Miracle-dz/UniFAR-FTUs huggingface dataset:
ENHANCED PRODUCTION OF ALKALINE PROTEASE BY BACILLUS SUBTILIS IN SUBMERGED FERMENTATION

An alkaline protease producing strain was isolated from the sample collected from a slaughter house in Krishnagiri (District), T.N. , India identified as Bacillus subtilis. Optimization of process parameters, surfactants as stimulants and strain improvement by UV for increased production of protease was carried out. Further the enzyme was partially purified and characterized. The most important factors such as temperature, pH, incubation period, carbon, nitrogen and trace elements were optimum at 35 ￻C, 12, 24 h, glucose, urea and MgSO4, respectively. Addition of surfactants such as Tween 20 (75%), Tween 80 (25%), sodium taurocholate (35%) and SDS (55%) resulted in an increase in the yield of protease when added after 18 h of incubation of culture. On the other hand Triton X5100 inhibited the enzyme production. A 29% increase in protease production was observed by mutant strain upon UV treatment (260nm) of the parent strain for 10 min. The specific activities of the enzyme in culture supernatant and partially purified (80% saturation) samples were 0.7 and 0.6 U/mg of protein, respectively. There is an increase of 6 folds purity of protease with the recovery of 80% yield. The temperature and pH optimum of purified protease were 70 ￻C and 12, respectively.
TOF Estimation of Multipath Waves Using PN Correlation Method with M-FOCUSS

This paper applies M-FOCUSS, which is a method of compressed sensing technologies, to the delay profile of multipath waves obtained by the PN correlation method with multi-antenna system. The computer simulation using the proposed method demonstrates the improvement of TOF estimation accuracy and separation performance of the multipath waves.
Measuring Semantic Similarity of Bengali Texts with Parts-of-Speech Tags and Word-Level Semantics

The semantic textual similarity is essential for many applications related to natural language processing. But measuring the semantic similarity is not an easy task. Because there are different types of sentences and the diversities of sentences’ structure make assessing the semantic similarity a formidable task. When two texts are lexicographically dissimilar but semantically similar, the traditional lexical matching cannot return the actual degree of semantic similarity. Besides these, the lack of well-recognized language processing resources for Bengali texts makes the semantic textual similarity calculation a difficult task. In this paper, we tried to measure the semantic similarity of Bengali texts using word-level similarity and parts-of-speech tags. To assess the semantic similarity, we exploit the Bengali parts-of-speech tagger and pre-trained word-embedding model. Then, the maximum word-to-word similarity of the words is employed if the words belong to identical parts-of-speech tag. We also introduced a grammatical role level similarity in our proposed method to measure sentences’ similarity. To validate the performance of our method, we conducted experiments on a publicly available benchmark Bengali dataset. The results of the experiments demonstrated that our proposed method is effective to measure the degree of similarity of Bengali texts and achieved state-of-the-art performance.
On the Impossibility of NIZKs for Disjunctive Languages From Commit-and-Prove NIZKs

This paper considers the problem of expanding a language class that can be proven by a non-interactive zero-knowledge proof system (NIZK) in a black-box manner in the common reference string model. Namely, given NIZKs for two languages, <inline-formula> <tex-math notation="LaTeX">${\mathcal L}_{0}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${\mathcal L}_{1}$ </tex-math></inline-formula>, can we construct an NIZK for <inline-formula> <tex-math notation="LaTeX">${\mathcal L}_{0} \vee {\mathcal L} _{1}$ </tex-math></inline-formula> in a black-box manner? NIZKs for disjunctive languages have a large number of applications, such as electronic voting. Therefore, such a black-box construction may enable the efficient constructions of such applications. However, Abe <italic>et al.</italic> (PKC 2020) showed that this is impossible if the two given NIZKs are simulation-sound. In this paper, we prove that it is also impossible if the two given NIZKs are constructed by the commit-and-prove methodology that is typically used in many cryptographic protocols, including NIZKs. This result suggests that if we want to augment the capability of NIZKs in terms of the languages they can prove, we should rely on certain properties or structures of the underlying NIZKs, such as algebraic structures.
Radar-Based Automatic Detection of Sleep Apnea Using Support Vector Machine

Early diagnosis of sleep-apnea-related breathing problems helps to avoid the increased risk they can cause. In this study, we performed simultaneous radar measurements and polysomnography on patients with sleep apnea. A support vector machine algorithm was applied to the radar data to automatically detect sleep apnea events. Support vector machine parameters were optimized using the relationship between the radar and polysomnography data. The support vector machine was found to be effective in noncontact detection of central/mixed sleep apnea events using radar data. The proposed approach achieved an accuracy of 79.5%, a recall of 71.2%, and a precision of 71.2%.
Open Framework of Gateway Monitoring System for Internet of Things in Edge Computing

Aiming at the problems of the diversity of communication protocols for IoT devices and insufficient cloud computing power, a monitoring system for IoT gateways based on edge computing is designed. Through a combination of simulation and experiment, the transmission performance of IoT gateways based on multiple communication protocols is tested and analyzed. The paper uses EdgeX Foundry edge computing framework, Raspberry Pi 3B+ as the core, and Docker virtualized container technology, distributed A microservice architecture was deployed and a temperature and humidity alarm system based on the Modbus communication protocol was constructed. The average delay of the system was 576 ms. The experimental results show that the transmission performance of the IoT gateway meets the needs of the factory, provides a new idea for the data collection and control of the equipment, and has high application value.
Fabrication Strategies and Measurement Techniques for Performance Improvement of Graphene/Graphene Derivative Based FET Gas Sensor Devices: A Review

In the ever-expanding arena of gas sensor research, graphene (or graphene derivative viz. reduced graphene oxide (rGO)) field-effect transistors (FET) are the most recent inclusions which have enormous potential to detect wide variety of oxidizing and reducing target species with high sensitivity. This review article aims to unify the various strategies adopted (during fabrication or during measurement) by the researchers to improve the performance index of graphene/rGO-FET gas sensor devices. These strategies can broadly be classified as; (i) fabrication/device level optimization and (ii) operational/measurement technique improvisation. In the first category, improvement of various sensing parameters of graphene/rGO FET through functionalization/surface modification/doping of channel, use of novel gate dielectric/ gate material and exploration of innovative device configurations (ionic top gate, single back gate or dual gate) have been discussed. While in the second category, optimized gate biasing, AC phase-sensitive measurement, quasi static/ real time conductance measurement and low-frequency noise spectra based measurement techniques have been elucidated. After critically comparing the performance improvement devoting the above two lines of approaches, with emphasis on the unique advantage and limitations of each of the schemes, the review concludes with the indication towards future research directions that lie ahead.
Robust Detection and Tracking Method for Moving Object Based on Radar and Camera Data Fusion

Obtaining the accurate and real-time state of surrounding objects is essential for automated vehicle planning and decision-making to ensure safe driving. In complex traffic scenarios, object occlusion, clutter interference, and limited sensor detection capabilities lead to false alarms and missed object detection, making it challenging to ensure the stability of tracking and state prediction. To address these challenges, in this study, we developed a robust multi-object detection and tracking method for moving objects based on radar and camera data fusion. First, the radar and camera perform target detection independently, and the detection results are correlated in the image plane to generate a random finite set with an object type. Then, based on the Gaussian mixture probability hypothesis density algorithm framework, the tracking process is improved using elliptic discriminant thresholds, an attenuation function, and simplified pruning methods. The experimental results demonstrate that the improved algorithm can accurately estimate the number and state of targets in object occlusion, measurement loss scenarios, and achieve robust continuous multi-object tracking. The proposed method could guide the design of safer and more efficient intelligent driving systems.
Motor Imagery Classification using local region CSP features with high-gamma band

In this paper, we present enhanced MI classification method in electroencephalogram(EEG)-based Brain-Computer Interfaces(BCI) with high-gamma band and local region CSP features. In order to improve the performance of MI classification, the use of the local region CSP feature with high eigenvalue disparity score and the high-gamma band related to advanced cognitive information processing such as reasoning and judgment can provide improved performance compared to existing CSP based methods. As a result of the experiment through BCI competition III-IVa dataset, it is shown that MI classification performance is improved through the proposed method.
Intelligent Handover using User-Mobility Pattern Analysis for 5G Mobile Networks

Millimeter Waves (mmWaves) offer gigabit data rates in fifth generation (5G) mobile networks. The challenge however is mmWaves are highly sensitive to user mobility and topographic dynamics. Sensitivity enhances occurrences of irregular Line of Sight (LOS) and non-LOS spots in the network that distort cell patterns causing instances of gradual and abrupt changes to user data rates. To maintain desired quality of service, Handoff (HO) schemes need to be versatile and intelligent to avoid target links likely to abruptly fail. Unfortunately, most HO schemes cannot forecast instances of abrupt changes in targeted links. This leads to too early, delayed or wrong HOs, at worst actual link failure. To mitigate the HO challenges, this paper adopts a Jump-Markov-Linear-System HO model. The scheme learns to forecast the likely deterioration pattern of the target mmWave link using maximum likelihood approximation. This in turn helps switch to links likely to remain in LOS longer/gradually deteriorate between HOs. Thus, the scheme does not just select target links with higher data rate/ Signal but considers there reliability too past a HO. Simulation results show selection of more reliable links than in classic schemes: users remain connected longer between HOs.
A Miniaturized Band-Notched Absorber for Wideband RCS Reduction

This paper presents a polarization-insensitive miniaturized band-notched absorber with a wide operating bandwidth. The proposed geometry is comprised of a resistive top layer, band-reflective middle layer, and a ground plane, separated from one another by air spacers. The structure exhibits an operational bandwidth of 132.7% where absorption band is spread across the frequency ranges of 2.48-5.23 GHz and 7.68-12.26 GHz, whereas a band-notch response is obtained in between the absorption bands. The proposed topology is also angularly stable upto 45o for both TE and TM polarizations. In addition, the overall footprint of the unit cell geometry is very small having lateral dimensions of 0. 08λL × 0. 08λL and thickness of 0. 085λL, where λL corresponds to the lowest absorption frequency. The prototype of the structure has been fabricated as well as experimentally verified.
Ambiguity Resolution in Amplitude-Monopulse Systems Using Broad-Beam Patterns

In this letter, the use of flat-top broad beams is proposed for the improvement of the robustness against direction-of-arrival (DoA) ambiguities in amplitude-monopulse systems, if compared to the conventional use of narrow tilted beams. Oscillations of the monopulse function outside the field-of-view are eliminated, thus suppressing false detections created by signals coming from outside the angular range of interest. Theoretical and experimental results using a modulated leaky-wave antenna, which synthesizes a broad-beam at 15 GHz, confirm this improvement.
Host trait prediction from human microbiome data for Colorectal Cancer

Microbiomes, micro organisms living in a host environment, play significant r ole i n t he r egular a ctivities and abnormalities of the host. Researches throughout the world has found symbiotic relations between the human microbiomes and human physiology, immunity, diseases etc. Outstanding development in sequencing technology has paved the way to analyze large number of samples with reasonable cost. Applying supervised machine learning techniques on these data can help to find out the most important m icrobiomes residing in the host environment and build a predictive model to classify unknown samples. In this study, we have applied different supervised classification a lgorithms a long w ith s ome e nsemble techniques to find a b etter p redictive m odel t o p redict t he t rait o f human host for the prognosis of colorectal cancer. Our study finds that, tree based classification a lgorithms w orks b est f or classifying the human microbiome data for colorectal cancer which are large, sparse and dispersed in nature. We have also identified important microbiomes that acts as a deciding factor behind colorectal cancer.
LDNNET: Towards Robust Classification of Lung Nodule and Cancer Using Lung Dense Neural Network

Lung nodule classification plays an important role in diagnosis of lung cancer which is essential to patients’ survival. However, because the number of lung CT images in current dataset is relatively small and the ratio of nodule samples to non-nodule samples is usually very different, this makes the training of neural networks difficult and poor performance of neural networks. Hence, LDNNET is proposed, which adopts Dense-Block, batch normalization (BN) and dropout to cope with these problems. Meanwhile, LDNNET is an adaptive architecture based on convnets combining softmax classifier which is utilized to alleviate the problems of training deep convnets. Follows are our main work: Firstly, we utilized LDNNET on database LUng Nodule Analysis 2016 (LUNA16) for lung nodule classification and database KAGGLE DATA-SCIENCE-BOWL-2017(Kaggle DSB 2017) for lung cancer classification; Secondly, the comparison experiments are designed to compare the performance of dense connection, pooling layer and the input pixel size of lung CT(Computed Tomography) images; Thirdly, data enhancement, dense connection and dropout layer were utilized in LDNNET to reduce overfitting; Fourthly, pre-processing methods, for instance enhanced contrast, median filtering, Laplacian filtering are compared to the no-processing method to explore the effect of pre-processing on lung CT images classification. Fifthly, accuracy, specificity and sensitivity on LUNA16 are 0.988396, 0.994585 and 0.982072 and these indicators on Kaggle DSB 2017 are 0.999480, 0.999652 and 0.998974. Furthermore, AUC for both two datasets is over 0.98. Consequently, this paper conducts experiments with uniform parameter settings on two publicly available databases and shows that even in challenging situation where lung images are directly utilized as input images without preprocessing, LDNNET is still the more advanced algorithm than other recent algorithms respectively. Moreover, a series of comparative experiments were conducted to further confirm that the proposed algorithm has the higher accuracy and robustness through verification and discussion.
Recent Applications of Different Microstructure Designs in High Performance Tactile Sensors: A Review

Flexible tactile sensors have become one of the most attractive research areas because of its great application prospect in electronic skin, healthcare monitoring and artificial intelligence. However, there are still great challenges in mimicking the human skin’s strong ability of tactile perception to the changes of external physical information. Nowadays, in addition to flexible polymers and conductive materials, the microstructure of sensing units is a key factor to improve the performance of tactile sensors such as sensitivity, fast response and robustness. In recent years, a variety of novel microstructure designs have been applied to applications of tactile information perception and numerous corresponding processing methods have also been developed, which provides a wealth of enlightening solutions for the production of electronic skins. Herein, a large number of research results of tactile sensor devices are systematically reviewed according to different three-dimensional geometric shapes of the microstructures which are grouped into several categories. At the same time, relevant processing technologies employed, working principles and chief performance characteristics are also briefly described. Relative merits of different types of microstructure applications are clearly elaborated through the classification and comparison of multiple microstructure geometries in this paper to provide valuable reference for sensing microstructure design and fabrication of tactile sensors in corresponding engineering applications. Moreover, current difficulties and challenges in fabrication of flexible microstructures and future investigative directions pertaining to this field have been discussed.
Hieff: Enabling Efficient VNF Clusters by Coordinating VNF Scaling and Flow Scheduling

A cluster of Virtual Network Functions (VNF) can serve massive fluctuating traffic by managing VNF instances and distributing flows. However, how to schedule the flows and manage VNF scaling efficiently in a VNF cluster is still an open question. Existing solutions such as hash based schemes encounter imbalance and passive flow remapping obstacles while flow table-based scheme suffers from high processing latency and flow entries overflow challenges. In this paper, we design and present Hieff, an efficient NFV system that coordinates VNF scaling and flow scheduling within a VNF cluster. The key idea of Hieff is to precisely manage the heavy flows with a flow table while simply allowing light flows be distributed by hash. Though this idea has been explored in previous work, we are the first to apply it with the VNF scaling process. We mathematically model the Hieff system and propose a heuristic algorithm to determine the optimized VNF scaling and flow scheduling strategies. We implement Hieff based on BESS and Click and use real-world tracing to evaluate the system. Results show that Hieff can handle co-existing massive flows efficiently with low latency while balancing the load of VNF instances at low cost.
Deep Matrix Factorization for Cross-Domain Recommendation

Recommender system can make personalized recommendation services for all users. Collaborative filtering is the main method can select suitable items to specific users among the recommendation algorithm. Common CF-based approaches use the users’ history behaviors which include explicit information or implicit feedback to make recommendation decisions. Matrix Factorization is the most popular idea in the field of collaborative filtering. Currently, many MF-based methods have been proposed and achieve great improvement. But matrix factorization can only fit linear features which limit the their performance in real-world dataset which contains complex and nonlinear feature, moreover, the sparsity of user-item interaction information is another bottleneck of matrix factorization methods. Recently, Deep learning is widely applied to many fields, there are many research has applied deep learning to recommender system. In this paper, we use multi-layer perceptron structures to learn the representation of users and items in ML-based method. On the other hand, in order to address the data sparsity problem exists in collaborative filtering, Cross-Domain Recommendation is a promising way. Combining with collaborative approach to extract the latent feature, we propose Deep Matrix Factorization for Cross Domain Recommendation (DMF-CDR). We test the proposed method on real-world dataset and show that it outperforms several recent popular models.
Smart Robot for Collection and Segregation of Garbage

This paper proposes a robotic bot to collect the litter thrown and segregate the garbage so as to reduce human effort and keep the environment clean. The proposed robotic bot autonomously identifies garbage and classifies it into biodegradable and non-biodegradable. The final collected garbage is, in a way, segregated automatically. The robot moves towards the garbage just like a rag picker does. When the robot's bins are full, a signalling system is created. This autonomous bot is capable of collecting, segregating and providing signals if the garbage bin is full. The yolov4 model was used to train a dataset of 10,000 images of both the categories and the model was able to detectthe test images with adequate efficiency of 99.66 percent, making it promising candidate for using as an alternative to human effort.
Indoor Localization with Machine Learning Based on Interpolated Received Signals of Phased Array

This paper presents indoor localization with machine learning, which estimates the position of a target three-dimensionally based on the interpolated received signals of a phased array. In a fundamental study, retro-directive signals of a small 2-by-2 phased array received from the target are input to a 3-layered neural network (NN) to estimate the location of the target. To reduce the time and cost for the training of this system, especially in the 3D case, the signals from a tag on a coarse grid are interpolated to prepare training data. The higher accuracy of localization with interpolated signals is demonstrated.
Flexible inkjet sensor fabricated by inkjet printing

In this contribution, we deal with the optimization of the inkjet printing technology process of materials with appropriate electrical properties. The materials tested here were conductive inks based on colloid silver and organic polymer (poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate)). Subsequently, after optimization of the printing process of various materials, the devices were designed and fabricated to evaluate the electronic properties of printed material inks. To demonstrate the developed technological process, the temperature sensor was designed and fabricated.
A Novel Service Composition Approach for Offloading in Mobile Edge Computing

Computational offloading in Mobile Edge Computing (MEC) environment can help reduce energy consumption and response time in network, thus improve users’ experience. However, the distributed nature of the mobile users and the complex applications make it challenging to schedule the tasks reasonably among multiple devices. In this paper, we propose to leverage the idea of Software-Defined Networking (SDN) and Service Composition (SC) for such problem. We propose a software-defined service composition model and formulate the low latency service composition as a Constraint Satisfaction Problem (CSP) to make it a user-centric approach. We also define the QoS model which provides the composition rule that forms the best possible service composition at the time of need. The experimental results demonstrate that our approach can obtain better performance than existing methods.