A Hybrid Brain-Computer Interface for Closed-Loop Position Control of a Robot Arm

Brain-Computer interfacing(BCI)has currently added a new dimension in assistive robotics.Existing braincomputer interfaces designed for position control applications suffer from two fundamental limitations.First,most of the existing schemes employ open-loop control,and thus are unable to track positional errors,resulting in failures in taking necessary online corrective actions.There are examples of a few works dealing with closed-loop electroencephalography(EEG)-based position control.These existing closed-loop brain-induced position control schemes employ a fixed order link selection rule,which often creates a bottleneck preventing time-efficient control.Second,the existing brain-induced position controllers are designed to generate a position response like a traditional firstorder system,resulting in a large steady-state error.This paper overcomes the above two limitations by keeping provisions for steady-state visual evoked potential(SSVEP)induced linkselection in an arbitrary order as required for efficient control and generating a second-order response of the position-control system with gradually diminishing overshoots/undershoots to reduce steady-state errors.Other than the above,the third innovation is to utilize motor imagery and P300 signals to design the hybrid brain-computer interfacing system for the said application with gradually diminishing error-margin using speed reversal at the zero-crossings of positional errors.Experiments undertaken reveal that the steady-state error is reduced to 0.2%.The paper also provides a thorough analysis of the stability of the closed-loop system performance using the Root Locus technique.

A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia

实时逆转录聚合酶链反应(RT-PCR)检测早期新冠病毒肺炎(COVID-19)患者的痰液或鼻咽拭子中的病毒RNA阳性率较低。同时,COVID-19的计算机断层扫描(CT)影像学的临床表现有其自身的特点,不同于甲型流感病毒性肺炎(IAVP)等其他类型的病毒性肺炎。本研究旨在应用深度学习技术,建立COVID-19、IAVP及健康人群肺部CT的早期筛查模型。本研究共采集618份CT样本,其中219份样本来自110例COVID-19患者(平均年龄50岁,其中男性63例,占57.3%),224份样本来自224例IAVP患者(平均年龄61岁,其中男性156例,占69.6%),175份样本来自健康人群(平均年龄39岁,其中男性97例,占55.4%)。所有CT样本均来自浙江省三家COVID-19定点收治医院。我们首先利用胸部CT图像集的三维(3D)深度学习模型分割出候选感染区域,然后利用位置敏感机制深度学习网络将这些分离的图像归类为COVID-19、IAVP以及与感染无关(ITI)的图像,并且输出相应置信度得分。最后,用Noisy-OR贝叶斯函数计算每份CT病例的感染类型及总置信度。测试数据集的实验结果表明,从整体CT病例来看,本研究利用深度学习系统建立的COVID-19患者的早期筛查模型的总体准确率为86.7%。该模型有望成为一线临床医生诊断COVID-19的一种有效的辅助方法。

Histopathological Diagnosis System for Gastritis Using Deep Learning Algorithm

Objective To develope a deep learning algorithm for pathological classification of chronic gastritis and assess its performance using whole-slide images(WSIs).Methods We retrospectively collected 1,250 gastric biopsy specimens(1,128 gastritis,122 normal mucosa)from PLA General Hospital.The deep learning algorithm based on DeepLab v3(ResNet-50)architecture was trained and validated using 1,008 WSIs and 100 WSIs,respectively.The diagnostic performance of the algorithm was tested on an independent test set of 142 WSIs,with the pathologists’consensus diagnosis as the gold standard.Results The receiver operating characteristic(ROC)curves were generated for chronic superficial gastritis(CSuG),chronic active gastritis(CAcG),and chronic atrophic gastritis(CAtG)in the test set,respectively.The areas under the ROC curves(AUCs)of the algorithm for CSuG,CAcG,and CAtG were 0.882,0.905 and 0.910,respectively.The sensitivity and specificity of the deep learning algorithm for the classification of CSuG,CAcG,and CAtG were 0.790 and 1.000(accuracy 0.880),0.985 and 0.829(accuracy 0.901),0.952 and 0.992(accuracy 0.986),respectively.The overall predicted accuracy for three different types of gastritis was 0.867.By flagging the suspicious regions identified by the algorithm in WSI,a more transparent and interpretable diagnosis can be generated.Conclusion The deep learning algorithm achieved high accuracy for chronic gastritis classification using WSIs.By pre-highlighting the different gastritis regions,it might be used as an auxiliary diagnostic tool to improve the work efficiency of pathologists.

Automatic Speaker Recognition Using Mel-Frequency Cepstral Coefficients Through Machine Learning

Automatic speaker recognition(ASR)systems are the field of Human-machine interaction and scientists have been using feature extraction and feature matching methods to analyze and synthesize these signals.One of the most commonly used methods for feature extraction is Mel Frequency Cepstral Coefficients(MFCCs).Recent researches show that MFCCs are successful in processing the voice signal with high accuracies.MFCCs represents a sequence of voice signal-specific features.This experimental analysis is proposed to distinguish Turkish speakers by extracting the MFCCs from the speech recordings.Since the human perception of sound is not linear,after the filterbank step in theMFCC method,we converted the obtained log filterbanks into decibel(dB)features-based spectrograms without applying the Discrete Cosine Transform(DCT).A new dataset was created with converted spectrogram into a 2-D array.Several learning algorithms were implementedwith a 10-fold cross-validationmethod to detect the speaker.The highest accuracy of 90.2%was achieved using Multi-layer Perceptron(MLP)with tanh activation function.The most important output of this study is the inclusion of human voice as a new feature set.

Insights into artificial intelligence in clinical oncology:opportunities and challenges

Cancer continues to be a major public health issue worldwide.Given the complexity of the etiology and pathophysiology of cancer,challenges always exist at every step of cancer management,including early screening,diagnosis,treatment selection,and surveillance.Emerging novel technologies hold promise in addressing a wide range of healthcare problems.Artificial intelligence(AI),which is capable of learning of features from given datasets.

Man Overboard Detection System Using IoT for Navigation Model

Security measures and contingency plans have been established in order to ensure human safety especially in the floating elements like ferry,roro,catamaran,frigate,yacht that are the vehicles services for the purpose of logistic and passenger transport.In this paper,all processes in the event of Man overboard(MOB)are initiated for smart transportation.In MOB the falling person is totally dependent on the person who first saw the falling person.The main objective of this paper is to develop a solution to this significant problem.If a staff member or a passenger does not see the fall into the sea,undesirable situations such as disappearance,injury and death can occur during the period until the absence of the fallen person is noticed.In this paper,a comprehensive and improved solution is provided in terms of personnel and passenger security especially in all the floating elements,in which human resources are intensively involved like passengers,freight,logistics,fishing,business,yacht,leisure and naval vessels.In this case,if the ship’s personnel or passengers fall into the sea in any way,it detected the fallen person into the sea by the sensors in the portable emergency device,which each person will carry.The warning system is activated via the in-ship automation system to which the information is transmitted by wireless communication.Thus,the case of MOB will be determined quickly.Internet of things(IoT)has a key role in identifying the location and information of the person falling into the sea through sensors,radio frequency,GPS and connected devices.Simultaneously,the alarm system on board will be activated and MOB flag(Oscar)will automatically be opened.This paper enables the Search and rescue(SAR)operations to be initiated and accelerated without losing time through decision-making process.

An Efficient and Reliable Multicasting for Smart Cities

The Internet of thing(IoT)is a growing concept for smart cities,and it is compulsory to communicate data between different networks and devices.In the IoT,communication should be rapid with less delay and overhead.For this purpose,flooding is used for reliable data communication in a smart cities concept but at the cost of higher overhead,energy consumption and packet drop etc.This paper aims to increase the efficiency in term of overhead and reliability in term of delay by using multicasting and unicasting instead of flooding during packet forwarding in a smart city using the IoT concept.In this paper,multicasting and unicasting is used for IoT in smart cities within a receiver-initiated mesh-based topology to disseminate the data to the cluster head.Smart cities networks are divided into cluster head,and each cluster head or core node will be responsible for transferring data to the desired receiver.This protocol is a novel approach according to the best of our knowledge,and it proves to be very useful due to its efficiency and reliability in smart cities concept because IoT is a collection of devices and having a similar interest for transmission of data.The results are implemented in Network simulator 2(NS-2).The result shows that the proposed protocol shows performance in overhead,throughput,packet drop,delay and energy consumption as compared to benchmark schemes.

Edge Metric Dimension of Honeycomb and Hexagonal Networks for IoT

Wireless Sensor Network(WSN)is considered to be one of the fundamental technologies employed in the Internet of things(IoT);hence,enabling diverse applications for carrying out real-time observations.Robot navigation in such networks was the main motivation for the introduction of the concept of landmarks.A robot can identify its own location by sending signals to obtain the distances between itself and the landmarks.Considering networks to be a type of graph,this concept was redefined as metric dimension of a graph which is the minimum number of nodes needed to identify all the nodes of the graph.This idea was extended to the concept of edge metric dimension of a graph G,which is the minimum number of nodes needed in a graph to uniquely identify each edge of the network.Regular plane networks can be easily constructed by repeating regular polygons.This design is of extreme importance as it yields high overall performance;hence,it can be used in various networking and IoT domains.The honeycomb and the hexagonal networks are two such popular mesh-derived parallel networks.In this paper,it is proved that the minimum landmarks required for the honeycomb network HC(n),and the hexagonal network HX(n)are 3 and 6 respectively.The bounds for the landmarks required for the hex-derived network HDN1(n)are also proposed.

Fast Evaluation Algorithm for NURBS Surfaces

Through a precise recursion of B-spline bases and the resursive expression of the derivatives of rational surfaces, this paper presents an efficient algorithm for the calculation of NURBS surfaces and all their directional derivatives. The algorithm requires less storage and proves to be stable.

Capturability-based Fuzzy Footstep Planner for a Biped Robot with Centroidal Compliance

Compliance motion and footstep adjustment are active balance control strategies from learning human subconscious behaviors.The force estimation without direct end-actuator force measurement and the optimal footsteps based on complex analytical calculation are still challenging tasks for elementary and kid-size position-controlled robots.In this paper,an online compliant controller with Gravity Projection Observer(GPO),which can express the external force condition of perturbations by the estimated Projection of Gravity(PoG)with estimation covariance,is proposed for the realization of disturbance absorption,with which the robustness of the humanoid contact with environments can be maintained.The fuzzy footstep planner based on capturability analysis is proposed,and the Model Predictive Control(MPC)is applied to generate the desired steps.The fuzzification rules are well-designed and give the corresponding control output responding to complex and changeable external disturbances.To validate the presented methods,a series of experiments on a real humanoid robot are conducted.The results verify the effectiveness of the proposed balance control framework.

Cognitive-emotional feasibility of the effect of visual quality of building form on promoting the sense of place attachment(Case study:Cultural iconic buildings of Iran’s contemporary architecture)

The present research studies the relationship between place attachment and the perception of form’s visual quality in fifteen outstanding contemporary Iranian architectural cultural buildings.This study puts forward the hypothesis that there is a correlation between the quality of building form and the sense of place attachment,in the sense that creating high visual quality through enhancing the quality of building form increases citizens’initial satisfaction with and subsequent attachment to the building.High visual quality influences people’s experience of the environment and improves the quality of life.Place attachment highlights how people,on a personal level,recreate a sense of place for themselves.The present study adopts the descriptive-analytical method as its theoretical framework and the survey as the empirical methodology.Questionnaires were developed using the Likert scale and distributed among experts and ordinary citizens.Data analysis using SPSS and the adoption of descriptive-analytical statistics,correlation analysis,and regression showed the relationship among the characteristics of indicators.The results show a positive correlation between form and place attachment mediated through visual quality,and they are causal conditions for one another.In addition,only some of the buildings under study evoke the same level of place attachment.

Machine learning-enabled optimization of melt electro-writing three-dimensional printing

Melt electrowriting(MEW)is a solvent-free(i.e.,no volatile chemicals),a high-resolution three-dimensional(3D)printing method that enables the fabrication of semi-flexible structures with rigid polymers.Despite its advantages,the MEW pro-cess is sensitive to changes in printing parameters(e.g.,voltage,printing pressure,and temperature),which can causefluid column breakage,jet lag,and/orfiber pulsing,ultimately deteriorating the resolution and printing quality.In spite of the commonly used error-and-trial method to determine the most suitable parameters,here,we present a machine learning(ML)-enabled image analysis-based method for determining the optimum MEW printing parameters through an easy-to-use graph-ical user interface(GUI).We trainedfive different ML algorithms using 168 MEW 3D print samples,among which the Gaussian process regression ML model yielded 93%accuracy of the variability in the dependent variable,0.12329 on root mean square error for the validation set and 0.015201 mean square error in predicting line thickness.Integration of ML with a control feedback loop and MEW can reduce the error-and-trial steps prior to the 3D printing process,decreasing the printing time(i.e.,increasing the overall throughput of MEW)and material waste(i.e.,improving the cost-effectiveness of MEW).Moreover,embedding a trained ML model with the feedback control system in a GUI facilitates a more straightforward use of ML-based optimization techniques in the industrial section(i.e.,for users with no ML skills).

Effects of Heart Rate Variability Biofeedback Therapy on Patients with Poststroke Depression： A Case Study

Poststroke depression （PSD） is one of the common complications of cerebrovascular diseases. Drug therapy and cognitive behavioral therapy are two commonly used methods in current clinical treatments of PSD. However, either method has its own drawbacks： The tbrmer has problems such as the slow onset of action and various side effects, and patients olden have a poor response to the latter Recently, scientists have started using the heart rate variability （HRV） biofeedback approach to treating patients with depression and found significant clinical efficacy. HRV biofeedback requires patients to synchronize heart rate （HR） oscillations, and breathe by slow abdominal breathing （about 6 times/min; i.e., at the resonance frequency of 0. 1Hz）, and thus maximizes HRV. The objective of this study is to examine the impacts of HRV biofeedback on patients＇ emotional improvement and to explore the potential of this approach as an effective, side-effect-free supplement for comprehensive recovery.

Rhythm and synchrony in animal movement and communication

Introduction Animal communication and motoric behavior develop over time. Often, this temporal dimension has communicative relevance and is organized according to structural patterns. In other words, time is a crucial dimension for rhythm and synchrony in animal movement and communication. Rhythm is defined as temporal structure at a second-millisecond time scale (Kotz et al. 2018). Synchrony is defined as precise co-occurrence of 2 behaviors in time (Ravignani .2017).

Low-cost livestock sorting information management system based on deep learning

Modern pig farming leaves much to be desired in terms of efficiency,as these systems rely mainly on electromechanical controls and can only categorize pigs according to their weight.This method is not only inefficient but also escalates labor expenses and heightens the threat of zoonotic diseases.Furthermore,confining pigs in large groups can exacerbate the spread of infections and complicate the monitoring and care of ill pigs.This research executed an experiment to construct a deep-learning sorting mechanism,leveraging a dataset infused with pivotal metrics and breeding imagery gathered over 24 months.This research integrated a Kalman filterbased algorithm to augment the precision of the dynamic sorting operation.This research experiment unveiled a pioneering machine vision sorting system powered by deep learning,adept at handling live imagery for multifaceted recognition objectives.The Individual recognition model based on Residual Neural Network(ResNet)monitors livestock weight for sustained data forecasting,whereas the Wasserstein Generative Adversarial Nets(WGAN)image enhancement algorithm bolsters recognition in distinct settings,fortifying the model's resilience.Notably,system can pinpoint livestock exhibiting signs of potential illness via irregular body appearances and isolate them for safety.Experimental outcomes validate the superiority of this proposed system over traditional counterparts.It not only minimizes manual interventions and data upkeep expenses but also heightens the accuracy of livestock identification and optimizes data usage.This findings reflect an 89%success rate in livestock ID recognition,a 32%surge in obscured image recognition,a 95%leap in livestock categorization accuracy,and a remarkable 98%success rate in discerning images of unwell pigs.In essence,this research augments identification efficiency,curtails operational expenses,and provides enhanced tools for disease monitoring.

3D bioprinted organ-on-chips

Organ-on-a-chip(OOC)platforms recapitulate human in vivo-like conditions more realistically compared to many animal models and conventional two-dimensional cell cultures.OOC setups benefit from continuous perfusion of cell cultures through microfluidic channels,which promotes cell viability and activities.Moreover,microfluidic chips allow the integration of biosensors for real-time monitoring and analysis of cell interactions and responses to administered drugs.Three-dimensional(3D)bioprinting enables the fabrication of multicell OOC platforms with sophis-ticated 3D structures that more closely mimic human tissues.3D-bioprinted OOC platforms are promising tools for understanding the functions of organs,disruptive influences of diseases on organ functionality,and screening the efficacy as well as toxicity of drugs on organs.Here,common 3D bioprinting techniques,advantages,and limitations of each method are reviewed.Additionally,recent advances,applica-tions,and potentials of 3D-bioprinted OOC platforms for emulating various human organs are presented.Last,current challenges and future perspectives of OOC plat-forms are discussed.

Nature:通过控制血管生长来开发治疗癌症等多种疾病的新型疗法

最近，一项刊登在国际杂志Nature上的研究报告中，来自耶鲁大学的研究人员通过研究揭示了如何通过控制血管发育来帮助开发治疗心血管疾病以及癌症的新型疗法。