Miracle-dz/UniFAR-FTUs huggingface dataset:
Monitoring of Input and Output Water Quality in Treatment of Urban Waste Water Using IOT and Artificial Neural Network

In the today's world the need of monitoring the water quality of water bodies, water treatment plant and industries is always a big challenge as this task is done by a human which includes a lot of efforts and it includes the use of very costly sensors. This system includes a lot of human efforts, money and time which was not suitable for performing this task. There was a need of a system which can collect data then analyze that data and generate results in real time. In this paper we are focusing on creating a low-cost smart device which can be used to monitoring water quality at different places. We focus of creating a device with Arduino along with some sensors and modules to collect data of water quality at different places. This device will be connected to internet with the help of a Wi-Fi module and it will send all data on a server to record values. At the same time, we are using Artificial Neural Network for the analysis of this data for generating high accuracy results, after this the corresponding authorities will be notified to take actions accordingly. This device will be able to check and calculate some most important factors of water quality like EC, pH, Dissolved Oxygen, Color and Turbidity to check the quality of the water and these values will be send to the server so that an authorized person can check that data for any use. This data will be available to be viewed either in the form of a graph or in a table with numeric values. The device will also be able to give alert based on values which it has calculated and along with this we will be using Artificial Neural Network to analyze this data which will help in treatment process.
Distributed Machine Learning Oriented Data Integrity Verification Scheme in Cloud Computing Environment

Distributed Machine Learning (DML) is one of the core technologies for Artificial Intelligence (AI). However, in the existing distributed machine learning framework, the data integrity is not taken into account. If network attackers forge the data, modify the data, or destroy the data, the training model in the distributed machine learning system will be greatly affected, and the training results are led to be wrong. Therefore, it is crucial to guarantee the data integrity in the DML. In this paper, we propose a distributed machine learning oriented data integrity verification scheme (DML-DIV) to ensure the integrity of training data. Firstly, we adopt the idea of Provable Data Possession (PDP) sampling auditing algorithm to achieve data integrity verification so that our DML-DIV scheme can resist forgery attacks and tampering attacks. Secondly, we generate a random number, namely blinding factor, and apply the discrete logarithm problem (DLP) to construct proof and ensure privacy protection in the TPA verification process. Thirdly, we employ identity-based cryptography and two-step key generation technology to generate data owner’s public/private key pair so that our DML-DIV scheme can solve the key escrow problem and reduce the cost of managing the certificates. Finally, formal theoretical analysis and experimental results show the security and efficiency of our DML-DIV scheme.
First Demonstration of CMOS Inverter and 6T-SRAM Based on GAA CFETs Structure for 3D-IC Applications

For the first time, CMOS inverters and 6T-SRAM cells based on vertically stacked gate-all-around complementary FETs (CFETs) are experimentally demonstrated. Manufacturing difficulties of vertically stacked source and drain electrodes of the CFETs have been overcome by using junctionless transistors, thereby reducing the number of lithographic steps required. Furthermore, with post metallization treatments, both the voltage transfer characteristics (VTCs) of CMOS inverters and butterfly curves of SRAM show significant improvements due to the symmetry of nMOS and pMOS threshold voltages. Simulation shows that 3-dimensional CFET inverters have lower input parasitic capacitance than standard 2-dimensional CMOS, leading to reduced gate delay and lower power consumption.
Robust Secure Beamforming for SWIPT-Aided Relay Systems With Full-Duplex Receiver and Imperfect CSI

This paper investigates an energy-constrained relay-assisted secure communication system in the presence of a legitimate source-destination pair and a passive eavesdropper with imperfect channel state information (CSI). The energy-constrained relay exploits a power splitting (PS) protocol to simultaneously harvest energy and decode information from the received signal from the source, and then forwards the confidential signal to the full-duplex (FD) destination while it simultaneously transmits artificial noise (AN) to confuse the passive eavesdropper. This robust beamforming design is developed by maximizing the secrecy rate under the constraints of energy harvesting (EH) requirement at the relay and AN transmit power at the destination. Specifically, we formulate the joint relay beamforming and PS ratio as well as the AN power optimization as a non-convex quadratically-constrained problem. In order to circumvent this non-convexity issue, we decompose the original problem into three subproblems which can be determined by the proposed semidefinite relaxation (SDR) and successive convex optimization methods. Furthermore, we derive closed-form expressions for the optimal solutions of each subproblem. Finally, the original non-convex problem can be solved through a convergence guaranteed iterative algorithm. Simulation results are provided to demonstrate the effectiveness and superior performance of the proposed robust design compared with the benchmark schemes.
An Infant Cry Recognition based on Convolutional Neural Network Method

In this paper, an investigation of crying signal spectra is used to classify categories of infant cries. Three different types of crying considered in this work are hungry, sleepy and burping need. These cries are preprocessed and converted for calculation of Mel-Frequency Cepstral Coefficients (MFCC) before being classified by Convolutional Neural Network (CNN). Experimental results show that CNN based deep learning achieves high performance of 84%.
Searching Optimal Values of Identification and Controller Design Horizon Lengths, and Regularization Parameters in NARMA Based Online Learning Controller Design

This paper presents an analysis on searching the optimal values of the system identification and tracking window lengths, and regularization parameter for the online learning NARMA controller algorithm. Both window lengths and regularization parameter are generally determined with exhaustive searches by researchers. Although the estimation of plant and controller parameters plays the essential role in online learning control algorithms, using non-optimal values of the window lengths and regularization parameter may deteriorate badly the estimation and so the performance of the controller. In the paper, the effects of the window lengths and the regularization parameter on the tracking performance of the NARMA based online learning controller are analyzed with a search method. The considered NARMA based online learning control method is performed on a rotary inverted pendulum model. While the effect of the regularization parameter is examined in the batch mode, the effects of identification and tracking error window lengths are studied for the online mode of the controller learning algorithm. The developed search method can provide the optimum values of the plant identification and tracking horizon lengths, and regularization parameter when a sufficiently large class of possible input, output and reference signals are taken into account in the search. The presented study may be extended, as future research in the direction of developing intelligent control systems, by determining the horizon window lengths and regularization parameter, in an automatic way, with efficient learning algorithms.
Low-Complexity Linear Equalization for OTFS Modulation

In this letter, we propose low-complexity linear equalizers for orthogonal time frequency space (OTFS) modulation that exploit the structure of the effective channel matrix in OTFS. The proposed approach exploits the block circulant nature of the OTFS channel matrix to achieve significant complexity reduction. For an <inline-formula> <tex-math notation="LaTeX">$N\times M$ </tex-math></inline-formula> OTFS system, where <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> are the number of Doppler and delay bins, respectively, the proposed approach gives exact minimum mean square error (MMSE) and zero-forcing (ZF) solutions with just <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}(MN \log MN)$ </tex-math></inline-formula> complexity, while MMSE and ZF solutions using the traditional matrix inversion approach require <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}(M^{3}N^{3})$ </tex-math></inline-formula> complexity. The proposed approach can provide low complexity initial solutions for local search techniques to achieve enhanced bit error performance.
452 MHz Bandwidth, High Rejection 5.6 GHz UNII XBAW Coexistence Filters Using Doped AlN-on-Silicon

5.66 GHz bulk acoustic wave filters, utilizing doped aluminum nitride (AlN), are reported. The filters exhibit high -3 dB bandwidth of 452 MHz (8% fractional bandwidth), a minimum insertion loss of 1.79 dB, rejection greater than 50 dB and power handling performance (near the upper band edge) up to 32.5 dBm. Resonators show k2eff of 10.24%, Qmax of 1479, and FOM of 151.
Single-loop Current Sensorless Control with Self-detection of Conduction Losses Applied to Neutral Point Clamped Multilevel Converter

Current sensor is usually considered a compulsory part in current controlled power electronic converters. This paper presents a single-loop current sensorless control method for neutral point clamped multilevel converter. In order to control the current shape, the proposed method does not use feedback from current sensor and contains only a single control loop with voltage feedback to stabilize the DC bus voltage. The current control is implemented by means of precise calculation of volt-second balance of the inductor with consideration of conduction losses. Considering that values of parasitic components may change during converter’s operation, a novel mathematical observer is presented to implement continuous monitoring of parasitic elements introduced by switching components and resistance of the inductor. Moreover estimation of inductance is proposed with a simple method.
Effects of Predictive Information on Pupil Dilation during the Evaluation of Food Images

It has been well established in previous research that predictive information has a large impact on decision-making in a variety of cognitive paradigms. Less is known about how predictive cues influence the sensory, perceptual and affective processes toward a subjective choice. Here, we designed an evaluative decision-making paradigm, with naturalistic food images as stimuli, using predictive cues to measure pupil dilation during information processing toward a subjective rating. In each trial, we used a predictive cue to generate an expectation about the upcoming target image. The color of the cue indicated the predictive validity (either 100% or 50% reliability); the shape of the cue indicated the predicted valence of the target image (either appetitive or aversive). We also varied the length of the delay between the predictive cue and the target image (either 1s or 9s). The participants were asked to rate the target food images on a continuous scale from -10 to 10 using a joystick. The results showed a difference in pupil dilation in response to the predictive cue as a function of the predicted valence, with more constriction following negative cues. The dilation was unaffected by color. There was also a notable difference in pupil dilation during the target image viewing as a function of the actual valence, with more constriction for appetitive images. The apparent contradiction in dilation as a function of item category may be due to the level of arousal, with complementary or opposing functions for valence during expectation versus actual sensory and perceptual processing. Future research is required to establish the exact relationship with arousal in order to uncover the underlying mechanisms of this novel finding.
Bacteria Classification using Image Processing and Deep Convolutional Neural Network

Bacteria is one of the reasons that cause many diseases, and the diagnosis is hard to be done because of its shape and complexity. Thus, regularly the examination needs to be done by experts or specialists to classify the species of them and it also takes long time with the possibility on incorrect recognition. In Thailand there is still not enough experts to deal with it so the aim of this paper is to bring the new approach to classify the species of bacteria from Digital Image of Bacterial Species (DIBaS) by using Image Processing and Deep Convolutional Neural Network (DCNN) model to reduce the time consumption and increase the classification accuracy in traditional way. The result of the model gave the average accuracy at 95.09%.
Analysis of Low-Oversampled Staggered SAR Data

The combination of SCan-On-REceive with continuous variation of the pulse repetition interval during transmission (staggered operation) is a viable option for the acquisition of high-resolution synthetic aperture radar (SAR) data over wide areas. Since the acquired data are not uniformly sampled and contain gaps within the synthetic aperture mainly due to the interruption of reception during transmission (i.e., due to blockage), proper reconstruction strategies must be considered in order to minimize artifacts. Conventionally, large oversampling rates are required to ensure low ambiguity levels, but this is not always feasible due to data-rate constrains. This article presents a detailed analysis of the blockage recovery in the low-oversampling case. Moreover, we propose a data adaptive strategy to optimally perform this step, ensuring the best performance for point-like targets and avoiding degradation for distributed scatters. This paper also presents simulation results that show the impact of staggered data for interferometric applications.
In-Field Grape Cluster Size Assessment for Vine Yield Estimation Using a Mobile Robot and a Consumer Level RGB-D Camera

Current practice for vine yield estimation is based on RGB cameras and has limited performance. In this letter we present a method for outdoor vine yield estimation using a consumer grade RGB-D camera mounted on a mobile robotic platform. An algorithm for automatic grape cluster size estimation using depth information is evaluated both in controlled outdoor conditions and in commercial vineyard conditions. Ten video scans (3 camera viewpoints with 2 different backgrounds and 2 natural light conditions), acquired from a controlled outdoor experiment and a commercial vineyard setup, are used for analyses. The collected dataset (GRAPES3D) is released to the public. A total of 4542 regions of 49 grape clusters were manually labeled by a human annotator for comparison. Eight variations of the algorithm are assessed, both for manually labeled and auto-detected regions. The effect of viewpoint, presence of an artificial background, and the human annotator are analyzed using statistical tools. Results show 2.8–3.5 cm average error for all acquired data and reveal the potential of using low-cost commercial RGB-D cameras for improved robotic yield estimation.
CLINICAL ANALYSIS OF THE SINOMENINE HYDROCHLORIDE SUSTAINED RELEASE TABLET ON THE ANKYLOSING SPONDYLITIS

[Objective] To evaluate the clinical efficacy and safety of sinomenine hydrochloride sustained release tablet in the treatment of ankylosing spondylitis. [Methods] A randomized controlled clinical trial was conducted. Patients in the experimental group received treatment of sulfasalazine (1.0g po. Bid) combination of sinomenine hydrochloride sustained release tablet; Patients in the control group only undertook treatment of sulfasalazine. Besides, every patient in the two groups received one kind of NSAIDs named Mobic and was followed up for one year. [Results] The indexes of experimental group were significantly better than those of control group including BASDAI, BASFI, stiffness time, and CRP (P﹤0.05). [Conclusion] The symptoms of patients treated with sinomenine hydrochloride sustained release tablet combination of sulfasalazine can be significantly relieved the clinical symptoms of ankylosing spondylitis, and effectively prevent the natural course of ankylosing spondylitis, while the adverse reactions do not showed to be increased.
Multiuser Selection Criteria for MIMO-NOMA Systems With Different Detectors

Non-orthogonal multiple access (NOMA) is an effective way to improve the spectrum efficiency of multiple-input multiple-output (MIMO) systems, where this advancement can be fully exploited by employing a proper user selection strategy in the multiuser MIMO system. However, for multiuser scenarios, information-theoretic throughput may not be properly used as the performance indicator since the constraints at the receiver (e.g., suboptimal detectors) are not taken into consideration. In this paper, we adopt the error probability (EP) as the performance metric, which is equivalent to the practical throughput efficiency and can take the critical constraints into consideration. We analyze the pair-wise error probability (PEP) performance of different detectors and propose two kinds of user selection methods to minimize the PEP. To facilitate the detection performance evaluation, we further investigate the multiuser diversity of the proposed selection methods and derive a tight lower PEP bound for the case with randomly selected users. From the analytical and numerical results, we demonstrate that the proposed multiuser selection methods can suppress the inter-user interference effectively and achieve better performance than that of the state-of-the-art method.
Empirical Analysis of Hardware-Assisted GPU Virtualization

The increasing use of Graphics Processing Unit (GPUs) for accelerating compute intensive tasks and graphics-related computations has led to their inclusion in High Performance Clusters and Cloud setups. Several cloud vendors provide virtual machine instances with GPU capabilities. With the advent of virtualization aware GPU hardware (NVIDIA vGPUs), allocating and sharing physical GPU among virtual machines has become easier and cost-efficient. The sharing mechanism and extent of sharing are determined by the vGPU scheduling algorithm and a configurable vGPU profile. As part of this work, we present a thorough empirical study of the hardware-assisted virtualized GPU setups. In particular, we quantify the virtualization overheads, study the interference effects of concurrently executing homogeneous and heterogeneous workloads, and impact of vGPU scheduling algorithms. We also demonstrate that the best vGPU configuration parameters are sensitive to the mix of workload characteristics that share the GPU. Our study also compares the performance of vGPU based virtualization with PCI passthrough based direct GPU assignment to virtual machines. Based on our evaluation using heterogeneous workloads setups and varying vGPU configurations, we observe that the virtualization overheads are up to 7% (in terms of reduced memory availability) and up to 20% increase in execution times. Further, we demonstrate using our setups, that co-placing of heterogeneous workloads can improve the efficiency of GPU multiplexing and decrease execution times to as low as 20% as compared to homogeneous workload placements.
Multilabel Remote Sensing Image Retrieval Based on Fully Convolutional Network

Conventional remote sensing image retrieval (RSIR) system usually performs single-label retrieval where each image is annotated by a single label representing the most significant semantic content of the image. In this scenario, however, the scene complexity of remote sensing images is ignored, where an image might have multiple classes (i.e., multiple labels), resulting in poor retrieval performance. We therefore propose a novel multilabel RSIR approach based on fully convolutional network (FCN). Specifically, FCN is first trained to predict segmentation map of each image in the considered image archive. We then obtain multilabel vector and extract region convolutional features of each image based on its segmentation map. The extracted region features are finally used to perform region-based multilabel retrieval. The experimental results show that our approach achieves state-of-the-art performance in contrast to handcrafted and convolutional neural network features.
A Novel Double-Density Hemi-Cylindrical (HC) Structure to Produce More than Double Memory Density Enhancement for 3D NAND Flash

A novel hemi-cylindrical (HC) 3D NAND Flash architecture is demonstrated for the first time. HC 3D NAND squeezes the gate-all-around (GAA) hole in one direction, followed by a slit cut to split the GAA device to produce twin cells. It creates ~ 2.6 times of memory density increase than standard GAA 3D NAND at the same stacking layer. Both experimental and simulation results clarify that the "non-local" effect of channel potential can essentially "smooth" the non-uniform curvature effect thus the HC 3D NAND device possesses regular P/E window characteristics even it is not a regular shape of GAA. Good 100K PE cycling endurance and post 10K-cycled 150C retention are demonstrated. HC 3D NAND structure has potential to further scale 3D NAND device in lateral dimension, with the advances of processing technologies.
Secure Internet-of- Things: Assessing Challenges and Scopes for NextGen Communication

The exponential rises in wireless communication technologies and allied application demands have revitalized academia-industries to achieve more efficient Quality of Service (QoS) centric communication solutions. To achieve it, in the last few years Internet-of-Things (IoT) technology has emerged as a potential solution. However, ensuring QoS provision and reliable communication across the network has always been a challenge for research community. Being a highly interfaced and complex paradigm IoT requires assuring secure communication across the network to achieve QoS and reliable data transmission. Assuring end-to-end security in IoT has revitalized academia-industries to develop more effective solution by developing advanced cryptosystem methods; however maintaining lightweight computation, seamless communication and QoS delivery simultaneously has been a driving force behind numerous ongoing researches. With this motive, in this paper emphasis is made on studying and assessing different security challenges in IoT ecosystem, solutions proposed so far and scopes for further optimization so as to develop a more effective IoT security system. The survey paper revealed that though numerous efforts have been made to augment IoT communication security, majority of the classical approaches either undergoes high computational overheads or impacts QoS delivery with real-time network solution. Additionally, it reveals that most of the existing systems focus on using single cryptosystem for communication security; however it fails alleviating different attack scenarios with different breaching actions. Majority of the existing approaches are found confined to address the security-vulnerabilities such asSmart Card Loss Attack (SCLA), offline password guessing using Brute Force attack, sensor node spoofing, privileged-insider and Session specific temporary information attack (SSTIA), etc. It suggests designing a robust security model with lightweight cryptosystem armored with multilevel security and authentication system.