Miracle-dz/UniFAR-FTUs huggingface dataset:
Two-Dimensional DOA Finding Method for Noncircular Signals with Unknown Mutual Coupling of Rectangular Arrays

Noncircular signals have promising application potential in array signal processing, especially for 2D direction finding. However, the performance of the existing algorithms for 2D DOA (Direction of Arrival) finding will be downgraded because of the unknown mutual coupling of arrays. On the other hand, most existing algorithms search in two-dimensional space, which requires a lot of computation. To address these problems, a two-dimensional DOA finding algorithm for noncircular signal sources under the condition of unknown mutual coupling of uniform rectangular array is proposed in this paper. Based on uniform rectangular array, the 2D spectral peak search is transformed to two-level 1D spectral peak search for estimation, which greatly reduces the computational complexity. At the same time, the noncircular signal is introduced, and the performance of the maximum estimation source number and parameter estimation can be improved by making use of its noncircular characteristics. Simulation results certificate both the low calculational complication and the efficiency of the proposed algorithm for noncircular sources parameter estimation.
Mapping Urban Slum Settlements Using Very High-Resolution Imagery and Land Boundary Data

Accurate mapping of slums is crucial for urban planning and management. This article proposes a machine learning, hierarchical object-based method to map slum settlements using very high-resolution (VHR) imagery and land boundary data to support slum upgrading. The proposed method is tested in Kingston Metropolitan Area, Jamaica. First, the VHR imagery is classified into major land cover classes (i.e., the initial land cover map). Second, the VHR imagery and land boundary layer are used to obtain homogenous neighborhoods (HNs). Third, the initial land cover map is used to derive multiple context, spectral, and texture image features according to the local physical characteristics of slum settlements. Fourth, a machine-learning classifier, classification and regression trees, is used to classify HNs into slum and nonslum settlements using only the effective image features. Finally, reference data collected manually are used to assess the accuracy of the classification. In the training site, an overall accuracy of 0.935 is achieved. The effective image indicators for slum mapping include the building layout, building density, building roof characteristics, and distance from buildings to gullies. The classifier and those features selected from the training site are further used to map slums in two validating sites to assess the transferability of our approach. Overall accuracy of the two validating sites reached 0.928 and 0.929, respectively, suggesting that the features and classification model obtained from one site has the potential to be transferred to other areas in Jamaica and possibly other developing Caribbean countries with similar situation and data availability.
Predicting Success of a Movie from Youtube Trailer Comments using Sentiment Analysis

The optimal success of a movie can be predicated using social media analytics. The audience expectations and movie buzz can be measured from the data gathered via social media platforms. Few researchers have tried to demonstrate by analyzing and scrutinizing various features of tweets sent during the movie release, that twitter-based prediction of box office revenue are more accurate and specific than market based predictions. Video-sharing websites like YouTube encourages interaction among its users through the provision of user reviewing facility of movie trailers. In this paper, we have categorized the comments of movie trailers on YouTube. Using these comments, we have defined three different Sentiment Indexes which measure the sentiment of movie reviews. Prediction of box office collection of movie is then done based on this sentiment index. This helps the distributors and movie-makers to ascertain the response rate for the movie in advance by observing the comments on the trailers. This also leads to prediction of the release-day earnings by considering at the present-day sentiments. In this paper novel approach of sentiment analysis and sentiment index are used to predict movie collection.
Low Cost Synchronization for Actively Replicated Data Streams

Active replication is an attractive fault tolerance approach for data stream applications as it provides an almost instantaneous recovery matching well the low latency requirements for real time data analytics. Although the approach offers a quick recovery, it is rarely used in industry as it requires complex mechanisms such as atomic broadcast to ensure correctness, and introduces a non-negligible overhead. The majority of data stream applications compute over event windows utilizing operators such as aggregations or joins which share the property of commutativity where the correctness of the result does not rely on the order of events within such windows. In this paper, we exploit this ordering flexibility by proposing (i) an epoch-based deterministic merge algorithm which provides correctness at a much lower cost than a full-fledged atomic broadcast protocol or deterministic execution to achieve strict ordering. We furthermore propose (ii) a leader-follower protocol as an extension to this approach that lowers the impact on latency caused by stragglers and stops the propagation of any non-determinism originating from source operators. Our evaluation shows that the throughput can be improved by an order of magnitude compared to a strict ordering while providing the same guarantees.
A Proposal for Developing EA Models toward Innovation

Along with economic development, customers' expectations for the commercial value of a product or service tend to be diverse. Innovation of enterprise service and information technology is required to solve customer pain points and create new value. Enterprises that are insensitive in service innovation and technology innovation may gradually lose their customers and markets. In order to visualize and manage the business processes, resources, and the entire organization, including the information systems, this paper integrates Job Theory (JT), Business Model Canvas (BMC) and Balanced Scorecard (BSC), and proposes a method for EA (Enterprise Architecture) model development based on ArchiMate framework. First of all, the basic elements of traditional EA modeling approaches are analyzed and integrated. Next, the integrated elements are converted into ArchiMate concepts, a model-based method using ArchiMate elements is proposed and the detailed steps of this proposed method are defined. Last but not least, a case study on a Mental Health Care-Patient Management System (MHC-PMS) is carried out to verify the effectiveness of the proposed method.
An evaluation study of modulation-domain wavelet denoising method by alleviating different sub-band portions for speech enhancement

In this study, we investigate and extend the capability of the method of modulation-domain wavelet denoising (ModWD) in speech enhancement primarily analyzing the unequal importance of different sub-band signals. The recently developed ModWD is shown to improve the speech quality in adverse noise environment by processing the magnitude spectrogram of a noisy speech signal with a one-level discrete wavelet transform (DWT) and then alleviating the obtained detailed portion, which is shown more vulnerable to noise. This study follows the idea of ModWD and use a wavelet packet decomposition (WPD) to decompose the magnitude spectral time series into four sub-band sequences at first. Then any of these four subband sequences is zeroed out while the other three ones are kept unchanged. Finally, these four sub-band sequences are used to construct the updated spectrogram. The main purpose of the aforementioned procedure is to evaluate the noise-robust capability of the magnitude series at different sub-bands which possess twice (modulation) frequency resolution compared with those used in ModWD. The presented method is conducted on a subset of the Aurora-2 connected digit database, and the speech quality evaluation results in terms of Perceptual Evaluation of Speech Quality (PESQ) scores reveal that diminishing the second highest frequency band (roughly within the range [25 Hz, 37.5 Hz]) gives rise to the optimal performance.
Method for Shaping Field Intensity in Target Area

Shaping field intensity in target areas is a challenging problem in electromagnetics. Most of the existing methods can realize a certain type of field like plane wave field and focusing field. In this paper, we present an array design and control approach for shaping the field intensity to different patterns without dependence on precise control of phase. At the same time, the excitation parameters of array can be theoretical calculated according to target distribution of field intensity. We have studied the tricorn distributions in a 1-D target area. The results of theoretical calculation and full-wave simulation demonstrate the effectiveness of the proposed method.
Filtering for Systems with Sensor Nonlinearities and Data Losses: An Interval Type-2 Fuzzy-Model-Based Method

This paper focuses on designing H∞ filter for networked nonlinear systems subject to sensor saturations and packet dropouts. To capture the system uncertainties effectively, an interval type-2 (IT2) fuzzy model approach is applied. When the sensor outputs are lost during the transmission through the network links, the redundant channels are adopted to reduce the negative effect of packet dropouts. An IT2 filter is designed, where the membership functions and the number of fuzzy rules need not as the same as those of the plant, such that the filtering error system is guaranteed to be stochastically stable with prescribed H∞ performance. Finally, an example is utilized to illustrate the effectiveness of the algorithm.
Adaptive event-triggered controller design for cyber-physical systems with complex cyber-attacks

This paper focuses on the adaptive event-triggered control for cyber-physical systems (CPSs) under complex cyber-attacks, which contain deception attacks and replay attacks. A complex cyber-attacks model is firstly built for describing deception attacks and replay attacks. To save limited bandwidth resources, an adaptive event-triggered mechanism is introduced, where event-triggering condition can be adaptively adjusted when ensuring the desired system performance. By considering the complex cyber-attacks, a design algorithm of adaptive event-triggered controller is proposed for CPSs. Moreover, the sufficient conditions, which can guarantee the stability of CPSs, and the controller gain are acquired under the assistance of the Lyapunov stability theory and linear matrix inequality technique. At last, a simulated example is supplied to prove the effectiveness of the proposed method.
A Wideband and Compact Monopolar Circular Patch Antenna

A new circular patch antenna is proposed and analyzed in this paper. The proposed antenna consists of a circular patch with two sets of curved slots and a set of shorting pins. With a substrate height of $0.06 \lambda_{0} (\lambda_{0}$ is the wavelength at 6.02 GHz) and a patch radius of $0.36 \lambda_{0}$, this antenna exhibits a −10-dB impedance fractional bandwidth of 36.2%, ranging from 4.93 GHz to 7.11 GHz. It can produce a stable monopole-like conical radiation pattern with a maximum gain of 6.30 dBi. It is appropriate to use this antenna with a compact size and a wide impedance bandwidth in the wireless communication systems.
An Attack Behaviors Prediction Model Based on Bag Representation in Time Series

At present, intrusion detection system (IDS) focuses on classifying the network traffic individually and may overlook underlying correlation among adjacent attack behaviors. This paper proposes an attack behaviors prediction model based on bag representation in time series. We aggregate traffic flows into bags, and describe the distribution of data within each bag using Gaussian mixture model (GMM). The change of history data is measured and added into current bag features. The experimental results based on the Kyoto 2006+ dataset show that the approach successfully predicting the number of security incidents.
A Multilevel FeFET Memory Device based on Laminated HSO and HZO Ferroelectric Layers for High-Density Storage

We report 1-3 bit/cell FeFET operation through optimized HSO and HZO ferroelectric laminate layers using alumina interlayers. Memory window up to 3.5V, switching speed of 300ns, 10 years retention, and 104 endurance are reported. The gate stack lamination merits are discussed with insight potential of FeFET as an MLC memory.
Lane Line Detection Optimization Algorithm based on Improved Hough Transform and R-least Squares with Dual Removal

In this study, an improved algorithm based on traditional Hough transform and least square method is proposed for lane line detection. In this paper, the road image is preprocessed to obtain the edge points of the lane line, and then two methods of lane line fitting are optimized to improve the accuracy and real-time performance of lane line detection. In the actual detection, a detection algorithm combining improved Hough transform and r-lsdr method is designed, and Kalman filter is used to track the lane line. The experimental results show that the optimized lane line detection algorithm improves the detection accuracy and operation efficiency.
Optimized Wireless Connectivity for Cyber-Physical Systems with Publisher/Subscriber Architecture

Rapid advancements in communications technology accompanied by ever-increasing computing power and web-based cyber universe pave the way to new hyper-connectivity concepts and solutions among persons and things. In terms of connectivity, the IEEE 802.11 Wireless Local Area Network (WLAN), commonly referred to as Wi-Fi, has become a de-facto standard solution for the last-hop network access due to its high data rate, low cost, and ease of deployment. In this paper, we present a fast, robust and reliable application-specific context-aware Wi-Fi-based connectivity framework to facilitate the expansion of new use-cases in hyper-connected environments. We propose a configurable and flexible architecture for different network topology scenarios. The proposed solution dynamically exploits the presence of hierarchical infrastructure, ad-hoc and/or broadcast mode. We will focus on Industry 4.0 Cyber-Physical Systems (CPS) addressing a broad range of potential applications, with humans either included in or excluded from the control loop using publisher/subscriber message-exchange architecture and ROS (Robot Operating System). Optimized wireless connectivity scenarios will be analyzed, providing latency, throughput and reliability evaluations. We will argue in which situations the conventional non-optimized solutions would suffice over the newly proposed one. We evaluate and compare the efficacy of our solution on a carefully controlled, traffic-shaped wireless testbed consisting of four nodes using a Commercial Off-The-Shelf (COTS) 802.11 hardware. The obtained experimental results validate the feasibility and effectiveness of our proposed solution and demonstrate its advantages over the conventional non-optimized one.
A Distributed Weight-Based TDMA Scheduling Algorithm for Latency Improvement in IoT

The Internet of Things (IoT) and the Wireless Sensor Networks (WSNs) attract attention of the research communities due to their rapid and promising development. These paradigms have been predicted to play a leading role in our daily life in the near future. The limited resources and the restricted number of communication channels in these networks create new challenges especially in collision and access conflicts. To bring appropriate solutions for these problems, several works, in the literature, focus on centralized and distributed approaches. Traditional distributed protocols assume that the MAC and Routing layers act independently. Moreover, the dynamic topology and the central point of failure restrict the development of centralized techniques. In this paper, we present a distributed weight-based TDMA-MAC scheduling algorithm (WB-DTSA). The proposed solution takes advantage of routing information to improve the communication latency and the schedule length. Simulation results show better performance, in comparison to similar distributed channel assignment approaches. The latency is improved by 47% and the schedule length is reduced by 23%.
Exploring a Comprehensive Approach for the Automated Assessment of UML

UML is an important tool in structured software design and is commonly taught in undergraduate software courses. UML defines a complex set of linked notations and mastery requires instruction and examples over many taught courses. Such examples are typically disparate, modelling subsets of distinct systems. Teaching UML benefits from an end-to-end approach where consistency between modelling, design and implementation are equally emphasised and regular feedback is provided. A drawback of this is that assessing learner-derived UML models is a time-intensive and error-prone task if both formative and summative assessment is required. In this paper a novel framework is presented for the automated assessment of UML. The framework allows learners to be provided with automatically generated formative feedback for self-directed learning in the development of UML skills. Emphasis is placed upon the identification of the consistency and coverage of learner diagrams as this is an important skill in the application of UML. By integrating an implementation of this framework, instructor-based UML teaching can be supplemented with an end-to-end tool which allows learners to receive automated formative feedback in their understanding and usage of UML.
Optimization of Torque Ripples in an Interior Permanent Magnet Synchronous Motor Based on the Orthogonal Experimental Method and MIGA and RBF Neural Networks

Interior permanent magnet synchronous motors (IPMSMs) have high power densities and speed control performance, and they are widely used in the industry. The problem of reducing the torque ripple of an IPMSM is one of the hot issues in the field of electrical machine design. In order to determine the optimal combination of the geometric parameters to reduce the torque ripple of an IPMSM, a range analysis was conducted on the data from the orthogonal experiments in this study, dividing the rotor geometric parameters into two categories (important and ordinary) based on their degree of impact on the torque ripples of the IPMSM. Thereafter, an optimization of the ordinary parameters was carried out based on the results of the range analysis, whereas the optimization of the important parameters was carried out through a method that combined a multi-island genetic algorithm (MIGA) and Radial Basis Function (RBF) neural networks. The torque ripple of the IPMSM was effectively reduced without materially affecting the output power. Finally, the results of this optimization process were verified using a finite element simulation. The optimization method used in this study divided the motor geometric parameters into two categories and applied a different method of optimization to each parameter type, so it was able to efficiently optimize multiple geometric parameters for the IPMSM.
Aspect-Based Sentiment Analysis with Adjustments to Irrelevant Sentimental-Related Features

Aspect-based sentiment analysis is a fine-grained task of sentiment classification for multiple aspects in a sentence. While the overall polarity of a sentence has been successfully predicted with neural network architectures, aspect-specific sentiment analysis still faces challenges. In this paper, we propose a novel neural network named IRP-BERT. It is based on BERT which is the newest breakthrough in natural language processing. IRP-BERT aims at improving the accuracy of predicting the right polarity of aspects in a sentence, especially the neutral ones, because some sentimental-related words such as "love" in a noun phrase which is neutrally sentimental could have a wrong impact on the polarity classification to aspects. More specifically, we design a module named Irrelevance-Removing Process (IRP) to counteract the impact from irrelevant sentimental-related features. The final prediction depends on word representations from the outputs of both IRP and BERT. Experiments on a twitter dataset show that our model outperforms both existing state-of-the-art methods and fine-tuned BERT baselines.
Analyzing Tweets to Discover Twitter Users' Mental Health Status by a Word-Frequency Method

Mental Illness is reflected in the changes in isolation or combination of thinking, emotions, and expression of an individual. If an individual contemplates mental illness as any other medical problem, it is addressable within the medical science limits. However, the identification of mental illness in oneself is difficult, and normally, an individual does not accept to be a 'mental illness patient. ' Social networks dominated in one's lifestyle, communication, expression, sharing the views, photos and voice, etc with the present technological advancement. Social Networks further connected people from far off places. Identification of Mental Health Status of an individual became important as his/her behavior is directly impact the social environment. There is a great advantage, if one can identify the Mental Health Status of an individual and find ways to measure it. We have attempted to identify the behavior of individuals through the tweets posted, and a relation between these illness and tweet words is established with our Word-Frequency method. Our analysis correlated well with the SVD method results.
On the simplification of multi-focus image fusion using dictionary-based sparse representation

This paper proposes a fast implementation for multi-focus image fusion using dictionary-based sparse representation. The proposed method reduces the computation complexity of the method in [1] by synthesizing feature signals from the trained sparse coefficient feature vectors for classifying each pixel in a source image as focused or defocused, which help remove the computation of the OMP algorithm [3] in [1]. As a result, the complexity of the proposed method can be only 1/100 of [1]. Simulation results further demonstrate that the fused image of the proposed method has the same quality as that of [1].
Design of a X-band down converter module

This paper introduces a design of X-band down converter module based on microwave hybrid integrated. The module receives microwave signals from 8GHz to 9GHz, and outputs IF signal of 2.43GHz. The module using single frequency conversion scheme, which matches, amplifies and attenuates for input RF signal, then mixed with LO signal by two times, and produces the IF signal. The IF signal is output after filtering, amplifying and attenuation. The module system and key circuits are designed by using Syscalc and Serenade software. The test results indicate that the module has performance of conversion gain $25 \pm 1\ \text{dB}$, noise figure less than 3.8dB, input and output return loss more than 18.5dB, mirror frequency and spurious signal suppression performance all good, all indicators are satisfied with the requirements of the system.