An Error-Based Approximation Sensing Circuit for Event-Triggered, Low Power Wearable Sensors

Event-based sensors have the potential to optimize energy consumption at every stage in the signal processing pipeline, including data acquisition, transmission, processing and storage. However, almost all state-of-the-art systems are still built upon the classical Nyquist-based periodic signal acquisition. In this work, we design and validate the Polygonal Approximation Sampler (PAS), a novel circuit to implement a general-purpose event-based sampler using a polygonal approximation algorithm as the underlying sampling trigger. The circuit can be dynamically reconfigured to produce a coarse or a detailed reconstruction of the analog input, by adjusting the error threshold of the approximation. The proposed circuit is designed at the Register Transfer Level and processes each input sample received from the ADC in a single clock cycle. The PAS has been tested with three different types of archetypal signals captured by wearable devices (electrocardiogram, accelerometer and respiration data) and compared with a standard periodic ADC. These tests show that single-channel signals, with slow variations and constant segments (like the used single-lead ECG and the respiration signals) take great advantage from the used sampling technique, reducing the amount of data used up to 99% without significant performance degradation. At the same time, multi-channel signals (like the six-dimensional accelerometer signal) can still benefit from the designed circuit, achieving a reduction factor up to 80% with minor performance degradation. These results open the door to new types of wearable sensors with reduced size and higher battery lifetime

Incorporation of non-nucleoside triphosphate analogues opposite to an abasic site by human DNA polymerases β and λ

A novel class of non-nucleoside triphosphate analogues, bearing hydrophobic groups sterically similar to nucleosides linked to the α-phosphate but lacking the chemical functional groups of nucleic acids, were tested against six different DNA polymerases (polymerases). Human polymerases α, β and λ, and Saccharomyces cerevisiae polymerase IV, were inhibited with different potencies by these analogues. On the contrary, Escherichia coli polymerase I and HIV-1 reverse transcriptase were not. Polymerase β incorporated these derivatives in a strictly Mn++-dependent manner. On the other hand, polymerase λ could incorporate some alkyltriphosphate derivatives with both Mg++ and Mn++, but only opposite to an abasic site on the template strand. The active site mutant polymerase λ Y505A showed an increased ability to incorporate the analogues. These results show for the first time that neither the base nor the sugar moieties of nucleotides are required for incorporation by family X DNA polymerase

Incorporation of non-nucleoside triphosphate analogues opposite to an abasic site by human DNA polymerases β and λ

A novel class of non-nucleoside triphosphate analogues, bearing hydrophobic groups sterically similar to nucleosides linked to the α-phosphate but lacking the chemical functional groups of nucleic acids, were tested against six different DNA polymerases (polymerases). Human polymerases α, β and λ, and Saccharomyces cerevisiae polymerase IV, were inhibited with different potencies by these analogues. On the contrary, Escherichia coli polymerase I and HIV-1 reverse transcriptase were not. Polymerase β incorporated these derivatives in a strictly Mn(++)-dependent manner. On the other hand, polymerase λ could incorporate some alkyltriphosphate derivatives with both Mg(++) and Mn(++), but only opposite to an abasic site on the template strand. The active site mutant polymerase λ Y505A showed an increased ability to incorporate the analogues. These results show for the first time that neither the base nor the sugar moieties of nucleotides are required for incorporation by family X DNA polymerases

Discovery of non-nucleoside inhibitors of HIV-1 reverse transcriptase competing with the nucleotide substrate

How odd! A new class of non-nucleoside reverse transcriptase inhibitors with a 6-vinylpyrimidine scaffold (1) has been found to inhibit HIV-1 reverse transcriptase (RT) by competition with the nucleotide substrate after binding to the non-nucleoside inhibitor binding pocket of the enzyme. Molecular modeling studies have been performed to elucidate their peculiar behavior

Pattern Recognition in Non-uniformly sampled electrocardiogram signal for wearable sensors

In this thesis, we explore 3 main topics: non-uniform (in time) sub-sampling, QRS detection in an event-based sub-sampled ECG (electrocardiogram) and implementation on a low-power MCU (Micro Controller Unit). The main idea behind this work is to reduce the energy consumption of a QRS detection algorithm by adapting the sampling frequency using the local frequency of the signal while maintaining the overall performance on the QRS detection without degradation. In particular, we will focus on the compréhension and re-adaptation of 2 popular algorithms for QRS detection: the Pan-Tompkins and the gQRS. This choice was guided by some constraints given by the event-based sub-sampling. The re-adaptation, in particular, was performed in 2 parts: the first step was to change the behavior of the algorithms in order to be able to work in an event-based sampled domain. The results achieved from the first step led to the selection of gQRS as the designed algorithm to undergo step 2: parallelization and optimization for the chosen low-power device. The results achieved are comparable to the results achieved by the original version in the classical uniform-sampled domain. The selected low-power device is the PULP platform, an MCU composed of a single core, low power CPU and a cluster composed of other 8 smaller cores

Event-based sampled ECG morphology reconstruction through self-similarity

Background and Objective: Event-based analog-to-digital converters allow for sparse bio-signal acquisition, enabling local sub-Nyquist sampling frequency. However, aggressive event selection can cause the loss of important bio-markers, not recoverable with standard interpolation techniques. In this work, we leverage the self-similarity of the electrocardiogram (ECG) signal to recover missing features in event-based sampled ECG signals, dynamically selecting patient-representative templates together with a novel dynamic time warping algorithm to infer the morphology of event-based sampled heartbeats. Methods: We acquire a set of uniformly sampled heartbeats and use a graph-based clustering algorithm to define representative templates for the patient. Then, for each event-based sampled heartbeat, we select the morphologically nearest template, and we then reconstruct the heartbeat with piece-wise linear deformations of the selected template, according to a novel dynamic time warping algorithm that matches events to template segments. Results: Synthetic tests on a standard normal sinus rhythm dataset, composed of approximately 1.8 million normal heartbeats, show a big leap in performance with respect to standard resampling techniques. In particular (when compared to classic linear resampling), we show an improvement in P-wave detection of up to 10 times, an improvement in T-wave detection of up to three times, and a 30\% improvement in the dynamic time warping morphological distance. Conclusion: In this work, we have developed an event-based processing pipeline that leverages signal self-similarity to reconstruct event-based sampled ECG signals. Synthetic tests show clear advantages over classical resampling techniques

An Event-Based System for Low-Power ECG QRS Complex Detection

One of the greatest challenges in the design of modern wearable devices is energy efficiency. While data processing and communication have received a lot of attention from the industry and academia, leading to highly efficient microcontrollers and transmission devices, sensor data acquisition in medical devices is still based on a conservative paradigm that requires regular sampling at the Nyquist rate of the target signal. This requirement is usually excessive for signals that are typically sparse and highly non-stationary, leading to data overload and a waste of resources in the full processing pipeline. In this work we propose a new system to create event-based heart-rate analysis devices, including a novel algorithm for QRS detection that is able to process electrocardiogram signals acquired irregularly and much below the theoretically-required Nyquist rate. This technique allows us to drastically reduce the average sampling frequency of the signal and, hence, the energy needed to process it and extract the relevant information. We implemented both the proposed event-based algorithm and a state-of-the-art version based on regular sampling on an ultra-low power hardware platform, and the experimental results show that the event-based version reduces the energy consumption in runtime up to 15.6 times, while the detection performance is maintained at an average F1 score of 99.5%

Adaptive Laser Welding Control: A Reinforcement Learning Approach

Despite extensive research efforts in the field of laser welding, the imperfect repeatability of the weld quality still represents an open topic. Indeed, the inherent complexity of the underlying physical phenomena prevents the implementation of an effective controller using conventional regulators. To close this gap, we propose the application of Reinforcement Learning for closed-loop adaptive control of welding processes. The presented system is able to autonomously learn a control law that achieves a predefined weld quality independently from the starting conditions and without prior knowledge of the process dynamics. Specifically, our control unit influences the welding process by modulating the laser power and uses optical and acoustic emission signals as sensory input. The algorithm consists of three elements: a smart agent interacting with the process, a feedback network for quality monitoring, and an encoder that retains only the quality critic events from the sensory input. Based on the data representation provided by the encoder, the smart agent decides the output laser power accordingly. The corresponding input signals are then analyzed by the feedback network to determine the resulting process quality. Depending on the distance to the targeted quality, a reward is given to the agent. The latter is designed to learn from its experience by taking the actions that maximize not just its immediate reward, but the sum of all the rewards that it will receive from that moment on. Two learning schemes were tested for the agent, namely S-Learning and Policy Gradient. The required training time to reach the targeted quality was 20 min for the former technique and 33 min for the latter

High Potency of Indolyl Aryl Sulfone Nonnucleoside Inhibitors towards Drug-Resistant Human Immunodeficiency Virus Type 1 Reverse Transcriptase Mutants Is Due to Selective Targeting of Different Mechanistic Forms of the Enzyme

Indolyl aryl sulfone (IAS) nonnucleoside inhibitors have been shown to potently inhibit the growth of wild-type and drug-resistant human immunodeficiency virus type 1 (HIV-1), but their exact mechanism of action has not been elucidated yet. Here, we describe the mechanism of inhibition of HIV-1 reverse transcriptase (RT) by selected IAS derivatives. Our results showed that, depending on the substitutions introduced in the IAS common pharmacophore, these compounds can be made selective for different enzyme-substrate complexes. Moreover, we showed that the molecular basis for this selectivity was a different association rate of the drug to a particular enzymatic form along the reaction pathway. By comparing the activities of the different compounds against wild-type RT and the nonnucleoside reverse transcriptase inhibitor-resistant mutant Lys103Asn, it was possible to hypothesize, on the basis of their mechanism of action, a rationale for the design of drugs which could overcome the steric barrier imposed by the Lys103Asn mutation