We propose BSC, a Bitcoin Smart Contract implementation. It integrates the functionality of smart contracts into the Bitcoin system, giving developers the ability to build decentralized applications on Bitcoin. BSC will require a new hard fork, on which Bitcoin holders can use their existing funds directly. BSC combines the unlimited creative space of smart contracts and the vast network effect of Bitcoin, which will bring even more possibilities to the cryptocurrency world.

In this paper, we present results from a world-first radiant cooling pavilion, demonstrating a method of cooling people without cooling the air. Instead, surfaces are chilled and thermal radiation is used to keep people cool. A thermally-transparent membrane is used to prevent unwanted air cooling and condensation, a required precursor to deploying radiant cooling panels without humidity control in tropical environments. The results from this thermal comfort study demonstrate the ability to keep people comfortable with radiation in warm air, a paradigm shifting approach to thermal comfort that may help curb global cooling demand projections.

The emergence of embedded and connected smart technologies, systems, and devices has enabled the concept of smart cities by connecting every “thing’‘ to the Internet and in particular in transportation through the Internet of Vehicles (IoV). The main purpose of IoV is to prevent fatal crashes by resolving traffic and road safety problems. Nevertheless, it is paramount to ensure secure and accurate transmission and recording of data in “Vehicle-to-Vehicle’‘ (V2V) and “Vehicle-to-Infrastructure” (V2I) communication. To improve “Vehicle-to-Everything” (V2X) communication, this work uses Blockchain technology for developing a Blockchain-based IoT system aimed at establishing secure communication and developing a fully decentralized cloud computing platform. Moreover, the authors propose a model-based framework to validate the proposed approach. This framework is mainly based on the use of the Attack Trees (AT) and timed automaton (TA) formalisms in order to test the functional, load and security aspects. An optimization phase for testers placement inspired by fog computing is proposed as well.

In order to quantify the manipulation process of acupuncture, in this article, a piezoelectric glove based wearable stress sensing system is presented. Served as the sensitive element with small volume and high tensile resistance, PVDF greatly meet the need of quantitative analysis. Through piezoelectric force sensing glove, the system is capable of detecting both perpendicular stress as well as shear stress. Besides, key parameters including peak stress at needle are detected and extracted, potentially allowing for a higher learning efficiency hence advancing the development of acupuncture.

[Paper accepted at ACNS 2020] In this paper, we propose a simple and effective attack on the recently introduced Smartphone Authentication with Built-in Camera Protocol, called ABC. The ABC protocol uses the photo-response non-uniformity (PRNU) as the main authentication factor in combination with anti-forgery detection systems. The ABC protocol interprets the PRNU as a fingerprint of the camera sensor built-in a smartphone device. The protocol works as follows: during the authentication process, the user is challenged with two QR codes (sent by the server) that need to be photographed with a pre-registered device. In each QR code, the server embeds a unique pattern noise (not visible to the naked eye), called probe signal, that is used to identify potential forgeries. The inserted probe signal is very similar to a genuine fingerprint. The photos of QR codes taken by the user are then sent to the server for verification. The server checks (i) if the photos contain the user’s camera fingerprint (used to authenticate the pre-registered device) and (ii) if the photos contain the embedded probe signal. If an adversary tries to remove (subtract) his own camera fingerprint and replace it with the victim’s camera fingerprint (computed from photos shared on social media), then he will implicitly remove the embedded probe signal and the attack will fail. The ABC protocol is able to detect these attacks with a false acceptance rate (FAR) of 0.5%. However, the ABC protocol wrongly assumes that the attacker can only determine his own camera fingerprint from the photos of the presented QR codes. The attack proposed in our work is able to get past the anti-forgery detection system with a FAR of 54.1%, simply by estimating the attacker’s camera fingerprint from a different set of photos (e.g. five photos) owned by the attacker. This set of photos can be trivially obtained before the attack, allowing the adversary to compute his camera fingerprint independently of the attack. The key to the success of our attack is that the independently computed adversary’s camera fingerprint does not contain the probe signal embedded in the QR codes. Therefore, when we subtract the adversary’s camera fingerprint and add the victim’s camera fingerprint, the embedded probe signal will remain in place. For this reason, the proposed attack can successfully pass through the anti-forgery detection system of the ABC protocol. In this paper, we also propose a potential fix based on analyzing signals from built-in motion sensors, which are not typically shared on social media.

With recent rapid advances in photonic integrated circuits, it has been demonstrated that programmable photonic chips can be used to implement artificial neural networks. Convolutional neural networks (CNN) are a class of deep learning methods that have been highly successful in applications such as image classification and speech processing. We present an architecture to implement a photonic CNN using the Fourier transform property of integrated star couplers. We show, in computer simulation, high accuracy image classification using the MNIST dataset. We also model component imperfections in photonic CNN and show that the performance degradation can be recovered in a programmable chip. Our proposed architecture provides a large reduction in physical footprint compared to current implementations as it utilizes the natural advantages of optics and hence offers a scalable pathway towards integrated photonic deep learning processors.

This paper proposed a radically new concept of a Stiff Seismic Base Absorber (SBA).

This is a preprint of an article published in Scientific Reports. The final authenticated version is available online at: https://doi.org/10.1038/s41598-022-08279-w . Electrical stimulation of biological samples such as tissues and cell cultures attracts growing attention due to its capability of enhancing cell activity, proliferation and differentiation. Eventually, profound knowledge of the underlying mechanisms paves the way for innovative therapeutic devices. Capacitive coupling is one option of delivering electric fields to biological samples and has advantages with regard to biocompatibility. However, the mechanism of interaction is not well understood. Experimental findings could be related to voltage-gated channels, which are triggered by changes of the transmembrane potential (TMP). Numerical simulations by the Finite Element method (FEM) provide a possibility to estimate the TMP. For realistic simulations of in vitro electric stimulation experiments, a bridge from the mesoscopic level down to the cellular level has to be found. A special challenge poses the ratio between the cell membrane (a few nm) and the general setup (some cm). Hence, a full discretization of the cell membrane becomes prohibitively expensive for 3D simulations. We suggest using an approximate FE method that makes 3D multi-scale simulations possible. Starting from an established 2D model, the chosen method is characterized and applied to realistic in vitro situations. A to date not investigated parameter dependency is included and tackled by means of Uncertainty Quantification (UQ) techniques. It reveals a strong, frequency-dependent influence of uncertain parameters on the modeling result.

In order to quantify the manipulation process of acupuncture, in this article, a piezoelectric glove based wearable stress sensing system is presented. Served as the sensitive element with small volume and high tensile resistance, PVDF greatly meet the need of quantitative analysis. Through piezoelectric force sensing glove, the system is capable of detecting both perpendicular stress as well as shear stress. Besides, key parameters including peak stress at needle are detected and extracted, potentially allowing for a higher learning efficiency hence advancing the development of acupuncture.

Fundamental unit commitment approaches are of central importance in energy system modeling for the generation of detailed power plant schedules. However, existing approaches, which reduce complexity in a multi-stage process, often fail to generate realistic electricity prices. A new type of single-stage approach considers market-coupling implicitly so that, in addition to detailed power plant schedules, electricity prices reflecting real prices very well can be generated. In this paper, we show in a back-test for 2014 that an endogenously modeled market-coupling is the driving factor for the quality of resulting electricity prices. Conversely, it can be concluded that conventional multi-stage approaches show a significant distortion of modeled electricity prices due to missing price signals from neighboring market zones. Against the background of expanding trading capacities between market zones within the European power system, this issue becomes increasingly relevant when fundamentally modeling energy prices.

This work is part of a study regarding an issue found in the characterization of a microtransformer when different voltage values were applied to measure the inductance and resistance values of the windings. Under such conditions it was found a variation on those parameters, for both primary and secondary sides.

Gait is closely associated with many chronic diseases. With the development of the Internet of Health Things (IoHT), long-term gait monitoring and corresponding analysis can be performed remotely, reducing a patient’s time and traffic cost, while providing doctors more valuable gait information. The presented work provides a feasible means for real-time, long-term, and accurate gait monitoring in an IoHT scenario. Through the experimental results, the high detection sensitivity of 54 mN and responsivity of 163 mV/N are achieved, thereby satisfying the need for analyzing various diseases. Furthermore, 16 hours continuous working time indicates its successful utilization during long-term gait monitoring.

This article describes the simulation results of an astable multivibrator aimed to be fed by a photovoltaic cell, with the purpose of energy harvesting for electronic systems. The circuit was simulated using metal-oxide semiconductor field effect transistors (MOSFETs) and bipolar junction transistors. The use of half-wave and full-wave rectifiers for DC output voltage supply was also analyzed. The circuit reached a peak efficiency of 28 % when using MOSFETs PMDXB550UNE and half wave rectifier.

With the tremendous growth of the internet, cyberspace is facing several threats from the attackers. Threats like spam emails account for 55\% of total emails according to the Symantec monthly threat report. Over time, the attackers moved on to image spam to evade the text-based spam filters. To deal with this, the researchers have several machine learning and deep learning approaches that use various features like metadata, color, shape, texture features. But the Deep Convolutional Neural Network (DCNN) and transfer learning-based pre-trained CNN models are not explored much for Image spam classification. Therefore, in this work, 2 DCNN models along with few pre-trained ImageNet architectures like VGG19, Xception are trained on 3 different datasets. The effect of employing a Cost-sensitive learning approach to handle data imbalance is also studied. Some of the proposed models in this work achieves an accuracy up to 99\% with zero false positive rate in best case.

Piezoelectric force touch panels are extensively utilized as human-machine interfaces for 3-dimensional touch sensing in internet of things (IoT) applications. However, the unstable force voltage responsivity issue induced by different touch orientations limits the successful use of piezoelectric touch panels. In this article, a piezoelectric touch panel, which is sensitive to both capacitive and force stimulation, is assembled; and a touch orientation classification technique is developed to calibrate the detected force amplitude by training a machine learning model with finger induced capacitive information. Finally, a high stable force voltage responsivity of 87.5% is achieved experimentally.

In this paper, we focus on simple data-driven approach to solve deep learning based on implementing the Mask R-CNN module by analyzing deeper manipulation of datasets. We firstly approach to affine transformation and projective representation to data augmentation analysis in order to increasing large-scale data manually based on the state-of-the-art in views of computer vision. Then we evaluate our method concretely by connection our datasets by visualization data and completely in testing to many methods to understand intelligent data analysis in object detection and segmentation by using more than 5000 image according to many similar objects. As far as, it illustrated efficiency of small applications such as food recognition, grasp and manipulation in robotics

A document by Babak Tavassoli . Click on the document to view its contents.

We report a broadband radio frequency (RF) channelizer with up to 92 channels using a coherent microcomb source. A soliton crystal microcomb, generated by a 49 GHz micro-ring resonator (MRR), is used as a multi-wavelength source. Due to its ultra-low comb spacing, up to 92 wavelengths are available in the C band, yielding a broad operation bandwidth. Another high-Q MRR is employed as a passive optical periodic filter to slice the RF spectrum with a high resolution of 121.4 MHz. We experimentally achieve an instantaneous RF operation bandwidth of 8.08 GHz and verify RF channelization up to 17.55 GHz via thermal tuning. Our approach is a significant step towards the monolithically integrated photonic RF receivers with reduced complexity, size, and unprecedented performance, which is important for wide RF applications ranging from broadband analog signal processing to digital-compatible signal detection.