Recently, we proposed weight-constrained nested arrays (WCNA) that have holes in the difference coarray at lags 1 and 2. While this helps in reducing the impact of mutual coupling, the ULA segment in the coarray is ‘one-sided’ from lag L1to L2where 012. In this work, we propose to use covariance interpolation to effectively utilize such arrays having ‘central’ holes in the coarray. After interpolation, a larger Toeplitz matrix is generated, which is used to estimate directions of arrivals (DOAs) using root-MUSIC. We demonstrate that using this approach, we can accurately identify up to twice as many DOAs as what is possible using only the one-sided ULA segment in coarray. Even when the number of DOAs is small, the DOA estimation error after interpolation is over an order of magnitude smaller than that using only the one-sided ULA segment in coarray. Thus, we can mitigate the disadvantage of having central holes in the coarray while maintaining the advantage of WCNAs in reducing the impact of mutual coupling on DOA estimation. This interpolation approach can also be used for other arrays from the literature that have central holes in the coarray, such as CADiS.

In today’s age of data-driven healthcare, the growing utilization of health data to inform critical aspects of patient care and medical research places an ever increasing significance on its governance. This study aims to explore the perspectives of individuals living with Parkinson’s Disease regarding their needs and preferences in relation to data governance. We first conducted a survey (n=52) to explore the types of data people with Parkinson’s generate through their self-care practices, and then conducted 3 workshops with 9 participants to understand their perspectives on the governance of this type of data. Through this work, we highlight the factors that motivate them to collect self-care data, and present their requirements for its governance, which could inform the design of future infrastructure to support these needs. We also showcase how speculative approaches can be used to engage communities in discussions around data collection and governance.

Recently, several sparse array constructions have been proposed to reduce the effect of mutual coupling on the direction of arrival (DOA) estimation, by reducing the number of sensor pairs with small separations. For a large number of sensors N and under aperture constraint, $\mathcal{O}\left( {{N^2}} \right)$ degrees of freedom and $\mathcal{O}\left( {{N^2}} \right)$ aperture may not be of interest, or desirable. In this paper, we consider sparse arrays with $\mathcal{O}(N)$ aperture and enforce the coarray weights at smaller lags to be zero. This helps reduce the effect of mutual coupling while maintaining $\mathcal{O}(N)$ aperture, which is desirable when there is an aperture constraint. We describe several specific ways in which such arrays can be generated by appropriately dilating uniform linear arrays and augmenting them with a few additional sensors. We perform Monte-Carlo simulations to demonstrate that the proposed arrays perform well in the presence of strong mutual coupling, unlike the uniform linear array. They can also perform better compared to $\mathcal{O}\left( {{N^2}} \right)$ aperture sparse arrays when the array aperture is constrained.

In low-and middle-income countries like India, people with severe mental illness (PSMI) rely on their families as a primary source of care, given the lack of support from healthcare systems. The demanding nature of caregiving places significant physical and mental demands on caregivers, who are the primary source of support to PSMI. We explore how caregivers in under-resourced settings can be better supported through everyday digital technologies. We conducted interviews with caregivers (from urban and rural India), as well as workshops with professionals from Indian NGOs that work directly with PSMIs. We found that technology has the potential to (1) provide carer-centred support that empowers carers who experience stigma and issues with existing support networks; (2) provide support for carers to overcome barriers and progress in the recovery of the PSMI. We conclude with design considerations, proposing how an online peer community can leverage carers’ expertise to actualise support provision.

Ramanujan sums have been shown to have interesting applications in signal processing. Ramanujan subspaces, Ramanujan dictionaries, and Ramanujan filter banks are useful in representing and denoising discrete-time periodic signals. In this paper, we theoretically investigate an ideal interpolation filter model for Ramanujan subspace signals wherein an expander ↑ M is followed by the ideal q-th Ramanujan filter Cq(ejω). The output space of this interpolation filter is, in general, only a proper subspace of the q-th Ramanujan subspace ${{\mathcal{S}}_q}$. For the special case when M and q are coprime, we prove that the output space is the entire Ramanujan subspace. We also discuss a more general form of this model for the representation of periodic signals, which may have a potential application in denoising periodic signals. When M and q are not coprime, we provide a bound on the dimension of the output space of the interpolation filter. For this general case, we also conjecture that the provided bound in fact equals the dimension of the output space.

Rational arrays were recently proposed for direction of arrival (DOA) estimation, and some of their advantages were discussed. In this paper we discuss further advantages of rational arrays, by considering their difference coarrays. Although integer arrays such as nested arrays, and coprime arrays are well-known for their ability to identify ${{\mathcal{O}}}\left({{m^2}}\right)$ uncorrelated sources using m sensors through difference coarray domain, they can do so only when a large enough aperture is available. However, rational arrays can do so even when the aperture is constrained. We demonstrate that adding a few sensors at non-integer locations in an otherwise integer array can add a large number of fractional lags at which autocorrelation can be estimated. Appropriately designed sparse integer arrays can also be scaled to produce rational arrays that fit available aperture and have large uniform coarray segments at rational locations. Monte-Carlo simulations are provided to demonstrate the advantages, and practical issues associated with shrinking, such as increased mutual coupling, are discussed.

In interdisciplinary spaces such as digital health, datasets that are complex to collect, require specialist facilities, and/or are collected with specific populations have value in a range of different sectors. In this study we collected a simulated free-living dataset, in a smart home, with 12 participants (six people with Parkinson’s, six carers). We explored their initial perceptions of the sensors through interviews and then conducted two data exploration workshops, wherein we showed participants the collected data and discussed their views on how this data, and other data relating to their Parkinson’s symptoms, might be shared across different sectors. We provide recommendations around how participants might be better engaged in considering data sharing in the early stages of research, and guidance for how research might be configured to allow for more informed data sharing practices in the future.

Linear arrays used in array processing usually have sensor positions riλ/2 where λ is the wavelength of the impinging signals and riare integers. This paper considers rational arrays, where riare rational numbers. In particular, sparse rational arrays such as coprime rational arrays are introduced. In order to do this, some rational extensions of integer number theoretic concepts such as greatest common divisor and coprime numbers are required, which are introduced as well. The advantages of rational arrays are demonstrated with the help of rational coprime arrays. For example, they improve the accuracy of DOA estimation when the sensors have to be distributed with a fixed aperture constraint.

Ramanujan filter banks (RFB) have in the past been used to identify periodicities in data. These are analysis filter banks with no synthesis counterpart for perfect reconstruction of the original signal, so they have not been useful for denoising periodic signals. This paper proposes to use a hybrid analysis-synthesis framework for denoising discrete-time periodic signals. The synthesis occurs via a pruned dictionary designed based on the output energies of the RFB analysis filters. A unique property of the framework is that the denoised output signal is guaranteed to be periodic unlike any of the other methods. For a large range of input noise levels, the proposed approach achieves a stable and high SNR gain outperforming many traditional denoising techniques.

Ramanujan filter banks have been used for identifying periodicity structure in streaming data. This letter studies the locations of zeros of Ramanujan filters. All the zeros of Ramanujan filters are shown to lie on or inside the unit circle in the z-plane. A convenient factorization appears as a corollary of this result, which is useful to identify common factors between different Ramanujan filters in a filter bank. For certain families of Ramanujan filters, further structure is identified in the locations of zeros of those filters. It is shown that increasing the number of periods of Ramanujan sums in the filter definition only increases zeros on the unit circle in z-plane. A potential application of these results is that by identifying common factors between Ramanujan filters, one can obtain efficient implementations of Ramanujan filter banks (RFB) as demonstrated here.

An interpretable generative model for handwritten digits synthesis is proposed in this work. Modern image generative models such as the variational autoencoder (VAE) are trained by backpropagation (BP). The training process is complex, and its underlying mechanism is not transparent. Here, we present an explainable generative model using a feedforward design methodology without BP. Being similar to VAEs, it has an encoder and a decoder. For the encoder design, we derive principal-component-analysis-based (PCA-based) transform kernels using the covariance of its inputs. This process converts input images of correlated pixels to uncorrelated spectral components, which play the same role as latent variables in a VAE system. For the decoder design, we convert randomly generated spectral components to synthesized images through the inverse PCA transform. A subject test is conducted to compare the quality of digits generated using the proposed method and the VAE method. They offer comparable perceptual quality yet our model can be obtained at much lower complexity.

BACKGROUND: Recent cancer genome studies on many human cancer types have relied on multiple molecular high-throughput technologies. Given the vast amount of data that has been generated, there are surprisingly few databases which facilitate access to these data and make them available for flexible analysis queries in the broad research community. If used in their entirety and provided at a high structural level, these data can be directed into constantly increasing databases which bear an enormous potential to serve as a basis for machine learning technologies with the goal to support research and healthcare with predictions of clinically relevant traits. RESULTS: We have developed the Cancer Systems Biology Database (CancerSysDB), a resource for highly flexible queries and analysis of cancer-related data across multiple data types and multiple studies. The CancerSysDB can be adopted by any center for the organization of their locally acquired data and its integration with publicly available data from multiple studies. A publicly available main instance of the CancerSysDB can be used to obtain highly flexible queries across multiple data types as shown by highly relevant use cases. In addition, we demonstrate how the CancerSysDB can be used for predictive cancer classification based on whole-exome data from 9091 patients in The Cancer Genome Atlas (TCGA) research network. CONCLUSIONS: Our database bears the potential to be used for large-scale integrative queries and predictive analytics of clinically relevant traits.