Miracle-dz/UniFAR-FTUs huggingface dataset:
An Innovative Approach For Fruit Ripeness Classification

An automatic vision-based system for sorting and analyzing strawberry fruit is discussed in this paper. Manual classification leads to discrete opinion and is more time-consuming. Hence, finding a cost-effective and highly accurate method for strawberry classification yet remains a problem. In this paper, an automated system is proposed to predict the ripeness level of strawberry fruit, using a convolutional neural network. For the success of classification, appropriate features must be extracted. Surface color, size and shape are the necessary features for classification. The surface color of strawberry fruit determines its ripeness level. CNN is used to extract color, size and shape features from strawberry surfaces. The system shows 91.6% accuracy.
Semisupervised Dual-Dictionary Learning for Heterogeneous Transfer Learning on Cross-Scene Hyperspectral Images

Lack of labeled training samples is a big challenge for hyperspectral image (HSI) classification. In recent years, cross-scene classification has become a new research topic. In cross-scene classification, two closely related HSI scenes are considered, one contains adequate labeled samples, namely source scene, while the other one contains only a few labeled samples, namely target scene. The goal of cross-scene classification is utilizing the labeled samples in source scene to benefit the classification in target scene. In most cases, different HSIs are imaged by different sensors, leading to different feature dimensions (numbers of bands) in different scenes. In this situation, heterogeneous transfer learning is demanded. In this article, we propose a heterogeneous transfer learning algorithm namely semisupervised dual-dictionary nonnegative matrix factorization (SS-DDNMF). SS-DDNMF consists of two contributions. 1) Dual-dictionary nonnegative matrix factorization (DDNMF): DDNMF trains two dictionaries for source and target scenes, respectively, aiming at projecting the source and target features to a shared low-dimensional subspace, eliminating the difference between feature spaces. In DDNMF, within-scene and cross-scene graphs are built to maintain the similarities between pixels. 2) Semisupervised learning for target scene: as the limited number of labeled pixels in target scene will affect the graph building of DDNMF, semisupervised learning is adopted in target scene. In details, superpixel segmentation is adopted to generate pseudolabels for some unlabeled pixels, thus more “labeled” pixels can be considered for building better graphs. The effectiveness of SS-DDNMF is verified by experiments on cross-scene HSIs.
Photovoltaic Power Station Modeling and Its Impact on Distribution Networks

The integration of Distributed Generation (DG) can affect the distribution network in many aspects, such as power flow, short-circuit current, distribution grid protection, etc. In this paper the influence of a MW photovoltaic (PV) station to a distribution network is mainly studied. The mathematical model and equivalent circuit of a photovoltaic cell are presented in the first part of this paper. Then a 1.6MW PV station model is established in the software of DIgSILENT PowerFactory. This model is not a precise one, but it is more convenient for the research of PV station’s affection to a power grid. In the last part of this paper, various simulations are performed to investigate the influence of the PV station to a distribution network. The result shows that the model is capable for our investigation and the influence varies with different operating modes of the PV station.
Zero-touch coordination framework for Self-Organizing Functions in 5G

Traditional mobile network services are built by chaining together multiple functional boxes on which creation of new services is rather static. With the advent of 5G technology the ability to offer agile on-demand services to the users is mandatory. Therefore lifecycle operations such as service initial deployment, configuration changes, upgrades, scale-out, scale-in, optimization, self-healing etc. should be fully automated steps. Self-Organized Networks Functions (SF) were proposed to provide self-adaptation capabilities to mobile networks on different fronts: configuration, optimization and healing and somehow reduce the error-prone human intervention.Nevertheless, conventional design of these SFs was based on single objective optimization approaches where SFs were considered as standalone agents aiming at one very specific local objective (e.g. reduce the interference or increase the coverage). Thus, complex inter-dependencies between SFs were at some extent unattended, so when more than one function is acting on the network, conflicts are inevitable. A well-studied conflict happens when Mobility Load Balancing (MLB) and Mobility Robustness optimization (MRO) functions are simultaneously set up: without coordination, performance degradation is expected because of the cross-dependencies between both SFs. To cope with these underlying conflicts, we propose a zero-touch coordination framework based on Machine Learning (ML) to automatically learn the dynamics between the selected SFs and assist the network optimization task.
Location-Free CSI Based Activity Recognition With Angle Difference of Arrival

Device-free activity recognition is an indispensable technology in Human-Computer Interaction (HCI). The activity recognition system based on WiFi signals relying on the wide coverage of WiFi makes HCI more convenient. The previous research on WiFi-based activity recognition system has achieved high recognition accuracy. While the challenge that activity recognition is limited to fixed location and complex background, remains unresolved. In this paper, we propose a location-free activity recognition system which leverages fine-grained channel state information (CSI) to recognize same activities regardless of different locations and background. With CSI recorded in the Network Interface Card (NIC), Angle Difference of Arrival (ADoA) is reckoned to eliminate the location and background information, which is only consistent with the activity tendency. Then the Principal Component Analysis (PCA) method is utilized to reduce the dimension and followed by curve smoothing to make the signal more smoother. Furthermore, Bidirectional Long Short-Term Memory (BiLSTM) network is selected as ideal training machine to deal with issues that are highly correlated with time series. We use two commercial wireless network cards in the typical life scene, and finally achieve 93.7 % of recognition accuracy.
Hmc: A Hopping-Based Multi-Channel Coordination Scheme For Urllc In Unlicensed Spectrum

IMT-2020 focuses on providing new services over diverse application scenarios, such as ultra-reliable low latency communications (URLLC). Regarding the increase of mobile traffic demand, the consensus of deploying 5G in unlicensed spectrum has been reached under the premise that the coexistence issue between 5G and Wi-Fi has been resolved. This paper proposes a hopping-based multi-channel coordination scheme, aiming at determining the minimal number of channels occupied for URLLC service without violating regulations. The proposed scheme uses the busy tone and full-duplex radio technologies to reduce the interference and collision. Moreover, by using the proposed scheme, the URLLC device can efficiently hop to the channel with less or no collision for data transmission. Numerical results validated that the proposed scheme actually achieves the reliability requirement of URLLC.
An Emerging Dynamics Approach for Synchronization of Linear Heterogeneous Agents Interconnected Over Switching Topologies

In this letter, we propose an emergent dynamics based tool to approximate the synchronized behavior in large networks of interconnected linear systems with switching interconnection topologies and linear coupling. We assume that the networks are heterogeneous, but the states of all systems are of the same dimension. The goal of this letter is to show that, in the case of sufficiently large coupling gains, the network is practically synchronized, and its’ synchronized behavior can be approximated by a reduced order switching system independent of the control gains. The results are ensured for strongly connected networks under fairly mild assumptions by introducing a minimum dwell-time between two consecutive switches. Numerical simulations illustrate the theoretical results.
Estimates of acoustic target strength for giant jellyfish Nemopilema nomurai Kishinouye in the coastal Northwest Pacific

Acoustic target strength (TS) measurements were made of ex situ giant jellyfish Nemopilema nomurai Kishinouye at 38 and 120 kHz. These TS data may be useful for developing acoustic scattering models, and surveying giant jellyfish distributions and biomasses. Each jellyfish was tethered in seawater using a monofilament line that vertically penetrated its bell’s centre. During the acoustic measurements, an underwater video camera was used to continuously monitor the jellyfish’s behaviour. Acoustic measurements were made using splitbeam transducers. TS measurements were made of 27 individual jellyfish, but data were analysed for 23 specimens (bell diameter in air, Dair ¼ 21– 65 cm) at 38 kHz, and 19 specimens (Dair ¼ 21– 46 cm) at 120 kHz, respectively. Least-squares regression fits of TS vs. log(Dair) were TS38kHz ¼ 20†log10Dair– 82.7 (r ¼ 0.76) and TS120kHz ¼ 20†log10Dair –86.7 (r ¼ 0.79). The mean TS values at 38 and 120 kHz, using the average Dair ¼ 40.3 cm and 35.5 cm, respectively, were 250.6 and 255.7 dB. The reduced TS, a function of the ratio of Dair to wavelength (l), was RTS(Dair/l) ¼ 26.1†log10(Dair/l) –36.1 (r ¼ 0.51). These RTS values decreased with increasing Dair/l. Symbiotic medusa shrimp (Latreutes anoplonyx Kemp) contributed negligible bias to our TS measurements of giant jellyfish. These ex situ TS measurements may be used in acoustic surveys to estimate the distributions and biomasses of N. nomurai.
Virtual Network Function Deployment Strategy in Clustered Multi-Mobile Edge Clouds

Software Defined Networking (SDN) and Network Function Virtualization (NFV) have been widely acknowledged as the fundamental architectural technologies for 5G and beyond networks by consolidating network functions into general-purpose hardware. Meanwhile, emerging Mobile Edge Computing (MEC) technology provides a promising solution to fulfill service requirements on high reliability and low latency, by extending cloud computing to the edge of networks. To provision a specific type of service, a virtualized network topology, such as Service Function Chain or Service Function Graph is constructed by logically connecting a set of virtual network functions (VNFs). In SDN/NFV-based mobile networks with the MEC cluster, it is imperative to develop an effective VNF deployment strategy to support various services with diverse Quality of Service requirements. In this paper, we propose a VNF deployment strategy for clustered MEC, including VNF placement and routing schemes, with the aim to minimize the average delay of service flows. We first formulate the problem as a two-dimensional knapsack problem, which is NP-Hard. To provide an efficient solution, we develop an improved genetic simulated annealing algorithm. Numerical results show that the proposed strategy can significantly reduce the end-to-end service delay in comparison with the state-of-the-art solutions.
Topology Analysis of Distribution Network based on Multi-Source Measurement Data

Aiming at the problem of poor real-time performance of the current distribution network network topology analysis, combined with the current situation where multiple measurement systems of the distribution network coexist. A method for network topology analysis of distribution network using multi-source measurement data is proposed. The distribution network Micro-synchronous Phasor Measurement Unit (µPMU) is used, the current phasor data of the distribution network can be collected quickly and accurately, and the real-time network topology reconfiguration judgment conditions are constructed on this basis. Data from Supervisory Control and Data Acquisition (SCADA) system and µPMU are used to estimate the state of the distribution network. Furthermore, the actual µPMU measurement data is used to match the operating mode with the state estimation results under various network topology modes, and the operating network topology structure can be determined. The IEEE 33-bus model is used for the algorithm validation.
Demand Response Program for Shiftable Modes in Variable Tariff Zones of an Utility

Load shifting is one of the main key components of the demand response (DR) program which reduces the peak loading of power generating stations of utility. In DR programs controlling methodology is a critical task that involves electricity producer to the consumer of the electrical utility. This investigation presents a logical shifting algorithm for shiftable modes of operations of schedulable loads of users. In this approach, we have considered a washing machine and dishwasher of residential shiftable loads due to its multiple modes of operation. A day ahead zonal forecasting pricing data of New York City is taken from the website for the proposed algorithm illustration. The mode shifting algorithm for schedulable loads controls the operational mode of the appliances. User participation in this program benefits the user in the cost savings by shifting the operational modes of the appliances. It also reduces the peak demand of the generating stations. The idea delay-in- mode (DIM) is to shift the mode of operation of schedulable appliances from high tariff time slots to possible low tariff time slots and thus reducing the cost of operational modes and appliances. Day-ahead forecasting tariff curve is divided into multiple zones and zone wise analysis done for each appliance. This deterministic algorithm is executed in MATLAB using the logical functions for shifting. The results of the proposed approach show a clear way to the user of utility to reduce the operational cost of schedulable loads in a variable tariff environment. The proposed approach provides a simple way of DR program participation of users.
End-to-end Throughput Optimization in Multi-hop Wireless Networks with Cut-through Capability

In-band full-duplex (FD) technique can efficiently improve the end-to-end throughput of a multi-hop network via enabling multi-hop FD amplify-and-forward relaying (cut-through) transmission. This paper investigates the optimal hop size of a cut-through transmission and spatial reuse to achieve the maximum achievable end-to-end throughput of a multi-hop network. In particular, we consider spatial reuse and establish an interference model for a string-topology multi-hop network with x-hop cut-through transmission, and show that the maximum achievable end-to-end throughput is a function of x and the spatial separation between two concurrently active cut-through transmissions. Through extensive numerical studies, we show that the achievable date rate of a cut-through transmission drastically decreases along with the increase of the hop size x. Furthermore, we find that the 2-hop cut-through transmission mode can always achieve the maximum end-to-end throughput using Shannon Capacity formula if the spatial reuse is properly addressed. On the other hand, the results show that the 5-hop cut-through transmission mode can obtain the maximum end-to-end throughput with discrete channel rates when the self-interference cancellation is perfect and the hop distance is small.
Cyber-Security Constrained Placement of FACTS Devices in Power Networks From a Novel Topological Perspective

Optimal placement of flexible AC transmission systems (FACTS) devices and the cyber-security of associated data exchange are crucial for the controllability of wide area power networks. The placement of FACTS devices is studied in this paper from a novel graph theoretic perspective, which unlike the existing approaches, purely relies on topological characteristics of the underlying physical graphs of power networks. To this end, the maximum matching principle (MMP) is used to find the set of required FACTS devices for the grid controllability. In addition, the cyber-security of the most critical data related to the FACTS controllers is guaranteed by introducing the concept of moderated-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-security where <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> is a measure of data obscurity from the adversary perspective. The idea of moderated-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-symmetry is proposed to facilitate the arrangement of the published cyber graph based on a permutation of nodes within the symmetry group of the grid, called generator of automorphism. It is then verified that the published cyber-graph can significantly obscure the data exchange over the cyber graph for adversaries. Finally, a similarity is observed and demonstrated between the set of critical nodes attained from the symmetry analysis and the solution of the FACTS devices placement that further highlights the importance of symmetry for the analysis and design of complex power networks. Detailed simulations are applied to three power networks and analyzed to demonstrate the performance and eligibility of the proposed methods and results.
Substantial progress made in the rearing of the European cherry fruit fly, Rhagoletis cerasi

Successful and reproducible rearing of Rhagoletis cerasi L. (Diptera: Tephritidae) is imperative for substantiated research on this main pest in cherries in Europe, for instance for the applied research on control of the pest, as well as for basic research on its physiology, behaviour, or reproduction. Temperate Rhagoletis species with an obligatory diapause are more difficult to rear in the laboratory than most of the tropical or subtropical fruit fly species (Boller, 1989). The host range of the monoor oligophagous Rhagoletis species comprises a few main host plants, for R. cerasi these are sweet cherry [Prunus avium L. (Rosaceae)], tart cherry (Prunus cerasus L.), and fly honeysuckle [Lonicera xylosteum L. (Caprifoliaceae); Boller & Bush, 1974]. Rearing of Rhagoletis spp. included an adequate oviposition device and a suitable larval diet. In previous studies, Boller and Haisch et al. worked intensively on both aspects for R. cerasi. Boller and Prokopy developed black-dyed ceresin wax domes for oviposition (Prokopy & Boller, 1970, 1971; Boller & Ramser, 1971; Katsoyannos et al., 1977; Boller, 1984). However, this wax is no longer available. Hence, a new wax with similar or improved features had to be found. Boller (1984) and Haisch (1975) described different larval diets based mainly on brewer’s yeast, wheat germ, sugar, organic acids, and vitamins. Both authors reported pupation rates between 40 and 46%, but no reports on repeated or continuous laboratory rearing were published until now. In the experiments reported here, we could not reproduce these pupation rates. Furthermore, a Rhagoletis diet is sold by Bio-Serv (Frenchtown, NJ, USA) without information on its efficacy. For decades, the lack of a reproducible rearing method has hampered progress in R. cerasi research. The urgent need of applied research on R. cerasi becomes increasingly obvious because of the ongoing re-evaluation of pesticide usage in the EU. New and environmentally sound control strategies of this pest rely on the knowledge ascertained from basic research, wherefore successful rearing is a prerequisite. Based on present rearing knowledge, a reproducible rearing method was developed with an alternative for the formerly used, but now unavailable, wax for egg devices, and with optimised larval diets. Adult emergence, oviposition, and larval development until pupation of R. cerasi were quantified in this study.
A High Sensitivity 20-MHz Endoscopic Transducer With Integrated Miniature Amplifier

Endoscopic transducer with small size and high frequency has been widely studied and applied in medical ultrasound imaging. However, attenuation affects high frequency ultrasound waves to a greater degree than lower frequency waves, which results in limited endoscopic ultrasound detection depth and poor signal-to-noise ratio in the image. High sensitivity transducer is an effective way to improve this problem. In this paper, a high sensitivity endoscopic ultrasound transducer (HSEUST) with an integrated miniature amplifier was proposed, where the amplifier can effectively enhance the received ultrasound signal and reduce the impact of noise. According to the tests and experimental results, this sensor is about 4 times more in sensitive, 20% more in resolution than conventional endoscopic transducer. With high sensitivity, this transducer holds the potential for visualization of deeper tissue in the body with high resolution, which will greatly expand the application capabilities and fields of endoscope ultrasound imaging.
Online Model Identification Method of Vanadium Redox Flow Battery Based on Multiple Innovation Recursive Least Squares

Vanadium redox flow battery (VRB) have a wide range of applications in renewable energy power generation, intelligent microgrids, and other fields due to their good safety and long cycle life. The internal parameters of vanadium redox flow battery will change in real time during operation. In order to ensure the reliable operation of the battery, an accurate electrical model needs to be established to monitor and predict the battery status in real time. In this paper, an RC equivalent model is established for vanadium redox flow battery, and the parameters are identified based on the model. In order to overcome the shortcomings of recursive least squares (RLS) algorithm, which is prone to data saturation and not tracking the time-varying parameters, this paper proposes a model parameter identification method based on the multiple innovation recursive least squares (MIRLS) algorithm. Based on the data collected in the pulse charging experiment, RLS and MIRLS algorithms were used to identify the model parameters. The experiments were performed on Wuhan University of Technology's 5kW / 3kWh VRB experimental platform, and the results show that the MIRLS algorithm has better convergence and accuracy in tracking time-varying parameters.
Training Sequence Based Doppler Shift Estimation for Vehicular Communication

To guarantee the safety requirements of autonomous driving vehicles, the efficient and robust sensing information sharing among vehicles is crucial to overcome the environment sensing limitations beyond a single sensor or vehicle. The millimeter wave (mmWave) technology is considered as one of the potential mobile broadband communication solutions to solve high data rate sensing information sharing problem among vehicles with the low latency constraint. However, the Doppler shift effect is much more severe, which will deteriorate the performance of vehicular communication links, especially in a high speed vehicle communication scenario in the mmWave frequency band. Therefore, we propose a training sequence based estimator (TSBE) by designing a hybrid structure combining the fixed section and the repeated sequences, aiming to achieve a fast and accurate Doppler shift estimation (DSE) in the mmWave vehicular scenario. The fixed section provides a fast estimation, and the accuracy of DSE can be effectively improved by the random correlation selection of repeated sequences. Furthermore, the key performances of our proposed DSE algorithm are evaluated by both the link-level simulation and the hardware testbed results. Results prove that the mean-squared error of our proposed TSBE algorithm outperforms other conventional algorithms under various signal to noise ratio values. The hardware testbed shows a remarkable performance improvement on the error vector magnitude and the bit error rate using our proposed TSBE and compensation algorithm.