Review on Different Microcontroller Boards Used in IoT

Today’s technology has been evolved into stand-alone systems which can do all necessary processes by themselves without any additional hardware. Advance microcontrollers have become microcomputers that are also known as single board computers. These systems take their power from powerful microcontrollers. These microcontrollers have many integrated circuits on board so they can achieve many different processes by themselves. They are being used in many applications from powerful industrial devices to simple home appliances. In today’s market, there are many different microcontrollers with different structure and capabilities. Therefore, understanding the concepts related to the microcontrollers is really important for choosing the best hardware. This paper presents the main concepts of microcontrollers and reveals the basis of their structure. Their components and abilities have been discussed and a comparison of well-known single board computers has been given. Keywords: Microcontrollers, Integrated Circuits, Arduino UNO, Raspberry PI, BeagleBone Black, ESP8266.

Recording Natural Head Position Using Cone Beam Computerized Tomography

The purpose of this study was to develop a technique to record the natural head position (NHP) of a subject using the scout images of cone beam computerized tomography (CBCT) scans. The first step was to align a hanging mirror with the vertical (XY) plane of the CBCT field-of-view (FOV) volume. Then, two scout CBCT images, at frontal and at sagittal planes, were taken when the subject exhibited a NHP. A normal CBCT scan on the subject was then taken separately. These scout images were used to correct the orientation of the normal CBCT scan. A phantom head was used for validation and performance analysis of the proposed method. It was found that the orientation detection error was within 0.88°. This enables easy and economic NHP recording for CBCT without additional hardware.

Detection of Outdated Sensors in Wireless Network via a New Protocol

A novel method is proposed using the nonlinear mapping with kernel functions to correctly locate the outdated sensors in a wireless sensor network (WSN). Such detection system used Cornell regression and solved via the vector support regression (VSR) plus multi-dimensional backup vector regression (MBVSR). The developed method was simplistic and effective without the need of any additional hardware for any measurement. It required only the vicinity and information of location from the anchor nodes to detect the outdated sensors. It was achieved in three stages including the measurements, kernel regression, and stepping stage. First step measured the proximity information from a given grid. The relationships between the proximity and geographic distance among the sensors’ nodes were generated in the kernel regression stage. For the stepping phase, every sensor node found its location in the distributed way via the kernel regression. Simulation results showed the robustness and high efficiency of the proposed scheme.

Gaze Tracking Using an Unmodified Web Camera and Convolutional Neural Network

Gaze estimation plays a significant role in understating human behavior and in human–computer interaction. Currently, there are many methods accessible for gaze estimation. However, most approaches need additional hardware for data acquisition which adds an extra cost to gaze tracking. The classic gaze tracking approaches usually require systematic prior knowledge or expertise for practical operations. Moreover, they are fundamentally based on the characteristics of the eye region, utilizing infrared light and iris glint to track the gaze point. It requires high-quality images with particular environmental conditions and another light source. Recent studies on appearance-based gaze estimation have demonstrated the capability of neural networks, especially convolutional neural networks (CNN), to decode gaze information present in eye images and achieved significantly simplified gaze estimation. In this paper, a gaze estimation method that utilizes a CNN for gaze estimation that can be applied to various platforms without additional hardware is presented. An easy and fast data collection method is used for collecting face and eyes images from an unmodified desktop camera. The proposed method registered good results; it proves that it is possible to predict the gaze with reasonable accuracy without any additional tools.

Hardware-free collision detection and braking for securing drone propellers

Purpose The widespread use of drones among the general public has led to an alarming increase in accidents, some with lethal consequences. As drone blades are made from rigid materials and rotate at very high speeds, their impact with a human body can result in fatal injuries. Reliable collision detection combined with near-instantaneous braking of the drone’s rotor(s) can substantially lessen the severity of injuries sustained. The purpose of this paper is to achieve a safety solution which can be easily integrated into new products, or retrofitted into existing systems. Design/methodology/approach Through a proof of concept, this paper demonstrates the possibility of detecting a collision with a drone propeller absent any hardware modifications to the drone’s instrumentation. The solution relies on current-sensor readings, ordinarily used for monitoring the battery status of electrically actuated drones. The braking is achieved purely by reconfiguring the motor’s control strategy, without the need for additional hardware, as has been the case in previous works. Findings This paper demonstrates the possibility of detecting a collision with a drone propeller absent any hardware modifications to the drone’s instrumentation. Originality/value Compared to previous works which require installing additional hardware, the solution is purely software. This makes it very easy to integrate into existing systems or new products, at no additional cost. In experiments conducted on a prototype system, the solution was shown capable of detecting a collision and braking the motor in fewer than 20 ms. This allowed attenuating centimetre-deep cuts made to a piece of meat by an unprotected rotor to mere superficial scratches.

TOWARDS MORE EFFICIENT UAS DATA ACQUISITION: CAMERA AUTO MOUNT PIVOTING OBLIQUE SURVEY

In this paper I present a new MAVLink command, enabling oblique aerial surveys, along with its implementation on the major open source flight stacks (PX4 and ArduPilot) and ground control station (QGroundControl). A key advantage of this approach is that it enables vehicles with a typical gimbaled camera to capture oblique photos in the same pass as nadir photos, without the need for heavier and more expensive alternatives that feature multiple cameras, at fixed angles in a rigid mount, thus are unsuitable for lightweight platforms. It also allows for flexibility in the configuration of the camera angles. The principle is quite simple, the command combines camera triggering with mount actuation in a synchronized cycle along the flight traverses through the region of interest. Oblique photos have also been shown to increase the accuracy of data and help filling holes in point clouds and related outputs of surveys with vertical components. To provide evidence of its benefits, I compare the results of several missions, in simulated and field experiments, flown with nadir only surveys versus oblique surveys, and different camera configurations. In both cases, ground control and check points were used to evaluate the accuracy of the surveys. The field experiments show the vehicle had to fly 44% less with the oblique survey to cover the same area as the nadir survey, which could translate in a 80% gain in efficiency in coverage area per flight. Furthermore, this new command is capable of enhancing functionality of Unmanned Aerial Systems (UASs) without any additional hardware, therefore its adoption should be straightforward.

Research on a Novel Locating Method for Track Inspection Based on Onboard Sensors in Maglev Train

Guideway inspection is of great significance to the operation safety and riding quality of a commercial high-speed maglev transportation system. When analyzing guideway inspection data, it is important to obtain the location information for each piece of raw data and convert it from the time domain to the spatial domain for the analysis afterward. Previous studies have used the method of adding additional hardware such as GPS (global positioning system) receivers, LRF (location reference flag) readers, or onboard CAN (controller area network) bus adaptors to obtain location information. This paper presents a novel method for indirectly obtaining the location information via the use of data from the levitation and guidance control sensors perpendicular to the longitudinal direction to extract the characteristic information from the track. The method can be used for a long stator linear motor-driven maglev system and similar contactless rail transit systems. The results showed that the method could accurately identify the required location information in each stator tooth during the entire operation simultaneously with the operating information such as train velocity, direction, and track ID, without additional hardware installation and vehicle network connection. Thus, it could improve the pertinence of the results of guideway inspection equipment, and at the same time, facilitate the miniaturization and independence of guideway inspection equipment.

Overhauser dynamic nuclear polarization (ODNP)-enhanced two-dimensional proton NMR spectroscopy at low magnetic fields

The majority of low-field Overhauser dynamic nuclear polarization (ODNP) experiments reported so far have been 1D NMR experiments to study molecular dynamics and in particular hydration dynamics. In this work, we demonstrate the application of ODNP-enhanced 2D J-resolved (JRES) spectroscopy to improve spectral resolution beyond the limit imposed by the line broadening introduced by the paramagnetic polarizing agent. Using this approach, we are able to separate the overlapping multiplets of ethyl crotonate into a second dimension and clearly identify each chemical site individually. Crucial to these experiments is interleaved spectral referencing, a method introduced to compensate for temperature-induced field drifts over the course of the NMR acquisition. This method does not require additional hardware such as a field-frequency lock, which is especially challenging when designing compact systems.

An Improving Method for Performance of DNN using 2-bit Quantization

Background/Objectives: Recently, interest in AI(Artificial Intelligence) has increased, and many studies are being conducted to enable AI to be used in embedded and mobile environments. Among them, quantization is one of the methods to reduce the size of the model, and most quantization of less than 8 bits cannot be implemented without additional hardware such as FPGA. With this in mind, in this paper, we propose two new algorithms that can implement 2bit quantization in software. Methods/Statistical analysis: In this paper, we propose a packing operation that quantizes a weight consisting of 32-bit real values into 2 bits, stores four 2-bit quantization weights in one 8-bit memory, and a Masking Matrix Multiplication function that performs the calculation of the packed weight and input values. These functions operate in parallel in the GPU memory. Findings: The quantization model using the above function showed about 16 times more memory saving and 4 times faster when comparing the operation with the existing 32bit model. Nevertheless, the DNN model showed an error of around 1% in learning using MNIST and HandWritten data, and the CNN model showed an error of around 1% in learning using EEG (Electroencephalograpy) data. Improvements/Applications: The function used in this study is focused on the domain of DNN, and although extended to CNN, quantization could be performed only in the FC (Fully Connected) part. To apply to the convolution layer, an additional function is required, and it is necessary to check whether the difference in accuracy is small even in a more complex data set in the future.

Controlling Teleportation-Based Locomotion in Virtual Reality with Hand Gestures: A Comparative Evaluation of Two-Handed and One-Handed Techniques

Virtual Reality (VR) technology offers users the possibility to immerse and freely navigate through virtual worlds. An important component for achieving a high degree of immersion in VR is locomotion. Often discussed in the literature, a natural and effective way of controlling locomotion is still a general problem which needs to be solved. Recently, VR headset manufacturers have been integrating more sensors, allowing hand or eye tracking without any additional required equipment. This enables a wide range of application scenarios with natural freehand interaction techniques where no additional hardware is required. This paper focuses on techniques to control teleportation-based locomotion with hand gestures, where users are able to move around in VR using their hands only. With the help of a comprehensive study involving 21 participants, four different techniques are evaluated. The effectiveness and efficiency as well as user preferences of the presented techniques are determined. Two two-handed and two one-handed techniques are evaluated, revealing that it is possible to move comfortable and effectively through virtual worlds with a single hand only.