Transmission Power Control in Wireless Sensor Networks Using Fuzzy Adaptive Data Rate.

As the technology of Internet of Things (IoT) becomes popular, the number of sensor nodes also increases. The network coverage, extensibility, and reliability are also the key points of technical development. To address the challenge of environmental restriction and deployment cost, most sensor nodes are powered by batteries. Therefore, the low-power consumption becomes an important issue because of the finite value of battery capacity. In addition, significant interference occurs in the environment, thereby complicating reliable wireless communication. This study proposes a fuzzy-based adaptive data rate for the transmission power control in wireless sensor networks to balance the communication quality and power consumption. The error count and error interval perform the inputs of a fuzzy system and the corresponding fuzzy system output is guard that is utilized for limiting the upper bounds of data rate and transmission power. The long-term experimental results are introduced to demonstrate that the control algorithm can overcome environmental interference and obtain low-power performance. The sensor nodes have reliable communication under an ultra-low-power consumption. The experimental results show that the total power consumption of the proposed approach has been improved 73% compared with the system without executing the algorithm and also indicate the Packet Error Rate (PER) is close to 1%. Therefore, the proposed method is suitable for the battery supply IoT system.

The Impact of Switching Intention of Telelearning in COVID-19 Epidemic's Era: The Perspective of Push-Pull-Mooring Theory.

Under the impact of COVID-19, medical telelearning education is increasingly becoming urgent to resolve the contradiction between the physical isolation of medical students and the need for on-site clinical teaching. In this study, the push-pull-mooring (PPM) theory is integrated into a comprehensive model as a conceptual PPM framework: push factors (information system quality and perceived risk), pull factors [telepresence (TP), trust, etc.], mooring factors (switching costs), and switching intention. The results show that most hypotheses were positive, but perceived risk did not influence user satisfaction significantly, and switching costs did not provide the impact on switching intention. This study provides a comprehensive empirical analysis of key factors influencing the choice of distance education by medical students through the integrated multi-model framework.

End-to-End Deep Learning by MCU Implementation: An Intelligent Gripper for Shape Identification.

This paper introduces a real-time processing and classification of raw sensor data using a convolutional neural network (CNN). The established system is a microcontroller-unit (MCU) implementation of an intelligent gripper for shape identification of grasped objects. The pneumatic gripper has two embedded accelerometers to sense acceleration (in the form of vibration signals) on the jaws for identification. The raw data is firstly transferred into images by short-time Fourier transform (STFT), and then the CNN algorithm is adopted to extract features for classifying objects. In addition, the hyperparameters of the CNN are optimized to ensure hardware implementation. Finally, the proposed artificial intelligent model is implemented on a MCU (Renesas RX65N) from raw data to classification. Experimental results and discussions are introduced to show the performance and effectiveness of our proposed approach.

A Low-Power WSN Protocol with ADR and TP Hybrid Control.

Most Internet of Things (IoT) systems are based on the wireless sensor network (WSN) due to the reduction of the cable layout cost. However, the battery life of nodes is a key issue when the node is powered by a battery. A Low-Power WSN Protocol with ADR and TP Hybrid Control is proposed in this paper to improve battery life significantly. Besides, techniques including the Sub-1GHz star topology network with Time Division Multiple Access (TDMA), adaptive data rate (ADR), and transmission power control (TPC) are also used. The long-term testing results show that the nodes with the proposed algorithm can balance the communication quality and low power consumption simultaneously. The experimental results also show that the power consumption of the node with the algorithm was reduced by 38.46-54.44% compared with the control group. If using AAA battery with 1200 mAh, the node could run approximately 4.2 years with the proposed hybrid control algorithm with an acquisition period of under 5 s.

A Network Sensor Fusion Approach for a Behaviour-Based Smart Energy Environment for Co-Making Spaces.

User behaviour and choice is a significant parameter in the consumption patterns of energy in the built environment. This paper introduces a behavior-based approach for developing smart energy applications. With the rapid development of wireless sensor networks and the Internet of Things (IoT), human-computer interfaces can be created through the mapping of user experiences. These applications can provide users with dynamic feedback on their energy consumption patterns in their built environment. The paper describes a "Sensible Energy System" (SENS) that is based on user experience design methods with sensor network technology. Through SENS, solar energy simulation is combined with device consumption data in order to achieve an IoT network to facilitate the interaction between user behaviors and electricity consumption. The interaction between users and devices through SENS can not only optimize power consumption, but also provide consumers with additional choice and dynamic decision making regarding their consumption. This article provides an (1) understanding and analysis of users' spatial interaction, explains the (2) planning of the new smart environment design and user experiences, discusses (3) designing a suitable Wireless sensor network (WSN) agent and energy connection, describes (4) the information that has been collected, and (5) incorporates a rooftop solar potential simulation for predicting energy outputs into the sensor network model.

An Innovative Test Method for Tensile Strength of Concrete by Applying the Strut-and-Tie Methodology.

Tensile strength is one of the important mechanical properties of concrete, but it is difficult to measure accurately due to the brittle nature of concrete in tension. The three widely used test methods for measuring the tensile strength of concrete each have their shortcomings: the direct tension test equipment is not easy to set up, particularly for alignment, and there are no standard test specifications; the tensile strengths obtained from the test method of splitting tensile strength (American Society for Testing and Materials, ASTM C496) and that of flexural strength of concrete (ASTM C78) are significantly different from the actual tensile strength owing to mechanisms of methodologies and test setup. The objective of this research is to develop a new concrete tensile strength test method that is easy to conduct and the result is close to the direct tension strength. By applying the strut-and-tie concept and modifying the experimental design of the ASTM C78, a new concrete tensile strength test method is proposed. The test results show that the concrete tensile strength obtained by this proposed method is close to the value obtained from the direct tension test for concrete with compressive strengths from 25 to 55 MPa. It shows that this innovative test method, which is precise and easy to conduct, can be an effective alternative for tensile strength of concrete.

Power Consumption and Calculation Requirement Analysis of AES for WSN IoT.

Because of the ubiquity of Internet of Things (IoT) devices, the power consumption and security of IoT systems have become very important issues. Advanced Encryption Standard (AES) is a block cipher algorithm is commonly used in IoT devices. In this paper, the power consumption and cryptographic calculation requirement for different payload lengths and AES encryption types are analyzed. These types include software-based AES-CB, hardware-based AES-ECB (Electronic Codebook Mode), and hardware-based AES-CCM (Counter with CBC-MAC Mode). The calculation requirement and power consumption for these AES encryption types are measured on the Texas Instruments LAUNCHXL-CC1310 platform. The experimental results show that the hardware-based AES performs better than the software-based AES in terms of power consumption and calculation cycle requirements. In addition, in terms of AES mode selection, the AES-CCM-MIC64 mode may be a better choice if the IoT device is considering security, encryption calculation requirement, and low power consumption at the same time. However, if the IoT device is pursuing lower power and the payload length is generally less than 16 bytes, then AES-ECB could be considered.

A robust variable sampling time BLDC motor control design based upon µ-synthesis.

The variable sampling rate system is encountered in many applications. When the speed information is derived from the position marks along the trajectory, one would have a speed dependent sampling rate system. The conventional fixed or multisampling rate system theory may not work in these cases because the system dynamics include the uncertainties which resulted from the variable sampling rate. This paper derived a convenient expression for the speed dependent sampling rate system. The varying sampling rate effect is then translated into multiplicative uncertainties to the system. The design then uses the popular µ-synthesis process to achieve a robust performance controller design. The implementation on a BLDC motor demonstrates the effectiveness of the design approach.

Computer-aided diagnosis in endoscopy: a novel application toward automatic detection of abnormal lesions on magnifying narrow-band imaging endoscopy in the stomach.

Gastric cancer is the fourth common cancer and the second major cause of cancer death worldwide. Early detection of gastric cancer by endoscopy surveillance is actively investigated to improve patient survival, especially using the newly developed magnifying narrow-band imaging endoscopy in the stomach. However, meticulous examination of the aforementioned images is both time and experience demanding and interpretation could be variable among different doctors, which hindered its widespread application. In this study, we developed a new image analysis system by adopting local binary pattern and vector quantization to perform pattern comparison between known training abnormal images and testing images of magnifying narrow band endoscopy images in the stomach. Our preliminary results demonstrated promising potential for automatically labeled region of interest for endoscopy doctors to focus on abnormal lesions for subsequent targeted biopsy, with the rates of recall 0.46-1.00 and precision 0.39-0.87.

A computer-aided method for automatic localization and thickness measurement of peritoneum in ultrasound images.

This paper presents a method of automatically measuring peritoneum thickness in ultrasound images. In our previous work, a method of manually selecting the region of interest (ROI) area has been developed. To achieve an automatic ROI area selection, two phases: Gaussian high-pass filtering and bilateral filtering, are used in the proposed method. In the bilateral filtering phase, the ultrasound image is enhanced for obtaining more details of the peritoneum so that probable areas can be extracted. In the other phase, the ultrasound image is processed with a Gaussian high pass filter, and the result is used to locate the precise area of peritoneum in the first phase result. The experimental results show that the proposed method has high accuracy and fast processing speed in determining the peritoneum area and its thickness distribution.

Pleural effusions in febrile medical ICU patients: chest ultrasound study.

STUDY OBJECTIVES: To assess the necessity of thoracentesis in febrile medical ICU (MICU) patients, and to evaluate the efficiency and reliability of sonographic effusion patterns for diagnosing empyema. DESIGN AND SETTING: A prospective, 1-year, tertiary-care hospital study of febrile MICU patients with physical, radiographic, and ultrasonographic evidence of pleural effusion. PATIENTS: During this study period, we screened 1,640 patients who had been admitted to the MICU; of these, 94 patients had a temperature > 38 degrees C for > 8 h with evidence of pleural effusion proven by chest radiography and ultrasound. INTERVENTION: Routine thoracentesis and pleural effusion cultures were performed in 94 febrile patients under portable chest ultrasound guidance. Three days later, if the first pleural effusion culture was inconclusive and the patient still had persistent fever of > 38 degrees C, we repeated the diagnostic thoracentesis and pleural effusion culture. In total, 118 procedures were performed in those 94 febrile patients. MEASUREMENTS AND RESULTS: In all, 58 patients (62%) had infectious exudates (parapneumonic, n = 36; empyema, n = 15; urosepsis, n = 3; liver abscess, n = 2; deep neck infection, n = 1; and wound infection, n = 1), 28 patients (30%) had transudates, and 8 patients (8%) had noninfectious exudates. The prevalence of empyema in febrile patients admitted to the MICU was 16% (15 of 94 patients). Analyses of the sonographic patterns of the 15 patients with empyema out of the 118 thoracenteses performed showed the following: anechoic pattern, 0% (0 of 47 procedures); complex nonseptated and relatively nonhyperechoic pattern, 0% (0 of 36 procedures); complex nonseptated and relatively hyperechoic pattern, 100% (2 of 2 procedures); complex septated pattern, 35% (11 of 31 procedures); and homogeneously echogenic pattern, 100% (2 of 2 procedures). Hemothorax was the only complication, and it occurred in two patients (2%). Both patients had a favorable outcome after drainage. CONCLUSION: Portable chest ultrasound examination and ultrasound-guided thoracentesis in febrile MICU patients are safe, feasible, and useful methods for diagnosing thoracic empyema. Our results suggest that only some sonographic patterns of pleural effusion (homogeneously echogenic, complex nonseptated and relatively hyperechoic, and complex septated) deserve aggressive assessment and rapid management.