Ehsan Rohani

Support Vector Machine Based Detection of Drowsiness Using Minimum EEG Features

Drowsiness presents major safety concerns for tasks that require long periods of focus and alertness. While there is a body of work on drowsiness detection using EEG signals in neuroscience and engineering, there exist unanswered questions pertaining to the best mechanisms to use for detecting drowsiness. Targeting a range of practical safety-awareness applications, this study adopts a machine learning based approach to build support vector machine (SVM) classifiers to distinguish between awake and drowsy states. While broadband alpha, beta, delta, and theta waves are often used as features in the existing work, lack of widely agreed precise definitions of such broadband signals and difficulty in accounting for interpersonal variability has led to poor classification performance as demonstrated in this study. Furthermore, the transition from wakefulness to drowsiness and deeper sleep stages is a complex multifaceted process. The richness of this process calls for inclusion of sub-band features for more accurate drowsiness detection. To shed light on the effectiveness of sub-banding, we quantitatively compare the performances of a large set of SVM classifiers trained upon a varying number of 1Hz sub band features. More importantly, we identify a compact set of neuroscientifcally motivated EEG features and demonstrate that the resulting classifier not only outperforms traditional broadband based classifiers but also is on a par with or superior than the best sub-band classifiers found by thorough search in a large space of 1Hz sub band features.

An iterative LR-aided MMSE extended soft MIMO decoding algorithm

This paper presents an iterative soft decision based lattice reduction (LR) aided Schnorr-Euchner (SE) multiple-input-multiple-output (MIMO) decoding algorithm, which reduces the gap in performance between suboptimal K-best and maximum likelihood (ML) detectors. Following IEEE 802.16e standard, we develop an iterative soft decoding algorithm for 4×4 MIMO with different modulation schemes. Using this method, we obtain 1.1 to 2.7 dB improvement over iterative soft decision based least sphere decoding (LSD) for different iterations. Then, using extensive simulation, we determine the optimum values for list size and saturation limit, which are the two governing parameters of our algorithm. Finally, we demonstrate that limiting the log likelihood ratio (LLR) values in LR-aided and LSD algorithm results in more than 8x reduction in list size as well as in the complexity of detectors and LLR calculation units.

Asynchronous baseband processor design for cooperative MIMO satellite communication

The challenges in satellite communication (SatCom) include but not limited to the customary complications of telecommunication such as channel condition, signal to noise ratio (SNR), etc. SatCom system is also prone to transient and permanent radiations hazards. Hence, in spite of the harsh environmental factors (weather phenomena, solar events, etc), a SatCom system must maintain reliable and predictable communication functions with limited source of power. This paper presents a SatCom system design for achieving both low-power and high fidelity communication. The design uses cooperative multiple input multiple output (MIMO) for spectral efficiency and diversity, low-density parity-check (LDPC) decoding for near Shannon-limit gain, and dynamic voltage and frequency scaling (DVFS)-assisted asynchronous circuit designs to achieve low-power and fault tolerance. The MIMO system permits uninterrupted service in the event of temporary/permanent link or unit failures. The results show that the resilience against injected radiation levels of upto about 25 fempto-Coulombs on critical path is achieved. This is more than 600 times the minimum charge required to logically flip a gate output in ordinary static CMOS gate.

An iterative soft decision based adaptive K-best decoder without SNR estimation

This paper presents an iterative soft decision based adaptive K-best multiple-input-multiple-output (MIMO) decoding algorithm. It has the flexibility of changing the list size, K with respect to the channel condition, although the accurate measurement of signal to noise ratio (SNR) is not required. Moreover, the concept of iterative soft decision based lattice reduction (LR)-aided minimum mean square error (MMSE) extended K-best decoder is applied instead of conventional hard decision based K-best algorithm to reduce computational complexity to a great extent It is found that the ratio of the minimum path metric to the second minimum can provide reliable estimation of channel condition. Hence, in the proposed algorithm, K is changed adaptively with respect to the ratio. Using this method with less number of K, we can obtain similar performance compared to the conventional LR-aided K-best algorithm operating with maximum list size of 64. Comparing to the fourth iteration of iterative soft decision based least sphere decoding (LSD), the proposed method with less K achieves 1.6 dB improvement at the bit error rate (BER) of 10-6. Therefore, similar performance can be obtained by the proposed adaptive K-best algorithm with less computational complexity of the tree search decoder.

Modeling and performance evaluation of IEEE 802.22 physical layer

IEEE 802.22, also called Wireless Regional Area Network (WRAN), is the newest wireless standard being developed for remote and rural areas. In this paper an overview of the standard, and more specifically its PHY layer is introduced. In order to evaluate the performance of the system, we model the PHY layer in MATLAB/SIMULINK and extract the Bit Error Rate (BER) of the system for different code rates and modulation schemes with noisy channel.

Low-Power On-the-Fly Reconfigurable Iterative MIMO Detection and LDPC Decoding Design

Manufacturing and operation of wireless systems require a practical solution for achieving low-power and high-performance when using advance communication apparatus such as that using multiple-input and multiple-output (MIMO). Often algorithm solutions achieve very high performance but over only in a narrow range of operating parameters. This paper presents a hardware design of MIMO detection that allows real-time switching between various algorithms and detection effort to achieve high performance over the wide-range of signal to noise ratio (SNR) found in realistic operating conditions. We illustrate a design with over 80% reduction in detection power that satisfies the required quality of service (QoS) in SNRs (Eb/No) as low as 8.7 dB.

Signal reconstruction processor design for compressive sensing

This paper presents a very-large-scale integration (VLSI) design to reconstruct compressively sensed data. The proposed digital design recovers signal compressed by specific analog-to-digital converter (ADC). Our design is based on a modified iterative hard threshold (IHT) reconstruction algorithm to adapt unknown and varying degree of sparsity of the signal. The algorithm is composed empirically and implemented in a hardware-friendly fashion. The reconstruction fidelity using fixed-point hardware model is analyzed. The design is synthesized using Synopsys Design Compiler with TSMC 45nm standard cell library. The post-synthesis implementation consumes 165 mW and is able to reconstruct data with information sparsity of 4%, at equivalent sampling rate of 1 gigasample-per-second (GSPS).

Asynchronous design for precision-scaleable energy-efficient LDPC decoder

This paper presents a low-density parity-check (LDPC) decoder design that uses scalable-precision calculation (SPC) and asynchronous circuit techniques to reduce power consumption. The decoder configures the computation precision to minimize circuit-level switching necessary for given target biterror rate (FER). The asynchronous circuit approach guarantees the completion of each compute-and-forward phase at necessary voltage levels. The voltage level is scheduled to ensure completion of minimum necessary decoding iterations. The proposed scheme is studied for the specific application of IEEE 802.16e to reduce the power consumption at a given target FER. The proposed design is evaluated on Nangate 45nm library. The results show that the proposed asynchronous design results in 51% reduction in terms of power consumption compared with full-precision decoding mode.

Hardware Considerations for Digital Audio Broadcasting System

In recent years, several implementations have been reported for Digital Audio Broadcasting (DAB) systems. Normally, implementation parameters of these systems are extracted from extensive system level simulations to adjust various parameters while maintaining the required performance. In this paper, the bit-true model of a DAB system is extracted and an accurate model simulation for the system is performed to find the word lengths of various parameters to approach the best trade off between performance and hardware cost. Here, the decimation-in-time algorithm for FFT/IFFT and adaptive LMS algorithm for time equalizer is adopted.