Keberkesanan program simulasi penapis sambutan dedenyut terhingga (FIR) terhadap kefahaman pelajar kejuruteraan elektrik

Kefahaman merupakan aset bagi setiap pelajar. Ini kerana melalui kefahaman pelajar dapat mengaplikasikan konsep yang dipelajari di dalam dan di luar kelas. Kajian ini dijalankan bertujuan menilai keberkesanan program simulasi penapis sambutan dedenyut terhingga (FIR) terhadap kefahaman pelajar kejuruteraan elektrik FKEE, UTHM dalam mata pelajaran Pemprosesan Isyarat Digital (DSP) bagi topik penapis FIR. Metodologi kajian ini berbentuk kaedah reka bentuk kuasi�eksperimental ujian pra-pasca bagi kumpulan-kumpulan tidak seimbang. Seramai 40 responden kajian telah dipilih dan dibahagi secara rawak kepada dua kllmpulan iaitu kumpulan rawatan yang menggunakan program simulasi penapis FIR dan kumpulan kawalan yang menggunakan kaedah pembelajaran berorientasikan modul pembelajaran DSP UTHM. Setiap responden menduduki dua ujian pencapaian iaitu ujian pra dan ujian pasca yang berbentuk kuiz. Analisis data berbentuk deskriptif dan inferens dilakllkan dengan menggunakan Peri sian Statistical Package for Social Science (SPSS) versi 11.0. Dapatan kajian menunjukkan kedua-dua kumpulan pelajar telah mengalami peningkatan dari segi kefahaman iaitu daripada tahap tidak memuaskan kepada tahap kepujian selepas menggunakan kaedah pembelajaran yang telah ditetapkan bagi kumpulan masing-masing. Walaubagaimanapun, pelajar kumpulan rawatan menunjukkan peningkatan yang lebih tinggi sedikit berbanding pelajar kumpulan kawalan. Namun begitu, dapatan kajian secara ujian statistik menunjukkan tidak terdapat perbezaan yang signifikan dari segi pencapaian markah ujian pasca di antara pelajar kumpulan rawatan dengan pelajar kumpulan kawalan. Sungguhpun begitu, penggunaan program simulasi penapis FIR telah membantu dalam peningkatan kefahaman pelajar mengenai topik penapis FIR

Bio-Inspired Compressive Sensing based on Auditory Neural Circuits for Real-time Monitoring and Control of Civil Structures using Resource Constrained Sensor Networks.

Recent natural hazard disasters including Hurricane Sandy (2012) and the Tohoku Earthquake (2011) have called public attention to the vulnerability of civil infrastructure systems. To enhance the resiliency of urban communities, arrays of wireless sensors and actuators have been proposed to monitor and control infrastructure systems in order to limit damage, speed emergency response, and make post-disaster decisions more efficiently. While great advances in the use of wireless sensor networks (WSNs) for the purposes of monitoring and control of civil infrastructure have been made, significant technological barriers have hindered their ability to be reliably used in the field for long durations. Some of these limitations include: reliance on finite, portable power supplies, limited radio bandwidth for data communication, and limited computational capacity. To resolve current bottlenecks, paradigm-altering approaches to the design of wireless monitoring and control systems are required. Through the process of evolution, biological central nervous systems (CNS) have evolved into highly adaptive and robust systems whose sensing and actuation capabilities far surpass the current capabilities of engineered (i.e., man-made) monitoring and control systems. In this dissertation, the mechanisms employed by biological sensory systems serve as sources of inspiration for overcoming the current challenges faced by wireless nodes for structural monitoring and control. The basic, yet elegant, methods of signal processing and data transmission used by the CNS are mimicked in this thesis to enable highly compressed communication with real-time data processing for WSNs engaged in infrastructure monitoring. Specifically, the parallelized time-frequency decomposition of the mammalian cochlea is studied, modeled, and recreated in an ultra-low power analog circuit. In lieu of transmitting data, the cochlea-inspired wireless sensors emulate the neurons by encoding filtered outputs into binary electrical spike trains for highly efficient wireless transmission. These transmitted spike train signals are processed for pattern classification of sensor data to identify structural damage and to perform feedback control in real-time. A key contribution of this thesis is the development and experimental validation of a bio-inspired wireless sensor node that exhibits large energy savings while employing real-time processing techniques, thus overcoming many of the current challenges of traditional wireless sensor nodes.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107302/1/cpeckens_1.pd

Compression vs Transmission Tradeoffs for Energy Harvesting Sensor Networks

The operation of Energy Harvesting Wireless Sensor Networks (EHWSNs) is a very lively area of research. This is due to the increasing inclination toward green systems, in order to reduce the energy consumption of human activities at large and to the desire of designing networks that can last unattended indefinitely (see, e.g., the nodes employed in Wireless Sensor Networks, WSNs). Notably, despite recent technological advances, batteries are expected to last for less than ten years for many applications and their replacement is often prohibitively expensive. This problem is particularly severe for urban sensing applications, think of, e.g., sensors placed below the street level to sense the presence of cars in parking lots, where the installation of new power cables is impractical. Other examples include body sensor networks or WSNs deployed in remote geographic areas. In contrast, EHWNs powered by energy scavenging devices (renewable power) provide potentially maintenance-free perpetual network operation, which is particularly appealing, especially for highly pervasive Internet of Things. Lossy temporal compression has been widely recognized as key for Energy Constrained Wireless Sensor Networks (WSN), where the imperfect reconstruction of the signal is often acceptable at the data collector, subject to some maximum error tolerance. The first part of this thesis deals with the evaluation of a number of lossy compression methods from the literature, and the analysis of their performance in terms of compression efficiency, computational complexity and energy consumption. Specifically, as a first step, a performance evaluation of existing and new compression schemes, considering linear, autoregressive, FFT-/DCT- and Wavelet-based models is carried out, by looking at their performance as a function of relevant signal statistics. After that, closed form expressions for their overall energy consumption and signal representation accuracy are obtained through numerical fittings. Lastly, the benefits that lossy compression methods bring about in interference-limited multi-hop networks are evaluated. In this scenario the channel access is a source of inefficiency due to collisions and transmission scheduling. The results reveal that the DCT-based schemes are the best option in terms of compression efficiency but are inefficient in terms of energy consumption. Instead, linear methods lead to substantial savings in terms of energy expenditure by, at the same time, leading to satisfactory compression ratios, reduced network delay and increased reliability performance. The subsequent part of the thesis copes with the problem of energy management for EHWSNs where sensor batteries are recharged via the energy harvested through a solar panel and sensors can choose to compress data before transmission. A scenario where a single node communicates with a single receiver is considered. The task of the node is to periodically sense some physical signal and report the measurements to the receiver (sink). We assume that this task is delay tolerant, i.e., the sensor can store a certain number of measurements in the memory buffer and send one or more packets of data after some time. Since most physical signals exhibit strong temporal correlation, the data in the buffer can often be compressed by means of a lossy compression method in order to reduce the amount of data to be sent. Lossy compression schemes allow us to select the compression ratio and trade some accuracy in the data reconstruction at the receiver for more energy savings at the transmitter. Specifically, our objective is to obtain the policy, i.e., the set of decision rules that describe the node behavior, that jointly maximizes throughput and reconstruction fidelity at the sink while meeting some predefined energy constraints, e.g., the battery charge level should never go below a guard threshold. To obtain this policy, the system is modeled as a Constrained Markov Decision Process (CMDP), and solved through Lagrangian Relaxation and Value Iteration Algorithm. The optimal policies are then compared with heuristic policies in different energy budget scenarios. Moreover the impact of the delay on the knowledge of the Channel State Information is investigated. Two more parts of this thesis deal with the development of models for the generation of space-time correlated signals and for the description of the energy harvested by outdoor photovoltaic panels. The former are very useful to prove the effectiveness of the proposed data gathering solutions as they can be used in the design of accurate simulation tools for WSNs. In addition, they can also be considered as reference models to prove theoretical results for data gathering or compression algorithms. The latter are especially useful in the investigation and in the optimization of EHWSNs. These models will be presented at the beginning and then intensively used for the analysis and the performance evaluation of the schemes that are treated in the remainder of the thesis

Latitude, longitude, and beyond:mining mobile objects' behavior

Rapid advancements in Micro-Electro-Mechanical Systems (MEMS), and wireless communications, have resulted in a surge in data generation. Mobility data is one of the various forms of data, which are ubiquitously collected by different location sensing devices. Extensive knowledge about the behavior of humans and wildlife is buried in raw mobility data. This knowledge can be used for realizing numerous viable applications ranging from wildlife movement analysis, to various location-based recommendation systems, urban planning, and disaster relief. With respect to what mentioned above, in this thesis, we mainly focus on providing data analytics for understanding the behavior and interaction of mobile entities (humans and animals). To this end, the main research question to be addressed is: How can behaviors and interactions of mobile entities be determined from mobility data acquired by (mobile) wireless sensor nodes in an accurate and efficient manner? To answer the above-mentioned question, both application requirements and technological constraints are considered in this thesis. On the one hand, applications requirements call for accurate data analytics to uncover hidden information about individual behavior and social interaction of mobile entities, and to deal with the uncertainties in mobility data. Technological constraints, on the other hand, require these data analytics to be efficient in terms of their energy consumption and to have low memory footprint, and processing complexity