Miracle-dz/UniFAR-FTUs huggingface dataset:
A CMOS Image Sensor With Thermal Sensing Capability and Column Zoom ADCs

This paper presents a CMOS image sensor (CIS) with a zoom ADC, to quantize in-pixel temperature sensors, as well as for faster readout speed of the image pixels while maintaining low quantization noise. The proposed 15 bit zoom ADC has a 4 bit Unit Capacitor Array (UCA) SAR and a 13 bit incremental 2nd-order delta-sigma ADC (DSADC), as its first and second stage, respectively. The proposed UCA with improved switching and decoding technique minimizes capacitor area and switching energy, by 50 % and 75 %, respectively, compared to a conventional binary weight array (BWA) counterpart. Measurement results on 4 chips show the proposed zoom ADC could operate at least twice as fast, when maintaining the same signal-to-noise ratio (SNR), or improve its SNR by 9 dB, when maintaining its sampling speed, compared to a DSADC only alternative. The proposed 15 bit ADC is measured a SNR of 80.1 dB and INL and DNL within ±1.5 LSB and ±1 LSB (full scale voltage is 1 Vp-p), when operating at 31 kHz. The incorporated imager-based temperature sensors are measured to have inaccuracies within ±0.6 °C on 4 chips, between −20 and 80 °C, when quantized by the same zoom ADC.
Customer Relationship Management (CRM) Implementation Evaluation Using Maturity Assessment in Telecommunication Industry: Case Study of an Indonesian Company

In the telecommunication service provider industry that has service similarity among competitors, Customer Relationship Management (CRM) plays an important role in business operational and analytical activities to understand customer needs comprehensively so the company can provide excellence customer service. The role of CRM become more important nowadays when conventional telecommunication company should transform into digital telecommunication company. CRM development strategies need to be planned so company can take the right steps in implementing CRM. The purpose of this paper is to give understanding on establishing CRM Change Management Strategy using proposed CRM Maturity Model (CRM3) assessment based on Detecon Model and evaluated using eTOM (Enhanced Telecom Operational Map). The assessment result provides a detailed description of function maturity in accordance with the best practice of telecommunication business process framework which can be mapped into the organization level. This case study of an Indonesian Telecommunication show that the implementation of CRM has maturity level of 3.67 (Established) on a scale of 5 and give 12 recommendations.
Cascaded Convolutional Neural Network with Attention Mechanism for Mobile EEG-based Driver Drowsiness Detection System

The road accidents are a common cause to the injury and death of people. As reported by The American National Highway Traffic Safety Administration (NHTSA), the drivers drowsiness accounts for nearly 100,000 accidents per year in the United States. Thus, we present a novel drivers drowsiness detection system in this paper using the techniques of deep learning(DL), mobile computing, wearable device and Electroencephalography (EEG). We employ the deep learning architecture designed by ourselves that can be easily implemented on the mobile phone to detect the drowsiness with a high accuracy. The EEG signal we use is only single channel that can be easily obtained by the wearable device. The EEG signal collector is designed and made by ourselves, which is like a hair band that makes the driver easier and more comfortable to wear it. The whole system mainly consists of two parts: one is the hardware consisting of EEG headband and sensor, the other is software consisting of Android application and web platform. The app contains the fine trained model to make real-time prediction based on the EEG signal and alert the driver, while sending the data to the backend synchronously. The web platform provides an interface for the monitor to observe the condition of the driver. Our system achieved an accuracy of 97.09% detecting the drivers drowsiness, which surpasses the SOTA methods. The model's size and predict latency are also within a smaller scale than present models that make it more applicable to mobile and embedded system.
A Novel Framework for Building Extraction with Dual-path Convolution Network

Building extraction from aerial images has always been a hot research topic in the field of remote sensing image analysis, which is benefit to a wide range of areas. In this letter, a novel framework, Dual-path Convolution Network (D-CN), is proposed for building extraction, which combines different contextual information. The benefits of this framework are twofold: first, the utilization of different area image will increase the confidence of the prediction area greatly. Second, the small size of the predicted feature map will reduce training time, accelerate convergence, but get better performance. Then, we test various variations of D-CN on a public building data set and compare it with Unet, the proposed method can save much training time but get almost the same or better accuracy.
DL-RANSAC: An Improved RANSAC with Modified Sampling Strategy Based on the Likelihood

This paper intends to improve the RANSAC by introducing new sampling method which provides prior knowledge of random population before selecting the hypothesis set. In traditional RANSAC algorithm, a minimal set of samples are chosen randomly from the population containing uneven noise and iteration continues until the desired model is found. Ambiguity remains to find the desired result within a short time because of the random sampling technique. The proposed method, DL-RANSAC (Descendant Likelihood sampling RANSAC), reduces the randomness by introducing descending likelihood based minimal set selection which converges to the desired result faster than the conventional RANSAC. Experiments shows the superiority of this proposed method in line fitting problem, correspondence points matching between a pair of images and loop closing of ORB-SLAM2. Less computational time and easy implementation ability make it beneficial to use over other methods.
HNEDTI: Prediction of drug-target interaction based on heterogeneous network embedding

Identifying drug-target interactions (DTIs) is an important task in drug discovery. Various computational models have been proposed to predict potential association between drugs and targets. However, it is still a great challenge to accurately predict the potential drug-target interactions with rare known drug-target interactions. In this work, we propose a heterogeneous network embedding model to predict drug-target interactions, called HNEDTI. Based on the assumption that similar drugs share similar patterns of relationships with target proteins, we integrate the drug-drug similarity network, target-target similarity network and known drug-target interactions into a heterogeneous network. HNEDTI can learn more accurate feature representation of drugs and targets by extract both local and global information of the heterogeneous network from different lengths of meta-paths. The low dimensional feature representation vectors of drugs and targets are applied to random forest model to predict whether the given drug-target pair has an interaction. The evaluation on four benchmark datasets (Enzyme, Ion Channel, GPCR and Nuclear Receptor) shows that our method HNEDTI outperforms the previous methods.
A clinical trial of 5-fluorouracil combined with low molecular weight Heparin for the prevention from proliferative vitreoretinopathy

AIM: To investigate the safety and efficacy of adjuvant combination therapy using 5-fluorouracil(5-FU) and low molecular weight heparin(LMWH) for prevention of proliferative vitreoretinopathy(PVR) after vitrectomy and retinal reattachment surgery.METHODS: In 132 high-risk patients undergoing either 5-FU and LMWH therapy or placebo,66 patients were assigned(to the) treatment group and 66 to the placebo group.Results of(standard) surgery with 5-FU and LMWH therapy or placebo were compared at the 6-month follow-up.Main outcome assessed were the rate of recurrent retinal detachment at month 3 and 6 after(surgery),development of postoperative PVR,best-corrected visual acuity and electroretinogram(ERG). RESULTS: At month 3,6.1%(4/66) of the 5-FU and LMWH group and 18.2%(12/66) of the placebo group had recurrent retinal detachment,then at month 6,7.6%(5/66) and 19.7%(13/66) had recurrent retinal detachment.There were significant differences in the primary outcome measure(P0.05). At month 3 in 7.6%(5/66) of the 5-FU and LMWH group and in 22.7%(15/66) of the placebo group (P0.05),then at month 6 in 10.6%(7/66) of the 5-FU and LMWH group and in 28.8%(19/66) of the(placebo) group(P0.05),postoperative PVR developed.The postoperative changes in visual acuity and electroretinogram were statistically significant in the 5-FU and LMWH therapy and the placebo group compared with preoperative status(P0.05). There were no differences in visual acuity and electroretinogram between the 2 groups(P0.05). The rates of recurrent retinal detachment and postoperative PVR at month 3 were not statis-(tically) different as compared with the rates at month 6(P(0.05)). CONCLUSION: There is a significant reduction in the(incidence) of postoperative PVR in patients receiving the 5-FU and LMWH therapy.This trial shows the combination of 5-FU and LMWH can effective prevent the postoperative recurrent PVR.
On the Ergodic Secrecy Rate of Massive MIMO Transmission with Partial Legitimate User CSI

In this paper, we consider the downlink transmission over a single-cell massive multiple-input-multiple-output (MIMO) system in the presence of multiple single-antenna eavesdroppers (massive MIMOME). We concentrate on the practical scenario where partial channel state information (CSI) of legitimate users and no CSI of eavesdroppers are available at the base station and consider both types of eavesdroppers including the non-colluding and colluding eavesdroppers. Random unitary beamforming (RUB) based scheme is used to describe the partial CSI of legitimate users. We derive the closed-form expression of ergodic secrecy rate for RUB based massive MIMOME transmission, and its single legitimate user particular case. We also present numerical results to illustrate the performance-complexity tradeoff among different massive MIMO transmission schemes. We show that RUB based scheme can enhance secrecy performance of massive MIMO transmission with lower implementation complexity.
Factors Affecting Network-Based Gene Prediction Across Diverse Diseases

There are many current efforts to integrate biological interaction data with disease information in order to predict new genes associated with complex diseases. Network-based learning methods such as logistic regression can utilize this information to identify disease genes, and are typically applied to protein-protein interaction networks. However, little is reported about what factors influence the performance of these network-based methods. Here, we explore features that affect network-based disease gene prediction performance. We devise two cross-validation schemes to evaluate the impact of various parameters, settings and disease qualities across a wide range of diseases. We demonstrate that including gene regulatory interactions and including low-confidence disease genes improves disease gene prediction performance. Further, network connectivity among high-confidence disease genes is a strong indicator of prediction performance. We demonstrate that network and input features can have a dramatic effect on prediction performance, and these should be carefully considered when designing network-based algorithms to find new disease genes.
Reduction of Current Collapse in GaN (MIS)-HEMTs Using Dual Material Gate

GaN high electron-mobility transistors suffer from various defects or trap states present either in AlGaN barrier or GaN buffer layer. This work presents a different approach to reducing the current-collapse effects in GaN-based transistors by using a dual material gate technology, whereby two different materials having different work functions are merged together to form a single gate, resulting in an improvement of current collapse of around 50%. The current transport efficiency also improves, thus offering improved transconductance. The presence of two gate materials of different work-function modifies the peak electric-field at the drain end, reducing current-collapse and dynamic-Rds,ON degradation.
A Low-Profile Dual-Band Dual-Polarized Antenna With an AMC Reflector for 5G Communications

A low-profile dual-band dual-polarized antenna with an artificial magnetic conductor (AMC) reflector is proposed for 5G communications. The antenna consists of a pair of crossed dual-polarized dual-band bowtie dipoles and a dual-band AMC reflector. By introducing trapezoidal slots and U-shaped slots on the bowtie dipoles, miniaturization and dual-band characteristics are achieved. Moreover, T-shaped feeding structures are utilized to broaden the bandwidth of the bowtie dipoles. By adopting a dual-band AMC reflector instead of a conventional perfect electric conductor (PEC) reflector, the distance between the radiator and the reflector can be reduced from <inline-formula> <tex-math notation="LaTeX">$0.25\lambda _{0}$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$0.08\lambda _{0}$ </tex-math></inline-formula> (where <inline-formula> <tex-math notation="LaTeX">$\lambda _{0}$ </tex-math></inline-formula> is the free-space wavelength at 3.5 GHz). The radiator can maintain the impedance bandwidth and a high gain can also be achieved, even if it is close to the AMC reflector. A geometrical optics model is used to explain the mechanism of the AMC. The optimal parameters of the AMC depend on the antenna operating frequency and the distance between the radiator and the reflector. Measurements show that the proposed dual-band antenna has an impedance bandwidth of 19.8% (3.14-3.83 GHz) and 13.2% (4.40-5.02 GHz), covering the sub-6 GHz frequency spectra of 5G mobile communications. The peak gain is 7.1 dBi in the lower band and 8.2 dBi in the upper band. Port isolation better than 20 dB is achieved. The proposed antenna can be used alone for 5G indoor base station, or as the element of an array for 5G outdoor base station.
Win-win Cooperation: A Novel Dual-Modal Dual-Label Algorithm for Membrane Proteins Function Pre-screen

Integral membrane proteins (MPs) make up a large proportion of the genomes of many organisms and the amount of sequenced proteins is growing at an unprecedented pace. As a result, MPs function pre-screen is of great necessity. To be functional, MPs must be expressed and localized through a series of elaborate sub-cellular processes. It is worth noting that subtle changes in sequence may lead to drastic changes in expression and localization. In light of the above observation, taking the complementary information of sequence-based proteins into consideration, a novel boosting Dual-Modal Dual-Label (DMDL) algorithm with hypothesis reuse is proposed. On the one hand, DMDL treats sequence features and structure features as dual-modal. On the other hand, DMDL considers eukaryotic expression and plasma membrane localization as dual-label, which would be of great value as a pre-screen for MPs function. Meanwhile, two label sets interact, which can not only make the best use of two modal features, but also could effectively exploit the relationship between two label sets. An assessment of real-world channelrhodopsins (ChRs) chimeras clearly validate the effectiveness of DMDL algorithm compared with state-of-the-art algorithms. In addition, extensive experiments are performed on adapted public datasets, showing effeceiveness of hypothesis reuse mechanism in DMDL.
Generalized Association and Deep Understanding Method for Big Video Data Processing

In Big Data Era, it is becoming more and more important to timely and efficient processing of massive videos, and mining of the value information contained in them. This paper studies chaotic compressed sensing theory and uses Long-term and short-term memory networks (LSTM) algorithm to generate a summary of the content of video behavior track. It has already to apply the proposed method in target recognition and tracking, and the recognition rate achieves more than 86%.
A Review on Reduced Switch Count Multilevel Inverter Topologies

Multilevel Inverters (MLIs) are becoming more and more popular in medium and high power applications. This is due to several inherent advantages of MLI over two-level inverters such as high-quality output, lower device ratings, and several others. While the classical topologies are still having applications in most of the key areas, there is a growing interest in newer multilevel topologies with an objective of reducing power semiconductor device count, gate drivers and/or isolated DC sources. In this paper, a comprehensive review of some of the recently proposed newer multilevel inverter topologies with the abovementioned objectives is presented. In this article, a detailed investigation in terms of total power semiconductor switch count, number of DC sources, passive component requirement, highest switch voltage rating, total standing voltage etc. has been presented.
Approaches of Detecting and Analyzing the Thyroid Disease: A Review

Speech signal processing is one of the most interesting areas to work with. Speech signal consists of scads of information like gender categorization, various voice features, emotion characteristics etc. Every speaker and speech has its own special signal characteristics. This review is a proposed approach which is an analysis of various methods for detecting the thyroid diseases. Basically, thyroid is butterfly-shaped gland present in the lower anterior of the neck. This controls the metabolism of the body. Different methods used for detecting thyroid disease differentiating into two ways i.e. Biomedical Analysis and Signal Processing. In this review, diverse bio-medical methods like voice self-perceptual assessment, statistical analysis, complication analysis and surgical analysis has been explained. In signal processing method, CAD (Computer Aided Diagnosis) and Acoustic Voice Analysis approaches are found. The main approach of this review is to illustrate the various methods and approaches available to detect thyroid disorders.
Client Side Channel State Information Estimation for MIMO Communication

Multiple-input multiple-output (MIMO) system relies on a feedback signal which holds channel state information (CSI) from receiver to the transmitter to do pre-coding for achieving better performance. However, sending CSI feedback at each time stamp for long duration is an overhead in the communication system. We introduce a deep reinforcement learning based channel estimation at receiver end for single user MIMO communication without CSI feedback. In this paper we propose to train the receiver with known pilot signals to analyse the stochastic behaviour of the wireless channel. The simulation on MIMO channel with additive white Gaussian noise (AWGN) shows that our proposed method can learn the different characteristics affecting the channel with limited number of pilot signals. Extensive experiments show that the proposed method was able to outperform the existing state-of-the-art end to end reinforcement learning method. The results demonstrate that the proposed method learns and predicts the stochastic time varying channel characteristic accurately at receiver's end.
Two-dimensional DOA Estimation for Coherently Distributed Noncircular Sources Using Rank Reduction Estimator

In this paper, we propose a direction-finding algorithm for coherently distributed noncircular sources using two parallel linear arrays. First, we establish the relation between the generalized array manifolds of the two linear sub-arrays. Secondly, taking advantage of non-circularity properties of noncircular signal, we use the receiving data and it’s conjugate to extend observation vector. Thirdly, the theory of rank reduction estimator (RARE) is used to estimate central direction of arrival (DOA) and non-circularity phase. The algorithm has good performance in low signal-to-noise ratio (SNR). Moreover, the sensor arrays are extended to estimate more DOAs. Computer simulations are shown to verify the efficacy of the proposed algorithm. It can also estimate the 2D DOA independently of deterministic angular signal distribution function.
A Review of Deep Learning in Computer-Aided Drug Design

Recently, Deep Learning has been applied to many medical domains, such as medical image analysis, bioinformatic, biochemistry, drug design, and so forth, to improve the performance that is superior to traditional computational approaches; especially in computer-aided drug design. Many AI-driven drug discovery startups have utilized deep learning methodology to achieve the significant improvement of searching candidate compounds, predicting functions, and so forth. Therefore, using AI to facilitate drug design is the trend in the coming future. In this study, a comprehensive review of the current state-of-the-art in Computer-Aided Drug Design using deep learning methods is presented. Meanwhile, the challenges and potential of these methods are also highlighted
Light-Induced Self-Written Optical Waveguide Fabrication by Near Infrared Continuous Wave Laser Light with Microwatt Power

We have successfully demonstrated light-induced self-written (LISW) waveguides at a wavelength of 1070 nm and 1310 nm, respectively. Microwatt level photopolymerization threshold value was obtained at both wavelengths. The results show a promising approach for optical interconnection in silicon photonics.
Cross-tier interference mitigation in a cellular system with small moving cells

In this paper, we consider the management of cross-tier interference in a HetNet cellular system employing small moving cells (SMCs). We manage the interference by using statistical channel information taking into consideration of SMC mobility. We represent the cross-tier interference channel in terms of dominant channel space and then determine the beam weight to minimize the effect of interference in the dominant channel space. Finally, the performance of the proposed scheme is verified by computer simulation.
Potential predictors of type-2 diabetes risk: machine learning, synthetic data and wearable health devices

The aim of a recent research project was the investigation of the mechanisms involved in the onset of type 2 diabetes in the absence of familiarity. This has led to the development of a computational model that recapitulates the aetiology of the disease and simulates the immunological and metabolic alterations linked to type-2 diabetes subjected to clinical, physiological, and behavioural features of prototypical human individuals. We analysed the time course of 46,170 virtual subjects, experiencing different lifestyle conditions. We then set up a statistical model able to recapitulate the simulated outcomes. The resulting machine learning model adequately predicts the synthetic dataset and can, therefore, be used as a computationally-cheaper version of the detailed mathematical model, ready to be implemented on mobile devices to allow self-assessment by informed and aware individuals. The computational model used to generate the dataset of this work is available as a web-service at the following address: http://kraken.iac. rm.cnr. it/T2DM.
Twin Robot Cooperation in Multi-Robot Environment: An Applied Q-learning

This paper provides a new approach of executing the twin robot operation with the application of classical Q-learning and improved Q-learning algorithm. This approach has significantly less space and time requirement for execution of the algorithm As only the best Q-value with matched actions are stored for each state, the space requirement is reduced. Similarly, only the best matched state-action pairs with best Q-value are executed, it requires less amount of time for execution. On application of the classical and improved Q-0learning in twin robot operation, the proposed algorithm takes the supremacy with respect to the space requirement and the traversal time.