Miracle-dz/UniFAR-FTUs huggingface dataset:
Shortened Color-Shape Descriptors for Point Cloud Classification from RGB-D Cameras

Deep learning techniques have become the standard approach for computer vision tasks. However, traditional methods are still advantageous in application domains with limited computing or battery power such as mobile robots. In this work, we address the task of point cloud classification from RGB-D cameras with a traditional approach using local feature descriptors. We modify CSHOT, an established feature descriptor, with the goal of radically reducing its dimensionality. The resulting Short-CSHOT descriptor is evaluated on common point cloud data sets for keypoint matching and object classification. The obtained results suggest that Short-CSHOT is able match the original CSHOT descriptor, while at the same time having a much lower dimensionality. Further, the number of dimensions of Short-CSHOT can be varied, allowing to trade-off between the memory footprint and accuracy. All code is made publicly available on https://github.com/vseib/PointCloudDonkey.
An Automated Method for Identification of Key frames in Bharatanatyam Dance Videos

Identifying k ey frames is the first and necessary step before solving the variety of other B haratanatyam problems. The paper aims to partition the momentarily stationary frames (key frame s) from this dance video’s motion frames. The proposed key frame s (KFs) localization is novel, simple, and effective compared to the existing dance video analysis methods. It is distinctive from standard KFs detection algorithms as used in other human motion videos. In the dance’s basic structure, the occurrence of KFs during performances is often not completely stationary and varies with the dance form and the performer. Hence, it is not easy to decide a global threshold (on the quantum of motion) to work across dancers and performances. The earlier approaches try to compute the threshold iteratively. However, the novelty of the paper is: (a) formulating an adaptive threshold, (b) adopting Machine Learning (ML) approach and, (c) generating the effective feature by combining three frame differencing and bit-plane technique for the KF detection. In ML, we use Support Vector Machine (SVM) and Convolutional Neural Network (CNN) as the classifiers. The proposed approaches are also compared and analyzed with the earlier approaches. Finally, the proposed ML techniques emerge as a winner with around 90% accuracy.
Data Augmentation and Faster RCNN Improve Vehicle Detection and Recognition

Vehicle detection is a technique that uses photographs to identify and label vehicles. Images for their learning may be taken in various scenarios, such as focus ranges or different lighting. Lower yields may have arisen from the photographs taken under normal conditions. Better accuracy can be achieved by improving the original frames by correcting the point of view conditions. The simulation can test machine learning performance by simulating different scenarios, such as whether the images are good. One method of simulating the initial image conditions is to use data augmentation. Several techniques for machine learning for image recognition have been developed, such as Neural Networks and Deep Learning, which can be used in large image datasets indefinitely. An attribute from each type or class’s images is used to classify the type or classes in Deep Learning. Faster R-CNN with Data Augmentation was proposed as a tool for classifying images learned from a large dataset. Data augmentation was used to replicate the images that could be produced to improve precision.
IoT Traffic Offloading with MultiPath TCP

The concept of a fully connected world in the fifth generation technology envisions the massive rollout of small and constrained Internet of Things (IoT) devices. In such IoT environments, machine-to-machine (M2M) communications is key for allowing these devices (e.g., sensors, actuators, smart meters/monitors) to collect data toward a remote server. We address this particular scenario by building and evaluating an IoT multi-access reference architecture, with a multipath TCP (MPTCP) gateway connected to multiple cellular networks to offload M2M data traffic. Experimental results from CoAP-emulated traffic indicate that the our proposed system together with the benefits of MPTCP improve IoT application performance. More specifically, multipath-based M2M data traffic offloading increases throughput and significantly reduces latency of CoAP requests as the number of sensor networks increases.
A Q-Learning-Based Resource Allocation for Downlink Non-Orthogonal Multiple Access Systems Considering QoS

As a technology that can accommodate more users and significantly improve spectral efficiency, non-orthogonal multiple access (NOMA) has attracted the attention of many scholars in recent years. The basic idea of NOMA is to implement multiple access in the power domain and decode the desired signal via successive interference cancellation (SIC). However, the resource allocation problem in such NOMA system is non-convex. It is difficult to directly solve this optimization problem through conventional methods. As such, we propose to apply a reinforcement learning (RL) approach based on cooperative Q-learning to solve the resource allocation problem in multi-antenna downlink NOMA systems. First, we formulate the resource allocation process as a sum rate maximization problem, subject to the power budget constraints and quality of service (QoS) condition. Second, we design a reward function to improve the sum rate while meeting the power and capacity constraints. Multiple Q-tables are created and cooperatively updated to get the optimal beamforming matrix. Then, we analyze the convergence of our proposed RL based power allocation method. Our simulations show that the proposed power allocation scheme yields excellent performance in terms of sum rate, energy efficiency, and spectral efficiency.
Towards the Identification of Security Tokens on Ethereum

Crypto tokens are digital assets, similar to the coins of a cryptocurrency, except that they do not have their own blockchain or distributed ledger. Rather, they are built on top of an existing one. Areas of application include their use as means of investment, as a local currency in a decentralized application, as well as means for building an ecosystem or a community. Depending on the purpose, it is common to categorize tokens into payment tokens, security tokens and utility tokens. The distinction is of interest since in most jurisdictions, security tokens are more heavily regulated than other tokens.In this paper, we present a heuristic approach towards automatic detection of security tokens from blockchain data. To this end, we first discuss several methods for the (semi-) automatic identification of token contracts. Then we attempt to identify the token type. For our analysis, we examine both the deployed bytecode and the calls to token contracts that we extract from transaction data of the Ethereum main chain up to block 9500000, mined on Feb 17, 2020.
Finite-Time Control of Myringotomy Surgical Device Based on Nonsingular Terminal Sliding Disturbance Observer

This paper presents a disturbance observer-based nonsingular terminal sliding mode control design for a myringotomy surgical device. The methodology is established to control a semi-automated hand-held ear surgical device for the treatment of otitis media with effusion. Otitis media with effusion is the collection of bacterial fluid in the middle ear space which can potentially lead to hearing loss and chronic infections. A finite-time terminal sliding surface is proposed and a nonsingular terminal sliding control is constructed. The control approach fulfils precise tracking of the desired motion trajectory in the presence of disturbances, nonlinearities and uncertainties. The stability of the control approach is analyzed. In addition, the tracking control of the myringotomy surgical device is proven mathematically and the simulation results confirm the applicability and efficiency of the proposed approach and a precise tracking performance following desired motion trajectory is demonstrated in the simulations.
Visual Information Literacy: Definition, Construct Modeling and Assessment

A major problem in education and visual information design is that, while tools to measure people’s reading and writing ability with texts and numbers are ripe, the ability to properly process information from data graphics – an ability that can be called Visual Information Literacy – is still off the radar, and even less interest is apparently devoted to its evaluation. The purpose of this research is that of presenting an exploration of methods and tools towards the measurement of data graphics effectiveness and efficiency, and of proposing a definition of ‘Visual Information Literacy’, together with the design of a model characterizing it as a developmental skills progression that covers the cognitive abilities activated when dealing with data graphics. A final goal of this paper is to report a first round of results assessing the validity of the model designed, by bringing statistical evidence that data graphics comprehension depends on the matching of users’ ability and data graphics difficulty. The contribution of this paper is twofold: comparing the current research on Visual Information Literacy and advancing it by designing a model for its characterization to allow the design of a Visual Information Literacy measurement scale standard.
Network Traffic WLAN Monitoring based SNMP using MRTG with Erlang Theory

Best internet network connection because of the smooth flow of data and affects performance. A network admin must supervise the internet network both in software and in terms of hardware. The main thing that a network admin does in the Central Room is to be able to realize a software such as the Multi Router Traffic Grapher (MRTG) which can function to monitor the internet network in the environment. Internet speed is determined by the amount of bandwidth available with many users accessing the network. This work was conducted using the SNMP-based MRTG application, which is used to monitor and analyze bandwidth usage. The results of the bandwidth usage will be calculated using the Erlang-B theory from the results of calculations carried out using the Erlang-B theory algorithm, a Grade of Service (GoS) value for the busiest hour is 0.095%, rounded to 0.001. This means that the probability value of all channels being active at that hour is 0.001%. This GoS value is good enough because failed access is unlikely.
A Multi-Agent Simulation Model for Analyzing Employee Morale during Organizational Innovation

Morale issues are considered to be a major factor in preventing realization of organizational reforms. Employees' morale during the organizational reform is known to depend upon trust in their management. A previous study has indicated that trust can be estimated as a function of the type of organizational changes, the category/size of the business, the employees’ job position and gender. To the best of our knowledge, few studies have quantitatively clarified the relationship between trust and the employees’ morale. Our previous study has developed a multi-agent simulation model to demonstrate the change of employees’ states as well as formulating a total employees’ morale during organizational reform. This study modified the model to show how sensitively the employees react to CEO’s behavior when the combination of the types of organizational changes varies.
Genetic Algorithm-Based Energy Efficiency Maximization for Social-Aware Device-to-Device Communications

In this paper, we propose a novel energy efficiency maximization scheme for social-aware device-to-device (D2D) communications based on a genetic algorithm (GA). The proposed scheme incorporates both social and physical parameters of users to model the energy efficiency maximization problem. The formulated problem considers the spectral reuse, spectral efficiency, and the transmit power constraints of both cellular and D2D users to satisfy their quality of service requirements. Moreover, an algorithm based on the self-adaptive penalty function is applied to convert the constrained problem into an unconstrained problem. Next, GA is utilized to maximize the unconstrained problem. The feasibility of the proposed scheme is shown by computing its time complexity in terms of big- $O$ notation. Moreover, the convergence of the proposed scheme is analyzed by comparing the maximum and average values of the overall energy efficiencies for different iterations. Likewise, the performance is evaluated in terms of overall energy efficiency and system throughput for various D2D communications scenarios. To demonstrate the efficiency of the proposed scheme, the results are compared with those for a static penalty-based GA algorithm. Furthermore, to demonstrate the significance of combining the two types of parameters (i.e., social and physical), the performance of the proposed scheme is compared with schemes based on only social or physical parameters.
A Pose Control Algorithm for Omnidirectional Robots

The pose control (position and orientation) of a robot is important to control how and when the robot gets to the desired pose at the desired time in order to perform some task. Controlling omnidirectional robots is of great interest due to their complete maneuverability. So, we use Proportional-Integrative (PI), Proportional-Derivative (PD), and Feed-Forward (FF) controllers to control the pose of an omnidirectional robot in space and in time. The proposed controller approximates the future trajectory (a subset of points) on parametric polynomials for computing the derivatives needed in the FF. In the simulations performed, it was analyzed the size of the future trajectory horizon for the controller depending on the robot’s velocity, and the proposed controller was compared to PD-only and a generic GoToXY controller. The results demonstrated that the proposed controller achieves better results than the other two both in space and in time.
WIP Adaptive evaluation for a systems theory course according to the learning context

The model developed for an adaptive evaluation in the blended learning system (2018) is described, and it is about replicating it in a system totally unknown (2020) to the teacher and the student, where the classes have been entirely non-face-to-face. This gives rise to the beginning of a review of the model or student profile that goes beyond learning styles identified by Multiclass Neural Network in machine learning, as well as the classification of the questions. So that model is in the training phase. In the first phase, the main objective was to compare the evaluation using the adaptive tests versus the normal tests, where the results of acceptable reliability in the adaptive and control tests were obtained, as well as a better result in the average student performance and a shorter time.
Securing Building Automation Systems

Security is a well discussed topic in computer networks and IT infrastructure. In contrast it is severely neglected in building automation systems. It is very common for new buildings to be equipped with smart systems responsible for heating, air conditioning, lighting amongst other systems. While they have a lot of positive features, there are also shortcomings. One of the huge problems is security. The used protocols got developed some tens of years ago and thus are not at all at the state of the art regarding security. Replacing old devices by new ones using secure protocols is not an option as this would mean a huge amount of costs. This means a suitable solution has to be found to counteract this problem and to improve the security in such systems as much as possible. In this paper different approaches are discussed. The different benefits as well as limitations of the solutions is shown. This enables the reader to get an overview about the different possible approaches to the topic.
Single Wavelength Strain Sensing Based on Double Taper Mach-Zehnder Interferometer

A novel interrogation method is presented in the application of strain sensing with a double taper Mach-Zehnder interferometer (DTMZI). The proposed sensing setup comprises a laser source and an optical power meter, which significantly lowers future integration cost and complexity. An analytical coupling model is established to study the sensor's strain-related modal behaviour. The design parameters of the DTMZI device are conditioned to satisfy a single-wavelength measurement. The spectral responses of several fabricated sensors are examined. Our experiments confirm the direct mapping between different strain values and particular transmission measures for the single wavelength case.
A Modern Solution for an Old Calibration Problem

Cameras endow a robot with a sense of vision to see the world. The data acquired from a camera is originally expressed in the camera coordinate system. However, a robot manipulator only accepts the data represented in the robot coordinate system. Under such circumstances, robotic applications that employ cameras necessitate the requirement of converting the camera-acquired data into the robot coordinate system. Since the robot coordinate system is often attached to the base of the robot, the relationship between the camera's and the robot's frames (commonly described by a homogeneous transformation matrix) is composed of two parts: the transformation matrix between the base frame and the end-effector frame of the robot manipulator; and the transformation matrix between the end-effector frame and the camera frame. As the first transformation matrix can be attained from robot kinematics, the problem of representing the camera-acquired data in the robot coordinate system boils down to the estimation of the transformation matrix between the robot's end-effector frame and the camera frame. This estimation problem is widely known as the hand-eye calibration problem since the end-effector and the camera are commonly regarded as the hand and the eye of a robot, respectively. The hand-eye calibration problem plays an important role in robotic applications as it enables the use of cameras in such applications. This problem also emerges in other applications such as visual servoing, 3D scanning systems, and other sensor calibrations.
The Frequency- and the Time-Domain Design of a Dual Active Bridge Converter Output Voltage Regulator Based on the D-Decomposition Technique

Commonly used variants of the proportional-integral, PI, and the integral-proportional, IP, compensator gains selections, merged with the <inline-formula> <tex-math notation="LaTeX">$\mathfrak {D}$ </tex-math></inline-formula>-decomposition technique are presented in this paper. The motivation to this work is the willingness to check whether such a combination can lead to unified and perhaps simplified approach in this matter. Therefore, the <inline-formula> <tex-math notation="LaTeX">$\mathfrak {D}$ </tex-math></inline-formula>-decomposition technique has been effectively combined with the frequency- and the time-domain driven requirements regarding the control dynamics. Criteria such as the gain- and phase-margins, <inline-formula> <tex-math notation="LaTeX">$(GM, PM)$ </tex-math></inline-formula>, the sensitivity, <inline-formula> <tex-math notation="LaTeX">$M_{\text {S}}$ </tex-math></inline-formula>, the pole placements by means of the <inline-formula> <tex-math notation="LaTeX">$(\sigma, \omega _{\text {d}})$ </tex-math></inline-formula> and the <inline-formula> <tex-math notation="LaTeX">$(\xi, \omega _{\text {n}})$ </tex-math></inline-formula>, and the overshoot with the rise time <inline-formula> <tex-math notation="LaTeX">$(\delta, t_{\text {r}})$ </tex-math></inline-formula> are considered. It has been shown that the control design effort can be reduced by the means of the <inline-formula> <tex-math notation="LaTeX">$\mathfrak {D}$ </tex-math></inline-formula>-decomposition to intuitive judgments in the proportional and integral gains coordinates <inline-formula> <tex-math notation="LaTeX">$(K_{\text {P}}, K_{\text {I}})$ </tex-math></inline-formula> with parametric curves. As such it can be thought of as a promising scenario in a case considered. The analyses are presented and discussed in details. They are conducted basing on example of output voltage closed loop control of the Dual Active Bridge, DAB, converter. The circuit operates under the phase shift control scheme. The control-to-output transfer function identification and the control circuit delays are included in the analyses. The case analysis have shown that the time domain requirements are less effectively met with the PI regulator when compared to its IP configuration. This is due to the commonly used simplifications during conversion between the <inline-formula> <tex-math notation="LaTeX">$(\xi, \omega _{\text {n}})$ </tex-math></inline-formula> and the <inline-formula> <tex-math notation="LaTeX">$(\delta, t_{\text {r}})$ </tex-math></inline-formula> in presence of uncompensated zero of the closed loop transfer function. The paper contains complete and intelligible approach to dedicated mathematical investigations verified experimentally.
Design and Development of Low Cost, Portable, On-Field I-V Curve Tracer Based on Capacitor Loading for High Power Rated Solar Photovoltaic Modules

This paper outlines a novel design of low-cost, portable, fast, and precise Current-Voltage Curve Tracer (IVCT) with automated parameter extraction for high power rated Solar Photovoltaic (SPV) modules to effectively and efficiently determine the outdoor operating status of SPV power generators. The developed IVCT is based on a Raspberry Pi microprocessor, a super-capacitive load, heat sinkable discharge resistances, and sensors with high sensitivity and resolution for measuring light irradiance, module temperature, current, and voltage. The proposed Outdoor Test Facility (OTF) consists of an Current-Voltage (<inline-formula> <tex-math notation="LaTeX">$I-V$ </tex-math></inline-formula>) and a Power-Voltage (<inline-formula> <tex-math notation="LaTeX">$P-V$ </tex-math></inline-formula>) curve tracer that uses a dynamic loading supercapacitor to safely and quickly scan the SPV module performance characteristics under real-world operating conditions. It also helps to achieve uniform sampling with better data accuracy. It uses Raspberry Pi as a central processing unit for low-cost data acquisition, data logging, and data computation. Furthermore, results from on-field testing of various small-scale SPV modules show that the <inline-formula> <tex-math notation="LaTeX">$I-V$ </tex-math></inline-formula> tracer can acquire higher-resolution characteristics curves and perform accurate model parameter recognition in real-time. Proposed IVCT can measure individual SPV modules without altering the electrical interconnection circuit, and the operating point can be shifted to 20 <inline-formula> <tex-math notation="LaTeX">$A$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$45~V$ </tex-math></inline-formula> in few seconds. The proposed system recomposes the SPV module <inline-formula> <tex-math notation="LaTeX">$I-V$ </tex-math></inline-formula> characteristics based on this variance, with accuracies of 1 to 3% for the region near maximum power.