Miracle-dz/UniFAR-FTUs huggingface dataset:
Field Programmable Gate Array based Real-Time Imaging System for Spectral-Domain Optical Coherence Tomography

We present a real-time imaging modality which utilizes the field programmable gate array (FPGA) for spectral-domain optical coherence tomography (SD-OCT). By adapting the FPGA as the operating and processing unit for an SD-OCT system, we could achieve a frame rate of 40 frames/sec with 1024 × 512 pixels per frame. The frame rate can be increased further, without changing the design but with optimization, which is significantly higher than computer based systems. This embedded system does not require any bulky desktop computer even for displaying tomographic images; it uses a VGA monitor through VDAC. The performance of the proposed system is presented with in vivo real-time images of biological samples.
On Evaluating the Renaissance Benchmarking Suite: Variety, Performance, and Complexity

The recently proposed Renaissance suite is composed of modern, real-world, concurrent, and object-oriented workloads that exercise various concurrency primitives of the JVM. Renaissance was used to compare performance of two stateof-the-art, production-quality JIT compilers (HotSpot C2 and Graal), and to show that the performance differences are more significant than on existing suites such as DaCapo and SPECjvm2008. In this technical report, we give an overview of the experimental setup that we used to assess the variety and complexity of the Renaissance suite, as well as its amenability to new compiler optimizations. We then present the obtained measurements in detail.
Next generation sequencing characterizes HLA diversity in a registry population from the Netherlands

Next generation DNA sequencing is used to determine the HLA‐A, ‐B, ‐C, ‐DRB1, ‐DRB3/4/5, and ‐DQB1 assignments of 1009 unrelated volunteers for the unrelated donor registry in The Netherlands. The analysis characterizes all HLA exons and introns for class I alleles; at least exons 2 to 3 for HLA‐DRB1; and exons 2 to 6 for HLA‐DQB1. Of the distinct alleles present, there are 229 class I and 71 class II; 36 of these alleles are novel. The majority (approximately 98%) of the cumulative allele frequency at each locus is contributed by alleles that appear three or more times. Alleles encoding protein variation outside of the antigen recognition domains are 0.6% of the class I assignments and 5.3% of the class II assignments.
Fast Pig Detection with a Top-View Camera under Various Illumination Conditions

The fast detection of pigs is a crucial aspect for a surveillance environment intended for the ultimate purpose of the 24 h tracking of individual pigs. Particularly, in a realistic pig farm environment, one should consider various illumination conditions such as sunlight, but such consideration has not been reported yet. We propose a fast method to detect pigs under various illumination conditions by exploiting the complementary information from depth and infrared images. By applying spatiotemporal interpolation, we first remove the noises caused by sunlight. Then, we carefully analyze the characteristics of both the depth and infrared information and detect pigs using only simple image processing techniques. Rather than exploiting highly time-consuming techniques, such as frequency-, optimization-, or deep learning-based detections, our image processing-based method can guarantee a fast execution time for the final goal, i.e., intelligent pig monitoring applications. In the experimental results, pigs could be detected effectively through the proposed method for both accuracy (i.e., 0.79) and execution time (i.e., 8.71 ms), even with various illumination conditions.
Towards Finding an Optimal Flight Zone for a Side-by-Side Tracking UAV w.r.t Extraction of Dynamic Vector Fields

In this paper we present work towards finding an optimal flight zone of an Unmanned Aerial Vehicle (UAV) as adhering to space-restricting factors brought upon by a dynamic vector field extraction algorithm. The objective of the UAV is to perform side-by-side tracking and following of a lightweight ground vehicle while acquiring high quality video of tufts attached to the side of the tracked vehicle. The recorded video is supplied to a dynamic vector field extraction algorithm that produces the positions and deformations of the tufts over time as they interact with the surrounding air, resulting in an airflow model of the tracked vehicle. The present limitations of wind tunnel tests and computational fluid dynamics simulation suggest the use of a UAV for real world evaluation of the aerodynamic properties of the shell of the vehicles exterior. The novelty of the proposed approach is alluded to defining specific flight zone restricting factors while adhering to the vector field extraction algorithm, where as a result we were capable of formalizing a locally-static and a globally-dynamic geofence attached to the tracked vehicle and enclosing the UAV. Moreover, a drone video quality and stability analysis tool was implemented to aid in quantifying the quality of the drone video and in constructing the geofence.
Controlling of Solar Powered S.E DC Motor using IMC Controller

Due to the economic challenges in surging oil costs and natural concern India is facing huge growth in renewable energy resources. By adopting artificial intelligence technique based input to the drive train mechanism, the effective utilization of the solar energy has been performed. The complete system has an array of solar panel, a boost converter, separately excited DC motor and Internal Model control (IMC) controller as a voltage regulator. IMC Controller have been designed to deal with the system nonlinearity and its associated uncertainty. Monitoring of system is based on LabVIEW virtual instruments. Key-Words: P&O MPPT, S.E DC Motor, IMC Controller.
A High Performance Edge Server System

The latest trend of connecting and controlling the technology is moving towards Internet of Things (IoT)s. Some common examples of IoTs are smart cars, smart homes, intelligent grids, virtual power generation units, smart health monitoring and many more. There are popular smart devices available in the market like wearable devices, smart outlets, active trackers, parking sensors, smart thermostat etc. Besides all the comfort and ease provided to the user by IoTs, they have some challenges. One of the challenges is that IoTs generate and transfer a lot of data, making Internet overloaded and requires high computation at main server placed distantly which not only causes a rise in the cost of data uploading but is also insecure and inefficient in cases where delay can perish the quality of data. In this work, we propose a High Performance Edge Server (HPES) that can compute and transfer data from and to the connected IoTs while being placed in the vicinity. After basic local computation, the HPES delivers the actual concerned required data to the data center by reducing Internet traffic and reducing computing effort of cloud analytics.
Identification and Classification of Maize Drought Stress Using Deep Convolutional Neural Network

Drought stress seriously affects crop growth, development, and grain production. Existing machine learning methods have achieved great progress in drought stress detection and diagnosis. However, such methods are based on a hand-crafted feature extraction process, and the accuracy has much room to improve. In this paper, we propose the use of a deep convolutional neural network (DCNN) to identify and classify maize drought stress. Field drought stress experiments were conducted in 2014. The experiment was divided into three treatments: optimum moisture, light drought, and moderate drought stress. Maize images were obtained every two hours throughout the whole day by digital cameras. In order to compare the accuracy of DCNN, a comparative experiment was conducted using traditional machine learning on the same dataset. The experimental results demonstrated an impressive performance of the proposed method. For the total dataset, the accuracy of the identification and classification of drought stress was 98.14% and 95.95%, respectively. High accuracy was also achieved on the sub-datasets of the seedling and jointing stages. The identification and classification accuracy levels of the color images were higher than those of the gray images. Furthermore, the comparison experiments on the same dataset demonstrated that DCNN achieved a better performance than the traditional machine learning method (Gradient Boosting Decision Tree GBDT). Overall, our proposed deep learning-based approach is a very promising method for field maize drought identification and classification based on digital images.
Efficient Energy Utilization in Fog Computing based Wireless Sensor Networks

Wireless Sensor Networks (WSNs) consist of thousands of sensing nodes that are deployed at remote locations for continuous probing of the surrounding environment in order to collect useful data. In general, each node is equipped with a fixed battery and limited working duration. The fixed battery condition is dominating these days and therefore energy efficiency is an important factor to be considered when the protocols are being designed. In this paper, we implement an enhanced version of Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol named as LEACH with Dijkstra’s Algorithm (LEACH-DA) under a cloud environment, which optimizes the power consumption or energy utilization based on shortest path selection. Also, the proposed framework incorporates load balancing by picking an appropriate cluster head (CH) node among its alternates by calculating their traffic situation with the sink/base station or cloud. In addition, we employ a fog computing model for our scenario (i.e. LEACH-DA-Fog) in order to increase lifespan of network as compared to the original implementation of the underlying protocol. Some test cases are presented to show that the proposed updates on the classical LEACH protocol improve the network efficiency as well as the durability of the whole network.
Air-Sea Interaction in Seasonal Forecasts: Some Outstanding Issues

All of the ECMWF seasonal forecasting systems initialized in spring failed to predict the intensification of El Niño 1997 during summer. This failure is attributed to the model inability to sustain the amplitude of an MJO event present in the initial conditions, and failure to propagate it from the Indian Ocean to the Western Pacific. Additional diagnostics suggest that the negative feedback resulting from the model heat flux response to a given SST warm anomaly is too strong. It is argued that this can be a consequence of the model warm bias, which is large enough for the non-linearities to play a role. Experiments with different initialization procedures conducted with the same coupled model indicate that the model warm bias is a direct consequence of the the initial adjustment process.
BabeBay-A Companion Robot for Children Based on Multimodal Affective Computing

The BabeBay is a children companion robot which has the ability of real-time multimodal affective computing. Accurate and effective affective fusion computing makes BabeBay own adaptability and capability during interaction according to different children in different emotion. Furthermore, the corresponding cognitive computing and robots behavior can be enhanced to personalized companionship.
A Lightweight and Efficient Secure Hybrid RSA (SHRSA) Messaging Scheme With Four-Layered Authentication Stack

For virtual private network and LAN-to-LAN tunnel sessions, End-to-End security is the main constraint in private messaging scenarios. Internet Protocol Security can negotiate new keys for every communication, but when the key is compromised, it is a problem. Perfect Forward Secrecy is the resolution. In addition, the messaging scheme should be efficient and lightweight for keeping parity with daily needs. The popular Rivest-Shamir-Adleman (RSA) cipher is omnipresent in secure communications but has many scientific problems. So here, in this paper, a lightweight and efficient Secure Hybrid RSA (SHRSA) messaging scheme with four-layered authentication stack is implemented and analyzed. The scheme is resolving the problem of asymptotic very low speed of decryption of RSA, the computational modular exponentiation complexity and partial key exposure vulnerability issues of RSA, and many more. The four-layered authentication stack have eliminated the need of the use of any password, external digital certificates, and a third party for authentication with its own four techniques in a staked way. We have found that in evolutions and analysis of the scheme, it is not only resolving various scientific problems of RSA but also occupying 2%–4% less CPU than main RSA and occupying 1%–3% less memory than main RSA. Its decryption average time has gained 8.858 times compared to the main RSA and gained 2.248 times compared to CRT RSA. We have found that the RSA, CRT-RSA, and SHRSA’s encryption throughput are alike-all are around 6 KB/Sec but decryption throughput of SHRSA has gained 8.5345 times than main RSA and gained 2.1174 times than CRT-RSA. The results obtained have shown us the relevancies of the SHRSA messaging scheme to be integratable as a cipher in Blockchain architectures, cyber-physical systems, and the Internet of Everything.
M1901097 - Information Sciences Campaign, KCI-IS-1: Cyber Fire and Maneuver in Tactical Battle: Dynamic Watermarking and Timing Analysis to Protect Vehicular Cyber-Physical Systems

Autonomous vehicles (AVs) can potentially bring about great benefits, such as higher safety and mobility, and lower congestion and fuel consumption; however, studies have shown that AVs are vulnerable to malicious cyber attacks. Since the breakdown of an AV can be catastrophic (e.g., passengers injured/killed), it becomes imperative to deploy defense techniques to enhance the cybersecurity of AVs, which is the motivation of this study. Dynamic watermarking (DW), an active defense technique for cyber-physical systems (CPSs), is adopted herein. Implemented in a real AV for an autonomous driving operation, the DW technique is shown to be robust and sensitive in the early detection of different types of malicious cyber attacks on the sensing system.
The basophil

Basophils are a member of a group of cells discovered by Paul Ehrlich in the 1879 [1]. They are referred to as a “white cell” comprising no more than 1% of this cell type [2]. They are the least common but largest of the granulocytes which include neutrophils and eosinophils, the other two groups of white cells. Basophils have the capability to produce histamine, serotonin, and are at least partially responsible for various allergic (immune) reactions in the body. If basophil levels are low, it may in certain circumstances (such as helminth infections) take longer for the body to respond to infections. Under these circumstances, patients are more at risk for more serious complications and in rare cases death.
Voltage-Frequency Droop Scheme for Standalone Hybrid Microgrids

The concept of hybrid microgrid is gaining attention as it offers advantages of both dc and ac systems. The interlinking converter (ILC) is a critical element in this grid as it manages bidirectional power and regulates global variables. This paper presents a voltage-frequency droop scheme for ILC in a standalone hybrid microgrid. The goal is to map any voltage deviations at dc bus to ac bus frequency variations so that both grids can adjust power accordingly to satisfy the loads. The voltage-frequency droop coefficient is defined by the ratio of two scaling factors. Compared to other primary control schemes of hybrid microgrid, the proposed strategy allows us to define the amount of power change in ac/dc grid owing to a load change in dc/ac gird. This is particularly significant for grids with different power generation capacities. Simulations performed in PSCAD/EMTDC confirm the validity of the scheme.
Structure-based Object Type Classiﬁcation using Graph Matching

Current feature-based object type classiﬁcation methods use texture and shape based information derived from image patches. Generally, input features such as aspect ratio are derived from some rough characteristics of the entire object. However, we derive input features from parts-based representation of the object. In this paper, we propose a method to distinguish object types using structure-based features described by a mixture of Gaussian distribution. Our approach uses the Gaussian ﬁtting onto object image to segment into several sub-regions, each of which is related to a physical part of the object. We model the object as a graph, where the nodes contain texture and shape information obtained from the corresponding segmented regions, and the edges contain the distance information between two connected regions. Calculating the distance of the reference graph and the input graph, we can classify an object into single-human/human-group/vehicle/bike using k-NN based classiﬁer. We demonstrate that we can obtain higher classiﬁcation performance when we use both conventional features and structure-based features compared to using only conventional features or only structure-based features.
BIOCLIMATIC CONCEPT FOR ASSESSMENT OF ATMOSPHERE AND FOREST LAND-COVER COUPLING AT A REGIONAL SCALE

A biophysical model for quantitative description of mountain forest cover in relation to environmental features (climate, relief, growing season) is developed. It is based on the climatoligical approach of Budyko and accounts for the long-term coupling between the forest functioning and atmosphere. Approximations of heatand waterbalances of mountain natural vegetation environment are framed in a bi-dimensional biosphere-atmosphere equilibrium scheme that defines forest-cover classes as Forest Functional Types (FFTs). The index of dryness of Budyko and potential evapotranspiration (net radiation estimates) during the growing season are used as bioclimatic indicators of the bi-dimensional framework to define the limits of FFTs stable equilibrium. Forest-vegetated land cover of South and South-eastern Bulgaria is considered. Modelling is run for the present-day climate by using meteorological data, which cover spaceand altitudinalenvironmental differences of the region. Human-induced land cover change is simulated and the potential impacts due to the ‘albedo effects’ through forest-atmosphere feedbacks are evaluated.
A Fast Solver to Calculate the Creeping Wave Fields From the NURBS Scatterers

In this paper, a new method to calculate geodesic on the Non-Uniform Rational B-spline (NURBS) surfaces is proposed. With Heuns method, the accuracy of geodesic can be improved, which is important in the future researches of scattered fields. The bridge between NURBS surfaces and the variables needed in the calculation of geodesic is also built in the paper. The validity and efficiency of the method is verified by numerical results.
A Simple Power Estimation With Triple Phase-Shift Control for the Output Parallel DAB DC–DC Converters in Power Electronic Traction Transformer for Railway Locomotive Application

Aimed at the input-independence and output-parallel dual-active-bridge (DAB) dc–dc converters applied to power electronic traction transformer (PETT), this paper proposes a simple power estimation scheme with triple phase-shift control (PES-TPS) to achieve the transmission power balance, dynamic performance improvement, and efficiency optimization, simultaneously. Through executing the current stress optimization (CSO) and introducing the power estimation based on operational parameters, the efficiency of converters can be further improved and the power balance among DAB cells can be achieved over the whole power range without adding extra sensors to sample the output/inductor current of each cell. In addition, the dynamic performance of the adopted converters can be improved by compensating power loss online. Especially, dynamic response of the output voltage can be enhanced under input voltage and load fluctuation conditions. Finally, a comprehensive experimental comparison of CSO with dual phase shift (CSO-DPS), CSO with TPS (CSO-TPS), and the proposed PES-TPS control is conducted in the DAB converters experimental prototype with three cells. The experimental results have confirmed the excellent performance of the proposed PES-TPS control and the correctness of the theoretical analysis.
ASSOCIATION OF SCHISTOSOMIASIS ENDEMICITY WITH SOCIODEMOGRAPHIC FACTORS IN GIKONKO AND KIBINGO HEALTH CENTRES, KIGALI-RWANDA

Schistosomiasis also known as bilharziasis is among the major endemic parasitic communicable disease commonly found in developing countries including Rwanda and world over. The research aimed at determing the correlation between schistosomiasis with socio-demographic factors and water contact behaviors. It was a descriptive, cross-sectional study that adopted mainly quantitative data collection and analysis approach in purposively selected population of “391” patients attending health services. Data was collected and analyzed using SPSS. Cross-tabulation and chi-square were used to determine the association between independent and dependent variable. Findings indicated a high prevelance of 32% being highly associated with none use of shoes among females (p-value = 0.029: CI 95), agriculture activities in swamps (p-value =0.001: CI 95%), Swimming (p-value=0.001: CI = 95) and bathing in water sources (p-value=0.001 : CI = 95. Conclusivelly, this study indicated that demographic, social and water contact behaviors are among the contributing factors to schistosomiasis endemicity in the research areas.In this case management planners need to include all age groups in mass treatment of the disease, have continuous Health education and community mobilization about the endemicity and morbidity of schistosomiasis in the study areas and country at large.
A Novel Fault Diagnosis Method of Gearbox Based on Maximum Kurtosis Spectral Entropy Deconvolution

Minimum entropy deconvolution (MED) is widely used in the gearbox fault diagnosis because it can enhance the energy of the impact signal. However, it is sensitive to single abnormal impulsive oscillation. This is because it takes kurtosis as the objective function and solves the optimal filter by iteration. In addition, the filter length is not adaptive and needs to be determined artificially. This paper proposes a maximum kurtosis spectral entropy deconvolution (MKSED) method and applies it to bearing fault diagnosis. Considering that the kurtosis spectral entropy has the advantage of highlighting the continuous impact oscillation, the kurtosis spectral entropy is chosen as the objective function of deconvolution. At the same time, kurtosis spectral entropy is also used as the fitness function of improved local particle swarm optimization algorithm (LPSO), and the filter length is optimized by LPSO, which makes that MKSED adaptively determines the length of the filter while solving the deconvolution, so that it can accurately extract the continuous pulse signal. The results of the simulation signal analysis show that the proposed MKSED method is superior to MED, and the proposed method is applied to bearing fault diagnosis, which verifies its ability to extract continuous impact.
Physics-based Neural Networks for Shape from Polarization

How should prior knowledge from physics inform a neural network solution? We study the blending of physics and deep learning in the context of Shape from Polarization (SfP). The classic SfP problem recovers an object's shape from polarized photographs of the scene. The SfP problem is special because the physical models are only approximate. Previous attempts to solve SfP have been purely model-based, and are susceptible to errors when real-world conditions deviate from the idealized physics. In our solution, there is a subtlety to combining physics and neural networks. Our final solution blends deep learning with synthetic renderings (derived from physics) in the framework of a two-stage encoder. The lessons learned from this exemplary problem foreshadow the future impact of physics-based learning.