A blind implementation of multi-dimensional matched filtering in a Maximum-Likelihood receiver for SIMO channels

In order to establish the optimal receiver strategy, in terms of error rate for Single Input Multi Output (SIMO) wireless channels, the Maximum Likelihood (ML) detection should be performed following a multi-dimensional matched filter. However, the implementation of the matched filter and the ML detection both need the estimation of the channel impulse response in advance. In this work, we propose a novel method to establish the matched filters of the SIMO channel blindly alongside a three-step technique for the blind and adaptive ML detection of the symbol vector. With the use of the novel method, the system will benefit from the bandwidth efficiency point of view due to the use of blind schemes. The constant modulus algorithm is utilized to perform the blind matched filtering operation and later Least Mean Squared algorithm is introduced for further correction on the matched filter estimate. The blindly estimated matched filters are incorporated into the ML detector so that the transmitted symbols are found and therefore the channel is equalized. Simulations are provided to present the equalization performance and convergence speed of the novel technique

Multi-dimensional matched filter identification technique for channel equalization deployed in spatial diversity receivers

This paper proposes a multi-dimensional matched filtering technique for spatial diversity receivers. The coefficients of the multi-dimensional matched filter are identified by making use of an adaptive filter, the update of which doesn't require the transmission of any training symbols within the transmitted data stream. Therefore the use of the proposed technique improves the data rate efficiency. Furthermore, it is well known that implementing multi-dimensional matched filtering is essential for equalization purposes to obtain the optimum error rate performance from spatial diversity receivers. For that reason the technique is designed not only to identify the unknown matched filter but also to simultaneously lead to the equalization of the channel too. In order to update the adaptive filter, the Constant Modulus Algorithm (CMA) is utilized, which is an implementation convenient algorithm. Therefore the proposed technique is not computationally complex in comparison to those identification algorithms proposed for spatial diversity receivers. Simulations are provided to present the equalization performance of the novel technique

Some aspects of nanocrystalline nickel and zinc ferrites processed using microemulsion technique

Nanocrystalline nickel and zinc ferrites synthesised using a microemulsion technique were characterised by high resolution transmission electron microscopy and vibrating sample magnetometry. A narrow and uniform distribution of crystals of size range 5 – 8 nm, distinguished by a clear lack of saturation magnetisation at 9 kOe, were obtained. Also, no coercivity or remanence was observed.

All-adaptive blind matched filtering for the equalization and identification of multipath channels: a practical approach

Blind matched filter receiver is advantageous over the state-of-the-art blind schemes due the simplicity in its implementation. To estimate the multipath communication channels, it uses neither any matrix decomposition methods nor statistics of the received data higher than the second order ones. On the other hand, the realization of the conventional blind matched filter receiver requires the noise variance to be estimated and the equalizer parameters to be calculated in state-space with relatively costly matrix operations. In this paper, a novel architecture is proposed to simplify a potential hardware implementation of the blind matched filter receiver. Our novel approach transforms the blind matched filter receiver into an all-adaptive format which replaces all the matrix operations. Furthermore, the novel design does not need for any extra step to estimate the noise variance. In this paper we also report on a comparative channel equalization and channel identification scenario, looking into the performances of the conventional and our novel all-adaptive blind matched filter receiver through simulations

Blind correlation-based DFE receiver for the equalization of single input multi output communication channels

In this paper, the correlation-based decision feedback equalizer (DFE), where the received data from multiple antennas are processed by a multi-dimensional matched filter and then combined prior to the equalization with a single input single output DFE, is discussed and its blind implementation is introduced. To perform the correlation-based DFE blindly, the multi-dimensional matched filter is replaced by an adaptive filter and the DFE filter weights are calculated via manipulating over the second order statistics of the received data. In the blind architecture, the adaptive filter converges to matched filter equivalents, therefore the matched filters of the corresponding communication channels are also blindly be estimated in addition to the blind equalization process. The mean-squared error of the estimation of matched filters and the equalization performance of the proposed blind architecture are also studied and simulated

The Effects of the Emotional State on an Observer in the Face in the Crowd Paradigm

The face in the crowd paradigm refers to a particular visual search task in which participants are asked to identify target facial expressions in a crowd of distractors. Previous research in this vein has suggested performance is enhanced for angry faces, an anger-superiority effect. There is however disagreement in many of these findings, and this disagreement may partly be explained by a failure to recognize the role of observer mood, response bias, and discrimination ability in the paradigm. The present study used a face in the crowd visual search task and assessed participant mood state using the Positive and Negative Affect Schedule. We hypothesized that mood state would be congruent to facial expressions most efficiently perceived. Multivariate analyses of variance showed instead that positive mood is associated with faster response times in emotional crowds, and negative mood is associated with faster response times in neutral crowds. A strong “no target present” response bias was also associated with neutral crowds, and this response was exacerbated by negative mood. These results suggest that mood does play an important role in visual search, one that may explain contradictory findings in the previous literature

How Effective Are African Health Systems? An Analysis of Guinea, Liberia and Sierra Leone

While developed countries in the rest of the world have found themselves overwhelmed by the Covid-19 pandemic, in comparison, Africa has been minimally affected given that it has reported lower case counts since the start of the pandemic in March 2020. However, given the destructive potential of this pandemic, this raises the question: how prepared are health systems in Africa to face major outbreaks? To answer this question, this article explored the state of health systems and epidemic preparedness in African countries using Guinea, Liberia and Sierra Leone as case studies. Given that these three countries were epicentre countries during the 2014-2015 West Africa Ebola Virus Disease Outbreak, an examination of key response contributions from national governments and local communities was performed. Additionally, data from the 2019 Global Health Security Index was analyzed to determine the current state of epidemic preparedness in the three countries. Positive response strategies were detected across the three countries specifically with the implementation of infection prevention control guidelines by governments and the active engagement of community members in response efforts. In contrast, some gaps remain in the detection, health sector and response capabilities of the three countries regarding epidemic preparedness. This assessment suggests that these positive strategies need to continue to occur in African countries in times of major outbreaks and that special collaborations between stakeholders in Africa and international partners need to take place in order to fortify health systems and better prepare the continent for future epidemics or pandemics

Room equalization based on iterative simple complex smoothing of acoustic impulse responses

This paper presents a room equalization method based on iterative simple complex smoothing of measured acoustic impulse responses. This is useful in cases of long duration impulse responses. Corresponding time reduced impulse responses are derived which conform to perceptual principles. The smoothed impulse responses are then used to design equalization filters. Results from an audio-conferencing reverberant room using objective and subjective tests show that we can improve the measured and perceived quality of audio reproduction

Area and Power Efficient Implementation of db4 Wavelet Filter Banks for ECG Applications Using Reconfigurable Multiplier Blocks

There is an increasing demand for wavelet-based real-time on-node signal processing in portable medical devices which raises the need for reduced hardware size, cost and power consumption. This paper presents an improved Reconfigurable Multiplier Block (ReMB) architecture for an 8-tap Daubechies wavelet filter employed in a tree structured filter bank which targets the recent Field-Programmable-Gate-Array (FPGA) technologies. The ReMB is used to replace the expensive and power hungry multiplier blocks as well as the coefficient memories required in time-multiplexed finite impulse response filter architectures. The proposed architecture is implemented on a Kintex-7 FPGA and the resource utilization, maximum operating frequency and the estimated dynamic power consumption figures are reported and compared with the literature. The results demonstrated that the proposed architecture reduces the hard- ware utilization by 30% and improves the power consumption by 44% in comparison to architectures with general purpose multipliers. Thus, the proposed implementation can be deployed in low-cost low-power embedded platforms for portable medical devices

IIR Wavelet Filter Banks for ECG Signal Denoising

ElectroCardioGram (ECG) signals are widely used for diagnostic purposes. However, it is well known that these recordings are usually corrupted with different type of noise/artifacts which might lead to misdiagnosis of the patient. This paper presents the design and novel use of Infinite Impulse Response (IIR) filter based Discrete Wavelet Transform (DWT) for ECG denoising that can be employed in ambulatory health monitoring applications. The proposed system is evaluated and compared in terms of denoising performance as well as the computational complexity with the conventional Finite Impulse Response (FIR) based DWT systems. For this purpose, raw ECG data from MIT-BIH arrhythmia database are contaminated with synthetic noise and denoised with the aforementioned filter banks. The results from 100 Monte Carlo simulations demonstrated that the proposed filter banks provide better denoising performance with fewer arithmetic operations than those reported in the open literature