importance of adopting an effective particle dispersion technique for producing such nano-composites is also experimentally validated. Three different dispersion techniques are contrasted in terms of the corresponding space charge behavior exhibited, and the interplay between increasing filler content and dispersion techniques is investigated and reported. It is observed that an effective particle dispersion technique results in a homogeneous distribution of nano-particles in the polymeric matrix, which in turn gives rise to evenly distributed trap sites and affects the local mobility of charge carriers, effectively restraining them. More significantly, since most of the developed trap-sites are located close to/in the interfacial zone between the injecting electrode and nano-composite specimen, an initial, reasonably homogeneous distribution of nano-particles at relatively lower loading (vol. %) is just as efficient in restricting bulk space charge accumulation as higher loaded nano-composite specimens - proving the importance of adopting an effective particle dispersion technique. Achieving lower levels of bulk space charge accumulation (as demonstrated in this work) results in smaller internal electric field distortion and stress in insulation material, thus having the beneficial effect of prolonging operational life-time and increased reliability.
Experimental Study on the Short-Circuit Instability of Cascode GaN HEMTs

This article presents an experimental investigation on the short-circuit (SC) instability of several commercially available cascode gallium nitride (GaN) high-electron-mobility transistors (HEMTs). In the SC test, self-sustained oscillation is observed during the SC transient. The SC oscillation features some unique characteristics. The gate resistor shows very weak damping effect on the SC oscillation. The SC oscillation thereby cannot be suppressed by utilizing a large gate resistor. With the increase in the dc-bus voltage, the SC oscillation greatly amplifies and becomes more unstable. When the dc-bus voltage reaches 200–250 V, catastrophic failure occurs. In the SC test, two distinct failure modes, which are related to the failure of low-voltage (LV) MOSFET and depletion-mode HEMTs (DHEMTs), are identified. Based on the experimental evidence and simulation study, the root causes of the failure are clarified in the end.
A Self-biased Current Source, using an Asymmetric Bulk-modified MOS Composite Transistor

In this work a new topology for a self-biased current reference, based on an asymmetric bulk-modified MOS (ABM) composite transistor is presented. Two current references based in this technique were designed: a 13.5nA current reference in a $1.5\mu \mathrm{m}$ CMOS technology, and a 100nA current reference in a $0.18\mu \mathrm{m}$ CMOS technology. The latter was designed to minimize the temperature dependence of the output current; the result was less than 5% from 0°C to 100°C, which is a very good result in comparison to other reported similar current references.
Small Deep Learning Models for Hand Gesture Recognition

Robust hand gesture recognition is a critical ingredient of many human-computer interaction applications. Recently, Deep Neural Networks (DNNs) models have been widely applied to solve different computer vision problems. However, few researchers have applied DNNs to recognize static hand gestures. In this paper, an approach is proposed that leverages the recent progress on Convolutional Neural Networks (CNNs) and uses deep-learning-based strategy to recognize 24 hand gestures from the American Sign Language (ASL). This approach is built on recently released small CNNs architectures to leverage their high accuracy rate, maintaining small model size and the feasibility to deploy on resource constrained-devices. Here, the architectures of SqueezeNet and MobileNets for both RGB and depth images are utilized, and a late-fusion strategy to concatenate features from both RGB and depth modalities is performed. Also, an effective encoding of depth images as rendered RGB images is proposed to handle depth data with CNNs, and thus improve recognition performance. Experiments are executed on a challenging publicly available American Sign Language – Finger Spelling (ASL-FS) dataset acquired by an RGB-D camera to evaluate the proposed architecture. Results show that the proposed approach achieves higher average accuracy, outperforming all previous methods, and has great feasibility to apply the models on resource-constrained devices and embedded visual applications.
Practices regarding prescription and dispensing of drugs in a tertiary care hospital in rural West Bengal, India

Prescription practices have always been one of the most important aspects of medical care, mostly because of its ability to single-handedly determine not only the general trends in the prescription and use of drugs in a population, but also the overall development and progress of the healthcare system in a particular region. Not only that, prescription practices also provide us with an insight into the general state of physician-patient relationship in a healthcare setting, and how it compares to that of the rest of the world. Therefore, determining the general trends of medical care practices among the healthcare professionals is immensely important in the understanding and improvement of the state of medical care in a geographical region.
Fast Method of Detecting Tomatoes in a Complex Scene for Picking Robots

At present, there are two main problems with fruit-and vegetable-picking robots. One is that complex scenes (with backlighting, direct sunlight, overlapping fruit and branches, blocking leaves, etc.) obviously interfere with the detection of fruits and vegetables; the other is that an embedded platform needs a lighter detection method due to performance constraints. To address these problems, a fast tomato detection method based on improved YOLOv3-tiny is proposed. First, we improve the precision of the model by improving the backbone network; second, we use image enhancement to improve the detection ability of the algorithm in complex scenes. Finally, we design several groups of comparative experiments to prove the rationality and feasibility of this method. The experimental results show that the f1-score of the tomato recognition model proposed in this paper is 91.92%, which is 12% higher than that of YOLOv3-tiny; the detection speed on a CPU can reach 25 frames/s, and the inferential speed is 40.35 ms, equivalent to that of YOLOv3-tiny. Through comparative experiments, we can see that the comprehensive performance of our method is better than that of other state-of-the-art methods.
Lateral Photocurrent-Induced High-Performance Self-Powered Photodetector Observed in CIGS Heterojunction

With the shortage of energy and the development of technology, self-powered devices are promptly needed and aroused much concern in the world. However, restricted to the working principle, external biases are usually needed to get high longitudinal photocurrent responses for the current prototype devices. Here, the lateral photovoltaic effect (LPE) is first introduced to study the lateral photocurrent (LPC) responses in the Cu(In,Ga)Se2 (CIGS) heterostructure. It is found that the CIGS heterojunction can be developed to both an LPC-based self-powered position-sensitive detector (PSD) and photodetector. This PSD shows ultralarge LPC responses in a wide spectral range with a position sensitivity of up to 4.66 mA/mm (at 532 nm) and can work in different contact distances as large as 10.2 mm with excellent linearities. Moreover, the LPC responses can be considerably improved by modulating the thickness of the indium tin oxides (ITOs) layer, which can be mainly attributed to the ITO thickness-dependent external quantum efficiency of the CIGS devices. For the photodetector, the responsivity reaches up to 0.197 A/W but exhibits a nearly reverse laser power- and contact distance-dependent changing tendency with the position sensitivity due to their different definitions.
An Overview of Recent Progress of Lane Detection for Autonomous Driving

Technology breakthroughs and novel ideas proposed by esteemed authors on lane detection systems are summarized in this paper to facilitate autonomous vehicle researchers to understand the concept briefly. The importance of autonomous driving, especially the Advanced driver assistance system (ADAS) and future scope of research are discussed. A high amount of sophistication and accuracy is expected from lane detection systems to prevent road accidents and achieve high automation. A single sensor, algorithm or methodology cannot meet the real-world urban lane expectations due to highly random traffic properties and road constraints. Hence, multiple perception sensors must be integrated at the sensor level, along with the system and algorithm level integration is required to design robust lane detection systems. A generalized lane detection procedure is also discussed for an overview of the detection process. Widely used sensor modalities, their characteristics and applications in ADAS are discussed in detail. A brief introduction to performance evaluation tools is addressed.
Statistical Analysis Based on Adaptive Progressive Hybrid Censored Sample From Alpha Power Generalized Exponential Distribution

For the alpha power generalized exponential model, the maximum likelihood and Bayes estimations of unknown distribution parameters are discussed under an adaptive Type-II progressive hybrid censored sample. The confidence intervals of the distribution parameters are calculated approximately, and further employ the delta method to obtain approximate interval estimations of the associated survival function and hazard function. The Bayes estimates and the credible intervals of these quantities are also obtained. The results of simulation and analysis of an actual life data set are used to evaluate the performance the proposed method and illustrate its application possibility.
Chest Wearable Apparatus for Cuffless Continuous Blood Pressure Measurements Based on PPG and PCG Signals

This paper describes an integrated chest wearable apparatus for continuous blood pressure (BP) measurements exploiting the heart-rate (HR) and the pulse arrival time (PAT) in a modified Moens-Korteweg model. The device embeds a miniaturized gas pressure sensor to record the phonocardiogram (PCG) of the heart sounds, a LED-photodiode pair to detect the photoplethysmogram (PPG) of the blood flow wave, a $\mu $ -controller, a wireless communication module and the power supply. With the proposed device no active participation would be required from human subjects for BP measurements, since the HR and the PAT are continuously extracted from the PCG and PPG signals. Dedicated signal processing algorithms were developed and implemented off-line to extract both HR and PAT. A subject-specific calibration protocol of the BP model was designed and implemented. The calibration and validation of the apparatus were performed on a cohort of 20 healthy subjects. A GIMA ABPM pressure Holter was chosen as reference device, and 8 measurements points, evenly distributed over a 10-minute interval, were used for model calibration for each subject. The range of DBP and SBP measurements were 52–85 mmHg and 90–141 mmHg, respectively. The results from Bland-Altman analysis showed that the mean±1.96SD for the estimated diastolic, systolic, and mean BP with the proposed method against reference were 0.01±7.55, 1.47±3.76, 0.74±4.38 mmHg, respectively. The corresponding mean absolute differences (MAD) were 3.06, 1.83, and 1.80 mmHg. These results demonstrates that the acquisition apparatus is able to continuously estimate the BP with an accuracy comparable to traditional cuff-based devices.
A Hybrid Local Search-Genetic Algorithm for Simultaneous Placement of DG Units and Shunt Capacitors in Radial Distribution Systems

Controlling active/reactive power in distribution systems has a great impact on its performance. The placement of distributed generators (DGs) and shunt capacitors (SCs) are the most popular mechanisms to improve the distribution system performance. In this line, this paper proposes an enhanced genetic algorithm (EGA) that combines the merits of genetic algorithm and local search to find the optimal placement and capacity of the simultaneous allocation of DGs/SCs in the radial systems. Incorporating local search scheme enhances the search space capability and increases the exploration rate for finding the global solution. The proposed procedure aims at minimizing both total real power losses and the total voltage deviation in order to enhance the distribution system performance. To prove the proposed algorithm ability and scalability, three standard test systems, IEEE 33 bus, 69 bus, and 119-bus test distribution networks, are considered. The simulation results show that the proposed EGA can efficiently search for the optimal solutions of the problem and outperforms the other existing algorithms in the literature. Moreover, an economic based cost analysis is provided for light, shoulder and heavy loading levels. It was proven, the proposed EGA leads to significant improvements in the technical and economic points of view.
Role of food taboos in energy, macro and micronutrient intake of pregnant women in western Kenya

The purpose of this paper is to examine food taboos/habits and dietary patterns of pregnant women in Kenya. ,In all, 205 individual 24-h recall interviews were conducted face to face to collect information on pregnant women dietary intake. Women focus group discussions were conducted in seven villages in Kakamega County to determine which foods are avoided during pregnancy and the reasons for avoiding them. ,The concept of “baby becoming big” was common advice to pregnant women to reduce intake of large amounts of starchy or high caloric foods. Restriction on the consumption of some parts of cow and chicken and consumption of eggs was widespread. Women with the highest education had significantly higher intake than women with lower education: energy (1,718 vs 1,436 kcal, p = 0.007), protein (45 vs 38 g, p = 0.03), vitamin C (59 vs 39 mg, p = 0.000), calcium (275 vs 222 mg, p = 0.04) and iron (8.5 vs 7.2 mg, p = 0.03). Multivariate binary logistic regression showed that participants with higher education were more likely to reach estimated average intake for energy (OR = 2.82, 95 per cent CI = 1.2-6.5) and vitamin C (OR = 4.1, 95 per cent CI = 1.5-11.0) than women with lower education level. ,The observed link between reduced intake of nutrients and low education levels suggests that education is a possible intervention strategy. Incorporating nutrition education in school curriculum and improving knowledge on nutrition among women with low education may help overcome unhealthy food taboos in pregnant women.
Bayesian Methods for the Yield Optimization of Analog and SRAM Circuits

As the technology node shrinks to the nanometer scale, process variation become one of the most important issues in IC designs. The industry calls for designs with high yield under process variations. Yield optimization is computationally intensive because traditionally it relies on the Monte-Carlo yield estimation. In this paper, we will first review the Bayesian methods that reduce the computational cost of yield estimation and optimization. By applying Bayes’ theorem, maximizing the circuit yield is transformed to identify the design parameters with maximal probability density, conditioning on the event that the corresponding circuit is “pass”. It can thus avoid repetitive yield estimations during optimization. The computational cost can also be reduced by using the Bayesian optimization strategy. By using the Gaussian process surrogate model and adaptive yield estimation, Bayesian optimization can significantly reduce the number of simulations while achieving even comparable yields for analog and SRAM circuits. We further propose a Bayesian optimization approach for yield optimization via maxvalue entropy search in this paper. The proposed max-value entropy search can better explore the state space, and thus reduce the number of circuit simulations while achieving competitive results.
Efficient MIMO Preprocessor With Sorting-Relaxed QR Decomposition and Modified Greedy LLL Algorithm

This paper proposes a high-efficient preprocessing algorithm for $16\times 16$ MIMO detections. The proposed algorithm combines a sorting-relaxed QR decomposition (SRQRD) and a modified greedy LLL (MGLLL) algorithm. First, SRQRD is conducted to decompose the channel matrices. This decomposition adopts a relaxed sorting strategy together with a paralleled Givens Rotation (GR) array scheme, which can reduce the processing latency by 60% compared with conventional sorted QR decomposition (SQRD). Then, an MGLLL algorithm is conducted to improve detection performance further. The MGLLL algorithm adopts a paralleled selection criterion, and only process the most urgent iterations. Thus the processing latency and column swaps can be reduced by 50% and 75%, respectively, compared with the standard LLL algorithm. Finally, the bit-error-rate (BER) performance of this preprocessing algorithm is evaluated using two MIMO detectors. Results indicate that this preprocessor suffers a negligible performance degradation compared with the combination of the standard LLL algorithm and SQRD. Based on this preprocessing algorithm, a pipelined hardware architecture is also designed in this paper. A series of systolic coordinated-rotation-digital-computer (CORDIC) arrays are utilized, and highly-pipelined circuits are designed, helping this architecture achieve high frequency performance. This architecture is implemented using 65-nm CMOS technology, which can work at a maximum frequency of 625 MHz to process channel matrices every 16 clock cycles. The latency is 0.9 us. Comparisons indicate that this preprocessor outperforms other similar designs in terms of latency, throughput, and gate-efficiency.
Smart DAG Task Scheduling with Efficient Pruning-Based MCTS Method

Efficient task scheduling algorithms are important for achieving high performance in heterogeneous computing systems. The DAG (Directed Acyclic Graph) tasks scheduling problem has been extensively studied. However, many previously proposed algorithms are typically heuristic and based on greedy methods, which can not effectively explore large search space of the problem. In this paper, we proposed a smart DAG task scheduling algorithm with efficient pruning-based Monte Carlo Tree Search (MCTS) method. In the task scheduling process, the algorithm adaptively balances the exploration and exploitation of search policies. To further reduce the search space, a new branch-and-bound based pruning method is proposed that significantly improves the algorithm's efficiency. Our experimental evaluation shows the effectiveness of the proposed algorithm. Under a range of different DAGs and heterogeneous configurations, the pruning-based MCTS scheduling algorithm outperforms HEFT, CPOP and PEFT algorithms in terms of scheduling makespan, while restricts the computing overhead below an acceptable threshold.