BUZZFEET: Blind Man Shoes

It is well known that visually disabled people often find it difficult to interact with their nearby environment. In this paper, we propose a design and show the real-time implementation of the smart embedded blind stick. The main components of the Smart Blind Stick comprise Arduino Uno, Ultrasonic Sensor, IR sensor, GPS sensor, and Buzzer. Here, the ultrasonic sensor and IR sensor are used for obstacle detection in the path of blind person, buzzer is used to make the person alert and GPS sensor is used to track the blind person if she or he lost their path. Arduino microcontroller will be used to control the whole scenario, where the smart stick is a closed-loop system to monitor the nearby environment continuously, and send us the output, by comparing the input result in the form of a buzzer sound.KeywordsIOTMicrocontrollerBlind stickSensorVisual impairmentArduino UnoObstacleUltrasonic sensorETA

Motorized accidents on many motorways are caused by sudden braking. Sudden braking occurs because of the distance between the driver and the obstacles in front that are too short, and the driving speed is too high, there is a delay in braking and a surge of force. Supposedly, vehicle motion when braking occurs must comply with the physics principle of following the kinematics concept of non-uniform motion slowing down between the driver with obstacles in front, and the concept of non-uniform circular motion slowing down on the wheels of a motorized vehicle. For this reason, an automatic braking instrumentation system is created using the principle of non-uniform motion slowing down and nonuniform circular motion slowing down, with distance and speed variables obtained from the proximity sensor and the speed sensor mounted on a motorized vehicle. The study began by conducting the first is characterization of the braking system of a motorcycle engine as a whole. Next, conducting a literature study from information obtained in the field. Next, conducting an appropriate braking control system study. Next, braking control system manufacturing and modeling mathematical as well as applications on motorbike vehicles. Next, tests are carried out in open fields and on the road starting from the engine starting the engine and moving forward until it stops again after braking. The braking control system development approach that is being carried out is the application of digital technology by balancing the needs of motorcyclists. This is done so that there is no gap between the needs of motorcyclists and the technology applied. In general, the technology applied includes the main parts, the controller unit as microcontroller, distance measuring unit use ultrasonic sensor, and speed sensor use hall effect sensor, servo as mechanical drive automatic brakes, and battery as power source. The units were developed according to the real conditions, and from the results of measurements of the effect of distance to deceleration by 93 percent.

Remote sensing devices have been implemented on the I-64 Bridges over US60 in Franklin County, KY. One of the girders in the westbound bridge has been previously repaired due to unexpected fatigue cracking. The exterior girder in the eastbound bridge has shown signs of impacts due to the traversing trucks on US60. Sensing and recording devices such as strain and temperature gauges, infrared sensors, ultrasonic height detectors, and an accelerometer have been installed. Specifically, eleven strain gauges are used on the repaired girder, impacted girders, and girders adjacent to them. Two sets of infrared sensors, ultrasonic detectors, and video cameras are placed to capture the impacting truck(s). Overall structural responses will be studied through data collected from the strain and temperature gauges, and accelerometer. Data are stored on-site, but the investigator has the flexibility of transmitting or viewing the data, live or stored, via an internet connection.

The most important measurement is surface analysis for railway track maintenance, as changes indicate any probable faults in the surface topography.This publication proposes a computer vision system based on a rail surface defect inspection method.Various algorithms related to noise reduction, filtering, and thresholding.Apply segmentation and feature extraction to process images of railroad surface defects and cracks.Mainly implemented on computers.To improve speed and complexity, Embedded systems should be used to implement algorithms.These techniques were created using the Intel OpenCV package for a variety of software platforms, including MATLAB and C++.Following that, adaptive thresholding and feature matching techniques are used to extract and discover the particular regions of interest linked to the flaws.The evaluation of the inspected rail head flaws is then determined by calculating the rail head wear rate and surface.The proposed algorithm's experimental results and benchmarks that used the software algorithm are presented in the results section.

Due to their numerous uses in manufacturing and modern society, Human Following robots have been studied and actively developed for many years. The robot needs to use a variety of methods, including robot control algorithm, human target detection, and obstacle avoidance. This project showcases a four-wheeled robot that follows people while equipped with an ultrasonic sensor to prevent collisions. The user is located by the robot using feedback from ultrasonic sensors. This robot can assist humans in carrying a variety of accessories, luggage, and highly sensitive chemicals in a number of different applications. Using ultrasonic sensors, an algorithm was created to track and position a human user while maintaining a constant distance from the robot. In the presence of numerous such obstacles, the IR sensors are used to uniquely identify the user. The most crucial element for an autonomous robot is communication between the robot and the user. There has been a noticeable increase in awareness of the use of these technologies. The creation of such robots is tangentially related to this research. Fully autonomous robots ought to be able to connect with the person controlling them in some way in addition to performing the tasks that are expected of them. Two IR sensors and an ultrasonic sensor are employed. Ultrasonic sensors are used to move the robot backward and IR sensors are used to follow a person or object.

The government of India aims at “Swachh Bharat Mission” and for that we need smart city with smart streets enabled with smart garbage monitoring system. We see garbage bins around us placed at public places overflowing with due to increase in waste and results into unhygienic conditions for people and leads to deadly diseases. Thus to improve this situation, we proposed this project. In our proposed system, there are multiple smart garbage trash bins on a microcontroller board platform (Arduino Board) located throughout any city or the campus or hospital. The Arduino Board is interfaced with GSM modem and ultrasonic sensor. Once the level of threshold is being crossed, then ultrasonic sensors will trigger the GSM module which in turn continuously alert the authorised person by sending SMS reminder after until the dustbin is cleaned. Beside this, we will also create the central system that will keep showing us the current status of garbage on mobile web browser with html page by wi-fi. With the help of this, we will create shortest path for garbage collection vehicles using Dijkstra Algorithm. This is real time waste management by using smart trash bins that can be accessed anytime anywhere by the concerned person. Keywords: IoT; Smart city; Smart Garbage Bins; Arduino; Ultrasonic Sensors; GSM; Real time Analytics; Route Optimization

The visually impaired are at a considerable disadvantage because they often lack the information for avoiding obstacles and hazards in their path. They have very little information on self-velocity, objects, direction - which is essential for travel. Previously developed navigation systems use costly equipment which is often not affordable by the common blind community. The navigation systems available are heavy and very complicated to operate. This research has been aimed at design and development of a smart and intelligent cane which helps in navigation for the visually impaired people. The navigator system designed will detect an object or obstacle using ultrasonic sensors and gives audio instructions for guidance. The signals from the ultrasound sensor are processed by a microcontroller in order to identify sudden changes in the ground gradient and/or an obstacle in front. The algorithm developed gives a suitable audio instruction depending on the duration of ultrasound travel which in turn is made available by an mp3 module associated with the system. This work presents a new prototype of navigation system on a cane which can be used as a travel aid for blind people. The product developed is light in weight, hence, does not cause fatigue to the user. This project is developed by keeping in view the affordability and reliability. An obstacle as close as 4cm can be detected by this module. A resolution of 15cm of obstacle distance has been designed and achieved. This system can also detect potholes on the path.

The intelligent devices have taken us to a convenient and fashionable era, however while we use a pedometer to calculate the number of steps, the blind even do not have the ability to walk independently. There is no doubt that they are eager for convenience and freedom, based on this, this article proposes an intelligent system that assists the blind in walking. The system consists of a crutch and a bracelet. The crutch can detect the obstacles in front with ultrasonic sensors which working direction is controlled by stepper motors and angle sensor. The bracelet would vibrate when the obstacles are detected to prompt the user. Furthermore, the Sensors on the bracelet can detect a blind man's fall, if the blind can't stand up in time, the bracelet would automatically make a voice to passers-by for help. The bracelet is equipped with GPS and 4G terminals, so position and need of the blind can be both sent to his family. By testing, the system can recognize obstacles such as pedestrians, trees, bricks and so on. The proper environment to use are also identified.

Aiming at the problems of traditional tracking, such as difficulty in deployment, low flexibility and high cost, this paper proposes and designs an automatic cruising car based on UWB positioning technology. The car adopts the Raspberry Pi 3B module as the core control unit, obtains the position through the UWB module, acquires the azimuth of the vehicle through the MPU9250 module, detects the obstacles in front through the ultrasonic sensor, and uses the incremental PID control algorithm to track the specified path. Different from the traditional way of laying magnetic needles and printing guide lines, position coordinate information is introduced in this design, and a series of coordinate points are used as the guiding path of the trolley. Tests show that the designed car can complete the function of automatic cruising according to the specified path, which has certain practical application value.

As of late, it is seen that dustbins set at a different spots like open places, for example, healing centers, instructive Institutes and Industries are flooding. This flooding of refuse canisters make unhygienic condition which can spread the sicknesses .Also fast increment in populace squander offer ascent to inappropriate waste administration. To maintain a strategic distance from this circumstance, we proposed new framework Smart City Garbage Collection and Monitoring System. In the recent decades, Urbanization has increased tremendously. In the meantime there is an expansion in waste creation. Waste management has been a pivotal issue to be considered. This paper is a way to deal with achieve this incredible inspiration. In this paper, smart bin is built on a microcontroller based platform Raspberry pi Uno board which is interfaced with GSM modem and Ultrasonic sensor and also the weight Sensor which is used for calculating the weight of the dustbins. The weight sensor is placed at the bottom of the dustbins which will measure the weight of the dustbins and also The Ultrasonic sensor is placed at the top of the dustbin which will read the status of the dustbin. The threshold limit is set as 10cm. Raspberry will be programmed in such a way that when the dustbin is being filled, the remaining height from the threshold height will be displayed. When the junk achieves the limit level ultrasonic sensor will trigger the GSM modem which will persistently caution the required expert until the trash in the dustbin is squashed. As indicated by the area, expert will sends the message to the separate administrator; waste vehicle can gather the refuse, which is finished with the assistance of robot component.

This paper aims at combining object detection at real time and recognition with suitable deep learning methods in order to detect and recognize objects position as well as the names of multiple objects detected by the camera using an object detector algorithm. This is to aid the visually impaired user without the help of any other person. The image and video processing algorithms were designed to take real-time inputs from the camera, Deep Neural Networks were used to predict the objects and uses Google’s famous Text-To-Speech (GTTS) API module for the anticipated voice output precisely detecting and recognizing the category or class of objects and locations contained. Our best result shows that the system recognizes 91 categories of outdoor objects and produces the output in speech i.e. in an audio format even when a reduced amount of spectral information from the data is available.

-Since the late 1970s, there has been talk of home automation. Yet, as technology and services improved, so did people's expectations, which affected how they perceived home automation systems. In this work, we use a modest PIR built around an Arduino controller to construct an advanced electronic security system. PIR and IR sensors detect an intruder's presence, and the controller interprets the signal from the sensors. Intruder detection activates the bell, places a call, and sends an SMS to a predetermined number using a GSM modem. In tandem, the controller activates a camera to record an intruder's image and send it to the person via GSM module AT instructions.A excellent security system is centrally located, making it easy to maintain a close eye on the entire area, which lowers personnel requirements and labour costs. Key Words: PIR sensor, IR sensor, arduino controller, GSM modem, Camera and Memory card

Visually impaired people face a lot of challenges in their day by day life. Due to blindness most of the time they depend on others for their daily movements. Many assistive technologies have been developed for blind people; most of them are expensive and designed in a complicated way. So in this paper, we represent a complete wearable navigation system for blind people based on the low expanse and truly subtle sensors, for example, Pi camera and Ultrasonic sensor. Live video analysis has been done to detect human faces and ultrasonic sensors are used to detect objects as obstacles. Raspberry Pi has been used as the main controller board. The indoor path has been pre-trained and saved in a database for blind assistance by voice command using Google Text To Speech (gTTS) API so that blind people can navigate independently. In an emergency, the blind person can seek help from the specific person by sending SOS short message service (SMS) through pressing an integrated button. This system has been tested continuously by both blindfolded and visually impaired people at various indoor locations. The outcome shows that it operates more efficiently than other assistive systems.

Abstract—Object detection is a computer vision technique that locates objects in images or videos by creating bounding boxes around them. In this paper, we propose a model based on object detection using deep learning technologies along with text to speech conversion.An object detection system uses a deep learningmodel to detect objects using YOLO (You Only Look Once) and text-to- speech (TTS) to synthesize a voice announcement about each object. The system we used is built using python OpenCV tool and Google text to speech (gTTS) is used to convert text into audio segment. First variations of YOLO algorithm are compared and then the best one is used according to result we get it by training it on COCO dataset. After the object is detected, the name of the detected object is displayed then the voice output is generated by using Google Text To Speech(gTTS) module. The contribution we make is to present a visual substitution system that uses features extraction and matching to recognize objects with a voice feedback. Index Terms—Object Detection, YOLO, Open CV, python, Google Text To Speech

For real-time object detection and speech alarms, Research offers a sophisticated blind aid solution that smoothly combines the YOLO algorithm with Open CV's DNN (Deep Neural Network) module. The main objective is to improve the safety and freedom of people with visual impairments by offering quick object recognition and audio feedback. The YOLO method, which is tuned for real-time inference, is used by the system to precisely recognize objects after using a webcam to record live video input. Then, using audio description, it produces speech notifications that provide crucial details about the things it has spotted. Because of its exceptional versatility, the research can provide speech outputs in the user's preferred language, increasing its usability and accessibility. Its versatility is further demonstrated by its capacity to precisely handle a variety of object classes, making it a priceless tool for greatly enhancing the lives of those who are blind or visually impaired. Challenges in real-time object detection include occlusion, scale variations, and cluttered environments. Researchers must navigate the trade-offs between accuracy and speed. Real- time object detection is pivotal in computer vision, enabling intelligent systems across diverse applications. Real-time object detection systems designed for assistive technologies are becoming increasingly important in empowering individuals with visual impairments. The research integrates the YOLO algorithm with OpenCV’s DNN module to deliver a highly responsive blind-aid solution capable of capturing live video through a webcam and processing it instantly. YOLO’s architecture, optimized for speed and accuracy, enables the system to detect and classify multiple objects within each video frame, even in dynamic or unpredictable environments. Once an object is identified, the system generates immediate speech output, providing descriptive audio alerts in the user’s preferred language, thus enhancing accessibility and user experience. This multilingual capability makes the solution versatile across different regions and cultures. Additionally, the system supports a wide range of object categories such as vehicles, household items, and obstacles helping visually impaired users navigate daily surroundings more safely and independently. However, the implementation faces challenges, including handling occlusions, varying object scales, low-light conditions, and cluttered backgrounds. These factors require careful balancing between detection accuracy and processing speed to maintain real-time performance. Despite these challenges, the integration of advanced deep learning models with user-friendly audio interfaces demonstrates significant potential to improve mobility, safety, and overall quality of life for people with visual impairments.