Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementing ROS Communications for Sensor Integration with the RB5 Collaborative Robot

This paper highlights the implementation of a ROS wrapper for the RB5 collaborative robot, specifically to enable easy sensor integration for use as a research platform. The RB5 is a new, low-cost robotic arm that was developed to be implemented in non-industrial environments. End users can program basic tasks using a tablet-based teaching pendant software. While the current interface allows users to create programs for specific routine tasks, it does not allow for more the development of more robust applications - especially as there is no alternative solution for end users wanting to interact with the robot. The need to provide a more general platform for application led us to developing a Robot Operating System (ROS) interface. This interface abstracts robot commands through simple function calls on the user end, leading to easy control of the robot. In addition, the sensor integration capabilities that ROS provides will allow researchers to test their applications of the RB5 in whichever human collaborative environment they see fit.
An Optimized FPGA Implementation of DCT Architecture for Image and Video Processing Applications

The development of portable devices such as digital cameras and mobile phones led to higher amount of research being dedicated to image and video processing systems. In particular, video processing coding technique such as High Efficiency Video Coding (HEVC) requires low power consumption for the multimedia applications, this leads to extensive development in low area and high speed algorithms. The log time Discrete Cosine Transform (DCT) is increasingly employed for compression standards, since it has remarkable energy compaction properties. Pipelined and MAC based DCT have been implemented in digital hardware to offer better compression at very low circuit complexity. In this paper, a Time division multiplexing (TDM) based DCT architecture is implemented using the concept of resource sharing principle which possess low computational complexity and high speed. This hardware implementation results in reduced area and increased speed compared to the conventional DCT architectures implemented in Field Programmable Gate Array (FPGA) devices.
Attack Detection based on Statistical Analysis of Smartphone Resource Utilization

Detection of anomalous behavior is critical to protect smartphones from emerging threats and ensure user security and privacy. Android has emerged as the prevalent mobile operating system and is one of the most attractive targets for potential attacks. Typical approaches for malware detection in Android rely on resource intensive analysis of cloud servers using Machine Learning (ML) algorithms. However, in practice, collecting and sending malicious data samples is cumbersome since it requires a persistent connection between smartphones and cloud servers. In this work, we propose a novel approach that leverages elements from statistical analysis and information theory to detect malware based attacks on smartphones. We parameterize CPU usage, RAM utilization, and network data of smartphones to detect anomalous usage patterns using the Kullback-Leibler divergence measure. The advantage of our proposed method is two-fold. First, it is less computation intensive than Machine Learning algorithms and thus can run efficiently on low-power devices. Second, the proposed approach is generic in that our model can be applied to diverse variety of smartphone usage patterns to detect malicious behavior. Evaluation of the proposed method shows that it achieves an accuracy of 86.24% for detecting malicious behavior in Android smartphones.
Comparison of MongoDB and Cassandra Databases for Spectrum Monitoring As-a-Service

Due to the growing number of devices accessing the Internet through wireless networks, the radio spectrum has become a highly contended resource. The availability of low cost radio spectrum monitoring sensors enables a geographically distributed, real-time observation of the spectrum to spot inefficiencies and to develop new strategies for its utilization. The potentially large number of sensors to be deployed and the intrinsic nature of data make this task a Big Data problem. In this work we design, implement, and validate a hardware and software architecture for wideband radio spectrum monitoring inspired to the Lambda architecture. This system offers Spectrum Sensing as a Service to let end users easily access and process radio spectrum data. To minimize the latency of services offered by the platform, we fine tune the data processing chain. From the analysis of sensor data characteristics, we design the data models for MongoDB and Cassandra, two popular NoSQL databases. A MapReduce job for spectrum visualization has been developed to show the potential of our approach and to identify the challenges in processing spectrum sensor data. We experimentally evaluate and compare the performance of the two databases in terms of application processing time for different types of queries applied on data streams with heterogeneous generation rate. Our experiments show that Cassandra outperforms MongoDB in most cases, with some exceptions depending on data stream rate.
Magnetic Field Design of a Full-Scale Prototype of the HL-LHC Beam Separation Dipole With Geometrical and Iron-Saturation Corrections

High Energy Research Organization (KEK) has developed three short-scale model magnets of the beam separation dipole, MBXF, which is to be installed for the High Luminosity Large Hadron Collider, HL-LHC, in CERN. We will soon undertake production of a prototype of MBXF and its magnetic design has to be finalized before the fabrication. We established a strategy for designing the new coil cross section by taking into account different error sources for its field quality. In this paper, focused on <inline-formula><tex-math notation="LaTeX">$b_3$</tex-math></inline-formula>, we describe how the final <inline-formula><tex-math notation="LaTeX">$b_3$</tex-math></inline-formula> integral, which should be within 2.9 units according to the acceptance criteria, is achievable.
A Framework To Generate Actionable Information For Mobile Based Information System That Empowers Users To Self-Manage Type 2 Diabetes

Very high mobile phone adoption makes it possible to use mobile phones to empower patients with type 2 diabetes to self-manage their diabetic condition. The mobile application allows patients to make informed choices by providing appropriate information in the right context. We present a framework that can generate actionable information for such a mobile application and the result of evaluation of the generated actionable information provided by a group of 15 patients and 8 health care providers. We have received positive feedbacks from patients and health care provider after carrying out the evaluation of the application.
Ischemic Stroke Lesion Segmentation Using Multi-Plane Information Fusion

Diffusion-weighted magnetic resonance imaging (DWI) is sensitive to acute ischemic stroke and is a common diagnostic method for the stroke. However, the diagnostic result relies on the visual observation of neurologists which may vary from doctor to doctor under different circumstance. And manual segmentation is often a time-consuming and subjective process. The time from onset to thrombus removal has a significant impact on the prognosis of patients with acute ischemic stroke. The shorter the time, the better the prognosis. For this purpose we present a novel framework to quickly and automatically segment the ischemic stroke lesions on DWI. We mainly have three contributions: firstly, we design a detection and segmentation network (DSN) to solve the two kinds of data imbalance; secondly, we propose a triple-branch DSN architecture, used for extracting different plane feature respectively; thirdly, we propose a multi-plane fusion network (MPFN), which aims to make final prediction more accurate. Extensive experiments on ISLES2015 SSIS DWI sequence dataset demonstrate the superiority of our proposed segmentation method. The dice reached 62.2% and the sensitivity reached 71.7%.
Design of a Nonlinear Model-based Predictive Controller for a Wind Turbine Based on PMSG Using an Augmented Extended Kalman Filter

In this paper, a novel nonlinear model-based predictive controller without speed/position sensors is designed for control of a wind turbine permanent magnet synchronous generator (PMSG) and grid. Here, both grid and generator sides are controlled via a predictive mechanism including an optimization subject to nonlinear constraints of current and voltage amplitudes as well as the harmonic distortion magnitude of currents. To have a sensorless design, a Kalman filter is also designed. First, an extended Kalman filter (EKF) is used to estimate the speed and then an augmented extended Kalman filter (AEKF) is designed to estimate the flux without the need to add complex equations. The simulation results show an acceptable performance of the proposed method despite the changes in the reference speed and disturbance.
Lane Keeping of Vehicle Using Model Predictive Control

The MPC approaches, namely, Implicit and Explicit MPC are described in this paper from implementation and analysis points of view for the Lane Keep Assist System of a passenger vehicle in simulated environment. The comparison of the closed loop performance of the said approaches is expected to help the Model in Loop validation of control software development. This could ease and speed up its implementation on the embedded hardware that serves as integral part of LKA feature of Advanced Driver Assistance System. The work uses fundamental equations and develops a simplified model for lateral dynamics of a vehicle as a Linear Parameter Variant state space model in time domain for a commonly followed speed range. The IMPC and EMPC controller actions are computed by constructing and solving optimization problems using Quadratic Programming approach and multi-parametric PWA solver approach respectively with the help of available toolboxes. The tracking performance is simulated for the future lateral deviation from the centre line of driving lane at a chosen look-ahead distance on a road profile of varying curvature. The MPC controllers are further complemented by adopting feed forward control action that may be incorporated by automatic control or by means of steering maneuvers by driver. A qualitative comparison of the two approaches is made along with the comments on trade-offs thereof. It is revealed that both the MPC approaches lead to acceptable performance criteria while IMPC is superior among all. The EMPC facilitates additional benefit of its feasibility to implement on comparatively low end microcontroller and may be considered for cost effective implementations of LKA system.
Affinity-based Fragmentation for Sensor Data

Affinity-based Fragmentation AFA method for fragmenting rapidly growing RDF data is a workload aware method that keeps frequently queried data in a fragment. It is demonstrated using sensor Linked Open Dataset LOD. Performance parameters are Query Execution Time QET and Data Loading Time DLT. QET averaged over all the query types is 47% faster for fragmented data. QET for frequently occurring query types, linear and star, increases linearly while for range and snowflake, it scales exponentially. Results reported here verify that AFA executes in constant time for scaled data hence will help in building faster analytics for weather forecast.
Prototype of Smart Trainable Robotic Arm

The robotic arm is a kind of manipulator, used to carry out most difficult tasks, which is the need of today's world and its applications are in vast fields. It can be used for many purposes such as to pick and drop and to work in the environment where a human cannot work i.e. harmful chemicals, high temperature and so on. One needs some skilled personnel to make that robotic arm functional as per requirement and for making some amendments in its functionality, one has to reprogram its parameters which needs an expert. A robotic arm that can learn from its mimicry done by a human which can be easily taught to perform certain operation as per its application by an unskilled person. We have designed a prototype of the robotic arm which can work according to the way one teaches furthermore, robotic arm does not need a skilled person, the mimicry feedback will be recorded in a processing unit i.e. microcontroller (Arduino) and on a single command of play that robotic arm will work continuously to perform that specific task.
Hybrid Feature Vector Generation for Alzheimer’s Disease Diagnosis Using MRI Images

In the advent of computer-aided diagnosis (CAD), analysis of medical images with the help of various feature extraction techniques is gaining a lot of interest. Nowadays, a lot of research is done in the analysis of neuro-images, particularly the MRI modality to study the structural changes or atrophy of the brain due to various neurological disorders. Alzheimer’s disease (AD) being one such illness, affects a vast proportion of elderly populations worldwide. The current diagnosis of AD which is done manually by a clinician using neuro-images can sometimes be inaccurate. Although image processing and machine learning have facilitated feature extraction and diagnosis of AD respectively, there are still many challenges that exist in the current systems. This work proposes a system that will diagnose AD using MRI images by generating a hybrid feature vector (HFV). The HFV is created from fusing features obtained from different techniques such as volumetric analysis of grey matter (GM) and white matter (WM), surface-based cortical thickness and tissue volumetric study of regions of interest (ROI) acquired from Voxel Based Morphometry (VBM) analysis. Machine learning techniques are later used to classify patients (AD) from normal controls (NC). The experimentation of the proposed system was carried out using the T1-weighted MRI scans of 100 subjects (50 AD and 50 NC) obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The maximum accuracy achieved by the proposed system is 93% using the SVM classifier which validates the promising performance of the HFV in AD diagnosis.
Sensorless Automatic Stop Control of Electric Vehicle in Semi-dynamic Wireless Charging System

Semi-dynamic wireless charging system provides electric power to electric vehicles (EVs) on the point of stopping. This paper proposes a scheme of positioning system in semi-dynamic wireless charging system via magnetic resonance coupling in order to perform sensorless automatic stop control in EVs by using parameters from wireless power transfer system (WPT). Furthermore, this proposed method also selects the stop position where occupies high transmission efficiency and short vehicular gap which leads to energy conservation and traffic jam problem reduction. The experiments demonstrated that the proposed method is able to stop the vehicle at the high transmission efficiency position without any visual sensor.
Sampled-data control of 2D Kuramoto-Sivashinsky equation under the averaged measurements

This paper deals with sampled-data control of 2D Kuramoto-Sivashinsky equation over a rectangular domain Ω. We suggest to divide the 2D rectangular Ω into N sub-domains, where sensors provide spatially averaged state measurements to be transmitted through communication network. We design a regionally stabilizing controller applied through distributed in space characteristic functions. Sufficient conditions ensuring regional stability of the closed-loop system are established in terms of linear matrix inequalities (LMIs). By solving these LMIs, an estimate on the set of initial conditions starting from which the state trajectories of the system are exponentially converging to zero. A numerical example demonstrates the efficiency of the results.
Hybrid Method for Modeling User Interests based on Social Network

The growing popularity of social networks and microblogging services has gradually increased the demand for personalized applications. The microblogging services such as Twitter has a powerful forum for users to share their personal interest and opinions. Mining and analyzing user's interests is a crucial factor in buying decisions and tracking the emotions of the public about their items, business, etc. Although, Twitter has a broad range of topics in real-time, it poses significant challenges because of the unstructured short text. In this paper, the best model for finding the user's topics of interest is being investigated by building the profile of individual users based on their tweets. A hybrid Topic-based model is proposed that combines both two unsupervised learning algorithms with sentiment consideration and user features. Thus, we show that the proposed hybrid model has a higher performance in the topic extraction of user's interests on Social Networks.
Transverse Spectral Analysis in Stenosis Diagnosis Using Transversely Oscillating Acoustic Field

Accurate estimation of maximum velocities in Doppler blood flow spectrograms is vital in diagnosis of cardiovascular anomalies such as stenosis. However, obtaining accurate maximum velocity is not straightforward due to intrinsic spectral broadening and variance in the power spectrum estimate. Therefore, employment of Doppler indices such as pulsatility index is commonplace. Moreover, the results attained by conventional blood spectrogram estimators fail when beam to flow angle is near to 90°. In this work, an approach for estimation of pulsatility index for a fully transverse blood flow is presented. The method has been validated using simulated Doppler signal with scatterers possessing the pulsating flow of femoral artery in Womersley-Evan’s model. By investigation of three scenarios of a normal vessel, and vessel with 20% and 40% stenosis, the possibility of stenosis diagnosis is discussed.
EASM: Energy-Aware Sink Mobility Algorithm to Prolong Network Lifetime in WSN

Traffic from all over the sensor network forwarded to the sink leads to quicker energy dissipation of the nodes situated around the sink compared to other sensor nodes deployed away from the sink results in energy hole problem. The aim is to gather data from all the sensor nodes, aggregate and forward it to the sink. WSN is logically divided into subgrids and each subgrid is uniquely identified and number of sensor under each subgrid is known to the sink. During the data aggregation process, the mobile sink move towards the potential subgrid so that the energy of all the sensor nodes is equally utilised. It is implemented through reconstructing the network and creating new routing paths in the network. The sink node moves to a new safe location where nodes have more residual energy, gets associated with new neighbor nodes which prolongs the network lifetime. In this paper, an Energy-Aware Sink Mobility (EASM) algorithm is proposed to enhance the lifetime of the sensor network. Simulation results are obtained and shows improvement in network lifetime.
Real Time Big Data Processing: A Comparative Study of Existing Approaches

Current paper analyzes different existing approaches for solving issues with Real Time Big Data Processing . On the background of identified inherent functional requirements of Real Time as well as Big Data processing system, comparative studies are carried out on all major technology solutions in the entire spectrum and conclusive decisions are made in order to adopting an option for design and development of a novel and Real Time Big Data Processing architecture.
IoT-Enabled Smart Child Safety Digital System Architecture

Safety of a child in a large public event is a major concern for event organizers and parents. This paper addresses this important concern and proposes an architecture model of the IoT-enable smart child safety tracking digital system. This IoT-enabled digital system architecture integrates the Cloud, Mobile and GPS technology to precisely locate the geographical location of a child on an event map. The proposed architecture model describes the people, information, process, and technology architecture elements, and their relationships for the complex IoT-enable smart child safety tracking digital system. The proposed architecture model can be used as a reference or guide to assist in the safe architecture driven development of the various child tracking digital systems for different public events.
A New Fuzzy Clustering Algorithm Using Uncertainty-based Entropy for Brain MR Image Segmentation

In this paper, we propose a new fuzzy clustering algorithm using uncertainty-based entropy for brain MR image segmentation. To overcome this problem, we introduce an uncertainty measure, which defines a degree of improbable of a pixel for becoming a member of a cluster. In addition, we use this uncertainty measure to calculate the entropy for a particular pixel. The pixels in the tissue boundaries have higher entropy as the region is blurred due to noise and intensity inhomogeneity. Finally, a similarity measure, which is characterized by a Gaussian density function, is integrated both with this uncertainty measure and the fuzzy membership function. We suitable formulate the objective function of the proposed algorithm by integrating the above parameters; thereby addressing the limitations of the standard fuzzy c-means (FCM) clustering algorithm. The simulation results of the proposed algorithm suggest that it is suitable for segmentation of brain MR images, especially in the presence of high percentage of noise and intensity inhomogeneity and even superior to some of the state-of-the art methods.
Towards a CubeSat Mission for a Wideband Data Transmission in E-Band

This paper presents ongoing activities towards an in-orbit verification of an E-band high bandwidth communication system to exploit a new spectrum for broadband satellite communication by demonstrating a data-downlink, using solid state technologies for the RF analog transmit front end, in the frequency range of 71-76 GHz with a data rate of 5 Gbit/s from a three-axis stabilized 6U nano-satellite in low earth orbit to a ground station. A recent plane-to-ground data transmission has successfully validated the technology platform, and dynamic link budgets suggest the feasibility of LEO to ground links exploiting E-band frequencies.
Roger that! Learning How Laypersons Teach New Functions to Intelligent Systems

Intelligent systems are rather smart today but still limited to built-in functionality. To break through this barrier, future systems must allow users to easily adapt the system by themselves. For humans the most natural way to communicate is talking. But what if users want to extend the systems' functionality with nothing but natural language? Then intelligent systems must understand how laypersons teach new skills. To grasp the semantics of such teaching sequences, we have defined a hierarchical classification task. On the first level, we consider the existence of a teaching intent in an utterance; on the second, we classify the distinct semantic parts of teaching sequences: declaration of a new function, specification of intermediate steps, and superfluous information. We evaluate twelve machine learning techniques with multiple configurations tailored to this task ranging from classical approaches such as na? ve-bayes to modern techniques such as bidirectional LSTMs and task-oriented adaptations. On the first level convolutional neural networks achieve the best accuracy (96.6%). For the second task, bidirectional LSTMs are the most accurate (98.8%). With the additional adaptations we are able to improve both classifications distinctly (up to 1.8%).