Testing and Analysis of Activities of Daily Living Data with Machine Learning Algorithms

It is estimated that 28% of European Union’s population will be aged 65 or older by 2060. Europe is getting older and this has a high impact on the estimated cost to be spent for older people. This is because, compared to the younger generation, older people are more at risk to have/face cognitive impairment, frailty and social exclusion, which could have negative effects on their lives as well as the economy of the European Union. The ‘active and independent ageing’ concept aims to support older people to live active and independent life in their preferred location and this goal can be fully achieved by understanding the older people (i.e their needs, abilities, preferences, difficulties they are facing during the day). One of the most reliable resources for such information is the Activities of Daily Living (ADL), which gives essential information about people’s lives. Understanding this kind of information is an important step towards providing the right support, facilities and care for the older population. In the literature, there is a lack of study that evaluates the performance of Machine Learning algorithms towards understanding the ADL data. This work aims to test and analyze the performance of the well known Machine Learning algorithms with ADL data

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

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 effect of vascular graft and human umbilical cord blood-derived CD34+ stem cell on peripheral nerve healing

AIM: There are many trials concerning peripheral nerve damage causes and treatment options. Unfortunately, nerve damage is still a major problem regarding health, social and economic issues. On this study, we used vascular graft and human cord blood derived stem cells to find an alternative treatment solution to this problem. MATERIAL AND METHODS: We used 21 female Wistar rats on our study. They were anesthetized with ketamine and we studied right hind limbs. On Group 1, we did a full layer cut on the right sciatic nerve. On Group 2, we did a full layer cut on the right sciatic nerve, and we covered synthetic vascular graft on cut area. On Group 3, we did a full layer cut on right sciatic nerve, and we covered the area with stem cell applied vascular graft. RESULTS: At the end of postoperative 8. weeks, we performed EMG on the rats. When we compared healthy and degenerated areas as a result of EMG, we found significant amplitude differences between the groups on healthy areas whereas there was no significant difference on degenerated areas between the groups. Then we re-opened the operated area again to reveal the sciatic nerve cut area, and we performed electron microscope evaluation. On the stem cell group, we observed that both the axon and the myelin sheet prevented degeneration. CONCLUSION: This study is a first on using synthetic vascular graft and cord blood derived CD34+ cells in peripheral nerve degeneration. On the tissues that were examined with electron microscope, we observed that CD34+ cells prevented both axonal and myelin sheath degeneration. Nerve tissue showed similar results to the control group, and the damage was minimal. © 2018 Ali Yilmaz, Abdullah Topcu, Cagdas Erdogan, Levent Sinan Bir, Barbaros Sahin, Gulcin Abban, Erdal Coskun, Ayca Ozkul