Miracle-dz/UniFAR-FTUs huggingface dataset:
Internet of Things based Development of Continuous Saline Monitoring and Control System

Healthcare is getting digitized due to advancement in technology and increase in the population rate. The healthcare principles are being combined with engineering principles to provide automated versions rather than using manpower. The very basic medication given to a patient is the saline which helps the sick to remain hydrated, being pain free and various other uses. From simple medication to large process like blood transfusion, saline is used. These saline bottles are monitored and controlled by manual process. The IV tube must be stopped once the saline level reaches the lower level because it may cause a tedious damage to the patient like loss of blood, and reverse flow of blood from veins to the IV tube. There is also a chance of air entering into the veins if the bottle becomes drained which would push the patient into a serious condition which may lead to death. Monitoring the IV tube is utmost necessary, so the manual method was revolutionized to the infusion pump which tells only about the rate of saline discharged from the bottle. Another setup was to just monitor the amount of fluid remaining in the bottle. The role of this proposed project is to automatically monitor and control the saline bottle using wireless modes of transfer. This method replaces the manual work, and the physician's frequent entry into the patient's room which will not be safer during the pandemic outreach. As the entire management and control process is done using IoT, it becomes easier for the physician to be in touch with the patient monitoring.
ERINet: Effective Rotation Invariant Network for Point Cloud based Place Recognition

Place recognition task is a crucial part of 3D scene recognition in various applications. Nowadays, learning-based point cloud place recognition approaches have achieved remarkable success. However, these methods seldom consider the possible rotation of point cloud data in large-scale real-world place recognition tasks. To cope with this problem, in this work, we propose a novel effective rotation invariant network for large-scale place recognition named ERINet, which captures the recent successful deep network architecture and benefits from holding the rotation-invariant property of point clouds. In this network, we design a core effective rotation invariant module, which enhances the ability to extract rotation-invariant features of 3D point clouds. The benchmark experiments illustrate that our network boosts the performance of the recent works on all evaluation metrics with various rotations, even under challenging rotation cases.
Deep Learning and Mosaic algorithm: An efficient combination for single cell segmentation

In biomedical industry, Deep Learning is considered one of the most effective tools to achieve high performance, especially in image segmentation [1]. In order to get high credibility, Deep Learning requires a lot of data to learn its pattern, while the resources of biomedical images are relatively small compared to other fields. To be able to completely and accurately segment individual cells in images is difficult due to the diversity of cell shapes, unequal frequency of cells in images, and the class distribution being extremely imbalanced. Cellpose model [2] which specializes in biomedical image segmentation was applied to perform our task. We trained our model on an open-source dataset called Sartorius, this dataset includes 606 images for training and 240 images for evaluating. The number of images is small and the data are also naturally imbalanced among the object classes. To deal with these problems, some data augmentation techniques and our customized Mosaic algorithm that is suitable for the dataset were used. The auxiliary model, Size Model was also trained which supports Cellpose in the inference phase. Our method overcomes these Sartorius's difficulties and also makes prediction more reliable. On the test set, our model achieves an AP score of 31.3%, compared with 25.3% of Mark Wijkhuizen who trained Mask RCNN [3] with backbone EfficientNetV2 [4] on this dataset.
Cluster Based Smart Random Walk for Data Aggregation in Wireless Sensor Network

Wireless Sensor Network (WSN) is a collection of sensor devices having limited communication range and battery power. It has unreliable wireless medium for data transmission hence reliable message delivery is a challenging task for routing protocols designed for WSN. In multiple scenarios a Random Walk (RW) in WSNs is proved to be energy efficient for routing and load balancing. Further clustering methodologies provide a novel idea of multi-hop data gathering and aggregation which significantly reduces redundant data transmission. The present work explores the integration of RW and clustering schemes with an objective to improve upon existing routing protocols in terms reliable data transmission and network lifetime. Hence, a hybrid Cluster-Based Smart Random Walk (CBSRW) routing technique for data collection and aggregation is proposed in this paper. It has two phases: Clustering & data aggregation and CBSRW. Experimental results and comparative analysis show that the proposed work achieves an enhanced lifetime and efficient packet delivery ratio compared to the conventional state of the art schemes designed for the domain.
User-Interactive Light Emitting Capacitor Display for Instantaneous Liquid Stimulus Visualization

Lightweight and flexible interactive display that offers tremendous benefit to simplify work task and improve human life was under intensive investigation for decades. Inorganic phosphor powder-based light emitting capacitor (LEC) lives with ambient manufacturing condition and simplest sandwich device feature received a lot of attention on exploring its potential in visualize mechanical, motion and thermal stimuli. Combined with microfluidic design concept, biomolecular interactive LEC (BIO-LEC) has been developed to facilitate the transduce of external liquid stimuli into light intensive signals. Here, a miniaturized biomolecular onsite interactive light emitting capacitor (BIOON-LEC) has been further studied with advancing in microfluidic design and using disposable batteries.
Force Distribution-Based Control Strategy of Landing Gear for the Unmanned Helicopter Landing on the Inclined Surface

The unmanned helicopter plays an important role in the fields of cargo transportation and rescue missions. Due to the limitation of the traditional landing gear structure, the unmanned helicopter has more stringent requirements on the landing terrain. In order to improve the ability to land on the inclined surface for the unmanned helicopter, a novel control strategy of the landing gear based on contact force distribution algorithm is proposed. The overall of landing process includes the desired position tracking stage, the following stage and the attitude adjusting stage. By obtaining the desired acceleration according to the landing and attitude correcting curve of the fuselage, and calculating the desired force based on contact force distribution algorithm, the adaptive motion and trajectory tracking of the landing gear during the landing process has been realized. The simulation results have demonstrated the effectiveness of the strategy when the unmanned helicopter with the initial roll angle of -3 degrees and pitch angle of 3 degrees lands on the inclined surface of 7.07 degrees. In the future, the control strategy can provide a reference for the dynamic landing of the unmanned helicopter while at sea.
Depressive states in healthy individuals lead to biased processing on frontal-parietal ERPs

According to Beck’s cognitive model of depression, depression has an effect on schema, which leads to biased cognition. Depressive states and depression symptoms are considered to occur on a continuum, with only qualitative differences between them. However, it remains unclear whether there are significant differences in information processing among healthy individuals. In this study, we investigated the event-related potentials (ERPs) and event-related spectrum perturbation (ERSP) of healthy individuals with various depressive states during emotional stimulation. We found that participants in a high depressive state had a lower P300 amplitude. Moreover, we identified significant differences in fast and slow neural responses in the frontal and parietal lobes, which might indicate a cognitive bias in healthy individuals.
Substorm‐injected protons and electrons and the injection boundary model

Observations of substorm-associated enhancements of proton and electron fluxes were made by Explorer 45 below L = 5.3 in the dusk magnetosphere on February 13, 1972. The particles were observed after a substorm that started at 1116 ± 0010 UT in the wake of an ssc at 0939 and that initiated a small magnetic storm with maximum Dst = 47 γ. The protons in the range between 1 and 40 keV exhibit strong dispersion effects in both energy and pitch angle. The electrons also show dispersion effects above 40 keV, while those below that energy are not observed until the satellite crosses the plasmapause. The observations are interpreted in terms of McIlwain's model electric field E3 and an injection boundary close to or coinciding with the plasmapause. Good quantitative agreement is obtained between the observed and the predicted proton energies and pitch angle dispersions and electron energy dispersions. In view of strong temporal fluctuations in the electron flux, no comparison in pitch angle dispersion of electrons is possible. The conclusion drawn is that in this event protons and electrons were injected at the onset of the substorm more or less instantaneously into the magnetosphere along or above the injection boundary and particles with energies below 10 keV were not convected in from the magnetospheric tail.
Capacitance of a System of Interdigitated Microelectrodes Used for Impedance Control of Liquid Substances

The capacitance of a interdigitated microelectrode structure is considered. To determine this capacitance the method is proposed which is based on an analytical approach. In the structure, a system of two film electrodes is distinguished for which the potential distribution at a constant field is analytically determined. Using the potential distribution, the form factor of the structure is found, which, in its turn, is used to determine its capacitance. Based on the proposed method, the dependences of the form factor and the capacitance of the interdigitated microelectrode structure on the thickness and materials of the substrate and the finger width of microelectrodes are determined. The dependencies of the structure capacitance obtained by the proposed method and based on the lossless microstrip coupled lines model are compared.
Exponential synchronization of stochastic impulsive perturbed chaotic Lur'e systems with time-varying delay and parametric uncertainty

This paper is devoted to investigating the scheme of exponential synchronization for uncertain stochastic impulsive perturbed chaotic Lur'e systems. The parametric uncertainty is assumed to be norm bounded. Based on the Lyapunov function method, time-varying delay feedback control technique and a modified Halanay inequality for stochastic differential equations, several sufficient conditions are presented to guarantee the exponential synchronization in mean square between two identical uncertain chaotic Lur'e systems with stochastic and impulsive perturbations. These conditions are expressed in terms of linear matrix inequalities (LMIs), which can easily be checked by utilizing the numerically efficient Matlab LMI toolbox. It is worth pointing out that the approach developed in this paper can provide a more general framework for the synchronization of multi–perturbation chaotic Lur'e systems, which reflects a more realistic dynamics. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed method.
CNN based Music Recommendation system based on Age, Gender and Emotion

Music is a soothing combination of sounds that can heal a person mentally and emotionally. Listening to music can improve their mood and productivity, as it helps to destress/reduce anxiety and enhance sleep quality. Here, the proposed music recommendation system is based on Age, Gender, and Emotion. For a better-personalized recommendation system, the age and gender should also be considered in addition to the emotion, further depending on different age groups and gender, the preference also changes. The entire system can then be divided into three neural network-based models to detect age, gender, and emotion, respectively, and depending on this combination, the personalized playlist has been suggested.
A Study on Mobility Management Schemes in Vehicular Communication

Vehicular communication allows vehicles to communicate with each other without connecting to a centralized infrastructure (i.e., base station). Vehicular communication including Vehicle-to-vehicle and vehicle-to-infrastructure communications also allows for the exchange of information between vehicles, which supports a number of applications, such as intelligent transportation systems and safety management. However, mobility management is a key aspect that has yet to be addressed properly for vehicular communication. Therefore, this paper reviews the existing mobility management and summarizes the resource allocation scheme for vehicular communication. We present a mode selection to maintain the connectivity of vehicular communication. Then, we simulate and discuss the existing mobility management scheme for vehicular communication.
Ripple Compensation Based On COT Control Mode

In the traditional Constant-On-Time (COT) control mode, the output bypass capacitor mostly uses ceramic capacitors, and its equivalent series resistance (ESR) is small, the phase of output voltage ripple lags behind the inductor current, causing system instability. For this reason, this paper adopts the ripple compensation technology. By injecting the external ramp and the internal inductor current ramp, a virtual resistance is introduced for the ESR, and the resistance value of the ESR is increased to meet the stability conditions. The chip is designed based on 18V 0.1Sum BCD process. The simulation results show that when the input voltage is 12V and the temperature is 27°C, the system conversion efficiency can reach 97%, and the system output voltage ripple is about 3.4mV.
Feasibility of LTE-Connected Unmanned Aerial Vehicle

The need for extended range Unmanned Aerial Vehicle (UAV) is increasing day by day. The cellular network seems a promising solution for controlling the UAV over an unlimited range. However, investigations need to be carried out to verify the feasibility of integrating the unmanned aerial system (UAS) with the cellular network. This research goes in line with that, as this paper investigates and presents the reference signal received power (RSRP) and reference signal received quality (RSRQ) for the cellular network at 3 different heights: ground level, 50m, and 100m in a rural area in Malaysia. The long-term evolution (LTE) network is provided by two Malaysian operators which are Celcom and Maxis. To conduct the test, a smartphone with Net Monitor Lite application has been attached to a flying fixed-wing UAV. The results show that the UAV RSRQ is vulnerable to more interference at high altitudes due to its exposure to more cells in line of sight (LOS). The outcomes of this study can serve as a basis for applying interference mitigation. The paper also showed the potential of deploying a dual network to enhance the UAV's RSRQ.
Design and Development of Reliable and Automated Hydroponics System

Internet of Things has made possible the arising of new technologies, and one such technology is the advanced hydroponics system, which is studied in this project. Before the use of modern technologies, manual maintenance was done for safeguarding the crops which had disadvantages like time consuming and harder to maintain a huge area of crop. To overcome this, a reliable automated hydroponics system is proposed in this paper. The main purpose of this project is to use machine learning algorithms for predicting the various parameters required for agriculture, and the predicted output will be useful for increasing the productivity of the crop. The detection algorithms used in this application is Convolutional Neural Network and Naive Bayes. These algorithms are designed to predict sensor values and perform comparison analysis. The algorithms are compared against each other in terms of accuracy for calculating the output in a very accurate manner. The testing of these algorithms is to make the correct prediction and to determine the algorithm which performs better than the other. The farmer can easily predict the problem and solve it through values that have been measured using the sensors, and the predicted result of the Machine Learning algorithm.
A Systematic Literature Review of Big Data Analytics Challenges in Cloud Computing

Big data development has been increasing rapidly especially in the industry and government sectors. Big data has many benefits if it can be analyzed. But since big data is huge, it cannot be analyzed by an ordinary computer due to the lack of computing resources. Cloud computing has those resources needed for analyzing big data and can be accessed by everyone with only a subscription. Although cloud computing has its advantages, it also presents challenges for big data analytics. In this paper, we use a systematic literature review (SLR) for several scientific papers explaining cloud computing models, the technologies used for big data analytics, and big data analytics challenges in cloud computing. This paper explained three cloud computing models: SaaS, PaaS, and IaaS. We also talked about Hadoop and MapReduce; two tools used in big data analytics. This paper also addresses the difficulties of big data analytics in cloud computing.
Bi-Directional LSTM Classification of Emotions in EEG Signals

In the field of neuroscience research, the analysis of EEG signals is crucial for comprehending both human behavior and the workings of the brain. The majority of research is being done to find out how EEG signals are usually used to distinguish between various psychological states and emotions. The proposed study has applied the Bi-LSTM approach for emotion categorization, which significantly raises the accuracy of emotion categorization based on the brain waves obtained from EEG. The results show that the system is reliable as the data has been trained by using a variety of classifiers but with the DEAP dataset, the proposed method obtains a higher and quicker accuracy rate with a training period of about 5ms and an average accuracy rate of 94.12%, and is working to classify the emotion such as valence, arousal, and dominance with single electrode channel with the highest accuracy.
Location-Dependent Task Bundling for Mobile Crowdsensing

The mobile crowdsensing (MCS) is an emerging sensing paradigm based on the mobile device. For location-dependent sensing tasks (LDSTs), when tasks are farther with low payment from workers, they can be difficult to complete. The completion rate of this unpopular task has always been an issue. Most existing researches mainly focus on how to increase payment for unpopular tasks, but the platform may suffer from it, because an incorrect increase results in an inability to raise the number of completed tasks. In this paper, we present a task bundling reorganized mechanism (TBRM) to improve the platform utility of MCS system. In the proposed mechanism, the unpopular and popular tasks are properly bundled to improve the platform utility. To decrease searching time for suitable bundles, two sub-policies are respectively utilized to design TBRM based on reinforcement learning: the area selection policy and the rule selection policy. Experimental results demonstrate that TBRM outperforms the three benchmark mechanisms, which reveals that TBRM can effectively bundle unpopular tasks and improve platform utility.
Effect of Mechanical and Electrical Stimulation on the Differentiation and Orientation of C2C12 Cells

The low differentiation and orientation rate of C2C12 cells is a key factor that restricts the performance improvement of the bio-hybrid robots driven by C2C12 cells. It has been demonstrated that the application of external stimuli during in the process of cell culture can contribute to C2C12 cells growth and differentiation. In this study, we applied different parameters of mechanical stimulation, electrical stimulation and coupled stimulation to C2C12 cells, and observed the differentiation and orientation of cells to investigate the optimal stimulation parameters and the effect of force-electric coupling on the differentiation and orientation of C2C12 cells. The results show that the optimal mechanical stimulation parameter is 10% static strain and electrical stimulation parameter is 1V/cm. The results of this study are a referable protocol for improving differentiation and orientation rate of C2C12 cells.
Design and Preliminary Evaluation of a Lightweight, Cable-Driven Hip Exoskeleton for Walking Assistance

The hip exoskeleton has demonstrated the capability of enhancing human locomotion performance during walking. However, few of them reached the ideal effect and went out of the lab due to excessive weight, lack of degree of freedom and user discomfort. In this paper, we present a cable-driven hip exoskeleton that concentrates on lightweight, comfortability and human-machine interaction. The total weight of the device is 3.15 kg excluding the real-time controller. Active cable-driven actuators and passive actuators based on torsional springs are developed to provide assistive force in the sagittal plane, and allow the hip to move freely in the coronal plane. We proposed a hierarchical controller that calculates the motor current command based on sensory information and user instruction, and validated the usability and effectiveness of the exoskeleton through a series of evaluation experiments. The results showed the exoskeleton can provide 14 % of energy supply per step on average during walking. Our work may provide a possible solution for the future lightweight, high-performance, and humanized hip exoskeleton.
On properties of functional principal components analysis

Summary. Functional data analysis is intrinsically infinite dimensional; functional principal component analysis reduces dimension to a finite level, and points to the most significant components of the data. However, although this technique is often discussed, its properties are not as well understood as they might be. We show how the properties of functional principal component analysis can be elucidated through stochastic expansions and related results. Our approach quantifies the errors that arise through statistical approximation, in successive terms of orders n−1/2, n−1, n−3/2, …, where n denotes sample size. The expansions show how spacings among eigenvalues impact on statistical performance. The term of size n−1/2 illustrates first‐order properties and leads directly to limit theory which describes the dominant effect of spacings. Thus, for example, spacings are seen to have an immediate, first‐order effect on properties of eigenfunction estimators, but only a second‐order effect on eigenvalue estimators. Our results can be used to explore properties of existing methods, and also to suggest new techniques. In particular, we suggest bootstrap methods for constructing simultaneous confidence regions for an infinite number of eigenvalues, and also for individual eigenvalues and eigenvectors.