Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of CNNs for Remote Sensing Scene Classification

Nowadays, deep learning are used widely in many applications related to remote sensing i.e. earth observation, urban planning, earth’s scene classification, and so on. The deep learning manner, especially CNNs, has proved its accuracy for these practical applications. Hence, in this article, CNNs models are reviewed and its five different architectures are applied for comparisons; namely, AlexNet, VGGNet, GoogleNet, Inception-V3, and ResNet-101. These models are carried out on seven different remote-sensing image datasets for image scene classification purpose; namely, WHU-RS19, UC-Merced Land Use, SIRI-WHU, RSSCN7, AID, PatternNet, and NWPU-RESISC45. These datasets have different spatial resolutions, ranging from 0.2 to 30, to differentiate the classification accuracy of the low and high resolution images. As well, the classification accuracy of each model is assessed by trying five different classifiers; namely, Naïve Bayes, Decision Tree, Random Forest, K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). The best accuracy credits to ResNet-101 model with SVM classifier; it has reached about 98.6±0.02 % of the high resolution dataset, PatternNet.
MISC Array: A New Sparse Array Design Achieving Increased Degrees of Freedom and Reduced Mutual Coupling Effect

Recently, nested and coprime arrays have attracted considerable interest due to their capability of providing increased array aperture, enhanced degrees of freedom (DOFs), and reduced mutual coupling effect compared to uniform linear arrays (ULAs). These features are critical to improving the performance of direction-of-arrival estimation and adaptive beamforming. In this paper, a new sparse array configuration based on the maximum inter-element spacing constraint (MISC) is proposed. The MISC array configuration generally consists of three sparse ULAs plus two separate sensors that are appropriately placed. The MISC array configurations are designed based on the inter-element spacing set, which, for a given number of sensors, is uniquely determined by a closed-form expression. We also derive closed-form expressions for the number of uniform DOFs of the MISC arrays with any number of sensors. Compared with the existing sparse arrays, the MISC array enjoys two important advantages, namely, providing a higher number of DOFs and reducing the mutual coupling effects. Numerical simulations are conducted to verify the superiority of the MISC array over other sparse arrays.
Program for Finite Element Based Design of Rotor Permanent Magnet Flux Switching Machine

Electrical machine design today is faced with greater challenges due to development of new electrical machine topologies, new types of winding layouts and new materials. While the use of well established empirical based methods is limited to design of conventional machines, most new types of electrical machines are designed by using Computer-Aided-Design tools (CAD). With limited availability and performance of appropriate commercial tools, development of novel machine topologies largely depends on custom made programs. In this paper, the methodology for finite element (FE) design of RPMFS (Rotor Permanent Magnet Flux Switching) machines is presented. Based on the proposed methodology, a program is developed using the Octave FEMM (Finite Element Method Magnetics) toolbox. The program enables fast and efficient simulation and performance assessment of various RPMFS designs with different inputs, such as core dimensions, number of slots, poles and even phases, while the appropriate winding layout is automatically assembled based on these inputs.
Audit of insulin prescription patterns and associated burden among diabetics in a tertiary health institution in Nigeria

Background Insulin is one of the most important anti-diabetic agents in the management of diabetes even among type 2 diabetic. Objective There was need to assess insulin adherence, mode of insulin delivery and burden of insulin usage among diabetics. Methods A cross-sectional, prospective questionnaire, orally administered at a Diabetes Clinic of a University Teaching Hospital, SouthWest, Nigeria. Participants were consecutive patients with diabetes who were 18 years or older presently on insulin either alone or in combination with other anti-diabetic agents for at least 3 months. Baseline demographic and insulin treatment information were obtained. Results Two hundred and thirteen (213) participants were studied. Of these, 21 (9.9%) had T1DM and 192 (90.1%) had T2DM, (means age, 58.6 ± 13.1 years, mean duration of diabetes, 7.0 ± 6.9 years). Insulin adherence was noted in 72.8% with better adherence among those who self-injected insulin compared to those who were injected by health care professionals (HCPs) or relations. Among the respondents, 80.8% were on human insulin and pre-mixed insulin was the most commonly used form of insulin (52.6%). Most participants (52.6%) were taking 10–20 units per day, only 22 (10.3%) were on >40units/day. Reuse of insulin needle was found in 74.6% of the participants. Major reasons for insulin omission were non-availability of insulin and patients being tired of insulin injection. Conclusion The insulin adherence among diabetics in this study was high. Non-availability of insulin, insulin injection pain and being tired of continual insulin usage were some of the reasons for non-adherent to insulin usage.
Production scheduling with ant colony optimization

The optimum solution of the production scheduling problem for manufacturing processes at an enterprise is crucial as it allows one to obtain the required amount of production within a specified time frame. Optimum production schedule can be found using a variety of optimization algorithms or scheduling algorithms. Ant colony optimization is one of well-known techniques to solve the global multi-objective optimization problem. In the article, the authors present a solution of the production scheduling problem by means of an ant colony optimization algorithm. A case study of the algorithm efficiency estimated against some others production scheduling algorithms is presented. Advantages of the ant colony optimization algorithm and its beneficial effect on the manufacturing process are provided.
A High-Efficiency Fully Convolutional Networks for Pixel-Wise Surface Defect Detection

In this paper, we propose a highly efficient deep learning-based method for pixel-wise surface defect segmentation algorithm in machine vision. Our method is composed of a segmentation stage (stage 1), a detection stage (stage 2), and a matting stage (stage 3). In the segmentation stage, a lightweight fully convolutional network (FCN) is employed to make a pixel-wise prediction of the defect areas. Those predicted defect areas act as the initialization of stage 2, guiding the process of detection to correct the improper segmentation. In the matting stage, a guided filter is utilized to refine the contour of the defect area to reflect the real abnormal region. Besides that, aiming to achieve the tradeoff between efficiency and accuracy, and simultaneously we use depthwise&pointwise convolution layer, strided depthwise convolution layer, and upsample depthwise convolution layer to replace the standard convolution layer, pooling layer, and deconvolution layer, respectively. We validate our findings by analyzing the performance obtained on the dataset of DAGM 2007.
Mediators of Inflammation in Topical Therapy of Skin Cancers

Taking into consideration that the immune system plays a very important role in the development of melanoma and non-melanoma skin cancers, which have a high prevalence in immunosuppressed patients and after prolonged ultraviolet radiation, the interest in developing novel therapies, in particular targeting the inflammation in cancer, has increased in the past years. The latest data suggest that therapies such as imiquimod (IMQ), ingenol mebutate (IM), 5-fluorouracil (5-FU), retinoids, and nonsteroidal anti-inflammatory drugs (NSAIDs) have been used with success in the topical treatment of some cancers. Herein, we review the topical treatment targeting the inflammation in skin cancer and the mechanisms involved in these processes. Currently, various associations have shown a superior success rate than monotherapy, such as systemic acitretin and topical IMQ, topical 5-FU with tretinoin cream, or IMQ with checkpoint inhibitor cytotoxic T lymphocyte antigen 4. Novel therapies targeting Toll-like receptor-7 (TLR-7) with higher selectivity than IMQ are also of great interest.
Performance of Adaptive Link Selection with Buffer-Aided Denoise-and-Forward Relays

In this paper, we study a two-way relaying network consisting of two source nodes and one half-duplex denoise-and-forward (DNF) relay node, where the relay node equips a buffer. A buffer-aided relaying scheme is proposed to employ adaptive link selection to improve the network throughput. In the proposed scheme, two cases considering the instantaneous signal-to-noise ratios (SNRs) of two source-to-relay links are introduced and analysed. Both of the finite and infinite size buffers are investigated. Simulation results show that our proposed buffer-aided scheme can significantly improve the throughput performance with the traditional non-buffer DNF two-way relaying network.
Exponential Synchronization of Neural Networks With Time-Varying Delays via Dynamic Intermittent Output Feedback Control

This paper addresses the exponential synchronization problem for neural networks with time-varying delays. First, a novel controller is presented by combining intermittent control with dynamic output feedback control. Next, a sufficient criterion is established based on the Lyapunov-Krasovskii functional approach and the lower bound lemma for reciprocally convex technique to ensure exponential stability of the resultant closed-loop system. Then, some solvable conditions of the proposed control problem are derived in terms of linear matrix inequalities. Notably, our results here extend the existing ones to the relaxed case because the derivative of time-varying delays is now an arbitrary bounded real number. Finally, a numerical simulation is provided to demonstrate the effectiveness of the proposed method.
Handwritten Signature Verification using Haar Cascade Classifier Approach

Handwritten Signature verification is an important personal identification. It is widely used in authorizing a cheque or legal documents. Signature verification is either online (dynamic) or offline (static). A machine learning, Haar Cascade Classifier (HCC) approach was introduced by Viola and Jones [1] to achieve rapid object detection based on a boosted cascade of Haar-like features. Here, for the first time the HCC approach was applied for the handwritten signature recognition and verification. Two datasets were used, UTSig dataset [2] for Persian writers which written from right to left and includes 8,280 images from 115 writers. GPDS synthetic Signature database [3] for English writers which written from left to right. It contains data from 4,000 synthetic individuals. A classifier was created for each one of the writers’ signatures after applying the preprocessing phase. Each classifier was trained and tested using enormous number of signatures generated from applying artificial noises on the signature images. The system was tested with real signature images and produced accuracy of 92.42% with UTSig dataset and 92% with GPDS dataset. The proposed approach achieved high results compared to approaches built using traditional machine learning approaches. The approach proof applicability with the different types of writers’ languages and style.
Intelligent MU-MIMO User Selection With Dynamic Link Adaptation in IEEE 802.11ax

IEEE 802.11ax high-throughput wireless access networks support multi-user multiple-input multiple-output (MU-MIMO)-based communication, where a set of spatially apart wireless stations forms a user group and uses different spatial streams for simultaneous transmission and reception. In this architecture, dynamic user group selection is an important aspect for maintaining high-throughput fair channel access. In addition, the physical and media access control parameters, like channel bonding levels, modulation, and coding schemes need to be tuned based on the selected user group to utilize the maximum available capacity. In this paper, we design an online learning-based approach over a centralized logical control architecture, called intelligent MU-MIMO user selection with link adaptation (IMMULA), where a central controller collects the performance statistics under various configuration space and applies a reinforcement learning strategy to select the best-suited configurations dynamically at periodic intervals. The performance of IMMULA is analyzed over a testbed consisting of 6 IEEE 802.11ac access points and 20 wireless stations. The results show that IMMULA improves network performances significantly compared to other baseline mechanisms.
Terrain‐Enhanced Precipitation Processes Above the Melting Layer: Results From OLYMPEX

Enhancement of precipitation processes aloft over complex terrain is documented using reflectivity data from an S‐band scanning radar (NPOL) that was deployed on the west coast of Washington State during the Olympic Mountains Experiment (OLYMPEX). From November 2015 through mid‐January 2016, NPOL obtained high‐resolution data within sectors over the ocean and over the windward slopes of the Olympic Mountains. Contoured Frequency by Altitude Diagrams of radar reflectivity highlight a higher frequency of occurrence of larger reflectivities for all heights between 2 and 8 km over land compared to ocean, with the largest difference in the 4‐ to 6‐km range indicating a robust signature of enhancement aloft over the windward slopes. This enhancement pattern is found to some degree under all environmental conditions considered but is especially pronounced during periods of high vapor transport, high melting level height, southwest low‐level winds, and neutral stability. These conditions are generally associated with warm sectors of midlatitude cyclones and atmospheric rivers. Past studies have postulated that a secondary enhancement in reflectivity aloft was an intrinsic part of atmospheric river type systems. However, these results show that further significant enhancement of this signature occurs as deep moist‐neutral, high water vapor content flow is lifted when it encounters a mountain range. Reflectivity data from the dual‐precipitation radar aboard the Global Precipitation Measurement satellite also documents this reflectivity increase aloft over the Olympic Mountains compared to the adjacent ocean, showing the potential for Global Precipitation Measurement to provide reliable estimates of precipitation structure over remote mountainous regions.
Functional Observer Design for Time-Delayed Systems With Application to Fault Diagnosis

This paper deals with the functional observer (FO)-based fault detection and isolation problem for time-delayed linear systems. First, a new method to design the low-order FO estimating the partial states for linear systems with time-varying delay is presented. Conditions for the existence of the proposed observer and the stability of resulting error dynamics are derived. Based on the proposed observer, a novel residual generator is constructed to detect actuator faults. Furthermore, a bank of residual generators for fault isolation is also constructed for time-delayed systems. The simulation results of the numerical example demonstrate that the proposed observer-based fault diagnosis scheme works effectively.
Method of intonation conversion for facilitating English communication between native and non-native speakers

With the growth of the Internet and globalization, hardships in English speech communication became more serious for non-native speakers. It would be desirable to develop a speech communication device which can convert English speech into a more intelligible one for listeners. In this study, English speech was hypothesized to be more intelligible to non-native listeners by being modified into the listeners’ native language speaking style. First, the characteristics of English between native and Japanese speeches were statistically analyzed. The analysis determined the modification model marked modification of intonation of native speakers’ speech so as to include Japanese characteristics. The hypothesis was realized by speech modification generated by a speech analysis, modification and synthesis system, STRAIGHT. Then, the modified speeches were evaluated on the basis of listening experiments involving Japanese listeners. The experiments consisted of dictation tests and preference tests, and resulted in an increase in the scores. The results, therefore, verified the hypothesis. This also revealed the potential feasibility of the speech conversion method which facilitates spoken English communication.
Location-Based Online Task Assignment and Path Planning for Mobile Crowdsensing

Mobile crowdsensing has been a promising and cost-effective sensing paradigm for the Internet of Things by exploiting the proliferation and prevalence of sensor-rich mobile devices held/worn by mobile users. In this paper, we study the task assignment and path planning problem in mobile crowdsensing, which aims to maximize total task quality under constraints of user travel distance budgets. We first formulate the problem mathematically when all task and user arrival information is known a priori and prove it to be NP-hard. Then, we focus on studying the scenarios where users and tasks arrive dynamically and accordingly design four online task assignment algorithms, including quality/progress-based algorithm (QPA), task-density-based algorithm (TDA), travel-distance-balance-based algorithm (DBA), and bio-inspired travel-distance-balance-based algorithm (B-DBA). All the four algorithms work online for task assignment upon arrival of a new user. The former three algorithms work in a greedy manner for assigning tasks, one task each time, where the QPA prefers the task leading to largest ratio of task quality increment to travel cost, the TDA tends to guide user to high-task-density areas, and the DBA further considers travel distance balance information. The last algorithm B-DBA integrates the travel-distance-balance-aware metric in the DBA and bio-inspired search for further improved task assignment performance. Complexities of the proposed algorithms are deduced. Simulation results validate the effectiveness of our algorithms; B-DBA has the best performance among the four algorithms in terms of task quality, and furthermore, it outperforms existing work in this area.
Tactile symbols for visually impaired older adults

This work investigates the performance of older adults in tasks related to recognition of tactile symbols. We conducted experiments to evaluate different tactile symbols with varying elevation, shape form (outline, fill), and symbol preference in general. Experiments were performed with the general population of visually impaired (N = 6, age mean = 46.3) and with visually impaired older adults (N = 9, age mean = 76.8). We used both qualitative and quantitative methods to evaluate the experiments. The results indicate that even older adults are capable of distinguishing between six different tactile symbols. Participates frequently failed to connect meanings of abstract symbols with functions of a hypothetical orientation system terminal.
Acoustic Scene Classification Using Joint Time-Frequency Image-Based Feature Representations

The classification of acoustic scenes is important in emerging applications such as automatic audio surveillance, machine listening and multimedia content analysis. In this paper, we present an approach for acoustic scene classification by using joint time-frequency image-based feature representations. In acoustic scene classification, joint time-frequency representation (TFR) is shown to better represent important information across a wide range of low and middle frequencies in the audio signal. The audio signal is converted to Constant-Q Transform (CQT) and Mel-spectrum TFRs and local binary patterns (LBP) are used to extract the features from these TFRs. To ensure localized spectral information is not lost, the TFRs are divided into a number of zones. Then, we perform score level fusion to further improve the classification performance accuracy. Our technique achieves a competitive performance with a classification accuracy of 83.4% on the DCASE 2016 development dataset compared to the existing current state of the art.
A Novel Dynamic Performance Analysis and Evaluation Model of Series-Parallel Connected Battery Pack for Electric Vehicles

Due to the low voltage and small capacity of a Li-ion battery cell, large numbers of cells are connected to construct a battery pack to satisfy the voltage and capacity requirement of the power system of an electric vehicle. Focusing on parallel and series connection mode of battery packs, the main contributions include the following. First, in order to increase the utilization rate of cells and enhance the performance of the battery pack, a method that makes the battery pack achieve their maximum initial capacity has been proposed. Second, a dynamic modeling and analysis method for the battery pack based on the equivalent circuit model has also been proposed. The results show that the battery pack in parallel and then in series has a better performance on charge/discharge capacity, efficiency, and utilization rate of cells. Third, the performance analysis and evaluation platform of the battery pack has been built, and the battery pack test results of different groups supported the conclusion of the simulation. The battery packs in parallel and then in series can achieve superior performance and greatly increase capacity and energy utilization.
Investigating Timing and Impact of News on the Stock Market

Predicting stock market movements is an interesting and challenging problem: researchers and traders have approached this task with different techniques, from time series prediction to technical and fundamental analysis. Nowadays, a huge amount of textual data can be used to lead a new source of information on this task, well known to be highly stochastic and temporal dependent. In this paper, we investigate the problem of analyzing the timing and the impact that news have on the stock market. We focus on two different important aspects: the possible contribution that new information can have on the stock price and its possible relation with the aggregate news recently published. Our approach involves using the information available now to predict different prices movements, from the recent past to the far future. Results on US market show that the information contained in news can be used as an accurate predictor for past price movements. When the future is considered, however, the correlation between news and stock market becomes less clear.
Antenna characteristics over perfect ground

In a radio link two identical antennas are used each one performing a different role. The directivity or gain is the natural parameter of interest for the transmitting antenna. Whereas the effective area characterized the antenna in the receiving role. Power transmission loss (Friis Equation) is only valid in free space as a general belief but in this paper it is proven that this principle is valid also for radio links over perfect ground. This statement permits the exclusion of any artificial factor to achieve the eadio link power budget. This fact takes into account the lack of loasds in the natural space between both antennas. This coecepe ise very important for antenna factor measurements or calculatioap nocsp-sary for electromagnetic compatibility (EMC) activities.