Miracle-dz/UniFAR-FTUs huggingface dataset:
REPRODUCTIVE BIOLOGY OF MOUTH-BROODING TILAPIINES IN THE KAFUE FLOODPLAINS

Invasive alien species have become a serious threat to the biodiversity of ecosystems of the world. Globally, the threat to biodiversity due to invasive species is considered second only to habitat loss. Invasive species are thus a serious impediment to conservation and sustainable use of global, regional and local biodiversity. This study concentrated on the reproductive biology of the Oreochromis species in the Kafue Floodplains, which is part of the Kafue River stretching from Itezhi-tezhi to the Kafue Gorge. Specific objectives for this study were to compare reproductive strategies i.e. egg size and Gonadosomatic Indices of the introduced Oreochromis niloticus and the local breams, Oreochromis andersonii and Oreochromis macrochir. Oreochromis niloticus is an invasive species that has been reported to cause a reduction in abundance of fish which are closely related to it due to its highly competitive nature. In this study, the egg sizes were noted as longest in Oreochromis niloticus measuring 1.825 ± 0.1892mm followed by Oreochromis andersonii with 1.696 ± 0.3117mm and Oreochromis macrochir had the smallest, which measured 1.4483 ± 0.777mm. The Gonadosomatic Index was highest in Oreochromis niloticus at 1.034 ± 0.0816 followed by Oreochromis macrochir with 0.918 ± 0.1332 and Oreochromis andersonii with 0.783 ± 0.0107. In all three species, the females had a higher Gonadosomatic Index than the males and the fish in the ripe gonad maturity stage also showed a higher Gonadosomatic Index value than the other stages. Though only six months of data were collected for Gonadosomatic Indices, distinct spawning peaks for all three species were noted as November and January for Oreochromis andersonii, October and December for Oreochromis macrochir and November through December and March for Oreochromis niloticus. From the results, it can therefore be concluded that larger egg sizes and high Gonadosomatic Index of Oreochromis niloticus compared to the other mouth-brooding tilapiines, Oreochromis andersonii and Oreochromis macrochir contribute significantly in giving it a competitive advantage. It is therefore recommended that the introduction of Oreochromis niloticus into the Zambian river systems be revised based on the findings from this study.
Voice Command Recognition for Drone Control by Deep Neural Networks on Embedded System

Speech recognition and its applications for controlling a system has been an important and attractive issue over the last few decades. Controlling electronic devices by speech commands allows us to manage systems quickly and easily since users would not need any additional information or remote controller. Being able to communicate a system by using speech commands also brings with the requirements of fast and accurate response. So, at the present, speech recognition algorithms are extensively performing on high performance computers. However, the improvements of system on a chip (SoC) board and deep neural network based algorithms, make it possible to execute such kind of programs on them. The proposed study defines a model for controlling a drone system by using Turkish speech directional commands in real time which is based on deep learning approaches using spectrogram images. At first, speech commands are detected in real time with the help of signal energy and zero crossing rate and these are converted to log spectrogram images. A CNN (three convolutional layers and a fully connected layer) structure is created and trained by feeding those images. Then, the trained model is moved to embedded board to achieve real time, low-cost performance. Speech commands are provided by the user instantaneously and transferred to the model as an input for decision. Then, algorithm decides which directional command is given by the user and desired operation is performed on the drone system. It is observed that, by using the proposed model, accuracies of 95.72% for offline dataset and 92,88% for real time classification are obtained.
Integration of Minimum Energy Point Tracking and Soft Real-Time Scheduling for Edge Computing

In the upcoming Internet of Things era, reducing energy consumption of embedded processors is highly desired. Minimum Energy Point Tracking (MEPT) is one of the most efficient methods to reduce both dynamic and static energy consumption of a processor. Previous works proposed a variety of MEPT methods over the past years. However, none of them incorporate their algorithms with practical real-time operating systems, although edge computing applications often require low energy task execution with guaranteeing real-time properties. The difficulty comes from the time complexity for identifying MEP and changing voltages, which often prevents real-time task scheduling. This paper proposes an approximated MEPT algorithm, which reduces the complexity of identifying MEP down to that of Dynamic Voltage and Frequency Scaling (DVFS). We also propose a task scheduling algorithm, which adjusts processor performance to the workload, and provides a soft real-time capability to the system. With these two methods, MEPT became a general task, and the operating system stochastically adjusts the average response time of a processor to be equal to a specified deadline. The experiments using a fabricated test chip show that the energy loss induced by the proposed algorithm is only 0.5% at most, and the algorithm does not sacrifice the fundamental real-time properties.
Sliding Mode and Inertial Delay Based Direct Yaw Moment Control for AGVs

The direct yaw moment control (DYC) obtained using active front-wheel steering and differential forces at each wheel is an effective technique in achieving vehicle stability. In this paper, a sliding mode and inertial delay control (IDC) is designed to achieve DYC. In this method, the uncertainties in vehicle parameters and external disturbances are estimated using IDC and used in the sliding mode control (SMC) framework to force the vehicle to track the desired yaw rate and sideslip angle. The proposed controller is free from chatter and does not need the uncertainties' bound. The stability of the controller is established using the Lyapunov theory. The potency of the prospective controller has been proved by executing the simulation for varying driving conditions.
An Innovative Algorithm to Process Imaging Data for Detection of Weak Chick Embryos in Vaccine Production

Efficient and accurate detection of weak embryos often requires the detection and classification of both weak and live embryos over a specific period. In this work, we image, detect and classify weak and live embryos hatched between the 9th and 15th day. We introduce a new method called Weak Embryo Detection Network (WEDNet), which is a weak embryo detection method based on a multiscale-feature fusion convolution neural network. First, we broaden the expand convolution layer of the Fire module to implement a multiscale feature extraction using a lightweight network SqueezeNet structure, where convolution kernels of different sizes are stored. A residual connection method is introduced between adjacent modules to achieve feature fusion between layers. Furthermore, a residual multiscale Fire block (RMFB) is proposed. WEDNet is then formed by a cascade of RMFB modules. A batch normalization layer is introduced into the network structure to speed up the network's convergence speed and a dropout method is adopted to suppress the overfitting phenomenon due to the increase of the network’s width and depth. Experimental results show that the method proposed in this paper can reach 99.35% detection accuracy, which can achieve good selection of weak embryos.
Image Captioning using Convolutional Neural Networks and Recurrent Neural Network

Image Caption is a concept of gathering the right description of the given image on the internet use Computer Vision and natural language processing. The following is achieved using the Deep learning techniques called as convolution neural network and recurrent neural network. The dataset used for implementation is called as the Flickr8_k Dataset. The model uses the combination of convolution neural network which helps in extraction whereas the recurrent neural network helps in generation of the right text
Vision based collision detection for a safe collaborative industrial manipulator

In this work we present an improved method for collision detection that extends the ability of collaborative systems to share the workspace between the operator and the robot. Our work is based on the state-of-the-art GPU-Voxels algorithm, with a dynamic swept volume method for collision detection that minimizes intervention in robot trajectories. We propose a sensor calibration method that allows multiple cameras to be placed around the robot to ensure coverage without shadows. In addition, we show how our previous work on advanced force control algorithms, which in combination with the collision detection algorithm presented, meets all safety requirements of the technical specification ISO/TS 15066. Our ultimate goal is to derive a framework for collaborative industrial manipulators, which are able to work side by side with people.
Summarizing Relevant Parts from Technical Videos

Software developers frequently watch technical videos and tutorials online as solutions to their problems. However, the audiovisual explanations of the videos might also claim more time from the developers than the text-only materials (e.g., programming Q&A threads). Thus, pinpointing and summarizing the relevant fragments from these videos could save the developers valuable time and effort. In this paper, we propose a novel technique – TechTube – that can be used to find video segments that are relevant to a given technical task. TechTube allows a developer to express the task as a natural language query. To account for missing vocabularies in the query, TechTube automatically reformulates the query using techniques based on information retrieval. The reformulated query is matched against a repository of online technical videos. The output from TechTube is a sequence of relevant video segments that can be useful to implement the task at hand. Unlike previous researches, our approach splits the video by detecting silence in video audio tracks. Experiments using 98 programming related search queries show that our approach delivers the relevant videos within the Top-5 results 93% of the time with a mean average precision of 76%. We also find that TechTube can deliver the most relevant section of a technical video with 67% precision and 53% recall that outperforms the closely related existing approach from the literature. Our developer study involving 16 participants reports that they found the video summaries generated by TechTube very accurate, precise, concise, and very useful for their programming tasks rather than the original complete videos.
Unified Compact Modeling of Charge Trapping in 1/f Noise, RTN and BTI

We review and critically discuss the unified statistical modeling of charge trapping in the context of 1/f noise, RTN and BTI. The focus is on circuit (electrical) level models to support circuit designers. Modeling is based on discrete device physics quantities, which cause statistical variability in the electrical behavior of MOSFETs. It allows for the derivation of analytical formulations for 1/f noise, RTN and BTI using a single modeling framework.
Virtual Morality: Using Virtual Reality to Study Moral Behavior in Extreme Accident Situations

Virtual Reality (VR) technologies are widely employed to investigate human behavior in dangerous situations that cannot be safely reproduced in the real world, allowing researchers to study in an ecological way complex scenarios such as training for risky jobs, safety procedures, emergencies and, more recently, moral dilemmas in driving context. Understanding how people act when facing severe accidents involving unavoidable collisions has extremely important implications for the design and development of the “decisional system” of Autonomous Vehicles (AV s). However, previous studies have not focused on the differences between being the driver acting in a complex moral situation or being in a self-driving car that chooses for you. In the present paper, we described a case study that uses a first-person virtual reality simulation to investigate people's emotional reactions, perceived sense of responsibility, and acceptability of moral behavior in human and autonomous driving modalities. The main findings showed that participants experienced a high sense of presence in our simulation and react differently to the two driving conditions, showing a greater arousal, a more negative valence, and an increased sense of responsibility when faced moral dilemmas as drivers. Instead, in scenarios that did not involve killing someone (non-moral dilemmas), being in a fully autonomous vehicle was judged less pleasant than being the actual driver. These results suggest that people prefer to be in control only in common driving situations and not when their actions have deadly consequences on other people, suggesting the need to consider emotional factors in studying decision-making applied to autonomous vehicles, as a mean to reach a more complete understanding of people's reactions to this new technology, and to possibly gain insights for the design of autonomous driving systems and, more generally, AI-driven machines.
A Rate-based Drone Control with Adaptive Origin Update in Telexistence

A new form of telexistence is achieved by recording videos with a camera on an Uncrewed aerial vehicle (UAV) and playing the videos to a user via a head-mounted display (HMD). One key problem here is how to let the user freely and naturally control the UAV and thus the viewpoint. In this paper, we develop an HMD-based telexistence technique that achieves full 6- DOF control of the viewpoint. The core of our technique is an improved rate-based control technique with our adaptive origin update (AOU), in which the origin of the coordinate system of the user changes adaptively. This makes the user naturally perceive the origin and thus easily perform the control motion to get his/her desired viewpoint changing. As a consequence, without the aid of any auxiliary equipment, the AOU scheme handles the well known self-centering problem in the rate-based control methods. A real prototype is also built to evaluate this feature of our technique. To explore the advantage of our telexistence technique, we further use it as an interactive tool to perform the task of 3D scene reconstruction. User studies demonstrate that comparing with other telexistence solutions and the widely used joystick-based solutions, our solution largely reduces the workload and saves time and moving distance for the user.
The research culture and the development of research ability in students of the faculty of social and health sciences of the Península Santa Elena State University, Ecuador, during the period 2018–2019

Training in the research culture of university students represents a great challenge in a social context dominated by globalization. The university student must assume an investigative and critical attitude in their training. Far from focusing on updating the latest advances in knowledge of your specific career, it must be assumed from the perspective of comprehensive, ethical, scientific and humanistic training. The objective of the study is to evaluate the influence of the research culture on the development of research skills, applying research methods, techniques and instruments, aimed at 300 students from the Faculty of Social and Health Sciences. A correlation test was applied to verify related dimensions between the variables, research culture and development of research skills. There is a direct correlation between the variables of research culture and development of research skills. It is concluded that, as long as an adequate research culture is encouraged, research skills will be developed in the student of the Santa Elena Peninsula State University.
Learning Based Placement Refinement to Reduce DRC Short Violations

With the increasing complexity of the design rules, the routability has become one of the most essential factors that should be considered in the placement stage; however, being the routable basis of the placer in the past, the congestion map given by global routing cannot display the trend of routabiliy nowadays. If we want more comprehensive and close to the actual routing information, we must execute the complete flow including global routing and detailed routing, which is time-consuming. Therefore, how to access the accurate routing information rapidly is an important issue.This paper proposes a machine learning method and put it into our placement flow to help us solve the above problem. In this machine learning model, the features contain the information of placement itself and the global routing congestion. We utilize the model to predict the position of the detailed routing violations and feed the information back to placement system, and generate a new placement result afterwards. Experimental results show that comparing with the result of the original placer, the proposed methodologies can effectively decrease the number of the DRC violations.
Research on The Dynamic Model of Human Lower Limbs Based on 3D Vision

In order to improve the perception ability of the walking-aid robot and make it more accurately understand the user's training status, propose an analytical method of human lower limbs dynamic model based on 3D vision for the walking-aid robot. First. , we create a point cloud set of the field of view, use the advantages of 3D vision to quickly segment the leg and background point cloud data, and optimize the lower limb point cloud data through down-sampling and statistical filtering algorithms to provide accuracy and real-time guarantee for analyzing the lower limbs posture. The oriented bounding box of the local point cloud based on principal component analysis(PCA) is used to analyze the posture of the lower limbs, which is used as the angle feature of the lower limbs dynamic model. Combine the angle feature with the design constraints to obtain the dynamic model of walking lower limbs. Finally, the effectiveness of the algorithm is verified by design experiments.
Design Aspects of Terahertz (THz) Antenna

In this paper the analysis of traditional metallic structures of dipole and monopole antennas, along with the dielectric resonant antenna (DRA) structures in THz frequency band is carried out. The purpose of this analysis is to represent the design aspects of THz antennas. Here the antenna parameters of metallic dipole, metallic monopole and DRA structures are compared in order to highlight the feasibility of metallic and dielectric antennas in THz band.
Hand Gesture Recognition Based on Shape Context Analysis

The technological advancement is evolving the human–computer interaction (HCI). The goal is to ameliorate the HCI to a level where computers can be interacted in a natural way. It is a demanding aim and keeps the contemporary HCI systems complex and challenging. This paper aims to develop an effective hand gesture identification piloted HCI. It is realizable by three stages of preprocessing, features extraction and classification. The system functionality is studied by using a colored images database. Each incoming instance presents a hand gesture. Firstly it is subtracted from the background template to focus on the intended hand gesture. Afterward the subtracted image is enhanced and then converted into the grayscale one which is then thresholded by converting it in a binary image. This segmented version is further enhanced by using the morphological filters. The features are extracted by using the grayscale pixel values and shape context analysis (SC). Gestures are automatically recognized by using the k-Nearest Neighbor (k-NN) classification algorithm. The system achieves 83.3% of gesture recognition precision. The classification decisions are conveyed to the front-end embedded controller for systematic actuations and actions.
A study of OFF-state Current and Impact of Dielectric Constant on ION/IOFF ratio of Carbon Nanotube Field Effect Transistor

This paper deals with three key non-ballistic effects of carbon nanotube field effect transistor (CNT-FET) and also shows the significance of band bending and tunneling effect on off-state current of the device. A high dielectric material for gate insulator has been proposed because it enhances electrostatic control and reduces the short channel effects. Thus, by using higher dielectric materials, a significant reduction in IOFF takes place. As a result, ION/IOFF ratio also increases. The effect of temperature has also been discussed throughout the work.
Design of 2xVDD-Tolerant Power-Rail ESD Clamp Circuit Against False Trigger During Fast Power-ON Events

A new 2xVDD-tolerant power-rail ESD clamp circuit with voltage-level detection realized by 1xVDD devices is proposed against false trigger issue under fast power-on condition. All the 1xVDD devices in the proposed 2xVDD-tolerant ESD circuit are safely operated without gate oxide reliability issue. The proposed ESD clamp circuit has been implemented and verified in a 0.18-μm CMOS technology with 1.8-V devices. The experimental results have confirmed that the proposed ESD clamp circuit sustains a good HBM ESD level of 5.25kV and high immunity against false trigger issue under fast power-on condition. Moreover, polysilicon diodes are also verified to further reduce the stand-by leakage current of the proposed 2xVDD-tolerant power-rail ESD clamp circuit.
Simultaneous Localization and Mapping of Mobile Robot using GMapping Algorithm

The main aspect of the mobile robot system is the ability to localize itself accurately and simultaneously, create an map of the unknown environment. Simultaneous localization and mapping (SLAM) is required for autonomous navigation. The framework of SLAM is based on Rao-Blackwellized Particle Filter (RBPF) that is applied through G-Mapping algorithm. The 2D LIDAR scanner and odometer is used as main sensor in the simulated robot model. The RBPF algorithm and the data of the LIDAR and odometer are fused to create the 2D map of the simulated environment as well as localization and navigation of the robot in unknown environment. Experiment Implementation is carried in Robotic Operating system (ROS) and gazebo simulator.