Miracle-dz/UniFAR-FTUs huggingface dataset:
A Techno-Economic Study of Rooftop Grid-Connected Photovoltaic-Energy Storage Systems in Qatar

Gulf Corporation Countries (GCC) are exposed to high levels of solar insolation throughout the majority of the year. Therefore, the use of photovoltaics (PV) is a viable, clean energy source for the GCC region. This paper presents a detailed techno-economic study for the implementation of a grid-connected rooftop photovoltaic and energy storage system (PV-ESS) in the State of Qatar. This study aims to present a detailed cost analysis as well as sizing information for a house in Qatar. A multi-objective optimization that considers generation and load profiles, cost of grid consumption, cost of materials, and house size is presented in the form of Pareto frontier curves. Load modulation is introduced as a method to lower capital expenditure and operational costs. The benefit to a developed country like Qatar is to improve the resilience of the electrical supply and reduce the carbon footprint. The benefit to a developing country is that it brings electricity to individual homes without needing an expensive utility grid. Although this paper is not intended to study energy policy, the techniques presented can be used to recommend energy policies. The results of this study are presented in the form of implementation steps to make rooftop PV-ESS systems feasible in Qatar based on factual information and detailed analysis.
MoOxbased hole selective contact - Inference from the Simulation

Transition metal oxide-based carrier selective contacts are under intense research. In this work, we compare the two different set of material parameters of Molybdenum Oxide (MoOx) using the numerical simulation and discuss different hypothesis while correlating with the reported experimental data. We found that a particular set of the parameter could better explain the transfer-length-method (TLM), dark and 1-sun J-V curve, and surface photo-voltage measurement for different annealing temperature. Also, it leads to the different inference that the hydrogen effusion-from the hydrogenated, intrinsic amorphous silicon-could have caused trap-limited transport instead of work function reduction.
Analysis of public complaint reports during the COVID-19 pandemic: a case study of Jakarta’s citizen relations management

COVID-19, as a newly discovered disease, has suddenly become a major challenging problem for many cities in the world, including Jakarta. This crisis affects all segments in day to day life. Jakarta citizens can report any problems related to COVID-19 through Citizen Relations Management, later known as CRM. This CRM system consists of 14 official complaint platforms that can be used by the citizens to report their complaints to the government and its working units. As a bridge between people and the government, this system is a form of problem-solving innovation. In this study, Exploratory Data Analysis (EDA) was carried out to analyze the pattern of complaint reports concerning COVID-19. The dataset used is CRM report data and daily COVID-19 positive case data. Through the analysis, a linkage is found between the cumulative number of reports related to COVID-19 and the cumulative number of COVID-19 positive cases. During the crisis period like the unfolding COVID-19 pandemic, transparency of information and citizen feedback can make invaluable contributions to an effective national response. This study is expected to encourage CRM to deal with the impact of COVID-19 effectively through the official platforms managed by Jakarta Smart City.
Broadband Sensing Around 1 THz Via a Novel Biquad-Antenna-Coupled Low-NEP Detector in CMOS

We report on the design and characterization of a novel backside-radiating antenna-coupled direct terahertz detector fabricated in 65 nm CMOS technology. The novelty of the design lies in the low-metal coverage of the biquad antenna geometry, which adapts well to the particular challenging conditions of on-chip antenna integration in silicon and allows optimization for a versatility of operation conditions. The biquad antenna was modified here to achieve wideband radiation and matching to a gate-coupled single-finger field-effect transistor with ac open condition at the drain terminal. The successful detector performance was the result of a careful treatment of transistor, antenna, and optics from a codesign perspective, since the beginning of the design. This included the frequency-dependent complex impedance for optimum matching, the technology restrictions to ensure proper chip fabrication, and the overall detection efficiency after backing the device with a silicon lens. Calibrated detector measurements for 7777 Hz modulation frequency yielded minimum optical noise-equivalent-power (NEP) of 25 pW/<inline-formula><tex-math notation="LaTeX">$\sqrt{\,}$</tex-math></inline-formula>Hz at 1 THz, with NEP values below 50 pW/<inline-formula><tex-math notation="LaTeX">$\sqrt{\,}$</tex-math></inline-formula>Hz in the 0.84–1.29 THz frequency range. These figures achieve state-of-the-art of wideband CMOS-based detectors and are only a factor of <inline-formula><tex-math notation="LaTeX">${{\sim}2}$</tex-math></inline-formula> inferior to the best reported narrowband devices close to 1 THz.
Chaos, Machine Learning and Deep Learning based Hybrid to forecast Consumer Price Index Inflation in India

In this study, we proposed a 2-stage hybrid approach for financial time series forecasting wherein chaos is modeled in stage-1 followed by forecasting is accomplished using machine learning and deep learning algorithms in stage-2. The effectiveness of the proposed hybrid is tested on forecasting Consumer Price Index Inflation of Food & Beverages, Fuel & Light, and Headline in India. This is a first-of-its-kind study where chaos is modeled and deep learning is employed in forecasting macroeconomic time series. From the results, it is inferred that Chaos + Machine learning hybrids yielded better forecasts than pure machine learning algorithms without Chaos in terms of Symmetric Mean Absolute Percentage Error (SMAPE), Theil’s U statistic and Directional statistic across all the data sets. A deep learning model namely, Long Short Term Memory (LSTM) was also employed but without much success. The results of 2-stage hybrid models are compared with models without accounting for chaos. The results are encouraging and these hybrids can be applied to predict other financial time series.
Adaptive Deep Co-Occurrence Feature Learning Based on Classifier-Fusion for Remote Sensing Scene Classification

Remote sensing scene classification has numerous applications on land cover land use. However, classifying the scene images into their correct categories is a challenging task. This challenge is attributable to the diverse semantics of remote sensing images. This nature of remote sensing images makes the task of effective feature extraction and learning complex. Effective image feature representation is essential in image analysis and interpretation for accurate scene image classification with machine learning algorithms. The recent literature shows that convolutional neural networks are mighty in feature extraction for remote sensing scene classification. Additionally, recent literature shows that classifier-fusion attains superior results than individual classifiers. This article proposes the adaptive deep co-accordance feature learning (ADCFL). The ADCFL method utilizes a convolutional neural network to extract spatial feature information from an image in a co-occurrence manner with filters, and then this information is fed to the multigrain forest for feature learning and classification through majority votes with ensemble classifiers. An evaluation of the effectiveness of ADCFL is conducted on the public datasets Resisc45 and Ucmerced. The classification accuracy results attained by the ADCFL demonstrate that the proposed method achieves improved results.
Optimizing stock market execution costs using reinforcement learning

Stock market trading is a complex process where traders aim to maximize their expected return while minimizing associated risks. With the increasing availability of digital historical records, using automated agents for stock market trading becomes of a significant interest. Reinforcement learning is a machine learning branch which circumvents the problem of defining explicit targets and tackles problems which require sequential decisions. Reinforcement learning has been applied in finance problems, yet execution costs optimization problem among others still gets little attention. The optimization of execution costs in stock markets is a vital problem, where a trader wants to minimize the cost of buying a predefined amount of shares over a fixed time horizon. In this study, we propose a novel reinforcement learning Q-trade model to address the execution costs optimization problem. We tested the Q-trade model using historical data of the Egyptian stock market and Nasdaq stock market, and it showed in both markets a significant improvement (more than 60% for some securities) over the compared strategies.
Comment on the free energy functional of block copolymer melts in the strong segregation limit

An elastic theory was previously developed for gently deformed periodic structures. However the elastic constants obtained are not consistent with those of Wang and Safran calculated recently by a different method. The main difference is the block ratio dependence of the equilibrium period D. Our result show a rather strong dependence of the block ratio f while Wang and Safran have derived D independent of f. The origin of the above discrepancy is explored
Performance Analysis of Improved Backoff Scheme for Full Duplex CSMA/CA in VLC Networks

IEEE 802.15. 7 visible light communication (VLC) networks suffer from performance degradation caused by the hidden device problems due to the directional transmission with narrow beamwidth. One of the solutions for the hidden device is to apply a full-duplex transmission technique to the VLC networks. This approach enables to mitigate collisions induced by hidden devices. As a side effect of applying the full-duplex transmission, however, the data-packet discard by reaching the retransmission limit occurs frequently in the networks. This paper proposes an improved backoff scheme to suppress the packet discard. In the proposed backoff scheme, the values of the backoff exponent (BE) and the number of backoff stage (NB) in IEEE 802.15. 7 are increased only when the data packet transmission fails. To evaluate the system performance theoretically, in addition, this paper establishes the analytical models, including operations of the improved backoff scheme. The results from simulation and theoretical analysis show the effectiveness of the proposed scheme.
Modified Fuzzy C-Means Clustering Approach to Solve the Capacitated Vehicle Routing Problem

Fuzzy C-Means clustering is among the most successful clustering techniques available in the literature. The capacitated vehicle routing problem (CVRP) is one of the most studied NP-hard problems. CVRP has attracted the attention of many researchers due to its importance within the supply chain management field. This study aims to develop a fuzzy c-means clustering heuristic to efficiently solve the CVRP with large numbers of customers by using cluster-first route-second method (CFRS). CFRS is a two-phase technique, where in the first phase customers are grouped into, and in the second phase each cluster is solved independently as a traveling salesman problem (TSP). This work is concerned the clustering phase of the CFRS. The second phase of the CFRS method is solved using traditional optimization software. A modified demand weighted fuzzy c-means clustering algorithm is developed to solve the clustering phase. Twentyfive instances are solved to evaluate the efficiency of the proposed algorithm. Some of them are large instances with more than 500 customers. Promising results in terms of accuracy and processing time are obtained.
A Comparative Study of Rectangular and Double- D Coil Geometries for the Inductive Link Design of Wireless EV Charging System

The performance of the magnetic couplers in an inductive power transfer system determines the overall feasibility of an entire system. In this paper, the basic theory behind wireless power transfer and the required components is analyzed, showing how design aspects, power pad physical dimensions, height and vertical displacement affect the results. Two coil geometries are analyzed and investigated in this work and their coupling characteristics and misalignment tolerance are studied and compared. This study aims at the design and Finite Element Modeling (FEM) simulations of the inductive link to maximize Figure-of-merit (kQ) and improve misalignment tolerance. Based on the simulation results, the geometry in which Double $\mathrm{D}$ coils are considered for primary and secondary charging pads provides the highest coupling factor and highest inductive link efficiency at various misalignment conditions up to + / - 800 mm, and up to 30 cm air gap.
High-Performance OTFTs Using Surface-Modified Alumina Dielectrics

We show novel and selective means to modify the dielectric surfaces in organic TFTs. Modification schemes include alkylphosphonic acid monolayers that have a strong affinity for alumina surfaces. Monolayers form robust, extremely uniform thin films and are deposited through simple spin-coating with a dilute solution of the monolayer precursor in solvent. Adding monolayers to organic TFTs has resulted in polycrystalline devices with mobilities nearly equal to single-crystal values while maintaining acceptable values of other device parameters (for example, the threshold voltage, on/off ratio, and subthreshold slope) required for fully functional integrated circuits.
Optimizing the Lifetime of Software Defined Wireless Sensor Network via Reinforcement Learning

Reinforcement learning (RL) is an unsupervised learning technique used in many real-time applications. The essence of RL is a decision-making problem. In RL, the agent constantly interacts with the environment and selects the next action according to previous feedback in terms of reward. In this paper, RL trains Software-Defined Wireless Sensor Networks (SDWSNs) controller to optimize the routing paths. We combine RL and SDN, where RL is applied to the SDN controller to generate the routing tables. We also propose four different reward functions for optimization of the network performance. RL-based SDWSN improves network performance by 23% to 30% in terms of lifetime compared with RL-based routing techniques. RL-based SDWSN performs well because it can intelligently learn the routing path at the controller. In addition, it has a faster network convergence rate than RL-based WSN.
Sequential Magnetic Mapping of Bacteria Loaded with Pd-Fe Nanoparticles

Magnetic nanoparticles are of widespread use in nanotechnology. Palladium nanoparticles are among the most unusual since they combine magnetism with high catalytic activity. These nanoparticles could be obtained biologically by exposing bacteria to a palladium salt. However, due to the small size and weak magnetism, it is challenging to measure their magnetic properties. One of the solutions to enhance their magnetism is to incorporate a small number of iron atoms into them. After this procedure, the nanoparticles and bacteria can be embedded in resin and characterized by magnetic force microscopy. This technique allows imaging cross-sections of the bacteria with nanoparticles but cannot give information from the depth of the sample. Here we report on an approach partially solving this problem. Its novelty lies in measurements of consecutive thin slices of resin, which allows mapping cross-sections of individual bacteria and different parts of the material surrounding the same bacterium. An interesting observed feature is the formation of magnetic chains of nanoparticles outside of the bacteria.
QoS-Aware Energy Management and Node Scheduling Schemes for Sensor Network-Based Surveillance Applications

Recent advances in wireless technologies have led to an increased deployment of Wireless Sensor Networks (WSNs) for a plethora of diverse surveillance applications such as health, military, and environmental. However, sensor nodes in WSNs usually suffer from short device lifetime due to severe energy constraints and therefore, cannot guarantee to meet the Quality of Service (QoS) needs of various applications. This is proving to be a major hindrance to the widespread adoption of WSNs for such applications. Therefore, to extend the lifetime of WSNs, it is critical to optimize the energy usage in sensor nodes that are often deployed in remote and hostile terrains. To this effect, several energy management schemes have been proposed recently. Node scheduling is one such strategy that can prolong the lifetime of WSNs and also helps to balance the workload among the sensor nodes. In this article, we discuss on the energy management techniques of WSN with a particular emphasis on node scheduling and propose an energy management life-cycle model and an energy conservation pyramid to extend the network lifetime of WSNs. We have provided a detailed classification and evaluation of various node scheduling schemes in terms of their ability to fulfill essential QoS requirements, namely coverage, connectivity, fault tolerance, and security. We considered essential design issues such as network type, deployment pattern, sensing model in the classification process. Furthermore, we have discussed the operational characteristics of schemes with their related merits and demerits. We have compared the efficacy of a few well known graph-based scheduling schemes with suitable performance analysis graph. Finally, we study challenges in designing and implementing node scheduling schemes from a QoS perspective and outline open research problems.
Longpass Distributed Bragg Reflector for Improved Radiation Tolerance and Current Matching in Multijunction Solar Cells

Reducing the thickness of the GaAs absorber in a multijunction solar cell can increase radiation tolerance, while incorporation of a distributed Bragg reflector (DBR) can maintain current density by maintaining optical path length. In a closely current matched multijunction design, low energy sidelobe reflections of the DBR parasitically affect the subcell below the GaAs junction. A variation of the DBR design is proposed to mitigate this loss mechanism by maximizing transmission below the main Bragg peak. Through simulated multijunction and experimental verification of test structures, this paper will show how this “longpass” DBR can be optimized to improve current matching while maintaining radiation hardening afforded by thinning the GaAs junction.
Comparison of ACR Methods for 360° Video Quality Assessment Subject to Participants’ Experience with Immersive Media

Watching 360° videos on head-mounted displays (HMDs) has become more popular in recent years. As such, subjective quality assessment of 360° videos on HMDs is needed to obtain a ground truth on the quality as perceived by users. Due to the lack of standardized methods, the absolute category rating (ACR) with hidden reference (ACR-HR) method for conventional videos has also been used for subjective quality assessment of 360° videos on HMDs. A modified ACR (M-ACR) method tailored for assessing 360° videos on HMDs has recently been proposed. In this paper, we compare the ACR-HR and M-ACR with hidden reference (M-ACR-HR) method regarding subjective quality assessment of 360° videos on HMDs. The comparison is conducted subject to participants’ experience with watching immersive media on HMDs. The findings include: 1) Average rating times to cast opinion scores is lower for the ACR-HR method irrespective of participants’ experience, 2) Mean opinion scores are similar for both methods irrespective of participants’ experience, 3) The M-ACR-HR method is more reliable, especially with experts and participants with no experience, 4) Discomfort is significantly lower for the ACR-HR method, especially, for participants with no experience.
Automated Brain Tumor Segmentation in MRI using Superpixel Over-segmentation and Classification

Brain tumor segmentation is a challenging task due to the strong fluctuation in intensity and shape. It has attracted the attention of medical imaging community for several years. This work introduces a fully automated brain tumor segmentation approach from multimodal MRI images. Segmentation in three different MRI modalities; T1 (gadolinium-enhanced), T2, and Fluid-Attenuated Inversion-Recovery (FLAIR) are compared to choose the best one. The proposed approach utilizes a super-pixel over-segmentation technique and applying a classification for each super-pixel which leads to more smooth segmentation. Several features including statistical, fractal, and texture features are calculated from each super-pixel of the normalized (T1, T2, and flair) images to ensure a robust classification. Additionally, the class imbalance problem is tackled to allow the algorithm to accurately segment abnormal tissue. The Random Forest (RF) classification algorithm is utilized for final segmentation. The RF classifier is being chosen in the proposed approach because it provides a better performance according to the confusion matrix results. The proposed approach has been trained using 10 Low-Grade and 20 High-Grade cases and evaluated using different 5 Low-Grade and 5 High-Grade cases from BRATS 2013 dataset. Dice, average precision, sensitivity, and F1-score metrics are used for segmentation accuracy evaluation. The average precision, sensitivity, fl-score and dice overlap for tumor segmentation are 92%, 95%, 96% and 94% for flair images, 89%, 92%, 90% and 93% for T2 and 89%, 90%, 89% and 90% for T1. Finally, the voting strategy is being used to get the best segmentation between these different modalities.
A Process-Aware Memory Compact-Device Model Using Long-Short Term Memory

With the immense increase in the processing data during the scaling down of semiconductor devices by Moore’s Law, it is in urgent need to use data analytics to meet the state of the art performance in both manufacturing and device compact modeling. In particular, managing the fabrication cost and promptly providing compact device models, especially for new or emerging devices, is challenging. To ease out these issues, we propose a unified, general-purpose, process-aware machine learning (ML) based compact model (CM) for resistive random-access memory (RRAM), and the same methodology can be used for any memory devices with hysteresis. A long short-term memory (LSTM) ML model is used to fit the RRAM current-voltage (I-V) characteristics. The memorizing capability of LSTM ensures one model can fit both RRAM low resistance state (LRS) and high resistance state (HRS). The fitted dataset is based on the fabricated RRAM samples using TaN/HfO2/Pt/Ti/SiO2/Si structure. The resultant fitting error is 0.0096 in sinusoidal wave input voltage and 0.0148 in random walk voltage sequences. In the process-aware demonstration, we use post-oxide annealing dataset from 300°C to 500°C. The root mean squared error (RMSE) in the process-aware RRAM compact model is 0.0028. Thus, the LSTM-based CM has the potential to compete with the conventional compact device models in terms of shorter developing time, better fitting capability in emerging devices and a large number of devices, easily incorporated process-aware models, and one unified model accounting for LRS and HRS ensuring differentiability. We propose that the LSTM based memory CM can be useful in intelligent manufacturing, process tuning, and simulation program with integrated circuit emphasis (SPICE) modeling in circuit simulation.