Design And Implementation Of An Autonomous Wireless Sensor-Based Smart Home

The Smart home has gained widespread attentions due to its flexible integration into everyday life. This next generation of green home system transparently unifies various home appliances, smart sensors and wireless communication technologies. It can integrate diversified physical sensed information and control various consumer home devices, with the support of active sensor networks having both sensor and actuator components. Although smart homes are gaining popularity due to their energy saving and better living benefits, there is no standardized design for smart homes. In this thesis, a smart home design is put forward that can classify and predict the state of the home utilizing historical data of the home. A wireless sensor network was setup in a home to gather and send data to a sink node. The collected data was utilized to train and test a classification model achieving high accuracy with Support Vector Machine (SVM). SVM was further utilized as a predictor of future home states. Based on the data collection, classification and prediction models, a system was designed that can learn, run with minimal human supervision and detect anomalies in a home. The aforementioned attributes make the system an asset for senior care scenarios

UWB communication systems acquisition at symbol rate sampling for IEEE standard channel models

For ultra-wideband (UWB) communications, acquisition is challenging. The reason is from the ultra short pulse shape and ultra dense multipath interference. Ultra short pulse indicates the acquisition region is very narrow. Sampling is another challenge for UWB design due to the need for ultra high speed analog-to digital converter.A sub-optimum and under-sampling scheme using pilot codes as transmitted reference is proposed here for acquisition. The sampling rate for the receiver is at the symbol rate. A new architecture, the reference aided matched filter is studied in this project. The reference aided matched filter method avoids using complex rake receiver to estimate channel parameters and high sampling rate for interpolation. A limited number of matched filters are used as a filter bank to search for the strongest path. Timing offset for acquisition is then estimated and passed to an advanced verification algorithm. For optimum performance of acquisition, the adaptive post detection integration is proposed to solve the problem from dense inter-symbol interference during the acquisition. A low-complex early-late gate tracking loop is one element of the adaptive post detection integration. This tracking scheme assists in improving acquisition accuracy. The proposed scheme is evaluated using Matlab Simulink simulations in term of mean acquisition time, system performance and false alarm. Simulation results show proposed algorithm is very effective in ultra dense multipath channels. This research proves reference aided acquisition with tracking loop is promising in UWB application

Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

Frame synchronization for PSAM in AWGN and Rayleigh fading channels

Pilot Symbol Assisted Modulation (PSAM) is a good method to compensate for the channel fading effect in wireless mobile communications. In PSAM, known pilot symbols are periodically inserted into the transmitted data symbol stream and the receiver uses these symbols to derive amplitude and phase reference. One aspect of this procedure, which has not received much attention yet, is the frame synchronization, i.e. the method used by the receiver to locate the time position of the pilot symbols. In this study, two novel non-coherent frame synchronization methods are introduced in which only the magnitude of received signal is used to obtain the timing of the pilot symbol. The methods are evaluated for both AWGN and frequency non-selective slow Rayleigh fading channels. One synchronization technique is derived by standard maximum likelihood (ML) estimation formulation, and the other is obtained by using maximum a Posteriori probability (MAP) with a threshold test. Signal processing in the receiver uses simplifying approximations that rely on relatively high signal-to-noise ratio (SNR) as consistent with the reception of 16-QAM. Computer simulation has been used to test the acquisition time performance and the probability of false acquisition. Several lengths and patterns of pilot symbol sequences were tested where every 10th symbol was a pilot symbol and all other symbols were randomly selected data symbols. When compared with the other published synchronizers, results from this study show better performance in both AWGN and fading channels. Significantly better performance is observed in the presence of receiver frequency offsets

A Definition and a Test for Human-Level Artificial Intelligence

Despite recent advances in many application-specific domains, we do not know how to build a human-level artificial intelligence (HLAI). We conjecture that learning from others' experience with the language is the essential characteristic that distinguishes human intelligence from the rest. Humans can update the action-value function with the verbal description as if they experience states, actions, and corresponding rewards sequences firsthand. In this paper, we present a classification of intelligence according to how individual agents learn and propose a definition and a test for HLAI. The main idea is that language acquisition without explicit rewards can be a sufficient test for HLAI

Signal fingerprinting and machine learning framework for UAV detection and identification.

Advancement in technology has led to creative and innovative inventions. One such invention includes unmanned aerial vehicles (UAVs). UAVs (also known as drones) are now an intrinsic part of our society because their application is becoming ubiquitous in every industry ranging from transportation and logistics to environmental monitoring among others. With the numerous benign applications of UAVs, their emergence has added a new dimension to privacy and security issues. There are little or no strict regulations on the people that can purchase or own a UAV. For this reason, nefarious actors can take advantage of these aircraft to intrude into restricted or private areas. A UAV detection and identification system is one of the ways of detecting and identifying the presence of a UAV in an area. UAV detection and identification systems employ different sensing techniques such as radio frequency (RF) signals, video, sounds, and thermal imaging for detecting an intruding UAV. Because of the passive nature (stealth) of RF sensing techniques, the ability to exploit RF sensing for identification of UAV flight mode (i.e., flying, hovering, videoing, etc.), and the capability to detect a UAV at beyond visual line-of-sight (BVLOS) or marginal line-of-sight makes RF sensing techniques promising for UAV detection and identification. More so, there is constant communication between a UAV and its ground station (i.e., flight controller). The RF signals emitting from a UAV or UAV flight controller can be exploited for UAV detection and identification. Hence, in this work, an RF-based UAV detection and identification system is proposed and investigated. In RF signal fingerprinting research, the transient and steady state of the RF signals can be used to extract a unique signature. The first part of this work is to use two different wavelet analytic transforms (i.e., continuous wavelet transform and wavelet scattering transform) to investigate and analyze the characteristics or impacts of using either state for UAV detection and identification. Coefficient-based and image-based signatures are proposed for each of the wavelet analysis transforms to detect and identify a UAV. One of the challenges of using RF sensing is that a UAV\u27s communication links operate at the industrial, scientific, and medical (ISM) band. Several devices such as Bluetooth and WiFi operate at the ISM band as well, so discriminating UAVs from other ISM devices is not a trivial task. A semi-supervised anomaly detection approach is explored and proposed in this research to differentiate UAVs from Bluetooth and WiFi devices. Both time-frequency analytical approaches and unsupervised deep neural network techniques (i.e., denoising autoencoder) are used differently for feature extraction. Finally, a hierarchical classification framework for UAV identification is proposed for the identification of the type of unmanned aerial system signal (UAV or UAV controller signal), the UAV model, and the operational mode of the UAV. This is a shift from a flat classification approach. The hierarchical learning approach provides a level-by-level classification that can be useful for identifying an intruding UAV. The proposed frameworks described here can be extended to the detection of rogue RF devices in an environment