With the rise of the Internet of Things (IoT), ambient vibration energy harvesting technology using piezoelectric transducers has drawn much research interest in realizing the power supply for wireless sensor networks (WSNs). To improve the efficiency of energy harvesting, an interface circuit is required to operate energy management action. The synchronized switch harvesting on inductor (SSHI) method is the most efficient method of extracting energy from the piezoelectric transducer. This paper presents a self-powered piezoelectric energy harvesting (PEH) interface circuit, which integrates a SSHI rectifier with self-adaptive switch control. The proposed circuit includes an active diode in each LC resonant tank to reduce the reverse current of the inductor. The SSHI rectifier, therefore, has high adaptability to external inductors and piezoelectric transducers. The switch control circuit of this paper is directly powered by the rectified voltage to avoid an external power supply for higher integration. Compared with the conventional SSHI rectifier, the proposed implementation achieves high voltage flipping efficiency with a small flipping inductor. The proposed circuit is designed in 180nm CMOS process, and the chip area is 0.163mm2. The simulation result shows that the proposed circuit achieves cold start-up operation without any external power supply and can work with high efficiency when the external inductor is varying from 1 to 1000μH. The simulated maximum output power of the proposed design is 2.5× higher than that of the ideal full-bridge rectifier.
Today's technology has allowed us to have within our reach services and experiences that previously could only be offered by specialized centers due to their high cost of manufacture and acquisition. Therefore, there are devices with which it is possible to monitor our cardiac system, inadequate postures, muscular ailments and nervous activity, among others. Now that our habits have changed and work and studies are done from home due to the pandemic that happened, it is common to suffer ailments in various parts of our body, most commonly in the back, for this reason it is necessary to adopt healthy habits such as a correct body posture in order to reduce the risks related to our spine and the development of injuries that derive from this. This scientific article deals with the design, simulation and construction of a low-cost IoT posture corrector, with commercial electronic materials, its effectiveness was evaluated through the analysis of cost-benefit, comfort and efficiency of the device by means of a perception of part of some tests performed to a total of 12 people during the period of 8 weeks. Satisfactory results were obtained with 75% comfort of use and 75% effectiveness by means of a rating scale evaluation.
Wireless receivers with multiple modes and standards require highly linear Pre-filters to prevent strong out-of-band blocker signals from corrupting in-band signals. A prefaced SAW(surface acoustic wave) filter needed in conventional receiver structures is neither tunable nor fully integrated. However, when the multiple frequency bands demand to be supported, the combination of multiple SAW filters increases the circuit's size and cost. This paper introduces a fully integrated, reconfigurable wideband wireless receiver based on N-path technology. The circuit utilizes 65nm CMOS technology and applies the Gain-Boosted approach to reduce the noise factor to 3.3dB. The out-of-band suppression ability of the circuit is improved by N-path technology, and the out-of-band IIP3 is up to +33.51dBm. The circuit's power consumes 46-72mW at 0.5-2GHz frequency, and the layout area is 700*1000μm2.
Network status prediction and efficient routing play important roles in low earth orbit (LEO) satellite networks. It is necessary to establish an accurate network model for state prediction in LEO satellite networks with high mobility. According to the predicted state, route planning is needed to satisfy requirements. In this work, we propose a satellite network state prediction and route planning method combining the Message Passing Neutral Network (MPNN) algorithm and Q-Leaning. Firstly, a network topology establishment method is proposed based on the idea of Virtual Node (VN). Then, the satellite network status is accurately predicted based on the MPNN algorithm with the attention mechanism. Q-Learning is further applied to obtain the optimal routing path after prediction. Finally, through simulation experiments, the accuracy and generalization of our algorithm is verified, and the route policy can be obtained.
This paper proposes a power flow analytical technique for a networked microgrid for grid-connected and islanded operations. This technique is convenient for solving the power flow of different topologies due to the changes in the switching status of interconnected switches in the planning and operating stages. Furthermore, it can be used to calculate the bus voltage, line flow, and system loss for scheduling and dispatching the battery energy storage system in energy management systems. First, the bus and branch data of the sample system were established in the OpenDSS software. Next, the load, photovoltaic, and the battery energy storage system data from the database were obtained from the metering system. Second, the OpenDSS, InfluxDB database, and Grafana web-based visualization tool programs were integrated with Python language. Then, the OpenDSS can solve the power flow after the switching state is changed. Finally, the proposed approach was used to solve the power flow of a networked microgrid under different operating conditions. The numerical results demonstrate that the proposed analytical technique helps solve the power flow of networked microgrids.
Deep learning (DL) application is proven helpful in a vast research field. One recent trend is to employ DL in radio frequency (RF)-based indoor localization. The fingerprint technique is the most used indoor localization technique known for its accuracy and performance. However, the fingerprint technique pays a high cost and effort in offline database construction, while its performance solely depends on the database density. Moreover, to apply deep learning, we also need a large dataset for it to learn efficiently. We propose to implement a DL-based fingerprint technique to tackle both problems of dataset scarcity and localization performance. We propose the DL's discriminative model, i.e., multilayer perceptron (MLP), for classification tasks. For the fingerprint database augmentation, we employed the generative model, i.e., Generative adversarial networks (GANs). We considered using a received signal strength indicator (RSSI) from a measurement campaign based on Wi-Fi devices for the database. The total area of interest is 25 m2 inside the typical classroom environment, and we consider the 25 fingerprint locations as labels. We have a dataset of 1,250 rows x 8 columns (from 8 reference points). From the results, by using only 50% of actual data combined with the 125 synthetic data, we can improve the accuracy by more than 200% compared to only using 50% of actual data and show a 60% improvement in the loss. The combination of 100% actual data and 125 synthetic data gives the best accuracy and loss performance of 0.76 and 0.85, respectively. It gives an improvement of 144% in accuracy and 200% loss performance. By implementing deep learning for fingerprint techniques for data augmentation and classification, we can achieve good performance and reduce the workload of fingerprint database construction.
Electric Vehicles (EVs) are extremely efficient and produce zero emissions which is a better alternative than conventional IC engine vehicles. The electric motor is the heart of every EV and is the key to realizing the optimum balance of top speed, acceleration, deceleration, and achievable distance per charge. In such conditions, the continuous monitoring of EV motors demands efficient methods to avoid catastrophic and vital loss. This paper proposed a multi-sensor-based approach for bearing fault diagnosis of an electric vehicle motor, i.e., a Switched Reluctance Motor (SRM), under constant and varying speed conditions. Initially, raw acoustic and vibration data are acquired at constant and varying speed conditions and decomposed by the Hilbert transform, followed by feature extraction. Thereafter, a Bayesian optimized Neural Network (BoNN) has been proposed for evaluating the performance of individual sensors using five bearing conditions of the SRM. The experimental findings depict that the proposed strategy involving different modality sensors provides promising and reliable results with a maximum accuracy of 100 %.
The Philippines is a southeastern Asian archipelago of over 7,640 islands. The Philippine National Police (PNP) is tasked with upholding the law, preventing and controlling crime, maintaining peace and order, and ensuring public safety and internal security with the active support of the community. Currently, the country has 1,766 police stations. Criminal identification procedures take time to complete due to geographical challenges. To address such challenges, the Mobile Automated Fingerprint Identification System (MAFIS) was developed. The integration of face recognition with the existing MAFIS makes it MABIS, a Mobile Automated Biometric Identification System. The MABIS allows law enforcers to use both fingerprint and face recognition to identify law offenders by searching the criminal database for existing records. If found, criminal records will be retrieved for investigation and referenced. If no information is found, a new record will be added. The goal of the paper is to integrate a Face Recognition (FR) system into an existing MAFIS by employing an open-source facial recognition service.
The circuit of diode envelope detector is simple and easy to implement, which is of great significance to the detection of ordinary amplitude modulation waves. In engineering practices, the designed detector is required to be able to restore the original signal very well, so its working principles and distortions are worth studying. In the following paragraphs, the characteristics of the output voltage waveforms of different types of detectors are compared, four kinds of common distortion cases are analyzed, and the ranges of parameters to avoid distortion are given. Since most books focus on the analysis of the theory such as the derivation of equations but lack direct presentation of the output of the circuit, the simulation solution based on the software Multisim is paid much attention to in the following text boxes, which can visualize the detection of the circuit. By scanning the ranges of values of the parameters, the effect of the relevant parameters on the distortion of the detector output voltage is specifically investigated to further verify the theoretical derivation. According to these analyses, it is easy to design diode envelope detectors and select reasonable device parameters.
The search for efficient alternatives for water generation in arid areas or areas where it does not rain regularly has affected agriculture to a great extent due to this scarcity of water resources. Despite being a growing problem and affected even more by climate change, the search for alternative solutions to generate water in these arid areas has affected agriculture to a great extent due to the scarcity of water resources. The search for alternative solutions to generate water in these agricultural areas ranges from the use of rainwater to the use of the atmosphere to obtain water using the thermoelectric effect. This article focuses on the latter section, the device was designed using Peltier cells, powered by a solar panel through a PID control using Ziegler-Nichols tuning and a MPPT regulator circuit, all the circuitry was placed in a 3D printed housing. The water production obtained from the designed circuit was approximately 0.8 - 1.1 mL/h. The device responded correctly to the implemented PID control, this obtained water can be used for irrigation of small nurseries and even increase the water production by placing in parallel more Peltier cells.
In this paper, we propose a no reference stereoscopic video quality assessment (SVQA) method that takes self-attention fusion and different resolution level into account, which reduces loss of detail information. Firstly, considering that left and right branches have semantically inconsistent information, we build a self-attention feature fusion (SAFF) module, which maintains high resolution in both channel and spatial dimensions in the feature fusion process. Secondly, we design a multi-scale module with asymmetric convolution (MSMAC) to obtain multi-scale information and to enhance representation ability of square convolution. Finally, we fuse and weight the features of different resolution levels and then get the final quality score with regressing weighted features. We also consider the depth information between left and right videos and construct the disparity branch. Experimental results on two public stereoscopic video quality databases show that our proposed method outperforms other methods.
This paper presents a method of sulfate reduction of lead-acid batteries using high-frequency pulses. It is a suitable electronic circuit that is attached in parallel to the two electrodes of each battery to continuously generate a high-frequency pulse with different duty cycle lengths. Experimental results show that charging a lead-acid battery with a high-frequency pulse gives very positive results, which are that the internal resistance of the battery is significantly reduced and the capacity is increased. Besides, the microcontroller-based system with sensors also allows to monitor of the temperature of the battery case and stops the function of the automatic pulse generator before reaching the failure threshold to protect and prolong the life of the battery.
Due to the needs of automation in today's industrial environment, the demand for the Internet of Things is increasing. Here, we use an innovative location-based Bluetooth MESH to provide the IoT MESH system in the factory or at home, and use the Bluetooth broadcaster function as the main wireless transmission of the network. By receiving the acknowledgement packet and the positions of the relay nodes, it provides a first-in, first-out Queue and a Fast-Scan routing method for the transmission algorithm, which can achieve one-to-one and many-to-many routing. Multiple PUB/SUB two-way communication can be transmitted through the cloud as the center or decentralized. Basically, the BLE nodes in the field can communicate with each other, and the transmission delay is in the range of tens to hundreds of milliseconds. It can monitor the physiological condition of the wearable device on the mobile users, and can also control the position of personnel, and can graft the wired sensor already installed in the factory into our BLE MESH system. One example realized is to connect the Modbus communication to our system, and in the future LE audio can also come into our system when it is officially launched on the market
Artificial intelligence has been developed in many fields due to the rapid spread of the Internet worldwide and the rapid development of computer computing power in the last decade. During this development, many branches of AI, such as computer vision, natural language processing, graph deep learning, and reinforcement learning, have been proposed and studied, making AI ubiquitous in people's lives today. In recent years, cross-domain applications have been a very hot topic, where the combination of graph deep learning and reinforcement learning has achieved good results in several fields. In our previous work, we used graph deep learning as an encoder to encode game states on Tetris game and fed it into a reinforcement learning algorithm for training game agents with success. In this paper, we build on our previous work and explore the impact of different graph construction methods on the performance of the game Agent. We compare the performance between graph structures with different number of edges and finally find that there are cases where the Game Agent can maintain good performance even when using graph structures with significantly fewer edges.
According to the World Health Organization (WHO), the harm caused by noise to human health ranks second only to air pollution in urban areas. In several of Vietnam's major cities, noise levels frequently exceed the allowable limit and tend to worsen. In order to improve urban management, particularly noise pollution, proper noise level assessment equipment is required. Furthermore, the collection and processing of audio data for automatic and intelligent control applications is currently a promising research field. For the reasons stated above, we have decided to carry out the graduation thesis on the topic "Design and implementation of a portable audio signal spectrum analyzer". The objective of the project is to study and implement a compact, affordable device for measuring and displaying sound intensity levels. The design employs the ESP32 central control board to communicate with the MEMS ICS43434 Microphone via the I2S (Inter-IC Sound) standard, allowing for high-quality and efficient sound recording directly from the environment. In real time, the audio data is analyzed using the Fast Fourier Transform (FFT) algorithm. The magnitude of each frequency component of the recorded sound can be directly observed on the LCD screen or magnified for display on an LED panel (if necessary). Experiments using the designed equipment to analyze a variety of sound sources yielded very promising preliminary results.
The increase in the number of people suffering from respiratory system diseases is related to environmental pollution. Pulse oximetry equipment, which measures blood saturation levels, is of great importance in the evaluation of patients with respiratory disorders. Oxygen levels and other vital signs are important indicators of an individual's health. Monitoring these signs can help protect against health problems. Historically, measuring blood oxygen levels has been difficult and time-consuming. Often, it is not possible to do so during risky situations, such as during surgeries. That said, current methods are much more accurate and can be used to monitor patients in a variety of situations, but are relatively expensive, depending on the equipment and its performance. In the present work, a pulse oximetry module is designed and implemented using a low-cost acquisition card, the NIDAQ USB 6008, to perform noninvasive measurements in hospitalized patients as well as at home. The assembly has the advantage of avoiding failures due to physical maintenance, integrated circuit cabling and/or calibration. Due to its connection to a computer, the data obtained can be contrasted with other vital signs obtained in other hospital centers so that the information can be shared by experts in emergency situations.
The rapid development of technology has impacted various aspects of life, including the way individuals, organizations, and governments deliver accurate, effective, and efficient information. XYZ local government, which is responsible for serving the community in the trade field, manages its information through the Communication and Information Agency (Diskominfo) of the XYZ region. Diskominfo employs technological advancements to provide the people of the XYZ region with direct access to accurate, precise, and reliable data through their website. However, the security of the website has become a crucial aspect to prevent attacks from malicious individuals that can cause damage to the system and harm the website owner. To analyze the website's security loopholes and vulnerabilities, the author performed a simulation of an attacker. The analysis aimed to evaluate the level of risk and confidence in the website. The results showed 42 alerts categorized into four risk levels: 9 vulnerabilities with a high-risk level, 13 vulnerabilities with a medium-risk level, 11 vulnerabilities with a low-risk level, and 9 vulnerabilities with an informational-risk level.
Biometric technology is a very advanced security method that is difficult to duplicate. Recently, fingerprint and face recognition have become popular with the public through their practical use in unlocking smartphones. Research on activity-based biometrics using data from smartphones, smartwatches, and small sensors has been conducted. This study presents a machine learning (ML)-based approach for automatically identifying users based on their daily activity patterns. We used a publicly available human activity dataset, which was collected from eight subjects using on-body three wearable sensors (accelerometer, gyroscope, and magnetometer). First, we extract 21 time and frequency-domain features. Secondly, more efficient features were selected using a minimum redundancy-maximum relevance (mRMR)-based feature selection method. Thirdly, four ML-based approaches, namely random forest, decision tree, 1-dimensional convolutional network, and extra tree (ET) were implemented for user identification. Our experimental results illustrated that the mRMR-based ET classifier produced a higher recognition accuracy rate of 99.5%. Our results indicate that our proposed method can more effectively identify users based on their daily living activities.
A banyan-type network is a multistage switching network, made up of unit switches with two inputs and two outputs. The network has been used as a component of various communication and computer systems. Banyan-type networks are categorized into blocking and rearrangeable networks. A rearrangeable network is significant for some applications because it can connect inputs and outputs for any request without blocking. However, previous studies have not completely defined and categorized the class of rearrangeable banyan-type networks. This study presents a systematic scheme for discovering previously unreported rearrangeable banyan-type networks. The presented scheme uses the conjunctive normal form–satisfiability (CNF–SAT) modeling of connection routing to test the rearrangeability. In addition, to find truly new rearrangeable networks, networks found to be rearrangeable are compared with known rearrangeable networks by a graph isomorphism algorithm. The scheme applies these processes to networks generated exhaustively by assigning link configuration rules expressed as bit permutations to interstage links. New rearrangeable networks will be discovered if they exist by performing the scheme. The results of performing the scheme are also presented. The result reveals previously unknown networks, which are highly probably rearrangeable and are not isomorphic to any known rearrangeable networks.
Technology is developing rapidly along with the times; one example of technological developments is the development of the use of websites in daily activities. Many institutions and entities have utilized the use of websites to support their business processes. For example, one of the faculties of XYZ University has used a website to help with administrative activities. One of the websites of the faculties at XYZ University is the final assignment proposal dashboard website which contains plots of the final assignment supervisor and the title of the final assignment. However, with the development of a technology, the development of vulnerabilities or attacks against the technology also increases. Therefore, it is necessary to carry out a vulnerability assessment method to be able to find out the vulnerabilities that exist on a website and also solutions that can be implemented to overcome these vulnerabilities. In this study, a vulnerability assessment will be carried out on the XYZ University students' final project proposal dashboard website using Nmap and Acunetix tools. The accuracy level of nmap is 13.25%, while Acunetix has 100% accuracy with the results obtained after the vulnerability assessment process, namely there are 12 vulnerabilities on the XYZ University student final project proposal dashboard website with Nmap detecting 3 medium risk vulnerabilities and 1 low risk vulnerability while Acunetix managed to detect 2 medium risk vulnerabilities and 6 low risk vulnerabilities
