Miracle-dz/UniFAR-FTUs huggingface dataset:
U-SLADS: Unsupervised Learning Approach for Dynamic Dendrite Sampling

Novel data acquisition schemes have been an emerging need for scanning microscopy based imaging techniques to reduce the time in data acquisition and to minimize probing radiation in sample exposure. Varies sparse sampling schemes have been studied and are ideally suited for such applications where the images can be reconstructed from a sparse set of measurements. Dynamic sparse sampling methods, particularly supervised learning based iterative sampling algorithms, have shown promising results for sampling pixel locations on the edges or boundaries during imaging. However, dynamic sampling for imaging skeleton-like objects such as metal dendrites remains difficult. Here, we address a new unsupervised learning approach using Hierarchical Gaussian Mixture Mod- els (HGMM) to dynamically sample metal dendrites. This technique is very useful if the users are interested in fast imaging the primary and secondary arms of metal dendrites in solidification process in materials science.
Long range radio location system employing autonomous tracking for sounding rocket

This work summarizes the development of a radio location system based on tracking antennas. The solution was designed to ensure a reliable wireless connection between a sounding rocket and a ground station. The system consists of both rocket and ground terminals, each including digital transceivers, which are responsible for a two-way communication within 868 MHz ISM frequency band. Another integral part of the solution is an autonomous tracking system placed on a movable ground platform. The tracking system is used to direct the maximum gain of a communication antenna towards the rocket. The terminal placed on board the rocket is used as a beacon. Real time GNSS location data is transferred to the ground station during the flight. The system was manufactured, optimized and verified during the flight campaign of the Magnitudo sounding rocket.
Joint Determination of Improved Variety Adoption, Productivity and Efficiency of Pulse Production in Bangladesh: A Sample-Selection Stochastic Frontier Approach

Pulses are an important source of protein and have recently gained international prominence. This paper jointly identifies the determinants of improved variety adoption, productivity and efficiency of 2700 pulse producers from 10 pulse-growing districts of Bangladesh using a Sample-selection Stochastic Production Frontier model. Result revealed that the decision to adopt improved pulse technology is significantly influenced by yield, farming experience, education and extension contact while subsistence pressure discourages adoption. Land, fertilizer, mechanical power, pesticides and labour are the significant determinants of improved pulse productivity. Productivity is significantly lower for improved varieties of lentil, blackgram and chickpea as compared to mungbean and for farmers who use own-sourced seed. Location of the growing area does matter. Improved pulse productivity is significantly higher in five of the ten districts. The mean level of technical efficiency of improved pulses is estimated at 0.73, implying that productivity can be substantially improved by eliminating inefficiency. Policy implications include investments in R&D and extension services by involving farmers in R&D endeavours and enhancing farmer-based seed production and distribution schemes to develop and disseminate improved pulse technology, improving farmers’ education and tenurial reforms to facilitate smooth operation of the land market and mechanical power services to increase pulse productivity and production in Bangladesh.
Design of smart home environment monitoring system based on raspberry Pi

Based on Raspberry Pi of ARM Cortex-A53 kernel, B/S(Browser/Server) architecture service, ESH (Eclipse smart home) project platform, wireless networking technology, smart home environment monitoring system is designed. The system is composed of smart home controller, EnOcean/Wi-Fi wireless sensor control network, smart home PC client. The system completes the hardware and software design of the smart home controller: transplant the embedded Linux operating system on the ARM platform; configure the wireless Wi-Fi network card driver; composed of EnOcean wireless sensor control network, complete the Program of controller node and the terminal node design, developed a data communication protocol; use the B/S architecture design client. The test results show that the sensor nodes in the network can transmit the detected information to the controller, and the smart home client can monitor the home environment through the smart home controller.
A method of classified HV circuit breaker fault signal based on EEMD and SOM neural network

During the operation of high voltage circuit breaker, the change of vibration signal reflects the mechanical state of the circuit beaker. Aiming at the nonlinearity and non-stationarity of high voltage circuit breaker vibration signal, this paper present a fault diagnosis method based on the ensemble empirical mode decomposition (EEMD) for correlation dimension and self-organizing feature mapping (SOM) neural Networks. First, the vibration signal is decomposed by EEMD, then we get the eigenvector of the vibration signal by calculating the correlation dimension of the intrinsic mode function (IMF). Finally, automatic fault identification is carried out in the SOM neural network, which is trained by input of the eigenvector. The results show that the method proposed in this paper can identify the fault types accurately and quickly, and has a certain reference value for the fault diagnosis of high voltage circuit breaker.
Business network formation among 5G providers

Virtualization ensures that in the approaching 5G era online services will be elastic and their deployments will be fast, fulfilling the demand of end-users rapidly and to a greater extent than what is feasible today. Telcos, cloud operators, and online application providers will join forces for delivering ICT services to customers globally. In order to support the mobility of customers, or the mere geographic span of an integrated enterprise application, the service deployments must span over many administrative domains and an assured quality of collaboration among various infrastructure and service providers is necessary. Therefore the vision of the 5G ecosystem is partly founded on the federation of these stakeholders in which they can seamlessly cooperate with the goal of creating the resource slices and the services within for a maximal geographic reach of customers. In this ecosystem, business aspects will greatly influence the technical capability and performance: we argue that the cooperative network of the actors will inherently determine availability and end-user prices of certain services. In this work we model the business relations of infrastructure providers as a variant of network formation games, and we derive conditions under which the current transit-peering structure of network providers remains intact.
Dynamical complexity analysis of beijing transportation network based on district structural information

Hierarchy is a fundamental characteristic of many complex systems. The methods of structural information have been taken as a prospective way for quantifying dynamical network complexity. This paper based on the study of the high-dimensional natural structural information entropy in networks. And then propose a new similarity District Structural Information (DSI) index, which takes the characteristics of network districts into consideration, to analyze the complexity of dynamical network districts. Based on the method, this paper apply the district structural information to explain the equilibrium problem in real-world networks. And take the Beijing traffic network and its districts to complete experiments that demonstrate the DSI index can reflect the equilibrium of the network and the districts effectively.
Controlled deployment in wireless sensor networks based on a novel Multi Objective Bee Swarm Optimization algorithm

Nowadays Wireless Sensor Networks (WSNs) stand out as an attractive research field in the world of computer networks. As sensor deployment is one of the essential part of WSNs and the most basic network services are affected by the deployment strategy, consequently, in WSNs, it is really needed that sensors be deployed precisely. Nevertheless, trading off between the numerous conflicting optimization criteria, including coverage and network connectivity is really challenging and complicated. In this article a controlled deployment algorithm based on Multi Objective Bee Swarm Optimization algorithm which is inspired by foraging behavior of honey bees is introduced. In this study the two most significant objectives in WSNs, i.e. network connectivity and coverage, is taken into consideration. The experiment's results reveal that the proposed algorithm along with innovations such as being effective in large scale three-dimensional and non-uniformed environments and utilizing sensors optimally, has the ability to deploy sensors in such a style that cover all the events completely and distribute sensors uniformly according to the position of events, in spite of a low number of sensor nodes. In comparison with NSGAII and IBEA algorithms, the two most famous multi objective algorithms, our novel method has remarkable advantages in terms of higher percentage of covering and higher quality of solutions and surpasses the previous presented algorithms.
A fast start-up modified LMS algorithm

Due to the time-delay spread of the channel, the equalizers for high data rate mobile communication systems has critical importance in mitigation of inter-symbol interference (ISI). One of the most important channel equalization algorithms is the least mean squares (LMS) algorithm. However, the slow convergence rate and the need for long training sequences are the major disadvantages of the LMS algorithm. In this paper, a faststart-up modified LMS (FSU-M-LMS) algorithm based on channel matched filter (CMF) has been proposed to increase the convergence speed and performance of the LMS algorithm. In order to prove the performance of the proposed method, computer simulations are performed on stationary and nonstationary frequency selective Rayleigh fading channels. The obtained simulation results illustrate that the proposed FSU-M-LMS algorithm has better mean square error (MSE), bit error rate (BER) and channel tracking performances than the conventional LMS, modified LMS (M-LMS), normalized LMS (N-LMS) and conventional RLS algorithm.
Formation path-following of multiple underwater vehicles based on fault tolerant control and port-controlled hamiltonian systems

This paper addresses the path-following formation keeping of a network of multiple autonomous underwater vehicles (AUVs) under unknown and possible time-varying disturbances, while achieving the fault tolerant control. We formulate this problem as the Port-controlled Hamiltonian form, and a feedback control is introduced to symmetrize the non-symmetric part of the inertia matrix caused by the forward speed. And fault tolerant is necessary to be considered due to the fact communication faults occur in the complex ocean environment. In the presence of time-varying disturbances, the internal model is applied to tackle the uncertainties. The passivity enables the closed-loop systems to converge to steady states. Numerical simulation results are given to illustrate the effectiveness of the approach.
Using Probabilistic Relational Models to generate synthetic spatial or non-spatial databases

When real datasets are difficult to obtain for tasks such as system analysis, or algorithm evaluation, synthetic datasets are commonly used. Techniques for generating such datasets often generate random data for single-table datasets. Such datasets are often inapplicable when it comes to evaluating data mining or machine learning algorithms dealing with relational data. To address this, our earlier works have dealt with the task of generating relational datasets from Probabilistic Relational Models (PRMs), a framework for dealing with probabilistic uncertainties in relational domains. In this article, we extend this work by proposing to use more efficient data sampling algorithms, and by using a spatial extension of PRMs to generate synthetic spatial datasets. We also present our experimental analysis on three different data sampling algorithms applicable in our method, and the quality of the datasets generated by them.
Face re-identification in thermal infrared spectrum based on ThermalFaceNet neural network

Face recognition is widely explored research area which achieved high level of maturity. It attracts scientists to explore new possibilities and various spectral domains. Thermal infrared spectrum seems to be a promising modality which may complement visible domain systems. Face recognition process consists of several stages such as image acquisition, face detection, feature extraction and matching. Extraction of features is the one of most important stage that can be performed using various approaches, such as appearance-based methods, local descriptor methods or convolutional neural networks. Lately, convolutional neural networks (CNN) become very popular. CNN is a structure based on various filtering layers to reduce size, extract features and finally to classify the input data. This technique allows to efficiently process large datasets. Extracted features can be used to perform multiclass classification or identity verification. Verification which refers to comparing two samples is often performed using distance metrics. This paper presents thermal face verification method based on Siamese convolutional neural network. We introduce ThermalFaceNet architecture and compare performance with existing state-of-the-art CNN architectures.
Numerical Improvement for the Mechanical Performance of Bikes Based on an Intelligent PSO-ABC Algorithm and WSN Technology

This paper proposed a novel hybrid optimization algorithm, particle swarm optimization-artificial bee colony (PSO-ABC), based on the PSO and ABC algorithms. The ABC algorithm can offset defects in the PSO algorithm that easily fall into a local optimization; combining the algorithms can improve the optimization ability of the PSO algorithm to a certain extent. Therefore, this paper applied the PSO-ABC hybrid algorithm and the finite-element method to systematically optimize the mechanical performance of the disc rotor of a bike and verified the numerical computation model via the wireless sensor network technology. The experimental test was completed with wireless sensor network technologies. To verify the optimized effects of the proposed PSO-ABC hybrid algorithm after parameter selection, the algorithm was compared with the traditional PSO and ABC models. The PSO, ABC, and PSO-ABC models adopted the same population to conduct a multi-objective optimization for vibration accelerations of the disc rotor. Comparing the results from these models proved that the proposed PSO-ABC method is superior for the optimization of vibration characteristics of the disc rotors.
SDE: A Novel Clustering Framework Based on Sparsity-Density Entropy

Clustering of data with high dimension and variable densities poses a remarkable challenge to the traditional density-based clustering methods. Recently, entropy, a numerical measure of the uncertainty of information, can be used to measure the border degree of samples in data space and also select significant features in feature set. It was used in our new framework based on the sparsity-density entropy (SDE) to cluster the data with high dimension and variable densities. First, SDE conducts high-quality sampling for multidimensional data and selects the representative features using sparsity score entropy (SSE). Second, the clustering results and noises are obtained adopting a new density-variable clustering method called density entropy (DE). DE automatically determines the border set based on the global minimum of border degrees and then adaptively performs cluster analysis for each local cluster based on the local minimum of border degrees. The effectiveness and efficiency of the proposed SDE framework are validated on synthetic and real data sets in comparison with several clustering algorithms. The results showed that the proposed SDE framework concurrently detected the noises and processed the data with high dimension and various densities.
Immersion and invariance based composite adaptive control of nonlinear high-order systems

A new nonlinear composite adaptive control based on immersion and invariance theory is presented for a class of high-order system with parametric uncertainty. The main feature of this control scheme lies in the construction of the adaptive law, which is driven by both tracking error and prediction error. The tracking-error based adaptation law is first constructed using immersion and invariance theory, then the prediction-error based adaptation law is added to it, thus makes a composite adaptive control law. The combination is applied to the immersion and invariance based adaptive control with improved performance. Stability analysis is presented using Lyapunov theory, which shows asymptotical convergence of the tracking error to zero. The validity of the proposed control scheme is illustrated through numerical simulations.
Code Reviewing in the Trenches: Challenges and Best Practices

Code review has been widely adopted by and adapted to open source and industrial projects. Code review practices have undergone extensive research, with most studies relying on trace data from tool reviews, sometimes augmented by surveys and interviews. Several recent industrial research studies, along with blog posts and white papers, have revealed additional insights on code reviewing “from the trenches. ” Unfortunately, the lessons learned about code reviewing are widely dispersed and poorly summarized by the existing literature. In particular, practitioners wishing to adopt or reflect on an existing or new code review process might have difficulty determining what challenges to expect and which best practices to adopt for their development context. Building on the existing literature, this article adds insights from a recent large-scale study of Microsoft developers to summarize the challenges that code-change authors and reviewers face, suggest best code-reviewing practices, and discuss tradeoffs that practitioners should consider. This article is part of a theme issue on Process Improvement.
Adaptive dynamic programming for optimal control of van der Pol oscillator

Adaptive dynamic programming has been considered as one of the effective methods to solve the optimal control of nonlinear systems and it has lots of applications in academic research and engineering. Firstly, we formulate the optimal control of van der Pol oscillator with the least energy consumption. Then, using an adaptive dynamic programming method to dynamically solve the approximate solution of this optimal control problem. A simplified neural network model is used to approximate the cost function. In addition, an admissible initial control policy for van der Pol oscillator is designed and Lyapunov stability criterion is established. On the basis of predecessors, we present a moving-data window recursive least-square method, which can overcome the computational difficulties and the problem that the parameter of oscillator maybe unknown and various slowly. Finally, the implementation steps of the method are proposed, and the feasibility is illustrated.
Embedded LQR controller design for self-balancing robot

This study aims at designing an embedded controller that can stabilize custom designed self-balancing two-wheeled robot (TWSBR) using velocity feedback alone. A state-space model for the custom designed TWSBR is obtained through dynamic analysis using a Lagrangian method. System identification experiments are proposed and performed to identify the parameters of the model. Based on the identified model, an LQR controller, which is a combination of optimal estimation and optimal control, is designed and implemented that can successfully stabilize TWSBMR. At last, few tests are carried out to evaluate the performance of the controller, showing how the proposed LQR controller outperforms classical controller in many aspects.
Securing IoT microservices with certificates

The Internet of Things (IoT) consists of distributed computing nodes. With increasing processor power such nodes can be used as hosts for microservices. IoT services routinely processes security critical data that affects the privacy, safety, and security of users. However, suitable security mechanisms remain missing. Fundamental open challenges are the authentication of services, securing the metadata of services, and validating the correct functioning of security mechanisms on distributed entities under different authorities. In this paper we present a certificate- based methodology for authenticating services, securely adding information to their executables, and validating the correct functioning of distributed entities of our design. We add X.509 certificates with extended attributes to the service executables. By introducing different trust anchors, services and their metadata are protected through their entire life cycle from developers to the computing nodes running them. Our solution enables distributed nodes to verify the security properties locally. It enables reliably changing certificate properties across the distributed IoT nodes. It features autonomous certificate management. We evaluate the traffic caused by our autonomous certificate management process quantitatively. The presented solution is churn tolerant and applicable to diverse distributed systems.
The Endocrine Brain: Pathophysiological Role of Neuropeptide-Neurotransmitter Interactions

Neurons in the human brain communicate with each other by releasing chemical messengers called neurotransmitters (electrical synapses are present, but in the distinct minority). The utility cycle of all neurotransmitter molecules is similar: they are synthesized and packaged into vesicles in the presynaptic cell; they are released from the presynaptic cell and bind to receptors on one or more postsynaptic cells, and once released into the synaptic cleft, they are rapidly removed or degraded. The total number of neurotransmitters is not known, but is likely to be well over 100. Despite this diversity, these agents can be classified into three broad categories: small-molecule neurotransmitters, neuropeptides, and unconventional transmitters. In general, small-molecule neurotransmitters mediate rapid reactions, whereas neuropeptides tend to modulate slower, ongoing brain functions. Abnormal transmitter functions may cause a wide range of neurological and psychiatric disorders; as a result altering the actions of neurotransmitters by pharmacological or other means is central to many modern therapeutic strategies.
Optimal products presentation in offer images for e-commerce marketplace platform

Offer images play an important role for e-commerce platforms in communicating features that are hard to express with plain text. Images affect consumer's attitude and emotions toward particular products and the whole platform quality. This paper makes contribution to show the way of providing measurable indicators for product images business quality. The term business quality means ‘pure’ item visualisation giving easy to capture information about products of each offer. Additionally, this paper exposes the superiority of deep learning techniques over standard ones used to detect information in images.