Miracle-dz/UniFAR-FTUs huggingface dataset:
Wide Angle SAR Subaperture Imaging Based on Modified Compressive Sensing

In wide angle synthetic aperture radar (WASAR), the scattering behavior varies with aspect angle. Subaperture methods can be adopted to accommodate the anisotropic behavior. Modified compressive sensing (MCS) focuses on reconstructing a sparse signal from a limited number of its linear projections when a part of its support is known. It can achieve the accurate recovery with less samples compared with the traditional compressive sensing (CS). Since the support sets of different subaperture images are highly overlapped across the whole aperture, MCS can be adopted in WASAR subaperture imaging. Iterative hard thresholding with partial known support (IHT-PKS) is adopted to recover the MCS model. Azimuth-range decouple operators based on backprojection are implemented in IHT-PKS to avoid huge memory cost of the measurement matrix. Compared with the traditional CS-based WASAR subaperture imaging method, the proposed method can reconstruct the aspect dependent scattering more accurately and is more robust to noise. The proposed method can also reconstruct the scene with less undesirable artifacts at downsampling rate. The simulation and experiment results validate the proposed method.
Grape leaf disease detection and classification using multi-class support vector machine

In the era of technology burst and usage of software as an alternative for the manual involvement for decision making, every field is trying to find its own comfort and cost cutting solutions in replacing software methods for best possible expert opinion. SVM, is initially proposed for binary classification technique, with simple manipulation can be used for a multiple class case. This project tries to attempt for improvement in classifying the leaf diseases. Most of the work until now involves extracting statistical features of RGB signal converted into LAB form[1]. HSI image has a reputation that the hue does not change even when the background light over the image changes. Hence few of the properties of HSI image are added to the database[2]. SVM is applied for classification for a larger space points. (properties)
Literature review of implementation of an entreprise resources planning: Dimensional approach

As information flows become more important and varied, with the emergence of information and communication technologies, these flows have experienced a change in the speed of diffusion, complexity, and ease of access. The sharing of information-data-becomes a concept of value creation. In this context of globalization-connected knowledge-the need to involve information systems (IS) in improving the performance of the business is turning into a major asset related to the survival of the organization. Implementing an Enterprise Resource Planning project successfully is a complex operation and is not without risks. This project involves multiple actors and elements interacting with the organization. Most of the problems that cause the failure of an ERP solution can be attributed to four sources including organizational change, organizational policies, and strategies, misunderstanding of the information system, and lack of skill and knowledge. This review will help practitioners in preparing to invest in IT related project focus on factors encouraging the success of the implementation. This review helps in presenting the key work done in the past and provides a humble guidance for future research work that can be conducted. This present paper purpose is to present the different thoughts on the key elements of the problems related to the implementation of an ERP and come out with a new multidimensional approach framework.
Intelligent IoT Traffic Classification Using Novel Search Strategy for Fast-Based-Correlation Feature Selection in Industrial Environments

Internet of Things (IoT) can be combined with machine learning in order to provide intelligent applications to the network nodes. Furthermore, IoT expands these advantages and technologies to the industry. In this paper, we propose a modification of one of the most popular algorithms for feature selection, fast-based-correlation feature (FCBF). The key idea is to split the feature space in fragments with the same size. By introducing this division, we can improve the correlation and, therefore, the machine learning applications that are operating on each node. This kind of IoT applications for industry allows us to separate and prioritize the sensor data from the multimedia-related traffic. With this separation, the sensors are able to detect efficiently emergency situations and avoid both material and human damage. The results show the performance of the three FCBF-based algorithms for different problems and different classifiers, confirming the improvements achieved by our approach in terms of model accuracy and execution time.
Socially Aware Dynamic Computation Offloading Scheme for Fog Computing System With Energy Harvesting Devices

Fog computing is considered as a promising technology to meet the ever-increasing computation requests from a wide variety of mobile applications. By offloading the computation-intensive requests to the fog node or the central cloud, the performance of the applications, such as energy consumption and delay, are able to be significantly enhanced. Meanwhile, utilizing the recent advances of social network and energy harvesting (EH) techniques, the system performance could be further improved. In this paper, we take the social relationships of the EH mobile devices (MDs) into the design of computational offloading scheme in fog computing. With the objective to minimize the social group execution cost, we advocate game theoretic approach and propose a dynamic computation offloading scheme designing the offloading process in fog computing system with EH MDs. Different queue models are applied to model the energy cost and delay performance. It can be seen that the proposed problem can be formulated as a generalized Nash equilibrium problem (GNEP) and we can use exponential penalty function method to transform the original GNEP into a classical Nash equilibrium problem and address it with semi-smooth Newton method with Armijo line search. The simulation results demonstrate the effectiveness of the proposed scheme.
Partial Replanning for Decentralized Dynamic Task Allocation

In time-sensitive and dynamic missions, multi-UAV teams must respond quickly to new information and objectives. This paper presents a dynamic decentralized task allocation algorithm for allocating new tasks that appear online during the solving of the task allocation problem. Our algorithm extends the Consensus-Based Bundle Algorithm (CBBA), a decentralized task allocation algorithm, allowing for the fast allocation of new tasks without a full reallocation of existing tasks. CBBA with Partial Replanning (CBBA-PR) enables the team to trade-off between convergence time and increased coordination by resetting a portion of their previous allocation at every round of bidding on tasks. By resetting the last tasks allocated by each agent, we are able to ensure the convergence of the team to a conflict-free solution. CBBA-PR can be further improved by reducing the team size involved in the replanning, further reducing the communication burden of the team and runtime of CBBA-PR. Finally, we validate the faster convergence and improved solution quality of CBBA-PR in multi-UAV simulations.
Simulation of the radar cross-section of dynamic human motions using virtual reality data and ray tracing

Radar returns from dynamic human motions are usually modeled using primitive based techniques. While the method is computationally simple and reasonably accurate in generating micro-Doppler signatures of humans, it is unreliable for predicting the radar cross-section (RCS) of the human especially at high frequencies. On the other hand, the shooting and bouncing ray method is effective for accurately measuring the RCS of humans. However, it has been carried out for only a single aspect or posture of the human. In this work, we present a method to simulate the radar cross-section of dynamic human motions across multiple postures by combining virtual reality data with shooting and bouncing ray (SBR) techniques. We convert each frame of human motion capture data to a poly-mesh structure of a human body and then incorporate the SBR technique for computing the resulting RCS. We verify the simulated results with measurement data at 24GHz.
A Novel Multiple-Read Screen for Metabolically Active Compounds Based on a Genetically Encoded FRET Sensor for ATP

Both glycolysis and mitochondrial energetics are targets of interest for developing antiproliferative cancer therapeutics. We developed a novel multiple-read assay based on long-term expression in K562 cells of a genetically encoded intramolecular Förster resonance energy transfer sensor for adenosine triphosphate (ATP). The assay, conducted in a fluorescent plate reader, can identify compounds that inhibit oxidative phosphorylation-dependent ATP production, glycolysis, or both after short-term treatment. We screened a National Cancer Institute (NCI) compound library, identifying inhibitors of oxidative phosphorylation-dependent ATP production and glycolysis. Three glycolysis inhibitors blocked hexokinase activity, demonstrating that our assay can serve as the initial step in a workflow to identify compounds that inhibit glycolysis via a defined desired mechanism. Finally, upon reviewing the literature, we found surprisingly little evidence that inhibiting glycolysis with small molecules is antiproliferative. Using NCI data on proliferation of K562 cells, we found that inhibitors of oxidative phosphorylation-dependent ATP production were no more antiproliferative than the overall library, whereas all glycolysis inhibitors were in the top third of most effective antiproliferative compounds. Our results thus present a powerful new way to screen for compounds that affect cellular metabolism and also provide important support for the idea that blocking glycosis is antiproliferative.
Power Allocation Study for Non-Orthogonal Multiple Access Networks With Multicast-Unicast Transmission

This paper considers a downlink single-cell non-orthogonal multiple access (NOMA) network, where the base station, which has multiple antennas, broadcasts the mixed multicast and unicast messages to multiple users with a single antenna. We propose two types of power allocation schemes for NOMA networks with multicast-unicast transmission to investigate the impact of performance, namely, cognitive radio inspired NOMA with dynamic quality of service (QoS) at multicast users (CR-NOMA-D-M), and CR-NOMA constrains the dynamic QoS at the unicast user (CR-NOMA-D-U). Based on the proposed schemes, we drive the exact/closed-form expressions for the secrecy outage probability, outage probability, as well as the approximate outage probability at high signal-to-noise ratio to study the diversity gain. Compared with existing works, the derived analytical results show that both proposed schemes cannot only significantly improve the outage performance for multicast users, but also remarkably enhance the diversity gain and the secrecy outage probability for the unicast user. Finally, the theoretical results are validated by the numerical results.
Overview study of on-chip interconnect modelling approaches and its trend

Several techniques related to the interconnect structure analysing and the modelling were outlined. Emphasis has been placed on modelling approach, starting from a one-dimensional and simple lumped issue and stretching to be a more general three-dimensional nonuniform problem. In addition, the advantages and disadvantages of each approach have highlighted to state the importance of these models for further research and their prospects. That verified through studying the approaches principles and an uncomplicated comparison.
Contacts-aware opportunistic forwarding in mobile social networks: A community perspective

Exploiting community structure for opportunistic forwarding decisions in mobile social networks offers a promising paradigm to improve the transmission performance and reduce the extra network overhead. Actually, people will have closer relationships and more opportunities to contact with each other if they are in the same community. In this paper, the activeness of nodes and the probability of reaching the destination are investigated based on the node contacts in the trace. Then the Contacts-Aware Opportunistic Forwarding (CAOF) scheme, which includes inter-community and intra-community phase, is proposed. In the inter-community phase, the node with higher global activeness and source-to-destination probability is selected to serve as the relay. Besides, in the intra-community phase, the forwarding decisions are determined by the local metrics. Furthermore, we compare the proposed CAOF scheme with several benchmark forwarding algorithms, including BUBBLE Rap, SPRINT, Epidemic and JDER. The validity of the modeling and the soundness of the analysis are verified through extensive experiments with real traces, which illustrates that it outperforms other routing strategies in heavy traffic scenarios.
A fast and omni-directional 3D reconstruction system of objects

In Amazon Robotic Challenge, we need to make unknown object's 3d model for recognizing and estimating 6D pose in limited time. The paper presents a unique autonomous system to build object's complete 3d model fast. This method is different with others for it can build object's model contains object's bottom side. We modify ElasticFusion to focus on building object's 3D model from object's up-side with assistant pose information from Aruco Markers. Then instead of using ICP or other merge algorithms, we merge object's upside point cloud and bottom-side point cloud easily by using Aruco Markers on transparent board. At last, we use octree and source point cloud to color poisson reconstruction mesh model to obtain object's final complete colored 3d model. Experiment results show that our approach can generate common object's complete model with acceptable size error in limited time.
Socially Aware Secrecy-Ensured Resource Allocation in D2D Underlay Communication: An Overlapping Coalitional Game Scheme

With the popularity of proximity-based services, device-to-device (D2D) communication underlaying cellular networks is a promising technology to cope with the growing demands by improving network resource utilization. However, the wireless communication’s broadcast nature is vulnerable to eavesdropping, and thus, ensuring a secrecy communication for both cellular user equipments (CUEs) and D2D pairs in an underlay network is a challenging issue. We investigate the problem of physical-layer secure transmission jointly with resource allocation in D2D communications. Different from existing works, we framed overlapping (partial) coalitional game where each D2D pair can access multiple CUEs’ spectral resources. Moreover, the multiple D2D pairs can share single CUE subchannel in multiple eavesdroppers scenario to ensure information security for both CUEs and D2D pairs and to maximize system sum rate in a socially aware D2D network. We incorporate the mutual interference and propose different transmission modes for a secrecy-ensured resource allocation-based overlapping coalition formation scheme with transferable utility to obtain a final stable partition. We further prove the proposed algorithm stability, convergence, and computational complexity. Both analytical and numerical results demonstrate the effectiveness of our proposed scheme, which ensures a system-wide security and at the same time improves the performance by maximizing the system sum rate.
A study on the performance of a complementary auxiliary antenna pattern for maisel sidelobe blanker

The problem of coupling between probability of target blanking (PTB ) and probability of blanking (PB ) in Maisel sidelobe blanker (SLB) is addressed and a complementary auxiliary antenna pattern is proposed for phased array radar systems. The numerical results indicate that the complementary pattern provides an improvement on PTB and PB especially for the cases where antennas have poor mainlobe-to-sidelobe ratio.
Fast online SVDD based on support vectors merging

Online support vector data description (SVDD) performs excellently when dealing with novelty detection problems. This method only uses the support vectors (SVs) as an initial training set and incrementally updates the SVDD classifier when receiving new samples. Therefore, as the SVs number tends to grow along with the number of training samples, the update time grows exponentially. To tackle this problem, we propose a new online SVDD method based on SVs merging procedure in this paper. SVs merging procedure bound the size of training set during online learning, thus the proposed method maintains a constant update time. Experimental results of the banana datasets, Mnist datasets and Cifar 10 datasets show that the proposed method achieves higher training speed than both the canonical online SVDD method and the incremental SVDD method, while maintaining a comparable classification accuracy.
Who Is Who in Adenosine Transport

Extracellular adenosine concentrations are regulated by a panel of membrane transporters which, in most cases, mediate its uptake into cells. Adenosine transporters belong to two gene families encoding Equilibrative and Concentrative Nucleoside Transporter proteins (ENTs and CNTs, respectively). The lack of appropriate pharmacological tools targeting every transporter subtype has introduced some bias on the current knowledge of the role of these transporters in modulating adenosine levels. In this regard, ENT1, for which pharmacology is relatively well-developed, has often been identified as a major player in purinergic signaling. Nevertheless, other transporters such as CNT2 and CNT3 can also contribute to purinergic modulation based on their high affinity for adenosine and concentrative capacity. Moreover, both transporter proteins have also been shown to be under purinergic regulation via P1 receptors in different cell types, which further supports its relevance in purinergic signaling. Thus, several transporter proteins regulate extracellular adenosine levels. Moreover, CNT and ENT proteins are differentially expressed in tissues but also in particular cell types. Accordingly, transporter-mediated fine tuning of adenosine levels is cell and tissue specific. Future developments focusing on CNT pharmacology are needed to unveil transporter subtype-specific events.
Autoencoder With Invertible Functions for Dimension Reduction and Image Reconstruction

The extreme learning machine (ELM), which was originally proposed for “generalized” single-hidden layer feedforward neural networks, provides efficient unified learning solutions for the applications of regression and classification. Although, it provides promising performance and robustness and has been used for various applications, the single-layer architecture possibly lacks the effectiveness when applied for natural signals. In order to over come this shortcoming, the following work indicates a new architecture based on multilayer network framework. The significant contribution of this paper are as follows: 1) unlike existing multilayer ELM, in which hidden nodes are obtained randomly, in this paper all hidden layers with invertible functions are calculated by pulling the network output back and putting it into hidden layers. Thus, the feature learning is enriched by additional information, which results in better performance; 2) in contrast to the existing multilayer network methods, which are usually efficient for classification applications, the proposed architecture is implemented for dimension reduction and image reconstruction; and 3) unlike other iterative learning-based deep networks (DL), the hidden layers of the proposed method are obtained via four steps. Therefore, it has much better learning efficiency than DL. Experimental results on 33 datasets indicate that, in comparison to the other existing dimension reduction techniques, the proposed method performs competitively better with fast training speeds.
Design of dual-band metasurface antenna

A metasurface antenna is proposed for the dual-band operation in 5G millimeter systems. The metasurface consists of a single-layered square patch array excited by two microstrip feeding slots cut onto the bottom ground. The characteristic mode analysis is used to optimize the metasurface patterns for the dual-band operation. The modes of metasurface resonant at 30 GHz, 38 GHz, and 41 GHz, respectively. To broaden the bandwidth, the etched slots on the ground are resonant as well. The impedance is matched over the frequency ranges of 23.75–28.8 GHz and 36.22–40.5 GHz with return loss larger than 10 dB. The simulated gain reaches 8.8–10.17 dBi and 9.46–12.6 dBi over the operating bands, respectively.
Food Overconsumption in Healthy Adults Triggers Early and Sustained Increases in Serum Branched-Chain Amino Acids and Changes in Cysteine Linked to Fat Gain.

Background Plasma concentrations of branched-chain amino acids (BCAAs) and the sulfur-containing amino acid cysteine are associated with obesity and insulin resistance. BCAAs predict future diabetes. Objective We investigated amino acid changes during food overconsumption. Methods Forty healthy men and women with a body mass index (mean ± SEM) of 25.6 ± 0.6 were overfed by 1250 kcal/d for 28 d, increasing consumption of all macronutrients. Insulin sensitivity and body composition were assessed at baseline (day 0) and day 28. Fasting serum amino acids were measured at days 0, 3, and 28. Linear mixed-effects models evaluated the effect of time in the total group and separately in those with low and high body fat gain (below compared with at or above median fat gain, 1.95 kg). At days 0 and 28, insulin-induced suppression of serum amino acids during a hyperinsulinemic-euglycemic clamp test and, in a subset (n = 20), adipose tissue mRNA expression of selected amino acid metabolizing enzymes were assessed. Results Weight increased by 2.8 kg. High fat gainers gained 2.6 kg fat mass compared with 1.1 kg in low fat gainers. Valine and isoleucine increased at day 3 (+17% and +22%, respectively; P ≤ 0.002) and remained elevated at day 28, despite a decline in valine (P = 0.019) from day 3 values. Methionine, cystathionine, and taurine were unaffected. Serum total cysteine (tCys) transiently increased at day 3 (+11%; P = 0.022) only in high fat gainers (P-interaction = 0.043), in whom the cysteine catabolic enzyme cysteine dioxygenase (CDO1) was induced (+26%; P = 0.025) in adipose tissue (P-interaction = 0.045). Overconsumption did not alter adipose tissue mRNA expression of the BCAA-metabolizing enzymes branched-chain keto acid dehydrogenase E1α polypeptide (BCKDHA) or branched-chain amino transferase 1 (BCAT1). In the total population at day 0, insulin infusion decreased all serum amino acids (-11% to -47%; P < 0.01), except for homocysteine and tCys, which were unchanged, and glutathione, which was increased by 54%. At day 28, insulin increased tCys (+8%), and the insulin-induced suppression of taurine and phenylalanine observed at day 0, but not that of BCAAs, was significantly impaired. Conclusions These findings highlight the role of nutrient oversupply in increasing fasting BCAA concentrations in healthy adults. The link between cysteine availability, CDO1 expression, and fat gain deserves investigation. This trial was registered at www.clinicaltrials. gov as NCT00562393.