Energy-Efficient Time-Stampless Adaptive Nonuniform Sampling

Nowadays, since more and more battery-operated devices are involved in applications with continuous sensing, development of an efficient sampling mechanisms is an important issue for these applications. In this paper, we investigate power efficiency aspects of a recently proposed adaptive nonuniform sampling. This sampling scheme minimizes the energy consumption of the sampling process, which is approximately proportional to sampling rate. The main characteristics of our method are that, first, sampling times do not need to be transmitted, since the receiver can compute them by using a function of previously taken samples, and second, only innovative samples are taken from the signal of interest, reducing the sampling rate and therefore the energy consumption. We call this scheme Time-Stampless Adaptive Nonuniform Sampling (TANS). TANS can be used in several scenarios, showing promising results in terms of energy savings, and can potentially enable the development of new applications that require continuous signals sensing, such as applications related to health monitoring, location tracking and entertainment

Divide and Conquer Partition for Fourier Reconstruction Sparse Inversion with its Applications

A partition method, with an efficient divide and conquer partition strategy, for the non-uniform sampling signal reconstruction based on Fourier reconstruction sparse inversion (FRSI) is developed. The novel partition FRSI(P-FRSI) is motivated by the observation that the partition processing of multi-dimensional signals can reduce the reconstruction difficulty and save the reconstruction time. Moreover, it is helpful to choose suitable reconstruction parameters. The P-FRSI employs divide and conquer strategy, and the signal is firstly partitioned into some blocks. Following that, traditional FRSI is applied to reconstruct signals in each block. We adopt linear or nonlinear superposition to determine the weight coefficients during integrating these blocks. Finally, P-FRSI is applied to two-dimensional seismic signal reconstruction. The superiority of the new method over conventional FRSI is demonstrated by numerical reconstruction experiments

Analog Signal Buffering and Reconstruction

Wireless sensor networks (WSNs) are capable of a myriad of tasks, from monitoring critical infrastructure such as bridges to monitoring a person\u27s vital signs in biomedical applications. However, their deployment is impractical for many applications due to their limited power budget. Sleep states are one method used to conserve power in resource-constrained systems, but they necessitate a wake-up circuit for detecting unpredictable events. In conventional wake-up-based systems, all information preceding a wake-up event will be forfeited. To avoid this data loss, it is necessary to include a buffer that can record prelude information without sacrificing the power savings garnered by the active use of sleep states.;Unfortunately, traditional memory buffer systems utilize digital electronics which are costly in terms of power. Instead of operating in the target signal\u27s native analog environment, a digital buffer must first expend a great deal of energy to convert the signal into a digital signal. This issue is further compounded by the use of traditional Nyquist sampling which does not adapt to the characteristics of a dynamically changing signal. These characteristics reveal why a digital buffer is not an appropriate choice for a WSN or other resource-constrained system.;This thesis documents the development of an analog pre-processing block that buffers an incoming signal using a new method of sampling. This method requires sampling only local maxima and minima (both amplitude and time), effectively approximating the instantaneous Nyquist rate throughout a time-varying signal. The use of this sampling method along with ultra-low-power analog electronics enables the entire system to operate in the muW power levels. In addition to these power saving techniques, a reconfigurable architecture will be explored as infrastructure for this system. This reconfigurable architecture will also be leveraged to explore wake-up circuits that can be used in parallel with the buffer system

Research of algorithms for the reconstruction of non-uniform sampled discrete-time signals with unknown sampling locations

В монографии изложены результаты исследований алгоритмов восстановления дискретных сигналов (ДС), заданных в узлах временной сетки (ВС) с точно неизвестными значениями координат ее узлов. Проведен анализ состояния предметной области, включая: существующие виды неравномерной дискретизации сигналов по времени и причины ее возникновения. Предложена классификация видов неравномерной дискретизации сигналов во времени. Приведены примеры реальных измерительных систем, на выходе которых получают ДС, заданные в узлах неравномерной временной сетки (НВС), а также методы восстановления ДС данного типа. Приведена постановка задачи восстановления ДС, заданного в узлах НВС, с неизвестными точно значениями координат ее узлов. Изложены результаты исследования особенностей восстановления ДС, заданных в узлах НВС с неизвестными точно значениями координат ее узлов, с помощью интерполяционных методов, а также методов, основанных на уточнении значений координат узлов НВС, и оценки их точности. Предложены новые алгоритмы восстановления ДС, заданных на НВС с точно неизвестными значениями координат ее узлов, продемонстрирована их работоспособность и получены оценки точности восстановления ДС с помощью данных алгоритмов. Проведен анализ точности восстановления периодических ДС, получаемых на выходе реальных цифровых систем (высокоскоростного 8-ми битного АЦП на основе КМОП-технологии 0.18 мкм, системы, состоящей из 8 параллельных 5-ти битных АЦП на основе КМОП-тепхнологии 65 нм), с помощью разработанных алгоритмов восстановления.In this treatise are considered the results of research of algorithms for the reconstruction of non-uniform sampled band-limited discrete-time signal with unknown sampling location. The condition of problem domain is analyzed, including: applying signal sampling schemes and causes of origin of the non-uniform sampling signals. Also is proposed classification of signal sampling schemes. Examples of real measurement systems, on the output of which registers non-uniform sampled signals with unknown sampling location as well as methods to reconstruct this type signals are given. The problem of signal reconstruction from non-uniform samples is considered. Interpolation methods to reconstruct a band-limited discrete-time signal from non-uniform samples with unknown sampling location, and optimization methods by estimating unknown sampling locations values are analyzed. Estimates of accuracy of interpolation methods for recovery of irregularly sampled signals are given. Also are given estimates of accuracy of methods to reconstruct a signal from non-uniform samples with unknown sampling locations by estimating unknown sampling locations values and presenting a solution of certain multiparameter problem of global optimization. Are proposed the new algorithms to reconstruct band-limited discrete-time signal from non-uniform samples with unknown sampling location and is demonstrated their performance. Estimate of accuracy of algorithms is compared and reconstruction errors are given in the numerical examples. The results of the research on the accuracy of the periodic signals reconstruction registered at the high speed 8 parallel 5-bit ADC system output based on the 65 nm CMOS technology and 8-bit high-rate ADC output based on the 0.18µm CMOS technology by means of special algorithms to reconstruct a signal from non-uniform samples with unknown sampling locations are analyzed

Time-Stampless Adaptive Nonuniform Sampling for Stochastic Signals

In this paper, we introduce a time-stampless adaptive nonuniform sampling (TANS) framework, in which time increments between samples are determined by a function of the m most recent increments and sample values. Since only past samples are used in computing time increments, it is not necessary to save sampling times (time stamps) for use in the reconstruction process. We focus on two TANS schemes for discrete-time stochastic signals: a greedy method, and a method based on dynamic programming. We analyze the performances of these schemes by computing (or bounding) their trade-offs between sampling rate and expected reconstruction distortion for autoregressive and Markovian signals. Simulation results support the analysis of the sampling schemes. We show that, by opportunistically adapting to local signal characteristics, TANS may lead to improved power efficiency in some applications. © 1991-2012 IEEE

Time-Stampless Adaptive Nonuniform Sampling for Stochastic Signals

In this paper, we introduce a time-stampless adaptive nonuniform sampling (TANS) framework, in which time increments between samples are determined by a function of the m most recent increments and sample values. Since only past samples are used in computing time increments, it is not necessary to save sampling times (time stamps) for use in the reconstruction process. We focus on two TANS schemes for discrete-time stochastic signals: a greedy method, and a method based on dynamic programming. We analyze the performances of these schemes by computing (or bounding) their trade-offs between sampling rate and expected reconstruction distortion for autoregressive and Markovian signals. Simulation results support the analysis of the sampling schemes. We show that by opportunistically adapting to local signal characteristics TANS may lead to improved power efficiency in some applications