Splines in Compressed Sensing

It is well understood that in any data acquisition system reduction in the amount of data reduces the time and energy, but the major trade-off here is the quality of outcome normally, lesser the amount of data sensed, lower the quality. Compressed Sensing (CS) allows a solution, for sampling below the Nyquist rate. The challenging problem of increasing the reconstruction quality with less number of samples from an unprocessed data set is addressed here by the use of representative coordinate selected from different orders of splines. We have made a detailed comparison with 10 orthogonal and 6 biorthogonal wavelets with two sets of data from MIT Arrhythmia database and our results prove that the Spline coordinates work better than the wavelets. The generation of two new types of splines such as exponential and double exponential are also briefed here .We believe that this is one of the very first attempts made in Compressed Sensing based ECG reconstruction problems using raw data.

Towards fog-driven IoT eHealth:Promises and challenges of IoT in medicine and healthcare

Internet of Things (IoT) offers a seamless platform to connect people and objects to one another for enriching and making our lives easier. This vision carries us from compute-based centralized schemes to a more distributed environment offering a vast amount of applications such as smart wearables, smart home, smart mobility, and smart cities. In this paper we discuss applicability of IoT in healthcare and medicine by presenting a holistic architecture of IoT eHealth ecosystem. Healthcare is becoming increasingly difficult to manage due to insufficient and less effective healthcare services to meet the increasing demands of rising aging population with chronic diseases. We propose that this requires a transition from the clinic-centric treatment to patient-centric healthcare where each agent such as hospital, patient, and services are seamlessly connected to each other. This patient-centric IoT eHealth ecosystem needs a multi-layer architecture: (1) device, (2) fog computing and (3) cloud to empower handling of complex data in terms of its variety, speed, and latency. This fog-driven IoT architecture is followed by various case examples of services and applications that are implemented on those layers. Those examples range from mobile health, assisted living, e-medicine, implants, early warning systems, to population monitoring in smart cities. We then finally address the challenges of IoT eHealth such as data management, scalability, regulations, interoperability, device–network–human interfaces, security, and privacy

Adapted Compressed Sensing: A Game Worth Playing

Despite the universal nature of the compressed sensing mechanism, additional information on the class of sparse signals to acquire allows adjustments that yield substantial improvements. In facts, proper exploitation of these priors allows to significantly increase compression for a given reconstruction quality. Since one of the most promising scopes of application of compressed sensing is that of IoT devices subject to extremely low resource constraint, adaptation is especially interesting when it can cope with hardware-related constraint allowing low complexity implementations. We here review and compare many algorithmic adaptation policies that focus either on the encoding part or on the recovery part of compressed sensing. We also review other more hardware-oriented adaptation techniques that are actually able to make the difference when coming to real-world implementations. In all cases, adaptation proves to be a tool that should be mastered in practical applications to unleash the full potential of compressed sensing

An ultra-low power dual-mode ECG monitor for healthcare and wellness

Technology scaling enables today the design of ultra-low cost wireless body sensor networks for wearable biomedical monitors. These devices, according to the application domain, show greatly varying tradeoffs in terms of energy consumption, resources utilization and reconstructed biosignal quality. To achieve minimal energy operation and extend battery life, several aspects must be considered, ranging from signal processing to the technological layers of the architecture. The recently proposed Rakeness-based Compressed Sensing (CS) expands the standard CS paradigm deploying the localization of input signal energy to further increase data compression without sensible RSNR degradation. This improvement can be used either to optimize the usage of a non volatile memory (NVM) to store in the device a record of the biosignal or to minimize the energy consumption for the transmission of the entire signal as well as some of its features. We specialize the sensing stage to achieve signal qualities suitable for both Healthcare (HC) and Wellness (WN), according to an external input (e.g. the patient). In this paper we envision a dual-operation wearable ECG monitor, considering a multi-core DSP for input biosignal compression and different technologies for either transmission or local storage. The experimental results show the effectiveness of the Rakeness approach (up to ≈ 70% more energy efficient than the baseline) and evaluate the energy gains considering different use case scenarios

Algoritmi per la reiezione dei disturbi nei sistemi di acquisizione dei segnali EEG basati sulla tecnica del Compressed Sensing

Il Compressed Sensing è emerso recentemente come un strumento che simultaneamente acquisisce e comprime segnali analogici su dispositivi a basso consumo. Rispetto alla sua caratterizzazione classica (CS), il sistema di acquisizione può essere adattato alla classe di segnali in ingresso (R-CS) ed è inoltre possibile garantire una buona reiezione dei disturbi (R-CSd). Quest’ultimo modello si basa sulla risoluzione di due problemi di ottimizzazione con un numero di variabili potenzialmente elevato. Il primo dei quali ha una risoluzione analitica, il secondo necessita di un risolutore software che, per un numero elevato di variabili, potrebbe non arrivare ad una soluzione in tempi ragionevoli. Il lavoro che viene presentato supera questo problema con un algoritmo scritto ad hoc che sfrutta l’applicazione del metodo del Gradiente Proiettato Discendente con Proiezioni Alternate, attraverso il quale si riesce a ridurre drasticamente il tempo richiesto dalla CPU per ottenere una soluzione, anche per un numero elevato di variabili. A conclusione del lavoro si è applicato questo metodo alla classe di segnali EEG con l’intento di attuare una reiezione dei disturbi a bassissima frequenza direttamente nello stadio di compressione. Il lavoro mostra la catena di elaborazione per il CS, il R-CS e per R-CSd. I casi analizzati sono: adattamento sulla classe di segnali EEG, adattamento sul singolo canale e divisione dei canali in due distinti cluster. Quello che si dimostra è che l’algoritmo R-CSd mostra le stesse performance di R-CS in tutti e tre i casi, facendo a meno dell’utilizzo di un filtro passa alto. La ricostruzione dei canali con la caratterizzazione dell’intera classe di segnali o con l’uso dei due cluster non si discosta troppo da quanto osservato per l’adattamento sul singolo canale con una conseguente semplificazione del sistema di acquisizione proposto