Miracle-dz/UniFAR-FTUs huggingface dataset:
Migration Kinetics of Ethylene Glycol Monomer from Pet Bottles into Acidic Food Simulant: Effects of Nanoparticle Presence and Matrix Morphology

In this study, migration of ethylene glycol from polyethylene terephthalate (PET) bottles (neat PET and PET nanocomposites) into acidic food simulant is investigated. Nanocomposite pellets were prepared by melt blending of PET and TiO2 nanoparticles. The stretch blow molding machine was used to make the preforms and bottles from neat PET and nanocomposites. DSC analyses showed that the presence of nanoparticles at 3 wt % noticeably affected the crystallization temperature, Tc, and the final degree of crystallinity of PET. The incorporation of nanoparticles in the PET matrix leads to a substantial decrease of water vapor permeability. Nanoparticles also decreased the diffusivity of migrants due to the higher degree of crystallinity. The acetic acid samples were stored at 45C for 15 days. Gas chromatography analysis demonstrated that the migration of ethylene glycol from nanocomposite bottles was less than the neat PET. The polymer-specific parameters (AP values) from mathematical migration models were derived from the experimental data. The diffusion coefficients (DP) based on Piringer's model showed a considerable difference between DP of nanocomposites and neat PET. It was concluded that the migration of monomers from food contact materials considerably depends on the structure of the manufactured packaging plastic. Practical Applications Polyethylene terephthalate (PET) nanocomposite pellets containing 3% wt TiO2 nanoparticles were prepared by melt blending and PET bottles were made by injection stretch blow molding. The results showed that incorporation of TiO2 nano-fillers in PET matrix considerably increased crystallinity and rigidity of PET packaging material and decreased the amorphous region in the polymer matrix. TiO2 nanoparticles with uniform distribution in the polymer matrix created a tortuous path, leading to reduce in monomer migration from packaging material into food simulant. Mathematical model used in this study was a practical tool for better understanding of migration process. Improved barrier properties and lower monomer migration from PET bottles into food could increase the application of PET nanocomposites in food packaging.
Night-Time Light Imagery Reveals China's City Activity During the COVID-19 Pandemic Period in Early 2020

The COVID-19 global pandemic has posed a serious impact on human life and health. Facing this unknown, unexpected, and devastating disease, China launched a resolute battle to prevent and control its spread. This study focused on assessing the recovery of city activity of 17 administrative regions in China around the COVID-19 pandemic period in early 2020. The night-time light (NTL) satellite remote sensing data, NPP-VIIRS dataset, was applied to evaluate the NTL changes during the coronavirus epidemic to understand the impact of the epidemic on city activity and the economy of China. A series of median composites was first constructed to analyze short-term NTL changes. Based on these median composites, the ratio map, difference map, wave index, and recovery ratio (RR) were defined to indicate the NTL recovery pattern and its spatial distribution characteristics. The results are as follows:first, the adopted median composites are able to describe the NTL changes in the short term; second, the recovery of city activity could not be exactly described by RR of a single year but could be evaluated by the comparison of RR between two adjacent years; third, a partial and spatially heterogeneous recovery of city activity is observed after the 2020 holiday compared to 2019, fourth, a better degree of recovery is observed after China announced its fully work resumption.
Beamforming and Waveform Optimization for OFDM-based Joint Communications and Sensing at mm-Waves

We consider a joint communications and sensing (JCAS) system operating at mm-waves that jointly maximizes both functionalities’ performance. Firstly, we propose a novel multibeam algorithm for JCAS systems, providing multiple simultaneous transmit beams to support efficient communications, while a separate beam simultaneously senses the environment. The proposed algorithm simultaneously implements interference control to mitigate possible interference stemming from the communication beam, while effective self-interference cancellation suppresses the direct transmitter–receiver leakage. Secondly, the joint waveform is optimized through the minimization of delay estimation error, which also ascertains that the performance of the communication system is at an acceptable level. The results showcase the trade-off between the performance of the communication system and the sensing functionality, but also demonstrate the high performance of the proposed JCAS system.
Towards the Basic Linear Algebra Unit : Replicating multi-dimensional FPUs to accelerate linear algebra applications

The ever improving silicon process nodes have provided chip manufacturer with a large available area. Once very expensive, floating-point units now constitute a small portion of the overall area of modern general purpose processors. In arithmetic heavy workloads, the processor peak performance is still directly proportional to this effective area. Chip manufacturers are always looking at ways to improve peak performance of each of their compute core. For regular arithmetic application, the introduction of vector extensions in ISA brought performance increases which linearly scale with core’s memory bandwidth. Though, the size of vector has reached technical and usability limits. We suggest a new type of architectural extensions for CPU: the Basic Linear Algebra Unit instructions and associated execution unit (BLAU). This new arithmetic architecture considers packing matrices in general purpose vector registers and implementing elementary matrix operations as part of the processor standard arithmetic pipeline. We study the dimensions of matrices compatible with integration into General Purpose Processor vector registers and arithmetic pipeline. We introduce feasible implementations of such units in current technologies for various arithmetic formats. We show that, for specific applications, the processor arithmetic efficiency can be greatly increased by such extensions with algorithmic technics to limit the impact on memory bandwidth usage.
Deep-Learning Approach to the Self-Piercing Riveting of Various Combinations of Steel and Aluminum Sheets

Deep-learning architectures were employed to simulate the self-piercing riveting process of steel and aluminum sheets and predict the cross-sectional joint shape with a zero head height. Four steels (SPRC440, SPFC590DP, GI780DP, SGAFC980Y) and three aluminum alloys (Al5052, Al5754, Al5083) were considered as the materials for the top and bottom sheets, respectively. The key objective was to consider the material properties of these metal sheets (Young’s modulus, Poisson’s ratio, and ultimate tensile strength) in a deep-learning framework. Two deep-learning models were considered: In the first model, the properties of the top and bottom sheets were adopted as the scalar inputs, and in the second model, the three properties were graphically assigned to the three channels of the input image. Both the models generated a segmentation image of the cross-section. To assess the accuracy of the predictions, the generated images were compared with ground truth images, and three key geometrical factors (interlock, bottom thickness, and effective length) were measured. The first and second models achieved prediction accuracies of 91.95% and 92.22%, respectively.
Disruption Kinetics of Bacterial Cells during Purification of Poly-β-Hydroxyalkanoate Using Ultrasonication

Poly-β-hydroxyalkanoate (PHA) is the intracellular product of Haloferax mediterranei In order to gain PHA. H. mediterranei cells are disrupted by an ultrasonication, and PHA is then purified by a centrifugal separation The results of purification are observed by SEM Most sizes of PHA granules range from 0.2 to 0.8 μm The SEM images demonstrate that the ultrasonication is an efficient mode for the cell disruption of H. mediterranei The Kick's size reduction la is used to describe the mean size variation during an ultrasonication operation The required energy is in proportional to the size reduction ratio Based on this size reduction mechanism, the disruption kinetics of H. mediterranei cells is studied and a theoretical model is derived to relate the cell survival fraction, acoustic power, size distribution index, and operation time This model provides a theoretical method to predict the cell disruption fraction by ultrasonication under various operating conditions with no parameter adjustment The results show that an increase in acoustic power or operation time leads to lower survival fraction of cells Use of this theoretical model, one can choose the optimum acoustic power and operation time to attain a specific cell disruption fraction
Identifying Depression Among Twitter Users using Sentiment Analysis

Major depressive disorder is one of the most crippling diseases, it accounts for 4.3% of the global disease burden. With depression being so prevalent, it is of paramount importance that a systematic method of diagnosis should exist. However, current diagnostic methods such as questionnaires and clinical diagnosis, consist of self-reported symptoms. Therefore, both methods are susceptible to patient manipulation. Nowadays social media websites such as Facebook, Twitter, Reddit, and Tumblr provide a method to obtain behavioral attributes to thoughts and interactions of a person. This research is focused on developing a machine learning model capable of analyzing linguistic patterns obtained from Twitter user data and determining whether a particular user exhibits depressive symptoms. We trained Support Vector Machine and Random Forest models for this purpose and, compare their efficiency of diagnosis and concluded that Random Forest gave the best results. We believe the results obtained from this research can be considered for the development of the new technique for effective identification of depressed users on social media platforms.
Power Efficient Multi-Carrier Baseband Processing for 5G and 6G Wireless

Power consumption is one of the significant challenges in millimeter wave (mmWave) systems due to the need to support wide bandwidths and large numbers of antennas. This paper explores energy efficient implementations of the baseband trans-receiver components for a multi-carrier 3GPP New Radio (NR) system. The analysis considers key components including channel selection filters, digital beamforming and FFT engines for the OFDM processing. A methodology is presented for optimizing bit widths in various components, which is critical in low power designs. Fully digital and analog beamforming architectures are also compared. Preliminary power estimates are provided using a TSMC 28 nm process for a 400 MHz system at 28 GHz similar to 5G systems today and a hypothetical 1.6 GHz system at 140 GHz for potential 6G deployment.
Security Challenges, Applications and Vehicular Authentication Methods in VANET For Smart Traffic Management

Vehicular Ad hoc Networks (VANET) is an emerging and promising technology that provides real-time assistance to automobiles. An increasing rate of accidents on road has shown a need for an Intelligent Transportation System (ITS) which can generate signals and can intimate nearby automobiles. In such a dynamic environment, authenticity is a major challenge. Various authentication schemes already prevailing are discussed with their implementation.
The Structural and Functional Study of GIT1 Paxillin Binding Domain

The G protein coupled receptor (GPCR)-kinase (GRK) interacting protein 1 (GIT1) is a multidomain protein that plays an important role in cell adhesion, motility, cytoskeletal remodeling, and membrane trafficking. GIT1 mediates the localization of p21-activated kinase (PAK) and PAK-interactive exchange factor (PIX) to focal adhesions, and its activation is regulated by the interaction between its C terminal paxillin-binding domain (PBD) and the LD motifs of paxillin. In this dissertation, we determined the solution structure of rat GIT1 PBD by nuclear magnetic resonance (NMR) spectroscopy. The PBD folds into a four-helix bundle, which is structurally similar to the focal adhesion targeting (FAT) domain and the vinculin tail (Vt) domain. The PBD is more stable than the FAT domain and there is no evidence of helix 1 swapping. Previous studies showed that GIT1 interacts with paxillin through the LD4 motif. However, studies in this dissertation demonstrated that in addition to the LD4 motif, the GIT1 PBD can also bind to the paxillin LD2 motif; and both LD2 and LD4 motifs competitively target the same site on the PBD surface. This dissertation also probed the function of paxillin splice variants by comparing their interaction with GIT1 PBD. It seems the paxillin isoforms did not play an important role in determining the affinity to GIT1. We also revealed that paxillin S272 phosphorylation does not influence GIT1 PBD binding in vitro. These results are in agreement with the notion that phosphorylation of paxillin S272 plays an essential role in regulating focal adhesion turnover. This dissertation also computationally derived the complex structures of GIT1 PBD bound with either LD2 peptide or LD4 peptide, based on the experimental binding site information. The LD2 and LD4 peptides bound to GIT1 PBD in a manner similar to the crystal structure of FAT-LD2 complex. The complex structures visualized the reason why both LD2 and LD4 can bind to the same GIT1 binding site. It also addressed the specificity problem in determining paxillin binding to GIT1 versus FAK. Our finding reconciles the controversial observations of earlier studies and provides a clearer picture of focal adhesion regulation. The structural studies of GIT1 PBD presented in this dissertation shed more light on the understanding of GIT functions. The novel findings also allow us to propose a working model regarding FA disassembly.
Multimodal Emotion Recognition using Deep Convolution and Recurrent Network

Automatic human emotion recognition is one of the most important and growing field of research in Human Computer Interaction (HCI) domain. It has huge impact on applications like Automatic Human Behaviour Analysis and multimedia retrieval system. Recently, Deep Neural Networks have gain a lot of success in terms of accuracy related to machine learning tasks. Automatic human emotion recognition have also been solved using the deep learning based Convolution Neural Network (CNN). Currently, most of deep learning based algorithms for human emotion recognition only focuses on the particular direction like Vision, Text and Audio. These algorithms are trained on specific modality (visual data, textual data, acoustic data) and performs well in a controlled environment, but failed to achieve the good results in most of the real-life cases. It is due to the unpredictable behaviour of the human nature. So to tackle this problem, a novel deep learning based multi modal architecture have been proposed in this paper. This algorithms utilizes visual, textual as well as audio features to enhance the accuracy on automatic human emotion recognition. Extensive experiments have been performed to improve the accuracy and transparency of our proposed work. Results have proved that we achieved good results as compared to the sate-of-the-art results.
MagWi: Benchmark Dataset for Long Term Magnetic Field and Wi-Fi Data Involving Heterogeneous Smartphones, Multiple Orientations, Spatial Diversity and Multi-Floor Buildings

The wide use of mobile devices introduced several new services for the consumer market which are collectively called location-based services, the name being indicative of the significance of the consumer position. Consequently, a rich variety of positioning technologies have been adopted to provide and enhance user location information. The mass deployment of Wi-Fi access points (APs) and the ubiquity of the magnetic field data make them attractive candidates for indoor positioning. Additionally, the availability of embedded magnetic and Wi-Fi sensors in smartphones helps to achieve positioning without additional infrastructure. Even though Wi-Fi and magnetic field data offer complementary characteristics for enhancing positioning accuracy, several challenges for these technologies remain unresolved. However, the lack of publicly available datasets for the magnetic field and Wi-Fi makes it very difficult to extensively investigate these characteristics. Also, the proposed approaches cannot be tested on common benchmark datasets to analyze the results of the state-of-the-art approaches. To resolve these issues, this study presents a dataset that comprises the magnetic field, Wi-Fi, and the data from the inertial measurement unit (IMU) sensors of the smartphone including accelerometer, gyroscope, and barometer. First, the important characteristics of both the Wi-Fi and the magnetic field that require further investigation are highlighted, and later the data are collected. The data are collected over a longer period spanning approximately five years involving five different smartphones used by four different users, both female, and males. Different path geometries are followed in different multi-floor buildings which are physically separated, comprising both small and large areas. Besides, three different orientations of the smartphone are considered for data collection covering corridors, halls, and laboratories. The data from the stairs help to test ‘stairs up’ and ‘stairs down’ events and approaches aiming at multi-floor positioning can be tested with the provided dataset.
Collaborative Edge Processing by mmWave Access Providing UAVs

A wireless IoT system consisting of unmanned aerial vehicles (UAVs) is investigated. They work with a ground base station to provide mmWave access to user equipment and IoT devices. In parallel, they collaboratively share edge processing load. We design a modified Lyapunov optimization framework for assigning tasks to multi-hop connected UAVs. The proposed strategy constructs a task flow control policy that is near-optimal with respect to energy consumption and response time. The best task migration and distribution over the UAVs can be found out at a bearable complexity. Numerical results confirm the soundness of the framework regarding power cost, execution latency and overhead data.
On the Performance of Power Splitting-Based SWIPT in Self-Energy Recycling Full-Duplex Relaying Networks

This paper investigates the outage performance of a cooperative relaying network, where the relay node is considered to be an energy-constrained device so that a power splitting-based simultaneous wireless information and power transfer scheme is employed. The relay is considered to operate in full-duplex (FD) mode so that both energy recycling and information decoding can be performed. For this purpose, the relay is assumed to be provided with two batteries which switch between the power supplying mode and charging mode at each transmission block. In particular, we assume that the self-interference inherent to FD mode is not completely suppressed; it is subject only to passive interference cancellation for self-energy recycling, while it is subject to both passive and active cancellation for information decoding. We derive a tight closed-form approximation to the outage probability for the considered FD mode-based scheme, as well as for the HD mode-based counterpart. We validate the obtained expressions via Monte Carlo simulations. The impact of self-energy recycling in FD mode on the system performance is assessed.
On the bending effects of a printed 4x4 antenna array

The bending effects of proximity coupled 4x4 patch arrays with printed patch elements operating in the X-band near 10 GHz is presented. The patch arrays have circular patch elements with inclusive slots residing on the top surface of four layers (10mil/layer) of DuPont Pyralux AP. The inclusive slots decrease the axial ratio of the antenna and increase the impedance bandwidth. Each array has a microstrip feed network of quarterwave transformers and T-junction power dividers in the middle of the four layer stackup. The microstrip lines are proximity (or capacitively) coupled to the patch elements. A ground plane on the bottom surface of the four layer stackup provides surface agnostic behavior. The patch elements and microstrip feed network are printed with DuPont CB028 conductive ink using a liquid dispense technique. For the purpose of comparison to PCB processed antennas, 4x4 patch arrays with 1$/_{2}$ oz. electroplated copper for the patch elements and microstrip feed network are also reported. The gain, 3 dB beamwidth, and VSWR bandwidth behavior have been studied for uniaxial bending of the two arrays for radii of curvature down to a 1 inch. The nominal (i.e., no bending) measured far-field gain of the arrays is 14.5 dBi (printed) and 14.7 dBi (copper). The printed and copper arrays behave very similarly with changes in gain of 9.3 dB (printed) and 8.3 dB (copper) for bending down to 1 inch in an axial direction and changes in gain of2.6 dB (printed) and 2.8 dB (copper) for bending down to 1 inch in a circumferential direction.
Low Cost Smart Navigation System for the Blind

One of the major problems faced by visually impaired people is navigating from one place to another. Commercially available walking canes only serve as obstacledetectors for these people. The need for an economical guidance and navigation system for the blind is long overdue. Existing solutions are highly cost-ineffective, rendering them available only to people on the higher end of the economic strata. A cheap and affordable piece of technology can help the blind commute to their workplace instead of relying on help from random strangers to even commute walkable distances. In this paper, we propose a design for a walking stick to help the visually impaired commute to their livelihood. The proposed solution works on the Internet of Things realm wherein the blind can “communicate” with the environment. This prototype is equipped with an ESP8266, a power source for the development board and coin motors along with a smartphone application, thereby making it accessible for even the working class visually impaired.
A Brief Review of Strategies Used for EMG Signal Classification

This article presents a brief review of machine learning/classification strategies for the classification of EMG signals in the context of Myoelectric controlled prosthesis. It focuses on certain parameters adopted for machine learning such as selecting the size of windows and frequency range adopted for different filters, the filters of three domains including time domain, frequency domain and time-frequency domain, and the classifiers commonly used and that of different statistical tests performed for evaluating the significant difference between the EMG and performance of features and classifiers. Also, the comparative analysis of different EMG related studies has been done in this article. The paper would contribute to selecting the parameters before evaluating the results using machine learning. The criteria for selection of the papers to present the review is set by looking at the frequently used features and classifiers that have been used by the researchers for EMG signals analysis in the past 2 decades.