Directional statistics approach based on instantaneous rotational parameters of tri-axial trajectories for footstep detection

Polarization of tri-axial signals is defined using instantaneous rotational characteristics of the three-dimensional (3D) trajectory. We propose a rotational model to parameterize the time evolution of the 3D trajectory as a sequence of scaled rotations. Using this model, the velocity-to-rotation transform is defined to estimate the eigenangle, eigenaxis and orientation quaternion that quantify the instantaneous rotational parameters of the trajectory. These rotational parameters correspond to p-dimensional directional random vectors (DRVs). We propose two approaches to discriminate between the presence and absence of an elliptically polarized trajectory generated by human footsteps. In the first approach, we fit a von Mises–Fisher probability density function to the DRVs and estimate the concentration parameter. In the second approach, we employ the Kullback–Leibler divergence between the estimated nonparametric hyperspherical probability densities. The detection performance of the proposed metrics is shown to achieve an accuracy of 97 % compared to existing approaches of 82 % for footstep signals

Convergence analysis of clipped input adaptive filters applied to system identification

One of the efficient solutions for the identification of long finite-impulse response systems is the three-level clipped input LMS/RLS (CLMS/CRLS) adaptive filter. In this paper, we first derive the convergence behavior of the CLMS and CRLS algorithms for both time-invariant and time-varying system identification. In addition, we employ results arising from this analysis to derive the optimal step-size and forgetting factor for CLMS and CRLS. We show that these optimal step-size and forgetting factor allow the algorithms to achieve a low steady-state misalignment

An adaptive subsystem based algorithm for channel equalization in a SIMO system

The principle of multiple input/output inversion theorem (MINT) has been employed for multi-channel equalization. In this work, we propose to partition a single-input multiple-output system into two subsystems. The equivalence between the deconvoluted signals of the two subsystems is termed as auto-relation and we subsequently exploit this relation as an additional constraint to the existing adaptive MINT algorithm. In addition, we provide analysis of the auto-relation constraint and show that this constraint confines the solution of equalization filters within a multi-dimensional space. We also explain through the use of convergence analysis why our proposed algorithm can achieve a higher rate of convergence compared to the existing MINT-based algorithms. Simulation results, using both synthetic and recorded channel impulse responses, show that our proposed auto-relation aided MINT algorithm can achieve a fast convergence compared to the existing MINT-based algorithms

A fast frequency-domain algorithm for equalizing acoustic impulse responses

The multiple-input/output inverse theorem (MINT) algorithm for multichannel equalization is computationally demanding. Although adaptive MINT reduces the computational complexity, it suffers from slow convergence. In this letter, we propose a low-complexity fast-converging adaptive algorithm for multichannel equalization. The novelty of the approach lies in the adaptive equalization for each frequency bin and its ability to achieve fast convergence in a single step. The proposed algorithm can achieve better equalization of high-order acoustic impulse responses with significant reduction in complexity

A variable step-size multichannel equalization algorithm exploiting sparseness measure for room acoustics

Non-adaptive multichannel equalization (MCEQ) algorithms based on multiple input/output inverse theorem (MINT) is computationally expensive as MINT involves the inversion of a convolution matrix with dimension that is proportional to the length of the acoustic impulse responses. To address this, we propose a MINT-based algorithm that estimates inverse filters by minimizing a cost function iteratively. To further enhance the convergence rate, we formulate an algorithm that employs an adaptive step-size that is derived as a function of the sparseness measure. The proposed algorithm is then applied to existing MINT-based equalization algorithms such as A-MINT and the currently proposed MCEQ-based algorithms

A linear source recovery method for underdetermined mixtures of uncorrelated AR-model signals without sparseness

Conventional sparseness-based approaches for instantaneous underdetermined blind source separation (UBSS) do not take into account the temporal structure of the source signals. In this work, we exploit the source temporal structure and propose a linear source recovery solution for the UBSS problem which does not require the source signals to be sparse. Assuming the source signals are uncorrelated and can be modeled by an autoregressive (AR) model, the proposed algorithm is able to estimate the source AR coefficients from the mixtures given the mixing matrix. We prove that the UBSS problem can be converted into a determined problem by combining the source AR model together with the original mixing equation to form a state-space model. The Kalman filter is then applied to obtain a linear source estimate in the minimum mean-squared error sense. Simulation results using both synthetic AR signals and speech utterances show that the proposed algorithm achieves better separation performance compared with conventional sparseness-based UBSS algorithms.Accepted versio

Toward better grade prediction via A2GP - an academic achievement inspired predictive model

Predicting student performance in an academic institution is important for detecting at-risk students and administering early-intervention strategies. We propose a new grade prediction model that considers three factors: temporal dynamics of prior courses across previous semesters, short-term performance consistency, and relative performance against peers. The proposed architecture comprises modules that incorporate the attention mechanism, a new short-term gated long short-term memory network, and a graph convolutional network to address limitations of existing works that fail to consider the above factors jointly. A weighted fusion layer is used to fuse learned representations of the above three modules—course importance, performance consistency, and relative performance. The aggregated representations are then used for grade prediction which, in turn, is used to classify at-risk students. Experiment results using three datasets obtained from over twenty thousand students across seventeen undergraduate courses show that the proposed model achieves low prediction errors and high F1 scores compared to existing models that predict grades and thereafter identifies at-risk students via a pre-defined threshold.Published versio

Online education evaluation for signal processing course through student learning pathways

Impact of online learning sequences to forecast course outcomes for an undergraduate digital signal processing (DSP) course is studied in this work. A multi-modal learning schema based on deep-learning techniques with learning sequences, psychometric measures, and personality traits as input features is developed in this work. The aim is to identify any underlying patterns in the learning sequences and subsequently forecast the learning outcomes. Experiments are conducted on the data acquired for the DSP course taught over 13 teaching weeks to underpin the forecasting efficacy of various deeplearning models. Results showed that the proposed multi-modal schema yields better forecasting performance compared to existing frequency-based methods in existing literature. It is further observed that the psychometric measures incorporated in the proposed multimodal schema enhance the ability of distinguishing nuances in the input sequences when the forecasting task is highly dependent on human behavior.NRF (Natl Research Foundation, S’pore)Accepted versio

Hidden Markov model for masquerade detection based on sequence alignment

A masquerade attack, in which an attacker impersonates a legitimate user to utilize the user's privileges, can be triggered either by someone within the organization or by an outsider. We propose the sequence alignment based hidden Markov model (SA-HMM) approach, where we incorporate the benefits of both the sequence alignment and continuous hidden Markov model (HMM). The sequence alignment module for the proposed algorithm allows the algorithm to tolerate variations in user activity sequence. The HMM module takes the positional information between the observations of users into account. The proposed approach achieves a high hit ratio of 94.1% outperforming existing masquerade detection approaches

Localization of taps on solid surfaces for human-computer touch interfaces

Localization of impacts on solid surfaces is a challenging task due to dispersion where the velocity of wave propagation is frequency dependent. In this work, we develop a source localization algorithm on solids with applications to human-computer interface. We employ surface-mounted piezoelectric shock sensors that, in turn, allow us to convert existing flat surfaces to a low-cost touch interface. The algorithm estimates the time-differences-of-arrival between the signals via onset detection in the time-frequency domain. The proposed algorithm is suitable for vibration signals generated by a metal stylus and a finger. The validity of the algorithm is then verified on an aluminium and a glass plate surface.Accepted versio