Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling and Noise Analysis of a Microcantilever-based Mass Sensor

Nanomechanical devices have the potential for practical applications as mass sensors. In microcantilever-based sensing, resonance frequency shifts are tracked by a phase-locked loop (PLL) in-order to monitor mass adsorption. A major challenge in minimizing the mass detection limit comes from the noise present in the system due to thermal, sensor and oscillator noise. There is numerical difficulty in simulating PLLs, as both low frequency phase estimates and high frequency mixing products need to be captured resulting in a stiff problem. By using linear system-theoretic modeling an in-depth analysis of the system is able to be conducted overcoming this issue. This provides insight into individual noise source propagation, dominant noise sources and possible ways to reduce their effects. The developed model is verified in simulation against the non-linear PLL, with each achieving low picogram sensitivity for a 100 Hz loop bandwidth and realistically modeled noise sources.
Smartphone PPG: signal processing, quality assessment, and impact on HRV parameters

Photoplethysmography (PPG) is a simple optical technique used to detect blood volume changes in the micro-vascular bed of tissue in order to track the heartbeat. Smart-phone PPG, performed with the phones camera, has became popular in recent years due to a boom in digital health apps that help people monitor their health parameters. However, many apps struggle with getting readings that are accurate enough to estimate heart rate variability (HRV) one of the most popular biomarkers in the preventive health space. The main obstacle is the multitude of factors that impact PPG results: unique technical characteristics of different smartphone models, frames per second (FPS) rate and the way color is recorded, brightness and ambient flash levels, finger placement, in-measurement movement, etc. These factors may decrease the accuracy of the signal extracted from the camera’s video stream and produce additional errors in the computation of HRV parameters. Thus, there is a need to estimate signal quality and predict possible bias in HRV parameter calculation. In this paper, we describe the method for processing signal from smartphone cameras, estimating signal quality, recognizing RR intervals, and predicting bias of simple HRV parameters.
Inferring Direct Causality from Noisy Data using Convergent Cross Mapping

Inferring causality between variables from time series data is of primary interest in various applications. Recently, convergent cross mapping (CCM) has been developed to address non-separable nonlinear dynamical systems based on nonlinear state space reconstruction. Unlike the idea in widely used Granger-causality, CCM measures the degree to which a cause can be recovered from its effect in the form of a cross map skill. Despite the superiority of CCM over GC, there are at least two known primary shortcomings. Firstly, detection of causal relationships using CCM in the presence of observational noise leads to spurious results. Secondly, it is unable to distinguish between direct and mediated causal relations. In this work, we address these two critical challenges. First, we investigate the effect of measurement noise on the functionality of cross map skill with sample size and propose a method for significance testing based on surrogate data analysis. Secondly, we develop a two-stage method to distinguish between direct and mediated cause. The first stage consists of pairwise analysis using CCM, while the second stage computes the regression coefficient between the hypothesised observed cause and all the recovered effects in the first stage. Direct connection is absent if the regression coefficient is zero between the observed cause and recovered causes from each effect. Simulation results are presented to illustrate the efficacy of the proposed method.
iTaskOffloading: Intelligent Task Offloading for a Cloud-Edge Collaborative System

With the development of technologies such as the Internet of Things and artificial intelligence, mobile applications are becoming more and more intelligent. Compared to traditional applications realized by mobile cloud computing technology, these novel applications have a higher requirement for a task offloading scheme. However, the traditional task offloading schemes are hard pressed to meet latency and personalization requirements of these new applications. For intelligent application, how to realize personalized and fine-grained task offloading is still a challenging problem. Therefore, we propose a scheme called intelligent task offloading (iTaskOffloading) for a cloud-edge collaborative system, which can provide personalized task offloading. To be specific, we first propose the architecture of iTaskOffloading which includes the local device layer, edge cloud layer, remote cloud layer, and cognitive engine. Then we analyze the method of iTaskOffloading, which contains coarse-grained computing and fine-grained computing. Finally, we build a testbed to evaluate the proposed iTaskOffloading scheme using a typical intelligent application of emotion detection. The experimental results show that compared to the traditional cloud computing scheme, iTaskOffloading has less task duration.
Extremely Reduced Data Sets Indicate Optimal Stimulation Parameters for Classification in Brain-Computer Interfaces

The time between the onset of subsequent auditory or visual stimuli – also known as stimulus onset asynchrony (SOA) – determines many of the event-related potential characteristics of the resulting evoked brain signals. Specifically, the SOA value influences the performance of an individual subject in brain-computer interface (BCI) applications like spellers. In the past, subject-specific optimization of the SOA was rarely considered in BCI studies. Our research strives to reduce the time requirements of individual BCI stimulus parameter optimization. This work contributes to this goal in two ways. First, we show that even the classification performance on extremely reduced training data subsets reveals the influence of SOA. Second, we show, that these noisy estimates are sufficient to make decisions for individual choices of the SOA that transfer to good classification performance on large training data sets. Thus our work contributes to individually tailored SOA selection procedures for BCI users.
A Novel Underactuated Robotic Finger with Variable Stiffness Joints*

Existing robotic hands mostly consist of rigid finger mechanism with constant joint stiffness, leading to poor handling performance and even unexpected safety issues. This paper proposed a novel underactuated robotic finger with variable stiffness joints based on human finger anatomy and electrostatic adhesion(ESA) principle. The proposed finger is unique in the 3D printable one-piece body structure consisting of three similar joints, actuated by only one linear actuator to mimic the flexion/extension movement of the human finger. It is characterized by simple actuation, light weight, low cost and compliant grasp. We constructed a portable finger prototype to investigate the variable stiffness performance. It turns out that the joint stiffness shows a growing trend as the applied voltage increases, which verifies the effectiveness of this design. The proposed novel finger indicates potential applications in service robots and prosthetic hands.
Visualizing and Aggregating Behavior for Trust Evaluation

The Internet of Things or more broadly the Internet of Everything is becoming a possibility in the near future. A vast number of entities (i.e., devices, people, processes etc.) will require to connect to the Internet. These communications could be facilitated by new technologies, such as 5G communication, which aim to be faster by providing higher bandwidth guarantees. This higher network capacity will increase exponentially the volume of data that could be extracted from the Internet regarding individual user or entities' behavior and concurrently the number of attacks that could be deployed. Thus, it is becoming increasing challenging to evaluate the trustworthiness of entities on the Internet. The contribution of this paper is a new methodology of aggregating behavior that aids in evaluating individual trust. We introduce a basic methodology for calculating Trust, which is later extended through clustering of malicious events. Based on the proposed methodology, a GUI is implemented that allows the quick and easy visualization of malicious behavior with the corresponding trust calculation. Overall, this work aims to support existing trust models in terms of aggregating behavior and translating it into trustworthiness.
A Powerful and Efficient Method for Enhancing Images of Different Types

This paper proposes to utilize combination of methods to obtain high quality images. Compared to the typical histogram the proposed method in this research work, the brightness of an original image is preserved during enhancing the contrast of a source image by providing a natural enhancement which can be utilized in many products of electronics. In this research work, various methods are combined to achieve efficient image quality. The proposed method used in this research work would provide subjectively and objectively good results for both true colour and gray scale images. Research work consists of an efficient algorithm for increasing the image contrast without disturbing the originality of an image. Reality of an image is checked and verified using ASP.NET program and proposed algorithm will be deployed in cloud server for service. In future this method will be deployed in cloud so that researchers can use this service to compare their results for quality of image. In future we have an idea to attempt to collaborate two emerging technologies of Information Technology like Digital Image Processing and Cloud Computing
Kinematic Redundancy Analysis during Goal-Directed Motion for Trajectory Planning of an Upper-Limb Exoskeleton Robot*

The kinematic redundancy of human arm imposes challenges on joint space trajectory planning for upper-limb rehabilitation robot. This paper aims to investigate normal motion patterns in reaching and reach-to-grasp movements, and obtain the unique solution in joint space for a five-DOF exoskeleton. Firstly, a six-camera optical motion tracking system was used to capture participants’ arm motion during goal-directed reaching or reach-to-grasp movements. Secondly, statistical analysis was executed to explore the characteristics of swivel angle, which revealed that the swivel angle can be approximated to the mean value (155° ± 5°) in resolving the arm redundancy problem. Thirdly, combined with the minimum-jerk trajectory of end-effector, the generated joint trajectory complied well with the joint trajectory captured in healthy humans. Consequently, the obtained results facilitate a new way for three-dimensional trajectory planning of the exoskeleton robot. Further, adaptive assist-as-needed control of the exoskeleton robot can be implemented based on the optimal reference trajectory, with aims to provide assistance according to the patient’s performance, and in turn promote neural plasticity.
First Evaluation of a Transcutaneous Carbon Dioxide Monitoring Wristband Device during a Cardiopulmonary Exercise Test*

We introduce an innovative wristband wireless device based on a dual wavelength NDIR optical measurement and an optimized thermo-fluidic channel to improve the extraction of the carbon dioxide gas from the blood within the heated skin region. We describe a signal processing model combining an innovative linear quadratic model of the optical measurement and a fluidic model. The evaluation is achieved using a cardiopulmonary exercise test (CPET). We compare carbon dioxide tension measurement at the forearm level using our device, with an electrochemical measurement at the forearm level, and an optical measurement of the end-tidal exhaled breath. These curves demonstrate a significant reduction of the variability of carbon dioxide pressure measurement with respect to the pressure dynamic range during the test.
Mining Gradual Itemsets Using Sequential Pattern Mining

Gradual itemsets model complex attributes covariation of the form "The more or less is A, the more or less is B". Recently, such kind of itemsets have received attention from the data mining community, where several formalizations and methods have been defined to automatically extract and maintain gradual patterns from numerical databases. However, mining gradual itemsets remains challenging as the task is more complex than ordering the transactions according to several dimensions or attributes. In fact, the order in which attributes are considered impacts the sorting operation. One can note that an ordering of the transactions according to a single attribute leads to a sequence of itemsets where items correspond to transaction identifiers. In this paper and from this observation, we propose a new formulation of the gradual itemset mining task as the problem of sequential pattern mining. This original reduction allows us to exploit sequential pattern mining algorithms to extract gradual itemsets. Experimental results obtained on several numerical datasets show the feasibility of our proposed framework.
Analysis of key Factors for Organization Information Security

Protecting sensitive information from illegal access and misuse is crucial to all organizations. An inappropriate Information Security (IS) policy and procedures are not only a suitable environment for an outsider attack but also a good chance for the insiders’ misuse. In this paper, we will discuss the roles of an organization in information security and how human behavior affects the Information Security System (ISS). How an organization can create and instill an effective information security culture in an organization to improve their information safeguards. The findings in this review can be used to further researches and will be useful for organizations to improve their information security structure (ISC).
Cloud, Big Data & IoT: Risk Management

The heart of research pumps for analyzing risks in today’s competitive business environment where big, massive computations are performed on interconnected devices pervasively. Advanced computing environments i.e. Cloud, big data and Internet of things are taken under consideration for finding and analyzing business risks developed from evolutionary, interoperable and digital devices communications with massive volume of data generated. Various risks in advanced computational environment have been identified in this research and are provided with risks mitigation strategies. We have also focused on how risk management affects these environments and how that effect can be mitigated for software and business quality improvement.
Effect of residue formed during the AC and DC dry-band arcing on silicone rubber filled with natural silica

This paper investigates the residue formed on silicone rubber filled with natural silica in the inclined plane tracking and erosion test under AC and DC voltages. Simultaneous thermogravimetric and differential thermal analyses reveal residue formation dependent on volatilization and crosslinking in silicone rubber. Thermo-oxidation of volatiles yields silica-based residue protecting the surface during dry-band arcing; whereas, crosslinking leaves carbonaceous residue leading to more severe dry-band arcing. Volume fraction of natural silica replacing silicone rubber is shown to be a main source of silica residue suppressing carbonaceous residue, thus preventing failure in the inclined plane test. More severe erosion under the equivalent +DC voltages as compared to standard AC and equivalent — DC voltages of the inclined plane test is attributed to more severe dry-band arcing heat promoted by carbonaceous residue. Silicone rubber filled with lower particle sizes of natural silica is demonstrated to deteriorate erosion performance, possibly as result of filler-polymer interactions or lower particle size reducing the integrity of the protective silica residue. The following paper highlights the importance of using critical AC and DC voltages during the inclined plane tracking and erosion tests.
Semantic CIM2Matpower: Ontology-Based Network Topology Processor and Converter for CIM Node-Breaker Models

Smart electricity grids are key to a successful transition towards a future sustainable energy system. Network topology processing (NTP) is an important and frequently reoccurring task in most power grid analysis and simulation applications' including power flow calculations, short circuit and contingency analysis. NTP is applied to a detailed node-break-er representation of the power grid, as provided by the IEC Common Information Model (CIM). It is performed in order to obtain a reduced bus-branch representation, which serves as input to downstream analysis and simulation applications. The present paper introduces a novel approach to NTP that utilises a semantic knowledge base for network model management and exploits semantic reasoning over the formal CIM ontology and instance data. The proposed system is tested on publicly available ENTSO-E data, validated by converting the generated bus-branch model to Matpower and running power flow calculations in a power grid modelling and simulation framework.
Instance Segmentation of Nematode Cysts in Microscopic Images of Soil Samples*

Nematodes are plant parasites that cause damage to crops. In order to quantify nematode infestation based on soil samples, we propose an instance segmentation method that will serve as the basis of automatic quantitative analysis. We consider light microscopic images of cluttered object collections as they occur in realistic soil samples. We introduce an algorithm, LMBI (Local Maximum of Boundary Intensity) to propose instance segmentation candidates. In a second step, a SVM classifier separates the nematode cysts among the candidates from soil particles. On a data set of soil sample images, the LMBI detector achieves near-optimal recall with a limited number of candidate segmentations, and the combined detector/classifier achieves recall and precision of 0.7. The pipeline only requires simple dot annotations and ≈moderately sized training data, which enables quick annotating and training in laboratory applications.
Toward clinically-relevant joint moment estimation during sit to stand: a feasibility study

Conventional motion analysis system to measure joint moment during sit to stand is impractical to be used in clinics. Inertial sensor-based motion analysis system has been proposed to improve usability. In this study, we propose a simple system that can predict joint moment, especially maximum joint moment, with a minimum number of an inertial sensor attached and force plate; shank and thigh angles were estimated with a kinematic model and an effective joint moment analysis period. Through a validation experiment with eight subjects, the estimated joint moments were comparable to be actual joint moment measured by the conventional system, also the previous study with the inertial system.
Feasibility of Microstrip Components on Paper Substrate

Feasibility of using “paper” as low cost organic substrate of filter and antenna is studied here. Cutoff frequency for the realized 3-pole low pass filter fabricated on paper substrate (dielectric constant=2.24) is around 2.77GHz having insertion loss less than 0.8dB and minimum attenuation less than −23dB at 7.61GHz (pole frequency). The realized filter has a rejection band from 5.5GHz to 9 GHz with value less than −15dB and achieves at same time acceptable pass band with sharpness factor of 4dB/GHz. A 7-pole low pass filter is also fabricated on same paper substrate. Here rejection band below −20 dB has been found from 2.355 GHz to 15GHz. So rejection bandwidth has been widened from 3.5 GHz to 12.645GHz in comparison with the 3-pole even at −20dB. This filter has sharpness factor of 21.14 dB/GHz which is improved by 17dB compared to 3-pole filter. These sharp low pass filters fabricated on paper substrate have major application in RF receivers.Next a simple circular-polarized antenna is proposed having a square metallic patch with two notches, a ground plane, a probe feed and ‘paper’ as substrate. Experimental result shows that the operating frequency is 2.5GHz with an impedance bandwidth (2:1 VSWR) of 90 MHz and axial ratio less than 0.5db. The simulated cross-polarization isolation obtained is greater than 15dB. The proposed circular-polarized antenna of 2.5 GHz has extensive applications in wireless communication, cellular & satellite communications.
An in Vitro Flow Model for Cardiovascular Inflammation *

Cardiovascular disease is modern-day plague with a vast number of lives claimed, and an enormous socio-economic cost incurred. Hemodynamics of the cardiovascular system play an important mechanistic role in disease development. For instance, atherosclerotic plaque depositions are often correlated with regions of turbulent flow patterns and disturbed hemodynamic shear stress. A simplified, rigid, in vitro, flow model of a real-size aortic arch is described. The flow in the arched vessel is attached and healthy at the outer curvature, while it is separated and disturbed at the inner curvature wall, which is an ideal setting to study cardiovascular disease. Endothelial cells can be cultured on the lumen of the aortic arch model under controlled flow conditions and extracted from the inner and outer curvature walls for biochemical signaling studies. The flow velocity field in the model is characterized using particle image velocimetry PIV which allows for the estimation of the wall shear stress. This helps in correlating the underlying hemodynamics to the biomechanical response of the endothelium.
Substrate Integrated Waveguide based Antenna for IoT Applications

In this paper, substrate integrated waveguide (SIW) compact antennas are presented as sensors to operate in the IEEE C-band. The main sensor is a SIW antenna designed at a frequency of 5.8 GHz for IEEE 802.11 WLAN application. The antenna has triangular patch and metallic vias along the edge thereby making it a conductive wall and hence a perfect electrical conductor (PEC) while absence of vias on the other side makes it a perfect magnetic conductive (PMC) wall. The antenna radiates linearly polarized wave corresponding to TM010 mode with a corresponding gain of 4.21dBi. The SIW designs are simulated using Arlon AD270 substrate as a dielectric and the simulated results have been obtained.
Wide Stopband Microstrip Diplexer Using a Novel Configuration for Frequency Division Duplex and GSM-4G Applications

This paper presents a high performance microstrip diplexer based on a novel geometrical structure. The realized diplexer consists of coupled lines and semicircular structures. It operates at 2.58 GHz and 2.72 GHz, for GSM-4G and wireless applications. The diplexer channels are close to each other, which fulfill the frequency division duplex (FDD) application needs. The wide stopband rejections for both S21 and S31 are provided up to 15 GHz. Despite of quite close channels, the simulated and measured isolation between the two channels (S32) is better than −20 dB, from DC up to 20 GHz. The designing method is based on providing a theoretical approach to attenuate the harmonics and tuning the resonance frequency. Meanwhile, several transmission zeros are created after the passbands, which improve the stopband properties. The proposed diplexer provides sharp transition edges for both passbands. The high performance is obtained using a compact size of 0. 047λ2. To verify the designing methodology, the proposed diplexer is fabricated and measured. Good agreement between simulation results and measurement results are observed.