Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling of Cymbopogon Oils Species Using k-Nearest Neighbours (k-NN)

Cymbopogon oil is derived from the Cymbopogon species which are lemongrass and citronella. The essential oil is primarily used as a mosquito repellent as a pesticide, and also has other insecticidal, acaricidal and herbicidal activity. It is not considered harmful to humans and pets but may cause skin irritation. The aim of this paper is to model the Cymbopogon oils species using k-Nearest Neighbours (k-NN). The work consists of Cymbopogon oils data extraction using GC and GC-MS machine for compound identification. After that, a quick observation on the major compound in the samples as significant compound identification was done. Then, pre-processing data was performed which consist of data randomization and data splitting into training and testing dataset with the ratio of 80%:20%, respectively. Next, the k-NN modelling was carried out using training dataset and also applying Euclidean as distance metric. It was followed by the tested of developed k-NN using testing dataset. The result showed that k=1 and k=2 achieved 100% accuracy for both testing and training dataset. This proven that k-NN modelling in this study was able to classify the Cymbopogon oil compounds according to its species which are citronella and lemongrass.
Analysis of the Effect of Automotive Ethernet Camera Image Quality on Object Detection Models

This paper centers on analyzing the effect of automotive Ethernet camera image quality on the object detection models. We choose the five pre-trained models from the TensorFlow model zoo and set various image bitrate to the comparison of detection accuracy on different vehicle object sizes. We use an automotive Ethernet camera to collect image dataset on actual roads and different weather. The experiments were performed to obtain the detection accuracy of each detection model for different object sizes under different image quality and hardware platforms. The contribution of this study is to show how to choose a feasible object detection model in terms of the bandwidth, image quality, object size, cost, detection speed, and detection accuracy requirements.
Simpler Design for Liquid Supply Line Leakage Monitoring

This paper introduces a method for flow monitoring through various supply lines by implementing a mathematical function, majority of which is dependent on standard deviation. Instead of including various sensors and numerous complex electrical connections which is still the common way to measure and monitor leakage or flow, this project just uses flow measuring sensor to collect data and process the data in such a way that actually focuses on user conditions and takes regular variation of flow into account and modifies its monitoring protocols regularly. The other systems in this sector implies various sensors to accurately detect leakage which is good but those systems lack the ability to collect, process and store regular data related to the supply line. For this reason, users often have to handle two different systems simultaneously, one to monitor leakage and the another for regular data collection regarding supply. To reduce that effort and cost, this system includes both flow measurement, data storing and monitoring as well as keeps an eye whether the flow is regular or there is any leakage or illegal line taken out. In the field of commercial distribution of oil and water, leakage and unauthorized or illegal supply is one of the main issues to be considered. Yet now, not many systems has been developed to prevent or monitor such losses. We have developed a system to monitor and locate line leakage, illegal supply lines and faulty supply lines in a much swift and simpler way which is cost effective too.
Automatic Generation of Custom Tourist Routes

In this paper we present a new tool for the automatic generation of custom tourist routes. Starting from the user preferences (place to visit, starting and ending points, available time, favourite types of attractions) our system is able to extract information from the Web and to suggest a custom route to the user. Our system is based only onto online information, which is dynamically extracted at the query time, so that it can work for every location in the world, with no restrictions. Furthermore, our method does not require any user intervention, unless the input parameters. Our system is also able to give to the users supplementary information about the route stops, as a photo slideshow and a textual description of the places. We implemented a Web application, which has been available online, and we test our prototype with the help of some Facebook focus groups. We collected users'feedback through an online evaluation form.
Optimized Hybrid Clustered Protocol for IoT Heterogeneous Wireless Sensor Networks

For the past few years, the Wireless Sensor Networks (WSNs) have become very popular because of the advancements and innovation in the micro-electro-mechanical system (MEMS) which creates more opportunities to utilize the WSN with their outstanding capability of sensing various type of environments as well as other physical conditions. WSN consists of huge number of small and less expensive sensor nodes, these nodes communicate with one another with different set of rules called protocols. Performance of these sensor nodes generally affected by some set of rules by which they perform in the network these are called protocols. Nodes are mostly operated on battery power therefore the energy utilization will always be the prime concern of WSNs. The Same analogy is applied for the WSN in the Cyber-Physical system and for IoT environment where different types of nodes or end devices have been found with different amount of initial energy. This paper optimizes the existing routing protocol and proposes a different protocol with improvised features such as new data relaying scheme and improved CHs selection procedure. Thereby linking the difference between the real world Wireless Sensor Networks and the genuine heterogeneous environment. The simulation results shows the comparison between the Hy-IoT and proposed protocol by using the performance measures shown in the result section.
Toward OS-Level and Device-Level Cooperative Scheduling for Multitasking GPUs

As one of the most popular accelerators, the graphics processing unit (GPU) has been extensively adopted throughout the world. With the burst of new applications and the growing scale of data, co-running applications on limited GPU resources has become increasingly important due to its dramatic improvement in overall system efficiency. Quality of service (QoS) support among concurrent general-purpose GPU (GPGPU) applications is currently one of the most trending research topics. Prior efforts have been focused on providing QoS support either with OS-level or device-level scheduling methods. Each of these scheduling methods possesses pros and cons and may be unable to independently cover all the scheduling cases. In this paper, we propose a cooperative QoS scheduling scheme (C-QoS) that consists of operating-system-level (OS-level) scheduling and device-level scheduling. Our proposed scheme can control the progress of a kernel and provide thorough QoS support for concurrent applications in multitasking GPUs. Due to the accurate resource management of the copy engine and execution engine, C-QoS achieves QoS goals 23.33% more often than state-of-the-art QoS support mechanisms. The results demonstrate that cooperative methods achieve 17.27% higher system utilization than uncooperative methods.
Optimized Resource and Power Allocation for Sum Rate Maximization in D2D- Assisted Caching Networks

Device-to-Device (D2D) communication has become a promising technology to enhance the performance of cellular networks. The major challenge for resource sharing in D2D networks lies in the co-interference between cellular users and the originated D2D connections. In addition, power allocation for D2D connections and cellular users sharing the same spectrum is also a problem for these networks. In D2D caching networks, caching contents at users' devices can significantly reduce large network headaches. This becomes a promising solution for controlling limited resources. Maximizing the cached portions delivered to users using D2D communications requires combined consideration for guaranteed D2D channels and their allocated power. In this paper, a joint optimization problem of caching content caching delivery with resource and power allocation awareness to improve the performance of D2D caching networks is presented. We aim to enhance the performance of such networks by maximizing the overall sum rate of D2D and cellular users. An optimization problem for sum rate is formulated under the constraint of power requirements and Signal-to-Interference plus Noise Ratio (SINR). The proposed optimization problem will be solved using an optimization algorithm by jointly optimize channel and power allocation. The simulation results show that the performance of the optimal scenario with effectively selecting suitable locality ratio and D2D power optimization can lead to valuable gains in the sum rate.
Segmenting Nodules of Lung Tomography Image with Level Set Algorithm and Neural Network

Lung cancer is the main cause of death in the world. It is diagnosed generally by analysing a tissue cluster formation called ‘nodule’ inside the lung. Computer aided diagnosis (CAD) plays an important role in medical field which helps radiologists to detect and localise lung nodule. The aim of this research is to develop an image segmentation algorithm for nodule detection in computed tomography (CT) image. Performance of the developed segmentation algorithm is analysed through testing on the lung CT image obtained from public online database, Lung Image Database Consortium (LIDC). To segment the lung nodule, preprocessing techniques are applied on the CT image followed by segmentation approaches to segment the nodule inside the lung. In this research, median filter is applied to improve the quality and filter the background noise of the image. Segmentation technique which is level set method (LSM) is applied to segment the nodule. After that, feature extraction process is carried out to obtain the essential information of the segmented nodules for further analysis or classification purpose. Here, the features extracted including centroid, major and minor axis length and area of the nodule. Performance of the segmentation algorithms is analysed using 20 lung CT images from LIDC dataset. Lastly, classification step is done using Artificial Neural Network (ANN) to classify the segmented nodule to different categories. From the analysis, LSM technique performs better in cases of bigger nodule compared to smaller size nodule. The result shows that the proposed segmentation method can effectively segment the lung nodule.
A Disturbance Activation Approach to Collision Avoidance Autonomous Driving

A very challenging control task for operating autonomous vehicles is to avoid collision with approaching pedestrians or vehicles while tracking a reference trajectory. This paper proposes a Linear-Quadratic Regulator (LQR)-based collision avoidance trajectory tracking controller for an autonomous driving scenario. In this approach, simply the control module activates a designed disturbance when there is a collision situation. A proper design of the disturbance causes the vehicle to prevent the collision and safely maneuver on the road. The asymptotic stability of this feedback LQR-based controller under an infinite horizon is shown. Simulation experiments are carried out to illustrate the performance of the proposed controller.
Dynamic Resource Distribution using SDN in Wireless Networks

When it comes to smart manufacturing, flexibility is an essential feature as it leads to on demand fabrication of various types of products in a factory with less down time and high throughput. This paper addresses dynamic communication infrastructure needed to provide the above mentioned flexibility. We consider a heterogeneous communication system, consisting of wired and wireless networks, that is managed by software defined networking and propose an admission control residing in the SDN controller and dynamically managing the bandwidth allocation among wireless nodes. To evaluate the proposed protocol we implement the admission control scheme in real hardware and perform a set of experiments to show the applicability and overhead of the protocol.
Design and Simulation of A Small Wind Turbine Blade with Qblade and Validation with MATLAB

This paper presents designing blades of small horizontal axis wind turbine for low wind speed area. In this case, Blade element momentum theory has been employed to find optimum value of chord length and twist angle. The blade performance has been illustrated in terms of coefficient of power, coefficient of thrust, coefficient of torque and so on. All this design procedure and analysis have been undergone with the help of Qblade, a dedicated as well as validated software for designing and simulating of wind turbine. Moreover, it has been validated with the help of MATLAB by implementing blade element momentum method. The simulation result from Qblade shows that rotor can extract maximum 48% wind energy having start-up speed of 2 m/s.
Comparison of the Effects on Stator Currents Between Continuous Model and Discrete Model of the Three-phase Induction Motor in the Presence of Electrical Parameter Variations

Three-phase induction motors are the most widely used electrical energy conversion machines due to their low cost, robustness, and ease of maintenance. Hence, the mathematical model analysis is fundamental to comprehend the behavior of the machine in order to implement a certain type of control. This article presents a comparison between the effects on the current, in steady-state, of electrical parameter variations of a three-phase induction motor model based on discrete-time with a continuous model by considering electrical parameters fixed in nominal values. Side by side comparisons between the application of pure sinusoidal waveforms and PWM waveforms generated by a voltage source inverter to the induction motor are presented. Simulation results are provided to show the parameter with the most significant impact over the machine when varying values.
Optimization of Active Voltage Rectifier / Doubler Designed in 90 nm Technology

This paper presents the optimization and comparison of three RF-to-DC circuit designs based on voltage rectifiers/doublers for Energy harvester systems. The first design is a 2-stage Differential Drive Cross-Coupled (DDCC) rectifier, the second one is a DDCC rectifier cascaded with a modified DDCC using a body-biasing technique for both NMOS and PMOS transistors and the third design is a DDCC using 2 pair of access transistors. These designs were simulated (TCAD Synopsys) in 90 nm CMOS Technology with three different output resistances ranging from 50, to 500 kΩ. Power (PCE) and voltage (VCE) conversion efficiencies were extracted and compared at a relatively high frequency (950MHz). The results show that after optimization for PCE, for all resistive loads used the DDCC with and without body biasing reach the best PCE% (70%) making these circuits good full wave rectifiers. However, design three gets the best VCE, enabling to properly duplicate the input voltage as required of any voltage doubler circuit.
Gold Coated Microstructure Optical Fiber Based Plasmonic Bio-sensor

A simple hexagonal lattice microstructure optical fiber (MOF) incorporating a gold nanofilm is demonstrated for refractive index (RI) sensing. The proposed MOF is numerically scrutinized by implementing finite element method (FEM) with perfectly matched layer (PML). Performances of the sensor are inspected based on the confinement loss peak (CLP) shift with the variation of RI. In the wide RI range from 1.36 to 1.44, a great CLP shift of $74\ \mu\mathrm{m}$ is found which leads to the maximum wavelength sensitivity (WS) up to 74,000 nm/RIU. Besides that, the maximum confinement loss (CL) of the proposed sensor is as low as 5.6 dB/cm that helps to enlarge the sensor length. However, simple structure, low loss, and high sensitivity makes this sensor suitable candidate for detecting of anonymous analytes.
Modeling of Electrochemical Batteries Behavior and Lifetime Degradation for PV Applications

The interest in studying electrochemical batteries in many applications is rising in the last years, due to increasing applications of storage systems in which batteries have a crucial role. For example, one can cite electric vehicles and in the future power systems, where storage can be used for load leveling, power variation damping and power quality improvement. Although batteries are a component known for a long time, modelling them is a very complex task. There is not a general model easily applicable in every situation. To address this issue, this paper presents the approaches commonly used in battery modelling and develops one model to represent the behavior of battery voltage and capacity during a discharge and charge cycle. Furthermore, another model is used to predict the lifetime and the degradation. These models are developed in order to simulate small scale battery systems for Demand Side Management applications with PV generation.
Improving Flow Scheduling Scheme With Mix-Traffic in Multi-Tenant Data Centers

Data centers need low-latency fabrics. Several flow scheduling schemes have been proposed to minimize the Flow Completion Time (FCT) based on Shortest Job First (SJF) heuristic. However, to mimic SJF, previous proposals sacrifice the generality (e.g., pFabric requires special hardware) or sacrifice the performance to guarantee the generality (e.g., PIAS loses some of pFabric’s performance). Especially, in multi-tenant data centers, traffic patterns from different applications are mixed together and vary over time, thereby creating even more challenges. In this paper, we investigated that the performance of information-agnostic scheme could be further improved by leveraging the unique characteristics of different traffic types. Based on this investigation, we present Traffic Prediction based Flow Scheduling (TPFS), aiming at achieving near-optimal performance and good generality in multi-tenant data centers with the mix-traffic pattern. To achieve near-optimal performance, we design a two-stage machine learning algorithm to first automatically cluster flows with the similar flow size distribution and then predict the priorities of flows based on the clustering results. Besides, we implement TPFS in virtual switches, which exerts fine-grained flow scheduling over the arbitrary network stacks of tenants. Testbed evaluation and simulations show that TPFS outperforms the previous information-agnostic flow scheduling scheme PIAS and greatly reduces the tail latency of the network.
A Minimum Supply Voltage Operational Transconductance Amplifier for Wireless Biomedical Acquisition Systems

A new solution for a fully differential bulk-driven Operational Transconductance Amplifier (OTA) for ultra-low-voltage applications is proposed herein. The amplifier eliminates the tail current source, performs a common-mode control and a transconductance adjust in parallel. All transistors in the project are biased in the weak inversion region. The minimum supply voltage is 150 mV using a transistor stack up of two. Simulations indicate that the Common Mode Rejection Ratio (CMRR), transconductance and Total Harmonic Distortion (THD) are compatible with biomedical applications.
Coexisting Analysis of 5G Networks with ISDB-T System in TV White Spaces

The efficient use of the electromagnetic spectrum becomes increasingly necessary due to the increase number of cellular devices. One possible solution is the opportunistic spectrum use in the VHF and UHF bands, allocated for television broadcasting. Therefore, it is necessary to evaluate the concurrent operation of broadcasting and mobile communication systems. This work aims to identify, analyze and measure the interoperability between those systems, by evaluating the feasibility of coexistence of different types of services. In this article, evaluations on two major candidates for the next cellular generation, the GFDM and the F-OFDM , operating along with the Integrated Services Digital Broadcasting Terrestrial – ISDB-T standard. The results show the flexibility of the GFDM and F-OFDM waveforms over the OFDM waveform, thus enabling opportunistic use of the spectrum over licensed and unlicensed users.
Diagonal Symmetric Pattern-Based Illumination Invariant Measure for Severe Illumination Variation Face Recognition

The center symmetric pattern (CSP) was widely used in the local binary pattern based facial feature, whereas never used to develop the illumination invariant measure in the literature. This paper proposes a novel diagonal symmetric pattern (DSP) to develop the illumination invariant measure for severe illumination variation face recognition. Firstly, the subtraction of two diagonal symmetric pixels is defined as the DSP unit in the face local region, which may be positive or negative. The DSP model is obtained by combining the positive and negative DSP units in the even <inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> even block region. Then, the DSP model can be used to generate several DSP images based on the <inline-formula> <tex-math notation="LaTeX">$2\times 2$ </tex-math></inline-formula> block or the <inline-formula> <tex-math notation="LaTeX">$4\times 4$ </tex-math></inline-formula> block by controlling the proportions of positive and negative DSP units, which results in the DSP2 image or the DSP4 image. The single DSP2 or DSP4 image with the arctangent function can develop the DSP2-face or the DSP4-face. Multi DSP2 or DSP4 images employ the extended sparse representation classification (ESRC) as the classifier that can form the DSP2 images based classification (DSP2C) or the DSP4 images based classification (DSP4C). Further, the DSP model is integrated with the pre-trained deep learning (PDL) model to construct the DSP-PDL model. Finally, the experimental results on the Extended Yale B, CMU PIE, AR, and VGGFace2 face databases indicate that the proposed methods are efficient to tackle severe illumination variations.