Miracle-dz/UniFAR-FTUs huggingface dataset:
An exploration of active hard mount vibration isolation for precision equipment

Vibrations due to environmental disturbances can cause a loss of accuracy in high-precision equipment. To reduce the vibration levels, the equipment is commonly mounted on soft mounts. However, the soft mount's low support stiffness may introduce difficulties in the response to direct disturbances and with the equipment levelling. In this thesis, an alternative vibration isolation concept is investigated, which uses stiff supports (hard mounts). The increased support stiffness circumvents the mentioned difficulties of soft mounts. Additional objectives for this vibration isolation concept are improvement of the damping in suspension and structural modes and achievement of floor vibration isolation performance as achieved by state-of-the-art soft mounts. The required additional damping is achieved by feedback control. Absolute motion sensors are shown to be preferred over force sensors and displacement sensors for this purpose. To reduce the transmission of floor vibrations further, feedforward compensation of measured floor vibrations is applied. The (adaptive) FxLMS algorithm (with several extensions), which is widely used in Active Noise Control applications, is used to find the optimal feedforward compensation. The standard Finite Impulse Response parametrization requires a large number of parameters to model the optimal feedforward controller with sufficient accuracy. Alternatively, an Infinite Impulse Response parametrization with fixed poles is proposed. Then, a similar performance can be achieved with less parameters, resulting in less demanding computational requirements. However, the performance depends critically on the choice of the fixed poles in the IIR filter. Experimental tests indicate that the vibration levels can be reduced by a factor of 20, to typical levels of 1 mm/s2 RMS, while maintaining a support stiffness 300 times larger than typical soft mounts. The residual vibration level is not yet comparable to typical soft mount vibration levels, as the performance is primarily limited by the sensor noise level and time delay in the signal conditioning.
Place of Computer Modeling in Design of Biodegradable Graphene Printed Antennas of Cellular Range

This paper presents the analysis of various ways of mounting the connector to a printed biodegradable graphene dipole antenna. The effect of parameter changes of the antenna and connector joint on the final characteristics of graphene dipole antennas is estimated, based on simulation. Further, the voltage standing wave ratio of the fabricated antenna samples with three different connector mounting methods is measured in accordance with the modeling performed by the authors. The method of mounting the connector is chosen (design of the antenna communication interface with the connector), based on the comparison of the experimental and simulated reflection coefficient and the voltage standing wave ratio. The analysis of the graphene printed antennas stability is made by various methods to single and repeating mechanical effects, such as twisting and bending, is carried out. As a result, the high stability of the graphene devices parameters is shown, as compared to their metal counterparts. It makes possible to increase the reliability of communication in emergency situations by printing graphene antennas with the presented interface on the equipment of rescuers and personnel of other response groups.
Ethnobotanical study of Penchalakona forest area of Nellore District, Andhra Pradesh, India.

An ethno botanical survey was undertaken to collect information from yanadi tribe of penchalakona forest area, Rapur mandal, Nellore district, Andhra Pradesh, India. The indigenous knowledge of traditional healers of this ethnic group has been disappearing due to lack of ancestors as well as followers. Only few people are practicing with little knowledge which was transmitted orally from their elders. The native plants used for medicinal purposes by few people were collected through questionnaire and personally interviewed during field trips. An aboriginal tribe called “Yanadi”, of this area has authentic information on medicinal values of different plant species. Yanadi tribal community being drifted from their natural way of life due to agro rural development activities, a few aged persons are still able to furnish very little traditional ethno botanical data and continue to depend on medicinal plants atleast for the treatment of primary healthcare. Because the area is located near the forest and 70 Kms from Nellore town.The study revealed that, the Yanadi tribe used 20 plants species belonging to 20 families to treat various diseases like worm infestations, scorpion stinge, headache, body pains, fevers, swelling of foots, skin diseases, heart diseases, stomach ulcers, diuretic, dysentery, snake bites, nerve disorders, rheumatic pains, antiseptic, helmentic disease, diabetes, cold and dental problems.These plants represents the major source for the pharmaceutical industries in view of their raw material. The information requires validation for further clinical usage.
Dual-Guidance-Based Optimal Resonant Frequency Band Selection and Multiple Ridge Path Identification for Bearing Fault Diagnosis Under Time-Varying Speeds

To perform bearing fault diagnosis under variable speeds, the optimal resonant frequency (ORF) band selection and diagnosis strategy are pivotal. Indexes, such as kurtosis, crest factor (CF) and smoothness index (SI), are extensively used for guiding ORF selection. Due to that each index has unique advantages, the hybrid of such indexes has been developed. However, applications of the current index hybrid method are impeded by problems of: 1) ineffectiveness for signal corrupted by impulsive noises and 2) equal segmentation of frequency band with human intervention. This paper, therefore, firstly proposes a dual-guidance based scheme with an embedded tunable Q-factor wavelet transform (TQWT) to address the problems. The so-called dual-guidance scheme contains two guidance procedures: 1) the SI guided pre-process for obtaining weight vectors and 2) the index hybrid output guided scheme for ORF selection. The embedded TQWT is used for frequency band segmentation and sub-band signal acquisition without subjective interventions. With the proposed scheme, the ORF band can be determined for bearing fault feature extraction. Then, an algorithm for multiple instantaneous frequency (IF) ridge identification is exploited based on the peak search algorithm for diagnosis. To tackle the difficulty that, at each time instance, the amplitudes of IF ridges of interest do not always dominate the time frequency representation (TFR), a starting point search tactic with a synchronization step is explored. A diagnosis vector can subsequently be obtained by calculating the average ratios of the identified ridges and bearing fault diagnosis can then be done by matching the elements of the diagnosis vector with fault characteristic coefficient (FCC). Comparisons are performed to illustrate the superiority of the proposed method. The experimental analyses are also conducted to validate the proposed method for bearing fault diagnosis under variable speeds.
Development of A Warning Receiver System for Public Display Based on Application-Controlled Video Switcher

Indonesia is prone to tsunami. One way to reduce the risk is to increase people preparedness. Indonesia government together with several Country in the world has built InaTWES as an Early Warning System. Currently, it used several mediums for dissemination, namely radio, TV, SMS, fax, and siren. We could broaden the range of dissemination by adding a new medium. One of the potential media is a public display. Unfortunately, most of the public display is designed as an advertisement platform. It doesn’t have support alerting service. Hence, we propose a system that could enable current public display as a warning receiver system. By using video source switcher that could switch the video source of public display, from its existing system to external system that could receive a warning message.
Development of Disease Prediction Model Based on Ensemble Learning Approach for Diabetes and Hypertension

Early diseases prediction plays an important role for improving healthcare quality and can help individuals avoid dangerous health situations before it is too late. This paper proposes a disease prediction model (DPM) to provide an early prediction for type 2 diabetes and hypertension based on individual’s risk factors data. The proposed DPM consists of isolation forest (iForest) based outlier detection method to remove outlier data, synthetic minority oversampling technique tomek link (SMOTETomek) to balance data distribution, and ensemble approach to predict the diseases. Four datasets were utilized to build the model and extract the most significant risks factors. The results showed that the proposed DPM achieved highest accuracy when compared to other models and previous studies. We also developed a mobile application to provide the practical application of the proposed DPM. The developed mobile application gathers risk factor data and send it to a remote server, so that an individual’s current condition can be diagnosed with the proposed DPM. The prediction result is then sent back to the mobile application; thus, immediate and appropriate action can be taken to reduce and prevent individual’s risks once unexpected health situations occur (i.e., type 2 diabetes and/or hypertension) at early stages.
On the Energy Requirements of UAVs Used for Blade Inspection in Offshore Wind Farms

Offshore wind energy is being seen as a major energy player as the quest for clean renewable energy solutions intensifies. Wind turbines operating at sea are subjected to a harsh and unforgiving marine environment that calls for timely inspections and maintenance in order to maintain high levels of availability. Inspections at height and especially at sea are inherently time-consuming, expensive and fraught with risks, so the automation of such procedures will benefit the industry at all levels. Unmanned Aerial Vehicles (UAVs) are finding niche applications as a remote sensing tool in various sectors, one of which is wind turbine inspection. This paper investigates the onboard energy requirements and operational flight duration of a drone used for offshore wind turbine inspections subject to aspects such as wind turbine size and site wind conditions. The energy requirements of the UAV are numerically modelled using a single-wind turbine blade inspection routine as a basis. Flight path distance is inherently linked to wind turbine blade length and, based on the need for four passes per blade during an inspection, this property featured prominently on the UAV's performance. The research then moved on to investigate UAV energy requirements for multiple-wind turbine arrays with factors such as wind speed and direction, inter-turbine spacing and array layout and rotor wakes being considered. This part of the study utilised the numerical model mentioned earlier and the WindPRO software package. The undisturbed wind speed was declared as that characteristic having the highest influence on energy consumption and flight duration. Wind direction, wind turbine rotor wake and inter-turbine downwind spacing were the other forcing factors considered, with the significance of their impacts in the same order respectively. The results of this study identify the effects of the various parameters on UAV energy requirements and inspection flight duration as a means of enabling better planning and optimisation of offshore wind turbine blade inspection using drones.
Acoustic Scattering from a one-dimension Gaussian surface on a pressure-release boundary

In this study, the impact of parameters for a random Gaussian surface with on the scattering field is analysed by solving an integral equation. The boundary is assumed to be a pressure release boundary. The bistatic scattering cross section of the scattering field is obtained over surface realizations. The focus is mainly on two parameters of the surface: the root mean square (rms) slope and the ratio of the incident wavelength to the surface correlation length , the former can be regarded as an indicator of surface roughness, while the latter a character describing the scale of surface roughness. For the correlation length with the same order of magnitude as the incident wavelength, the scattering energy exists on the whole space. However the correlation lengths in other scales only affect the region around the specular direction. As correlation length is identical with the incident wavelength in magnitude, the rms slope angle as an indicator of the surface roughness affect the distribution of scattering energy between the specular direction and remaining region.
An Analytical Framework for Effective Joint Scheduling Over TDD-Based Mobile Networks

The growing demand for data along with the emergence of new communication standards have reshaped wireless networks through a denser base station deployment, an increasing traffic heterogeneity, and an additional complexity in quality of service (QoS) assurance. Orthogonal Frequency Division Multiple Access (OFDMA) is considered as one technique to be used in next generation wireless networks. Utilizing time division duplexing (TDD) aids the management of resources and providing effective QoS. In the literature, common approaches in assessing effectiveness attempt to capture performance using single indicators that reflect one aspect of the network’s operation. consequently, multi-objective evaluations are not easy and require intuitively considering isolated descriptions, plots, visualizations, and holistically performing multiple comparisons. In this paper, we propose an analytical framework that aims to classify the effectiveness of joint scheduling algorithms over TDD-OFDMA networks per combined heterogeneous properties. In the suggested framework, a designer benefits from a bouquet of carefully customized indicators that can lead to quality evaluations, performance classifications beyond traditional approaches, and accurate improvements. Validation includes exhaustive simulations and the assessment of different scheduling and performance classification schemes. The obtained results confirm the validity of the framework and confirms its effectiveness in application.
A Look on Ships Detection techniques using SAR Images

Several techniques have been studied, invented and reformulated to enhance automatic ships detection using SAR images. Depending on how the information is gathered and processed each technique presents different performances and results. Nowadays there are several missions on going, and the need to get better results in ships detection, oil-spills or any kind of sea activity is fundamental to preserve and promote navigation safety as well as a constant and accurate monitoring of the surroundings for, for example, illegal fishing activities, pollution or drug trafficking. This paper is written in the context of a MSc dissertation which the purpose of looking on a full hand of recent and most used techniques for ships detection and summarizing them in terms of basic principles, advantages and disadvantages and performance in order to provide a basis guideline to choose which technique shall provide the best and more promising results for a task at hands and also give a practical example on Sentinel data set material.
A Rerouting Framework Against Routing Interruption for Secure Network Management

In this paper, we consider the problem of routing interruption, which has received significant interest in recent years. To solve this problem, rerouting is an effective way. However, how to design an effective secure and applicable rerouting algorithm remains a significant challenge. To this end, we propose a fast rerouting framework for routing interruption. Our framework consists of two parts: (1) Diagnose routing interruption. We determine the location of interruption by directly interrogating the control plane IPv6 stack. (2) Implement fast rerouting. We propose the rerouting algorithm to make the router match and reroute all prefixes affected by interruptions. This paper updates the path only by adding a forwarding rule, thus the interruption recovery time is reduced. We conduct a comprehensive evaluation for our rerouting framework. Experimental results show that our method is better than FCP, OSPF and DLF in speed and accuracy.
Text Mining Analysis in the Log Discussion Forum for Online Learning Recommendation Systems

These studies aim to determine the degree of similarity between student and lecturer posts. The level of similarity then we validated using a lecture note. This research is a continuation of the research that we are currently doing related to the development of smart LMS with one of the supporting features of the recommendation system. Using the analysis in the forum log post discussion of this research was carried out through several stages. The first stage, the random selection of 5 classes taken by the forum data, the second stage, carried out text mining analysis from the posts of students and lecturers, the third stage, analyzing text validation using lecture notes that have been used as data sets in the form of corpus. This study uses the doc2v algorithm with vectorization. The results of this study found that the percentage of similarities between lecturers’ posts, students and lecture notes only reached 49% of the target we expected at least 80%. Because discussion forums are a substitute for face-to-face sessions on face-to-face learning. On the other hands this study found that the similarity of discussion between lecturers and students on discussion forums had a significant influence on student learning outcomes (assignment) and this reinforces the need for a system of recommendations for online learning.
Underwater threat recognition: Are automatic target classification algorithms going to replace expert human operators in the near future?

In this paper, different human/machine strategies are tested in order to evaluate their performance in underwater threat recognition. Sonar images collected using synthetic aperture sonar (SAS) and side scan sonar (SSS) during real mine countermeasures exercises are used. Data are collected over a test area on the Belgian Continental Shelf, where several targets were deployed. Image resolution is divided in three categories: (1) up to 5cm pixel size, (2) between 5cm and 10cm pixel size, (3) larger than 10cm pixel size. Soil complexity is also evaluated and used to build up different strategies. Results demonstrate the utility of considering the human operator as an integral part of the automatic underwater object recognition process, as well as how automated algorithms can extend and complement human performances.
An Energy-Efficient Cross-Layer-Sensing Clustering Method Based on Intelligent Fog Computing in WSNs

The conventional data-based routing protocols are usually vulnerable to a large number of energy voids or hotspots in Wireless Sensor Networks (WSNs). In order to address this problem, we propose Mobile Intelligent Fog Computing: An Energy-efficient Cross-layer-sensing Clustering Method (ECCM). The first, according to the cross-layer projection principle, the proposed algorithm employs the sensing-event-driven mechanism to project the fog nodes onto the sensing layer, and constructs a powerful virtual control node. Then the control procedure of the cluster-based routing protocol in sensor networks is uploaded to the fog layer and the fog computation is employed to achieve the distributed clustering of the event-field nodes. The second, the optimized data aggregation routing is constructed, which centers the projectile fog nodes. The data in the bottom-layer routing of the sensor network is thus replaced, and the network load is balanced and reduced. The third, in the optimization of the routing protocol, we introduce the Particles Swarm Optimization, (PSO) algorithm and elect a group of optimal nodes as the cluster heads, without the cost of any competition overhead, the energy overhead of the network can be effectively reduced and balanced, which curbs the rapid exhaustion of the node energy and prolongs the network lifetime. Finally, it is shown by the simulation results that the construction and the maintenance of the routing structure are small, which could optimize the data aggregation efficiency and improve the network performance.
Organically Packaged Components and Modules: Recent Advancements for Microwave and mm-Wave Applications

The wireless industry is moving at a faster pace than ever before. The number of mobile phone applications is growing rapidly; high-definition video, automotive radars, and high-resolution imaging markets are creating a demand for high-speed communication that goes well beyond what today's wireless systems can handle. Component integration, size miniaturization, cost reduction, reliability, and performance enhancement have become the primary focus of current wireless systems. To achieve these objectives, advanced packaging techniques have been developed that replace traditional bulky package configurations. These concepts include system-on-chip (SoC), system-in-package (SIP), and systemon-package (SoP) technologies, as illustrated in Figure 1.
Hemagglutination by Human Anti‐Leukocyte Serums

Isoantigenic variation in human leukocytes, and specific serums defining several leukocyte antigen systems, have been described [3, 4, 6, 101. Most of these antigens have been found to occur on the cells of several different organs [l, 2, 71, some have been limited in their distribution t o a specific cell strain [6], but until now none have been demonstrated on erythrocytes. This preliminary report will describe hemagglutinating activity of anti-leukocyte serums, the specificity of which differs from that of antibodies defining known red cell blood types.
Transient analysis of the external magnetic field via MUSIC methods for the diagnosis of electromechanical faults in induction motors

In the induction motor predictive maintenance area there is a continuous search for new techniques and methods that can provide additional information for a more reliable determination of the motor condition. In this context, the analysis of the external magnetic field has drawn the interest of many researchers. The simplicity, low cost and potential of this technique makes it attractive for complementing the diagnosis provided by other well-established methods. More specifically, the study of this quantity during transient operation of the motor (e.g. under the starting) has been recently proposed as a valuable tool for the diagnosis of certain electromechanical faults. Despite this fact, the research in this approach is still incipient and the employed signal processing tools must be still optimized for a better visualization of the fault components and, therefore, for a better determination of the machine condition. This paper presents an advanced algorithm based on MUSIC for enhancing the visualization of the harmonics caused by different motor failures in the electromotive force signals induced by the external magnetic field. Two faults are considered in the work: rotor problems and misalignments. Also, different positions of the external coil sensor are studied. The results prove the potential of the MUSIC algorithm for the reliable diagnosis of electromechanical faults.