Saeed Mian Qaisar

An adaptive resolution computationally efficient short-time Fourier transform

The short-time Fourier transform (STFT) is a classical tool, used for characterizing the time varying signals. The limitation of the STFT is its fixed time-frequency resolution. Thus, an enhanced version of the STFT, which is based on the cross-level sampling, is devised. It can adapt the sampling frequency and the window function length by following the input signal local characteristics. Therefore, it provides an adaptive resolution time-frequency representation of the input signal. The computational complexity of the proposed STFT is deduced and compared to the classical one. The results show a significant gain of the computational efficiency and hence of the processing power.

Adaptive Rate Filtering Fora Signal Driven Sampling Scheme

This work is a contribution to enhance the signal processing chain required in mobile systems. The system must be low power as it is powered by a battery. Thus a signal driven sampling scheme based on level crossing is employed, adapting the sampling rate and so the system activity by following the input signal variations. In order to filter the non-uniformly sampled signal obtained at the output of this sampling scheme a new adaptive rate FIR filtering approach is devised. The idea is to combine the features of both uniform and nonuniform signal processing tools to achieve a smart online filtering process. The computational complexity of the proposed approach is deduced and compared to one of the classical FIR filtering approach. It promises a significant gain of the computational efficiency and hence of the processing power.

Adaptive rate filtering a computationally efficient signal processing approach

Filtering is a basic operation, almost required in every signal processing system. The classical filtering is time-invariant, the sampling frequency and the filter order remains unique. Therefore it can render a useless increase of the processing activity, especially in the case of sporadic signals. In this context, adaptive rate filtering techniques, based on a level crossing sampling are devised. They adapt the sampling frequency and the filter order by analyzing the input signal local variations. They correlate the processing activity to the signal variations. The computational complexities and output qualities of the proposed techniques are compared to the classical one for a speech signal. Results show a drastic computational gain, of the proposed techniques compared to the classical ones, along with a comparable quality. It promises a significant processing power reduction of the proposed solutions compared to the classical ones.

Analog to digital converters for high temperature applications: The modeling approach issue

The analog to digital converter (ADC) is an essential part of measurement, communications and processing systems. ADC modeling is a rich trend in the ADC research areas. High temperature applications have witnessed a real growth in last years. The motivation of this article is to describe an appropriate choice of the ADC modeling approach for high temperature ranges. The context of high temperature electronics systems is briefly described here and allows us to understand the challenges of such modeling. Comparing most known techniques enables us to make a suitable modeling choice for high temperature. An appropriate method for modeling was presented.

Cloud-based mobile platform for EEG signal analysis

It is estimated that there are millions of people with epilepsy around the world. Seizure detection and prediction systems are built to improve lifestyle of patients. Closed-loop systems are designed to predict and detect seizures and inform patient and caretakers. Ideally, wireless technologies are used in order not to interfere with patients life. We build a prototype for closed-loop systems consisting of Mind Wave EEG capturing device and Android application communicating via Bluetooth. The application can store signals locally or send them to cloud and then process them for different applications such as BCI, Neurofeedback, epileptic seizure prediction, etc.

Power efficient analog to digital conversion for the Li-ion battery voltage monitoring and measurement

In modern era the utilization of battery empowered systems is growing exponentially. In the goal to achieve an optimal system performance, the employment of a BMS (Battery Management System) is inevitable. The recent sophistications in the area of BMSs are demanding more and more processing resources. Reducing the BMS power consumption is becoming one of the most difficult industrial challenges. Most of efforts to achieve this goal are focused on improving the embedded systems design, but very few studies target to exploit the input signal time-varying nature. This work aims to achieve power efficiency by smartly adapting the system activity to the input signal local variations. In this context a novel A/D conversion approach is derived. The proposed solution, based on the LCSS (Level Crossing Sampling Scheme) presents an ADC (A/D Converter), able to adapt its acquisition rate according to the input signal variations. In fact, the principle is to smartly exploit the signal local characteristics to acquire only the relevant signal parts at relevant sampling rate. The idea offers a significant reduction in acquired number of samples and hence promises a drastic reduction in the system power consumption compared to the classical approach.

An evaluation methodology for the Li- Ion battery multiplexed voltage measurement systems

In modern era the utilization of battery empowered systems is growing exponentially. In the goal to achieve an optimal system performance, the employment of a BMS (Battery Management System) is inevitable. Data acquisition is an elementary operation of the BMS. Usually a battery is composed of several cells. Therefore, a multiplexed ADC (A/D Converter) is employed for acquiring each cell data. It renders an effective solution in terms of cost and power consumption. However, the multiplexing can render A/D conversion latencies. In this context a performance comparison between two multiplexed data acquisition systems, based respectively on the ΔΣ and the SAR (Successive Approximation Register) ADCs is made. The application is to measure the Li-Ion battery voltage. A measurement methodology is presented. The pros and cons of both considered systems are described with the experimental results.

High-level synthesis of an event-driven windowing process

This work is a contribution to enhance the signal processing chain required in modern systems. The idea is to take advantage of the interesting features of both event-driven and well-established uniform sampling and signal processing algorithms. In this context, authors have proposed original windowing techniques for the event-driven sampled signal, activity selection and local parameter extraction. These are called as Activity Selection Algorithms (ASA). The proposed techniques correlate the windowing function length, shape and resampling frequency with the input signal time variations. In this paper, the ASA and an adaptive rate resampler with zero order interpolation are implemented in C. Using high level synthesis tools, synchronous and asynchronous register transfer level descriptions are synthesized on a commercial CMOS 40 nm technology. The synthesis results are presented. A comparison of the average power consumption between asynchronous and synchronous implementations is made. The proposed system functionality is also verified with the help of analytic test signals. Results have shown a compression, in terms of samples, of 1.5 to 1.6 respectively compared to the simple event-driven level-crossing sampling and by 5.8 to 9.3 compared to the uniform sampling based systems. The asynchronous (resp. synchronous) implementation of ASA and resampler only consumes in average 28pJ/sample (resp. 238 pJ/sample).

Time-domain characterization of a wireless ECG system event driven A/D converter

Heart strokes are one of the leading cause of global deaths. In order to prevent such strokes, a variety of wearable devices have been proposed recently. These are based on the wireless Electrocardiogram (ECG) acquisition, analysis and transmission. An efficient acquisition, analysis and transmission of such signals can be achieved by adapting the system activity according to the input signal local characteristics. In this context, an event driven analog-to-digital converter (ADC) based on level-crossing sampling scheme (LCSS) is designed for the ECG acquisition. The analog-to-digital (A/D) conversion error is a vital parameter to characterize its performance. It implicitly provides information about the ADC effective resolution. In this context, a novel method is proposed to measure the conversion error in time domain of the designed level-crossing ADC (LC-ADC). In the aim of acquiring the desired conversion precision, a criterion for properly choosing the system parameters is also discussed.

A VHDL based Moore and Mealy FSM example for education

With recent technological advancements, modern societies are becoming more and more dependent on the automated machines. It is in order to cope with their fast-going lives. Modern automated machines adapt their sequence of actions depending on their environment and events. The FSM (Finite state machine) is used to mathematically express those sequences of actions or instructions. In this article two FSM machines types, Moore and Mealy, are discussed. Showing different results in order to demonstrate the importance of FSM modeling. An edge detector circuit is designed by employing both Moore and Mealy machines. It is a FSM design example, can be used for students concepts building and demonstration. These designs are implemented in VHDL. A comparison is also made based on both implementations.