Miracle-dz/UniFAR-FTUs huggingface dataset:
Environmental Indices – Predicting Air Temperatures and Water Temperatures in the Northwest Atlantic By Time Series Analysis

Univariate seasonal ARIMA and intervention models are used to forecast monthly mean air and bottom water temperatures from 3 sites in the Northwest Atlantic region, up to one year in advance. These models explain a reasonable amount of the total variability, with results showing a good agreement between the forecasts and the observed mean air and bottom water temperatures. The structure of the random processes that generated the temperature time series was specified for most cases as ARIMA models with moving-average terms. All fitted interventions appeared during wintertime (December-March), and therefore prediction of temperatures during these months must be taken with caution.
Exchange Rate Prediction using ANN and Deep Learning Methodologies: A Systematic Review

Advance study about exchange rate prediction is reviewed in this paper. Different proposed new methods forforecast exchange rate prediction, from 2000 to 2019 are taken into consideration. In the protected period within the examine, the effects acquired observed some new proposed models like Artificial Neural Network (ANN), Functional Link Artificial Neural Network (FLANN), Hidden Markov Model (HMM), Support Vector Regression (SVR), Auto Regressive (AR) models are displayed in this paper. But, some of the proposed new neural networkmodelforforecastingthatconsideredtheoreticalsupport and a systematic procedure in the construction of model. This leadstoconveyingofnewmodelsofdeepneuralnetwork.
Composite Body-Tracking: Device Abstraction Layer with Data Fusion For Virtual Reality Applications

In a time where Virtual Reality has become increasingly popular, many companies have launched new body tracking hardware. These devices deliver various types of tracking information such as a simple full body skeleton (e.g. Microsoft Kinect v2) or a detailed hand skeleton (e.g. Leap Motion). Various device abstraction layers exist, making it easy to connect to such diverse input devices. However, when an application is connected to two or more individual input devices, these devices often provide complementary or multiple, but inconsistent tracking information. To make best use of the available data, and by that, achieving the best tracking quality, proper fusion of the information is required.Hence, we present the design and architecture of the ALVR system (Abstraction Layer for Virtual Reality). On the one hand, ALVR decouples vendor-specific interfaces from the application. On the other hand, it builds and maintains a data model representing a human skeleton. ALVR may receive data from various tracking devices. It then merges redundant tracking data and integrates unique tracking data into this data model. Thereby, application developers gain access to holistic tracking information. This integrated approach - data fusion with device abstraction - allows application developers to maximize the usage of their tracking devices while optimizing the information provided by them, e.g. for gesture recognition.
Continuous-Time Deterministic Policy Gradient-Based Controller for Morphing Aircraft without Exploration*

A controller is optimized using limited flight data of morphing aircraft without dynamic model. Due to the nonlinear and nonaffine in control nature of the morphing aircraft dynamics, the integral reinforcement learning scheme and the deterministic policy gradient-based learning method are incorporated to develop to train the parameterized control input. The stability of the learnt control input is analyzed when the corresponding action-value function is converged. Unlike online algorithms, the parameters in the control input is hard to converge, and therefore a constrained learning strategy is implemented. The performance of the proposed method is demonstrated through numerical simulation for a longitudinal morphing aircraft system.
On the Optimization of User Association and Resource Allocation in HetNets With mm-Wave Base Stations

This article investigates the problem of joint user association and resource allocation, defined by the number of allocated time-slots, in hybrid heterogeneous networks with the coexistence of sub-6-GHz base stations and millimeter wave (mm-Wave) base stations. To do so, we formulate a joint optimization problem to improve the efficiency of resource utilization by maximizing the number of associated users and minimizing the number of allocated time-slots. The optimization problem is formulated as a binary integer linear program and is proved to be NP-hard. Accordingly, we propose two efficient heuristic algorithms to solve it. The first one is centralized and relies on complete information, whereas the second one is distributed and is based on a reinforcement learning approach. The proposed distributed learning algorithm aims to find the best association for each user based on its past experience, automatically and independently from others. Simulation results show that the performances of both proposed algorithms are close-to-optimal with an important reduction in computational complexity.
Identification of Pathological Disease in Plants using Deep Neural Networks - Powered by Intel® Distribution of OpenVINO™ Toolkit

This paper deals with an algorithm for the easy identification or classification of pathological diseases in plant species via a mobile or web application. The entire system is an intelligent framework that enables users to identify a pathological disease via a deep learning and computer vision based smart system – A user merely needs to open the app, click a picture, and view the result. Input for the system can be either an image or live video feed of the plant species, and the result is in the form of a bounding box with the name of the identified pathological disease and the accuracy of the identification. Once the identification is accurately done the user can get more insights into the cause of the disease and how to do a proper medication.For this experimental research purpose, we are targeting five pathological diseases: Blister Blight in Tea, Citrus Canker, Early Blight, Late Blight, Powdery Mildew in Cucurbitaceae. This paper illustrates how the solution is built using deep learning and computer vision algorithms powered by the Intel® Distribution of Open VINO™ toolkit Model Optimizer.
Research on the Influence of Psychological Capital on the Management of Research Projects of University Teachers

Innovation performance is an important measure of project management. How to strengthen the management of research projects and improve the performance of research projects under the condition of limited resources has become an urgent problem to be solved in Colleges and universities. Through the analysis of the influence of psychological capital of teachers on the performance of innovation of research projects, it is found that, as an important regulator to stabilize emotion and behavior, psychological capital can effectively regulate the influence of external factors on individual behavior and improve the performance of innovation of research projects. Based on this, this paper puts forward strategies to improve the performance of innovation of research projects.
Reinforcing Cyber World Security with Deep Learning Approaches

In the past decade, the Machine Learning (ML) and Deep learning (DL) has produced much research interest in the society and attracted them. Now-a-days, the Internet and social life make a lead in most of their life but it has serious social threats. It is a challenging thing to protect the sensitive information, data network and the computers which are in unauthorized cyber-attacks. For protecting the data’s we need the cyber security. For these problems, the recent technologies of Deep learning and Machine Learning are integrated with the cyber-attacks to provide the solution for the problems. This paper gives a synopsis of utilizing deep learning to enhance the security of cyber world and various challenges in integrating deep learning into cyber security are analyzed.
Highly Secured all Optical DIM Codes using AND Gate

Optical Code Division Multiple Access (OCDMA) is an inevitable innovation to cope up with the impediments of regularly expanding information traffic and numerous user accesses in optical systems. In Spectral Amplitude Coding (SAC)-OCDMA systems cross correlation and Multiple Access Interference (MAI) are utmost concerns. For eliminating the cross correlation, reducing the MAI and to enhance the security, in this work, all optical Diagonal Identity Matrices codes (DIM) with Zero Cross-Correlation (ZCC) and optical gating are presented. Chip rate of the proposed work is 0.03 ns and total 60 users are considered with semiconductor optical amplifier based AND operation. Effects of optical gating are analyzed in the presence/absence of eavesdropper in terms of Q factor and received extinction ratio. Proposed system has advantages for service provider because this is mapping free technique and can be easily designed for large number of users.
Design and Implementation of Hypothermia Symptoms Early Detection With Smart Jacket Based on Wireless Body Area Network

Hypothermia is a state where the body cannot balance the body heat due to excessive cold temperature pressure so that there will be loss of heat in the body. The result will be a quickly decreasing body temperature. A condition where a body temperature is less than 35°C and that it is accompanied by a decrease in consciousness may cause a death threat. Hypothermia is more common when climbers perform activities in cold temperatures and strong winds, and when they wear wet clothes. It is not easy for a mountain climber to find out his own body temperature as well as that of his friends and to determine whether the body temperature is safe or affected by hypothermia symptoms. Therefore, this research formed a device to detect hypothermia. It consists of temperature sensor, pulse sensor, and accompanied by heating elements based on wireless body area network (WBAN). This device will connect to the user’s smartphone by using Bluetooth, so the user can see the condition of his body, adjust the temperature of the heating element, and make a group of climbers. The experiment result shows that the device can detect hypothermia symptoms and inform these symptoms to the users and groups of mountaineers in WBAN area, and the climbers can be more vigilant in responding to hypothermia so that mortality cases caused by hypothermia can be minimized.
Nanorobots-assisted Tumor Sensitization and Targeting for Multifocal Tumor

We have proposed a new tumor sensitization and targeting (TST) framework, named in vivo computation, in our previous investigations. Our previous works are focusing on the TST for unifocal tumor, where we proposed the weak priority evolution strategy to adapt to the actuating mode of the homogeneous magnetic field used in the state-of-the-art nanorobotic platforms, and some in vitro validations were performed. In this paper, we focus on the problem of TST for multifocal tumor, which can be seen as a multimodal optimization problem for in vivo computation. To overcome this issue, we propose a sequential targeting strategy to complete TST for the lesions one by one with the assistance of nanorobot swarms. The proposed tumor targeting strategy is performed in the in vitro experiment, where Janus particles are used as nanorobots and a 2-D microchannel network plays the role of the tumor vascular network. This paper describes the theoretical knowledge, experimental validation scenario and shows the experimental result on the targeting efficiency by using the proposed targetlng strategy.
In-situ gold nanoparticle synthesis in polymer films

A versatile method for fabricating both plasmonic and conductive metal structures in situ in polymer materials is presented. The process relies on UV induced radical polymerization of acrylic resin and gold nanoparticle formation by the reduction of a gold precursor in the presence of a photoinitiator. The room temperature fabrication process provides control over nanoparticle size, growth and distribution within the polymer matrix.
Investigating the Impact of Antenna Heights on Path Loss Models in an Indoor Corridor Environment

Mobile data traffic has rapidly increased in the last few years because of the continuous demand for higher data rates through a rapidly growing number of connected devices and the advancing technology of smartphones. Consequently, researchers have focused on frequency bands greater than 6 GHz because of their ability to meet the requirements of the upcoming fifth-generation (5G) wireless system, and other multimedia services that support high-speeds up to several gigabits per second. This research paper presents propagation measurements that have been conducted in a typical indoor corridor environment for both line-of-sight (LOS) and non-line-of-sight (NLOS) communication scenarios at three frequencies in the super high frequency (SHF) band, which are 14, 18, and 22 GHz. Moreover, this work aims primarily to investigate the effect of changing the antenna height on the performance of two well-known path loss prediction models. The models considered in this work are the close-in (CI) free space reference distance model and the floating-intercept (FI) model. Furthermore, this study presents mean square error (MSE) between the same and different antenna heights. The results observed that the CI model provides more stability than the FI model at the frequency bands selected in the LOS communication scenario. In contrast, both models have comparable performance in the NLOS scenario. Furthermore, the 22 GHz frequency band has attractive behavior since the impact of the antenna's height is negligible at this band compared to 14 and 18 GHz frequency bands at both the CI and FI models in the LOS and NLOS scenarios. Finally, the results show that both models are suitable for predicting path loss in enclosed environments like corridors.
Classification of EEG signal using LSTMs under Audiovisual Stimuli

This paper presents the latest developments in the field of Electroencephalogram (EEG) signals classification and further classifies the BCI data generated due to Audio/Visual Stimuli using a variety of Long Shot Term Memory’s (LSTM) deep learning models. The techniques involved in the classification are Vanilla LSTM, Bidirectional LSTM and Stacked LSTM. An arduous task at hand was to preprocess the irregular data using Independent Component Analysis (ICA) and using MATLAB’s EEGLAB software. The dataset was recorded from readings generated from users being subjected to tones and flashes at regular and irregular intervals. Another analysis was performed to find out the best optimizer for BCI data. It was seen that the most efficient classifier was Bidirectional LSTM pointing to 84% accuracy.
Drug Discovery and Drug Identification using AI

The paper deals with identifying and creating new drugs using AI technique. We are implementing a process using Intel Open VINO toolkit for identification of drugs. With this detection technique we can identify the reactants which are added as drugs and automates the entire flow. We are using Intel OpenVINOtoolkit with custom object detection technique to train the model using the faster Region Based Convolutional Neural Network (R-CNN)method with labeled drugs (organic compounds) which act as Reactants. Using this approach, the entire drug discovery process of clinical trial for the process can be reduced to very small time of 3-4 months (which generally takes 10-12 years) and we can generate simulated drugs to see the behavior and implementation becomes faster. We are also creating a customized dataset of drugs or molecules which are used for identifying the drugs. We are using the Canonical SMILES for the molecules so we will map SMILES with organic compounds being detected.
Speech Separation Using a Composite Model for Complex Mask Estimation

Speech spectrograms exhibit strong contextual dependencies along both time and frequency dimensions. In this paper, a novel composite model integrating a long short-term memory (LSTM) and convolutional neural network (CNN) to exploit temporal and spectral contextual speech information, respectively, is proposed for speech separation. LSTM and CNN operate in a parallel fashion to speed up the process and independently extract a complementary set of speech features. A fully-connected network then maps these features to the real and imaginary components of a ratio mask to enhance the magnitude and phase of the corrupted speech simultaneously. In the CNN path, a new delicately designed CNN with frequency dilated one-dimension (1D) convolutional layers is employed to expand the receptive field of CNN kernels without increasing the complexity. Furthermore, this CNN benefits from residual learning and skip connections to facilitate training and accelerate convergence. In spite of different neural networks included in the composite model, the proposed separation system not only has a low computational complexity, but also significantly outperforms some other deep learning-based methods.
Comparison of Controller Performance for UGV-Landing Platform Self-Leveling

This paper focuses on a comparative study of linear, nonlinear and adaptive controller performance to level within 1° of the horizon - regardless of terrain configuration, i.e., rough, asphalt, off-road - a custom-built landing platform for quadrotors and small-scale helicopters to take-off and land. The landing platform is built on top of the commercially available Argo J5 Unmanned Ground Vehicle (UGV). A detailed and accurate landing platform model is first derived based on the Euler-Lagrange formulation. Then, PD, PID, LQR, feedback linearization and a Passivity Based Adaptive Controller (PBAC) are designed, tested and compared via extensive MATLAB/Simulink simulations and by using the V-REP Simulation Environment for further validation and evaluation of leveling capabilities under disturbance and disturbance-free conditions, where the disturbance torque is added to the controllers. The comparative study considers real Argo J5 and platform physical and functional constraints and limitations. Results show that the PD and PBAC achieve faster platform settling time under disturbance-free conditions, while the PBAC and LQR achieve leveling under external disturbances. Thus, it is concluded that the PBAC is the best candidate controller configuration. Obtained results also reveal platform design limitations that lead to improved UGV-platform configurations before actual experimentation and use.
A Low-power Reconfigurable Readout Circuit with Large DC Offset Reduction for Neural Signal Recording Applications

This paper presents a fully reconfigurable readout circuit including a chopper-stabilized neural amplifier and a successive approximation register (SAR) analog-to-digital converter (ADC) for neural signal recording applications. Since the target neural signals - action potentials (APs) and local field potentials (LFPs) differ in the peak amplitude while occupying different frequency bandwidths, gain, and bandwidth reconfigurability would be advantageous in improving power and noise performance. The readout circuit is designed in 180 nm standard CMOS technology. It achieves the mid-band gain of 50.3 dB in the frequency band of 0.1 Hz - 250 Hz to detect the LFPs, and 63.4 dB in 267 Hz - 20.8 kHz for detecting the APs. The neural amplifier consumes a total power of 1.54 µW and 1.94 µW for LFP and AP configurations, respectively. The input-referred noises have been achieved as 0.97 µVrms (0.1 Hz - 250 Hz), and 0.44 µVrms (250 Hz - 5 kHz), leading to a noise efficiency factor (NEF) of 1.27 and 1.21, for the two configurations, respectively. It rejects the generated large DC offset up to 40 mV at the electrode-tissue interface, by implementing a DC servo loop (DSL). The offset voltage with the DSL becomes 0.23 mV, which is acceptable for the neural experiments. Enabling the impedance boosting loop, the DC input impedance is found to be within the range of 1.77 - 2.27 GΩ, introducing the reconfigurability in impedance for matching with the electrode impedance. The SAR-ADC having a varying sampling frequency ranging from 10 - 40 ksamples/s demonstrates to digitize the APs and the LFPs with the resolution from 8 - 10 bits. The entire AFE provides good compatibility to record the neural signal while lowering the large DC offset down to 0.23 mV.
Model Predictive Current Control for Fault-Tolerant Bidirectional Voltage Source Converter With Open Circuit Fault and Unbalanced Grid Voltage

To improve the fault-tolerant operation capability of bidirectional voltage source converter (BVSC), an improved model predictive current control (IMPCC) for fault-tolerant BVSC, is proposed with balanced DC-link capacitor voltage under unbalanced grid voltage. The proposed method can maintain continuous operation even if power device faults and unbalanced grid voltage faults occur together. A current predictive model of the fault-tolerant BVSC is established. By using grid voltages and 90° lagging signals in the $\alpha \beta $ stationary coordinate system, the reference current calculation method is designed for BVSC to eliminate power ripple under unbalanced grid voltage, which can avoid the complex positive and negative sequence extraction. DC-link split capacitor voltage balancing is achieved by the improved cost function. Based on the current predictive model and improved cost function, the optimal space voltage vectors are selected for fault-tolerant BVSC. Compared to the existing predictive methods, the proposed IMPCC can balance DC-link capacitor voltage and eliminate the power ripples for fault-tolerant BVSC under unbalanced grid with simple implementation. The experimental results validate the effectiveness of the proposed control scheme.
Windowed Integration Sampling in Bio-Signal Front-End Design

The application of closed-loop windowed integration sampling (WIS) to the design of a CMOS closed-loop, low-noise, low-power front-end circuit for bio-signal acquisition is described. The inherent anti-aliasing of the WIS scheme enables direct sampling and amplification of input signals from sensors with minimal aliasing. The resulting signals are further conditioned by a switched-capacitor filter (SCF) prior to digitization. Consequently, the overall closed-loop transfer function is determined by precision capacitor ratios. The concepts are validated via a peripheral nerve signal (PNS) recording front-end designed and fabricated in a TSMC 180nm CMOS process. The circuit draws 17.3 µA from VDD = 1.8 V, achieves an input-referred noise of 1.68 µVrms (integrated from 10 Hz - 50 kHz) and a noise efficiency factor (NEF) of 2.84. The bandwidth extends from 220 Hz to 9.1 kHz with an overall mid-band gain of 56.9 dB. The die area is 0.24 mm2 including the clock generator.
Charge Analysis in SAR ADC with Discrete-Time Reference Driver

This paper provides detailed mathematical analysis that investigate the effect of charge-sharing between an analog-to-digital converter (ADC) reference decoupling capacitor and a charge-redistribution based differential split-monotonic capacitive digital-to-analog converter (CDAC). A discrete-time reference driver is used to charge the decoupling capacitor in the sampling phase, forming a closed-system in the hold phase which allows us to apply a charge-based analysis to determine the voltages at several nodes within the system such as the reference capacitors and comparator inputs. The generalized mathematical model can be used to accurately determine the voltage shift on the comparator inputs and hence quantify the effect on the SAR comparator decision level with a varying reference decoupling capacitor which can ultimately be used to optimize the size of the capacitor while maintaining high SNDR/SFDR. In this design, we utilize a differential decoupling capacitor which provides a 4x capacitor area decrease compared to its single ended counterparts.