Fingerprint indoor positioning based on user orientations and minimum computation time

Indoor Positioning System (IPS) has an important role in the field of Internet of Thing. IPS works based on many existing radio frequency technologies. One of the most popular methods is WLAN Fingerprint because this technology has been installed widely inside buildings and it provides a high level of accuracy. The performance is affected by people who hold mobile devices (user) and also people around the users. This research aimed to minimize the computation time of kNN searching process. The results showed that when the value of k in kNN was greater, the computation time increased, especially when using Cityblock and Minkowski distance function. The smallest average computation time was 2.14 ms, when using Cityblock. Then the computational time for Euclidean and Chebychev were relatively stable, i.e. 2.2 ms and 2.23 ms, respectively

A Review of Hybrid Indoor Positioning Systems Employing WLAN Fingerprinting and Image Processing

Location-based services (LBS) are a significant permissive technology. One of the main components in indoor LBS is the indoor positioning system (IPS). IPS utilizes many existing technologies such as radio frequency, images, acoustic signals, as well as magnetic sensors, thermal sensors, optical sensors, and other sensors that are usually installed in a mobile device. The radio frequency technologies used in IPS are WLAN, Bluetooth, Zig Bee, RFID, frequency modulation, and ultra-wideband. This paper explores studies that have combined WLAN fingerprinting and image processing to build an IPS. The studies on combined WLAN fingerprinting and image processing techniques are divided based on the methods used. The first part explains the studies that have used WLAN fingerprinting to support image positioning. The second part examines works that have used image processing to support WLAN fingerprinting positioning. Then, image processing and WLAN fingerprinting are used in combination to build IPS in the third part. A new concept is proposed at the end for the future development of indoor positioning models based on WLAN fingerprinting and supported by image processing to solve the effect of people presence around users and the user orientation problem

Modelling the Effect of Human Body around User on Signal Strength and Accuracy of Indoor Positioning

WLAN indoor positioning system (IPS) has high accurate of position estimation and minimal cost. However, environmental conditions such as the people presence effect (PPE) greatly influence WLAN signal and it will decrease the accuracy. This research modelled the effect of people around user on signal strength and the accuracy. We have modelled the human body around user effects by proposed a general equation of decrease in RSSI as function of position, distance, and number of people. RSSI decreased from 5 dBm to 1 dBm when people in LOS position, and start from 0.5 dBm to 0.3 dBm when people in NLOS position. The system accuracy decreases due to the presence of people. When the system in NLOS case (ΔRSSI = 0.5 dBm), the presence of people causes a decrease in accuracy from 33% to 57%. Then the accuracy decrease from 273% to 334% in LOS case (ΔRSSI = 5 dBm)

Automatic energy and carbon emissions monitoring using OPC: unified architecture

Global warming and climate change is being an issue for more than a decade. It has given the negative impact to the nature, such as rise of global temperature, unpredictable weather and many other natural disasters. Human activities that are resulting in emissions of Greenhouse Gases (GHG) contribute to these problems. These gases will trap heat into the atmosphere that cause the rising of earth temperature. Based on IPCC report in 2007 (as shown in Figure 1), Carbon Dioxide (CO2) is the main contributor to GHG emissions which are closely related to natural and human activities

OPC Protocol Application for Real-Time Carbon Monitoring System for Industrial Environment

Global warming is referred to the rise in average surface temperatures on earth primarily due to the Greenhouse Gases (GHG) emissions such as Carbon Dioxide (CO2). Monitoring the emissions, either direct or indirect from the industrial processes, is important to control or to minimize their impact on the environment. Most of the existing environmental monitoring system is being designed and developed for normal environment monitoring. Hence, the aim of this project is to develop industrial CO2 emission monitoring system which implements industrial Open Platform Communications (OPC) protocol in an embedded microcontroller. The software algorithm based on OPC data format has been designed and programmed into the Arduino microcontroller to interface the sensor data to any existing industrial OPC compliant Supervisory Control and Data Acquisition (SCADA) system. The system has been successfully tested in a lab with the suitable environment for real-time CO2 emissions measurement. The real-time measurement data has been shown in an industrial SCADA application which indicates successful implementation of the OPC communications protocol

Modelling the effect of human body around user on signal strength and accuracy of indoor positioning

WLAN indoor positioning system (IPS) has high accurate of position estimation and minimal cost. However, environmental conditions such as the people presence effect (PPE) greatly influence WLAN signal and it will decrease the accuracy. This research modelled the effect of people around user on signal strength and the accuracy. We have modelled the human body around user effects by proposed a general equation of decrease in signal strength as function of position, distance, and number of people. Signal strength decreased from 5 dBm to 1 dBm when people in line of sight (LOS) position, and start from 0.5 dBm to 0.3 dBm when people in non-line of sight (NLOS) position. The system accuracy decreases due to the presence of people. When the system is in NLOS case, the presence of people causes a decrease in accuracy from 33% to 57%. Then the accuracy decrease from 273% to 334% in LOS case

Comparison of hash function algorithms against attacks: a review

Hash functions are considered key components of nearly all cryptographic protocols, as well as of many security applications such as message authentication codes, data integrity, password storage, and random number generation. Many hash function algorithms have been proposed in order to ensure authentication and integrity of the data, including MD5, SHA-1, SHA-2, SHA-3 and RIPEMD. This paper involves an overview of these standard algorithms, and also provides a focus on their limitations against common attacks. These study shows that these standard hash function algorithms suffer collision attacks and time inefficiency. Other types of hash functions are also highlighted in comparison with the standard hash function algorithm in performing the resistance against common attacks. It shows that these algorithms are still weak to resist against collision attacks

A review of MyGDI: the catalyst of the evolution of Geographical information systems in Malaysian public sector

Spatial Data Infrastructure (SDI) is the base for the development and implementation of geospatial direction in many countries. SDI is made of framework of geographic data, Standards, Policies, Tools, Geographical information systems (GIS), Technical Infrastructure, Metadata and legal procedures. GIS being one of the components in the Spatial Data Infrastructure is important for dissemination of geospatial information and services. In Malaysia the Spatial Data Infrastructure or Geospatial Data Infrastructure is known as the Malaysia Geospatial Data Infrastructure (MyGDI). MyGDI enables the evolution of geographical information Systems in Malaysia Public Sector. Over the years many GIS applications have emerged through the development of MyGDI at the federal, state and the local authority levels. GIS application can be categorized into different disciplines such as public safety, disaster management, transportation, traffic control, tracking, health, environment, natural resources, mining, agriculture, utilities and many more. The aim of the paper is to discuss on how MyGDI has facilitated the evolution GIS in Malaysian Public Secto

Breast cancer classification using deep learning approaches and histopathology image: a comparison study

Convolutional Neural Network (CNN) models are a type of deep learning architecture introduced to achieve the correct classification of breast cancer. This paper has a two-fold purpose. The first aim is to investigate the various deep learning models in classifying breast cancer histopathology images. This study identified the most accurate models in terms of the binary, four, and eight classifications of breast cancer histopathology image databases. The different accuracy scores obtained for the deep learning models on the same database showed that other factors such as pre-processing, data augmentation, and transfer learning methods can impact the ability of the models to achieve higher accuracy. The second purpose of our manuscript is to investigate the latest models that have no or limited examination done in previous studies. The models like ResNeXt, Dual Path Net, SENet, and NASNet had been identified with the most cutting-edge results for the ImageNet database. These models were examined for the binary, and eight classifications on BreakHis, a breast cancer histopathology image database. Furthermore, the BACH database was used to investigate these models for four classifications. Then, these models were compared with the previous studies to find and propose the most state-of-the-art models for each classification. Since the Inception-ResNet-V2 architecture achieved the best results for binary and eight classifications, we have examined this model in our study as well to provide a better comparison result. In short, this paper provides an extensive evaluation and discussion about the experimental settings for each study that had been conducted on the breast cancer histopathology images