Miracle-dz/UniFAR-FTUs huggingface dataset:
Simultaneous Multi-View Camera Pose Estimation and Object Tracking With Squared Planar Markers

Object tracking is a key aspect in many applications, such as augmented reality in medicine (e.g., tracking a surgical instrument) or robotics. Squared planar markers have become popular tools for tracking since their pose can be estimated from their four corners. While using a single marker and a single camera limits the working area considerably, using multiple markers attached to an object requires estimating their relative position, which is not trivial, for high-accuracy tracking. Likewise, using multiple cameras requires estimating their extrinsic parameters, also a tedious process that must be repeated whenever a camera is moved. This paper proposes a novel method to simultaneously solve the above-mentioned problems. From a video sequence showing a rigid set of planar markers recorded from multiple cameras, the proposed method is able to automatically obtain the three-dimensional configuration of the markers, the extrinsic parameters of the cameras, and the relative pose between the markers and the cameras at each frame. Our experiments show that our approach can obtain highly accurate results for estimating these parameters using the low-resolution cameras. Once the parameters are obtained, tracking of the object can be done in real time with a low computational cost. The proposed method is a step forward in the development of cost-effective solutions for object tracking.
A Multi-Point Switchable and Self-Adaptive Ultrasonic Sensor Using Fiber Bragg Gratings in a Fiber Ring Laser

We propose and demonstrate a multi-point, self-adaptive fiber ultrasonic sensor based on fiber Bragg gratings (FBGs). The sensor is constructed from matched FBG pairs (FBGPs) and a tunable optical filter in a fiber ring laser. FBGPs with different Bragg wavelengths are used as both the comb filter to produce potential lasing lines and the ultrasonic sensing elements. The ultrasonic signal is directly detected by monitoring the intensity response at each laser channel to the cold-cavity loss modulation from the spectral shift of matched FBGPs. The laser channel between the FBGPs is actively switched using the tunable optical filter, thereby achieving multi-point and switchable detection yet avoiding mode competition in the lasers. Meanwhile, to achieve self-adaptability, the two FBGs of the FBGP are fixed side by side at the same location of the structure such that both FBGs respond similarly to static strain and temperature variation. The multi-point system is promising for the robustness in the practical applications because it is insensitive to static strain ranging over 1400 μϵ and temperature variation from 20 °C to 50 °C, respectively.
Pinning Synchronization of Nonlinear Coupled Lur’e Networks Under Hybrid Impulses

This brief focuses on the pinning synchronization problem of impulsive Lur’e networks with nonlinear and asymmetrical coupling. In order to study the situation in which synchronizing impulses and desynchronizing impulses are allowed to occur simultaneously, a single pinning impulsive controller is designed to investigate the synchronization of Lur’e networks with hybrid impulses based on the methods of average impulsive interval and average impulsive gain. By employing the Lyapunov method, some sufficient conditions are derived to guarantee the exponential synchronization of Lur’e networks with synchronizing impulses and desynchronizing impulses simultaneously. Two numerical examples are given to illustrate the results.
Aggregation and Mapping of Social Media Attribute Names Extracted from Chat Conversation for Personalized E-Learning

Nowadays learners are using social media applications to perform collaborative learning tasks. Learners from the same class can perform the task through discussions and conversations using different applications. These discussions generate valuable chat conversations contain information related to learners’ characteristics. For educators, to understand the personal characteristics of those learners, collecting and analyzing these conversations is required. Due to the varied structure of these applications, an aggregation and mapping mechanism is required. This paper presents an aggregation model for the conversation data collected from different social media applications. This aggregation is built, based on the attributes required to enhance personalization services. The attributes identified have been used to construct a unified format for the chat data generated, using different social web applications. To perform the aggregation task, similarity matching technique using ontology-based model has been adopted. The promising results from the matching process indicate the usefulness of the model.
DC-Bus Dual-Level Control Strategy for PV Power System With Dual-Mode Operation

An autonomous dc-bus dual-level control (DDLC) strategy is proposed for the two-stage three-phase photovoltaic power system. During the grid-fault situation, the second-stage inverter may lose the ability to regulate the dc-bus voltage and the first-stage boost converter has to change its mode from maximum power point tracking mode to dc-bus voltage control (DCVC) mode. However, the communication between the two-stage circuits must be established. The circuit complexity and cost will increase while the system's scalability and flexibility will be reduced. With the proposed DDLC strategy, the dc-bus voltage is controlled into different levels for different operation modes, without the need of communication mechanism. No extra sensors or circuits are required. Two important design parameters for the proposed DDLC are derived thoroughly. The computer simulations and hardware experimental results of a 4 kVA prototype circuit are presented to validate the performance of the proposed DDLC strategy.
ROKA: A Software Development Methodology for Industrial Automation

The industry invests great amounts of resources in automation and maintenance systems. To encourage more efficient systems a design of a methodology is needed, it must work for software development in industrial automation processes. To obtain guidelines to design such a methodology, a literature review was performed. As a result, a methodology was developed based on good practices and lessons learned from Software Engineering. It was made for the development of advanced structured software in industrial automation systems. Through user experiences analysis a positive validation was obtained, it confirms that a methodology for mechatronic and automation systems is necessary, also it is an almost unknown area, additionally, some observations about how to teach the methodology were also obtained.
Small Signal Stability Analysis of Multi-Infeed Power Electronic Systems Based on Grid Strength Assessment

The increasing penetration of renewable energy resources, such as wind and solar, is changing AC grid dynamics. Potential dynamic stability issues such as small signal instability may occur when these resources are connected to a grid with low strength, i.e., a weak grid. In this paper, the relationships between small signal stability and grid strength measured with short circuit ratio (SCR), along with the relationships between the stability boundary and critical SCR (CSCR) are analytically studied in a single-infeed power electronic system (SIPES). On this basis, these relationships are extended to a multi-infeed power electronic system (MIPES) through eigenvalue decomposition techniques to define the so-called generalized short circuit ratio (gSCR) and critical gSCR (CgSCR) for small signal stability analysis. The defined gSCR and CgSCR are the generalized representation of SCR and CSCR for SIPESs in MIPESs. The gSCR can assess the grid strength of a MIPES in terms of its small signal stability while the CgSCR can characterize the stability boundary of a MIPES. In addition, the condition of CgSCR = CSCR is satisfied for a MIPES with homogeneous power electronic devices. This enables the proposed method based on the gSCR to simplify the complex stability analysis of MIPESs into the stability study of SIPESs. The efficacy of the proposed gSCR and CgSCR is validated with eigenvalue analysis and time-domain simulations.
Causal Inference in Geoscience and Remote Sensing From Observational Data

Establishing causal relations between random variables from observational data is perhaps the most important challenge in today’s science. In remote sensing and geosciences, this is of special relevance to better understand the earth’s system and the complex interactions between the governing processes. In this paper, we focus on an observational causal inference, and thus, we try to estimate the correct direction of causation using a finite set of empirical data. In addition, we focus on the more complex bivariate scenario that requires strong assumptions and no conditional independence tests can be used. In particular, we explore the framework of (nondeterministic) additive noise models, which relies on the principle of independence between the cause and the generating mechanism. A practical algorithmic instantiation of such principle only requires: 1) two regression models in the forward and backward directions and 2) the estimation of statistical independence between the obtained residuals and the observations. The direction leading to more independent residuals is decided to be the cause. We instead propose a criterion that uses the sensitivity (derivative) of the dependence estimator, the sensitivity criterion allows to identify samples most affecting the dependence measure, and hence, the criterion is robust to spurious detections. We illustrate the performance in a collection of 28 geoscience causal inference problems, a database of radiative transfer models simulations and machine learning emulators in vegetation parameter modeling involving 182 problems, and assessing the impact of different regression models in a carbon cycle problem. The criterion achieves the state-of-the-art detection rates in all cases, and it is generally robust to noise sources and distortions. The presented approach confirms the validity in observational bivariate problems in the earth sciences.
Electric Demand Forecast with Few Historical Data

En este trabajo se presenta una aplicación de las redes neuronales artificiales para pronóstico de la demanda de energía eléctrica a corto plazo. Se utiliza una arquitectura del tipo feed-forward con 97 entradas, dos capas ocultas de 100 y 70 neuronas respectivamente, y 48 datos de salida. Los datos de entrada a la red son la temperatura y demanda de energía del día anterior al que se desea pronosticar, y el día de la semana que se desea predecir. En la salida se obtiene la demanda pronosticada para el próximo día. Los datos de demanda y temperatura están muestreados cada 30 minutos. El método propuesto fue diseñado usando las herramientas del Neural Network Toolbox de MATLAB, y los resultados se analizaron trabajando con 21 datos de la demanda eléctrica del área centro del sistema eléctrico de Argentina.
SMS Classification Method for Disaster Response Using Naïve Bayes Algorithm

The bulk of SMS that the Quick Response Team and Rescue Agencies received during disasters made it hard for them to categorize responses based on priorities. This paper provides a method that classifies SMS received by the agency as Spam, Invalid, Alert 1 Alert 2, and Alert 3. This method allows proper response to be extended to those asking for it based on prevailing needs. This also provides a chance to ignore insignificant messages and save precious time that may be incurred by merely dealing with unimportant messages. The implementation of Naïve Bayes Algorithm, a self-learning algorithm, and together with Natural Language Processing was utilized in this research. Extension of the method is however devised in order to cover the irregularity of the data to process. Test results of the classification method showed success in its implementation and since it is a self-learning process, the method gets better and became more accurate through time.
Localized Deep-CNN Structure for Face Recognition

In face recognition, there currently exists a significant challenge, most prominent when complex conditions such as large expression, pose, illumination, and even low resolution are introduced. Therefore, there exists a demand to address this issue, particularly concerning the key challenge of efficient feature representation, appreciating the extensive feature space that can be utilized to improve efficiency. This paper proposes a new approach in which a localized Deep-CNN structure is applied to demonstrate its' effectiveness and efficiency. The main contribution of this model is to directly learn the localized visual features by splitting the learning face into four local blocks. Intuitively, the Localized Deep-CNN model mimics the primary visual context to joint feature representations by extraction to produce local relational visualized features for the learning face. Our Deep-CNN model achieves 97.13% accuracy rate on the Labeled Faces in the Wild (LFW) dataset, compared to the humanlevel recognition rate which is 98.76%.
UNSUPERVISED SEMANTIC SEGMENTATION OF KIDNEYS USING RADIAL TRANSFORM SAMPLING ON LIMITED IMAGES

Efficient training of supervised deep learning models for semantic segmentation requires a massive volume of annotated data. In this paper, we propose an unsupervised semantic segmentation method through the application of a radial transform method in the polar coordinate system to unannotated images. This method generates radial transformed images up to the number of pixels in the input image. Each generated image corresponds to a pixel in the original image, which is a spatial representation of the selected pixel with respect to other pixels, in the polar coordinate system. A dimension reduction method, such as a convolutional autoencoder (CAE), can extract features of these representations for clustering and later, labeling the original image by mapping the labels back to the original image. The advantage of the proposed radial transform technique is that it generates a massive number of training images by sampling pixels in the polar coordinate system from a very limited number of original images in the Cartesian coordinate system. The proposed approach achieved 88.20% accuracy in pixel-level segmentation of left kidney, right kidney, and non-kidney pixels in contrast-enhanced computed tomography (CT) images.
First Steps Towards Simultaneous Isolation and Detection of Exosomes with Carbon Nanotube-Based SMRs

Exosomes are small (20-150nm)intercellular vesicles carrying valuable information regarding disease presence or evolution. Their isolation from body fluids is one of the most challenging targets. Although many techniques already exist, these are still inefficient, expensive and time-consuming. One of these new methods is nanofiltration. Within this technique, carbon nanotube (CNT)forests have been proposed as good candidates owing to their easily controllable tube separation and growth. Previous works have proven the isolation of different particles; however, optical read-out methods are still needed to confirm the success of the process. Here we propose and start studying the integration of CNT forests in solidly mounted resonators to simultaneously isolate exosomes and gravimetrically estimate the trapped amount. As a first step we prove the ability of our devices to trap and detect CD63 antibodies, due to their high affinity to exosomes. We demonstrate a frequency shift of around 280±8 kHz with an antibody concentration of 8 nM, four times lower than previously reported concentrations used for the detection of antibodies with thin film acoustic resonators, for similar frequency shifts.
Hypothesis Testing for Privacy of Smart Meters With Side Information

Privacy concern is often underlined as the main obstacle to the path for the large deployment of smart meters (SMs). An energy management unit (EMU) along with a proper battery system charge/discharge schedule can mask the consumer’s actual energy use and to some extent alleviate privacy concerns. This paper speculates a hypothesis testing problem focused on SM privacy. The proposed model consists of an energy consumer, battery storage, EMU, and SM. The EMU, which controls the energy consumption policy, chooses between two hypotheses. The first one considers a normal-mode consumption policy in which EMU utilizes the consumer’s entire energy demand from the grid. In the second hypothesis, EMU takes some part of the energy consumption from the grid independent of the consumer’s actual demand. In this situation, the battery with a limited capacity undertakes supplying the remaining load or absorbing the excessive energy. The second approach would conceal the consumer’s profile from an adversary. The energy provider, which has access to a side information, could predict the hypothesis utilized by the consumer. We denote the reliability criterion as probability of error in choosing a correct hypothesis. A case study is given in the support of effectiveness of proposed methodology.
NETTED: Non-Standard CAN ID Extraction System Based on Deep Neural Networks

With increased interest in smart driving technology, interest in understanding the current state of the vehicle using CAN data has also increased. Standardized CAN information such as vehicle speed and engine RPM information can be easily acquired and used. However, most of the CAN data related to the driving information such as the angle of the steering wheel is not standardized. To solve this problem, this paper proposes and implements a system for extracting nonstandard CAN IDs based on deep neural networks. The system can estimate the current state of the steering wheel and brake pedal by learning data collected from GPS and IMU. Then, the appropriate CAN ID and byte position are extracted based on the estimated current state information. Experimental results show that the estimation accuracy of the steering wheel and brake pedal are 87.63% and 94.81%, respectively. Also, it is confirmed that CAN data area is reduced by about 90% through the pre-filtering process. Finally, the proposed system extracts two desired CAN IDs.
Iterative UAV Trajectory Optimization for Physical Layer Secure Mobile Relaying

With the rapid development and growth of unmanned arial vehicle (UAV) technology and industry, there is increasing research interests in UAV communications. In this work, the mobility and deployment flexibility of UAV is used to provide assistance to terrestrial point-to-point communications. In particular, design of the UAV-enabled mobile relaying system is investigated from the physical layer security perspective to achieve higher overall secrecy capacity. A dynamic channel model that changes with the trajectory is established based on the UAV relay's mobility. By optimizing the UAV's moving trajectory, secrecy capacity of the relay-assisted wireless communication system is maximized. The problem formulated is shown to be non-convex, which is difficult to solve in general. To make the problem tractable, we decompose the original problem and optimize the trajectory to approximate the optimal flight path by optimizing the increments at each trajectory iteration. Simulation results show that the method of finding the optimal trajectory based on the displacement updating iteration is effective and fast converging. When the number of iterations is sufficiently large, the trajectory of the UAV converges, and the optimized trajectory significantly improves the system's secrecy capacity performance. Based on the optimized trajectory, it is investigated through numerical results how the overall secrecy capacity is impacted by the total flight time and the maximum instantaneous UAV speed. It is revealed that higher flight speed is desirable in finding a secure UAV trajectory, and there exists a minimum total flight time to achieve the best average secrecy rate performance
Challenges and Direction of Hybrid SDN Migration in ISP networks

Software Defined Networks (SDN) is a networking paradigm that separates the control plane from the data plan of a network, and controls data flow with a SDN controller. This networking model would be useful for ISP (Internet Service Provider) networks in order to control their own networks via the controller. Unfortunately, it is difficult to fully implement SDN in ISP networking due to financial and operational constraints. In this article, we will identify the various migration challenges for hybrid SDN in ISP networks, and investigate on the network planning tools currently available in the market that with regards to facilitate the migration process. Lastly, we will discuss on the future direction of research and development in the field of hybrid SDN migration.
The Use of Ontology in Retrieval: A Study on Textual, Multilingual, and Multimedia Retrieval

Web contains a vast amount of data, which are accumulated, studied, and utilized by a huge number of users on a daily basis. A substantial amount of data on the Web is available in an unstructured format, such as Web pages, books, journals, and files. Acquiring appropriate information from such humongous data has become quite challenging and a time-consuming task. Trivial keyword-based information retrieval systems highly depend on the statistics of data, thus facing word mismatch problem due to inevitable semantic and context variations of a certain word. Therefore, this marks the desperate need to organize such massive data into a structured format so that information can be easily processed in a large context by taking data semantics into account. Ontologies are not only being extensively employed in the semantic Web to store unstructured information in an organized and structured way but it has also raised the performance of diverse information retrieval approaches to a great extent. Ontological information retrieval systems retrieve data based on the similarity of semantics between the user query and the indexed data. This paper reviews modern ontology-based information retrieval methods for textual, multimedia, and cross-lingual data types. Furthermore, we compare and categorize the most recent approaches used in the above-mentioned information retrieval methods along with their major drawbacks and advantages.
Pre-Charged Pneumatic Soft Gripper With Closed-Loop Control

Pneumatic soft grippers have nonlinear continuum deformation enabling them to adapt to irregular object shapes. Most of the existing pneumatic soft grippers use only open-loop control. Attempts for closed-loop control of soft grippers are all based on air pressure regulation, which is inaccurate and cumbersome due to nonlinear performance of the soft actuator and the compressible nature of air. In this letter, we present a controllable soft gripper based on pre-charged pneumatic soft actuators. The soft actuators, with the same design as a normal bending pneumatic soft actuator, are pre-charged with air to a preset pressure through a one-way check valve. The bending angle and bending speed of soft actuators are controlled by servomotors through tendons based on feedback data from force and proximity sensors. Kinematic models of the soft actuators are developed. Dynamic properties are experimentally studied. A prototype gripper with closed-loop control is developed for controllable grasping demonstration.