On Indoor Localization Using WiFi, BLE, UWB, and IMU Technologies.

Indoor localization is a key research area and has been stated as a major goal for Sixth Generation (6G) communications. Indoor localization faces many challenges, such as harsh wireless propagation channels, cluttered and dynamic environments, non-line-of-sight conditions, etc. There are various technologies that can be applied to address these issues. In this paper, four major technologies for implementing an indoor localization system are reviewed: Wireless Fidelity (Wi-Fi), Ultra-Wide Bandwidth Radio (UWB), Bluetooth Low Energy (BLE), and Inertial Measurement Units (IMU). Sections on Data Fusion (DF) and Machine Learning (ML) have been included as well due to their key role in Indoor Positioning Systems (IPS). These technologies have been categorized based on the techniques that they employ and the associated errors in localization. A brief comparison between these technologies is made based on specific performance metrics. Finally, the limitations of these techniques are identified to aid future research.

Thermal Effects of 445-nm Diode Laser Irradiation on Titanium and Ceramic Implants.

This study aimed to evaluate temperature changes in titanium and ceramic implants after using a 445-nm diode laser under different in vitro conditions. Titanium (Ti) and ceramic (Zr) dental implants were placed into a bone analog, and an intrabony defect was created at each implant. A 445-nm diode laser was used to irradiate the defects for 30 seconds, noncontact, at 2 W in continuous wave (c.w.) and pulsed mode. The experiment was done at room temperature (21.0 ± 1°C) and in a water bath (37.0 ± 1°C). Two thermocouple probes were used to record real-time temperature changes (°C) at the coronal part of the implant (Tc) and the apex (Ta). The temperature was recorded at time 0 (To) and after 30 seconds of irradiation (Tf). The average temperature change was calculated, and a descriptive analysis was conducted (P < .05). The Ti implant resulted in the highest ΔT values coronally (29.6°C) and apically (6.7°C) using continuous wave at 21°C. The Zr implant increased to 26.4°C coronally and 5.2°C apically. In the water bath, the coronal portion of the Ti and Zr implants rose to 14.2°C and 14.01°C, respectively, using continuous waves. The ΔT values for Ti were 11.9°C coronally and 1.7°C apically when placed in a water bath using pulsed mode. The lowest ΔT occurred on the Zr implant with ΔTc and ΔTa of 4.8°C and 0.78°C, respectively. Under in vitro conditions, the 445-nm diode laser in pulsed mode seems to be safe for use on ceramic implants and should be used with caution on titanium implants.

A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges.

The Department of Transport in the United Kingdom recorded 25,080 motor vehicle fatalities in 2019. This situation stresses the need for an intelligent transport system (ITS) that improves road safety and security by avoiding human errors with the use of autonomous vehicles (AVs). Therefore, this survey discusses the current development of two main components of an ITS: (1) gathering of AVs surrounding data using sensors; and (2) enabling vehicular communication technologies. First, the paper discusses various sensors and their role in AVs. Then, various communication technologies for AVs to facilitate vehicle to everything (V2X) communication are discussed. Based on the transmission range, these technologies are grouped into three main categories: long-range, medium-range and short-range. The short-range group presents the development of Bluetooth, ZigBee and ultra-wide band communication for AVs. The medium-range examines the properties of dedicated short-range communications (DSRC). Finally, the long-range group presents the cellular-vehicle to everything (C-V2X) and 5G-new radio (5G-NR). An important characteristic which differentiates each category and its suitable application is latency. This research presents a comprehensive study of AV technologies and identifies the main advantages, disadvantages, and challenges.

The Effect of Miss and Tuck Stitches on a Weft Knit Strain Sensor.

Weft knitted conductive fabrics can act as excellent textile strain sensors for human motion capture. The loop architecture dictates the overall electrical properties of weft knit strain sensors. Therefore, research into loop architecture is relevant for comprehensively investigating the design space of e-textile sensors. There are three main types of knit stitches, Knitted loop stitch, Miss stitch, and Tuck stitch. Nevertheless, most of the research into weft knit strain sensors has largely focused on fabrics with only knitted loop stitches. Miss and tuck stitches will affect the contact points in the sensor and, consequently, its piezoresistivity. Therefore, this paper investigates the impact of incorporating miss and tuck stitches on the piezoresistivity of a weft knit sensor. Particularly, the electromechanical models of a miss stitch and a tuck stitch in a weft knit sensor are proposed. These models were used in order to develop loop configurations of sensors that consist of various percentages of miss or tuck stitches. Subsequently, the developed loop configurations were simulated while using LTspice and MATLAB software; and, verified experimentally through a tensile test. The experimental results closely agree with the simulated results. Furthermore, the results reveal that increases in the percentage of tuck or miss stitches in weft knit sensor decrease the initial and average resistance of the sensor. In addition, it was observed that, although the piezoresistivity of a sensor with tuck or miss stitches is best characterised as a quadratic polynomial, increases in the percentage of tuck stitches in the sensor increase the linearity of the sensor's piezoresistivity.

Super Resolution Generative Adversarial Network (SRGANs) for Wheat Stripe Rust Classification.

Wheat yellow rust is a common agricultural disease that affects the crop every year across the world. The disease not only negatively impacts the quality of the yield but the quantity as well, which results in adverse impact on economy and food supply. It is highly desired to develop methods for fast and accurate detection of yellow rust in wheat crop; however, high-resolution images are not always available which hinders the ability of trained models in detection tasks. The approach presented in this study harnesses the power of super-resolution generative adversarial networks (SRGAN) for upsampling the images before using them to train deep learning models for the detection of wheat yellow rust. After preprocessing the data for noise removal, SRGANs are used for upsampling the images to increase their resolution which helps convolutional neural network (CNN) in learning high-quality features during training. This study empirically shows that SRGANs can be used effectively to improve the quality of images and produce significantly better results when compared with models trained using low-resolution images. This is evident from the results obtained on upsampled images, i.e., 83% of overall test accuracy, which are substantially better than the overall test accuracy achieved for low-resolution images, i.e., 75%. The proposed approach can be used in other real-world scenarios where images are of low resolution due to the unavailability of high-resolution camera in edge devices.

Wheat Yellow Rust Disease Infection Type Classification Using Texture Features.

Wheat is a staple crop of Pakistan that covers almost 40% of the cultivated land and contributes almost 3% in the overall Gross Domestic Product (GDP) of Pakistan. However, due to increasing seasonal variation, it was observed that wheat is majorly affected by rust disease, particularly in rain-fed areas. Rust is considered the most harmful fungal disease for wheat, which can cause reductions of 20-30% in wheat yield. Its capability to spread rapidly over time has made its management most challenging, becoming a major threat to food security. In order to counter this threat, precise detection of wheat rust and its infection types is important for minimizing yield losses. For this purpose, we have proposed a framework for classifying wheat yellow rust infection types using machine learning techniques. First, an image dataset of different yellow rust infections was collected using mobile cameras. Six Gray Level Co-occurrence Matrix (GLCM) texture features and four Local Binary Patterns (LBP) texture features were extracted from grayscale images of the collected dataset. In order to classify wheat yellow rust disease into its three classes (healthy, resistant, and susceptible), Decision Tree, Random Forest, Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and CatBoost were used with (i) GLCM, (ii) LBP, and (iii) combined GLCM-LBP texture features. The results indicate that CatBoost outperformed on GLCM texture features with an accuracy of 92.30%. This accuracy can be further improved by scaling up the dataset and applying deep learning models. The development of the proposed study could be useful for the agricultural community for the early detection of wheat yellow rust infection and assist in taking remedial measures to contain crop yield.

Thermal Effects of Diode Laser-Irradiation on Titanium Implants in Different Room Temperatures In Vitro.

Objective: The aim of this study was to determine the thermal effects of diode laser irradiation on titanium implants. Methods: An implant (3.5 × 11 mm) was placed into a bovine bone block. A three-wall intrabony defect was created to simulate peri-implant defect. Two thermocouples were secured to the apical and coronal surfaces to measure temperature changes (ΔT) during irradiation. The block was placed in a 37°C water bath and at room temperature (21°C). The defect was irradiated with different diode lasers (fiber 300 µm), while the coronal part of the implant was slightly emerging from the water. While the laser tip was positioned parallel to the implant, the defect was irradiated for 30 sec at 2 W in continuous and pulsed mode. Twenty laser irradiations were performed for each laser wavelength for assessment of ΔT. The linear mixed model was used for comparative statistics. Results: The 980 nm pulsed laser resulted in the highest ΔT (°C) at the coronal (22.45 ± 2.1/14.15 ± 0.13) and apical level (5.4 ± 0.56/3.56 ± 0.35) when this laser was used in both room temperature and water bath conditions, respectively. Similarly, highest ΔT (p < 0.0001) for the 810 nm was 14.3 ± 1.6/12.51 ± 0.63 and apical 3.42 ± 0.52/2.58 ± 0.25, for the 970 nm was 13 ± 1.4/9.93 ± 0.47 and apical 2.89 ± 0.19/2.01 ± 0.19 compared to the 940 nm laser coronally 10.1 ± 0.6/9.19 ± 0.35 and apically 1.67 ± 0.34/1.80 ± 0.17. The coronal part of the implant surpassed the critical threshold of 10°C when irradiated with each of the lasers in the room temperature conditions. Conclusions: Within the limitations of the study, the 940 nm laser seems to control better the risks of overheating during implant irradiation.