Miracle-dz/UniFAR-FTUs huggingface dataset:
Multifractal Spectrum Analysis of Heart Rhythm during Internally and Externally Operative Attention

Heart and brain are two most complex physiological structures of the human body and both continually interplay to mediate the human cognitive information processing. This paper identifies non-linear variations of heart rhythm acquired during internally and externally operative attention using multifractal analysis. To study internally and externally operative attention, fourteen healthy subjects performed the experiment designed as per Posner's spatial orienting paradigm. The multifractal spectrum width for the baseline and recovery session was observed as $\mathbf{1.35}\ \pm\ \mathbf{0.21}$ and $\mathbf{1.41}\ \pm\ \mathbf{0.14}$ (mean $\pm$ standard deviation) respectively, which reveals that the heart rhythm exhibit existence of long-range correlation and Gaussian spectrum shape. Moreover, for internally operative attention task, the subjects shown significant variation from baseline and recovery session with spectrum width $\mathbf{1.28}\ \pm\ \mathbf{0.21}$ and no change in multifractal spectrum shape. However, for the externally operative attention task, subjects shown significant variation from baseline, while insignificant variation from recovery session with spectrum width $\mathbf{1.31}\ \pm\ \mathbf{0.19}$ and no change in spectrum shape. Although for internally and externally operative attention task the multifractal-dimension analysis reveals that heart rhythm possessed fractal behaviour with reduced spectrum width and no change in shape of multifractal spectrum with respect to baseline and recovery session. Fractal variations of heart rhythm found to quantify internally and externally operative attention with respect to baseline and recovery session, which may support us to design robust braincomputer interfaces, accelerated sports and defense training paradigms.
3D Object Classification Using a Volumetric Deep Neural Network: An Efficient Octree Guided Auxiliary Learning Approach

We consider the recent challenges of 3D shape analysis based on a volumetric CNN that requires a huge computational power. This high-cost approach forces to reduce the volume resolutions when applying 3D CNN on volumetric data. In this context, we propose a multiorientation volumetric deep neural network (MV-DNN) for 3D object classification with octree generating low-cost volumetric features. In comparison to conventional octree representations, we propose to limit the octree partition to a certain depth to reserve all leaf octants with sparsity features. This allows for improved learning of complex 3D features and increased prediction of object labels at both low and high resolutions. Our auxiliary learning approach predicts object classes based on the subvolume parts of a 3D object that improve the classification accuracy compared to other existing 3D volumetric CNN methods. In addition, the influence of views and depths of the 3D model on the classification performance is investigated through extensive experiments applied to the ModelNet40 database. Our deep learning framework runs significantly faster and consumes less memory than full voxel representations and demonstrate the effectiveness of our octree-based auxiliary learning approach for exploring high resolution 3D models. Experimental results reveal the superiority of our MV-DNN that achieves better classification accuracy compared to state-of-art methods on two public databases.
A State-based Extension to STPA for Safety-Critical System-of-Systems

Automation of earth moving machinery enables improving existing production workflows in various applications like surface mines, material handling operations or material transporting. Such connected and collaborating autonomous machines can be seen as a system-of-systems. It is not yet clear how to consider safety during the development of such system-of-systems (SoS). One potentially useful approach to analyze the safety for complex systems is the System Theoretic Process Analysis (STPA). However, STPA is essentially suitable to static monolithic systems and lacks the ability to deal with emergent and dysfunctional behaviors in the case of SoS. These behaviors if not identified could potentially lead to hazards and it is important to provide mechanisms for SoS developers/integrators to capture such critical situations. In this paper, we present an approach for enriching STPA to provide the ability to check whether the distributed constituent systems of a SoS have a consistent perspective of the global state which is necessary to ensure safety. In other words, these checks must be capable at least to identify and highlight inconsistencies that can lead to critical situations. We describe the above approach by taking a specific case of state change related issues that could potentially be missed by STPA by looking at an industrial case. By applying Petri nets, we show that possible critical situations related to state changes are not identified by STPA. In this context we also propose a model-based extension to STPA and show how our new process could function in tandem with STPA.
Big Data and Hadoop Ecosystem: A Review

We are living in the 21st century, world of Big Data. Big Data is a huge collection of structured, semi-structured and unstructured data that is impossible to be processed with the existing data processing system. So, to overcome this problem Hadoop, an open source framework came into existence as a solution. With the help of Hadoop users can run their application on Hadoop clusters. This paper is aimed to demonstrate Big data characteristics, problems and challenges. This paper also represents a review on Hadoop Ecosystem & its components and discussed different domains where Big Data Analytics used.
Stealthy Rootkits in Smart Grid Controllers

This paper presents a stealthy and persistent attack on a Cyber-Physical System (CPS), namely the smart grid and a multi-layer approach to detect such an attack. The attack on the CPS controller uses a rootkit-based malware. When activated, the rootkit overwrites operator commands to the smart grid relays while evading detection by the operator control station. The rootkit sends valid replies to the operator while corrupting the controller operation through a dynamically loaded library, which is hidden by the rootkit. The attack persists even when the controller stops and restarts since the rootkit automatically restarts the process with the malicious library by using a background daemon, which the rootkit hides from user-space tools. Using a high-fidelity simulation of the smart grid CPS, we show that the attack drastically impacts the CPS, especially when the adversary strategically chooses the target relays to attack. We design an ensemble of detectors to detect the attack and uncover its persistence and insertion mechanisms. The detector uses measures such as hardware performance counters (HPCs), change detection in binary signatures, change detection in system calls, and detection of hidden processes and file system entries.
Lattice-Superposition NOMA for Near-Far Users

A novel non-orthogonal multiple access scheme based on lattice superposition is proposed. Sixteen points from the D4lattice are picked as the signal constellation in the four-dimensional real space. Superposition coding for two users is applied at the downlink of the communication system. While the far user detects its intended signal directly, the near user performs interference cancellation before detects its own intended signal. The performance of our proposed scheme is compared with a benchmark orthogonal scheme using 16-QAM. When the link gain difference between the two users is large, our proposed lattice superposition scheme outperforms the benchmark significantly. In particular, when the link gain of the far user is 20 dB smaller than that of the near user, with a word error probability of 10−3, an energy gain of 6 dB can be obtained.
Static Synchronous Compensator based on the Matrix Converter

In this paper, a Static Synchronous Compensator based on the direct Matrix Converter (MC-STATCOM) is proposed for reactive power compensation. The MC-STATCOM allows eliminating the energy storage device used in conventional STATCOM topologies, besides improving some of its operational characteristics. This device is controlled through a linear control strategy in order to ensure its stability and dynamic response. The MC-STATCOM performance is verified through several time domain simulations carried out in MATLAB /Simulink©. The results obtained validate the feasibility of the topology proposed.
Influence of significant target on image quality assessment via EEG

The significant target in the image shows how much people pay attention to certain objects in the image when viewing the image. These significant targets will affect human perception of image quality when quality change occurs. But in recent years, most of the image quality perception research based on elsctroencephalography(EEG) were not considered of these significant targets. Therefore, this paper obtains the EEG signals of the subjects through an image rendering experiment for the presence or absence of significant targets, and compares the signal differences. The experimental results show that there is no significant effect on the image quality perception in the perceptible range. If there is a large degree of distortion, whether there is significant target has an important impact on the image quality perception.
Sum-Rate Analysis of MIMO Based CR-NOMA Communication System

In this paper, we have exploited the key technologies of the next generation communication systems which are cognitive radio (CR), non-orthogonal multiple access (NOMA) and multiple-input-multiple-output (MIMO) and have proposed a framework of simultaneous use of these technologies for the spectral efficient design, named as MIMO based CR-NOMA communication systems. The proposed communication system is defined for the specific case that is MIMO with 2 × 2 antennas and NOMA serving two users. Moreover, the data rate at each antenna of users in derived numerically and further, the sum-data rate for the cognitive users (CUs) is computed. The relationship between sum-data rates of two NOMA users with two antennas each, is described with simulation results.
Gate-Diffusion Input based Linear Feedback Shift Register : A Review

GDI technique reduces the power intake, delay and digital circuit area and also maintains a less complex logic design. The CMOS technology, commonly used for implementing the Null-Conventional logic circuits. CMOS has major disadvantages in the field of area consumption and power dissipation being large. A low power design technique GDI can overcome these restrictions. But the circuit suffers from a notable voltage drop, thus Modified-GDI (m-GDI)was introduced. But GDI and m-GDI had threshold loss, to overcome this loss, FullSwing-GDI(FS-GDI) was introduced, which is considered to be more effective for low power digital design which supports smaller area consumption in contrast to CMOS technique. This paper highlights the implementation of Linear feedback shift register (LFSR) using FS-GDI resulting in area and power efficient design.
Detecting and Predicting Performance Degradation Caused by Impaired Cache Isolation

As the shared last level cache (LLC) in multicore processors has been shown to be a critical resource for system performance, much work has been proposed for improving the quality of service (QoS) and throughput on LLC. Cache Allocation Technology (CAT) and Adaptive Cache Replacement Policies (ACRP) are two of the techniques that are featured in recent Intel processors. CAT implements way partitioning and provides the ability to control the cache space allocation among cores. ACRP works with multiple replacement policies and enables the cache to adapt to the cache replacement policy with less cache misses. In this paper, we first show an interesting finding that ACRP technique can violate the performance isolation provided by CAT. We find the cause for this problem is that the ACRP chooses the cache replacement policy upon the global information even although the cache space partitioning is being enabled by CAT. As the result, the cache/performance isolation can be impaired by the interference on cache replacement policy. To deal with this problem, we propose a low overhead method to predict the worst execution time degradation caused by the replacement policy adaptation. Thus, in the partitioned cache space, if the worst execution time estimated by our method is not beyond the response time required for this program, the QoS for this program can be quaranteed no matter how the cache replacement policies alternate.
Machine Learning Applied to Energy Efficiency of Large Consumers

The use of electric energy is rapidly increasing, so it is essential that users improve their understanding of electric consumption, thus reducing waste and improper use. The present work uses Machine Learning techniques (specifically Linear Regression and Random Forest) to model the relationship between electricity consumption and climatic conditions within the Federal Institute of Education, Science and Technology of Santa Catarina (IFSC) and the Ministries Esplanade (headquarters of the Brazilian Executive, in Brasilia). Historical data of active power, ambient temperature and atmospheric pressure obtained from the database of the project PGEN. The results show that the model allows predicting the instantaneous energy consumption of the localities with an average error of 22.75% KW. The building of the Ministries Esplanade obtained the lower errors, and the campus Florianópolis obtained the bigger erros.
Variable Stiffness and Damping Impedance Control with Disturbance Observer

This paper proposes a variable stiffness and damping impedance controller with a new closed-loop impedance architecture containing the negative power type stiffness and damping parameters. The sliding mode control technique is employed to construct an asymptotically stable reduced order system using PID sliding surface. The external disturbance is well considered, which easily propagate to the preset impedance architecture to degenerate the impedance performance. To address this problem, a disturbance observer is introduced into the control system to reduce the adverse effect by partially eliminating the disturbance. The proposed impedance architecture owns better transient performance compared with the trivial spring-damper impedance architecture, which is well illustrated by numerical results.
Reliability and Maintainability Analysis of Material Handling Machinery in Golegohar Mine No. 1

Loading and hauling machines are of the most important operational units in open pit mines, which their failure can lead to reduction in production and profitability of mines. Therefore, it is very important to do a reliability and maintainability analysis of mining machines in order to make a better decision with respect to operation and maintenance of machines as well as production control in the mine. Awareness of transport units operational conditions and precise control over them are the key elements for preventing unwanted stops and improving the performance of machines. The aim of this study is to evaluate the reliability and maintainability of loading and hauling equipment in Golegohar Mine No. 1 in Iran, which produce 12 million tons of iron ore annually. For this purpose, the failure data of transport units including dump trucks, mining shovels and backhoe excavators were collected over a period of one year. Then the data were analyzed based on the statistical method to produce the reliability and maintainability parameters of each mining equipment. The time corresponding to reliability and maintainability levels of 90 and 50 percent are also calculated and presented.
A Natural inclusion of Motives inside BDI agents

The Belief-Desire-Intention (BDI) model is amongst the most popular approaches to design agents with flexible behaviors. It is based essentially on the processing of beliefs and goals. On the other hand, it is well accepted that the motive is another relevant concept for the agent’s reasoning and its flexibility. As the BDI model does not include this important concept, several BDI agent’ s extensions, with a new and a special component expressing the motive, were proposed in the literature. In this paper, we show that it is not necessary to add this new component to express a motive, as a motive can be expressed by a particular type of goal. This allows including and taking advantage from the reasoning about motives without additional components.
Circuit Reliability of Low-Power RRAM-Based Logic-in-Memory Architectures

Logic circuits based on Resistive RAM (RRAM) devices and the material implication logic (IMPLY) are promising solutions for low-power logic-in-memory (LiM) architectures. Still, their diffusion is limited by their high design complexity resulting from device and circuit non-idealities. These non-idealities are usually overlooked in the design phase when using simplified RRAM models, thus leading to unreliable designs. In this work, we derive correct design strategies for reliability of RRAM-based LiM circuits and quantitatively evaluate circuit performances using a physics-based compact model.
Augur Justice: A Supervised Machine Learning Technique To Predict Outcomes Of Divorce Court Cases

Machine Learning and Law are two disciplines that are rapidly gaining everyones attention due to Machines ability to understand, process, and learn the data. Legal data currently is present in tremendous amounts that are produced every day. If this legal data can be effectively classified and trained corresponding to a specific domain, it can prove to be a great help to the general public. This research paper focuses on one of the domains of law, which is of marriage and divorce belonging to three different religions, namely Hindu, Muslim, and Christian. The objective is to allow the user feed in information about their case relating to marriage and divorce domain of law. For the given user, the religion is detected, and for predicting the probability of winning or losing the case described by the user, the laws of the user's religion play a vital role. It is thus making laws on each of the three religions an important aspect of this work. Based on the pivotal features present in the trained data set of the previously fought court cases of the similar domain, the probability of losing or winning the case is determined through legitimate processes. Along with this tool which helps the user to determine the losing or winning probability of their case, this research paper also aims at attaining a comparative analysis of various Supervised Machine Learning algorithms on the domain of law and thus showing the proposed algorithms capability to predict the outcome better than that of commonly used Supervised Machine Learning Techniques.
Classification of Environmental Sounds Using Convolutional Neural Network with Bispectral Analysis

To realize a useful acoustic environmental recognition system, we propose a new method that classifies sound signals using a slice bispectrogram, which is a third-order version of a spectrogram. The classified sound was used as input data for a convolutional neural network. We conducted a fundamental classification experiment using UrbanSound8k, which was an open dataset consisting of 10 classes of environmental sounds. Our proposed method gave high accuracy and stability. Furthermore, a relationship between the accuracy and non-Gaussianity of sound signals was confirmed.
Continuous Improvement Model to Systematize Curricular Processes in Engineering Education

The continuous improvement model is presented, which is composed of two levels: The first level corresponds to the process of continuous improvement associated with the curriculum and the second level corresponds to the process of continuous improvement related to the measurement of learning outcomes in each of the subjects and its purpose is to establish the plan for annual improvement of teaching and the systematization of the processes of measurement and evaluation of student learning outcomes. The proposed model incorporates a cyclic process that allows self-regulation the process of continuous improvement at the level of courses and at the curriculum level. For the evaluation of learning outcomes are proposed various measuring instruments what allows to obtain a better quality of information. The implementation of the model can improve the quality of engineering education because it allows systematic monitoring of teaching-learning processes
The Effect of Different Position and Size of U-Shape Slot on a Microstrip Patch Antenna for IoT Application

A slotted rectangular shape microstrip patch antenna was designed by using the transmission line method with FR-4 as the substrate. The antenna resonant at a frequency of 5.8GHz which can be used for IoT applications. CST software version 2014 was used to simulate and optimize the antenna. The main objective of this paper is to operate the slotted rectangular microstrip patch antenna for IoT application and attain a high return loss and gain. To achieve this objective, A parametric study was carried out to discover the best position to place the two U-shape slots that are loaded into the conducting patch of the antenna. Twenty-four (24) different position and size of the U-shaped slot was analysed. The best position to place the slot which is position H resonant at a frequency of 5.745GHz with a return loss of 35.91dB, bandwidth of 162.2MHz and a gain of 2.47dB. In conclusion, From the result obtained, A considerable high return loss and bandwidth was achieved.
Cyclic Beneš Network Based Logic Encryption for Mitigating SAT-Based Attacks

Cyclic logic encryption currently constitutes the most robust technique against Boolean satisfiability (SAT) based attacks on logic encryption; however, recent "Cyclic SAT" (CycSAT) attacks have overcome this resistance to a large scale. In this paper we develop an improved version of cyclic logic encryption using a common permutation network (the "Beneš network"). The cyclic nature is implemented by channeling some of the keys of the permutation network through the permutation network itself, and feeding them back to the target nodes instead of direct connection. It is observed that although the standalone Beneš network is highly susceptible to SAT based attacks, the proposed architecture has the two properties required for it to be immune to SAT attack, namely oscillatory and stateful. We also show that the procedure of enumerating cycles without node duplicity ("simple cycles") during the preprocessing step of CycSAT attacks, as considered in some previous works, is not technically correct; instead, we should consider cycles without edge duplicity. We then prove that the number of cycles without edge duplicity in our proposed technique grows as Ω(n! (n – 1)! ), where n is the number of feedback paths. The logic synthesis tool distributes the different stages of our proposed network across the obfuscated circuit, thereby making it immune to trivial netlist tracing based detection and removal attacks. The robustness of the technique to the CycSAT attack has been validated for ISCAS-85 benchmark circuits.