Miracle-dz/UniFAR-FTUs huggingface dataset:
All-Printed Flexible Tactile Sensors with High Sensitivity and Large Detection Range

This paper presents an all-printed flexible capacitive tactile sensor fabricated by a single desktop printer. The main components of the sensor are a soft silica gel dielectric layer sandwiched between two layers of silver electrodes. The fabrication process includes inkjet printing, used to deposit silver layer as the electrodes on polyethylene terephthalate (PET) substrates, and direct ink writing (DIW), used to print silica gel elastic microstructures as the dielectric layer. Cross-sectional micro-dome shaped silica gel lines are printed to improve the sensitivity for low pressure range and increase the detection range for high pressure range, similar to the responsivity behavior as human tactile sensation. As a result, the sensor has a high sensitivity of 1 kPa−1 below 0.4 kPa and a large detection range up to 2 MPa but with a low sensitivity of 0.0006 kPa−1• Finally, loading cycles applied on the printed sensor shows good stability and small hysteresis.
Analytical Investigation of Trenched Multi-layered Gate Silicon on Nothing MOSFET with Graded Work-function

This paper presents a silicon-based grooved multi-layered gate architecture with air as buried oxide layer to improve the self-heating effect (SHE) and also capable of enhancing the short channel effects (SCEs). This structure takes the benefit of the trenched stack gate to decrease the hot carrier effect (HCE) and linearly varied work-function along the gate region to enhance the carrier transport efficiency. An analytical model is formulated for the presented graded work-function (GW) trenched rectangular stacked gate (TRSG) silicon on nothing (SON) MOSFET and validated through TCAD device simulator. The proposed device performances are upgraded with its physical parameters like negative junction depth (NJD). The performance parameters of GW-TRSG SON MOSFET are compared with trenched rectangular gate (TRG) silicon on insulator (SOI) MOSFET in terms of sub-threshold parameters, electron mobility, and higher-order trans-conductance for better linearity and proficiency.
Data-free Knowledge Distillation via Adversarial*

Network Compression is a challenging task, but it is crucial for using the deeper network in the low-performance device. If the original training datasets could be obtained, the traditional network compression approaches are useful for training a compact deep model. This paper proposes a novel framework for knowledge distillation without original training datasets via Generative Adversarial Network(GAN). We arrange the fixed pre-trained deeper network and the compact network as the discriminators to generate the training dataset. We also use the deeper network and the compact network as the generators, then introduce one simple full connection network as the discriminator to compress the complex network. We propose (i) a series of new images generation loss functions. (ii) a knowledge distillation method via generating adversarial networks. Finally, we show the superiority of our approach by contrasting with SOTA by benchmark datasets.
Low-Resistance Hole Contact Stacks for Interdigitated Rear-Contact Silicon Heterojunction Solar Cells

Achieving low contact resistivity for the p-contact in silicon heterojunction (SHJ) solar cells is challenging when classic n-type transparent conductive oxides (TCOs), such as indium tin oxide (ITO), are used in the contact stack. Here, we report on SHJ solar cells with interdigitated back-contact (IBC) and a direct aluminum (Al) metallization applied to the p-contact. We find that carefully annealing an Al/a-Si:H(p) (p-type amorphous silicon) contact at moderate temperatures leads to a specific contact resistivity that is half as low as its silver (Ag)/ITO counterpart. This is explained by Al diffusing into a-Si:H(p) upon temperature treatment, forming a partially crystallized aluminum silicide layer. For a sufficiently high doping level in a-Si:H(p), this enables an efficient tunnel-recombination of holes from a-Si:H(p) to the Al contact. An estimate for this tunneling-dominated specific contact resistivity is calculated as a function of the interface doping density. Best fabricated IBC SHJ solar cells with Al p-contact yield a fill factor of 77.5% and a power conversion efficiency of 22.3%. The main differences to devices with an Ag/ITO/a-Si:H(p) contact stack are a decrease in open-circuit voltage by 14 mV and a slightly higher series resistance (Rs). While the first aspect can be ascribed to increased interface recombination, the second one is unexpected and requires further investigation. Interestingly, omitting an intermediate TCO does not lead to current losses in devices with Al contacts, which is further investigated by optical simulations. Finally, electrical equivalent circuit simulations are conducted to describe the electrical behavior of the investigated devices.
Predicting the Impact of Disruptions to Urban Rail Transit Systems

Service disruptions of rail transit systems become more frequent in the past decades in urban cities like Singapore, due to various reasons such as power failures, signal errors, etc. We study and predict the impact of disruptions to transit systems and commuters. This benefits service providers in making both short and long term plans to improve their services. Specifically, we define two metrics, stay ratio and travel delay, to quantify the impact. To tackle the main challenge of abnormal data scarcity, i.e., only 6 observed disruptions in our one-year data records, we propose to format the problem into a training problem on a feature space relevant to alternative route choices of the commuters. We demonstrate the new feature space corresponds to more similar data distribution among different disruptions, which is beneficial for training more generalisable predictors for future disruptions. We implement and evaluate our approach with a real-world transit card dataset. The result clearly shows that our method outperforms a range of baseline methods.
Event-Driven Continuous-Time Feature Extraction for Ultra Low-Power Audio Keyword Spotting

In the context of autonomous keyword spotting and sound detection, this paper proposes a low power feature extraction unit generating spectrograms that represent a unique signature allowing the classification of audio signals. This system is composed of a continuous-time digital signal processing feature extractor combined with a convolutional neural network engine. The study evaluates the hardware requirements to implement the feature extraction unit using an advanced CMOS process. Furthermore, a simulation of the complete system using Matlab® reveals that the recognition accuracy remains higher than 90% while offering a power consumption 4000X lower than a conventional discrete time system.
A Learning-based Method for Computing Shortest Path Distances on Road Networks

Computing the shortest path distances between two vertices on road networks is a core operation in many real-world applications, e.g., finding the closest taxi/hotel. However existing techniques have several limitations. First, traditional Dijkstra-based methods have long latency and cannot meet the high-performance requirement. Second, existing indexing-based methods either involve huge index sizes or have poor performance. To address these limitations, in this paper we propose a learning-based method which can efficiently compute an approximate shortest-path distance such that (1) the performance is super fast, e.g., taking 60-150 nanoseconds; (2) the error ratio of the approximate results is super small, e.g., below 0.7%; (3) scales well to large road networks, e.g., millions of nodes. The key idea is to first embed the road networks into a low dimensional space for capturing the distance relations between vertices, get an embedded vector for each vertex, and then perform a distance metric (L1 metric) on the embedded vectors to approximate shortest-path distances. We propose a hierarchical model to represent the embedding, and design an effective method to train the model. We also design a fine-tuning method to judiciously select high-quality training data. Extensive experiments on real-world datasets show that our embedding based approach significantly outperforms the state-of-the-art methods.
Electromigration Improvement by Graphene on Cu Wire for Next Generation VLSI

The electro migration (EM) failure mode of copper (Cu) interconnects poses a major reliability concern. It has been found that using graphene as a capping layer on Cu could improve the EM failure. However, no reliability data have been reported for graphene grown on Cu damascene structures with foundry's back-end compatible process. In this study, we show that graphene can be selectively grown on Cu damascene structures as the capping layer on 12” wafers. An improvement in the EM lifetime was achieved through the graphene capping layer on the Cu line, and a nanotwin Cu structure with a graphene capping layer was also compared.
Research on elevator speed governor calibration software based on image processing

As a momentous device to ensure the safety of the elevator operation, the speed governor plays an important protective role of the safety control components in the event of overspeed and rope breaking. In order to judge the action of the speed governor, this article research and develop a software which carries out color processing, statistics and detection on the images according to the photos of the governor. The software can achieve picture browsing and reading function, image processing and parameter adjustment function, statistical results display function and log output function. Through processing the image and outputting the situation of the governor, the software provides professional reference of the elevator safety for the inspector. By comparing the obtained electric action speed with the specified value in the standard, the inspector can judge whether the operation speed of the speed governor meets the requirements.
Observer-based Control of Biomass Concentration under Discrete and Delayed Measurements

Wastewater treatment is a biological process which involves the streaming of solid waste products in tubes and makes use of sensors which provide outputs at distinct instants of time. These issues lead to problems in the control of the output, which is the substrate concentration. In this work, we present an observer and a controller which is used to control the biomass concentration of such bioreactors in the presence of delayed and discrete measurements. The aim of this paper is to make a realistic analysis of the proposed observer-based control system and conclude on the effects of delayed measurements on the maximum possible sampling partition diameter as well as to study the effect of the sampling rate on the maximum possible delay. The results obtained confirmed the satisfactory performance of the system.
Fragmentation learning based on Internet technology

Fragmented learning is a way for people to use spare time to learn. It forms small units that can learn quickly and flexibly by dividing learning content and learning time. It has the characteristics of flexible time, targeted content and high absorption rate. In the Internet era, with the rapid popularization of smart phones, tablet computers and high-speed bandwidth, learning based on mobile terminals has become a trend. Mobile and networked learning create an appropriate situation for fragmented learning and significantly promote the deep learning ability of college students.The application of Internet technology promotes the individualization of college students' learning time arrangement and knowledge learning. College students can arrange their learning independently according to their learning interests, realize the integration of fragmented knowledge with the help of internet tools, and improve their deep learning ability.
M-Type and CD-Type Carrier Based PWM Methods and Bat Algorithm-Based SHE and SHM for Compact Nine-Level Switched Capacitor Inverter

This paper presents new and efficient modulation techniques applied on the recently introduced compact nine-level switched-capacitor inverter (C9LSCI). The paper also discusses the performance of the inverter under selective harmonic elimination (SHE) and mitigation (SHM) based on the heuristic Bat-Algorithm (BA) technique. Two new modulation techniques employing CD-type carrier waves and M-type carrier waves are proposed, showing a rise in efficiency, an increase in RMS voltage, reduction in capacitor voltage ripple, and a reduction in harmonic distortion (THD). Four recently introduced modulating signals, namely the third harmonic injection, thirty-degree bus clamped PWM (THTDBCPWM), third harmonic injection sixty-degree bus clamped PWM (THSDBCPWM), one pole clamped PWM (OPCPWM). Loss balancing two-pole clamped PWM (LBTPCPWM) are applied to the inverter with the proposed carriers, and a comparative analysis is carried out on different performance parameters. The proposed carriers’ efficacy in Alternate Phase Opposition Disposition (APOD), Phase Opposition Disposition (POD), and Phase Disposition (PD) is validated. Further, Bat algorithm (BA) is applied as a heuristic approach to determine the optimum angles for the application of SHE and SHM. The efficacy of all the above techniques on the C9LSCI is verified in MATLAB/Simulink environment and further validated on an experimental setup.
An Empirical Evaluation of Stacked Ensembles With Different Meta-Learners in Imbalanced Classification

The selection of a meta-learner determines the success of a stacked ensemble as the meta-learner is responsible for the final predictions of the stacked ensemble. Unfortunately, in imbalanced classification, selecting an appropriate and well-performing meta-learner of stacked ensemble is not straightforward as different meta-learners are advocated by different researchers. To investigate and identify a well-performing type of meta-learner in stacked ensemble for imbalanced classification, an experiment consisting of 19 meta-learners was conducted, detailed in this paper. Among the 19 meta-learners of stacked ensembles, a new weighted combination-based meta-learner that maximizes the H-measure during the training of stacked ensemble was first introduced and implemented in the empirical evaluation of this paper. The classification performances of stacked ensembles with 19 different meta-learners were recorded using both the area under the receiver operating characteristic curve (AUC) and H-measure (a metric that overcomes the deficiencies of the AUC). The weighted combination-based meta-learners of stacked ensembles have better classification performances on imbalanced datasets when compared to bagging-based, boosting-based, Decision Trees, Support Vector Machines, Naive Bayes, and Feedforward Neural Network meta-learners. Thus, the adoption of weighted combination-based meta-learners in stacked ensembles is recommended for their better performance on imbalanced datasets. Also, based on the empirical results, we identified better-performing meta-learners (such as the AUC maximizing meta-learner and the H-measure maximizing meta-learner) than the widely adopted meta-learner – Logistic Regression – in imbalanced classification.
Comparision of Some Image Thresholding Techniques with Morphological Operations and Quantitative Evaluation for Lung Segmentation

Segmentation of lung parenchyma is a critical step on the computer aided diagnostic pipeline determining the success and failure of the overall system. Thresholding is one of the most frequently used operations carried out in most of the state of art methods for segmentation of lung parenchyma. This work presents a typical thresholding and morphological operation based lung segmentation framework, applies some of the variants of thresholding techniques, viz. fixed, histogram based, entropy based on 3D lung volume slice by slice and evaluates the quality of results obtained using five evaluation metrics and presents the results and discussion on the same.
Dual-Wavelength Light Source Assisted Frequency Response Measurement Method of Photodetectors

Dual-wavelength light source assisted frequency response measurement of photodetectors based on double-sideband suppressed-carrier modulation is proposed in this letter. In this method, a directly modulated laser with a modulation frequency of <inline-formula> <tex-math notation="LaTeX">$f_{1}$ </tex-math></inline-formula> combined with an optical tunable filter is used to generate a dual-wavelength carrier, which can be highly stable since only one light source is introduced. The dual-wavelength carrier generates two pairs of first-order sidebands under double-sideband suppressed-carrier modulation of the Mach-Zehnder modulator at frequency <inline-formula> <tex-math notation="LaTeX">$f_{m}$ </tex-math></inline-formula>. Afterwards, the modulated signal is injected into the photodetector under test, and the frequency response of the photodetector can be extracted by recording the electric power at specific frequencies. Based on this method, we used a 35-GHz modulator to measure the frequency response of photodetectors with bandwidth of 15 GHz and 43.5 GHz respectively. Moreover, a comparison of the results is performed between our method, the swept frequency method, and electro-absorption modulator deduction method. The results show that the new method extends the frequency response measurement range from <inline-formula> <tex-math notation="LaTeX">$f_{m}$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$f_{1}+2f_{m}$ </tex-math></inline-formula>, and the influence of the modulator on the frequency response measurement results is reduced simultaneously.
Improving Effectiveness of Spectrum-based Software Fault Localization using Mutation Testing

The performance of software fault localization (SFL) is significant to software reliability and the software debugging process. Test suite effectiveness is crucial in improving the performance of spectrum-based fault localization (SBFL). Although code coverage metric (percentage of code merely executed by test cases) is a useful factor to assess the test cases quality but it has a drawback that it does not tell how much code actually checked and therefore this metric alone is not sufficient to measure the quality of test suites. For this reason, in this paper, we make use of mutation testing for the selection of effective test suites to improve the performance of spectrum-based fault localization (SBFL). We use two classic SBFL techniques Ochiai and Jaccard in our experiments to show the impact of test suite effectiveness on the performance of SBFL. We have used an open source mutation testing tool PIT to evaluate and select test cases and the unit testing framework JUnit has been used for writing and executing the test suites. Our experimental results show that the proposed approach can significantly improve the suspiciousness ranking of faulty statements. The results demonstrate that our approach effectively reduces the number of statements examined by an average of approx 50%.
L-shaped piezoelectric energy harvester for low frequency application

In this paper, the L-shaped piezoelectric (PZ) cantilever structure has been designed, which is suitable for low frequency application. The size of the designed structure is within 5 mm. This makes it suitable to power any wireless sensor nodes for machine monitoring system. The L-shaped cantilever structure helps in reducing resonant frequency. It is able to achieve resonant frequency of 79.5 Hz. The proposed structure is simulated at 1g excitation acceleration and generates the output voltage of 11.6 V at its natural frequency. The proposed L-shaped PZEH generates an output power of 10.8 μW at its resonant frequency.
Improved Dynamic Response of DC Microgrid Under Transient Condition Using Inertia by Virtual Generation

This article proposes new control architecture to improve the dynamic performance of DC microgrid by enhancing system inertia. The DC microgrids are gaining more importance due to the controlled and combined operation of multiple distributed energy resources (DERs). The DC microgrid is dominated by controlled power electronics converters. These converters are not having inbuilt inertia like rotating generators. Therefore, the DC microgrid has a fast rate of change of bus voltage under fluctuations of load, change in available power capacity of sources due to their randomness, etc. This affects the voltage profile and stability of the system under dynamic variation. The proposed closed loop control scheme estimates the amount of inertia required in the DC microgrid to minimize the voltage variation. The new power references are given to sources based on virtual generation to improve the inertia in the system. The proposed control scheme maintains the stability of DC microgrid. It also achieves effective power management based on available power in DERs with a minimum burden on the energy storage system (ESS). The bus voltages are regulated at reference value. The stability analysis of the system is carried out for DC microgrid under different operating conditions. The proposed control method is tested experimentally under diverse operating conditions.
Predictive Modelling Methodologies for Bi-Material Wafer Warpage

Wafer warpage modeling is challenging for semiconductor industry because simulation tools need to consider multi-physics behavior and non-linear material properties. This paper conducted a wafer warpage experiment and simulation on bi-material wafer which consists of silicon and substrate's polymer materials. In the experiment, the bi-material wafer was thermally cured along a temperature profile with warpage measurement. For modelling study, pressure-volume-temperature-cure (PVTC) effect and visco-elastic (VE) with time-temperature shift factor (TTS) were considered in the polymer material model. The simulation results were compared with the experiment data.
A fast and efficient method for detection of seizure in electroencephalogram using log-energy entropy and support vector machine

We present a method for automatic detection of seizures in EEG that might help clinicians by speeding up the process of seizure detection. The method consists of extraction of Log-Energy Entropy from band-passed EEG and use of a Support Vector Machine (SVM) classifier. Furthermore, using multiple regression analysis, we evaluated the effect of some characteristics of the patients on the performance of the method. We found that the type of epilepsy is the major factor, which influenced the performance of the method. The high performance of the method makes it feasible also for real-time applications.
Hardware Realization of Low power and Area Efficient Vedic MAC in DSP Filters

VLSI experiences a key position in many of the signal processing applications. Multiply and Accumulation process is one among the mostly used operation. Power, area and speed are the metrics used to determine the efficiency of a MAC unit. For certain cases each of these metrics plays a key role. In some cases, speed is only concentrated, so the other parameters are not given much priority in that case. This work focuses on two metrics. The power and area is concentrated for the better efficiency in this work. Through the deep analysis of adders, Ripple Carry Adder has shown less area and power consumption than other adders. The processes that are involved in MAC are multiplication, addition and accumulation. The addition of Vedic techniques in a MAC is always an added advantage. So, this work includes development of a 32-bit multiply and accumulate unit using Vedic sutra (Urdhva Tiryakbhyam sutra), accumulation unit involving ripple carry adder (RCA) and its implementation in a 4-tap FIR filter. The results show an efficiency of 5% in area improvement and 9% in power.