Multiscale Bidirectional Input Convolutional and Deep Neural Network for Human Activity Recognition

In this paper, we proposed a multiscale and bidirectional input model based on convolutional neural network and deep neural network, named MBCDNN. In order to solve the problem of inconsistent activity segments, a multiscale input module is constructed to make up for the noise caused by filling. In order to solve the problem that single input is not enough to extract features from original data, we propose to manually design aggregation features combined with forward sequence and reverse sequence and use five cross-validation and stratified sampling to enhance the generalization ability of the model. According to the particularity of the task, we design an evaluation index combined with scene and action weight, which enriches the learning ability of the model to a great extent. In the 19 kinds of activity data based on scene+action, the accuracy and robustness are significantly improved, which is better than other mainstream traditional methods.

Computation of Constant Gain and NF Circles for 60 GHz Ultra-low noise Amplifiers

Wireless communication is a constantly evolving and forging domain. The action of the RF input module is critical in the radio frequency signal communication link. This paper discusses the design of a RF high frequency transistor amplifier for unlicensed 60 GHz applications. The Transistor used for analysis is a FET amplifier, operated at 60GHz with 10 mA at 6.0 V. The simulation of the amplifier is made with the Open Source Scilab 6.0.1 console software. The MESFET is biased such that Sll = 0.9<30°, S12 = 0.21<-60°, S21= 2.51<-80°, and S22 = 0.21<-15o. It is found that the transistor is unconditionally stable and hence unilateral approximation can be employed. With these assumptions, the maximum value of source gain of the amplifier is found to be at 7.212 dB and the various constant source gain circles and noise figure circles are computed. The transistor has the following noise parameters: Fmin = 3 dB, Rn = 4 Ω, and Γopt = 0.485<155°. The amplifier is designed to have an input and output impedance of 50 ohms which is considered as the reference impedance.

Instrumentation of realtime monitoring system towards level of C6H12O6, C2H5OH, CO2, temperature in tapai fermentation process

Abstrak. Tapai merupakan makanan yang dihasilkan dari hasil fermentasi, salah satunya berbahan dasar dari ubi kayu. Fermentasi oleh ragi (saccharomyces serevesiae) menjadikan perubahan kimia pada substrat karena aktivitas enzim yang dihasilkan mikroorganisme. Parameter-parameter yang ditinjau adalah perubahan kadar C6H12O6, gas C2H5OH, gas CO2, suhu dan kelembapan dalam proses fermentasi melalui sistem pengukuran elektronik berbasis mikrokontroller Arduino Uno. Rangkaian sensor mengandung modul input yaitu sensor FC-28, sensor MQ-3, sensor MG-811, sensor DHT-11 dan modul pemroses mikrokontroler ATMEGA238 dengan sistem Arduino Uno dan pada komponen output menggunakan layar LCD 2X16. Kadar karakterisasi berdasarkan keluaran ADC (Analog to Digital Converter) untuk C6H12O6 adalah 535 untuk tapai ubi kayu. Kadar C6H12O6 akan terus menurun dari hari pertama sampai hari keempat yang mencapai 175 pada tapai ubi kayu. Diperoleh nilai akhir kadar gas C2H5OH yaitu 582. Kadar gas CO2 406 untuk tapai ubi. Selanjutnya nilai suhu 31oC untuk tapai ubi dengan nilai kelembaban 95RH. Waktu panen tapai dapat dipersingkat yaitu dari 7 hari menjadi 4 hari. Penelitian ini diharapkan dapat memberikan informasi pemakaian yang dapat dipergunakan untuk tampilan komposisi tapai secara komersial atau untuk tujuan kesehatan. Abstract. Tapai is food produced from fermentation, one of which is made from cassava. Fermentation by yeast (Saccharomyces serevesiae) causes chemical changes in the substrate due to the activity of enzymes produced by microorganisms. The parameters reviewed are changes in levels of C6H12O6, C2H5OH gas, CO2 gas, temperature and humidity in the fermentation process through an electronic measurement system based on the Arduino Uno microcontroller. The sensor circuit contains an input module, namely FC-28 sensor, MQ-3 sensor, MG-811 sensor, DHT-11 sensor and ATMEGA238 microcontroller processing module with the Arduino Uno system and the output component uses a 2X16 LCD screen. The grade based on the ADC (Analog to Digital Converter) output for the C6H12O6 is 535 for cassava tapai. Levels of C6H12O6 will continue to decline from the first day to the fourth day reaching 175 in cassava tapai. Obtained the final value of C2H5OH gas content is 582. CO2 gas content of 406 for cassava tapai. Furthermore, the temperature value of 31oC for cassava tapai with a humidity value of 95RH. The harvest time for tapai can be shortened from 7 days to 4 days. This research is expected to provide usage information that can be used to display tapai composition commercially or for health purposes. Keywords: fermentation, tapai, cassava, FC-28, MQ-3, MG-811, DHT11, Microcontroller.

McpT, a broad range carboxylate chemoreceptor in Sinorhizobium meliloti

Chemoreceptors enable the legume symbiont Sinorhizobium meliloti to detect and respond to specific chemicals released from their host plant alfalfa, which allows the establishment of a nitrogen-fixing symbiosis. The periplasmic region (PR) of transmembrane chemoreceptors act as the sensory input module for chemotaxis systems via binding of specific ligands, either directly or indirectly. S. meliloti has six transmembrane and two cytosolic chemoreceptors. However, only the function of three of the transmembrane receptors have been characterized so far, with McpU, McpV, and McpX serving as general amino acid, short-chain carboxylate, and quaternary ammonium compound sensors, respectively. In the present study, we analyzed the S. meliloti chemoreceptor McpT. High-throughput differential scanning fluorimetry assays, using Biolog Phenotype Microarray TM plates, identified fifteen potential ligands for McpT PR , the majority classified as mono-, di-, and tri-carboxylates. S. meliloti exhibited positive chemotaxis toward seven selected carboxylates, namely, α-ketobutyrate, citrate, glyoxylate, malate, malonate, oxalate, and succinate. These carboxylates were detected in seed exudates of the alfalfa host. Deletion of mcpT resulted in a significant decrease of chemotaxis to all carboxylates except for citrate. Isothermal titration calorimetry revealed that McpT PR bound preferentially to the monocarboxylate glyoxylate, and with lower affinity to the dicarboxylates malate, malonate and oxalate. However, no direct binding was detected for the remaining three carboxylates that elicited an McpT-dependent chemotaxis response. Taken together, these results demonstrate that McpT is a broad range carboxylate chemoreceptor that mediates chemotactic response via direct ligand binding and an indirect mechanism that yet needs to be identified. IMPORTANCE Nitrate pollution is one of the most widespread and challenging environmental problems, mainly caused by the agricultural over-application of nitrogen fertilizers. Biological nitrogen fixation by the endosymbiont Sinorhizobium meliloti enhances the growth of its host Medicago sativa (alfalfa), which also efficiently supplies the soil with nitrogen. Establishment of the S. meliloti - alfalfa symbiosis relies on the early exchange and recognition of chemical signals. The present study contributes to the disclosure of this complex molecular dialogue by investigating the underlying mechanisms of carboxylate sensing in S. meliloti . Understanding individual steps that govern S. meliloti -alfalfa molecular cross-talk helps in the development of efficient, commercial bacterial inoculants that promote the growth of this most cultivated forage legume in the world and improves soil fertility.

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.

An Improved Segmentation Method for Automatic Mapping of Cone Karst from Remote Sensing Data Based on DeepLab V3+ Model

The South China Karst, a United Nations Educational, Scientific and Cultural Organization (UNESCO) natural heritage site, is one of the world’s most spectacular examples of humid tropical to subtropical karst landscapes. The Libo cone karst in the southern Guizhou Province is considered as the world reference site for these types of karst, forming a distinctive and beautiful landscape. Geomorphic information and spatial distribution of cone karst is essential for conservation and management for Libo heritage site. In this study, a deep learning (DL) method based on DeepLab V3+ network was proposed to document the cone karst landscape in Libo by multi-source data, including optical remote sensing images and digital elevation model (DEM) data. The training samples were generated by using Landsat remote sensing images and their combination with satellite derived DEM data. Each group of training dataset contains 898 samples. The input module of DeepLab V3+ network was improved to accept four-channel input data, i.e., combination of Landsat RGB images and DEM data. Our results suggest that the mean intersection over union (MIoU) using the four-channel data as training samples by a new DL-based pixel-level image segmentation approach is the highest, which can reach 95.5%. The proposed method can accomplish automatic extraction of cone karst landscape by self-learning of deep neural network, and therefore it can also provide a powerful and automatic tool for documenting other type of geological landscapes worldwide.

Development of a data acquisition program to measure the exhaust time of a pneumatic valve

This paper deals with development of a data acquisition program to measure the exhaust time of a mechanical actuated pneumatic valve. The volume, which will be exhausted is determined by a so-called end of line testing procedure. The data acquisition unit is an Advantech ADAM 4117 8 channel analogue input module. The measuring and logging program is developed in LabVIEW software. The program provides the data in comma separated value file format for postprocessing opportunities.

Inherent regulatory asymmetry emanating from network architecture in a prevalent autoregulatory motif

Predicting gene expression from DNA sequence remains a major goal in the field of gene regulation. A challenge to this goal is the connectivity of the network, whose role in altering gene expression remains unclear. Here, we study a common autoregulatory network motif, the negative single-input module, to explore the regulatory properties inherited from the motif. Using stochastic simulations and a synthetic biology approach in E. coli, we find that the TF gene and its target genes have inherent asymmetry in regulation, even when their promoters are identical; the TF gene being more repressed than its targets. The magnitude of asymmetry depends on network features such as network size and TF-binding affinities. Intriguingly, asymmetry disappears when the growth rate is too fast or too slow and is most significant for typical growth conditions. These results highlight the importance of accounting for network architecture in quantitative models of gene expression.