A Software Package for the Modeling of Electrophysiological Activity Data

A complex of programs has been developed for computer modeling of multichannel time series recorded in various experiments on electromagnetic fields created by the human body. Sets of coordinates and directions of sensors for magnetic encephalographs of several types, electroencephalographs and magnetic cardiographs are used as models of devices. To study the human brain, magnetic resonance tomograms are used as head models; to study the heart, a body model in the form of a half-space with a flat boundary is used. The sources are placed in the model space, for them the direct problem is solved in the physical model corresponding to the device used. For a magnetic encephalograph and an electroencephalograph, an equivalent current dipole model in a spherical conductor is used, for a magnetic cardiograph, an equivalent current dipole model in a flat conductor or a magnetic dipole model is used. For each source, a time dependence is set and a multichannel time series is calculated. Then the time series from all sources are summed and the noise component is added. The program consists of three modules: an input-output module, a calculation module and a visualization module. The input-output module is responsible for loading device models, brain models, and field source parameters. The calculation module is responsible for directly calculating the field and transforming coordinates between the index system and the head system. The visualization module is responsible for the image of the brain model, the position of the field sources, a graphical representation of the amplitude-time dependence of the field sources and the calculated values of the total field. The user interface has been developed. The software package provides: interactive placement of field sources in the head or body space and editing of the amplitude-time dependence; batch loading of a large number of sources; noise modeling; simulation of low-channel planar magnetometers of various orders, specifying the shape of the device, the number of sensors and their parameters. Magnetic and electric fields produced by sources in the brain areas responsible for processing speech stimuli are considered. The resulting multichannel signal can be used to test various data analysis methods and for the experiment planning.

Graphite Classification Based on Improved Convolution Neural Network

In the development of high-tech industries, graphite has become increasingly more important. The world has gradually entered the graphite era from the silicon era. In order to make good use of high-quality graphite resources, a graphite classification and recognition algorithm based on an improved convolution neural network is proposed in this paper. Based on the self-built initial data set, the offline expansion and online enhancement of the data set can effectively expand the data set and reduce the risk of deep convolution neural network overfitting. Based on the visual geometry group 16 (VGG16), residual net 34 (ResNet34), and mobile net Vision 2 (MobileNet V2), a new output module is redesigned and loaded into the full connection layer. The improved migration network enhances the generalization ability and robustness of the model; moreover, combined with the focal loss function, the superparameters of the model are modified and trained on the basis of the graphite data set. The simulation results illustrate that the recognition accuracy of the proposed method is significantly improved, the convergence speed is accelerated, and the model is more stable, which proves the feasibility and effectiveness of the proposed method.

Electronic Guidance Cane for Users Having Partial Vision Loss Disability

Vision is, no doubt, one of the most important and precious gifts to humans; however, there exists a fraction of visually impaired ones who cannot see properly. These visually impaired disabled people face many challenges in their lives—like performing routine activities, e.g., shopping and walking. Additionally, they also need to travel to known and unknown places for different necessities, and hence, they require an attendant. Most of the time, affording an attendant is not easier and inexpensive, especially when almost 2.5% of the population of Pakistan is visually impaired. There exist some ways of helping these physically impaired people, for example, devices with a navigation system with speech output; however, these are either less accurate, costly, or heavier. Additionally, none of them have shown perfect results in both indoor and outdoor activities. Additionally, the problems become even more severe when the subject/the people are partially deaf as well. In this paper, we present a proof of concept of an embedded prototype which not only navigates but also detects the hurdles and gives alerts—using speech alarm output and/or vibration for the partially deaf—along the way. The designed embedded system includes a cane, a microcontroller, Global System for Mobile Communication (GSM), Global Positioning System (GPS) module, Arduino, a speech output module speaker, Light-Dependent Resistor (LDR), and ultrasonic sensors for hurdle detection with voice and vibrational feedback. Using our developed system, physically impaired people can reach their destination safely and independently.

Hospital Paperless Communication Technologies

Nowadays, patient-related records are kept in cumbersome file cabinets that result in wasted effort, during burdensome searches. As a result, when a patient goes to a different hospital, all those records need to be copied or all those tests have to be repeated for the same patient. In the present research, a secure, paperless operating room architecture (PORA) has been implemented which provides easily accessible patient information that can be safely shared between different hospitals. PORA is composed of three modules. The modules are the patient data input module, operating room server module, and treated patient information output module. In all, the modules allow researchers to edit, review and analyze patient-related data easily; as well as giving patients access to their healthcare information. Near Field Communication (NFC) technology supported with symmetric encryption is employed in PORA to provide the information security of transmitted data. NFC is utilized during the collection of medical records through wireless communication. This solution achieves better communication and accuracy among OR staff members. The PORA has been effectively used to help healthcare personnel and patients receiving treatment across different hospital operating rooms. PORA might be a unique solution for seamless patient information sharing between independent operating rooms.

Detection Method and System of the Human Body Characteristic Index Based on TCM

As traditional Chinese medicine (TCM) has gained more and more recognition in the world, Chinese medicine has also played its important role. However, traditional Chinese medicine equipment is relatively deficient, with insufficient functions and low degree of digitalization. For example, existing auscultation equipment can obtain few human characteristic indicators, which is difficult to meet the needs of reference in traditional Chinese medicine diagnosis. Based on this, this paper designed a human body characteristic index detection system based on the principle of traditional Chinese medicine, which includes respiratory and heartbeat signal acquisition device, meridian and acupoint signal acquisition device, temperature signal acquisition device, pulse and blood pressure acquisition device, processing module, keyword module, and output module. The respiratory and heartbeat signal acquisition device is used to collect the respiratory and heartbeat signal of human body. Meridian acupoint signal acquisition device is used to collect human meridian acupoint radio signals. The temperature signal acquisition device is used to collect the infrared temperature light wave signal of human body. Pulse and blood pressure acquisition devices are used to collect pulse and blood pressure signals. The processing module is used to obtain one or more human body characteristic indicators according to one or more of the respiration and heartbeat signals, meridians and acupoints signals, temperature signals, pulse, and blood pressure, including Qi and blood characteristic indicators, viscera and six meridian characteristic indicators, and temperature characteristic indicators. The keyword corresponding module is used to obtain the corresponding keyword representing the physiological state information of human body according to the one or more human body characteristic indicators. The output module is used to output the human body characteristic index and the key words. It includes the key words of Qi and blood state information, the key words of viscera state information, the key words of Qi and blood state information, etc. The system can be used for serious disease screening, chronic disease management, and risk early warning.

Design of Effective Smart Communication System for Impaired People

In communication medium, sharing a conversation dialogue between the normal person and deaf and dumb person is one of the challenging tasks still. The dumb person can practice hand gesture language in their community but not to others. This research article focuses to minimize the difficulty level between these two communities with smart glove devices. Besides, the author believes that result of the proposed model provides a good impact on the dump community. The smart glove contains input, control, and output module to get, process, and display the data respectively. Our proposed model is used to help these communities to interact with each other continuously without any error. The proposed model is constructed with good specification flex sensors. Little change of resistance in flex sensor is providing changes in their gesture language. So this orientation direction is calculated well and gives better results over existing methods. The wireless set can be made with Bluetooth technologies here. Here the gestures are assigned based on the alphabet letter. The sign language performs and gives audible output in the display section of the proposed model. It gives good results in our experimental setup. This research work focuses on good recognition rate, accuracy, and efficiency. The good recognition rate shows the continuous conversation between the two persons. Moreover, this research article compares the recognition rate, accuracy, and efficiency of the proposed model with an existing model.

Network Visualization and Pyramidal Feature Comparison for Ablative Treatability Classification Using Digitized Cervix Images

Uterine cervical cancer is a leading cause of women’s mortality worldwide. Cervical tissue ablation is an effective surgical excision of high grade lesions that are determined to be precancerous. Our prior work on the Automated Visual Examination (AVE) method demonstrated a highly effective technique to analyze digital images of the cervix for identifying precancer. Next step would be to determine if she is treatable using ablation. However, not all women are eligible for the therapy due to cervical characteristics. We present a machine learning algorithm that uses a deep learning object detection architecture to determine if a cervix is eligible for ablative treatment based on visual characteristics presented in the image. The algorithm builds on the well-known RetinaNet architecture to derive a simpler and novel architecture in which the last convolutional layer is constructed by upsampling and concatenating specific RetinaNet pretrained layers, followed by an output module consisting of a Global Average Pooling (GAP) layer and a fully connected layer. To explain the recommendation of the deep learning algorithm and determine if it is consistent with lesion presentation on the cervical anatomy, we visualize classification results using two techniques: our (i) Class-selective Relevance Map (CRM), which has been reported earlier, and (ii) Class Activation Map (CAM). The class prediction heatmaps are evaluated by a gynecologic oncologist with more than 20 years of experience. Based on our observation and the expert’s opinion, the customized architecture not only outperforms the baseline RetinaNet network in treatability classification, but also provides insights about the features and regions considered significant by the network toward explaining reasons for treatment recommendation. Furthermore, by investigating the heatmaps on Gaussian-blurred images that serve as surrogates for out-of-focus cervical pictures we demonstrate the effect of image quality degradation on cervical treatability classification and underscoring the need for using images with good visual quality.

Neighborhood Attentional Memory Networks for Recommendation Systems

Deep learning systems have been phenomenally successful in the fields of computer vision, speech recognition, and natural language processing. Recently, researchers have adopted deep learning techniques to tackle collaborative filtering with implicit feedback. However, the existing methods generally profile both users and items directly, while neglecting the similarities between users’ and items’ neighborhoods. To this end, we propose the neighborhood attentional memory networks (NAMN), a deep learning recommendation model applying two dedicated memory networks to capture users’ neighborhood relations and items’ neighborhood relations respectively. Specifically, we first design the user neighborhood component and the item neighborhood component based on memory networks and attention mechanisms. Then, by the associative addressing scheme with the user and item memories in the neighborhood components, we capture the complex user-item neighborhood relations. Stacking multiple memory modules together yields deeper architectures exploring higher-order complex user-item neighborhood relations. Finally, the output module jointly exploits the user and item neighborhood information with the user and item memories to obtain the ranking score. Extensive experiments on three real-world datasets demonstrate significant improvements of the proposed NAMN method over the state-of-the-art methods.

Inter-sectoral CO2 flows implied in trade for Zhejiang Province, China

The transfer of carbon dioxide (CO2) implied in inter-sectoral trade is significantly affecting the process of reducing CO2 emissions in China. This phenomenon also affects Zhejiang Province, which has the top five GDP in China. In this study, a universal modeling system is developed to clarify CO2 emission reduction responsibilities and visualize relationships of each pair of transfers in Zhejiang Province. The system includes “three modules”, namely input-output module, CO2 emission factor module and ecological network module. The proposed modelling system is employed for sectors of Zhejiang province. Research results demonstrate that industry should assume more responsibility for emission reduction; the existing development models of various industries need to be further adjusted. Achievements of this research will provide a scientific reference and a strong basis for decision-makers to formulate reasonable emission reduction policies in Zhejiang Province.