Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of a Novel Functional Near-Infrared Spectroscopy System for Human Brain Imaging

Many countries are facing the dilemma of ageing society and the fatality rate due to degenerative brain diseases is rising. Therefore, swift advancements in the field of neuroscience are vital to our rapidly aging society. Functional near-infrared spectroscopy (fNIRS) has become a promising neuroimaging modality for measuring the concentration changes of blood chromophores during a neuronal activity. This paper describes the development and working of our lab-developed fNIRS system which measures the concentration changes in oxyhemoglobin and deoxyhemoglobin from the optical near-infrared (NIR) light signals. 128 light emitting diodes (LEDs) consisting of two wavelengths (i.e. 735 nm and 850 nm) are used in our system. This NIR light intensity passes through the brain tissue and get absorbed and scattered, is received and measured by using only single photodiode (PD) in order to compute the desired blood chromophores. The drive circuit of LEDs was designed by using serial peripheral interface based output expansion chips. Wireless communication based on Wi-Fi was employed in the system to transfer the light intensity values to our lab-developed software running on a Windows based computer. The software has the capacity to show the data plots in real-time as well as store the information for offline evaluation. The functionality of the system was tested using a phantom of known scattering and absorption coefficients instead of human brain. The LEDs and PD were placed firmly on the phantom and different arrangements for light shooting were utilized to yield rigorous testing data. The tested system exhibited the measured optical signal intensity values for both wavelengths that are used to measure oxy and deoxyhemoglobin changes in 128 channels.
Adaptive Multimodal Localisation Techniques for Mobile Robots in Unstructured Environments : A Review

Mobile robots can be integrated as an entity in the new paradigm of the Internet of Things (IoT) and can be instrumental in extending sensing and manipulation capabilities to remote environments where the installation of sensor networks is unfeasible. Many anticipated applications of autonomous mobile robots require for them to navigate in diverse complex environments without support from exterior infrastructures. To perform this on-board navigation, the robot must make use of the available sensor technologies and fuse the most reliable data respective to the present environment in an adaptive manner. This paper will review recent efforts to develop onboard navigation systems which can seamlessly transition between outdoor and indoor environments and different terrains seamlessly. The methodologies surveyed include visual SLAM, Odometry and Place Recognition. An overview of the state-of-the-art is provided with a focus on approaches which are adaptive to dynamic sensor uncertainty, dynamic objects and dynamic scenes. In addition, the paper also provides an analysis of the most common sensor modalities and the factors affecting sensor uncertainty for the same.
Decentralized Voltage Optimization based on Network Partitioning in Distribution Systems with DGs

As well known, the large-scale spreading of Distributed Generators (DGs) in distribution systems can cause a significant impact on the voltage profiles along the feeders. On the other hand, the reactive power injections by DGs can be used to reduce such an impact and regulate the nodal voltages. In this paper, the basic idea of a decentralized voltage optimization problem is proposed: it minimizes the deviations of the nodal voltages from their reference values so as to determine the optimal set-points of the reactive powers injected by DGs. To reduce the number of measures from the grid and to avoid a large number of non linear equality constraints, the partition of the network in two voltage control zones (VCZs) with pilot nodes (PNs) and a linear method for the steady-state operation of the distribution system are used. The decomposition of the problem into two problems is based on the theory of Lagrange multipliers and it results into a quadratic programming problem of limited dimension to be solved by each VCZ. The overall problem solution is obtained by iterate the VCZ solutions and exchanging a limited number of scalar quantities between PNs. With respect to a centralized approach, the proposed decentralized voltage optimization provides the same solution with a reduction of the requirements for the communication infrastructure. Numerical results in a case study are presented to give evidence of the improvement of the voltage profiles along the feeders of a 24-nodes LV distribution grid with DGs.
Enhanced Distributed Connectivity Control in Low Probability of Detection Operations

For most military land vehicles, communication systems are the main contributor to RF emissions from the vehicle. To reduce detectability of the vehicle, the transmission power of the communications systems should be managed. Accomplishing this without adversely effecting the ability of vehicles to communicate requires a coordinated approach. Previous work has developed algorithms that aim to use distributed connectivity control to minimise the received power of network communications at an estimated adversary location. This paper builds upon that work. It proposes a number of possible enhancements that aim to reduce the optimisation time of distributed connectivity control algorithms. The proposed enhancements include partitioning the graph using articulation points to reduce the search space, the ability to eliminate multiple links at once and the elimination of redundant bids in the auctioning process. This paper shows in simulation that these enhancements, combined, significantly reduce the optimisation time compared to the baseline approach.
Implementation of 6LoWPAN and Controller Area Network for a Smart Hydroponics System

Hydroponics is the practice of growing plants and crops in soilless culture. It is becoming more prevalent nowadays because of decreasing available land due to urbanization, and the various advantages it has as compared to soil-based farming. To study and optimize the various factors that affect plant growth in hydroponics set-ups (such as ambient temperature, light intensity, humidity, etc.), a hybrid sensor network system, with both wireless (6LoWPAN) and wired (CAN Bus) components, was proposed. The proposed system was then implemented using off-the-shelf devices such as the CC2650 MCU, MSP432 launchpad, Digilent PMOD CAN, and the Raspberry Pi 2. The total number of 6LoWPAN nodes in the intended application setup was shown to be reduced by 87.26% compared to fully-wireless setup. The system was shown to be functional and the packet drop rate when time interval between messages is 800ms drops to almost 0%. An initial design for the visualization of the web application was also presented.
A Rapid Low-Thrust Trajectory Design Approach Based on Bi-Impluse Extension

The low-thrust propulsion has broad application prospects in deep space explorations, while it also brings forward a formidable challenge to the low-thrust trajectory design. To determine whether there is a feasible low-thrust trajectory connecting given points and to provide an initial guess for further trajectory optimization, we present a rapid low-thrust trajectory design approach based on bi-impulse extension. The approach employs the bi-impulse obtained by solving the Lambert's problem to indicate the direction of low-thrust and updates the state of spacecraft by numerical integration using low-thrust. After several iterations, a feasible low-thrust trajectory can be obtained by the presented approach. Results show that it is effective for both Venus-flyby mission and Mars-rendezvous mission.
An Event Prediction-based IP Multimedia Subsystem Service Exposure

Exposure of relevant services aligned with user’s needs is nowadays not only a guarantee of the latter's satisfaction but above all, a way for service providers to stand apart from competitors. In this context, the IP Multimedia Subsystem (IMS), which is an implementation of the Next Generation Network (NGN) concept, aims to provide a seamless access to a rich and varied range of services regardless the access technology employed by the service requestor. Moreover, the multitude of existing services makes service exposure a challenging exercise. To meet this challenge, in this paper we propose a service exposure approach based on event prediction. Our approach is proactive and based on an online service recommendation implicitly initiated by the service requestor. Our work is compared to the classic service exposure approach and to the predictive collaborative service exposure approach we proposed in our previous work. The obtained results illustrate the good performance of our approach in terms of response time, CPU utilization, memory use; but poorer performance in terms of service targeting accuracy compared to the aforementioned approaches.
Predicting Probing Rate Severity by Leveraging Twitter Sentiments

Probing is the first step to gain access to a network. Predicting the rate levels of probing against a network sufficiently ahead of time could be insightful to security analysts and practitioners. Indeed, an accurate prediction would help to understand the potential threats and attacks menacing an organization’s network. However, this prediction problem is a challenging task; prior works make predictions over time horizons not exceeding a few hours. In this work, we propose a machine learning approach to predict the next day probing rate levels for a network telescope by leveraging Twitter users’ sentiments toward the country hosting the network telescope. First, we investigate the relationship between probing rates and Twitter sentiments. Second, we cluster the probing rates to determine the probing severity levels. Finally, we predict future rate levels using several classifiers. We show that incorporating negative sentiments improves significantly the prediction performance. This demonstrates the importance of incorporating social signals as predictors when predicting future probing rates.
A Hybrid of Dual and Meet-in-the-Middle Attack on Sparse and Ternary Secret LWE

The dual attack is one of the most efficient attack algorithms for learning with errors (LWE) problem. Recently, an efficient variant of the dual attack for sparse and small secret LWE was reported by Albrecht (Eurocrypt 2017), which forces some LWE-based cryptosystems, especially fully homomorphic encryptions (FHE), to change parameters. In this paper, we propose a new hybrid of dual and meet-in-the-middle (MITM) attack, which outperforms the improved variant on the same LWE parameter regime. To this end, we adapt the MITM attack for NTRU due to Odlyzko to LWE and give a rigorous analysis for it. The performance of our MITM attack depends on the relative size of error and modulus, and hence, for a large modulus LWE samples, our MITM attack works well for quite large error. We then combine our MITM attack with Albrecht’s observation that understands the dual attack as a dimension-error tradeoff, which finally yields our hybrid attack. We also implement a sage module that estimates the attack complexity of our algorithm upon <italic>LWE-estimator</italic>, and our attack shows significant performance improvement for the LWE parameter for FHE. For example, for the LWE problem with dimension <inline-formula> <tex-math notation="LaTeX">$n=2^{15}$ </tex-math></inline-formula>, modulus <inline-formula> <tex-math notation="LaTeX">$q=2^{628}$ </tex-math></inline-formula>, and ternary secret key with Hamming weight 64 which is one parameter set used for <italic>HEAAN</italic> bootstrapping (Eurocrypt 2018), our attack takes 2<sup>112.5</sup> operations and 2<sup>70.6</sup> bit memory, while the previous best attack requires 2<sup>127.2</sup> operations as reported by the <italic>LWE-estimator</italic>.
OptoShield: A Low-Cost Tool for Control and Mechatronics Education

Commercial educational tools for teaching feedback concepts in control systems engineering and mechatronics are generally expensive, large, complex and sensitive instruments; therefore cannot be used for homework assignments outside the classroom. This paper presents a novel open-source teaching aid that can be readily manufactured under AC3 and as such, is financially accessible to students and universities operating on a tight budget. The device proposed in this paper implements a simple optical experiment, allowing students to follow a prescribed brightness level using feedback control concepts. In addition to real-time control, the apparatus is also suitable for teaching signal processing and system identification principles. The educational aid presented here is attached to the popular Arduino microcontroller prototyping platform in a form of a miniature extension shield, thereby using a standardized physical layout and software ecosystem. We describe the hardware, the application programming interface (API) and library written in C/C++ for the Arduino integrated development environment (IDE) and the accompanying Simulink toolbox; moreover present sample exercises for system identification and proportional-integral-derivative (PID) control.
Evaluation of temperature changes of a loaded DC motor in autonomous mobile robots

In this paper, the analysis of the temperature changes of a loaded DC motor is presented. The motor is being used in autonomous robots in MegaSumo category. The research object was Maxon Motor RE40 150W, which is a frequent choice of autonomous mobile robots' constructors. High power, small size, simplicity and precision of control are primary factors that make these motors popular. This study aimed to develop a protection scheme of MegaSumo robot's power components, battery, motors, and their drivers through estimation of safe work time of a loaded motor in the robot activity time based on instantaneous power loss in the motor. In order to evaluate the thermal response of the motor's internal components, a mathematical model was created basing on both experimental and motor manufacturers data. The research based on a model was carried out in Matlab/Simulink software suite.
LSTM Based Multiple Beamforming for 5G HAPS IoT Networks

This article proposes a novel 5G Internet of Things (IoT) networks with High Altitude Platform Station (HAPS) in the sky. In this proposed method, the 5G HAPS that consists of airplane and uniform rectangular array (URA) can achieve reliable connections between ground gateway (GW) and user equipments (UEs), at the same time GW requires the relative position information of the HAPS. HAPS is susceptible to various factors in the sky, resulting in system capacity reduction. Correspondingly, we propose a direction of arrival (DoA) prediction method based on the latest Long Short-Term Memory (LSTM) model to solve the above problems. Simulation results show that our approach makes a significant improvement to the 5G Internet of Things networks with High Altitude Platform Station.
Design of a switched line phase shifter for reconfigurable mm-wave Antennas

The use of smart antennas with fully adaptive properties is a promising solution for next generation wireless networks aiming to optimize the performance of radio channel and reduce interference. Reconfigurable antennas are desirable in many applications due to their ability to change their pattern, polarization and resonance frequency dynamically. One of the reconfigurable techniques is to use phase shifter loaded with Positive Intrinsic Negative (PIN) diode. In this paper, we propose the design of a switched-line phase shifter at 28 GHz millimeter wave band based on microstrip transmission lines and PIN diodes. The design was achieved using Ansys High-Frequency Structure Simulator (HFSS). A phase shift of 145° ± 7° along the band 24–29 GHz is obtained with an insertion loss below 5dB and a return loss better than 14dB. This proposed circuit can be used in the design of reconfigurable antenna arrays in the mm-wave band.
Procedure of Selecting Stochastic Search Multi-agent Algorithm Parameters by the Example of Solving Discrete Optimization Problems

A list of practical discrete optimization problems, which due to their high dimensionality, nonlinear nature of objective functions and restrictions cannot be solved by means of conventional methods, is presented. Application of stochastic search multi-agent algorithms with island computing organization scheme for solving indicated problems is validated experimentally. Multi-agent algorithm parameters found experimentally and approximating dependences to determine them, which in solving problems of various dimensionalities provide obtaining the highest possible quality of result within restricted time, are presented. The possibility is verified experimentally, and mathematical dependences, the use of which enables controlling accuracy and time of solving by means of algorithm parameter variation, are defined.
An Intelligent Deterministic D2D Communication in Narrow-band Internet of Things

To enable the internet of things (IoT) devices with increased coverage and optimized power consumption, the 3rd generation partnership (3GPP) standardizes the idea of narrowband IoT (NB-IoT) technology in the fifth generation (5G) of cellular communication. Re-transmission of control and data packets due to a poor link between user equipment (UE) and the base station (BS), is considered as one of the key feature in NB-IoT to ensure the data delivery of delay-sensitive applications, e.g. ambulance services and body sensor networks (BSN). This phenomenon degrades the energy efficiency of the already resource constrained systems. One key solution for NB-IoT UE is to exploit the device-to-device (D2D) communication using two hops instead of transmitting on a direct uplink, due to which the system performance increases. In an attempt to transmit the NB-IoT UE uplink data packet, splendid researchers have focused towards developing a D2D communication based strategy, which typically optimizes the expected packet delivery ratio (EDR) and end-to-end delay (EED) through an opportunistic method. However, such methodology imposes an additional delay due to the unavailability of active relaying nodes and increases the overall energy consumption of the system. This necessitates us to design an intelligent deterministic D2D (2D2D) relay selection strategy for delay sensitive NB-IoT UEs. The EDR and EED have been improved using deterministic programming based algorithm. Simulations with various parameters are carried out, and results are presented. Simulation results show that the deterministic algorithm gives better performance with a 10% increase in EDR and overcomes the additional delay.
Storage kind recognition of truck's timber load

Every truck heading into the paper mill of company MONDI, Slovakia have to be checked if storage of its timber load meets all safety rules. Each truck is scanned by a group of 2D scanners. Then the evaluation of timber load is done by software with use of the data received from the scanners. Checking software is universal for every kind of storage of timber loads. This article deals with a problem of recognition a kind of storage of timber load on a semi-trailer. The image recognition is done with use of data recorded by a set of cameras situated with its view on the driveway. By detecting a type of storage, it is possible to evaluate the safety check for a specific type of storage with better result than the universal check.
Power Line Detection for Aircraft Safety Based on Image Processing Techniques: Advances and Recommendations

In this paper, an extensive survey on PLD methods for low-altitude aircraft safety based on image processing is presented. First, primary features of power lines are introduced. Then, main studies on PLD are summarized, including PLD approaches based on tradition image processing methods and machine learning. Besides, a few latest research works, such as PLD based on deep learning and infrared images, are discussed. By discussing and comparing current methods, we have proposed future development directions of PLD, with the aim of providing a starting point for researchers in PLD. Specifically, image fusion and multimodal sensor fusion should be able to provide better detection performance and lower false alarm rate by exploiting complementary information from different sensors. In addition, AI will be an important technique in PLD, with the help of transfer learning to utilize limited training data and of multimodal learning to deal with different kinds of information.
Bitwise Neural Network Acceleration: Opportunities and Challenges

Real-time inference of deep convolutional neural networks (CNNs) on embedded systems and SoCs would enable many interesting applications. However these CNNs are computation and data expensive, making it difficult to execute them in real-time on energy constrained embedded platforms. Resent research has shown that light-weight CNNs with quantized model weights and activations constrained to one bit only {-1,+ 1} can still achieve reasonable accuracy, in comparison to the non quantized 32-bit model. These binary neural networks (BNNs) theoretically allow to drastically reduce the required energy and run-time by reduction of memory size, number of memory accesses, and finally computation power by replacing expensive two's complement arithmetic operations with more efficient bitwise versions. To make use of these advantages, we propose a bitwise CNN accelerator (BNNA) mapped on an FPGA. We implement the Hubara'16 network [1] on the Xilinx Zynq-7020 SoC. Massive parallelism is achieved performing 4608 parallel binary MACs in total, which enables us to archive real-time speed up to 110 fps, while using only 22% of the FPGA LUTs. In comparison to a 32-bit network, a speed up of 32 times is achieved, and a resource reduction of 40 times is achieved, where the memory bandwidth is the main bottleneck. The provided detailed analysis of the carefully crafted accelerator design exposes the challenges and opportunities in bitwise neural network accelerator design.
Adding and Segmenting Educational Videos: Experiences of Teacher Users in an Educational Portal

Educational portals are environments that provide student and teacher users access to a variety of learning objects, among which are the educational videos. However, searching for such videos can be time-consuming and unproductive if only general syntactic descriptions are considered. Thus, an architecture of an educational semantic portal prototype, AvaOne, was conceived, implemented, and evaluated. Four experts in user profile and interaction design participated in the heuristic evaluation of this portal, and the results allowed changes and implementations of improvements in the system user interface, to make it more pleasant and engaging. The usability tests of AvaOne were conducted with university professors and graduate students through experiments and questionnaires. The results indicated that metadata embedding into educational videos through related descriptors facilitates and defines the search and access to specific content in the videos, which is favored by the semantic correspondence established between the data, which validates the hypothesis of this study. Therefore, AvaOne is recognized as a real, scalable, and flexible solution that enables the reuse of educational videos through its segmentation.