Miracle-dz/UniFAR-FTUs huggingface dataset:
Coset Probability Based Majority-logic Decoding for Non-binary LDPC Codes

This paper presents a majority-logic decoding (MLgD) algorithm for non-binary LDPC codes based on a novel expansion of the Tanner graph. The expansion introduced converts the Q-ary graph into a binary one, which makes the new MLgD algorithm more attractive for hardware implementations. Proposed algorithm performs significantly better than the existing MLgD algorithms in the waterfall region, and it shows a much lower error-floor as well. Algorithm only requires integer additions, comparisons, finite field operations and some binary operations. Thus, it offers an effective trade-off between performance and complexity in decoding non-binary LDPC codes.
Computer-Aided Diagnosis of Chronic Kidney Disease in Developing Countries: A Comparative Analysis of Machine Learning Techniques

The high incidence and prevalence of chronic kidney disease (CKD), often caused by late diagnoses, is a critical public health problem, especially in developing countries such as Brazil. CKD treatment therapies, such as dialysis and kidney transplantation, increase the morbidity and mortality rates, besides the public health costs. This study analyses the usage of machine learning techniques to assist in the early diagnosis of CKD in developing countries. Qualitative and quantitative comparative analyses are, respectively, conducted using a systematic literature review and an experiment with machine learning techniques, with the k-fold cross-validation method based on the Weka© software and a CKD dataset. These analyses enable a discussion on the suitability of machine learning techniques for screening for CKD risk, focusing on low-income and hard-to-reach settings of developing countries, due to the specific problems faced by them, e.g., inadequate primary health care. The study results show that the J48 decision tree is a suitable machine learning technique for such screening in developing countries, due to the easy interpretation of its classification results, with 95.00% accuracy, reaching a nearly perfect agreement with an experienced nephrologist‘s opinion. Conversely, random forest, naive Bayes, support vector machine, multilayer perceptron, and k-nearest neighbor techniques, respectively, yield 93.33%, 88.33%, 76.66%, 75.00%, and 71.67% accuracy, presenting at least moderate agreement with the nephrologist, at the cost of a more difficult interpretation of the classification results.
Improved Speed Control of BLDC Motor using Luo converter By Sliding Mode Control

The classical buck converter for BLDC motor applications do not meet the load requirement containing more ripples on the output voltage and parasitic effects. In order to overcome this effect, the additional filter elements are added in the Luo-converter to eliminate the output ripples and effectively enhance the output voltage level. The output stage of the Luo converter is comprised of an inductor and capacitor so it naturally acts as filter. The output stage stores and delivers energy to the load and smoothens the output voltage to produce a constant output voltage. The Luo Converter acts as both buck and boost converter by varying the duty cycle. Thus this Luo Converter is used for the proposed BLDC Motor Drive. The Sliding Mode Controller is used to make the speed of the System constant in a small amount of time.
The Nature of Controlling Shareholders, Executive Compensation and Investment Efficiency: Data from Chinese Capital Markets

Inefficient investment is a common phenomenon in Chinese listed companies. And the non-efficient investment behavior of non-state-owned listed companies is more than that of state-owned listed companies. Also the inefficient investment of locally controlled listed companies is more serious than that of centrally controlled listed companies. Executive monetary compensation has a significant inhibitory effect on over-investment and under-investment in listed companies. The author finds that State-owned listed companies have greater inhibitory effects than non-state-owned listed companies and the centrally controlled listed company has a greater inhibitory effect than the locally controlled listed listed company. Centrally controlled listed companies have a greater inhibitory effect on over-investment than in under-investment behavior. Equity compensation is negatively correlated with over-investment and under-investment, but the test results are not significant.
ISI-Mitigating Channel Codes for Molecular Communication Via Diffusion

One promising technique for communicating at the nanoscale is molecular communication (MC). In a molecular-communication-via-diffusion-scenario, the memory component of the channel is very high. This gives rise to what is known as inter-symbol interference. Traditional channel coding schemes cannot be utilized in MC due to the high memory. In this paper, a novel low complexity channel coding method is proposed for the molecular communication domain. The design of the proposed channel code takes into account the capability of a nano-device and the characteristics of the molecular communication channel. Simulation results confirm that the proposed method provides a significant improvement in terms of bit error rate. Moreover, a proof-of-concept implementation of the proposed coding scheme is done on a macro-scale testbed. The reliability of the communication link is shown to be significantly increased.
A Robust 13T Single Ended Schmitt Trigger Based SRAM Cell

Static Random Access Memory (SRAM) cells are widely used in many applications, such as cache and registers in various electronic circuits. Recently, due to internet of things (IoT) the memory devices need low voltage operation. The SRAM cell has already been implemented using six transistors in ultra-deep submicron technology to lower the power consumption. However, the conventional 6T SRAM cells implemented at lower voltages are suffering from the lower tolerance against the process, voltage, and temperature (PVT) variations. The Schmitt trigger (ST) based SRAM cell is used to overcome such variations. In this paper, a robust 13T-ST based SRAM has been implemented. Further, the performance of the 13T SRAM cell is compared with the conventional 6T SRAM cell. The comparison is made by varying the supply voltage from O.5V to 1V. Further, we have obtained the access time, power, and static noise margin (SNM) for both designs. Simulation results show that the 13T Schmitt trigger based SRAM has O.41x write ‘1’ access time, 2.31x write SNM, 2.23x read SNM, 1.1x hold SNM, 0.80x leakage power as compared with conventional 6T SRAM at 700mV.
Spine (L3-L4) Mechanics

Studies of spine mechanics have shown that the lumbar region experiences higher tensile and shear deformations compared to other regions which increase the probability of disc injury. Hence, studies on lumbar spine mechanics are vital to understand failure of vertebrae and related mechanics. This research paper focuses on spine mechanics of L3-L4 lumbar using Computer Aided Design (CAD) and Finite Element Analysis (FEA). Various CAD tools are used to build the FE Model of a lumbar spine. The L3-l4 spine segment was generated from a CT scan image and converted into surface model using Mimics and then refined by means of Geomagic. HyperMesh is used to obtain the optimum mesh size that enable for FE analysis. The results of spine mechanics are compared with other related studies.
Nondestructive Banana Ripeness Classification using Neural Network

Banana contributes to the total fruit production in Indonesia. To ensure the quality of a banana, Indonesian government with its national standard agency (BSN) has issued the national standard of fruit. This study proposed the classification method of a banana using the neural network based on color and texture. Data was collected before model creation with numbers of banana of ripeness level 2, 3, and 4 were 30, 30, and 45 respectively. Three neural network model with three different variables, i.e. color image and texture. Testing result show a high accuracy result of 95.24%.
Knowledge, risk perception and attitudes of Dentistry students with regard to HIV/AIDS

Objective: To assess the knowledge, perception of risk and attitudes of Dentistry students regarding HIV/AIDS.Methods: A descriptive, observational, cross-sectional study was performed by applying a questionnaire containing 33 objective questions to students enrolled in the Dentistry course at Southwest Bahia State University, between the second and final years.Results: The response rate was 74%, with an average overall score of 64.1% for the knowledge variable, considered to be good. The average overall score for risk perception was 61.7% (considered inadequate), borne out by a significant rate of accidents involving sharps (25%). An average overall score of 54.3% revealed negative attitudes, showing that some students feel apprehensive and unprepared to treat HIV+ patients.Conclusion: In view of the fact that the attitude and risk perception variables studied showed results that fall short of expectations, even with a knowledge variable considered to be good, a greater articulation between theory and practice is suggested.
Application of Single Pulse Dynamics to Model Program and Erase Cycling-Induced Defects in the Tunnel Oxide of Charge-Trapping Devices

3D NAND, the mainstream technology for high density Flash application [1], is typically based on the charge trapping paradigm, i.e. it relies on the storage of information as electrons trapped in a charge trapping layer (CTL). The useful life of these devices is limited by the number of program and erase (P/E) operations, because repeated tunneling of electrons and holes progressively creates defects in the tunneling dielectric (TuOx) that increase the charge leakage from the CTL, compromising retention. In this paper we propose a model that describes the creation of traps in TuOx according to the degradation dynamics within each pulse of a P/E cycle, unlike previous models that describe the creation of traps only as a function of the number of P/E cycles [2]. This enables a precise calculation of created traps in TuOx based on arbitrary stress pulses and workloads.
Latent Fingerprint Enhancement Based on DenseUNet

The image quality of latent fingerprints is usually poor with unclear ridge structure and various overlapping patterns. Enhancement is an important processing step to reduce the noise, recover the corrupted regions and improve the clarity of ridge structure for more accurate fingerprint recognition. Existing fingerprint enhancement methods cannot achieve good performance for latent fingerprints. In this paper, we propose a latent fingerprint enhancement method based on DenseUNet. First, to generate the training data, the high-quality fingerprints are overlapped with the structured noises. Then, a deep DenseUNet is constructed to transform the low-quality fingerprint image into the high-quality fingerprint image by pixels-to-pixels and end- to-end training. Finally, the whole latent fingerprint is iteratively enhanced with the DenseUNet model to achieve the image quality requirement. Experiment results and comparison on NIST SD27 latent fingerprint database are presented to show the promising performance of the proposed algorithm.
Tone Mapping Beyond the Classical Receptive Field

Some neurons in the primary visual cortex (V1) of human visual system (HVS) conduct dynamic center-surround computation, which is thought to contribute to compress the high dynamic range (HDR) scene and preserve the details. We simulate this dynamic receptive field (RF) property of V1 neurons to solve the so-called tone mapping (TM) task in this paper. The novelties of our method are as follows. (1) Cortical processing mechanisms of HVS are modeled to build a local TM operation based on two Gaussian functions whose kernels and weights adapt according to the center-surround contrast, thus reducing halo artifacts and effectively enhancing the local details of bright and dark parts of image. (2) Our method uses an adaptive filter that follows the contrast levels of the image, which is computationally very efficient. (3) The local fusion between the center and surround responses returned by a cortical processing flow and the global signals returned by a sub-cortical processing flow according to the local contrast forms a dynamic mechanism that selectively enhances the details. Extensive experiments show that the proposed method can efficiently render the HDR scenes with good contrast, clear details, and high structural fidelity. In addition, the proposed method can also obtain promising performance when applied to enhance the low-light images. Furthermore, by modeling these biological solutions, our technique is simple and robust considering that our results were obtained using the same parameters for all the datasets (e.g., HDR images or low-light images), that is, mimicking how HVS operates.
Adapted Compressed Sensing: A Game Worth Playing

Despite the universal nature of the compressed sensing mechanism, additional information on the class of sparse signals to acquire allows adjustments that yield substantial improvements. In facts, proper exploitation of these priors allows to significantly increase compression for a given reconstruction quality. Since one of the most promising scopes of application of compressed sensing is that of IoT devices subject to extremely low resource constraint, adaptation is especially interesting when it can cope with hardware-related constraint allowing low complexity implementations. We here review and compare many algorithmic adaptation policies that focus either on the encoding part or on the recovery part of compressed sensing. We also review other more hardware-oriented adaptation techniques that are actually able to make the difference when coming to real-world implementations. In all cases, adaptation proves to be a tool that should be mastered in practical applications to unleash the full potential of compressed sensing.
On the Impact of the Radiation Pattern of the Antenna Element on MU-MIMO Indoor Channels

This paper presents an analysis of the effect that the radiation pattern of the antenna element that makes up the base station array has on the structure of multi-user multiple-input multiple-output (MU-MIMO) channels. In this paper, the analysis focuses on the uplink. It is well known that the antennas are an inseparable part of the radio channel. The use of more or less directional antennas as elements of the base station array of a MIMO system influences the channel in two fundamental parameters that affect the performance of MU-MIMO systems: on the one hand, the coherence bandwidth, which determines the necessary overhead in the channel estimation; and, on the other hand, the orthogonality of the subchannels between the multiple users and the base station, which influences the degree with which the condition of “favorable propagation” is fulfilled. Based on an experimental analysis using both omnidirectional and directional antennas, conclusions are drawn about how these two parameters are affected and their influence on the spectral efficiency obtainable. The measurement campaign was carried out in an indoor environment in the 3 to 4 GHz band.
Design of Planar Low Pass Filter with Razor-sharp Roll-off

The design and development of a microstrip low pass filter with sharp selectivity and wide fractional stop band bandwidth by employing high and low impedance resonators is presented in this paper. The designs of individual resonators are studied with equivalent circuit. The measured results validate the simulated results. Filter exhibits a razor-sharp roll-off of 289 dB/GHz after 1.98 GHz cut-off and achieves a wide relative stop band bandwidth of 147% with suppression level better than 20 dB.
Narcotics Addiction: Related Criminal Careers, Social and Economic Costs

A sample of 279 male heroin addicts admitted to methadone maintenance programs in Southern California, interviewed between 1978 and 1980, reported high rates of drug trafficking and over 250,000 property crime-days, which resulted in 6,251 arrests. Analyses indicate that offense rates and related social and economic costs were at their highest during periods of addiction. The aggregate cost to society, including criminal justice system and drug treatment intervention, is conservatively estimated at $85 million, averaging $20,000 per subject per year. These findings provide an empirical basis against which to evaluate the cost-effectiveness of alternative interventions.
Fake News Detection Using Deep Learning Techniques

News is crucial part of our life. In day to day life current news are helpful to enhance knowledge what happen around the world. So most of peoples prefer watching news most of the peoples generally prefer reading newspaper early in the morning enjoying with cup of tea. If news is fake that will mislead peoples sometimes fake news utilized to spread rumors about things or it will affect some political leader positions just because of fake news. So it’s crucial to find the fake news. So we proposed system to detect fake news but now a day’s data on web or social media is increasing vastly and it is so hectic to detect news is fake or not by looking all data and it is time consuming so we utilize classification techniques to classify huge data. Here we proposed fake news detection system based on classification such as Logistic regression (LR), Naïve bayes (NB), Support vector machine (SVM), Random forest (RF) and deep neural network (DNN). We compare all machine learning techniques for detecting fake news.
Advanced Techniques for 60 GHz Fronthaul Links

In this paper, we review our recent work in 60 GHz radio-over-fiber (RoF) fronthaul links. We investigated coordinated-multipoint (CoMP) and non-orthogonal-multiple-access (NOMA) to improve edge user performance in a multi-user environment via transmission diversity.
Sexual behavior and prevalence of HIV and syphilis among young men who have sex with men

Objective To know the status of HIV and syphilis infection,HIV/AIDS knowledge and condom use among young men who have sex with men(MSM). Methods Overall 150 young MSM were recuited in Beijing,Harbin,Zhengzhou and Chengdu,respectively,with snowballing sampling.The participants were investigated with a questionnaire on demographics,HIV/AIDS related knowledge,sexual bebavior and condom use as well as HIV and syphilis testing.Results Among the participants,the prevalence of HIV infection,syphilis infection and concurrent infection of HIV and syphilis was 6.7%,8.3%,and 1.5%,respectively.The prevalence of HIV infection was 4.5% in young MSM with insertive anal sex,6.3% in young MSM with receptive andal sex,and 8.1% in the MSM with versatile anal sex,and the prevalence of syphilis infection was 11.2%,10.2%,and 7.8% accordingly.There was no signifecant difference in HIV/AIDS knowledge,condom use and prevalence of HIV infection between different anal sex players(P0.05). In the previous 6 months,the rates of self-report of unprotected insertive,recptive,oral,and vaginal intercourse were 52.5%,45.0%,87.5%,and 22.5% in HIV positive participants,and 31.1%,37.7%,72.9%,and 14.3% in negative participants.Conclusion The rates of HIV and syphilis are high among young MSM in cities of China.The risk of HIV infection is common among young MSM with receptive or insertive anal intercourse.
SPECIAL SECTION ON DATA MINING FOR INTERNET OF THINGS

In this work, we consider the broadcast mechanisms for both reliable links and unreliable links in the Internet of Things (IoT) networks. Focusing on the minimum transmission broadcast (MTB) problem, we propose an efficient algorithm, termed as the Connected Dominating Set Algorithm based on Coalitional Graph Game (CDSA-CGG), to find the minimum connected dominating set (MCDS). Distinct from related work in the literature which adopts a non-cooperative game approach and assumes the interaction among nodes is limited to neighboring nodes, we formulate the interaction and cooperation among nodes over reliable links and over unreliable links as a connected forwarding graph based on coalitional graph game. We prove that the forwarding graph constructed by CDSA-CGG is a Nash network. In addition, we also prove that the final resulting graph is pairwise stable. The simulation results show that the proposed coalition graph game-based algorithm performs well in terms of the number of transmissions, convergence rate, and lifetime of nodes. INDEX TERMS Coalitional graph game, IoT, minimum transmission broadcast, connected dominating set, pairwise stability.
Practical Privacy-Preserving Face Authentication for Smartphones Secure Against Malicious Clients

We propose a privacy-preserving face authentication system for smartphones that guarantees security against malicious clients. Using the proposed system, a face feature vector is stored on a remote server in encrypted form. To guarantee security against an honest-but-curious server who may try to learn the private feature vector, we perform a Euclidean distance-based matching score computation on encrypted feature vectors using homomorphic encryption. To provide security against malicious clients, we adopt a blinding technique. We implement the proposed system on a mobile client and a desktop server. Through an experiment with real-world participants, we demonstrate that secure face verification can be completed in real time (within 1.3 s) even when a smartphone is involved, with an Equal Error Rate (EER) of 3.04%. In further experiments with two public face datasets, CFP and ORL, face verification is completed in approximately 1 s with EER of 1.17% and 0.37%, respectively. Our system is two orders of magnitude faster than previous privacy-preserving face verification method with the same security assumptions and functionalities. To achieve this secure real-time computation, we improve the Catalano-Fiore transformation which converts a linear homomorphic encryption scheme into a quadratic scheme, and parallelize the decryption procedure of our system.
A Comprehensive Review of Medical Expert Systems for Diagnosis of Chronic Kidney Diseases

The prediction of the kidney diseases is essential to eliminate it on time, so that the life of patient may be saved. The early-stage detection of such diseases is helpful for increasing the life span of suffering patient. In addition, it also decreases the cost of treatment. It is better to build an accurate expert system, which helps to diagnose these diseases before leading to worse conditions. Therefore, many lives can be saved by using these intelligent systems. This review paper compares the performance parameters retrieved from various methods used in various existing studies to diagnose renal disorders or chronic kidney diseases. It provides the comprehensive review of medical expert systems and presents the best methods used to recognize chronic kidney disease (CKD).
Women and HIV/AIDS.

The incidence of AIDS in women over the age of 13 is increasing dramatically in the United States, with 40,702 cases reported by the end of September 1993 (Department of Health and Human Services, 1993). Some data indicate women are becoming infected at an earlier age and have a shorter survival time than men. The authors provide a background to the female epidemic, discuss the unique pathophysiologic progress of HIV disease in women, and present the sociocultural and economic issues that contribute to women's risk. Nurses must become knowledgeable of the issues directly impacting on women in order to provide appropriate education, counseling, care, and leadership to women.