Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-time detection and localization using SSD method for oyster mushroom picking robot*

Mushroom picking robot could significantly decrease the labor cost during production process. As well as humans, robots need to know what the mushroom is, where it is, and finally harvest it. This paper proposes an accurate and real-time capable object detection and localization approach for the use on oyster mushroom picking robot. Detection information of a neural network is combined with depth information from a RGB-D camera, which is mounted on the picking robot platform. The SSD object detection algorithm is used as convolutional neural network. In order to find the detected object in the 3D environment, the depth image based on binocular and structured light principles is also used to locate the precise position. In the evaluation part, the labelled images (4000 training, 300 validation and 300 test) of this study are tested. The results of picking robot experiments in the greenhouse showed that the recognition FI score of oyster mushroom had reached 0.951, and the 3D localization error of the vision system was only 2. 43mm, which guarantee the real-time performance and positioning requirements of oyster mushroom picking robots.
About the possibility to use the model based testing for system test automation

This paper presents the basic concept of using model-based testing to define and generate automated testes for automotive systems. It is described the development of model-based testing for the fuel level indication and the application of the model in the system testing. For developing the model, we define a consume curve of the fuel level and the results of the model are used in developing automated testing using an internal tool (uTAS) for the testing at the system level.
Analyzing parking lots vacancy detection algorithms using Mask R-CNN implementations

Parking is a very common problem that the modern society is facing nowadays. Numerous countries are working to manage their current transportation systems and road infrastructure to enhance traffic flows, mobility, and safety. The city of Timisoara has already taken the first steps towards this in the past, but the city is still afflicted by excessive traffic. The aim of this paper is to present, analyze and compare two algorithms for detecting occupied and vacant spaces in the parking place using the Mask R-CNN implementations. The potential implications include a drastic reduction in parking time and, consequently, overall traffic.
A Resistance-to-time Convertor to enhance Resistive Sensitivity of Embedded Systems

A Resistance-to-time convertor consists of a voltage follower, a bridge amplifier and Schmitt trigger to increase the resistive sensitivity and resistance range of an embedded system like Arduino. Arduino has an Analog input Read scale between 0 to 1024 and max input voltage reading of 5V, due to which its resistance sensing capability is restricted and the accuracy is hindered. Therefore, the R to t convertor is used to improve its resistance sensing capability. Experimental values are also given to demonstrate the above.
Resume Scanning and Emotion Recognition System based on Machine Learning Algorithms

In the current smart world, everything should be done faster, smarter, and accurate way. The various organization’s recruitment processes will be done face to face in an arranged venue. But, during some pandemics like Covid-19 face to face recruitment process will be very difficult. In the proposed system, a smarter way of performing the recruitment processes anywhere around the world based on the company requirements is performed. The aim of this article deals with making the process of candidate recruitment easier for companies. The amount of manual work that goes into recruiting processes is reduced and the initial scanning process of candidates was performed. By eliminating the redundant candidates helps in retaining only the applicable ones. Achieve this through the help of resume scanning, initial aptitude testing of candidates, and an interview session where the candidate answers questions asked by the interviewer. With this model, all the time and manual labor that is wasted in eliminating the redundant candidates is accomplished. It chooses the one who is best applicable to a job by comparing it with the job description based on the resumes received. Our model is working accurately for some of the predefined parameters of the company in a recruitment process by providing more security and reliability.
Multi-Scale Video Inverse Tone Mapping with Deformable Alignment

Inverse tone mapping(iTM) is an operation to transform low-dynamic-range (LDR) content to high-dynamic-range (HDR) content, which is an effective technique to improve the visual experience. ITM has developed rapidly with deep learning algorithms in recent years. However, the great majority of deeplearning-based iTM methods are aimed at images and ignore the temporal correlations of consecutive frames in videos. In this paper, we propose a multi-scale video iTM network with deformable alignment, which increases time consistency in videos. We first a lign t he i nput c onsecutive L DR f rames a t t he feature level by deformable convolutions and then simultaneously use multi-frame information to generate the HDR frame. Additionally, we adopt a multi-scale iTM architecture with a pyramid pooling module, which enables our network to reconstruct details as well as global features. The proposed network achieves better performance compared to other iTM methods on quantitative metrics and gain a significant visual improvement.
Deep Reinforcement Learning for Distributed Computation Offloading in Massive-user Mobile Edge Networks

This work investigates the distributed task offloading problem in a mobile edge computing network with a massive number of users and one single base station (BS). Each user can decide, in a distributed manner, whether to compute its task locally or to offload to a common edge server equipped at the BS for execution. If edge computing, the user transmits its task data along with a unique but potentially non-orthogonal pilot sequence to the BS using a grant-free multiple-access scheme, and then the BS detects the offloading user and decodes the task data. The misseddetection case may occur, in which the missed-detection probability increases with the total number of offloading users. Thus, the offloading decision of each user depends highly on the decisions of all other users. More specifically, if the number of users that decide to offload increases, the risk that each user is miss-detected becomes higher, resulting in extra system cost compared with local computing only. To solve this issue, we apply Deep Q-Network (DQN) on each user and assume that the user can perceive the actions of other users in the last time slot as the observed state. Then, users make decisions on task computation only based on the local task parameters and few information broadcasted by the BS. The simulation results demonstrate that the algorithm can significantly minimize the system computation cost and show remarkable advantages when the number of user is large.
New Strategies Based on Improved Fruit Fly Optimization Algorithm for Unknown Indoor Odor Source Location

It has great importance to locate the leaked source of hazardous odor in a ventilated indoor environment, for chemical warehouse safety and anti-terrorist explosion, etc. The distribution of odor plume is time-varying and intermittent in the indoor environment, which makes it difficult for single robot to locate the odor source. The multi-robot cooperative location of odor sources can reduce the interference of the above features and achieve high success rate of odor source localization. In this paper, the fruit fly optimization algorithm is applied to the odor source location problem, and a Fixed-step Fruit Fly Optimization Algorithm (FS-FOA) is proposed. To further improve the efficiency of the search and localization in the larger search range, a new strategy with adaptive algorithm for odor source localization is put forward, which is Concentration-Adaptive -Step Fruit Fly Optimization Algorithm (CAS-FOA). And the efficiency of the strategy in dynamic plume environment is validated in our experiments.
A semantic labeling framework for ALS point clouds based on discretization and CNN

The airborne laser scanning (ALS) point cloud has drawn increasing attention thanks to its capability to quickly acquire large-scale and high-precision ground information. Due to the complexity of observed scenes and the irregularity of point distribution, the semantic labeling of ALS point clouds is extremely challenging. In this paper, we introduce an efficient discretization based framework according to the geometric character of ALS point clouds, and propose an original intraclass weighted cross entropy loss function to solve the problem of data imbalance. We evaluate our framework on the ISPRS (International Society for Photogrammetry and Remote Sensing) 3D Semantic Labeling dataset. The experimental results show that the proposed method has achieved a new state-of-the-art performance in terms of overall accuracy (85.3%) and average F1 score (74.1%).
Single Band Frequency Selective Surface Suitable for C Band in Satellite Communication

The intent of this article is based on designing an investigation on a single band frequency response of a single-layer frequency selective surface. It is less manufacturing cost and a few dependencies on the polarization of the waves. The main two aspects of this study that the patch type band-reject filter resonate the frequency at C Band and maintaining the quality factor at higher Bandwidth. The single-layer FSS is presented by arranging the square-shaped metallic patch periodically in the dielectric sheet (FR-4 Epoxy). The proposed FSS provides a quality factor of 1.25 over the maximum rate of data transfer is about 80 % of higher bandwidth. The key factor of this article is to diminish the dimensions of the unit cell maintain to maintain the higher range of resonant frequency. The design and simulation were performed using the HFSS 2018.2 software and the ANSYS group.
Design of A Dual-Band SIW Antenna Array

In this letter, a differential-fed dual-band substrate integrated waveguide (SIW) antenna array is proposed for 5G communication. By employing the shoring pins and slots on the SIW element radiators, the $TM_{110}$ and $TM_{210}$ modes are combined in the lower band, while the $TM_{310}$ mode is used in the upper band. Then a $1 \times 4$ array is used to increase the gain. Due to the excellent electromagnetic shielding ability of the SIW structure, the coupling between the element antennas is much lower compared to traditional patch antennas, which can improve the beam scanning capability. The simulated results show that the realized gain is higher than 10.2 dBi in the lower band and 12.6 dBi in the upper band, and the beam scanning angles of the array are ±41° and ±25° in the lower and higher band, respectively. The proposed SIW antenna array has the advantages of low profile, high isolation and relatively high gain, which is a competitive candidate for 5G applications.
Portfolio Management based on Deep Reinforcement Learning with Adaptive Sampling

Quantitative trading finds stable and profitable trading strategies by observing historical data through statistics or mathematics methods. However, existing studies are still insufficient for the generalization of trading strategies. Therefore, this study takes the constituents of the Dow Jones Industrial Average as the target and applies the problem of optimizing the portfolio. The goal is to construct a portfolio of five assets from the constituent stocks, and this portfolio could achieve excellent performance through our trading strategy. Also, this study proposes a sampling strategy to determine which data is worth learning by observing the learning condition in order to save computational time. From the result of the experiment, we could observe that the model with our sampling strategy performed 6-7 % better than our baselines in terms of Sharpe ratio.
On Segmentation of Maxillary Sinus Membrane using Automatic Vertex Screening

The purpose of this study is to develop an automatic technique to segment the membrane of the maxillary sinus with morphological changes (e.g. thickened membrane and cysts) for the detection of abnormalities. The first step is to segment the sinus bone cavity in the CBCT image using fuzzy C-mean algorithm. Then, the vertices of inner bone walls of sinus in the mesh model are screened with vertex normal direction and angular based mean-distance filtering. The resulted vertices are then used to generate the bony sinus cavity mesh model by using Poisson surface reconstruction. Finally, the sinus membrane morphological changes are segmented by subtracting the air sinus segmentation from the reconstructed bony sinus cavity. The proposed method has been applied on 5 maxillary sinuses with mucosal thickening and has demonstrated that it can segment thin membrane thickening (< 2 mm) successfully within 4.1% and 3.5% error in volume and surface area respectively. Existing methods have issues of leakages at openings and thin bones, and inaccuracy with irregular contours commonly seen in maxillary sinus. The current method overcomes these shortcomings.
High aspect ratio topography reconstruction in sub-resonant atomic force microscopy exploiting stick-slip dynamics*

Sub-resonant tapping atomic force microscopy enables topography measurements with limited lateral tip-sample forces, while overcoming the limitations of excessive damage and measurement errors that occur during amplitude modulated operation on high aspect ratio samples. Therefore, it is a candidate to enable process quality monitoring in semiconductor device fabrication. However, at steep edges of such device geometries, the technique often results in stick-slip events for the probe, preventing the desired quasi-static motion of the cantilever and causing errors in the measured topography. To understand the stick-slip dynamics, a model is presented in this article that accurately predicts the consequences of stick-slip episodes. Employing this model, a time-domain based reconstruction technique is proposed to obtain an enhanced topography image. The proposed technique has been tested on a commercially available atomic force microscope with high aspect ratio tip probe and is shown to allow extraction of the topography of a grating sample with overhang features. Comparison of this technique with a conventional AFM method shows improved profile accuracy.
A Tile-based Fused-layer CNN Accelerator for FPGAs

The acceleration of Convolutional Neural Networks (CNNs) on FPGAs is becoming increasingly popular for computer vision tasks. However, the limited memory and bandwidth of these devices pose some challenges to the design of conventional CNN accelerators, which use external DRAM to store the intermediate results of each layer. To mitigate these criticalities, researchers have proposed the fused-layer methodology, which diminishes the accesses to the external DRAM by accelerating simultaneously multiple subsequent layers on the same chip. In this work, we propose a configurable fused-layer accelerator that exploits output tiling and the half-precision float datatype to reduce resource utilization. We assessed its effectiveness with experiments on VGG-16 and Yolo-Lite CNNs, targeting a Xilinx Zynq ZU6EG FPGA. Our design achieved up to 42% speedup and up to 95% fewer transfers from external memory compared to a single-layer baseline solution. Moreover, to ease and quicken the design space exploration, we developed a Machine Learning model that predicts the performance and the resource utilization of our accelerator with an accuracy > 90% on the reported dataset.
A 16 Gbps 10:1 Serializer with Active Inductor Based CTLE for High Frequency Boosting

This paper presents a half-rate 8–16 Gbps 10:1 serializer with an active inductive-peaking, capacitive-degeneration (AIPCD) based continuous-time linear equalizer (CTLE) for a SerDes system. The serializer uses an efficient 5:1 multiplexer, which also serves as a generic template for a 5*N:1 serializer. The transmitter receives ten low speed 0.8/1.6 Gbps parallel PRBS-31 bit sequences and outputs a high-speed 8/16 Gbps serial data signal. The AIPCD-CTLE presented in this work outputs good discernible eye-opening for up to 18 dB of channel loss at the Nyquist frequency, which can obviate the requirement of a feedforward equalizer. Implemented in CMOS 65 nm technology, for the output data rate of 16 Gbps, the transmitter achieves a 0.33UIPP eye width at a BER of 10−12, and 16.59 dB channel loss at the Nyquist frequency with 90 mW of power consumption.
Emotion-based AI Music Generation System with CVAE-GAN

Music emotion is important for listeners’ cognition. With the rapid development of technology, the variety of music has become more diverse and spread faster. However, the cost of music production is still very high. To solve the problem, the AI music composition has gradually gained attention in recent years. The purpose of this study is to establish an automated composition system that includes music, emotions, and machine learning. The system includes the music database with emotional tags as input, and deep learning trains the CVAE-GAN model as the framework to produce the music segments corresponding to the specified emotions. The subjects listen to the results of the system and judge that music corresponds to the original emotion.
Resiliency Analysis of Grid connected Solar PV System with Battery Support

Study on the grid outages and impact of integration of renewable energy sources along with energy storage systems on power system network is a present day need. The analysis of integration of PV system along with battery energy storage system with power system during the grid outages is discussed in this paper. Resiliency analysis, which is based on capability and duration of the system to survive during natural calamities and providing reliable supply to have a stable system, is performed here for the system considered. The application of energy storage systems in coordination with the Solar PV to estimate the resiliency of whole system is done. The study gives the probable solutions towards the energy management between available sources, the size of the energy storage units and also the duration for which it is to be connected to meet the critical loads, for an already existing power system network. The study has been carried out in a 10kW Solar PV system in PSCAD / EMTDC platform.
Biometric Identification Using Gait Analysis by Deep Learning

The walking pattern of a human being is termed GAIT. These Gait can be used for biometric identification. The COVID-19 pandemic situation has prompted us to change the ways of doing certain things in a traditional approach one of which includes, traditional biometric authentication systems which are physical contact based. This technology can be used as a method of contactless biometric authentication system in which contact chain forming between the users is prevented. The project that may be supported by supervised learning will acknowledge someone from the Gait features.
Data Transmission Time Minimization for LEO Satellite-Terrestrial Integrated Networks

In this paper, we investigate data transmission time minimization problem in low earth orbits (LEO) satellite-terrestrial integrated networks (STINs) with a central earth station (CES) for relay, which non-orthogonal multiple access (NOMA) scheme is used for data transmission from the Internet of Things (IoT) devices to CES, and orthogonal multiple access (OMA) scheme is used for data transmission from CES to satellite. Firstly, to tackle this problem, we decouple this problem into two sub-problems: subcarriers assignment in terrestrial network and data allocation in satellite subcarriers. Secondly, we formulate the subcarriers assignment as a many-to-one matching, and proposed the matching algorithm for subcarriers assignment. To keep fairness in satellite channels, we make that the rate in each satellite subcarriers is the same, and propose a minmax algorithm to minimize the max completion time. Finally, the simulations have shown that our proposed scheme has better performance than the existing approach, and the NOMA scheme always outperforms the OMA scheme.
A Voltage-Zone Based Power Management Scheme With Seamless Power Transfer Between PV-Battery for OFF-Grid Stand-Alone System

There is a requirement for a simple solution that provides seamless power management control and eliminates the issue of toggling between different operating modes in the stand-alone photovoltaic (PV) battery based hybrid system. The solution for the same is given in this article that uses a simple voltage control strategy. The given solution uses a voltage band technique for eliminating the toggling between charging and discharging modes. Further, the toggling between intermediate PV mode with charging and discharging mode is eliminated using the inner voltage band. Thus, the proposed control strategy provides a smooth and seamless control between different modes based on load voltage information. It avoids the overlap of charging and discharging modes within the switching cycle to improve the life-span of the battery. Also, the given control strategy does not require the power information and complex computations like division, etc., for its operation. The given solution can be easily implemented on any PV-battery-based hybrid system having independent control for charging and discharging of the battery with maximum power point tracking (MPPT). A simulation was done to verify the operation in various modes using seamless control. The simulation results are further justified with experimental results from the developed lab prototype.
Sinusoidal Voltammetry for the Analysis of Carbohydrates at Copper Electrodes

A digital approach for the collection and analysis of electrochemical frequency domain spectra is presented for the oxidation of carbohydrates at a copper electrode using a continuous, large-amplitude sine wave as an excitation waveform. The background charging current response is a phase-shifted sine wave with the major frequency component concentrated at the fundamental frequency. A nonlinear faradaic response due to the oxidation of sugars produces significant signal intensities at the higher harmonics as well as the fundamental frequency. Examination of the frequency spectra of glucose and maltose leads to selective and sensitive detection of these sugars at a copper electrode. The selectivity of this measurement relies on the inherent difference in the frequency domain spectra (i.e., magnitude and phase of each harmonic) of sugars of different sizes. This frequency distribution is dramatically affected by temperature, indicating the effect of kinetics in the mechanism for the oxidation of sugars. The.