Miracle-dz/UniFAR-FTUs huggingface dataset:
Compact Microstrip Bandpass Filter using Stepped-Impedance Resonators and Stepped-Lumped Resonators for 5G Wi-Fi and WLAN Applications

This article presents a novel and compact microstrip bandpass with enhanced upper stopband characteristics to fulfil the requirements of the WLAN/5G Wi-Fi. The proposed bandpass filter (BPF) uses the stepped-impedance resonators and the stub-loaded resonators to achieve the desired BPF response for 5G Wi-Fi and WLAN applications. The rectangular slots on both sides of the microstrip are used to improve the stopband performance and eliminates the spurious response. The designed BPF covers the frequency band of sub-band 6 GHz that is useful for released FCC’s band for WLAN/ 5G Wi-Fi and the free use of the microwave spectrum in the range of 5150-5250 MHz band, 5250-5350 MHz, 5470-5725 MHz, and 5725-5875 MHz permitted by the Indian government. The proposed BPF is designed on an RT/Duroid 5880 Substrate with the thickness of 0.508 mm and surface area of 31×27 mm2.
Design of Current-mode High Frequency linear Analog Circuit

A systematic synthesis and design methodology is proposed for current-mode high frequency linear analog circuit using the current-mode device, OTA (Operational Transconductance Amplifier). The proposed biquadratic filter is designed using first order filter and lossless integrator. Hierarchical synthesis approach is preserved through the design of programmable higher order filters utilizing the proposed biquad. Cut-off frequency and Q of the biquadratic filter are controlled by the transconductance of the OTAs. All the proposed theoretical predictions are justified through SPICE simulation in 0.13< technology. The proposed design technique keeps all the sensitivities to lower values. The operating range of the designed biquadratic circuit is 900 KHz - 1.5 GHz.
Improvement of Hardware Firewall’s Data Rates by Optimizing Suricata Performances

Suricata is a high-performance, multi-threaded Network IDS, IPS and Network Security Monitoring engine that can monitor networks in real time to protect against attacks. With its diverse features, Suricata is the choice of modern Unified Threat Management (UTM) systems to help networks secure their boundaries. As the network capacity increases to 40Gbps and beyond, it becomes important to tune Suricata to provide a lossless detection to the network. This paper describes the different tunings that were done to Red Piranha’s Crystal Eye appliances to achieve 60Gbps Suricata throughput. Suricata throughput, as described in this paper, defines the amount of data that can be handled by Suricata engine without any drops. We describe the hardware configurations as well as the Suricata configurations that can help achieve high detection rates. We have also added the test results for single NIC and dual NIC systems and discussed the impact of hardware on Suricata performance.
A High Throughput And Speed Architecture of Lightweight Cipher LEA

A high-speed cryptography algorithm is the necessity of today’s world to communicate between two resource-constrained tools. We have proposed a 128-bit block size and 256-bit key size LEA cipher for high speed and throughput purpose. High speed is an essential requirement with the secure transmission of information. Pipelining is the best way to achieve these requirements with higher throughput. The implementation results have shown that this method has a higher capability of speed and throughput as compared to previous ciphers. LEA has security against all existing cipher attacks and compatible with both hardware and software. Proposed work is 73.7% and 62.85% improved version of compared paper for respectively throughput and speed.
Population based study on arsenic induced blood samples employing hybrid metaheuristic optimization based ML approach

Long term exposure to arsenic may lead to the development of multi-organ ailments with several non-dermatological, non-malignant outcomes and hence considered as a paradoxical human carcinogen, requires major attention with comprehensive clinical data analysis. An experimental investigation has been pursued to analyze microarray data set and classify biologically arsenic affected genes endorsed by different machine learning approaches. Microarray data consists of a huge number of biologically insignificant genes. Thus it leads to an expensive computation and restrains the classification accuracy. A thorough and critical analysis has been performed to determine a small set of nonredundant genes to classify the arsenic disease. The analysis outcomes could be further put into action to interpret the therapeutic measures to be taken care of the disease. An experimental investigation has been carried out to deal with this population-based learning problem with the objective of classifying a the small set of gene expressions of the disease for particular demography. The analysis has been accomplished with the implementation of a hybrid metaheuristic optimization based machine learning methods for classification of arsenic disease into various classes.
A New Routing Scheme for Multi-cluster Flying Ad hoc Networks

Recently, the applications of remotely aerial nodes, e.g., unmanned aerial vehicles (UAVs) are growing swiftly for numerous civil and military areas by creating flying ad hoc networks (FANETs). Due to increasing number of UAVs and fast changing topology, FANETs require an adaptable, delay-bounded, and reliable communications among UAVs while maintaining the desired network quality of services. Multi-cluster FANETs handle resource scarcity problems and optimizes network performances over single cluster by efficient management of UAVs. In this research, IEEE 802.15. 4 MAC protocol is considered in multi-cluster FANETs for both beacon enabled and beacon-less modes among UAVs communication. Therefore, an efficient routing strategy is presented with two mobility models, e.g., Random Way Point (RWP) and Gauss Markov (GM). In addition, the performance analysis of different network parameters (i.e., jitter, throughput, packet delivery ratio, end to end delay, and normalized routing load), are illustrated for four routing protocols (AODV, DSR, DSDV, and OLSR).
Implementing the Real-Time Voltage Stability Ananlysis Tool on a Large Scale System Model for IROL Assessment in the Western Interconnection

This paper introduces a success story about how Peak Reliability (formerly, WECC Reliability Coordinator) collaborated with V&R Energy and Peak member entities to implement an online voltage stability analysis tool in a control room setting for real-time assessment of Interconnection Reliability Operating Limits (IROLs) in the Western System. Major technical challenges and the resolutions, including modeling aspects, software improvements, validation and integration efforts, are presented in the paper. Lessons learned and future work are discussed in the end.
Dynamic Pricing Mechanism With the Integration of Renewable Energy Source in Smart Grid

Day-ahead electricity pricing is an important strategy for electricity providers to improve grid stability through load scheduling. In this paper, we investigate a general framework for modelling electricity retail pricing based on load demand and market price information. Without any a priori knowledge, we have considered a finite time approach with dynamic system inputs. Our objective is to minimize the average system cost and rebound peaks through energy procurement price, load scheduling and renewable energy source (RES) integration. Initially, the energy consumption cost is calculated based on market clearing price and scheduled load. Then, through reformulation and subsequent modification of optimization problem, we utilize a day-ahead price information to construct individualized price profiles for each user, respectively. To analyse the applicability of proposed pricing policy, analytical solution is obtained which is further validated through comparison with solution obtained from genetic algorithm (GA). From results, it is observed that proposed price policy is non-discriminatory in nature and each user obtained a fair electricity tariff rather than a day-ahead price, which is based on load demand and consumption variation of other users. We also show that optimization problem is sequentially solved with bounded performance guarantee and asymptotic optimality. Finally, simulations are carried in different scenarios; aggregated load and market price, and aggregated load, individualized load, market price and proposed price. Results reveal that our proposed mechanism can charge the price to each user with 23.77% decrease or 5.12% increase based on system requirements.
GPOL: Gradient and Probabilistic approach for Object Localization to understand the working of CNNs

Convolutional neural networks have been a revolution in the field of Computer Vision and are being extensively used for the purpose of image classification, object detection, generation of captions etc. CNNs are mostly considered black boxes where the internal functioning is not known. The objective of this work is to provide an explanation of the functioning of the the predictions made by the CNN. We propose a new technique for comprehending the functioning of the middle layers of the neural network and the classifier operations. The proposed approach is capable of analyzing multifarious models which are trained for applications such as object detection and recognition. In this work, probabilistic approach and gradient based approach have been used for the purpose of object localization. Geometric mean of heatmaps of both the approaches has been done. In the former approach, the true object’s gradient’s are made to flow into the last convolutional layer for the purpose of determining the most significant points which would help to predict that particular object. In the probabilistic approach, CNN’s top down attention has been used which serves the purpose of generation of attention maps which are task specific. A probabilistic scheme (to select a significant neuron in the network) has been used during backpropagation of signals from top to down in the hierarchy of network. The proposed work has been executed on CLS-LOC dataset which is a part of Imagenet dataset. The proposed work is then compared with the previously developed techniques such as saliency maps, SmoothGrad, GradCam, Top Down Neural approach to exhibit the better accuracy of the proposed work.
Time and Computation Efficient Malicious Android Application Detection Using Machine Learning Techniques

Malware has become one of the major threats to information security in this rapid growth of internet applications. This has led the researchers to develop distinguished methods for detecting malware in this respect. To address the issue, machine learning techniques have proven itself efficient in detecting malware. But one of the major challenges is the reduction of attributes or components that are less important in malware detection process. Applying Principal Component Analysis (PCA) with other machine learning techniques, successful reduction of components is possible without any alternation in detection accuracy. In this paper, an approach based on PCA has been proposed which is time and computation efficient in detecting malware than the existing ADROIT approach that doesn’t use PCA. Experimental results have also shown the best suited approach for further development in dynamic malware detection process.
Development of RGB LED Based Compact Multipurpose Colorimeter for Hemoglobin Estimation of Blood

In this paper, an inexpensive, portable, lightweight and compact colorimeter has been proposed, which finds application in multiple fields such as biomedical instrumentation, chemical analysis, academics, etc. The instrument employs a customized tricolor RGB Light emitting Diode as light source, and a silicon photodiode as detector. It combines the use of a programmable source of light, and light to voltage integrated circuit, with an open source Arduino microcontroller for wavelength control and data processing. A successful attempt has been made in miniaturizing the bulky optical components of a conventional spectrophotometer thereby cutting down the weight, cost, and tediousness of the instrument. For validation purposes, the device has been harnessed as a hemoglobin analyzer. Observed hemoglobin values were compared with those obtained from a standard laboratory hematology analyzer. Using fivefold cross validation method, a minimum absolute error of 0.024 g/dL and an average root mean square error of 1.283 g/dL was obtained.
Repair Task Scheduling of Transmission Systems Considering Conditional Setup Times

The restoration planning of transmission system is crucial for achieving the goal of a more resilient power grid in face of high impact events such as natural hazards, which involves the optimal scheduling of repair tasks under limited resources. In this paper, a model is proposed to deal with the repair task scheduling and power system operation problems simultaneously. The sequence dependent setup times for each task, considering both resource pick-up and shift changing process are incorporated in the model. A novel two-stage bi-pheromone heuristics based on ant colony optimization is developed to solve this combinatorial problem with high complexity. The model is implemented on a modified IEEE 30-bus test system. The simulation results show that the proposed method is able to find better solution than the existent approximation methods, while outperforming the traditional integer programming method in terms of computational efficiency.
Lung Nodules Classification Using Massive-Training Self-Organizing Map and Learning Vector Quantization

The abnormal growth of cells in the lungs leads to the development of nodules and the overgrowth of lung nodules will eventually form a cancerous cell. Detection of lung nodules in the early stage is vital in such a way that proper treatments can be applied before the lung nodules grow into lethal lung cancer. In recent decades, machine learning has been widely used in the computer aided system to provide second opinion to the radiologists in the detection of abnormality on medical images. The aim of this paper is to implement a machine learning algorithm in the classification and enhancement of lung nodules on computed tomography (CT) images. The classification model – Massive-Training Self-Organizing Map and Learning Vector Quantization (MTSOM-LVQ) is implemented to classify the sub-regions based on the teaching Gaussian values. Each sub-region is associated with its teaching value generated by using Gaussian distribution function. The results show that MTSOM-LVQ is able to enhance nodules and suppressing non-nodules on CT images. Adjustment on the parameters such as map size, training iteration and size of the training sample would affect the performance of the MTSOMLVQ. Besides, the performance of the MTSOM-LVQ is validated and 90% classification sensitivity is achieved. As a conclusion, the training accuracy can be further improved by choosing the optimized parameters for MTSOM-LVQ in future research.
Factorization Meets Neural Networks: A Scalable and Efficient Recommender for Solving the New User Problem

Predicting new user behavior has always been a challenging issue in intelligent recommender systems. This challenge is mainly due to the extreme asymmetry of information between new users and old users. Existing factorization models can efficiently process and map asymmetric information, but they are not good at mining deep relationships between contexts when compressing high-dimensional data. In contrast, neural network methods can deeply exploit the relationship between contexts; however, their training cost is much larger than factorization approaches. Therefore, this paper proposes a scalable and efficient recommender to solve the new user problem by filling the gap between factorization models and neural networks. The scalable part is a neural network that can jointly encode, compress, and fuse various types of contexts. The efficient part is a factorization model based on a correlation constraint mechanism and a projection strategy, which enables an asymmetric mapping of information between old and new users. The entire recommender fuses the two parts so that the factorization model and the neural network can complement each other. The experimental results show that our approach can achieve a good balance between performance and training efficiency compared to state-of-the-art methods.
Geometric Characteristics of Saturation Tests in Synchronous Machines

The main machine testing standards present the necessary procedures in order to conduct saturation tests. The analysis of the measured data, however, depends on personal judgment when deciding the slopes of straight lines and the end of the linear part along with the beginning of saturation. This paper presents statistical models to fit measured data in a completely automatic fashion with $X_{d}$ and SCR calculation. The suitability of the proposed models is proven with several case studies with applications.
Single Line to Ground Fault Detection in a Non-effectively Grounded Distribution Network

In the event of a single line to ground (SLG) fault in a non-effectively grounded distribution network, the faulted current is weak (only a few amperes or less) and the existing devices cannot accurately judge the faulted feeder. In this paper, we proposed algorithms that combine CEEMDAN (Complete ensemble empirical mode decomposition with adaptive noise) and Hilbert-transform to construct a multi-criteria comprehensive voting method. First, CEEMDAN algorithm is used to decompose the zero-sequence current to obtain the IMF1 (the first intrinsic mode function) component and the Hilbert transform is used to calculate the instantaneous amplitude and instantaneous phase. Then, according to the three largest instantaneous amplitudes information, we constructed the CIP (Characteristic instantaneous phase), CIE (Characteristic instantaneous energy) relative entropy and CICP (Characteristic instantaneous zero sequence current polarity) criteria from the phase, energy and polarity, respectively. Last, we proposed a comprehensive voting method, which is specifically shown as follows: when two or more criteria show that one feeder or the bus has a SLG fault, it is voted that the feeder or the bus has a SLG fault. In contrast, if the judgment results of the three criteria are inconsistent, then we would return to recalculation and then vote. Compared with existing method, simulation tests and field experiments show that the method proposed in this paper has higher accuracy and a faster calculation speed.
Implementation of a real-time ISDB-TB LDM receiver using SDR

This paper presents an implementation of a realtime ISDB-TB LDM receiver in Software Defined Radio/GNU Radio Companion. The main idea of this work is to provide a hybrid digital TV broadcasting system that can be used as an option in a transition period between the current Brazilian system (ISDB-TB) and a possible new standard, such as a modified version of ATSC 3.0. The Core Layer is fully compatible with ISDB-TB and is multiplexed with an Enhanced Layer using a powerful channel coder, high order modulations and some other ATSC 3.0 features and techniques. With the use of the LDM in such systems, it is possible to deploy UHDTV applications. The implementation is done using the GNU Radio Companion software along with a Software Defined Radio platform to transmit and receive the Radio Frequency signal in real-time.
Autonomic Resource Allocation Frameworks for Service-based Cloud Applications: A Survey

Cloud is a collection of heterogeneouscomputing resources that are presented to the users on the payment basis. Cloud and the associated resources are dynamic in nature. The provisioning of resources in such dynamic environment is one of the critical issues in the cloud. Various Quality of Service (QoS) parameters contribute to the provisioning of appropriate resources in the cloud. A right proportion of resource provisioning is necessary to improve the performance of the system. To mention, energy wastage and cost increase due to over-provisioning, while, under-provisioning may seem to effect Service Level Agreements (SLA) and Service Quality (QoS). Hence, the virtual resources should be allocated accordingly to fulfil the current dynamic demand of various applications. This paper presents a deeper survey on the various resource allocation frameworks for service-based cloud applications.
Review on PD Ultrasonic Detection using EFPI -Part II: the Research and Development

Partial discharges (PD) detection method based on optical fiber sensing technologies has the advantages of safety, insulation and anti-electromagnetic interference, so it is suitable to be designed as embedded or built-in sensors installed on the inner or outer shell of power equipment for PD on-line monitoring or live detection with prospects for development. In this paper, based on the introduction of the PD measurement method of optical fiber sensing technologies, the extrinsic Fabry-Perot interferometer (EFPI) optical fiber sensing for PD ultrasonic detection is introduced. The principle and research progress of detecting PD ultrasonic signal based on EFPI optical fiber sensor and its performance tests including amplitude-frequency response, static pressure, distance attenuation and directionality are described in detail. The key parameters and application problems in the development of the EFPI PD optical fiber sensor are analyzed. Finally, the development trend of the EFPI PD optical fiber sensor is expected.
Intelligent Control Strategies for Tuning PID of Speed Control of DC Motor - A Review

DC motor are commonly used in industrial application, because of that advantages over other types of motors. Some applications of DC motor require more advanced control strategies of speed and position. The classic PID controller has been taking the majority of the control system worldwide. Due to its inefficiency in non linerity handling, some of the soft computing and artificial intelligent (AI) techniques were adopted to solve the uncertainties/nonlinearities existing in the system. In this paper, the several AI techniques were compared based on their performance for tuning PID of DC motor speed control. The parameters of PID controller to be tuned in terms of rise time, settling time, steady-state error and maximum overshoot. In addition, the theoretical complexity of the strategies is also compared. In general, this paper presents a brief review and comparison of intelligent control strategies for tuning PID parameters of DC motor speed control based on early research.
Sentiment Analysis on Product Reviews

Sentiment Analysis is a process of analyzing and categorizing the emotion or sentiment over any given review or text piece in order to know what the reviewer wants to express in the form of positive, negative or neutral. Today, people are highly interested in buying things online from any e-commerce site or they search for a product review in order to know the quality and one’s perception toward that product before buying. The product provider also gets to know about the user’s opinion over a product. This can help the company to improve its marketing strategy and quality of product in their favor. Sentiment analysis uses various semantic approaches like on these online reviews to extract as much feature it can and categorize the type of opinion. Some techniques also help in rating the product value based on user’s opinion. This paper is a literature survey including various authors and their sentiment techniques on product or online review.