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An automated embedded system with overweight and location detection of a vessel has been planned, developed and executed. To eliminate overweight issues and able to locate the vessel for stopping accidents, an embedded system has been developed. Based on “Archimedes Principle Formula” an algorithm has been formed, it works on data that have been co...
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Navigating through an environment can be challenging for visually impaired individuals, especially when they are outdoors or in unfamiliar surroundings. In this research, we propose a multi-robot system equipped with sensors and machine learning algorithms to assist the visually impaired in navigating their surroundings with greater ease and indepe...
It is common to use facial expressions to tell if a student is engaged in learning, but there are many situations where this is not practical or reliable. These expressions may simply be too subtle to see or difficult to interpret. In most cases, there are too many students to look around at, and quickly assess the entire room. The solution uses a...
In this study, the design and development of a sensor made of low‐cost parts to monitor inclination and acceleration are presented. Α micro electro‐mechanical systems, micro electro mechanical systems, sensor was housed in a robust enclosure and interfaced with a Raspberry Pi microcomputer with Internet connectivity into a proposed tilt and acceler...
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... The RX, TX and GPIO 0 pins must be disconnected after transferring the program. Secondly, established the connection between the ESP8266 Wi-Fi module and Arduino [29]- [31]. The RX pin of ESP8266 will be connected to TX pin 8 of Arduino and The TX pin of ESP8266 will be connected to RX pin 9 of Arduino which is defined in the code. ...
span>In Bangladesh’s rural regions, almost 30% of the population lives in poverty. Rural residents also have restricted access to nursing and diagnostic services due to obsolete healthcare infrastructure. Consequently, as cardiac failure occurs, they usually fail to call the services and adopt the facilities. The internet of things (IoT) offers a massive advantage in addressing cardiac problems. This study proposed a smart IoT-based electrocardiogram (ECG) monitoring system for heart patients. The system is divided into several parts: ECG sensing network (data acquisition), IoT cloud (data transmission), result analysis (data prediction) and monetization. P, Q, R, S, and T are ECG signal properties fetched, pre-processed, analyzed and predicted to age level for future health management. ECG data are saved in the cloud and accessible via message queuing telemetry transport (MQTT) and hypertext transfer protocol (HTTP) servers. The linear regression method is utilized to determine the impact of electrocardiogram signal characteristics and error rate. The prediction was made to see how much variation there was in PQRST regularity and its sufficiency to be utilized in an ECG monitoring device. Recognizing the quality parameter values, acceptable outcomes are achieved. The proposed system will diminish future medical costs and difficulties for heart patients.</span
... This system is developed so that the system can detect the gas leakage immediately (gas in the air between 1.8% to 10%) and take action for ensuring safety. Design the proposed system to detect or read the value (gas) from specific environment air and analyze the collected value for safety from unexpected incidents [15]. Firstly, it is required to set up the embedded system where LPG gas is used in home (Kitchen). ...
... The device in question can be a smartphone, laptop or tablet (González-Murillo, 2021). After entering the ID and password then proceed with checking the suitability of the ID and password (Shamrat, 2020). If the password and ID match, then you can directly access the lab computer that is already connected to the lab equipment (Purnomo et al., 2021). ...
During the pandemic situation, laboratories with a limited number of equipment cannot be used to their full capacity to avoid over crowded. Thus, innovation is needed in the design and arrangement of laboratories so that they can be accessed remotely with flexible schedule. However, when the community service team studied cases of teaching and learning practices during the pandemic, problems were found, among others, the imbalance between the number of tools and users, as well as limited practical opportunities in online-based learning. Therefore, in this community service a remote laboratory which could be accessed from where the users were as long as they connected to the internet was designed to provide solution to the problems. The designed remote laboratory utilized the Internet of Things (IoT) to help students do practicum on the topic Microspcope by using a remote desktop employing TeamViewer technique as a tool to access the lab computers with the users’ device. As expected, it is proven that remote laboratory can be used to overcome the imbalance between the number of laboratory equipment and laboratory users, and to avoid dangerous crowds during the pandemic as it can be accessed from where the users are located as long as they are connected to the internet.
... Modern technology is all about performance and speed [29][30] [39]. Today is the scientific and technical era. ...
One such complicated and exciting problem in computer vision and pattern recognition is identification using face biometrics. One such application of biometrics, used in video inspection, biometric authentica-tion, surveillance, and so on, is facial recognition. Many techniques for detecting facial biometrics have been studied in the past three years. However , considerations such as shifting lighting, landscape, nose being farther from the camera, background being farther from the camera creating blurring, and noise present render previous approaches bad. To solve these problems, numerous works with sufficient clarification on this research subject have been introduced in this paper. This paper analyzes the multiple methods researchers use in their numerous researches to solve different types of problems faced during facial recognition. A new technique is implemented to investigate the feature space to the abstract component subset. Principle Component Analysis (PCA) is used to analyze the features and use Speed up Robust Features (SURF) technique Eigenfaces, identification, and matching is done respectively. Thus, we get improved accuracy and almost similar recognition rate from the acquired research results based on the facial image dataset, which has been taken from the ORL database.
... If we should send a new picture as knowledge again, the means are played out again and we create a new histogram with database photographs. But we have to examine two histograms to find the coordination image [13][14][15][16]. ...
Human face recognition is distinguished by a method of identifying facts or confirmation that tests personality. The technique essentially relies on two stages, one is face identification, and another is face recognition. Facial recognition applies to a PC device with a few implementations in which human faces can be identified in pictures. Usually, facial identification is achieved by using 'right' data from full-frontal facial photographs. While there are, as a general rule, sufficient situations where full frontal faces will not be available, a valid example is the blemished faces that come from CCTV cameras. Subsequently, the use of fractional facial data as tests is still, to a large extent, an unexplored field of research on the PC-based face recognition problem. In this research, through using incomplete facial evidence, we concentrate on face recognition. By implementing critical analysis to evaluate the presentation of AI using the Haar Cascade Clas-sifier, we proposed and built our framework. Three phases of the proposed face detection method involve the Face Data Gathering (FDG) process, Train the Stored Image (TSI) phase, Face Recognition using the Local (FRUL) Binary Patterns Histograms (LBPH) algorithm, and this classifier computation was tested by splitting it into four phases. In this analysis, to complete the detection phase, we apply Haar feature selection, generating an integral image, Adaboost preparing, Cascading Classifiers. To complete this venture's human protection facial recognition framework with face detection, we used Local Binary Patterns Histograms (LBPH) estimate. In LBPH, a few parameters are used and a dataset is obtained by implementing an algorithm. By adding the LBPH operation and extracting the histograms, I got the Final computational part. "Image Processing Based Human Face Recognition Using Haar Cascade Classifier" Image Processing Based Human Face Recognition Using Haar Cascade Classifier
... This is shown in a tabular format below. [21][22][23][24]. Each algorithm is designed to address the problems in the dataset and make sure they do not affect the outcome of the algorithm. ...
Data is the most valuable resource in the present. Classifying the data and using the classified data to make a decision holds the highest priority. Computers are trained to manage the data automatically using machine learning algorithms and making judgments as outputs. Several data mining algorithms can be obtained for Artificial Neural Network classification, Nearest Neighbor Law & Baysen classifiers, but the decision tree mining is most commonly used. Data can be classified easily using the decision tree classification learning process. It's trained on a training dataset and then implemented on a test set from which a result is expected. There are three decision trees (ID3 C4.5 and CART) that are extensively used. The algorithms are all based on Hut's algorithm. This paper focuses on the difference between the working processes, significance, and accuracy of the three (ID3 C4.5 and CART) algorithms. Comparative analysis among the algorithms is illustrated as well.
... It is seen that the location execution and precision can be improved extraordinarily utilizing enormous scope preparing information [15][16][17][18][19][20][21][22]. This implies contrasting various calculations we need to utilize similar preparing information. ...
This paper intends to evaluate previous works done on different cascading classifiers for human face detection of image data. The paper includes the working process, efficiency, and performance comparison of different cascading methods. These methods are Dynamic Cascade, Haar Cascade, SURF cascade, and Fea-Accu Cascade. Each Cascade classifier is described in the paper with their working procedure and mathematical induction as well. Each technique is backed with proper data and examples. The accuracy rate of the method is given with comparison to analyze the performance of the methods. In this literature, the human face detection process using cascading classifiers from image data is studied. From the study, the performance rate and comparison of different cascading techniques are highlighted. This study will also help to determine which methods are to be used for achieving an accurate accuracy depending on the data and circumstances.
... In this section,basically different papers are discussed with their method,pros and cons of detecting DOS attacks on wireless sensor [1][2][3][4][5] networks. In [6] and [7], Denial of Service Attacks are categorized . ...
Wireless sensor networks are the new emerging technologies that are the combination of wireless devices, small, effective sensors and special embedded system design with them. Basically WSN gathers data from very sensitive and harsh environments. Then after processing,they transmit all the information to base station or user application for their further use. But in their design,there is some design constraints like less memory,power or less secured system. For this they have faced lots of attacks. Denial of service (DOS) is one of the most crucial of them which attacks the whole network system on each layer separately and makes the whole network paralysed and jeopardized. In this review paper, all the attacks of DOS are discussed and their countermeasures are also discussed here attack wise. Introduction: Wireless sensor networks are getting much attention and popularity day by day because of its vast application on different parts of human life. It is basically making life easier by getting the updated information from its combination of wireless technology, tiny sensors and embedded systems and devices. WSN can work in any environment like rain , sunlight, cold breeze and also in harsh environment. So it also has to face some attack on it. Denial of service (DOS) is one of those attacks. Because of its design constraints , it is much weaker against those attacks. So in order to get the proper feedback from the sensor nodes of WSN proper counter measures should be taken against those attacks. Wireless sensor networks are basically a sensory system which sense the different parts of environment and gather needed information. It is used in different sectors like monitoring of traffics, to diagnosis of healthcare problems, nuclear plantation, military network communication,weather update and information collection, ensuring security of a system etc. Wireless sensor networks must deliver security, integrity and correct output. But because of low power consumption, their tiny body structure and limitations of memory, DOS attack easily takes place and security vulnerability increases. Wireless sensor networks are much easier to implement in any situation and environment ,it is also very cost effective super fast than any other sensory device. tacks .
... If the ambient temperature becomes too high, the controller activates the fan to preserve the crop's desired temperature. The Arduino controller turns on the water pump to supply water to the crops if the soil moisture level is insufficient [19]. A remote IoT platform may be used to keep track of the crop's condition. ...
... From the prediction result of both the SURF and CNN models, it is observed that both models demonstrate very high accuracy. Therefore it is safe to use either of the models for facial recognition [45][46][47][48][49]. However, it is to be understood that, CNN model [50], [51] requires a large dataset. ...
In computer vision, facial recognition technology
is used to recognize every person. This approach is a revolution
that is perfect for analyzing a graphic image or a video frame
specially or differently. There are, however, systemic methods
where facial appreciation schemes initiative, typically, and the
effort by comparing chosen facial features from a specific image
with faces in a database. It's also known as a Biometric Artificial
Intelligence-based app that can see a person in extraordinary
detail by dissecting structures related to their facial exteriors
and figures. The professional employs a variety of techniques in
order to complete the mission. The SURF and Neural Network
methods are two of these methods. The writers of this paper
address the methods mentioned above and how they operate.
The emphasis of the debate is on the methods' accuracy rates
and determining which approach produces the most reliable
outcome based on facial image results.
