Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparative Study of CNN and AlexNet for Detection of Disease in Potato and Mango leaf

Deep Learning (DL) is a fastest growing and a broader part of machine learning family. Deep learning uses Convolutional Neural Networks (CNN) for image classification as it gives the most accurate results in solving real- world problem. CNN has various pre-trained architecture like AlexNet, GoogleNet, DenseNet, SqueezeNet, ResNet, VGGNet etc. In this study, we have used CNN and AlexNet architecture for detecting the disease in Mango and Potato leaf and compare the accuracy and efficiency between these architectures. The dataset containing 4004 images were used for this work. The images for potato were taken from plantvillage website, while images for mango were collected from GBPUAT field location. The results show that accuracy achieved from AlexNet is higher than CNN architecture.
Dynamic Movement Primitives: Volumetric Obstacle Avoidance

Dynamic Movement Primitives (DMPs) are a framework for learning a trajectory from a demonstration. The trajectory can be learned efficiently after only one demonstration, and it is immediate to adapt it to new goal positions and time duration. Moreover, the trajectory is also robust against perturbations. However, obstacle avoidance for DMPs is still an open problem. In this work, we propose an extension of DMPs to support volumetric obstacle avoidance based on the use of superquadric potentials. We show the advantages of this approach when obstacles have known shape, and we extend it to unknown objects using minimal enclosing ellipsoids. A simulation and experiments with a real robot validate the framework, and we make freely available our implementation.
The Quality of e-Village Budgeting Service : An Empirical Research in Banyuwangi, Indonesia

Electronic Village Budgeting (e-VB) is an information system of village budgeting owned by Banyuwangi District. But at the beginning of the implementation of e-VB, there were several errors and constraints experienced by users so that an evaluation is needed to measure the extent of the quality of e-VB services. To measure the quality of e-VB service, this research used 33 attributes from 6 dimensions of e-GovQual; ease of use, trust, functionality of the interaction environment, reliability, content and performance of information, and citizen support. While the Importance Performance Analysis (IPA) method is used to map the services performance based on the level of user importance. Respondents in this research were 44 respondents spread across 22 villages in Kabat and Licin sub-Districts. Based on the results of e-GovQual analysis was known that the quality of service performance is 3.16. This shows that the performance of the e-VB service is good according to the user's perception. However, based on the assessment of user importance of 3.45, it creates a gap value between performance and importance of -0.29, indicating that the quality of e-VB services is good, but has not fulfilled the user's importance. Based on the results of the IPA quadrant analysis, it produces several attributes that need a special priority for improvement; providing informed consent, accessibility of site, and loading speed. The results of the research are expected to help the provider of e-VB to be able to provide services that are in accordance with the user's importance.
Dual-Wideband Dual-Polarized Metasurface Antenna Array for the 5G Millimeter Wave Communications Based on Characteristic Mode Theory

A dual-wideband dual-polarized antenna using metasurface for the fifth generation (5G) millimeter wave (mm-wave) communications is proposed. It is designed and analyzed based on characteristic mode theory (CMT). The proposed metasurface is mainly composed of a $3\times 3$ square-patch, in which its four corner patches are further sub-divided into a $4\times 4$ sub-patch array, while the size of the other four edge patches is reduced and the center patch is etched with a pair of orthogonal slots. By doing so, the side lobe level can be effectively reduced and the main beam radiation can be enhanced. The metasurface is excited by a pair of orthogonally arranged substrate-integrated-waveguide (SIW) to grounded-coplanar-waveguide (GCPW) dual-polarized feeding networks that help to reduce the insertion loss and expand the frequency bandwidth of the feeding ports. In order to yield higher gain, four proposed metasurfaces are fed by a pair of 1-to-8-way power divider feeding networks including a pair of low-transmission-loss E-plane phase shifter. Measured results show desirable impedance bandwidths of 13.85% (24.2-27.8 GHz) and 14.81% (36.9-42.8 GHz) in the lower and upper frequency bands, respectively, and their corresponding average gains are 13.96 and 15.46 dBi.
Active Disturbance Rejection Attitude Control for Flapping Wing Micro Aerial Vehicle With Nonaffine-in-Control Characteristics

This study presents attitude control research of flapping wing micro aerial vehicles with nonaffine-in-control characteristics via the active disturbance rejection control technique. With consideration of the uncertainties in moment of inertia caused by wings flapping and external disturbances, an extended state observer based on the unit quaternion is first designed to estimate the angular velocity and total uncertainties. The norm constraint on the unit quaternion can be guaranteed theoretically. The requirement of accurate value of moment of inertia is circumvented in the extended state observer. Based on the designed extended state observer, an output feedback controller using dynamic inversion method is presented to resolve the nonaffine-in-control characteristics caused by the uncertainties in moment of inertia. Rigorous closed-loop system stability is proven by employment of Lyapunov functions and the proposed control structure possesses the multi-time-scale property. Finally, numerical simulations are provided to validate the effectiveness and good tracking performance of the proposed control scheme.
Study on Vision Measurement for Levitation Gap of Magnetic Levitation Ball Based on Convolutional Neural Network

With the development of deep learning, the Convolutional Neural Network (CNN) is widely used in object classification and pattern recognition. It has enabled computer to achieve better performance than humans in specialized computer vision tasks. This paper takes the magnetic levitation ball system as the research object. Aiming at the shortcomings of traditional methods for levitation gap measurement, a new method is proposed by combining machine vision and CNN image processing technology. The convolution neural network algorithm is used to build the gap measurement model, and the training set is used to train the model. The experimental results show that using convolution neural network image processing technology to realize the levitation gap measurement of magnetic levitation ball system has high distance measurement accuracy and good performance. The proposed CNN model provides correct gap data with the maximum error of 0.16mm for full scale and the average error of 0.07mm for full scale in the test set.
Real-Time Object Detection on 640x480 Image With VGG16+SSD

Convolutional neural networks (CNNs) show high performance in computer vision tasks including object detection, but a lot of weight storage and computation requirement prohibits real-time processing, 30 frames per second (FPS). This demonstration will show an CNN accelerator that can process real-time object detection on the 640x480 image. A high performance, complex CNN was implemented, single-shot multibox detector (SSD) with VGG16. The number of weights is reduced by a pruning scheme. For the higher utilization of operators, the accelerator-aware pruning was applied. The weights of the pruned network can be entirely stored in the internal memory. The proposed design reaches 42 FPS on XC7VX690T FPGA, showing VOC07 test mAP of 78.13%.
A Data-Driven Approach to Problem Assessment, Technology Design, and Sustainable Business Planning for m-Health Delivery

This paper presents a data-driven qualitative research approach to identifying the role of technology for chronic disease management and control in large underserved communities. Within the context of a knowledge translation framework, the main emphasis of this work is on tailoring knowledge from data provided by a community using mobile health (m-health) solutions followed by analysis leading to synthesizing models that are ultimately used for providing feasible technology solutions. The research design is described, along with an application case study for data capturing from a slum community in India. The reported results present the methodology and capturing process for data-driven grounded theory and include details of text-mining and qualitative results.
Voltage-Controlled and Input-Matched Tunable Microstrip Attenuators Based on Few-Layer Graphene

This article presents novel tunable microstrip attenuators based on few-layer graphene. The proposed topology consists of a microstrip line sided by pairs of grounded metal vias, with graphene pads located between the microstrip line and the vias. The possibility to control the graphene resistance through an applied bias voltage is exploited with the aim to modify the insertion loss of the attenuators. Several pairs of metal vias are adopted to achieve large tuning range, good input matching, and broadband operation. A systematic investigation of the structure with two, three, and four pairs of vias is presented, along with the experimental validation. Prototypes operating in the frequency range from 1 to 10 GHz and with maximum insertion loss exceeding 60 dB are fabricated and tested.
Secure Transmission in HARQ-Assisted Non-Orthogonal Multiple Access Networks

This paper investigates the secure transmission in hybrid automatic repeat request (HARQ)-assisted non-orthogonal multiple access (NOMA) networks, where a security-required (SR) user is paired with an opportunity-served (OS) user to perform NOMA. Different from the previous works where NOMA and HARQ are separately considered for secure transmissions, in this paper, both NOMA and HARQ are utilized to enhance the security of the SR user. We derive the closed-form expressions for connection outage probability (COP), secrecy outage probability (SOP), reliable and secure transmission probability (RSP) and effective secrecy throughput (EST) of the SR user in both maximum ratio combining (MRC) and selection combining (SC) schemes. The security-reliability trade-off (SRT) results of the SR user in both MRC and SC schemes are also provided. Analysis and simulation results show that HARQ improves secrecy performance in MRC and SC schemes in terms of RSP, especially in low transmit power region. Besides, since every retransmission is utilized in the MRC scheme, it strictly outperforms the SC scheme in terms of the SRT metric. However, the SC scheme has the potential to achieve higher EST than the MRC scheme by optimizing the transmit power or the power allocation factor. Significantly, the quality of service (QoS) of the OS user can be guaranteed by pairing an SR user who is far away from the transmitter and the secrecy performance of the SR user can be enhanced by pairing an OS user.
Circuit Design Challenges of ADC for the Application in Multiple Physiological Parameters Detection System

Design challenges of ADC for the application in multiple physiological parameters detection system is discussed in this paper. Based on requirements in the physiological signal detection applications, design difficulties of these high precision Sigma-Delta ADCs, including multi-channel high effective resolution, ultra-low power and low channel mismatch, are analyzed with design tradeoff considerations. Results of a Sigma-Delta modulator for physiological parameters detection applications is given. The noise floor of the modulator is about −120dB, the SNR is 101.4dB, and ENOB is 16.55 bit, with the power consumption of 18 mW.
Methodology for Predicting Flexible Photovoltaic Cell Life using Accelerated Tests

Long term stability is critical for maturing any photovoltaic (PV) technology. Understanding the environmental impact on the PV cell performance is crucial to assess its reliability. In this work, an accelerated test methodology has been developed for evaluating an unencapsulated flexible PV cell performance. A semi-empirical model has been developed and validated, which enabled predicting the flexible PV cell life under typical factory environmental conditions. This framework can be used as a guideline for defining appropriate storage conditions for PV cell to avoid unintended degradation before laminating the cell to a module.
Avoiding Power Clipping Losses by Inverter having High DC-to-AC Loading Ratio in Grid Connected Solar PV Plant Using Battery Energy Storage System

This paper presents a MATLAB model simulation as a solution to capture and avoid energy losses due to power clipping by Inverter having high DC to AC loading ratio in grid connected solar PV plant using voltage controlled bidirectional DC-to-DC converter interfaced with Lithium Ion battery. Battery is connected on DC link of grid connected solar PV plant. Complete topology is modeled in MATLAB®. Model uses a unidirectional DC-to-DC converter with MPPT (Maximum Power Point Tracking) for Solar PV Interface and Voltage Source Inverter to inject power into the grid from Solar PV or Battery. If the power from Solar PV is higher than the capacity of Inverter, some PV power is diverted to the battery in Buck mode of voltage controlled bidirectional DC-DC Converter. This avoids PV power clipping and loss of energy by inverter due to inverter capacity limitation. When Solar PV generation is less than Inverter capacity, power from the battery is discharged into the grid through same bidirectional DC-DC converter in Boost mode and Inverter. Model is simulated in MATLAB and results are validated.
A Simulation of Enhanced Oil Recovery of Surfactant Flooding Using Sodium Lignosulfonate by CMG-STARS

Many countries in the world still rely on crude oil as their major source of energy. In Indonesia, the production rate of crude oil decreases steadily with the current production rate of about 800 000 bbl oil/day in 2018. Current state happens because in Indonesia there are still a lot of old wells become mainstay to produce oil. Hence, there is an urgent need to introduce technology to improve oil production. Enhanced oil recovery (EOR) has been considered as the main alternative to boost the production of mature fields in Indonesia. A simulation study of EOR is an important support to the implementation of EOR in Indonesia. In this paper we demonstrate a simulation study of lab-scale core flooding test using Sodium Lignosulfonates (in-house surfactant product of Universitas Gadjah Mada) for EOR by using Berea core. CMG STARS software with Black Oil Model has been used as a simulator to track the fractional flow of water, oil, and surfactant in axial position. The result of the simulator model was validated with the experimental results from the lab.
Prevention of Agricultural Commodities using Artificial Intelligence

In India the major source of living and economy is through agriculture and agricultural industry. Rotting or over ripening of agricultural commodity has always been a major problem for the farmers. Farmers aren't able to keep a check on every single crop that turns out to be the reason of crops getting destroyed resulting in less amount of fertile crops. This paper mainly focuses on proposing a model which will help the farmers in crop monitoring and to know about the rotting of their crops before the crop gets rotten or over ripe. This paper also includes techniques of Artificial Intelligence in agriculture that will help the farmers to prevent the agricultural commodities. The objective of this project is to build a system which protects the crops from rotting and over ripening. The aim is to propose a new framework and develop a system to make some advances towards prevention of crops.
Research on Thangka Image Edge Detection Based on Differential Operator

Edge detection is not only the early work of image segmentation, but also the basis of image understanding, differential operator is a simple technique for edge detection and is typically accomplished by calculating gradients of horizontal and vertical gray values. However, there are many curves that two-direction operator can detect that is not so clear in Thangka image. In this paper, the edge detection effects of several common differential operators on Thangka image are compared. On the basis, a new algorithm for Thangka image detection is proposed. Fist the image is grayed. The the grays image is contrast reduced. At last edge detection of image is carried out by using four-direction Sobel operator.
The impact of non-holistic decision making on product development projects - A case study

A non-holistic approach and lack of systems thinking during the strategic decision-making process has its implications on the entire value chain in an organization which in turn affects its competitive advantage. This paper presents a case study from a Swedish manufacturing industry with focus on decision-making process during product development. The challenges faced during a product development process (PDP) are explored and major areas for improvement are identified using exploratory case study research method. Practical examples from the case company such as impact of requirements on quality and product cost are also discussed to support the results. A major observation made during the work is that for holistically improving the decision-making, PDP should not be stand-alone, rather be taken as a system of systems (SoS). This implies that during the decision-making process, in addition to product design and implementation, other organizational elements such as marketing strategy, logistics, management practices and company culture, etc. are also to be considered.
An Adaptive Affinity Matrix Optimization for Locality Preserving Projection via Heuristic Methods for Hyperspectral Image Analysis

Locality preserving projection (LPP) has been often used as a dimensionality reduction tool for hyperspectral image analysis especially in the context of classification since it provides a projection matrix for embedding test samples to low dimensional space. However, the performance of LPP heavily depends on the optimization of two parameters of the graph affinity matrix: k-nearest neighbor and heat kernel width, when one considers an isotropic kernel. These two parameters might be optimally chosen simply based on a grid search. In case of using a generalized heat kernel where each feature is separately weighted by a kernel width, the number of parameters that need to be optimized is related to the number of features of the dataset, which might not be very easy to tune. Therefore, in this article, we propose to use heuristic methods, including genetic algorithm (GA), harmony search (HS), and particle swarm optimization (PSO), to explore the effects of the heat kernel parameters aiming to analyze the embedding quality of LPP's projection in terms of various aspects, including 1-NN classification accuracy, locality preserving power, and quality of the graph affinity matrix. The results obtained with the experiments on three hyperspectral datasets show that HS performs better than GA and PSO in optimizing the parameters of the affinity matrix, and the generalized heat kernel achieves better performance than the isotropic kernel. Additionally, a feature selection application is performed by using the kernel width of the generalized heat kernel for each heuristic method. The results show that very promising results are obtained in comparison with the state-of-the-art feature selection methods.
A Segmentation Method of Lung Parenchyma From Chest CT Images Based on Dual U-Net

Segmentation of lung parenchyma as a pretreatment step for the detection of some lung lesions requires high accuracy to provide more reliable information for lesion detection. In this paper, we proposed a segmentation method of lung parenchyma from chest CT Images by Dual U-Net algorithm based on the characteristics of different lung region. Firstly, the original lung region is extracted from the full chest CT images of patient by an automatic threshold selection method. Then, the images of the different regions of lung is obtained by calculating the area. Finally, the Dual U-Net is trained according the different lung regions. The first U-Net algorithm operates these middle chest images and other images are operated by the second U-Net algorithm to reduce the interference of other images. The experimental results shown that the segmenting results of lung parenchyma by this method are more accurate than the results by single U-Net and U-Net++ algorithm.
Traceability in Permissioned Blockchain

In this paper, we have achieved privacy protection and high transparency in a permissioned blockchain. There is a sidechain that connects the permissionless blockchain and the permissioned blockchain. The behavior in the permissioned blockchain is almost a black box from the perspective of the permissionless blockchain. While this fact is useful for privacy protection, there is room for improvement in terms of transparency. To improve the transparency of the permissioned blockchain under privacy protection, we consider traceability in the permissioned blockchain consisting of the following three properties: trade privacy (who trades with whom and at what asset amount), preservation (the total amount inside the permissioned blockchain, including deposits and withdrawals to the permissionless blockchain, is immutable), and noninvolvement (some members in the permissioned blockchain are not involved in some trades, and it is possible to prove that specified members performed the transaction). To the best of our knowledge, we are the first to achieve both preservation and noninvolvement while protecting the privacy of transactions. Our approach is as follows. We model traceability based on the hidden Markov model. Because the proof of traceability requires the calculation of more than quadratic degrees, we encrypt this model by homomorphic encryption. The number of participants in the permissioned blockchain corresponds to the number of additions in the model. Then, we can construct the encrypted model by employing somewhat homomorphic encryption. The establishment of the original model is verifiable by applying the noninteractive zero-knowledge proof of the knowledge that the plaintext is equal to zero. This is an adaptation of Benhamouda et al. (Asiacrypt 2014).
Performance Analysis of Grid Connected Hybrid PV-Wind System

Renewable sources have always been an area of interest considering the energy requirements for the future generations. In this paper, a two-stage Photo-voltaic (PV) system is combined with a Wind System. Interfacing of two sources with each other and then with grid requires matching of frequency. Phase locked loop (PLL) is required in order to match the frequency. Modeling of PV system includes modeling of sub-stages i.e. 3-Diode model, boost converter and an inverter. 3-Diode model provides the PV array characteristics, boost converter provides amplification of PV system output voltage and inverter simply converts DC output voltage of boost into AC voltage. Wind system uses a doubly fed induction generator (WTIG) model. Active and reactive power is controlled using transformation in dq0 domain at unity power factor.