Miracle-dz/UniFAR-FTUs huggingface dataset:
Combining Domain Adaptation and Spatial Consistency for Unseen Fruits Counting: A Quasi-Unsupervised Approach

Autonomous robotic platforms can be effectively used to perform automatic fruits yield estimation. To this aim, robots need data-driven models that process image streams and count, even approximately, the number of fruits in an orchard. However, training such models following a supervised paradigm is expensive and unpractical. Extending pre-trained models to perform yield estimation for a completely new type of fruit is even more challenging, but interesting since this situation is typical in practice. In this work, we combine a State-of-the-Art weakly-supervised fruit counting model with an unsupervised style transfer method for addressing the task above. In this sense, our proposed approach is quasi-unsupervised. In particular, we use a Cycle-Generative Adversarial Network (C-GAN) to perform unsupervised domain adaptation and train it alongside with a Presence-Absence Classifier (PAC) that discriminates images containing fruits or not. The PAC produces the weak-supervision signal for the counting network, that can then be used on the target orchard directly. Experiments on datasets collected in four different orchards show that the proposed approach is more accurate than the supervised baseline methods.
One-Meter Deployable Mesh Reflector for Deep-Space Network Telecommunication at ${X}$ -Band and $Ka$ -Band

This article presents the design and optimization of a deployable 1 m mesh reflector compatible with a 12U-class CubeSat. This antenna is designed for telecommunication and is compatible with NASA’s deep-space network (DSN) at <inline-formula> <tex-math notation="LaTeX">${X}$ </tex-math></inline-formula>-band (i.e., uplink: 7.145–7.19 GHz; downlink: 8.4–8.45 GHz) and <italic>Ka</italic>-band frequencies (i.e., uplink: 34.2–34.7 GHz; downlink: 31.8–32.3 GHz). Three right-handed circularly polarized (RHCP) antennas, both transmit and receive, are introduced here: <inline-formula> <tex-math notation="LaTeX">${X}$ </tex-math></inline-formula>-band only, <italic>Ka</italic>-band only, and <inline-formula> <tex-math notation="LaTeX">${X}$ </tex-math></inline-formula>-/<italic>Ka</italic>-band. For the <inline-formula> <tex-math notation="LaTeX">${X}$ </tex-math></inline-formula>-band-only antenna, a gain of 36.1 and 36.8 dBic is achieved at uplink and downlink frequency bands, respectively. This translates into an aperture efficiency of 72% and 62%. For the <italic>Ka</italic>-band-only antenna, a gain of 48.4 and 48.7 dBic is obtained at downlink and uplink frequency bands, which translate into an aperture efficiency of 62% and 72%, respectively.
Performance Comparison of BCube and Fat Tree Topology Data Center Network using Named Data Networking(NDN)

One of the challenges in research on the Data Center Network (DCN) is to assess the effectiveness of the performance of the DCN architecture without having to build the original infrastructure first. Daji Wong et al. Have made a DCN simulation module based on the open-source NS-3 platform that allows obtaining unbiased DCN simulation results. The initial design used for host-to-host communication is no longer relevant to the current internet usage that is more widely used to distribute content. An alternative communication protocol is needed to tackle the existing problem. One of which is Named Data Networking (NDN). NDN is one of the technology protocol network based on Content-Centric Networking (CCN) which focus on the location where the data is located and only focus on the name. NDN could be applied to the network Internet and also Data Center Network (DCN) with an important role as the facility place for hosting data and housing computer systems through the network. In this research, our purpose is to use the NDN protocol for DCN. The topology types we used are Fattree and Bcube. The result of this research is that BCube with NDN has less delay than FatTree with NDN, but Fat-Tree with NDN has more throughput out data than Bcube with NDN.
FilterLAP: Filtering False-positive Mutation Calls via a Label Propagation Framework

Benefiting from the recent advantages of genomic sequencing, detecting genomic mutations becomes a routine work in precise diagnoses and treatments for cancers. In clinical practices, many factors, such as tumor purity, clonal structure, etc., interfere the performance of calling mutations. The computational pipelines prefer to sensitively report the candidate calls, while a filter is applied for removing the false-positive calls. The existing filters rely on the whole genome/exome sequencing data, which can provide sufficient samples for training the filters. However, the gene-panel sequencing is more popular in clinical practices, but there is no practical filter for limited training samples. In light of this, we develop a semi-learning filter for gene-panel sequencing data, FilterLAP, which implemented via a label propagation framework. Given few labeled samples with a set of unlabeled ones, its basic idea is to predict the label information of unlabeled nodes from the label information of labeled nodes, and establishes a complete graph model by using the relationship between samples, by combining transductive inference with label propagation algorithm. For each node in the network, tags are propagated to adjacent nodes according to similarity and the probability distribution of similar nodes tends to be similar and can be divided into a class. We perform multiple sets of experiments on gene-panel sequencing data captured from Illumina platform. FilterLAP outperforms on both SNV and INDEL filtering, where the AUCs reach 0.90-0.97, and the average accuracies on overall mutation calls are over 90%. Comparing to GATK hard filters, FilterLAP present a 5% improvement on accuracy. These results demonstrate that the proposed method can better reduce the false positive mutation calls on gene-panel sequencing data. In addition, it is stable and efficient, which can be used as a practical tool for mutation call filtering for gene-panel sequencing data.
Analysis and Comparison of SMES Device Power Losses Considering Various Load Conditions

Reliability concerns and continuous availability of superconducting magnetic energy storage (SMES) devices in photovoltaic (PV) power generation systems have been greatly increased. The PV generation systems are highly affected by the continuously changing solar irradiance levels in addition to the operating ambient temperatures. A practical power losses model based on the available experimental measures of SMES devices is proposed in this paper. In addition, this paper presents power losses performance comparison for SMES devices in PV systems with considering the fluctuations in PV generations and the various types of loading. The new proposed model is simple to be integrated with the power management controllers, in addition to being modular for various SMES ratings. A hybrid PV-SMES energy system is selected as a case study with various types of loads for validating the new proposed loss model.
Automatic Tongue Image Segmentation For Real-Time Remote Diagnosis

Tongue diagnosis, one of the essential diagnostic methods of Traditional Chinese Medicine (TCM), is considered an ideal candidate for remote diagnosis methods because of its convenience and noninvasiveness. However, the trade-off between accuracy and efficiency and the variation of tongue images pose great challenges in real-time tongue image segmentation. To remedy these problems, in this paper, a light weight architecture based on the encoder-decoder structure is proposed. The tongue image feature extraction (TIFE) module is designed to generate features with larger receptive fields without sacrificing spatial resolution. The context module is used to increase the performance by aggregating multi-scale contextual information. The decoder is designed as a simple yet efficient feature upsampling module to fuse different depth features and refine the segmentation results along tongue boundaries. The loss module is proposed to deal with misclassifications causing by class imbalance. A new tongue image dataset (FDU/SHUTCM) is constructed for model training and testing, which contains 5,600 tongue images and their corresponding high quality masks. We demonstrate the effectiveness of the proposed model on BioHit, PolyU/HIT, and our datasets, achieving the performance of 99.15%, 95.69%, and 99.03% IoU accuracy, respectively. Segmentation of a $513\times 513$ image takes 165 ms on CPU.
Performance evaluation of Intended Interference Lenient Secure Spectrum Sensing (IILSSS)

The plethora of the wireless applications combined with the state of the art technology have led to the dearth of the spectrum. The spectrum is such a commodity which cannot be created. The only alternative is to exploit the available spectrum which is already assigned to the Primary User (PU) who is the authorized user of the allotted spectrum. FCC (Federal Communications Commission) has taken a decision to allow the unauthorized users called Secondary Users (SU) to use the licensed spectrum when it not being used. Cognitive Radio (CR) is the technology which allows the imminent users to transmit the signals without causing intrusion to PU. The major challenge of Cognitive Radio Ad Hoc Network (CRAHN) is sensing the indolent spectrum and effectively managing the spectrum by nodes of SU. The sensing of spectrum can be augmented by allowing the coordination among nodes to share information concerning the state of spectrum in the CRAHN. However, the likelihood of Spectrum Sensing Data Falsification (SSDF) intrusions by the spiteful nodes of CRAHN is very much possible. In this paper, the neighboring nodes authenticity is evaluated through the spectrum state verification (SSV) procedure. The model referred as Intended Interference Lenient Secure Spectrum Sensing (IILSSS) by PSV is targeted to impede the SSDF attacks which is intended to cause interference by falsifying the data related to SSV. The experiments carried out with typical network situations and attacks of SSDF shows that the method has reduced the fault-value of the decision of spectrum and the identification rate of the malevolent cognitive nodes is improved. The Quality of Service (QoS) of the SU is also important and the performance is assessed using throughput, Packet Delivery Ratio (PDR) and End-to-End Delay.
High Gain DC-DC Converter using Coupled Inductors for Renewable Application

A family of high gain DC-DC converters is reported in literature with voltage gain up to 30. The high gain DC-DC converter find its application in integration of solar and other renewable energy resources. As the output voltage of photovoltaic cell varies continuously and not matches with the dc grid ratings, it requires a high gain dc-dc converter with proper closed loop control. The proposed converter comprises of two coupled inductors that are associated in parallel and series amid storing and releasing energy respectively. The switches with two distinctive duty ratios are the major benefit of the proposed converter to accomplish high voltage gain without utilizing large duty ratio.
MPPT System Using Incremental Conductance for Solar Cell in Normal and Partial Shading Conditions

In this research, MPPT (maximum power point tracking) system has been created on solar cells to get maximum power from solar cells. The designed system consists of 200 watt solar cells, voltage sensors, current sensors, microcontroller, DC-DC converter, and resistive loads. The MPPT system created uses incremental conductance algorithm to set the switching duty cycle on the DC-DC converter, with input parameters in the form of ΔV and AI to produce maximum power. This system is tested on solar cells with normal or partial shading conditions and under conditions of shadow-covered solar cells of 8%, 16%, 30%, 41% and 50% with a load of 10 Ohm, 25 Ohm, and 35 Ohm. The results obtained that the MPPT system can stabilize the output power of solar cells both under normal and shadow conditions and are able to maintain output power when there is a change in input power with the maximum efficiency of the MPPT system at a load of 10 Ohm of 78,38% with a standard deviation at the input power of 12,68 and standard deviation at output power of 7,74. Maximum efficiency at 25 Ohm load of 80,23% with standard deviation at input power of 3,59 and standard deviation at output power of 2,90. Maximum efficiency at 35 Ohm load is 82,7% with standard deviation at input power of 4,14 and standard deviation at output power of 3,29.
Single-Phase AC–DC Converter With SiC Power Pulsation Buffer for Pulse Load Applications

Single-phase ac–dc converter for low-frequency pulse load normally requires high-capacitance electrolytic capacitors, which reduces the lifetime and may cause reliability problem. To meet the challenges of voltage ripple, this article introduces a power pulsation buffer (PPB) that smooths the output voltage and provides essential pulse power to the load simultaneously. The proposed PPB is realized by a discontinuous current mode (DCM) bidirectional buck/boost converter with real-time duty-cycle calculation (RDC) and peak voltage control. The proposed control method can relieve the capacitance requirement and achieve good dynamic response. The electrolytic capacitor is replaced with the film capacitor which has better reliability. SiC devices are adopted to further reduce the inductor volume of PPB. The core-less design is also proposed to design an integrated air-core inductor for further volume deduction. To meet the DCM requirement of PPB in various operating conditions, the phase shift between the line voltage and pulse load is taken into account. An electrolytic capacitor-less prototype with 1800-W peak power is built to verify the advantages of the proposed method.
A Squeeze Convolutional Network For MRI Right Ventricle Segmentation

Segmentation of the right ventricle (RV) is difficult due to the variable shape and fuzziness of RV boundaries. In this paper, we propose an effective squeeze Convolutional Network for RV segmentation. A squeeze expand attention (SEA) module is constructed by combining squeezing, expanding, and adaptive weighting of features to generate feature maps for prediction. Our SEA module is introduced into a U-shaped network architecture to extract RV features in the coding layers and make end-to-end decisions in the decoding layers. Besides, a loss function constrained by RV shape constraints is proposed in our network to attenuate abnormal prediction results. Our proposed network is with fewer parameters and is rapid in training. Experimental results on public datasets show that our proposed network achieves more accurate segmentation accuracy compared with the existing methods significantly in terms of accuracy and speed.
Computationally Efficient Finite-Set Model Predictive Current Control of Interior Permanent Magnet Synchronous Motors with Model-Based Online Inductance Estimation

In this paper, solutions to the main drawbacks of the traditional finite-set model predictive current control (FS-MPCC) of interior permanent magnet synchronous motors (IPMSM) are proposed. These drawbacks are: high computational load and high sensitivity to any model mismatch. The proposed computationally efficient FS-MPCC is based on finding the optimal voltage vector (VV) that would enhance the flow of the desired currents analytically. Based on its location in the stationary $\alpha- \beta$ plane, only three iterations of a modified cost function will be needed. Furthermore, a novel model-based online inductance estimation technique is proposed to enhance the robustness of the controller against model mismatch.
An evolutionary data model for the implementation of collective cloud manufacturing to maintain individual value-added networks

Cloud Manufacturing (CM) offers the possibility to use and provide production services within an as-a-service concept. The CM paradigm enables more efficient resource utilization in the context of industry 4.0 through fully automated order processing. Existing architecture solutions, however, follow a central platform approach with a global neutral data model. As a consequence, the individuality of companies within the CM is not given any room for expression and symbioses between companies are not established. The result is a disadvantage particularly for SMEs, which generate their competitive advantage from innovative production technologies and symbiotic connections with customers and other SMEs. The focus of Collective Cloud Manufacturing (CCM) is the preservation of these characteristics, in which an over-approximation of costs through the integration of production-related simulations is to be reduced and the data security of the customer is to be enhanced. So in this work, a data model for an existing concept for CCM is presented. The requirements for the data model and the runtime behavior are defined. Subsequently, the state of the art of existing models in the domain of CM is presented. The developed data model is presented with an application evaluation.
Fast Nonnegative Matrix Factorization Using Nested ADMM Iterations

As a constrained low-rank decomposition technique nonnegative matrix factorization (NMF) finds a wide variety of applications, especially in the analysis and design of pattern recognition systems for large-scale datasets. In this paper, we present a new algorithm for NMF based on nested alternating direction method of multipliers (ADMM) iterations. The paper describes the algorithm with a great deal of technical details, and includes a case study to demonstrate the algorithm’s ability to handle large-scale datasets with improved efficiency in comparison with those not using nested ADMM iterations.
Adaptive Neural Network Stabilization Control of Underactuated Unmanned Surface Vessels With State Constraints

This paper studies the stabilization problem for the non-diagonal inertia and damping matrices underactuated unmanned surface vessels (USVs) in the presence of unknown time varying environment disturbances and state constraints. In this framework, we first convert the mathematical model into a form of two subsystems, which is easier amenable for stabilization, by applying several transformations. It is proved that the investigated problem can be reduced to one of the second subsystem. A novel time-varying state feedback control scheme based on backstepping method and prescribed Lyapunov function is proposed to ensure state constraints and guarantee the global stability of the reduced control system, as well as sufficient conditions to ensure controller feasibility are given. With adaptive neural networks (NNs), the controller can be easily extended to compensate uncertainty induced by unknown time-varying external disturbances (e.g., wind, waves, and currents). It is proved that all the states and stabilization functions in the overall closed-loop are globally uniformly bounded. Finally, simulation results demonstrate the effectiveness of the proposed control scheme.
ATOM: Construction of Anti-tumor Biomaterial Knowledge Graph by Biomedicine Literature

With the rapid development of anti-tumor biomaterials, biomedicine literature with respect to anti-tumor biomaterials has been leveraged for tumor treatment as it provides abundant and useful information. A large number of biomedicine literature contains unstructured data, making it difficult for researchers to obtain desired messages from it. Knowledge Graphs (KGs) provides structured relationships among entities and can be served as a solution. However, no existing tool can be found in constructing an anti-tumor biomaterial knowledge graph from biomedicine literature. To fill this gap, a novel approach, ATOM, was proposed to construct an anti-tumor biomaterial knowledge graph from biomedicine literature through a series of process including the recognition of anti-tumor entities, the simplification of sentences, the extraction of triples, and the predicate mapping. Experiments demonstrated that ATOM is able to effectively express the extracted anti-tumor entities and their relationships.
Web Application Attack Detection Based on Attention and Gated Convolution Networks

This paper proposes an anomaly detection model based on the reconstruction error to detect malicious requests in a Web application. Our model combines a multi-head attention network and gated convolution network to capture the pattern of a normal request. Moreover, we use a novel segmentation method to enhance the structural representation of a request and embed a raw request into a feature matrix. The result of this experiment indicates that our model has good ability to distinguish between normal and abnormal requests.
Robust Adaptive Control Design Based on RBFNN with an Unmanned Quadrotor for Transporting Tasks

A quadrotor helicopter demonstrates excellent maneuverability in complex environments, which makes it an ideal platform as a data collection and device delivering system. In this paper, the position control problem for a quadrotor helicopter with a cable suspended transporting payload is investigated. During the transporting process, it is very challenging to eliminate the time-varying external disturbances. A Radial Basis Function neural network (RBFNN)-based robust controller is proposed that is able to control the quadrotor helicopter’s position with suspended cable attached. The convergence of position and attitude errors together with the Lyapunov function based analysis are employed to prove the stability of the nonlinear transportation system. The proposed controller is tested in an outdoor environment and the experimental results on the quadrotor transportation system are provided to verify the controllers’ reliability.
Visual SLAM in Human Populated Environments: Exploring the Trade-off between Accuracy and Speed of YOLO and Mask R-CNN

Simultaneous Localization and Mapping (SLAM) is a fundamental problem in mobile robotics. However, the majority of Visual SLAM algorithms assume a static scenario, limiting their applicability in real-world environments. Dealing with dynamic content in Visual SLAM is still an open problem, with solutions usually relying on direct or feature-based methods. Deep learning techniques can improve the SLAM solution in environments with a priori dynamic objects, providing high-level information of the scene. This paper presents a new approach to SLAM in human populated environments using deep learning-based techniques. The system is built on ORB-SLAM2, a state-of-the-art SLAM system. The proposed methodology is evaluated using a benchmark dataset, outperforming other Visual SLAM methods in highly dynamic scenarios.
Three Level T-Type Buck-Boost Voltage Source Inverter

This paper presents a three level T-type buck-boost voltage source inverter ($\mathbf{3LT}^{\mathbf{2}}$-BBVSI) to be used with distributed generation systems. The $\mathbf{3LT}^{\mathbf{2}}$-BBVSI topology combines a three level T-type inverter with DC-DC boost converter in one power stage; thus, high boosting gain along with multi-level shaping of the output voltage are acquired. Compared to other single-stage boost inverters, the proposed one achieves significant reduction in the passive components count; thus, reduced size along with higher efficiency can be realized. In this paper, the topology description along with the steady state analysis are presented. The operation of $\mathbf{3LT}^{\mathbf{2}}$-BBVSI is verified using OPAL-RT LAB tools. In addition, experimental results using $\mathbf{3LT}^{\mathbf{2}}$-BBVSI prototype are introduced.