Miracle-dz/UniFAR-FTUs huggingface dataset:
High Gain Graphene-Based Magneto-Electric Antenna for 5G Communications

This paper introduces a wideband graphene-based magneto-electric (ME) antenna with reconfigurable radiation characteristics for 5G communications. It consists of graphene patches deposited on silicon oxide substrate arranged horizontally and vertically to act as a ME-antenna. The reconfigurable conductivity of graphene is used to control the operating bandwidth of the antenna. The single element has an impedance matching bandwidth of 78.3 % with circular polarized band of 61.2% for applying chemical potential, µc=2 eV. The effect of changing the graphene reconfigurable conductivity on the radiation characteristics of the ME-antenna is investigated. The operating bandwidth is controlled by proper biasing the graphene sheet. The peak gain and the antenna efficiency are increased with increasing the chemical potential value due to the increase in graphene conductivity. The mutual coupling between ME-antenna elements is investigated for linear and circular arrangements. Octagonal array consists of 8-similar elements are constructed to produce electronic beam switching in different directions. Single beam, dual-beams, and omni-directional beam are achieved by controlling the graphene conductivity of single, two, and all the ME-elements in the array.
CPG-Based Industrial Manipulators on AVR System for PTP Applications

This paper intends to develop a cost-effective model of industrial manipulators on AVR system, which incorporates high-precision and real-time trajectory transition to perform repetitive Point-to-Point (PTP) tasks. We opt for Central Pattern Generator (CPG), a bio-inspired network that generates rhythmic trajectories without the aid of any sensory feedback. To implement this controller in AVR system, we have proposed an AVR compatible model of CPG; a custom cosine function is designed for numerical efficiency, and several techniques are outlined to ease implementation. A simulation is carried out on three degrees of freedom (DOF) manipulator as an exponent of effectiveness of the proposed methods. From simulation result, it appears that the robot generates smooth and precise trajectories in Cartesian space and has no problem dealing with either random perturbations or abrupt changes in control parameters.
Chloroplast Genome of the Soap Bark Tree Quillaja saponaria

Quillaja saponaria (Quillay) is an endemic Chilean tree species within theQuillajaceae family, from the Fabales order that is adapted to grow under dry temperate conditions and poor soils (Villagran andHinojosa, 1997; Luebert, 2014). Its high tri-terpenoid saponin contentmakes it an economically attractive species. Quillay contains amphipathic glycosides called saponins that are used by the pharmaceutical and cosmetic industries (Hostettmann and Marston, 1995; Guo et al., 1998). Their harvest has conventionally been done using natural Quillay populations as the raw material, damaging natural ecosystems (Santelices and Bobadilla, 1997). This unsustainable overexploitation of native Quillaja forest has compelled the establishment of new plantations that would satisfy an increased demand for Q. saponaria products (Donoso et al., 2011). In this study, we present the first draft of Quillay’s chloroplast genome sequence (cpDNA) and functional annotation. Our study provides information for Quillay germplasm characterization, to address genetic diversity and gene flow from parental trees through uniparental mode of chloroplast inheritance (Daniell et al., 2016), which may contribute to the long-term goal of selection for superior saponin-producing trees amenable for commercial plantations.
Fault Diagnosis of Induction Motor Bearing Using Cepstrum-based Preprocessing and Ensemble Learning Algorithm

Monitoring the condition of rolling element bearing and diagnosing their faults are cumbrous jobs. Fortunately, we have machines to do the burdensome task for us. The contemporary development in the field of machine learning allows us not only to extract features from fault signals accurately but to analyze them and predict future bearing faults almost accurately as well in a systematic manner. Utilizing an ensemble learning method named Gradient Boosting (GB) our paper proposes a technique to previse future fault classes based on the data obtained from analyzing the recorded fault data. To demonstrate the cogency of the method, we applied it on the REB fault data provided by the Case Western Reserve University (CWRU) Lab. Employing this supervised learning algorithm after preprocessing the fault signals using real cepstrum analysis, we can detect and prefigure different types of bearing faults with a staggering 99.58% accuracy.
Modeling of Mixed CNT Bundle for Sub-threshold Interconnects

In this paper, we report the analysis of Mixed Carbon nanotube bundle (MCB) as potential interconnects in the sub-threshold regime. MCBs with specific and random arrangement of non-identical MWCNT diameters are proposed. The sub-threshold crosstalk delay has been obtained analytically for different MCB arrangements and is compared with SPICE simulation results. It is found that there is significant improvement in crosstalk induced delay for MCB where SWCNTs are located in the centre while MWCNTs are at periphery in the bundle.
Pre-processing and Feature Extraction Methods for Smart Biomedical Signal Monitoring: Algorithms and Applications

Pre-processing and Feature Extraction Methods for Smart Biomedical Signal Monitoring : Algorithms and Applications Abderrazak Chahid Human health is monitored through several physiological measurements such as heart rate, blood pressure, brain activity, etc. These measurements are taken at predefined points in the body and recorded as temporal signals or colorful images for diagnosis purposes. During the diagnosis, physicians analyze these recordings, sometimes visually, to make treatment decisions. These recordings are usually contaminated with noise caused by different factors such as physiological artifacts or electronic noises of the used electrodes/instruments. Therefore, the pre-processing of these signals and images becomes a crucial need to provide clinicians with useful information to make the right decisions. This Ph.D. work proposes and discusses different biomedical signal processing algorithms and their applications. It develops novel signal/image pre-processing algorithms, based on the Semi-Classical Signal Analysis method (SCSA), to enhance the quality of biomedical signals and images. The SCSA method is based on the decomposition of the input signal or image, using the squared eigenfunctions of a Semi-Classical Schrodinger operator. This approach shows great potential in denoising, and residual water-peak suppression for Magnetic Resonance Spectroscopy (MRS) signals compared to the existing methods. In addition, it shows very promising noise removal, particularly from pulse-shaped signals and from Magnetic Resonance (MR) images. In clinical practice, extracting informative characteristics or features from these pre-processed recordings is very important for advanced analysis and diagnosis. Therefore, new features and proposed are extracted based on 5 the SCSA and fed to machine learning models for smart biomedical diagnosis such as predicting epileptic spikes in Magnetoencephalography (MEG). Moreover, a new Quantization-based Position Weight Matrix (QuPWM) feature extraction method is proposed for other biomedical classifications, such as predicting true Poly(A) regions in a DNA sequence, multiple hand gesture prediction. These features can be used to understand different complex systems, such as hand gesture/motion mechanism and help in the smart decision-making process. Finally, combining such features with reinforcement learning models will undoubtedly help automate the diagnoses and enhance the decision-making, which will accelerate the digitization of different industrial sectors. For instance, these features can help to study and understand fish growth in an End-To-End system for aquaculture environments. Precisely, this application’s preliminary results show very encouraging insights in optimally controlling the feeding while preserving the desired growth profile.
Floorplanning as a practical countermeasure against clock fault attack in Trivium stream cipher

The fault injection in ciphers operation is a very successful mechanism to attack them. The inclusion of elements of protection against this kind of attacks is more and more necessary. These mechanisms are usually based on introducing redundancy, which leads to a greater consumption of resources or a longer processing time. This article presents how the introduction of placement restrictions on ciphers can make it difficult to inject faults by altering the clock signal. It is therefore a countermeasure that neither increases the consumption of resources nor the processing time. This mechanism has been tested on FPGA implementations of the Trivium cipher. Several tests have been performed on a Spartan 3E device from Xilinx and the experimental measurements have been carried out with ChipScope Pro. The tests showed that an adequate floorplanning is a good countermeasure against these kind of attacks.
Transitivity of Transformation Matrices to Bridge Word Vector Spaces Over Time

We proposed a search method with a query by example in a known domain for information in an unknown domain. It seems natural the relation that “Pikachu in 2000s” which is similar to “Dumbo in 1980s. ” Pikachu and Dumbo are the popular animation characters in these decades. For example, given three entities, such as Pikachu, 2000s and 1980s, we search Dumbo by the relation “Pikachu in 2000s as Dumbo in 1980s. ” The proposed method is based on transformation matrices to bridge word vector spaces. Especially, we focus on transitive relations such as the relation that “Dumbo in 1980s” is similar to “Garfield in 1990s” and to “Pikachu in 2000s” to solve the problem. Experimental results show that the proposed method has some advantage when there are a little common words between source and target terms.
Clustering and Data Aggregation in Wireless Sensor Networks Using Machine Learning Algorithms

Wireless Sensor Networks (WSN) are resource constrained. Clustering and data aggregations are used to reduce the energy consumption in the network by decreasing the amount of data transmission. Machine Learning algorithms such as swarm intelligence, reinforcement learning, neural networks significantly reduce the amount of data transmission and use the distributive characteristics of the network. It provides a comparative analysis of the performance of different methods to help the designers for designing appropriate machine learning based solutions for clustering and data aggregation applications. This paper presents a literature review of different machine learning based methods which are used for clustering and data aggregation in WSN and proposes an improved similarity based clustering and data aggregation, which uses Independent Component Analysis (ICA).
Imaging of Red-Shifted Light From Bioluminescent Tumors Using Fluorescence by Unbound Excitation From Luminescence

Early detection of tumors is today a major challenge and requires sensitive imaging methodologies coupled with new efficient probes. In vivo optical bioluminescence imaging has been widely used in the field of preclinical oncology to visualize tumors and several cancer cell lines have been genetically modified to provide bioluminescence signals. However, the light emitted by the majority of commonly used luciferases is usually in the blue part of the visible spectrum, where tissue absorption is still very high, making deep tissue imaging non-optimal, and calling for optimized optical imaging methodologies. We have previously shown that red-shifting of bioluminescence signal by Fluorescence Unbound Excitation from Luminescence (FUEL) is a mean to increase bioluminescence signal sensitivity detection in vivo. Here, we applied FUEL to tumor detection in two different subcutaneous tumor models: the auto-luminescent human embryonic kidney (HEK293) cell line and the murine B16-F10 melanoma cell line previously transfected with a plasmid encoding the Luc2 firefly luciferase. Tumor size and bioluminescence were measured over time and tumor vascularization characterized. We then locally injected near infrared emitting Quantum Dots (NIR QDs) in the tumor site and observed a red-shifting of bioluminescence signal by (FUEL) indicating that FUEL could be used to allow deeper tumor detection in mice.
Spider Monkey Optimization to Solve Traveling Salesman Problem

Traveling Salesman Problem (TSP) is the most well-known combinatorial optimization real-world problem. TSP is also very popular to check proficiency in any newly developed optimization method. In addition, the optimization methods, which are developed for other tasks (e. g., numerical optimization), also test their proficiency in TSP. This study investigates a new technique to solve TSP based on a recently developed optimization technique stimulated through the foraging conduct of spider monkeys. Standard Spider Monkey Optimization (SMO) is established for numerical optimization which has six phases and each one has a different purpose. In this study, SMO is modified and updated to solve TSP; and Swap Operators (SOs), and Swap Sequence (SS) are considered to adapt SMO for TSP. In the proposed method, each spider monkey is considered as a TSP solution and SS is considered to update the solution. SS is an arrangement of several SOs in which each one holds two particular positions of a solution that might be swapped to make a new solution. All SOs of a SS is applied on a specific tour maintaining order and thus ramifications of the SS change the TSP tour into another one. The SOs are generated using the experience of a specific spider monkey as well as the experience of other members (local leader, global leader, or randomly selected spider monkey) of the group. The proposed strategy has been examined on a huge number of benchmark TSPs and final consequences are compared to other prominent methods. SMO shows 11 best result out of 15 benchmark TSP problem compare to Ant Colony Optimization (ACO) and Velocity Tentative PSO (VTPSO). Experimental consequences show that the proposed strategy is a decent technique to solve TSP.
Olfactory Proteins in Timema Stick Insects

Most animals use olfaction to obtain important information from the environment, including the presence of food or mates. Insects detect odorants through receptors that are expressed in the sensory neurons of the olfactory sensilla, which cover the surface of the antennae. The olfactory capacities of an insect thus depend largely on the repertoire of the odorant receptors. Here, we study the repertoire of olfactory proteins in the stick insect Timema cristinae. We first generate transcriptomes from the antennae of adult males and females and identify, via homology searches, putative olfactory proteins of three different families: odorant binding proteins, odorant receptors, and chemosensory proteins (CSPs). We then attempt to categorize olfactory proteins likely involved in sexual communication by comparing gene expression between adults and juveniles, as well as between males and females. Notably, the olfactory proteins involved in the perception of food or abiotic environmental components, should be expressed in both adults and juveniles. By contrast, the olfactory proteins involved in sexual communication, such as the detection of sex pheromones, should be expressed in adults and often comprise different repertoires in males and females. Finally, we also tested whether olfactory proteins in general and the subset, with putative roles in sexual communication in particular, are under relaxed selection in the asexual species T. monikensis, a close relative of T. cristinae. We found that olfactory proteins are typically differentially expressed between juveniles and adults, but there is little overlap of differential expression between developmental stages and the level of sex bias in adults. Furthermore, while we find evidence that olfactory proteins are indeed under relaxed selection in the asexual species, there is no evidence that this is necessarily the case for olfactory genes with a putative role in sexual communication. Nevertheless, the list of olfactory genes generated in our study provides a useful tool for future studies on olfaction in Timema and other stick insects.
Influential Gene Identification for Cancer Classification

In the field of cancer diagnosis, the DNA microarray technology is used to estimate the level of expression of thousands of genes, which can then be used to differentiate between cancer and control subjects. For the diagnosis purpose, the number of genes is often more than our computational limit. Selecting a small number of influential genes can also lead to a better classification accuracy. To meet this criteria, a popular approach is feature selection. If a limited number of influential genes is identified, drug designing for cancer becomes more practical and easier. Previously, some statistical approaches have been used for this purpose. However, it is still nowhere near enough and much more research is needed in this field. Thus, to make further contribution, we have identified influential genes for colon cancer and leukemia and then biomarker genes are selected from them. In this paper, we have used Kruskal-Wallis test and Bonferroni correction to select features of the microarray gene expression data. We have also differentiated the up-regulated and down-regulated genes using FC values and heatmap plot. Finally, we have identified 11 influential genes with 93.33% classification accuracy for colon cancer dataset and 4 influential genes with 91.67% classification accuracy for leukemia dataset using support vector machine. All the files and codes used in our work is available at https://github.com/UtshaDas/Influential-Gene-Identification-for-Cancer-Classification.
Toward BX-Based Architecture for Controlling and Sharing Distributed Data

This paper proposes an architecture for controlling and sharing distributed data, based on the techniques of bidirectional transformation (BX). The novel idea of the proposed architecture, called the Dejima architecture, is that BX is not used between peers but used inside each peer. This idea enables each peer to specify what data is shared and control how data is used in terms of BX. This paper also addresses known issues of the proposed architecture when data update is cascadingly propagated, and shows our current approach to resolving the issues.
Predicting Time-Bounded Purchases During a Mega Shopping Festival

Shopping festivals such as Black Friday are an important period for online retail businesses. Shopping behaviors during these periods tend to be rushed and time-bounded due to many short-lived promotions, which makes it challenging for businesses to expect traffic and provision their service. In this paper, we examine the click action logs of five million shoppers from a large online retail market during a mega sale event in China and present a model of shopper behavior. Our model takes into account a wide range of features including the product category, time of day, and click sequences. Our model could identify precursors of purchase from as early as two weeks prior to the shopping festival. These findings help online retailers better prepare for future events by efficiently classifying shoppers and thereby reducing the operational costs associated with provisioning any rushed shopping moments.
Seasonal Dependency Analysis of Tasseled Cap Components for KOMPSAT-3 Data in Land Cover Classification

This paper investigates the seasonal dependency of three most significant Tasseled Cap (TCT) components namely brightness, greenness, and wetness derived by Gram-Schmidt orthogonalization process for KOMPSAT-3 sensor data in land-cover classification. Three KOMPSAT-3 multispectral images obtained from May 2013, March 2014, and December 2014 over Korea were considered. Canonical discriminant analysis (CDA) technique was used to test how seasonal dependency affects TCT components in land-cover classification. Results in CDA biplot showed that water, road, and coniferous classes can be separable correctly since they are not overlapped over March. In May, farmland and agriculture showed maximum separation from each other and broadleafwas found dominant. However, farmland and agriculture overlapped with water coniferous, respectively. Images of March and May together can help to accurately classify agriculture and farmland classes because farmland and agriculture do not overlap with water and coniferous in March image, respectively. Similarly, CDA biplot from December image exhibited that coniferous is dominant and other classes overlapped among them. Above overall consequences indicate that the TCT components are sensitive to seasonal climate change and are not stable among different seasons. Hence, these components extracted from one seasonal image are not enough to accurately classify all the land-cover types. Nevertheless, they can be successfully applied for land cover classification if KOMPSAT-3 images of different seasons are transformed with components appropriate to the inspected seasons.
A New Nine Level Inverter with Low TSV and Fewer Numbers of Components for Renewable Energy Systems

A new nine-level inverter (NLI) topology is proposed in this paper which generates nine voltage levels in the output with fewer numbers of components. This NLI is equipped with ten semiconductor switches, four capacitors and one dc voltage source. The nine voltage levels are produced through different plausible combinations of the dc source and capacitors by using the power semiconductor switches. Here, in any mode, maximum two switches conduct at a time. This reduces the conduction losses and improves the efficiency of the system. Another important feature of this topology is the low total standing voltage (TSV) of the power semiconductor switches. To reduce the switching loss of the power switches, gate signals are generated following a proposed modulation strategy. To validate the NLI, a number of simulations are performed in Simulink. A comparison of the proposed inverter with the existing topologies shows that the number of required components and de sources are significantly lower. Therefore the proposed NLI structure can be used in renewable energy systems to make the power generation process more efficient.