Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Transition Metals on the Structure and Performance of the Doped Carbon Catalysts Derived From Polyaniline and Melamine for ORR Application

In this work, the effects of the addition of transition metals (Mn, Fe, Co, Ni, Cu) on the structure and performance of the doped carbon catalysts M-PANI/C-Mela are investigated. The results show that the doping of various transition metals affected structures and performances of the catalysts significantly. Doping with Fe and Mn leads to a catalyst with a graphene-like structure, and doping with Co, Ni, and Cu leads to a disordered or nanosheet structure. The doping of transition metals can enhance the performance of the catalysts, and their ORR activity follows the order of Fe > Co > Cu > Mn > Ni, which is consistent with the order of their active N contents. We suggest that the various performance enhancements of the transition metals may be the result of the joint effect of the following three aspects: the N content/active N content, metal residue, and the surface area and pore structure, but not the effect of any single factor.
Method Development and Validation of RP-HPLC Method for Determination of New Antipsychotic Agent Asenapine Maleate in Bulk and Pharmaceutical Formulation

A novel isocratic reverse phase liquid chromatography method for determination of Asenapine Maleate was developed and validated after optimization of various chromatographic conditions. A Hiber C18, 5 μm column having 250×4.6 mm i.d. , with mobile phase containing 0.05 M potassium dihydrogen phosphate : acetonitrile (60:40, v/v, pH 2.7 adjusted with 1% o-phosphoric acid) was used. The flow rate was 1.0 mL min -1 and effluents were monitored at 270 nm. The retention time of asenapine was 4.2min. The linearity for Asenapine maleate was in the range of 0-150 μg mL -1 with coefficient of correlation 0.999 . The proposed method was validated with respect to linearity, accuracy, precision and robustness.
Polyester resin and carbon nanotubes based nanocomposite as new-generation coating to prevent biofilm formation

ABSTRACT In this article, we have developed protective nanocomposite coatings using unsaturated polyester resin and multiwalled carbon nanotubes showing antimicrobial activity and mechanical durability. Carbon nanotubes retain the mechanical resilience of polyester resin in the nanocomposite, improve its hydrophobic character, and increase the adhesion features of the coating, preventing its stiffness from decreasing due to water absorption or exposure to UV rays. Nanocomposite coating exhibits an appreciable antimicrobial property and a lower level of toxicity compared to pure resin. All these features make this material a good candidate for its use in the field of anti-biofilm coatings.
Deep Learning-Based Automated Vehicle Steering

Autonomous Vehicle applications are full of open challenges. Despite the advanced technologies, the lack of robust systems still exists due to the high complexity of the surrounded environments. The automated steering is one of the most complex autonomous driving system’s application. Model predictive control is the most common control strategy used to implement the automated steering tasks due to its ability to solve an online quadratic optimization problem in the real-time, in addition to its efficiency in handling the constraints of the system’s environments. MPC controller is used to drive the vehicle autonomously along the centerline of the road based on two main factors, the lateral deviation and relative yaw angle. Deep learning technology has been widely used in recent years because of the promising performance achieved in different applications and tasks. In this context, we suggested that the implementation of the Deep Neural Network (DNN) will provide a great improvement and it can be more computationally efficient than solving an online quadratic problem (QP), that will naturally lead to reduce the time, the complexity, and the computational loads of implementations. The main aims of this paper are to design a deep learning-based approach for automated vehicle steering based on the behaviour of the traditional MPC controller. In addition, to study the efficiency of the full replacement of the MPC controller by the suggested DNN model. The study is based on performing a comparison between the implementations of both controllers (MPC and DNN model) in terms of the performance and the execution time. The performance indicator is the ability of the controller to drive the decision variables (lateral deviation and yaw angle) to be close to zero in order to drive the vehicle autonomously along the desired path.
User-Centered Rating of Well-Being in Older Adults

Predictions estimate that the future will entail several devices related to IoT (Internet of Things). Most of these will be present in our homes, collecting useful information and triggering informed actions. Much work has been done on collecting data and triggering such devices. However, there is not much work on how to make use of such information to measure the well-being of a person. In the context of older adults, it would be useful to define a means to estimate their well-being, provide them some feedback, and eventually share it with a family member or caregiver. This article emphasizes how to measure well-being through a user-centered Personal Well-being Rating (PWR). Although the proposed rating is idealized as a general equation, our case study is mainly centered on older adults. These are undeniably a group of society that can enrich their lives, by integrating possible solutions implemented considering the PWR. This interpretation opens the door to the development of future interfaces, which can be supported by an explicit way of measuring the well-being of someone inside a home.
An Actor-Critic Ensemble Aggregation Model for Time-Series Forecasting*

Ensemble models are widely used as an effective technique in time-series forecasting, and recently, are inclined toward leveraging meta-learning methods due to their proven predictive advantages in combining individual models in an ensemble. However, finding the optimal strategy for ensemble aggregation is an open research question, particularly, when the ensemble needs to be adapted in real-time. In this paper, we pro-pose a novel meta-learning approach for aggregation of linearly weighted ensembles for the task of time-series forecasting. We outline a deep reinforcement learning framework with a coherent design of the components of the environment and the objective function as an aggregation method in our task. In this framework, the combination policy in ensembles is modeled as a sequential decision making process which is able to capture the temporal behavior in time-series, and an actor-critic model aims at learning the optimal weights in a continuous action space. An extensive empirical study on various real-world datasets demonstrates that our method achieves excellent or on par results in comparison to the state-of-the-art approaches as well as several baselines.
Area and Power-Efficient Capacitively-Coupled Chopper Instrumentation Amplifiers in 28 nm CMOS for Multi-Channel Biosensing Applications

This paper presents two designs of a capacitively-coupled chopper instrumentation amplifier (CCIA) successfully implemented in 28 nm CMOS for biopotential sensing applications. The first design is a compact CCIA using offset-blocking for chopping ripple reduction, DC servo loop (DSL) for electrode offset (<inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {EOS}}$ </tex-math></inline-formula>) suppression, and a high pass filter (HPF) for handling common-mode (CM) artifacts. An inverter-based self-biased input stage is used for noise and power efficiency. The two gain stages of the CCIA achieve a low-frequency open-loop gain >80 dB. Realized in an area of 0.05 mm<sup>2</sup>, the CCIA handles <inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {EOS}}$ </tex-math></inline-formula> up to 50 mV and tolerates CM artifacts up to <inline-formula> <tex-math notation="LaTeX">$1.5~V_{\mathrm {pp}}$ </tex-math></inline-formula>. The CCIA achieves a mid-band gain of 39.5 dB with a 1 kHz bandwidth by consuming <inline-formula> <tex-math notation="LaTeX">$1~\mu \text{W}$ </tex-math></inline-formula>. The integrated input-referred noise (IRN) over 200 Hz bandwidth is <inline-formula> <tex-math notation="LaTeX">$2.15~\mu V_{\mathrm {rms}}$ </tex-math></inline-formula>. The second design presents a dual-channel CCIA (DCCIA) using an orthogonal frequency chopping for multi-sensor array applications. An inverter-based input stage is used for noise efficiency. The measured results show that the DCCIA achieves a low-frequency gain of 39.5 dB with a 3-dB bandwidth up to 1 kHz by consuming <inline-formula> <tex-math notation="LaTeX">$0.71~\mu \text{W}$ </tex-math></inline-formula> per channel. The measured noise density is 136 nV/<inline-formula> <tex-math notation="LaTeX">$\surd $ </tex-math></inline-formula>Hz with an integrated noise of <inline-formula> <tex-math notation="LaTeX">$2.67~\mu V_{\mathrm {rms}}$ </tex-math></inline-formula> over 200 Hz bandwidth. The DCCIA suppresses <inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {EOS}}$ </tex-math></inline-formula> up to 50 mV using the DSL. Realized in a compact area of 0.04 mm<sup>2</sup> per channel, an excellent gain matching error of 0.29% is achieved with the crosstalk higher than 58 dB between the channels. This work is a measured report on the instrumentation amplifier using, to the best of the authors’ knowledge, one of the smallest CMOS process nodes.
A Layer-Reduced Neural Network Based Digital Backpropagation Algorithm for Fiber Nonlinearity Mitigation

A layer-reduced neural network based digital backpropagation algorithm called smoothing learned digital backpropagation (smoothing-LDBP), is proposed in this paper. The smoothing-LDBP smooths the power terms in nonlinear activation functions to limit the bandwidth. The limited bandwidth of the power terms generates fewer in-band distortions, thus reduces the required layer for a given equalization performance. Simulation results show that the required layers of smoothing-LDBP are reduced by approximately 62% at 6.7% HD-FEC compared with learned digital backpropagation. Owing to the layer reduction, the latency and the complexity are reduced by 69% and 51%, respectively. The layer-reduced property of smoothing-LDBP is also validated by a proof-of-concept experiment.
OCTAI: Smartphone-based Optical Coherence Tomography Image Analysis System

Identifying diseases in Optical Coherence Tomography (OCT) images using Deep Learning models and methods is emerging as a powerful technique to enhance clinical diagnosis. Identifying macular diseases in the eye at an early stage and preventing misdiagnosis is crucial. The current methods developed for OCT image analysis have not yet been integrated into an accessible form-factor that can be utilized in a real-life scenario by Ophthalmologists. Additionally, current methods do not employ robust multiple metric feedback. This paper proposes a highly accurate smartphone-based Deep Learning system, OCTAI, that allows a user to take an OCT picture and receive real-time feedback through on-device inference. OCTAI analyzes the input OCT image in three different ways: (1) full image analysis, (2) quadrant based analysis, and (3) disease detection based analysis. With these three analysis methods, along with an Ophthalmologist's interpretation, a robust diagnosis can potentially be made. The ultimate goal of OCTAI is to assist Ophthalmologists in making a diagnosis through a digital second opinion and enabling them to cross-check their diagnosis before making a decision based on purely manual analysis of OCT images. OCTAI has the potential to allow Ophthalmologists to improve their diagnosis and may reduce misdiagnosis rates, leading to faster treatment of diseases.
Electric Field and Self-Heating Effects on the Emission Time of Iron Traps in GaN HEMTs

In this article, we separately investigate the role of electric field and device self-heating (SHE) in enhancing the charge emission process from Fe-related buffer traps (0.52 eV from <inline-formula> <tex-math notation="LaTeX">${E}_{c}$ </tex-math></inline-formula>) in AlGaN/GaN high electron mobility transistors (HEMTs). The experimental analysis was performed by means of drain current transient (DCT) measurements for either: 1) different dissipated power (<inline-formula> <tex-math notation="LaTeX">${P}_{D,\mathrm {steady}}$ </tex-math></inline-formula>) at constant drain-to-source bias (<inline-formula> <tex-math notation="LaTeX">${V}_{\mathrm {DS,steady}}$ </tex-math></inline-formula>) or 2) constant <inline-formula> <tex-math notation="LaTeX">${P}_{D,\mathrm {steady}}$ </tex-math></inline-formula> at different <inline-formula> <tex-math notation="LaTeX">${V}_{\mathrm {DS,steady}}$ </tex-math></inline-formula>’s. We found that: 1) an increase in <inline-formula> <tex-math notation="LaTeX">${P}_{D,\mathrm {steady}}$ </tex-math></inline-formula> yields an acceleration in the thermally activated emission process, consistently with the temperature rise induced by SHE and 2) on the other hand, the field-effect turned out to be negligible within the investigated voltage range, indicating the absence of the Poole–Frenkel effect (PFE). A qualitative analysis based on the electric field values obtained by numerical simulations is then presented to support the interpretation and conclusions.
Design optimization for ADMM-Based SVM Training Processor for Edge Computing

This paper presents an optimized support vector machine (SVM) training processor employing the alternative direction method of multipliers (ADMM) optimizer. Low-rank approximation is exploited to reduce the dimension of the kernel matrix by employing the Nyström method. Verified in four datasets, the proposed ADMM-based training processor with rank approximation reduces $32\times$ of matrix dimension with only 2% drop in inference accuracy. Compared to the conventional sequential minimal optimization (SMO) algorithm, the ADMM-based training algorithm is able to achieve a $9.8\times 10^{7}$ shorter latency for training 2048 samples. Hardware optimization techniques, including pre-computation and memory sharing, are proposed to reduce the computational complexity by 62% and the memory usage by 60%. As a proof of concept, an epileptic seizure detector is designed to demonstrate the effectiveness of the proposed optimization techniques. The chip achieves a $153,310\times$ higher energy efficiency and a $364\times$ higher throughput-to-area ratio for SVM training than a high-end CPU. This work provides a promising solution for edge devices which require low-power and real-time training.
Enhanced Human Activity Recognition based on Activity Tracker Data Using Secure Homomorphic Encryption Techniques

The fitness tracker data for human activity recognition is playing an important role in improving health and monitoring the daily movements of human such as health care and fitness. This detects human activities such as step count, sleeps rate, calories burned, and person mood for the data collected by the activity tracker device and mainly helps in identifying the Apnea-Hypopnea Index (AHI) which is caused due to Obstructive Sleep Apnea. Medical data is very susceptible for many external threats that affect its security and privacy. The privacy and security of the data shared across the medical field is very difficult to maintain. To overcome these problems, Homomorphic Encryption techniques are proposed, which is used for privacy preserving out-sourced storage and computation. The encrypted technique helps in protecting sensitive data from outsourced. The two Homomorphic techniques are Gorti’s Enhanced Encryption Scheme and Carmichael’s Encryption Scheme that are used to encrypt the fitness tracker data. The computation is made with the encrypted data without revealing the original data to providers. The analysis is made on the encrypted data by computing the AHI to predict the disease based on the range given in AHI. Thus, security of the data is maintained. The performance of the schemes is measured based on the time taken for encryption and decryption of the medical data and analysis performed on the encrypted data.
Challenges and Solution of CMOS Instrumentation Amplifier Design for Bio-electric Signals

This paper explores challenges and solution of various types CMOS Instrumentation amplifier for identification of various bio electric signals using different CMOS technologies. The Bio-potential amplifiers plays a vital role in various biomedical Instruments. The regressive survey of such amplifiers is carried out based on different CMOS techniques. The different techniques are studied for the performance improvement of the CMOS Instrumentation Amplifier. The comparison for existing bio-potential amplifiers in terms of various parameters is presented in this paper.
On the Analytical Optimal Load Resistance of RF Energy Rectifier

RF wireless power transfer (WPT) is an essential building block for simultaneous wireless information and power transfer (SWIPT) and wireless powered communication (WPC) systems. It has been shown that the efficiency of RF-DC conversion of a rectifier is dependent on both input power and the load resistance. In this paper, we present a novel analytical solution for the optimal load resistance in terms of DC power on the resistive load for two harvester topologies, namely the series-diode half-wave rectifier and Greinacher voltage doubler. Additionally, closed-form solutions are presented for low input power to obtain intuitive insights. The proposed method models the diode with the equivalent Schottky diode model, taking the parasitic and packaging effects into consideration. The validity of the method is verified by simulations with both continuous sinewave (CW) and multi-sinewave input.
Utilizing Delta Trees for Efficient, Iterative Exploration and Transformation of Semi-Structured Contents

The keywords data exploration or data wrangling summarize various different query workload scenarios in which users aim to explore or tailor data to their needs. For semi-structured data, next to commonly used SQL-style select-from-where and aggregation queries, also the structure of the possibly-nested schema-free data can be altered, schema attributes renamed, and so on. This typically involves various rounds of refining or discarding queries—imposing that intermediate results as well as the original sources cannot be eliminated. In this work, we extend our prior work on JODA, a vertically scalable, versatile JSON data processor, to make use of so-called delta trees for the succinct representation of incrementally created query results.
Hand Gesture Recognition Based Virtual Mouse Events

This paper proposes a virtual mouse application based on the tracking of different hand gestures. The system eliminates the dependency on any external hardware required to perform mouse actions. A built-in camera tracks the user’s hands, predefined gestures are recognized and the corresponding mouse events are executed. This system has been implemented in Python using OpenCV and PyAutoGUI. Researchers have studied background conditions, effects of differences in illuminance and skin colour individually. However, the proposed system aims to take into account all the above factors to build an application most suitable in the real world.
A Review on Clinical Gait Analysis

Walking plays a very important role in our day to day life. Analysis of gait leads to detection and diagnose of various health issues at early stage and hence leads to great improvement in the treatment. Various rehabilitation are using analysis of gait in order to check the effect of treatment on once body. The aim of this research is to review various approaches for Gait Analysis and specifically clinical gait analysis. This paper includes the discussion on the background details of gait, related terminology and parameters, followed by the detail discussion on the approaches from subjective used in the early times for analysis of gait to vision based approaches used nowadays for analysis of gait. Narrative discussion done in this paper will help the researchers to work in the field of gait analysis. Future scope of gait in discussed in the end.
Design and Development of High Precision Magnetic Encoder Based on TMR MEMS Device

The MEMS magneto-sensitive chip based on the TMR principle is used as the sensitive element of the magnetic encoder. In terms of hardware development, after optimizing the design of the magnetic circuit, developing a precise rotating structure, designing a stabilized power supply circuit and a low-noise signal conditioning circuit, and digitally converting the analog voltage signal with high signal-to-noise ratio through analog-to-digital conversion and then entering the microprocessor. A high-precision magnetoelectric rotary encoder was designed and developed, and an automatic acquisition system was used to collect and store the encoder data. In terms of program design, a data processing, correction, and calibration algorithm was designed and written for the original output data of sensitive components, and MATLAB software was used to simulate, calculate and verify the angle data calculated by the encoder for further verification. The developed encoder has an angle measurement accuracy under actual working conditions. The encoder is installed on a high-precision time grid turntable system for testing, and the encoder's testing accuracy has reached a good level of 0.0469°.
A 3-bit chipless RFID Tag for IOT Applications

Chipless RFID plays a vital role due to its enormous advantages over barcode in modern communication. Chipped and chipless RFID is normally used as a sensor for interfacing with devices and other health care applications. This paper holds the chipless RFID tag intend to produce resonating frequencies at 1.5 GHz, 3.5 GHz, and 4.6 GHz to achieve nearfield and far-field interaction. In this particular work, it is emphasized to improve the RCS using the rectangular-shaped resonator. The RCS is found to be -23 dBsm for the proposed design. The distinct current distribution at the resonating frequencies validates the 3-bit coding capacity of the transponder. It can provide 3-bit information coding for various industrial and IoT applications.
Numerical Simulation of GaN-BTG MOSFET for Suppression of SCEs

The detailed study of GaN-BTG MOSFET where GaN is used as a substrate in lieu of silicon and at the gate terminal, a stacking of silicon dioxide (SiO2) and hafnium dioxide (HfO2) is placed in a conventional trenched gate structure to obtain the proposed device. This paper addresses the comparative study of electrical characteristics such as transfer characteristics, output characteristics, transconductance, higher-order transconductance, transconductance generation factor, output conductance has been obtained for the proposed device by performing simulation using TCAD. The results validate the superiority of the proposed device in terms of performance.
Atomic study on the deform mechanism of CuTa/Cu and CuTa/Ta nanolaminates

The purpose of this paper is clarifying the deform mechanism of CuTa/Cu and CuTa/Ta nanolaminates during nanoindentation test. Firstly, two kinds of amorphous and nanocrystalline interface model were built. Additionally, the Cu layer thickness in CuTa/Cu are λ between 3 Å and 12 Å. The Ta layer thickness in CuTa/Ta are μ between 3 Å and 12 Å. Secondly, nanoindentation test were introduced to get the elastic modulus and hardness of nanolaminates. As the results claimed that different deform mechanisms play the dominant role during the indentation load and unload. More exactly, when the Cu layer is less than 7 Å the Cu layer dominate the deform mechanism, the elastic modulus and harness increase with increasing Å. As Å increasing much more, the CuTa layer will dominate the deform mechanism. In the contrast, the elastic modulus and hardness will decrease with increasing Å. Similarly as CuTa/Cu model, CuTa/Ta model has an obviously change in the dominant role of deform mechanism. In the end, the influence of penetrate rate was discussed.