Miracle-dz/UniFAR-FTUs huggingface dataset:
Fusion of Geometric and Texture Features For Ear Recognition

The field of ear recognition has been getting a lot of attention in recent years in biometric recognition systems field, which have seen a rise in interest in the present day, not only because it provides a better substitute for conventional identification and authentication systems, but also because of the high rise in global insecurities which makes the biometric research field a very active research field. The aim of our study is to contribute to the field of ear recognition by applying both texture and geometric features. This research in respect to its aims and objectives carried out experiments on AMI ear database by extracting the Local Binary Patterns features, and also applying Laplacian filter on the raw images separately to extract the geometric features. The ear database was processed by dividing ear images into four quarters and experimented on them individually in a pursuit of identifying the region of high interest in human ear images. Texture and geometric features are then fused and experiments were carried out on to verify the contribution of the fused features.
Seasonal Air Density Variations over The East of Scotland and The Consequences for Offshore Wind Energy

In this communication, offshore wind energy is studied around the East of Scotland, where, among other farms, the pioneering floating wind farm Hywind-Scotland is located. SIEMENS 160/6 turbines have been implemented in this farm, and we have thus used this turbine for our study. The main purpose is to compute the Capacity Factor (CF) changes due to air density variations in the study area. The impact of seasonal air density changes has been assessed as percentage reduction, but individual extreme cases have also been considered at Hywind-Scotland farm. Temperature, pressure, and wind speed data from the reanalysis ERA5 have been used for that. As a results, in winter, the CF increment due to low temperatures and denser air reaches values around 3% and summer-winter difference can reach the 4%, that is, 0.52 GWh of energy production for one SIEMENS 160/6.
Lane Detection Based Camera to Map Alignment Using Open-Source Map Data

For accurate vehicle self-localization, many approaches rely on the match between sophisticated 3D map data and sensor information obtained from laser scanners or camera images. However, when depending on highly accurate map data, every small change in the environment has to be detected and the corresponding map section needs to be updated. As an alternative, we propose an approach which is able to provide map-relative lane-level localization without the restraint of requiring extensive sensor equipment, neither for generating the maps, nor for aligning map to sensor data. It uses freely available crowdsourced map data which is enhanced and stored in a graph-based relational local dynamic map (R-LDM). Based on rough position estimation, provided by Global Navigation Satellite Systems (GNSS) such as GPS or Galileo, we align visual information with map data that is dynamically queried from the R-LDM. This is done by comparing virtual 3D views (so-called candidates), created from projected map data, with lane geometry data, extracted from the image of a front facing camera. More specifically, we extract explicit lane marking information from the real-world view using a lane-detection algorithm that fits lane markings to a curvilinear model. The position correction relative to the initial guess is determined by best match search of the virtual view that fits best the processed real-world view. Evaluations performed on data recorded in The Netherlands show that our algorithm presents a promising approach to allow lane-level localization using state-of-the-art equipment and freely available map data.
Application of Artificial Bee Colony Programming to Two Trails of the Artificial Ant Problem

Automatic navigation of mobile robots has an increasing importance in many application fields such as robotics, mining industry, underwater exploration, aerospace research, virtual environments and games. In this study, we use artificial bee colony programming (ABCP), which is a novel evolutionary computation based automatic programming method, to solve the artificial ant problem that is considered as one of the basic test problems in robotic path planning. In order to see the performance of this method, a series of experiments are carried out on Santa Fe and Los Altos Hills trails. The results are compared with those of genetic programming.
Test Time Reduction on Testing Delay Faults in 3D ICs Using Boundary Scan Design

A boundary scan design with embedded time-to-digital converter (TDCBS) has been proposed for testing small delay faults. In this paper, the TDCBS is applied for testing TSVs in 3D IC. To reduce test application time of the TDCBS, we propose a modified TAP controller that utilizes the bypass mode for reducing unnecessary scan shifts during observation of the captured results. The simulation for an experimental circuit is shown to evaluate the effectiveness of the proposed method.
Cell Wall Synthesis, Development of Hyphae and Metabolic Pathways Are Processes Potentially Regulated by MicroRNAs Produced Between the Morphological Stages of Paracoccidioides brasiliensis

MicroRNAs are molecules involved in post-transcriptional gene regulation. In pathogenic fungi, microRNAs have been described at different morphological stages by regulating targets involved in processes such as morphogenesis and energy production. Members of the Paracoccidioides complex are the main etiological agents of a systemic mycosis in Latin America. Fungi of the Paracoccidioides complex present a wide range of plasticity to colonize different niches. In response to environmental changes these fungi undergo a morphological switch, remodel their cellular metabolism and modulate structural cell wall components. However, the underlying mechanisms regulating the gene expression is not well understood. By using high performance sequencing and bioinformatics analyses, this work characterizes microRNAs produced by Paracoccidioides brasiliensis. Here, we demonstrated that the transcript encoding proteins involved in microRNA biogenesis were differentially expressed in each morphological stage. In addition, 49 microRNAs were identified in cDNA libraries with 44 differentially regulated among the libraries. Sixteen microRNAs were differentially regulated in comparison to the mycelium in the mycelium-to-yeast transition phase. The yeast parasitic phase revealed a complete remodeling of the expression of these small RNAs. Analyses of targets of the induced microRNAs, from the different libraries, revealed that these molecules may potentially regulate in the cell wall, by repressing genes involved in the synthesis and degradation of glucans and chitin. Furthermore, mRNAs involved in cellular metabolism and development were predicted to be regulated by microRNAs. Therefore, this work describes a putative post transcriptional regulation, mediated by microRNAs in P. brasiliensis and its influence on the adaptive processes of thermal dimorphic fungus.
Brain Tumor Classification via Convolutional Neural Network and Extreme Learning Machines

Tumor identification is one of the main and most influential factors in the identification of the type of treatment, the treatment process, the success rate of treatment and the follow-up of the disease. Convolution neural networks are one of the most important and practical classes in the field of deep learning and feed-forward neural networks that is highly applicable for analyzing visual imagery. CNNs learn the features extracted by the convolution and maxpooling layers. Extreme Learning Machines (ELM) are a kind of learning algorithm that consists of one or more layers of hidden nodes. These networks are used in various fields such as classification and regression. By using a CNN, this paper tries to extract hidden features from images. Then a kernel ELM (KELM) classifies the images based on these extracted features. In this work, we will use a dataset to evaluate the effectiveness of our proposed method, which consists of three types of brain tumors including meningioma, glioma and pituitary tumor in T1-weighted contrast-enhanced MRI (CE-MRI) images. The results of this ensemble of CNN and KELM (KE-CNN) are compared with different classifiers such as Support Vector Machine, Radial Base Function, and some other classifiers. These comparisons show that the KE-CNN has promising results for brain tumor classification.
The Application of Deep Learning in Mathematical Education

A novel method of facial expression recognition based on convolutional neural network is proposed in this paper, we design a new convolutional neural network and expand the expression database. On the basis of CK+ and JAFFE databases, adding 200 sleepy expressions and focused expressions. The sleepy expression and the focus expression is the most common expression in the students’ learning process, recognizing that can help teacher know the students’ learning situation and change the point of teaching. In comparison to the traditional method of facial expression, the experimental results show that the proposed network is more accurate and efficient.
SAR ADC with DAC and SC Low-Pass Filter for Positron Emission Tomography Application

For positron emission tomography (PET), successive approximation register (SAR) analog-to-digital converter (ADC) and switched-capacitor (SC) low-pass filter implemented in tsmc 0.18-um CMOS process is presented. To reduce DAC switching energy and layout size, a hybrid resistor-capacitor DAC is applied. To save energy, asynchronous control logic to drive the ADC is used. A pre-amplifier based comparator circuit is built to reduce the kickback noise from the dynamic latch. The proposed filter uses cascades of first-order and second-order biquad seting blocks. In order to reach the largest possible input dynamic range, the method of dynamic range scaling and minimum capacitor scaling is used.
Salient Object Detection with Segment Features Using Mean Shift Algorithm

The object recognition has attracted high attention for its diverse applications in everyday life. Due to its importance in this field, academics proposed different algorithms to recognize the desired object in the shortest possible time. This paper introduce a new fast method for saliency object detection in images. This method has four steps: regional feature extraction, segment clustering, saliency score computation and post-processing. This dataset has a diverse set of images including single, multiple and complex object images. The main aim of this paper is the detection of objects in complex images. Introduced method has better performance compared to other methods which were evaluated based on ECSSD dataset. This procedure had shown better performance in compared to RRFC, RFC, DRFI, CHC, and RC. As indicated in the presented results, F-measure of our method was better as 0.03-0.1 compared to other methods.
The Epigenome in Multiple Myeloma: Impact on Tumor Cell Plasticity and Drug Response

Multiple myeloma (MM) is a clonal plasma cell malignancy that develops primarily in the bone marrow (BM), where reciprocal interactions with the BM niche foster MM cell survival, growth, and drug resistance. MM cells furthermore reshape the BM to their own needs by affecting the different BM stromal cell types resulting in angiogenesis, bone destruction, and immune suppression. Despite recent advances in treatment modalities, MM remains most often incurable due to the development of drug resistance to all standard of care agents. This underscores the unmet need for these heavily treated relapsed/refractory patients. Disruptions in epigenetic regulation are a well-known hallmark of cancer cells, contributing to both cancer onset and progression. In MM, sequencing and gene expression profiling studies have also identified numerous epigenetic defects, including locus-specific DNA hypermethylation of cancer-related and B cell specific genes, genome-wide DNA hypomethylation and genetic defects, copy number variations and/or abnormal expression patterns of various chromatin modifying enzymes. Importantly, these so-called epimutations contribute to genomic instability, disease progression, and a worse outcome. Moreover, the frequency of mutations observed in genes encoding for histone methyltransferases and DNA methylation modifiers increases following treatment, indicating a role in the emergence of drug resistance. In support of this, accumulating evidence also suggest a role for the epigenetic machinery in MM cell plasticity, driving the differentiation of the malignant cells to a less mature and drug resistant state. This review discusses the current state of knowledge on the role of epigenetics in MM, with a focus on deregulated histone methylation modifiers and the impact on MM cell plasticity and drug resistance. We also provide insight into the potential of epigenetic modulating agents to enhance clinical drug responses and avoid disease relapse.
Small Signal Stability Constrained Optimal Dispatch of Islanded AC Microgrid

The purpose of this study is to determine optimal values of static active power droop constants of dispatchable distributed generation units in an islanded AC microgrid to simultaneously minimize the fuel cost and enhance the small signal stability of the system. Power flow solutions are used to determine quiescent operating points of the system. The nonlinear model is then linearised about the quiescent operating points to extract the small-signal linear model. Particle swarm optimization is used to determine optimal static active power droop coefficients to meet the desired objectives. Bi-objective optimization is carried out in fuzzy domain using max-min composition. The proposed method is validated by simulation studies and comparison with the controlled elitist multi-objective genetic algorithm.
Perception of Mobile Commerce: A case study of Taiwan’s Agribusiness

This paper examines organization–s perception of using innovative m-commerce in relation to agribusiness demographic attributes such as geographical location, experiences in agriculture, and types of business that cause farmers adoption decisions of mobile commerce. This study discusses the fact of mobile commerce using the Technology Acceptance Model (TAM), influenced perceptions in relation various demographic factors. A quantitative approach using a questionnaire survey has adopted. Collected data was analyzed using SPSS-24 software program. First and foremost, regarding to geographical location results indicate that agribusiness located in northern Taiwan perceive more benefits in adopting mobile commerce than the agribusiness located in southern Taiwan feel. Besides, concerning experiences in agriculture, results show that newly established agribusiness feel more comfortable on conducting mobile commerce than the elder business. Finally, the perception of mobile commerce in general does not exhibit significant differences among the types of agribusiness.
An Autonomous Vehicle for Parcel Delivery in Urban Areas

The flexible and individualized transportation of goods is a central task of today's e-economy. In urban and highly populated areas autonomous electric vehicles are a promising solution for this task while simultaneously addressing ecological issues. While in indoor environments transport robots are well adopted, autonomous transport vehicles are hardly seen outdoors. In this paper, we aim at this gap and adapt and transfer concepts usually used in robotics to autonomous vehicles for an outdoor environment. We present an autonomous vehicle that is able to safely navigate in urban environments while able to deliver parcels efficiently. In particular, we will discuss a scalable and robust mapping and navigation process that forms the basis for the capabilities of the delivery vehicle. Moreover, we show preliminary results of a deployment of the system in two urban scenarios.
[Regular Paper] Adjacent Network for Semantic Segmentation of Liver CT Scans

Fully convolutional neural networks have shown remarkable success in performing semantic segmentation. The use of convolutional layers for the entire architecture and skip connections to combine different resolution features or predictions have been adopted in successful networks, such as U-Net and DenseNet. However, these models employ several max-pooling layers that cause the network to lose spatial information and require them to mimic an autoencoder architecture to perform semantic segmentation at the original input resolution. In this paper, we propose a network that extracts features automatically with convolutional layers, like the fully convolutional neural network, but retains the spatial information of each of the extracted features. It then utilises the extracted features to make predictions with an efficient upsampling method. We evaluate the network performance on a liver segmentation task where it performs with comparable accuracy to other state-of-the-art networks while being much smaller in terms of the number of parameters as well as faster in computation time.
Spatial-Wideband Effect in Massive MIMO with Application in mmWave Systems

Massive MIMO, especially in the millimeter- wave frequency bands, has been recognized as a promising technique to enhance spectrum and energy efficiency, as well as network coverage for wireless communications. Most research in massive MIMO just uses the extended conventional MIMO channel model by directly assuming that the channel dimensionality becomes large. With massive numbers of antennas, however, there exists a non-negligible propagation delay across the large array aperture, which then causes a transmitted symbol to reach different antennas with different delays, thereby rendering conventional MIMO channel models inapplicable. Such a phenomenon is known as the spatial-wideband effect in the areas of array signal processing and radar signal processing, and introduces the beam squint effect in beamforming. However, the spatial-wideband effect and the related beam squint issue are seldom studied in massive MIMO communications. To design a practical massive MIMO system, it is important to understand when the spatial-wideband effect appears and how it affects signal transmission, how the spatial-wideband effect interacts with the frequency-wideband effect (frequency selectivity), especially for multi-carrier modulations such as orthogonal frequency- division multiplexing (OFDM), and how we should re-design the transceiver. In this article we suggest a new massive MIMO channel model that embraces both the spatial- and frequency- wideband effects, and discuss these issues.
Active-Flux-Based Super-Twisting Sliding Mode Observer for Sensorless Vector Control of Synchronous Reluctance Motor Drives

This paper proposes an active-flux-based rotor position and speed estimation scheme for sensorless-vector- controlled synchronous reluctance motor (SynRM) drives, which is comprised of two main parts: 1) a super-twisting sliding mode observer (ST-SMO) for estimating the derivatives of active flux components; and 2) a rotor position and speed estimator based on the phase-locked loop (PLL). The ST-SMO is designed based on the active flux model of the SynRM in the stator reference frame. The estimated active flux components, which are obtained based on the ST-SMO and the integrator, are received as inputs of the PLL, and the estimated rotor position and speed are provided as outputs of the PLL. The feasibility and effectiveness of the proposed estimation scheme have been verified by the simulation results.
Lifetime-Improvement Routing Protocol for Wireless Sensor Networks

In this paper, we proposed a lifetime improvement fixed clustering energy awareness routing protocol for WSNs; named Load Balancing Cluster Head (LBCH) protocol. The LBCH protocol mainly targets reducing energy consumption in the network and balancing the workload over all nodes within the network. In order to evaluate the performance of LBCH protocol, thorough simulations have been conducted and the results are compared with other related protocols. The simulations showed that LBCH protocol overcomes other related protocols for continuous-based data models.
Image-based Navigation using Visual Features and Map

Building on progress in feature representations for image retrieval, image-based localization has seen a surge of research interest. Image-based localization has the advantage of being inexpensive and efficient, often avoiding the use of 3D metric maps altogether. This said, the need to maintain a large number of reference images as an effective support of localization in a scene, nonetheless calls for them to be organized in a map structure of some kind. The problem of localization often arises as part of a navigation process. We are, therefore, interested in summarizing the reference images as a set of landmarks, which meet the requirements for image-based navigation. A contribution of the paper is to formulate such a set of requirements for the two sub-tasks involved: map construction and self localization. These requirements are then exploited for compact map representation and accurate self-localization, using the framework of a network flow problem. During this process, we formulate the map construction and self-localization problems as convex quadratic and second-order cone programs, respectively. We evaluate our methods on publicly available indoor and outdoor datasets, where they outperform existing methods significantly.
Continuous Authentication System to Increase Security Level of User Workstations

The article examines the possibility of improving the security of user workstations, equipped with web cameras. This is achieved by means of the implementation of a continuous user authentication through face recognition. This method significantly increases a security level of the system from the threat of gaining access while the user is not at the workplace. Moreover, with appropriate settings, on time activation of computer energy saving mode allows reducing power consumption and prolonging the battery life in case of mobile devices. Testing various approaches to solving the problem was performed, and the reliability of the system was estimated.
Using Wireless Tags to Monitor Bodily Oscillation

Traditional systems for monitoring and diagnosing patients' health conditions often require either dedicated medical devices or complicated system deployment, which incurs high cost. The networking research community has recently taken a different technical approach of building health-monitoring systems at relatively low cost based on wireless signals. However, the RF signals carry various types of noise and have time-varying properties that often defy the existing methods in more demanding conditions with other body movements, which makes it difficult to model and analyze the signals mathematically. In this paper, we design a novel wireless system using commercial off-the-shelf RFID readers and tags to provide a general and effective means of measuring bodily oscillation rates, such as the hand tremor rate of a patient with Parkinson's disease. Our system includes a series of noise-removal steps, targeting at noise from different sources. More importantly, it introduces two sliding window-based methods to deal with time-varying signal properties from channel dynamics and irregular body movement. The proposed system can measure bodily oscillation rates of multiple persons simultaneously, even when the individuals are moving. Extensive experiments show that our system can produce accurate measurement results with errors less than 0.3 oscillations per second when it is applied to monitor hand tremor.