Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of Waste Management System using the Concept of ‘Basura Advantage Points’ through Artificial Neural Network

One of the most pressing problems of the world is the growing solid waste pollution. With current demands for sustainable development, the researchers developed a waste segregator machine that satisfies efficient segregation and introduces the concept of reward system to motivate people to throw their waste into the machine. In this paper, the researchers created an automatic segregating machine that uses Artificial Neural Network (ANN) as an algorithm for machine learning and embedded with the concept of “Basura Advantage Points”. The ANN acts as the brain of the machine for sorting out the plastic bottles as one category and other waste materials for the other category. The “Basura Advantage Points” is a novel concept wherein whenever people throw a garbage into the segregating machine, they can earn points which can then be used to redeem awards set by policy makers. Survey results show that the machine is appealing to the public. Based on the sample wastes, the accuracy of the machine is around 80 percent. From positive feedbacks to successful evaluation, the study highlighted a good model to decrease improper waste disposal and encourage people to participate in proper waste segregation.
Hamiltonian Energy-Based Sliding Mode Control Approach for a Multi-port Bidirectional EV Charger via Zero Dynamic

In this article a combinatorial nonlinear control system has been proposed for a multi-port bidirectional integrated on-board EV charger, as a variable structure system. This bidirectional EV charger has a bilinear dynamic model that is a specific classification of nonlinear dynamic, hence, these systems need a nonlinear control method for stabilizing in a wide range of state variables variations and external disturbances. At first, bilinear dynamic model of the mentioned converter is achieved by using Euler-Lagrange description, then sliding mode controller is used for one of the system state variables and after that an energy-based controller is implemented by zero dynamic of sliding mode controller for output voltage stabilization. The utilized energy-based controller is called Hamiltonian energy function which is operated on incremental energy of the converter state variables such that, all of the states tracking errors are approached to zero asymptotically. This proposed controller has been simulated via MATLAB\SIMULINK simulation software for this mentioned converter and it has been compared to a cascade linear controller.
3D Experience on a 2D Display

As we witness an explosion in terms of image data availability, the need for richer visualization also is increasing. While there are significant and successful efforts about creation of 3D datasets (point clouds) and visualization, most systems are today available only for industrial or commercial use, and a full end-to-end economically viable solution is not available for the common man. The solution proposed in this paper focuses on filling this gap – offering solutions to create 3D datasets and interactively visualizing the same on normal 2D displays. This is truly inter-disciplinary in nature – an amalgamation of 3D geometry, computer vision, machine learning, computer science, etc. We report a successful implementation of a prototype satisfying all these aspects.
Discrete-time Inverse Optimal Control for Consensus of Multi-Agent Systems via a Novel Meta-Heuristic Algorithm

This paper illustrates a novel perspective for inverse optimal control of the multi-agent systems (MAS) via a linear quadratic regulator (LQR) based on Jaya algorithm (JA), teaching-learning algorithm (TLBO) aSnd a novel meta-heuristic algorithm which is called advanced teaching-learning (ATLBO). In this regard, first the consensus protocol is designed and then the cost function is optimized via meta-heuristic algorithms for finding the consensus controller. The Laplacian matrix is a key tool in this paper, and this research sheds light on the fact that the Laplacian matrix is inverse optimal for the related cost function. Moreover, this paper shows that the LQR approach based on these three meta-heuristic algorithms leads all agents to reach the optimal consensus. The novel meta-heuristic algorithm (ATLBO) optimizes the cost function faster than the other mentioned algorithms therefore it decreases the runtime for the problem. Also, the combination of inverse optimal control approach and meta-heuristic algorithms is suggested for solving the consensus problem considering the least cost. Finally, simulation results illustrate the usefulness of the novel algorithm and method.
Operation of the AC-AC Converter Based Dynamic Voltage Restorer in Weak Distribution Systems

Dynamic voltage restorer (DVR) is a series compensation device designed to compensate for voltage-related power quality (PQ) problems. In the weak distribution systems, the Thevenin impedance of the system is high. On the other hand, the AC-AC converter-based DVR takes the required compensation energy from the grid so that its current increases and extra voltage drop on the system impedance occurs worsening the situation. This paper investigates the operation challenges of the AC-AC converter-based DVR in weak distribution systems and determines the voltage sag compensation capability of the AC-AC converter-based DVR in such a condition. The detailed analysis of the voltage sag compensation capability of the DVR in weak grid is presented and the effect of different parameters such as the grid impedance, load current, and the load power factor are considered and analyzed. The analysis given in the paper are verified by simulation studies on a single-phase 220V DVR.
Converter switching mechanism scheduling by type-2 fuzzy approach for PV/battery/Fuel systems

In this paper a type-2 (T2) fuzzy logic system (FLS) based controller is presented for power/voltage control of PV/batery/Fuel system. The switching mechanism of the converters are adjusted by the use of FLS based controllers. The rules of T2-FLSs are tuned on basis of Lyapunov approach. The tuning rules are determined such that the stability to be ensured and also the regulation to be obtained. Simulations verify that the deigned controllers result in a desired regulation in versus of fully unknown dynamics of PV and all other units and also the obtained control signals have a desired implementable shape.
Evaluating Performances and Importance of Venture Capitals: A Complex Network Approach

Venture capital market is one of the most important financial markets, which plays an important role in promoting industrial innovation and economic development. In this study, the complete data set of all venture capital events occurred in China from 2009 to 2020 was used to construct two kinds of complex networks, whose topological property and dynamic evolution trend of the network are studied. Specifically, we construct the co-investment network among investor, as well as the binary complex network of investor-entrepreneur, and propose the evaluation method of the importance and performance of venture capital institutions. Based on the proposed method, we identify some leading venture capital institutions with high performance and importance. Furthermore, this paper enlightens venture capital practitioners by discovering the investment behavior and preference of these leading institutions.
SRIF: Scalable and Reliable Integrate and Fire Circuit ADC for Memristor-Based CIM Architectures

Emerging computation-in-memory (CIM) paradigm offers processing and storage of data at the same physical location, thus alleviating critical memory-processor communication bottlenecks suffered by conventional von-Neumann architecture. Storage of data in a CIM architecture is analog in nature and therefore computation is performed in analog domain i.e. inputs and outputs are analog values. Since the outside computing environment is digital, analog-to-digital converters (ADC) are utilized to perform the output data conversion. However, ADC designs are bulky, power-hungry circuits that are prone to design variations and therefore, play an important role in determining the computing efficiency of CIM architectures. In this paper, we present a scalable and reliable integrate and fire circuit ADC (SRIF-ADC) design for CIM architectures, suitable for stringent power and area constraints. We devise a technique to stabilize the node receiving analog inputs that allows more rows to be activated at the same time, thereby increasing the operand size of input vectors. This allows better scalability in terms of higher parallelism of operations. We employ a self-timed variation-aware design approach and design measures to drastically reduce read disturb of memristor devices that address reliability issues related to the ADC design. In addition, we present a compact, built-in sample-and-hold circuit to replace the large-sized capacitance and built-in weighting technique to alleviate the need for post-processing. For multiply-and-accumulate (MAC) operation, our simulation results show that we can improve the computational parallelism by 3X as well as ADC conversion speed and energy efficiency are improved by 2X and 11.6X, respectively, compared to the state-of-the-art design.
Design and Analysis of a Novel Dual-Airgap Dual Permanent Magnet Vernier Machine

A novel dual-airgap permanent magnet Vernier machine (DADPMVM) is proposed and analyzed in this paper. The key of this design is combining the double-concentric-rotor structure, the dual permanent magnet structure along with the Vernier structure to improve the torque density while remaining the motor’s volume unchanged. The maximized use of the winding length and the reduction of copper loss are also the main merits of this design. The machine structure and operating principle are discussed, and its transient performance is analyzed by using the finite element method (FEM). Two different configurations of pole pairs and two different structures are designed in the same peripheral dimension configuration and compared with the proposed machine by FEM. The results of the comparison are used to confirm the outstanding performance of the proposed machine. The torque density per machine volume can exceed 100 kNm/ m3 with the proper cooling method.
Virtual Voltage Vector Based Predictive Control of High Performance Modified Quasi-Z-Source Inverter with the Aim of Constant Common-Mode Voltage

Removal of the transformer from quasi-Z-source inverters (q-ZSI) in photovoltaic systems creates an oscillating common-mode voltage (CMV) on the parasitic capacitors. Fluctuations in this voltage cause leakage current to flow in the system. Due to the shoot-through (ST) state in q-ZSIs, the CMV fluctuations are also higher than those of the conventional voltage source converters. In this paper, the predictive control method and the concept of virtual voltage vectors (VVVs) and their combination with the odd PWM (OPWM) method are proposed for elimination of the oscillation of the common state voltage in the q-ZSI. In the proposed method, the CMV fluctuations are kept constant for a complete switching period. In addition, the total harmonic distortion (THD) of the output current of the converter decreases, and consequently, the converter’s operating range and performance increase. By simulating a two-level q-ZSI, the performance of the proposed method are proved.
Uplink Massive Access in Mixed RF/FSO Satellite-Aerial-Terrestrial Networks

This paper investigates the massive access for a satellite-aerial-terrestrial network (SATN), where a high-altitude platform (HAP) is deployed as a relay to assist the uplink transmission from terrestrial user equipment (UE) to satellite. Unlike previous works, we adopt radio frequency (RF) and free space optical for the aerial-terrestrial and satellite-aerial links, respectively. Specifically, by assuming that imperfect angular information (IAI) of each UE is known at the HAP, we develop a space division multiple access (SDMA) scheme to maximize the ergodic sum rate (ESR). To this end, we first exploit the IAI to calculate the analytical expression of channel correlation matrix. Then, by considering the limitation of array freedom, we propose a subspace-based UE grouping and scheduling scheme to cluster all UEs into groups. Next, we present a computationally effective beamforming (BF) scheme for each UE at HAP to efficiently implement SDMA in the RF link. Furthermore, a closed-form expression for the ESR of the SATN is derived to validate the proposed BF and SDMA schemes. Finally, simulation results corroborate the derived theoretical formulas and reveal the impacts of array size, angular estimation error, the number of UEs and scheduling threshold on the system performance.
Performance Analysis of Array Database Systems in Non-Uniform Memory Architecture

Array Database Management Systems (Array databases) support query processing over multi-dimensional data. Data storage is implemented with non-linear structures to mitigate the shortcomings of the relational model when dealing with raw binary data, such as images, time series, and others. Due to data-hungry nature of multi-dimensional data applications, array databases must ideally provide a linear speedup when using a multi-processing system. When dealing with Non-Uniform Memory Access (NUMA) machines, array databases may require massive data movement across the nodes resulting in a severe performance impact, depending on the user operation. In this paper, we analyze the performance impact of the NUMA architecture in the SAVIME and SciDB array databases running five different well-known static thread pinning strategies. Our experiments showed a maximum speedup of these different strategies by 2.49x for SAVIME and up to 1.40x for SciDB. We also observed that these static strategies only yield 48% from the potential speedup (and 26% of the energy reduction), opening a new research topic.
Investigation of Intelligent approaches in Micro aneurysms Detection

Diabetic retinopathy is a leading eye disease in the present medical field that damages retina blood vessels due to the high blood sugars and those who are having this kind of diseases loss their eye visions slowly and it leads to blindness. Some new abnormal blood vessels can grow on the retina that can affect the vision. Diabetic retinopathy causes vision loss in people with various age group from 20 to 64 and above. In diabetic retinopathy the initial clinical sign is microaneurysm. Microaneurysms can be detected accurately using image processing along with deep learning mechanisms. This paper focuses on various studies on the detection of microaneurysms. It also brief various methods to detect microaneurysms and also the comparative analysis of microaneurysms detection on different methods and finally summarized the various classifiers.
An Adaptive PV Frequency Control Strategy Based on Real-Time Inertia Estimation

The declining cost of solar Photovoltaics (PV) generation is driving its worldwide deployment. As conventional generation with large rotating masses is being replaced by renewable energy such as PV, the power system’s inertia will be affected. As a result, the system’s frequency may vary more dramatically in the case of a disturbance, and the frequency nadir may be low enough to trigger protection relays such as under-frequency load shedding. The existing frequency-watt function mandated in power inverters cannot provide grid frequency support in a loss-of-generation event, as PV plants usually do not have power reserves. In this article, a novel adaptive PV frequency control strategy is proposed to reserve the minimum power required for grid frequency support. A machine learning model is trained to predict system frequency response under varying system conditions, and an adaptive allocation of PV headroom reserves is made based on the machine learning model as well as real-time system conditions including inertia. Case studies show the proposed control method meets the frequency nadir requirements using minimal power reserves compared to a fixed headroom control approach.
Non-Profiled Deep Learning-Based Side-Channel Preprocessing With Autoencoders

In recent years, deep learning-based side-channel attacks have established their position as mainstream. However, most deep learning techniques for cryptanalysis mainly focused on classifying side-channel information in a profiled scenario where attackers can obtain a label of training data. In this paper, we introduce a novel approach with deep learning for improving side-channel attacks, especially in a non-profiling scenario. We also propose a new principle of training that trains an autoencoder through the noise from real data using noise-reduced labels. It notably diminishes the noise in measurements by modifying the autoencoder framework to the signal preprocessing. We present convincing comparisons on our custom dataset, captured from ChipWhisperer-Lite board, that demonstrate our approach outperforms conventional preprocessing methods such as principal component analysis and linear discriminant analysis. Furthermore, we apply the proposed methodology to realign de-synchronized traces that applied hiding countermeasures, and we experimentally validate the performance of the proposal. Finally, we experimentally show that we can improve the performance of higher-order side-channel attacks by using the proposed technique with domain knowledge for masking countermeasures.
The effect of buoyancy forces in buildings on the efficiency of unbalanced regenerative air handling units

Over the years, ventilation in old residential buildings has developed and modernized, leading to more efficient ventilation systems and heat recovery units. In individual rooms, small houses, one-two storey buildings, and greenhouses it is very appropriate to use small single Air Handling Units (AHU) with heat recovery. It has been established in the literature that such devices for heat recovery are influenced by external factors – the outdoor and indoor parameters of the air, and especially the pressure difference in the buildings. These factors cause an unbalanced operating mode of the regenerative AHU which can significantly change its efficiency. The main aim of this paper is to simulate the unbalanced operating modes of AHU with a fixed-bed regenerator for heat recovery in multi-storey buildings. Simulations were performed for two cases of room ventilation, the first case refers to a single device in a room, and the second case refers to a pair of devices in a room. The results show different ventilation modes for each of the cases, which leads to different pressure distributions, air change rates, room velocity vector fields, and different regenerators’ thermal efficiency.
Detecting and Capturing the Intensity of a Brain Tumor using MRI Images

Mutated cells in the brain are known as tumors. There are two types of tumors present in the brain, and they are malignant and benign tumors. Benign tumors are non-cancerous tumors which reside inside a person’s brain having a primitive shape and size. Malignant tumors are cancerous brain tumors which spread by transforming the cells into the cell type next to the malignant tumor and have no clearly defined edge or shape(cloudy). Treatment planning and detection is the most efficient way of treating a patient and improving the condition. Magnetic Resonance Imaging (MRI) is the established method to detect the tumors. After the detection classification of the tumor manually takes reasonable time and sometimes it is impossible to detect the type with the naked eye. This results in having to conduct a biopsy which is a risk as well as sometimes impossible. One of the main factors to be considered in segmenting the tumor is the edge intensity of the tumor. For detecting the intensity of the tumor edge a novel completely automatic and reliable detection based on CNN is proposed. Pre-processing and running the improved images through CNN to get the best possible features to be extracted from the MRI images. Canny edge detection and Wavelet transform are applied to detect the edge and finally, Hough transform is used to detect the intensity of the edge. Edge detection method that proposed in this paper will have a wide verity of surgical applications.
Growth Law of Electrical Tree in Glass/Epoxy Resin Composite

This paper presents the growth law of electrical tree in glass/epoxy resin composite (GER). Two influencing factors on electrical tree growth, the relative permittivity of the glass and the adhesion type of the interface, are investigated. An improved WZ model is built to further explore the mechanism of electrical tree growth in GER. The electric field between the needle electrode and the glass is enhanced when the relative permittivity of glass is higher than that of epoxy resin, which is responsible for the attractive effect of the glass. The breakdown strength of the interface determines the growth mode of electrical tree. When the breakdown strength of the interface is as low as the air gap, the electrical tree showed ‘linear growth’ at the interface. High breakdown strength of the interface lead to ‘step growth’ of electrical tree and prolong the growth time compared to ‘linear growth’.
A 24-Channel Digitizer With a JESD204B-Compliant Serial Interface for High-Speed Detectors

A 24-channel application-specific integrated circuit (ASIC) for the readout of high-speed CMOS active pixel sensors for charged particle detection is presented. The chip comprises 24 preamplifiers, 24 distinct 12-bit, 25 MSPS Pipelined analog-to-digital converters (ADCs) with self-calibration, an internal phase-locked loop (PLL), and a 3 Gbps serial interface that conforms to the JESD204B standard. To simplify interfacing with a variety of sensors, the ASIC also includes an automatic offset calibration loop. The high level of integration of the ASIC reduces overall system cost and area, and exploiting the signal characteristics of the image sensor allows the ADC to be optimized for reduced power dissipation. The use of an integrated serializer and an industry standard protocol simplifies integration of the ASIC into a complete camera system. The ASIC, called the High-Speed Image Preprocessor Targeted for Electron Readout, or HIPSTER, with a die area of 64.26 mm2, is packaged in a 480-ball grid array (BGA) and is fabricated in 180-nm CMOS technology. HIPSTER achieves typical differential nonlinearity (DNL) < 0.55 LSB, input-referred thermal noise of $114.5~\mu \text{V}$ -rms, and a bit error rate (BER) of better than 10−14. The power dissipation is 98 mW/channel.
Change Detection Using Time and Look Angle Series of Geocoded Tandem-X Staring Spotlight Images

This article describes the analysis of changes in time and look angle series of TanDEM-X (TDX) and TerraSAR-X (TSX) staring spotlight images over the regions around Kjeller near Oslo in Norway and the town Nikel in Russia near the Norwegian border. It is shown that change detection may be done with TDX/TSX with shorter intervals than the repeat cycle using time series of geocoded images with varying look angles. Using look angle series of geocoded images can help to identify changes due to look angle variation. Accurate science orbit data and very good overlap of scenes both in time and look angle make the TDX/TSX data very attractive for both coherent and incoherent change detection.