Smarter City: Smart Energy Grid based on Blockchain Technology

The improvement of the Quality of Life (QoL) and the enhancement of the Quality of Services (QoS) represent the main goal of every city evolutionary process. It is possible making cities smarter promoting innovative solutions by use of Information and Communication Technology (ICT) for collecting and analysing large amounts of data generated by several sources, such as sensor networks, wearable devices, and IoT devices spread among the city. The integration of different technologies and different IT systems, needed to build smart city applications and services, remains the most challenge to overcome. In the Smart City context, this paper intends to investigate the Smart Environment pillar, and in particular the aspect related to the implementation of Smart Energy Grid for citizens in the urban context. The innovative characteristic of the proposed solution consists of using the Blockchain technology to join the Grid, exchanging information, and buy/sell energy between the involved nodes (energy providers and private citizens), using the Blockchain granting ledger

Hardware design of LIF with Latency neuron model with memristive STDP synapses

In this paper, the hardware implementation of a neuromorphic system is presented. This system is composed of a Leaky Integrate-and-Fire with Latency (LIFL) neuron and a Spike-Timing Dependent Plasticity (STDP) synapse. LIFL neuron model allows to encode more information than the common Integrate-and-Fire models, typically considered for neuromorphic implementations. In our system LIFL neuron is implemented using CMOS circuits while memristor is used for the implementation of the STDP synapse. A description of the entire circuit is provided. Finally, the capabilities of the proposed architecture have been evaluated by simulating a motif composed of three neurons and two synapses. The simulation results confirm the validity of the proposed system and its suitability for the design of more complex spiking neural network

Comparison of Low-Complexity Algorithms for Real-Time QRS Detection using Standard ECG Database

Today, thanks to the development of advanced wearable devices, it is possible to track patient conditions outside hospital setting for several days. One of the most important bio-signals used for health analysis is the electrocardiographic (ECG) signal. It provides information about the heart rate, rhythm, and morphology of heart. Many algorithms are proposed over years for automated ECG analysis. Due to their computational complexity, not all these techniques can be implemented on wearable devices for real-time ECG detection. In this frame, a particular interest is toward the algorithms for automatic QRS detection. Different algorithms have been presented in the literature. Among all, more suitable class for the implementation on embedded systems is based on the use of signal derivatives and thresholds. These algorithms are composed by pre-processing stage, for the noise removal, and decision stage for the QRS detection. In literature, the different algorithms were discriminated only with respect to their pre-processing stages. Furthermore, not all algorithms were tested with standard database: this makes the results difficult to compare and evaluate. Moreover, the algorithms performance in case of pathological behaviours was not compared. This paper is motivated by the need to perform a comparison of the whole algorithms, both pre-processing and decision stages, under a standard database (MIT-BIH ECG database of Physionet), either for non-pathological and pathological signals. The results confirm that the Pan & Tompkins algorithm has the best performance in terms of QRS complex detection. However, in some cases, its performance is comparable with the other algorithms ones. For this reason, in the applications in which the reduced of computational complexity is an important constraint, it is possible to implemented algorithms with comparable performance but with lesser complexity with respect to P&T algorithm

Energy Consumption Saving in Embedded Microprocessors Using Hardware Accelerators

This paper deals with the reduction of power consumption in embedded microprocessors. Computing power and energy efficiency are becoming the main challenges for embedded system applications. This is, in particular, the caseof wearable systems. When the power supply is provided by batteries, an important requirement for these systems is the long service life. This work investigates a method for the reduction of microprocessor energy consumption, based on the use of hardware accelerators. Their use allows to reduce the execution time and to decrease the clock frequency, so reducing the power consumption. In order to provide experimental results, authors analyze a case of study in the field of wearable devices for the processing of ECG signals. The experimental results show that the use of hardware accelerator significantly reduces the power consumption

e health iot universe a review

The Internet of Things (IoT) devices are able to collect and share data directly with other devices through the cloud environment, providing a huge amount of information to be gathered, stored and analyzed for data-analytics processes. The scenarios in which the IoT devices may be useful are amazing varying, from automotive, to industrial automation or remote monitoring of domestic environment. Furthermore, has been proved that healthcare applications represent an important field of interest for IoT devices, due to the capability of improving the access to care, reducing the cost of healthcare and most importantly increasing the quality of life of the patients. In this paper, we analyze the state-of-art of IoT in medical environment, illustrating an extended range of IoT-driven healthcare applications that, however, still need innovative and high technology-based solutions to be considered ready to market. In particular, problems regarding characteristics of response-time and precision will be examined. Furthermore, wearable and energy saving properties will be investigated in this paper and also the IT architectures able to ensure security and privacy during the all data-transmission process. Finally, considerations about data mining applications, such as risks prediction, classification and clustering will be provided, that are considered fundamental issues to ensure the accuracy of the care processes

811. Correction of Laminin-5 β3 Chain Deficiency in Human Epidermal Stem Cells by Transcriptionally Targeted Lentiviral Vectors

Mutations in any of the genes encoding the laminin 5 heterotrimer (|[alpha]|3, |[beta]|3 and |[gamma]|2) cause junctional epidermolysis bullosa (JEB), a severe and often fatal skin adhesion defect. We and others have shown that expression of a retrovirally transferred |[beta]|3-chain cDNA in keratinocytes from affected patients reconstitutes normal synthesis, assembly and secretion of laminin 5, and corrects the adhesion defect in vitro and in vivo. We have recently started a phase-I clinical trial of gene therapy of JEB based on transplantation of cultured skin derived from autologous epidermal stem cells transduced with a MLV-derived retroviral vector. Since gamma- retroviral vectors have raised safety concerns for the genotoxic risk associated with the insertion of LTR elements into the human genome, we developed an alternative gene transfer strategy based on LTR- modified, HIV-derived lentiviral vectors. Two self-inactivating (SIN) lentiviral vectors were built, in which expression of either GFP or a LAMB3 cDNA is under the control of either a constitutive promoter (PGK) or the keratinocyte-specific, 2.2-kb promoter-enhancer of keratin 14 (K14). In a third construct, expression of the transgene is under the control of the viral LTR, modified by replacing the U3 region with two K14 enhancer elements. Analysis in human keratinocyte cultures and in full-thickness human skin equivalents reconstituted onto immunodeficient mice showed that GFP expression directed by the K14 elements is tissue-specific and restricted to the basal layer of the epidermis. Expression of laminin5 from the three alternative vectors was evaluated in keratinocyte cultures derived from skin biopsies of JEB patients. Biochemical and cell kinetics assays demonstrated transduction of epidermal clonogenic stem/progenitor cells and full phenotypic correction of JEB keratinocytes with all vectors. Southern blot analysis of individual cell clones showed that LTR-modified lentiviral vectors are genetically stable and integrate in multiple copies in the human genome. This study shows that the use of lentiviral vectors transcriptionally targeted to the basal keratinocytes by the insertion of restricted enhancer elements is an effective, and potentially safer, alternative for gene therapy of JEB

Smarter City: Smart Energy Grid based on Blockchain Technology

The improvement of the Quality of Life (QoL) and the enhancement of the Quality of Services (QoS) represent the main goal of every city evolutionary process. It is possible making cities smarter promoting innovative solutions by use of Information and Communication Technology (ICT) for collecting and analysing large amounts of data generated by several sources, such as sensor networks, wearable devices, and IoT devices spread among the city. The integration of different technologies and different IT systems, needed to build smart city applications and services, remains the most challenge to overcome. In the Smart City context, this paper intends to investigate the Smart Environment pillar, and in particular the aspect related to the implementation of Smart Energy Grid for citizens in the urban context. The innovative characteristic of the proposed solution consists of using the Blockchain technology to join the Grid, exchanging information, and buy/sell energy between the involved nodes (energy providers and private citizens), using the Blockchain granting ledger

comparison of low complexity algorithms for real time qrs detection using standard ecg database

Today, thanks to the development of advanced wearable devices, it is possible to track patient conditions outside hospital setting for several days. One of the most important bio-signals used for health analysis is the electrocardiographic (ECG) signal. It provides information about the heart rate, rhythm, and morphology of heart. Many algorithms are proposed over years for automated ECG analysis. Due to their computational complexity, not all these techniques can be implemented on wearable devices for real-time ECG detection. In this frame, a particular interest is toward the algorithms for automatic QRS detection. Different algorithms have been presented in the literature. Among all, more suitable class for the implementation on embedded systems is based on the use of signal derivatives and thresholds. These algorithms are composed by pre-processing stage, for the noise removal, and decision stage for the QRS detection. In literature, the different algorithms were discriminated only with respect to their pre-processing stages. Furthermore, not all algorithms were tested with standard database: this makes the results difficult to compare and evaluate. Moreover, the algorithms performance in case of pathological behaviours was not compared. This paper is motivated by the need to perform a comparison of the whole algorithms, both pre-processing and decision stages, under a standard database (MIT-BIH ECG database of Physionet), either for non-pathological and pathological signals. The results confirm that the Pan & Tompkins algorithm has the best performance in terms of QRS complex detection. However, in some cases, its performance is comparable with the other algorithms ones. For this reason, in the applications in which the reduced of computational complexity is an important constraint, it is possible to implemented algorithms with comparable performance but with lesser complexity with respect to P&T algorithm

Impact of a Shorter Brine Soaking Time on Nutrient Bioaccessibility and Peptide Formation in 30-Months-Ripened Parmigiano Reggiano Cheese

Reducing the salt content in food is an important nutritional strategy for decreasing the risk of diet-related diseases. This strategy is particularly effective when applied to highly appreciated food having good nutritional characteristics, if it does not impact either upon sensory or nutritional properties of the final product. This work aimed at evaluating if the reduction of salt content by decreasing the brine soaking time modifies fatty acid and protein bioaccessibility and bioactive peptide formation in a 30-month-ripened Parmigiano Reggiano cheese (PRC). Hence, conventional and hyposodic PRC underwent in vitro static gastrointestinal digestion, and fatty acid and protein bioaccessibility were assessed. The release of peptide sequences during digestion was followed by LC–HRMS, and bioactive peptides were identified using a bioinformatic approach. At the end of digestion, fatty acid and protein bioaccessibility were similar in conventional and hyposodic PRC, but most of the bioactive peptides, mainly the ACE-inhibitors, were present in higher concentrations in the low-salt cheese. Considering that the sensory profiles were already evaluated as remarkably similar in conventional and hyposodic PRC, our results confirmed that shortening brine soaking time represents a promising strategy to reduce salt content in PRC