Miracle-dz/UniFAR-FTUs huggingface dataset:
The economic and environmental impact of replacing geyser with air source heat pump water heater

The study focused on the evaluation of the energy cost saving and greenhouse gas emission reduction when a 3.0 kW electric geyser is replaced by a 1.2 kW split type ASHP water heater. Both hot water devices were of 150 L capacity and are set up to operate simultaneously under specific volumes of hot water drawn off (150, 50 and 100 L) in the morning, afternoon and evening each day over a year. The data acquisition system comprising of flow meters, temperature sensors, power meters, ambient temperature and relative humidity sensor and a data logger. The data acquisition system was built and installed to monitor the performance of the hot water devices. The results depicted that the average monthly energy saving and CO2 reduction achieve with the ASHP system due to the 300 L of daily volume of hot water drawn off was 203.67 kWh and 201.63 kg, respectively for the summer season. The average monthly cost saving for the summer period based on the flat rate tariff (FR) and the time of use (TOU) tariff was R25S.66 and R 296.35. In addition, the average monthly energy and CO2 impact of the ASHP water heater due to the daily 300 L of hot water drawn off was 249.24 kWh and 246.74 kg, respectively for the winter season. The corresponding average monthly flat rate and time of use cost saving for the winter season was R 378.84 and R 719.26. The average month-day COP for both the summer and winter seasons was 2.90 and 2.49, respectively. A firm conclusion can be established that the time of use cost saving is higher than the flat rate cost saving due to the relatively higher electricity tariff structure attributed to the time of use electricity plan especially during the peak periods. Despite, the low average month-day COP experienced by the ASHP water heater in the winter as opposed to the COP for the average month-day in the summer period, the electrical energy saving of the ASHP water heater was higher due to the lowered makeup water temperature. These lowered makeup water temperature is associated with a greater electrical energy consumed and thermal energy gained before hot water set point temperature is attained after the hot water drawers.
Aln Based Piezoelectric Micromachined Ultrasonic Transducers for Continuous Monitoring of the Mechano-Acoustic Cardiopulmonary Signals

This paper presents, for the first time, Aluminum Nitride (AlN) based Piezoelectric Micromachined Ultrasonic Transducers (PMUTs) with high fill-factor and high acoustic sensitivity for continuous monitoring of the mechano-acoustic cardiopulmonary signals. The sensing cells of the reported PMUTs are designed and arranged as honeycomb architecture. The honeycombs innovative structural system not only achieves high fill-factor, but also enhances the acoustic sensitivity of the PMUTs. An 8 × 9 array of 180 µm side-length, 1.08 MHz PMUTs is fabricated based on an AlN-on-Cavity Silicon-on-Insulator (CSOI) platform. The fabricated PMUTs array achieves an acoustic pressure sensitivity of -178 dB (re: 1 V/µPa) and a signal-to-noise ratio of around 52 dB at 500 Hz. The PMUTs array is die-attached on their condition circuit and packaged with medical ultrasonic couplants to acquire wideband mechano-acoustic physiological signals, which achieves continuous monitoring of varieties of health factors associated with the cardiopulmonary system, such as heart rate, heart sound, respiratory rate and lung sound.
ISWC 2020

The International Symposium on Wearable Computers (ISWC) is the flagship conference on wearable computing focusing on design, algorithmic foundations, and deployments. It is the ideal venue to present and learn about the latest research in the field. The authors share their observations from the most recent gathering, held online in September 2020. Each year the International Symposium on Wearable Computing showcases the best and most innovative body worn technologies and the algorithms that support the novel insights we gain from them. This year was no exception, despite the remote nature of the conference. ISWC received a total of 70 paper submissions, among them 58 notes, and 12 briefs. The papers had (first) authors from 14 countries, with a balanced representation from the Americas, Asia/Oceania, and Europe. The program committee had 25 members, representing all the major areas of wearable technology expertise. The final acceptance rate across all ISWC publication formats was 30%. We are grateful for the hard work our PC members and the external reviewers put in to make ISWC 2020 happen, maintaining the scientific standards and rigor the community is proud of.
Blockchain enabled Ambient Assisted living for Critically ill Patients

The blockchain technology of the 4th industrial revolution was introduced to fundamentally change the online world by moving the entire system to a decentralized platform instead of the prevailing centralized system. It is an equitable system where the data is open to everyone thus any technology that makes use of blockchain framework retains lucidity thus making it hassle-free for its customers. The most popular implementation of blockchain technology, bitcoin is famous for its security and privacy, attributed to its proof of work mining algorithm. On the other hand, its enormous energy consumption hampers its implementation in resource-restrained IoT applications. While the traditional blockchain operation demands a solely dedicated mining machine, contrarily, IoT devices seems not capable enough to entertain the high cost, value, and validity of the blockchain domain. Therefore, some simplification in the maximum energy-consuming part of the blockchain which is the mining process is required to be done. This work implements a simplified energy-efficient blockchain for ambient assisted living applications notably for critically ill patients. It collects data from five digital sensors on a single board computer for a medical facility, which computes PoW for up to a particular level of difficulty as allowed by available computation power. The emulation results obtained on comparison of the recommended blockchain with respect to traditional blockchain exhibit that simplified blockchain implementation is energy efficient in the terms of computation time and more pertinent to the resource-constrained IoT domain.
Standard CMOS Integrated Ultra-Compact Micromechanical Oscillating Active Pixel Arrays

This work demonstrates an ultra-compact low power oscillating micromechanical active pixel array based on a 0.35 µm back-end of line (BEOL)-embedded CMOS-MEMS technology. Each pixel consists of a 3-MHz clamped-clamped beam (CCB) MEMS resonator and a power scalable transimpedance amplifier (TIA) that occupies a small area of 70 × 60 µm2 and draws only 85 µW/pixel. The MEMS resonator is placed next to the TIA with less than 10 µm spacing thanks to the well-defined etch stops in the titanium nitride composite (TiN-C) CMOS-MEMS platform. A multiplexing phase-locked loop (PLL)-driven oscillator is employed to demonstrate the chip functionality. In particular, a nonlinear operation of the resonator tank is used to optimize the phase noise (PN) performance and Allan deviation (ADEV) behavior. The ADEV of 420 ppb averaged over best 3-pixels is exhibited based on such a nonlinear vibration operation.
Stacked Autoencoder based Intrusion Detection System using One-Class Classification

The paper presents a study of deep learning based approach for Intrusion Detection System. Already existing models for classification were based on supervised learning methods which fails to classify instances of unknown attacks. Effective prediction of network packets as normal or attack, known and unknown to the model, is imperative requiring detection with minimal false alarm rate. Even for the attacks not known to the model, Stacked Autoencoder turns out to be one such deep learning architecture which identifies complex pattern leading to generation of the best latent representation of inputs. The proposed model was trained on single labeled instances from KDD Cup 99 dataset along with standardizing the inputs using batch normalization to minimize the problem of internal covariance shift and vanishing gradient to some extent. Experimental results obtained show that the proposed method outperforms all the other algorithms giving accuracy of 98.17% and false alarm rate of 0.38%.
Solar PV Array fed Modified PMBLDCM Drive Scheme for Blower Applications

A solar PV array fed PMBLDCM drive system for blower applications is presented in this paper. The power optimization of the solar PV array is done using modified DCDC converter based MPPT technology. The scheme is efficient, reliable and suitable for rural areas where the grid supply is limited. The PMBLDCM based blower is also works during bad weather conditions at lower speed. The scheme found highly suitable due to various advantages like wide MPPT operating range, simple configuration and high efficiency. The scheme is simulated and tested at various operating conditions (steady state and dynamic) and at different environmental condition (different insolation levels). The results validate the suitability of the proposed system for solar powered blower application.
A Single Chip Directional Loudspeaker Based on PMUTS

This paper reports a highly directional air-coupled parametric loudspeaker using a single chip of piezoelectric micromachined ultrasonic transducers (pMUTs). Compared with the state-of-art technologies, three distinctive advancements have been achieved: (1) a micromachined parametric array based on CMOS-compatible bimorph AlN pMUTs; (2) generation of highly directional audible sound with a half power beam width less than 5 degrees; and (3) generation of multiple audible frequency using a single chip with 246 pMUT elements in a footprint of 13mm by 13mm. As such, the proposed AlN pMUT parametric array could open up a new class for low-power portable directional loudspeakers for private communication and so on.
Optimal Sizing of ESS in a Hybrid Wind-Diesel Power System Using NAR and NARX Model

To solve the problem of ever increasing power crisis and to employ renewable sources of power generation into the existing grid, the focus has shifted to Energy Storage System (ESS) which not only improves the voltage profile but also reduce the power system cost. However, the use of insufficient size of ESS can lead to extreme cases of power system voltage stability and may lead to voltage outages in the grid. Whereas overrated Energy Storage System may lead to poor efficiency as well as low economy. In this paper, a novel method of calculating the size of ESS and its placement on sensitive buses has been proposed based on the data of Alberta province (Canada). To solve the problem, Power-Voltage curves have been plotted to identify the sensitive buses and the size of the ESS has been calculated using NAR and NARX model. All the results have been obtained using MATLAB Simulink and IEEE 14 bus model.
Decentralized Consensus Decision-Making for Cybersecurity Protection in Multimicrogrid Systems

Multimicrogrid (MMG) systems play an increasingly important role in the smart grid. They come with various potential cyberattacks, which may cause power supply interruption or even human casualties. Therefore, decision-making for timely mitigation of cyberattack risks is highly desirable in the security protection of power systems. However, there is a lack of effective decentralized decision-making strategies that are able to deal with MMG scenarios through distributed consensus. To address this issue, a decentralized consensus decision-making (DCDM) approach is proposed in this article for the security of MMG systems. It achieves decentralized consensus without the need of a trusted authority or central server, making it distinct from existing consensus methods. Meanwhile, it guarantees the consistency and nonrepudiability of consensus results, which are stored on the blockchain in sequence. In each of the distributed agents, the approach consists of a fuzzy static Bayesian game model (FSB-GM) to determine the optimal security strategy and a hybrid consensus algorithm to achieve consensus. The FSB-GM considers the fuzzy preferences of different types of attackers and defenders. The hybrid consensus algorithm is implemented by the fusion improvement of two consensus mechanisms in the blockchain. The effectiveness of the presented approach is demonstrated through a case study on an MMG system.
Shuffled Model of Federated Learning: Privacy, Accuracy and Communication Trade-Offs

We consider a distributed empirical risk minimization (ERM) optimization problem with communication efficiency and privacy requirements, motivated by the federated learning (FL) framework (Kairouz <italic>et al</italic>., 2019). Unique challenges to the traditional ERM problem in the context of FL include <inline-formula> <tex-math notation="LaTeX">$\textsf{(i)}$ </tex-math></inline-formula> need to provide privacy guarantees on clients’ data, <inline-formula> <tex-math notation="LaTeX">$\textsf{(ii)}$ </tex-math></inline-formula> compress the communication between clients and the server, since clients might have low-bandwidth links, <inline-formula> <tex-math notation="LaTeX">$\textsf{(iii)}$ </tex-math></inline-formula> work with a dynamic client population at each round of communication between the server and the clients, as a small fraction of clients are sampled at each round. To address these challenges we develop (optimal) communication-efficient schemes for private mean estimation for several <inline-formula> <tex-math notation="LaTeX">$\ell _{p}$ </tex-math></inline-formula> spaces, enabling efficient gradient aggregation for each iteration of the optimization solution of the ERM. We also provide lower and upper bounds for mean estimation with privacy and communication constraints for arbitrary <inline-formula> <tex-math notation="LaTeX">$\ell _{p}$ </tex-math></inline-formula> spaces. To get the overall communication, privacy, and optimization performance operation point, we combine this with privacy amplification opportunities inherent to this setup. Our solution takes advantage of the inherent privacy amplification provided by client sampling and data sampling at each client (through Stochastic Gradient Descent) as well as the recently developed privacy framework using anonymization, which effectively presents to the server responses that are randomly shuffled with respect to the clients. Putting these together, we demonstrate that one can get the same privacy, optimization-performance operating point developed in recent methods that use full-precision communication, but at a much lower communication cost, i.e., effectively getting communication efficiency for “free”.
Improved Specific Detectivity to 107 for CMOS-MEMS Pyroelectric Detector Based on 12%-Doped Scandium Aluminum Nitride

CMOS-MEMS pyroelectric detectors based on 12%-doped ScAlN are fabricated using 8-inch MEMS wafer technology and characterized. The ScAlN pyroelectric material is deposited at a low temperature of ~200oC. The results show D* as high as 4.3 x 107 (cm√Hz)/W and NEP as low as 1.26 x 10-9 W/√Hz. This is the first demonstration of a functional CMOS-MEMS pyroelectric detector using ScAlN as the pyroelectric sensing material. Compared to AlN that usually presents D* in the range of 105-106, Sc-doped AlN brings on the promise of better performing CMOS-MEMS pyroelectric detectors that could be miniaturized, integrated with CMOS circuits and provide wider applications.
Differential Thermal Conductivity CO2 Sensor

In this paper we present a novel MEMS technology for CO2 detection, based on differential thermal conductivity. Holes have been positioned across the thin-film dielectric membrane to induce an asymmetric temperature profile in conjunction with a centrally heated tungsten resistor, leading to distinct magnitudes of gas interaction across the sensor, all of which is substantiated by a numerical model. Differential readings are taken from four surrounding resistors and shown to correlate to CO2 concentration. The best resistor combination is investigated and is shown to provide a sensitivity of 0.05 mV/%CO2. Using separate heating and sensing elements greatly lowers the noise and a detection resolution of < 100 ppm is calculated. This proof-of-concept article introduces a device that could lead to low-cost, low-power, high-sensitivity, scalable gas detection.
ArcVein-Arccosine Center Loss for Finger Vein Verification

This article proposes a new loss function termed arccosine center loss, which can learn interclass and intraclass information simultaneously, to improve the discriminative ability of convolutional neural networks for finger vein verification. Specifically, the purpose of arccosine center loss is to reduce intraclass distance and increase the interclass distance through network model training. With the combination of softmax loss and arccosine center loss, the proposed network model can extract features with the interclass dispensation and intraclass compactness, which improves the discriminative ability of learned features. Experimental studies have confirmed the effectiveness and efficiency of the proposed method for finger vein verification.
Flood Risk Visualization and Prediction Information System: Case of City Manila, Philippines

Nowadays, the need for flood management using non-structural measures is becoming vital to experts and decision-makers. While technological intervention using information systems for flood risk visualization is widely recognized, the assimilation of flood risk prediction is nascent in the literature. This research presented the Flood Information System as an assistive tool for managing floods in the urban capital integrating analytics and machine learning algorithms for a more dynamic flood risk visualization and prediction. This paper shows that flood management information systems added value in monitoring and predicting flood risk areas in Manila City, Philippines.
Wind Power Forecasting Using Attention-Based Recurrent Neural Networks: A Comparative Study

Wind power is one of the most efficient renewable resources without emissions. Nonetheless, it is difficult to exactly forecast wind power generation given historical power and wind speed information, the failure of which may cost the risk of large-scale outages. This article takes a close look at the artificial recurrent neural network framework in the application of wind power forecasting. More intelligent mechanisms using attention to capture spatial-temporal patterns within historical data are emphasized in this work and are shown to be state-of-the-art for short-term wind power forecasting. Our experiments at a wind farm in southeast Australia using only the historical wind power generation and wind speed records from ambient weather stations show that, e.g., 7.4750% in mean absolute error (MAE) and 0.3345 in the coefficient of variation in the root mean squared error (CV-RMSE) for half-hour-ahead prediction. To interpret how the three models under consideration—the long- and short-term time-series network (LSTNet), the temporal pattern attention-based long short-term memory (TPA-LSTM) and the dual-stage attention-based recurrent neural network (DA-RNN)—work, we visualize and analyze the details of the models so that further improvement can be made by combining the advantageous components of the models.
High transition temperature shape memory polymer composites based on bismaleimide resin

Bismaleimide–allyl phenol polymer system exhibiting shape memory characteristics and possessing transition temperature of up to 220°C (Ttrans) was synthesized by coreacting 2,2-bis 4[(4-maleimidophenoxy)phenyl]propane (BMIP) and o,o′-diallylbisphenol A (DABA) with a bismaleimide-end-capped poly(tetramethyleneoxide) (BMI-PTMO). BMI-PTMO that served as shape memory polymer was synthesized and characterized using Fourier-transform infrared (FTIR), nuclear magnetic resonance, chemical analysis and size exclusion chromatography. The cured network containing varying proportions of poly(tetramethyleneoxide) was synthesized by varying the concentration of BMI-PTMO in the ternary blend. Cure characteristics were investigated by FTIR, differential scanning calorimetry and rheometry. Elastic memory composites were processed using carbon fabric from varying blend composition at fixed resin to reinforcement ratio. PTMO enhanced the flexibility of the network. The system as a whole possessing high transition temperature, its storage modulus decreased with increase in PTMO content. The polymer possessed good shape recovery characteristics and the recovery time decreased with increasing concentration of the shape memory component. The system showed shape recovery of 88–98% in about a minute at Ttrans. The extent of shape recovery was a direct function of PTMO concentration. The recovery time showed an inverse relationship to the modulus ratio.
Path Planning and Obstacle Avoiding of the USV Based on Improved ACO-APF Hybrid Algorithm With Adaptive Early-Warning

Path planning is important to the efficiency and navigation safety of USV autonomous operation offshore. To improve path planning, this study proposes the improved ant colony optimization-artificial potential field (ACO-APF) algorithm, which is based on a grid map for both local and global path planning of USVs in dynamic environments. The improved ant colony optimization (ACO) mechanism is utilized to search for a globally optimal path from the starting point to the endpoint for a USV in a grid environment, and the improved artificial potential field (APF) algorithm is subsequently employed to avoid unknown obstacles during USV navigation. The primary contributions of this article are as follows: (1) this article proposes a new heuristic function, pheromone update rule, and dynamic pheromone volatilization factor to improve convergence and mitigate finding local optima with the traditional ant colony algorithm; (2) we propose an equipotential line outer tangent circle and redefine potential functions to eliminate goals unreachable by nearby obstacles (GNRONs) and local minimum problems, respectively; (3) to adapt the USV to a complex environment, this article proposes a dynamic early-warning step-size adjustment strategy in which the moving distance and safe obstacle avoidance range in each step are adjusted based on the complexity of the surrounding environment; (4) the improved ant colony optimization algorithm and artificial potential field algorithm are effectively combined to form the algorithm proposed in this article, which is verified as an effective solution for USV local and global path planning using a series of simulations. Finally, in contrast to most papers, we successfully perform field experiments to verify the feasibility and effectiveness of the proposed algorithm.
Kirigami-Structured and Self-Powered Pressure Sensor Using Electroactive Polymer

Rapid advances in sensing technologies are leading to the development of wearable electronics for biomedical applications. Ionic polymer-metal composite (IPMC), an electroactive polymer, has excellent potential to be designed as a wearable sensor because of its self-powered sensing capability. Here, a unique four-arm pinwheel kirigami pattern has been integrated with a stretchable IPMC pressure sensor for an enhanced performance. Dynamic tests with a range of displacements were performed to validate the sensor design. The proposed sensor can produce a linear voltage output with a sensitivity of 36 mV/mm. The voltage output of the proposed kirigami-structured IPMC sensor shows superior performance with 3 times improvement compared to the conventional non-kirigami IPMC sensor.