Miracle-dz/UniFAR-FTUs huggingface dataset:
New Approach for Automated Epileptic Disease Diagnosis Using an Integrated Self-Organization Map and Radial Basis Function Neural Network Algorithm

Currently, epilepsy disease (ED) is considered to be one of the gradual diseases in brain function over a period of several months or years. Seizure status is the primary common cause of ED. The main goal of this paper is to discover the seizure and epilepsy status using the prediction algorithm on the test results received from patient medical reports. This paper proposed an automatic epilepsy diagnostic method based on a self-organization map (SOM) method using a radial basis function (RBF) neural networks approach. The hybrid technique sought to enhance epilepsy diagnosis precision and to decrease the misdiagnosis of seizure disease. The SOM algorithm was employed to differentiate the unknown patterns of the seizure and epilepsy dataset. The experiments were performed on various RBF neural network algorithms with integrated SOM algorithms to predict and classify the standard epilepsy disease dataset. The hybrid method was tested on the UCI epilepsy dataset. The overall detection accuracy with 10-fold cross validation using SOM-RBF method achieved 97.47%. The results were compared with other modern classification techniques for seizure prediction and detection in terms of the evaluation factor.
Preprocessing and Framework for Unsupervised Anomaly Detection in IoT: Work on Progress

A robust increasing on smart sensors in Internet of Thing (IoT) results huge and heterogenous data and becomes a challenge in data prepocessing and analysis for anomaly detection. The lack of IoT publicly available dataset is one issue in anomaly detection research. To resolve that problem, a testbed topology is proposed in this research. In addition, a high-dimensionality data analysis faces a computational complexity. The purpose of this study is to presents a global framework for anomaly detection in IoT and proposes a distributed preprocessing framework. Unsupervised learning approach has been chosen to reduce dimensionality of IoT data traffic.
Measuring Customer Satisfaction Using CRM Scorecard in Canteen FASILKOM UNSRI

Fasilkom canteen as businesses can compete with other canteens on Sriwijaya University both in relation to gain or maintain customers. Problems faced by fasilkom canteen pertaining to customers can be overcome by Customer Relationship Management (CRM) model. This paper is written with objective of knowing the service has been accepted by students as one of the ultimate. Methods used is CRM Scorecard. That these findings it will give evaluation future for the fasilkom canteen in doing management of customers. The total number of respondents of the questionnaires about 59 people consisting of the students fasilkom 2013–2016. Customer satisfaction to canteen fasilkom worth less, well that needs to be improved concern for seating, courtesy and patience when serve their customers. Other perspective are seller performance 3.59 that is the greatest value of four perspective, 3.35 process, 3.48 customer and 3.26 infrastructure proven it has indicated good value. But must keep improved because of it is near from enough range.
Robotic Grasping Estimation by Evolutionary Deep Networks

The problem of robotic grasping is addressed in this work. In order to make a fast and robust inference of robotic grasping points by a convolutional neural network (CNN), an estimator, based on the Particle Swarm (PS) algorithm, is presented. The cost function of the PS is decided to consider two main aspects to increase the accuracy and repeatability. First, a CNN is to classify features as either good or bad grasping features. Second, a magnet mechanism is designed to create convergence of particles towards the object center. The algorithm also includes a confidence factor that aims at reducing misinterpretation between one and none grasping points. The experimental results showed a slight improvement in the accuracy with a significant increment of the inference speed. Compared to the previous deep-learning methods, the proposed method is applicable for practical robotic grasping applications.
Security of Functionally Obfuscated DSP Core Against Removal Attack Using SHA-512 Based Key Encryption Hardware

Digital signal processing (DSP) kernel-based intellectual property (IP) core forms an integral ingredient of consumer electronics devices. Thus, protection of these IP cores against reverse engineering attack is crucial. Functional obfuscation serves as a powerful mechanism to counter this hardware threat. However, functional obfuscation methodologies for DSP cores use security logics that are not lightweight and are prone to removal attack by an adversary. This paper presents a novel security mechanism for protecting functionally obfuscated DSP core against removal attack using low-cost, low-power key encryption hardware. The proposed methodology using lightweight secure hashing algorithm (SHA-512)-based key encryption custom hardware reconfigures the key-bits (resulting into structural reconfiguration) of the locking logic in a functionally obfuscated DSP design augmented with the complete logic synthesis of the design. This hinders the detection of the locking logic in the obfuscated design due to camouflaging. The proposed mechanism integrated with the functional obfuscation framework yielded lower power, lower gate count, and enhanced security compared with an existing approach. An average reduction of 25.86 % in gate count and power as well as the average enhancement of 43.75 % in security against removal attack was obtained in the proposed approach compared with a recent existing approach.
Image-based EEG signal processing for driving fatigue prediction

This study proposes a EEG-based prediction system that transform the measured EEG record into an image-liked data for estimating the drowsiness level of drivers. Drowsy driving is one of the main factors to the occurrence of traffic accident. Since drivers themselves may not always immediately recognize that they are in the drowsy state, the risk of traffic accident increases while the driver is in the low vigilance state. In order to address this problem, the estimation of drowsy driving state via brain-computer interfaces (BCI) becomes a major concern in the driving safety field. This study transforms the measured EEG record into a image-liked feature maps, and then passes these feature maps to a Convolutional Neural Network (CNN) to learn the discriminative representations. The proposed drowsiness prediction system is evaluated by leave-one-subject-out cross-validation. The results indicate that our approach provides impressive and robust prediction performance on the EEG dataset without artifact removal process.
Analysis of the Load Growth Direction Variation in the Dynamic Security Assessment

The Voltage Stability Margin (VSM) is a widely used index in the operation centers and requires a load/generation growth direction to be calculated. However, different directions can provide different VSMs and then the VSM may not be reliable. In dynamic security assessment is highly desirable to identify limit regions where the power system can operate safely and these regions can be defined based on the values of VSM. This paper presents an analysis to evaluate the impacts of load growth direction variation in the dynamic security assessment. A modeling of the system incorporating this direction variation based on areas is presented. A direct method is used to compute the stability margin of the power system for a given load direction. The analysis was performed in a reduced south-southeast Brazilian system composed of 107 buses.
A Wideband HTS Filter Using Strong Coupling Coplanar Spiral Resonator Structure

A novel coplanar spiral resonator structure, which has a stronger coupling strength than a normal coplanar waveguide (CPW) structure, is introduced. This structure helps to realize the remarkable strong coupling required for low-frequency wideband filters. Although no grounded stubs are deployed to balance the ground plane, the slot-line mode of the filter is not excited. An eight-pole wideband high-temperature superconducting filter, with a fractional bandwidth of 50% and a center frequency of 322 MHz, is developed. The filter is designed and fabricated on a 0.5-mm-thick LaAlO$_3$ substrate with a core size of 41 mm × 22 mm. The measured minimum insertion loss is 0.05 dB and the first spurious peak appears at 842 MHz, which is about 2.6 times the center frequency. The measured results agree well with the simulated results.
Modeling the magnetic signature of diel vertical migrations of zooplankton

Diel vertical migration (DVM) of zooplankton can cause velocity fluctuations and a respective increase in the dissipation rate of turbulent kinetic energy dependent on zooplankton concentration (Dean et al. 2016). In this work, we used a 3D non-hydrostatic computational fluid dynamics (CFD) model (ANSYS Fluent) with Lagrangian particle injections (proxy for migrating organisms) to simulate the effect of turbulence generation by DVM. We tested a range of organism concentrations. The simulation at the extreme (10,000 organisms/m3) concentration of zooplankton showed an increase in dissipation rate of turbulent kinetic energy by two to three orders of magnitude during DVM over background turbulence (10-8 W kg-1). At the low (1000 organisms/m3) concentration, almost no turbulence above the background level was produced by DVM in the model. Seawater is an electric conductor; as a result, the motion of seawater in the magnetic field of the Earth induces electrical currents and, consequently, secondary magnetic fluctuations. Therefore, the turbulence produced by DVM can have a measureable magnetic signature. We have applied a magnetohydrodnamics (MHD) module to the CFD model to test this hypothesis. The MHD model results indicate that DVM of the extreme concentration of zooplankton create a magnetic signature on the order of 0.1 nT, which is relatively small but are well within the range of modern magnetometers.
Separation of Rectangularly Symmetric Modes of Light With Fan-Out Elements

We present a method to efficiently separate the rectangularly symmetric modes of light with two phase-only holograms. The holograms, known as fan-out elements, can split the input modes into multiple copies. A subsequent lens focuses these modes to different lateral positions. We use two spatial light modulators to create the desired optical elements. The separation of rectangularly symmetric modes, such as eigenmodes in rectangular waveguides and Hermite–Gaussian modes, is experimentally demonstrated.
Robust Control Synthesis for Linear Differential Systems with Parametric Uncertainty

This paper presents a robust control synthesis method for linear time-invariant (LTI) differential systems with parametric uncertainty. The uncertain dynamics is described by a parametric kernel representation. We use parametric quadratic differential forms (QDF) to represent the dissipativity properties (both storage function and supply rate) of the uncertain system. The control design utilizes sum-of-squares (SOS) programming to search for a QDF supply rate for the controller such that the supply rate of the closed-loop system satisfies robust stability and performance conditions. Such a supply rate is then applied for control synthesis based J-factorization.
Design of Voltage Amplifiers with optimization of Multiple Design Parameters

In the current age of having sophisticated appliances with sophisticated circuits, it becomes necessary to have circuit designs that are exploited to their full potential by making minimal changes in their basic designs. Low Power, high gain, high voltage swing and stable operating voltage amplifiers are required at various stages in these appliances. Therefore, the paper focuses on improving designs that have two stages. The three designs, namely, the simple two stage operational amplifier, the two stage operational amplifier with cascoding in the inner stage and lead compensation, and the two stage operational amplifier with cascoding in the inner stage, lead compensation and gain boosting are compared. The circuits are designed and simulated using the GPDK045 and Analog Library in the Cadence Virtuoso Design Environment with Length of MOSFETs as 180nm and VDD as 2V. Out of the three methods, the two stage operational amplifier with cascoding in the inner stage and lead compensation is found to give the highest unity gain bandwidth of 1.1436 GHz and the method with gain boosting is found to give the highest DC Gain of 117.1087dB. The corresponding phase margin and power dissipation of the two methods are 57.8963 degrees, 57.3776 degrees and 0.69mW, 0.235mW.
Adaptive Polling for Underwater Data Collection Systems

Acoustic networks fill an important gap in undersea communications by enabling intermediate and long-distance communication. Advanced Medium Access Control (MAC) protocols suitable for acoustic communication are required to efficiently share the undersea acoustic channel among deployed assets. In this paper our previously proposed adaptive polling-based MAC protocol, APOLL, is further extended to consider the network conditions including traffic load and its prioritization/precedence in determining the polling schedule. To improve the channel utilization and overcome the negative impacts from the long acoustic propagation delay, the difference in both Angle of Arrival (AoA) and propagation delays are utilized in APOLL to fulfill the spatiotemporal reuse of the acoustic channel among polled assets.
Secure and Tamper-resilient Distributed Ledger for Data Aggregation in Autonomous Vehicles

The phases those turn the wheels of an autonomous vehicle, are perception, decision and actuation. Among these, the major highlight of recent research has been perception through diverse sensors, and decision through an ever-aggressive cloud/fog/mist computing setup. In this paper, we take a closer look into the flow of data, both internal and external to the autonomous vehicle. We argue that confidentiality, integrity and availability of these data are critical to the eventual adoption of higher-level security and privacy mechanisms in autonomous vehicles. To that effect, we propose a secure and tamper-resilient distributed ledger as an underlying enabler for intra-vehicular data aggregation, and study its security and privacy issues under appropriate adversarial models, where the distributed ledger is instantiated as a standard consortium blockchain.
Jointly Optimized Energy-Minimal Resource Allocation in Cache-Enhanced Mobile Edge Computing Systems

Mobile edge computing (MEC) has attracted extensive studies recently due to its ability to augment the computational capabilities of mobile devices. This paper considers a cache-enhanced multiuser MEC system where the task can be cached in the MEC servers to avoid the transmission of duplicate data. To further improve the energy efficiency and satisfy the users’ requirement on delay, we jointly optimize caching, computation, and communication resources in this system. The formulated problem is a mixed integer non-convex optimization problem that is very challenging to solve. We thus propose an efficient iterative algorithm by jointly applying the block coordinate descent and convex optimization techniques, which is guaranteed to converge at least a suboptimal solution. Specifically, the formulated joint optimization problem is decomposed into two subproblems to optimize caching policy and resource allocation, respectively, which are alternately optimized by convex optimization in each iteration. To further speed up the algorithm convergence, an efficient initialization scheme based on the linear weighted method is proposed for caching policy. The extensive simulation results are provided to demonstrate that the proposed jointly optimizing caching, computation, and communication method can improve the energy efficiency with lower time cost compared with other benchmark methods.
Distributed constraint optimization for autonomous multi AUV mine counter-measures

In this paper, Mine Counter-Measures (MCM) operations with multiple cooperative Autonomous Underwater Vehicles (AUVs) are examined within the Distributed Constraint optimization Problem (DCOP) framework. The goal of an MCM-operation is to search for mines and mine-like objects within a predetermined area so that ships can pass the area through a safe transit corridor. Performance metrics, such as the expected time of completion and the level of confidence that all mine-like objects within the area have been detected, are used to quantity the utility of the operation. The AUVs coordinate their individual search segments in a distributed manner in order to maximize the global utility. The segmentation is optimized by the Compression-DPOP (C-DPOP) algorithm, which allows explicit reasoning by the AUVs about their actions based on the performance metrics. After initial segmentation of the mine threat area, subsequent optimizations are triggered by the AUVs based on the variations in sonar performance. The performance of the C-DPOP algorithm is compared to a static segmentation approach and validated using the high-fidelity Unmanned Underwater Vehicle (UUV) simulation environment based on the Gazebo simulator.
Generation of PUF-Keys on FPGAs by K-means Frequency Clustering

Physical unclonable functions (PUFs) generate secret keys that are unique and derived from the manufacturing process variations of the device. For high-security applications, generating the keys on the device is more secure than storing it on the device due to its resistance to attacks like tampering, probing and side-channel attacks. Also, the PUF-key generated should be very unique and reliable. In this paper, we are proposing a novel technique of generating a Ring Oscillator (RO) based PUF-keys on FPGAs by K-means clustering of RO frequencies. The placement of ROs on the FPGA fabric will have an impact on the variation of their oscillating frequencies. Based on the similarities in their frequency variations, the RO frequencies are grouped into k-clusters by K-means clustering and, the comparison of within-cluster RO frequencies will generate the PUF-keys. Our proposed method is evaluated on a large population of 193 Xilinx Spartan FPGAs. With the proposed method, the average inter-die Hamming Weight of the PUF responses is 48.92% which is close to the ideal value of 50%, the average Hamming Weight of the n-bit PUF response is 49.90% and the average error rate due to changes in temperature and voltage is 3.43%.
Parametric study of stub loaded loop resonator

In the current trends of RF and microwave developments, it became increasingly important to remove unwanted signals so that the information in the signal is clear. A filter does this and the paper proposes a compact stub loaded band stop filter which can be turned for a range of frequencies without changing the foot print of the filter. An edge coupled stub loaded band stop filter with cover type feed is presented here. For validation of these concepts, a band stop filter operating in S band has been designed, simulated, fabricated and tested.
A Complex-Valued VGG Network Based Deep Learing Algorithm for Image Recognition

At present, many deep neural networks are applied to image recognition. But most of them are based on real-valued operations and represents. Since the algorithm of complex operation has been put forward, we apply the VGG model to the complex domain in the paper. We provide the advantages which the complex-valued network possesses in terms of the depth and width of networks by calculating. When possess the same parameters, the complex-valued network is deeper and wider than the real-valued network. We test both complex-valued VGG network and real-valued one on image recognition. Experiments show that the complex-valued VGG network has better performance comparing with the traditional real-valued VGG network in terms of stability and convergence speed.
Flux-Weakening Space Vector Control Algorithm for Permanent Magnet Brushless DC Machines

A Flux-Weakening Space Vector Control algorithm (FW-SVC) for Permanent Magnet Brushless DC Machine (PMBDCM) is presented in this paper. It is developed referring to a novel ft reference frame, which enables distinguishing field and torque current components clearly. Therefore, a voltage follower PI regulator has been introduced in order to synthesize the reference field current based on the DC-link voltage deficit. This goal requires coping with non-linearities of the PMBDCM model in order to achieve a suitable trade-off between dynamic and steady-state performances. The effectiveness of the proposed solution is verified through numerical simulations, which regard the comparison between the proposed FW-SVC and both a conventional Current Commutation Control (CCC) and an SVC with no field current injection.
Application of Non-local Mean Filtering in Real Head MR Image*

MR images have high resolution for soft tissues, which have been widely used for brain research. During the acquisition of MR images due to the interference of the hardware device and external environment, it introduces a amount of noises, which will affect the clinical accuracy of the diagnosis of the disease. The basic principle of non-local mean filtering is similar to the mean filter, but the non-local mean filter adds the weight value of each point to the calculation, so it can be ensured that when neighboring and very different points are averaged in the box, which also retains a lot of details on the edge of the image, so that the image will look more clear. In this paper, the non-local mean denoising algorithm is compared with three kinds of preserving and denoising algorithms: median filtering, anisotropic diffusion filtering, and bilateral filtering. It is verified that it has good performance in denoising while maintaining the edge, and applied in the denoising of real head MR images.
A Hierarchical Multi-Metric Framework for Item Clustering

Item clustering is commonly used for dimensionality reduction, uncovering item similarities and connections, gaining insights of the market structure and recommendations. Hierarchical clustering methods produce a hierarchy structure along with the clusters that can be useful for managing item categories and sub-categories, dealing with indirect competition and new item categorization as well. Nevertheless, baseline hierarchical clustering algorithms have high computational cost and memory usage. In this paper we propose an innovative scalable hierarchical clustering framework, which overcomes these limitations. Our work consists of a binary tree construction algorithm that creates a hierarchy of the items using three metrics, a) Identity, b) Similarity and c) Entropy, as well as a branch breaking algorithm which composes the final clusters by applying thresholds to each branch of the tree. The proposed framework is evaluated on the popular MovieLens 20M dataset achieving significant reduction in both memory consumption and computational time over a baseline hierarchical clustering algorithm.