Miracle-dz/UniFAR-FTUs huggingface dataset:
Preoperative Planning Algorithm for Robot-Assisted Minimally Invasive Cholecystectomy Combined With Appendectomy

Preoperative planning for robot-assisted minimally invasive surgery is critical stage. Recently, many studies focus on the preoperative planning of the robot-assisted minimally invasive single-site surgery. However, the preoperative planning for the robot-assisted minimally invasive combined surgery based on the optimization algorithm has not been reported. In order to improve the dexterity and coordination of the manipulators in the surgical areas and to reduce the preoperative adjustment time for the combined surgery, this paper proposes a preoperative planning algorithm based on the non-dominated sorting genetic algorithm II (NSGA-II) for robot-assisted minimally invasive Cholecystectomy combined with Appendectomy (RAMICA). The preoperative planning algorithm simultaneously optimizes the entry ports and configurations of the manipulators. The optimization objective functions of the preoperative planning algorithm consist of a novel global dexterity index (GDI) based on the coefficient of variation and the coordination index (CI) that reflects hand-eye coordination and instrument coordination. The constraints of the preoperative planning algorithm include the port placement constraint and the non-collision constraint. The preoperative planning scheme based on the optimization algorithm are verified by comparative simulations to provide the better dexterity and coordination of the manipulators. Finally, the contrast experiments are carried out to demonstrate the effectiveness and superiority of the preoperative planning scheme obtained by the optimization algorithm.
Performance Analysis of a Dq Power Flow-Based Energy Storage Control System for Microgrid Applications

This paper presents a dq power flow based energy storage control system for reliable and stable operation of a renewable power generation based microgrid system. The control objectives are storing the excess energy from the microgrid into the storage unit or supplying energy deficit from the storage unit to the microgrid to achieve power equity between the generation and load, and regulation of voltage and frequency during stand-alone microgrid operation. Whereas during grid-connected microgrid operation, the control objective is to ensure storing energy in the storage unit and exchange power between the microgrid and the utility grid. The proposed controller is developed for inverter interface energy storages using dq power flow. The dq power flow is formulated using bus voltage components and the bus admittance matrix in dq frame. The dq power flow in the developed controller generates command (reference) active and reactive powers for the inverter interfaced storage unit connected to the microgrid buses. In addition, the implemented current controller of the inverter assures such command powers exchange between the storage unit and the microgrid. The developed dq power flow based storage unit (DQPFSU) control system is tested under various operating conditions for both in grid-connected and stand-alone microgrid operation. The test results of the developed DQPFSU controller illustrates satisfactory performance in generating fast control actions to ensure reliable and stable microgrid operation under various changing conditions. Moreover, the validity of such control actions has examined from the frequency response and bus voltages of the case study microgrid under various tested operational conditions.
End-to-End Anti-Attack Iris Location Based on Lightweight Network

With the widespread application of iris recognition in high security fields such as public safety, banking, and mobile payment, robust iris location and pseudo iris attack detection have become hotspots in recent years. How to achieve high accuracy in iris location, prevent the pseudo iris attack and guarantee both the real-time implementation and robustness of the algorithm is an open problem. This paper proposes a lightweight anti-attack iris location algorithm named Lite Anti-attack Iris Location Network (LAILNet). This algorithm can filter out pseudo-iris attacks and achieve robust and highly accurate iris location. LAILNet's feature extraction network adopts dense connection mechanisms and depthwise separable convolutions, which can reuse the features and speed up the network training with the greatly reducing amount of network parameters and calculations. The multi-scale iris location network utilizes six kinds of scales to locate different sizes of irises, improving the accuracy of small-sized iris location. In order to better verify the effectiveness of the algorithm, the continuous infrared iris database IPITRT is constructed in this paper. Real iris data in this database contains extremely disturbing samples with such as far and near focal lengths, blur, changes in ambient lighting, reflected light spots, head movements and blinks. The IPITRT's pseudo iris dataset is obtained by the high-resolution printing and photographing of the real iris data, which has a high similarity with the real iris data. The results show that the average false positive rate of the LAILNet algorithm on the three databases (IPITRT, CASIA-Iris-V4.0 and CASIA-Iris-Fake) is only 0.145%, the average iris positioning accuracy is 99.87%, the model size is 1.3MB, and single frame processing time is only 1.6ms. The LAILNet algorithm can effectively filter out false iris attacks while maintaining high accuracy and high robustness in the iris location. From the perspectives of model size and processing time, the RLAILNet algorithm is lightweight and achieves real-time performance.
Dual-loop Control Scheme with Optimized Type-III Controller based on Genetic Algorithm for 6-phase Interleaved Converter in Electric Vehicle Drivetrains

This paper presents an optimization procedure using a genetic algorithm (GA) for the dual-loop control scheme based on type-III controllers in a 6-phase interleaved converter. Four different objective functions (i.e. integration of squared and absolute errors and their time-weighted variants) are selected as the fitness functions of the GA. Based on the optimization procedure, step and dynamic responses of current and voltage controllers are investigated for those objective functions in terms of overshoot and settling time. In this study, another approach called classical ‘k-factor’ is utilized as a benchmark for the proposed type-III controller. Furthermore, two experimental tests are performed on a 60kW SiC-based prototype to verify the controller performances. The obtained results have demonstrated that the optimized controller design exhibits better dynamic response compared to the conventional counterpart.
Design of a Multiple-Beam Cassegrain Antenna With Quasi-Optical Isolator at 200 GHz for Target Tracking

This letter proposes a multiple-beam Cassegrain antenna with quasi-optical isolator at 200 GHz for target tracking. The proposed antenna is designed with a special topology, in which a multichannel transceiver feed integrated with a quasi-optical isolator is designed and placed in the back of the main reflector, aiming to isolate the transmitting and receiving channels, and to avoid the aperture blockage. Four receiving feeds are laterally defocused to produce offset beams and each of them is received and sampled individually without comparators, which simplifies the structure and provides flexibility for signal processing. An optimization is performed to achieve the best compromise design considering the conflicts induced by its special structure. The fabricated antenna is measured and the results demonstrate an excellent radiation performance of the proposed antenna. Four well-performed offset beams are obtained as expected and the antenna works stably within a bandwidth of 10 GHz. The measured 3 dB beamwidths vary around 0.6° and the sidelobe levels are lower than −12.5 dB for all the beams. The superiorities of the proposed antenna show its potential application in high-performance tracking and imaging systems.
Effects of dietary rapeseed meal and peas on the performance and meat quality of broilers

Performance, meat quality and litter quality were determined in 4000 male and female Ross 308 broilers fed on starter diet (soybean meal or rapeseed meal and peas) and grower-finisher diets based on either soybean meal (control) or substituting rapeseed meal or rapeseed meal and pea in different proportions (100/0%, 75/25% or 50/50%) for soybean meal in iso-nitrogenous basis. Performance of the birds fed with soybean meal diets was superior compared to that of the birds fed with rapeseed meal and pea based diets. Bird performance was the poorest and mortality the highest for the birds fed with the rapeseed meal grower diet. In this experiment birds did not reach performance target for the age and strain of bird. Overall, diet based solely on rapeseed meal protein is not suitable for broiler production. However, dietary rapeseed meal inclusion clearly improved fatty acid profile of breast meat. The omega-6/omega-3-ratio was 4.1, 2.4 and 2.7 for soybean meal, rapeseed meal, and rapeseed meal and peas based diets, respectively. There were no differences in sensory quality evaluation of breast meat between the treatments.
Intrusion Detection System using Machine Learning Techniques: A Review

The rapid growth in the use of computer networks results in the issues of maintaining the network availability, integrity, and confidentiality. This necessitates the network administrators to adopt various types of intrusion detection systems (IDS) that help in monitoring the network traffics for unauthorized and malicious activities. Intrusion is the breach of security policy with malicious intent. Therefore, intrusion detection system monitors traffic flowing on a network through computer systems to search for malicious activities and known threats, sending up alerts when it finds those threats. The detection of malicious activities is of two types, the misuse or signature-based detection in which the IDS collects information, analyzes it and then compares it to the attack signatures stored in a large database. The second detection is the anomaly detection which assumes malicious activity as any action that deviates from normal behavior. The proposed paper presents an overview of various works being done on building an efficient IDS using single, hybrid and ensemble machine learning (ML) classifiers, evaluated using seven different datasets. The results obtained by various works were discussed and compared which gives a clear path and guide for future work.
Back-propagation Based Contribution for nonlinear fault diagnosis

This paper proposes a novel fault diagnosis method by means of back-propagation based contribution (BBC) for nonlinear process. As a method based on the deep learning model, BBC can deal with the nonlinear problem in process monitoring by utilizing the nonlinear features extracted by auto-encoder (AE). Moreover, the smearing effect is an important factor affecting the performance of fault diagnosis. In order to solve this problem, BBC utilizes the basic idea of reconstruction based contribution (RBC), and describes the propagation of fault information by back-propagation (BP) algorithm. The validity of the proposed method is tested and verified by a nonlinear numerical example and the Tennessee Eastman benchmark process.
Communication-Free Secondary Frequency and Voltage Control of VSC-Based Microgrids: A High-Bandwidth Approach

In this paper, a decentralized secondary control strategy for microgrids, with fast dynamic response is proposed. This high bandwidth approach is realized by applying a finite control set, model predictive control (FCS-MPC) at the primary control level of the voltage source converters (VSCs) control. At the upper control level, a novel decentralized secondary control structure is proposed to regulate the islanded microgrid voltage and frequency subsequent to load change, with no need of any communication infrastructure. The proposed control strategy, restores the microgrid frequency and voltage to the nominal value while maintaining accurate power-sharing of the droop mechanism. Experimental results are also provided to verify the effectiveness of the proposed approach.
A formative assessment tool to support Computational Thinking in the classroom

Computational thinking (CT) is a set of abilities, which has lately acquired great relevance because it brings to students the components to handle in-formation and develop analytical skills, which allows them to perform ac-cording to today’s society. In this study, the authors describe the development of a formative assessment tool for Scratch programming. This web tool allows school teachers to design challenges for their students, supports the assessment of CT dimensions, gives feedback to students through the challenges’ results and a gamified strategy, and keeps a track of the development of CT for each student.
Navigation of Simultaneous Localization and Mapping by Fusing RGB-D Camera and IMU on UAV

Simultaneous localization and mapping (SLAM) is one of the key components of the navigation system of unmanned aerial vehicles (UAVs). In this paper, a novel system that enables a UAV to navigation in a GPS-denied environment with an RGB-D (Red, Green, Blue, and Depth) camera and Inertial Measurement Unit (IMU) is presented. Such a system is realized via a host computer on a UAV, in which the IMU information read from the flight controller and the image information read from the RGB-D camera are fused using extended Kalman filter (EKF). Experiments are designed to test the feasibility of the proposed navigation scheme. Accurate assessment of our idea shows a good response when rapidly moving and rotating the UAV.
A 2.4-GHz Quadrature Local Oscillator Buffer Insensitive to Frequency-Dependent Loads for Bluetooth Low Energy Applications

In this letter, a 2.4-GHz quadrature local oscillator (LO) buffer insensitive to frequency-dependent loads is presented for Bluetooth low energy (BLE) applications. An <italic>LC</italic> tank is used as a load in the proposed buffer to provide a sufficient voltage gain and large voltage swing at the operating frequencies of BLE. Furthermore, the effect of the frequency-dependent impedance of the <italic>LC</italic> tank on the gain and phase mismatches is reduced by adding compensating transistors. The proposed buffer can provide accurate quadrature signals without additional circuitry in the LO path, minimizing the current consumption of the receiver. It is implemented in 65-nm CMOS technology. It achieved a voltage gain of 5.6 dB, a gain mismatch less than 0.3 dB, a phase mismatch less than 1.1°, and an output <inline-formula> <tex-math notation="LaTeX">${P} _{{1dB}}$ </tex-math></inline-formula> of 3 dBm. It draws an average current of 3.8 mA from a supply voltage of 0.8 V. The active die area is 0.55 mm<sup>2</sup>.
ONTOGENY OF SCLEROCEPHALUS HAEUSERI OBSERVATIONS ON THE POSTCRANIAL MORPHOLOHY, ONTOGENY AND PALAEOBIOLOGY OF SCLEROCEPHALUS HAEUSERI (AMPHIBIA: ACTINODONTIDAE) FROM THE LOWER PERMIAN OF SOUTHWEST GERMANY

The temnospondyl amphibian Sclerocephalus is described in four ontogenetic stages: larval, juvenile, adult and late adult. In particular the postcranial anatomy was observed. Some specimens preserve stomach contents, consisting of paramblypterid fishes and small amphibians (Micromelerpeton, Apateon). In one specimen, the remains of a small Sclerocephalus were found. Larval and juvenile individuals probably lived in a different habitat than adult and late adult ones. In the juvenile, adult and late adult stages, Sclerocephalus was the top predator in its environment. Amphibia, Limnarchia, Actinodontidae, Sclerocephalus haeuseri, postcranial morpholgy, ontogeny, palaeobiology, Lower Permian, Germany
Review of Deep Reinforcement Learning-Based Object Grasping: Techniques, Open Challenges, and Recommendations

The motivation behind our work is to review and analyze the most relevant studies on deep reinforcement learning-based object manipulation. Various studies are examined through a survey of existing literature and investigation of various aspects, namely, the intended applications, techniques applied, challenges faced by researchers and recommendations for minimizing obstacles. This review refers to all relevant articles on deep reinforcement learning-based object manipulation and solutions. The object grasping issue is a major manipulation challenge. Object grasping requires detection systems, methods and tools to facilitate efficient and fast agent training. Several studies have proposed that object grasping and its subtypes are the main elements in dealing with the environment and agent. Unlike other review articles, this review article provides different observations on deep reinforcement learning-based manipulation. The results of this comprehensive review of deep reinforcement learning in the manipulation field may be valuable for researchers and practitioners because they can expedite the establishment of important guidelines.
Unmanned Aerial Vehicle and Artificial Intelligence for Thermal Target Detection in Search and Rescue Applications

Abstract- Recent developments in unmanned aerial vehicles (UAV), artificial intelligence and miniaturized thermal imaging systems represent a new opportunity for search and rescue (SAR) experts to survey relatively large areas. The system presented in this paper includes thermal image acquisition, the video processing pipeline that performs object detection and classification of people in need of SAR in outdoor environments. The system is tested on thermal video data from ground based and test flight footage and is found to be able to detect all the target people located in the surveyed area. The training dataset is a combination of gathered data and internet sourced data. The initial data procurement utilised online academic thermal databases and also the simulation of the UAV mounted camera environment. Ground based data was collected at Kangaroo Point cliffs in Brisbane, Australia, giving an approximate elevation of 26m. Airborne datasets were collected at South Bribie Island in Queensland, Australia, at a range of heights and vegetation density. These datasets where collected at different times of the day, allowing for a range of contrast levels between background and intended target. Once all data was collected, individual frames where extracted from each image and augmentation and annotation was completed. The images were gaussian blurred, lightened and darkened, once all annotation were completed. A total of 2751 original images were annotated, with the augmented dataset comprising of 10380 images. The YOLOV3 algorithm was selected as the neural network (NN) to be used for this experiment throughout training and classification. The ‘Experiencor’ GitHub pipeline was also used throughout the entirety of this project for data output and analysis purposes. The algorithm training was implemented on the Queensland University of Technology (QUT) High Performance Computing (HPC) cluster. Two main models were trained using different hyperparameters for comparison purposes. The first model had a loss of 3.81, AP of 98.6 after ~88 hours of training, with model two having a loss of 4.73, AP of 97.7 after ~184 hours of training. The comparison shows the importance of the chosen parameters when training detection algorithms of this nature, as minor changes can be the difference between an efficiently trained model and an inefficient failed training attempt. The prediction testing was completed on the test data sets that were not included in the training of the two models. This was done to remove any bias from the system, although it is noted that due to the shared environments of two of the test sets with the training sets, a small amount of bias will exist. Predictions made on the two data sets sharing environmental conditions with the training data, showed good prediction results for both trained models, with very limited false positive and missed detections. The system is flexible in that the user can readily define the types of objects to classify and the object characteristics that should be considered during training.
Evaluation of Nutritional Fact and Study of Various Physico-chemical Parameters of Black, Green and Red Grapes Samples in the Local Fruit Market

Aims: Aim of our study was to analyze various nutritional fact of three types of grape fruits and compare their facts in terms of consumption. The grape is one of the most edible fruit with high amount of nutritional and medicinal properties. Study Design: Physico-chemical analysis by using various chemical analysis and instrumental methods for analysis of various grape samples. Place and Duration of Study: Department of Applied Sciences, Chemistry section, University of Technology and Applied Sciences, Muscat, Oman. The study was performed during the May 2018 – December 2018. Methodology: We have selected three different grapes samples from the local market for the analysis (Green grapes, Red grapes and Black grapes). Our research study includes testing of various physico-chemical parameters for nutritional fact of fruit, such as water, carbohydrates, proteins, fat, vitamin C, and different minerals. We analyzed various physic-chemical parameters. Original Research Article Patil et al.; AJOCS, 8(3): 1-8, 2020; Article no.AJOCS. 61330 2 Results: We found that pH in the range of 3.775 – 4.14, conductivity 1.39 MS/cm – 1.553 MS/cm, titratable acidity was measured in terms of tartaric acid was found 0.712 g/100ml 1.065 g/100ml and Brix% value 9.5% 15.5%. Amount of water in berry was 77.964 % – 80.146 %, Ash in the range of 0.72 – 3.911 %. Protein 6.245 g/100ml – 7.928 g/100ml, Fiber content 1.112 % – 1.143% and good amount of potassium, magnesium, calcium, iron and zinc. Conclusion: Analysis of three types of grapes we found, acidity of green grapes was highest. It might be depending upon ripening stage of berry. That are correlated to sugar ( 0 Brix). Fiber value was very close to each other. Iron was highest in red grapes and then green grapes. Potassium, magnesium, calcium and zinc was found to higher in green grapes. Based on this green grape looks batter nutritional value.
An On-chip Analysis of the VLSI designs under Process Variations

Present digital world anticipates the high performance, low cost, and low power consumption in the semiconductor industry accompanied with a rapid growth in technology. Nowadays, the economy relies on the chip system, so that the semiconductor business enters the innovation in the transistor at each hub and further this innovation comes with a lot of difficulties like reduced quality, manufacturing and multi-faceted planning. Variety with primarily random and organized combinations is one of the essential challenges in assembling the boundaries. One of the most important differences observed from these variants is on-chip variation. During this work, the on-chip variation method was briefly addressed with the aid of CMOS technology.
3D Human Pose Estimation With Adversarial Learning

In recent years, due to the deep learning and the development of computer vision, great progress has been made in the 3D human pose estimation from RGB images. However, due to the lack of depth information in RGB images, this task still faces great challenges. In this paper, we propose a method of adversarial learning to estimate the 3D pose of the human body. Our framework consists of two parts, a pose generator and a discriminator. Using the 3D pose descriptor, we designed for adversarial learning can effectively increase the accuracy and visual effect of 3D pose estimation results. We performed ablation experiments on the public dataset, which is a good improvement compared to our baseline.