Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards the optimization of excitation waveform for electrostatic resonant sensors with enhanced frequency stability

We study a novel excitation scheme for gap-closing electrostatically-actuated MEMS resonators. We show that this scheme can be used to cancel out the impact of feedback phase fluctuations on oscillator frequency stability, and hence on resonant sensor resolution. Furthermore, as opposed to previous noise evasion schemes, the proposed excitation scheme can be applied without any loss in drive efficiency compared to the linear case, and irrespectively of the softening or hardening nature of the dominant nonlinearity of the system.
Research of Heterogeneous Acceleration Optimization of Convolutional Neural Network Algorithm for Unmanned Vehicle Based on FPGA

Convolutional neural network algorithm has been widely used in unmanned 3D perception and object detection, and FPGA has been more widely used in implementing convolutional neural network with its ultra-low power and ultra-high performance. In this paper, the heterogeneous computing method based on FPGA and various optimization methods are adopted to design the heterogeneous computing structure that meets the performance requirements of the unmanned vehicle algorithm, as well as the specific methods and optimization strategies for heterogeneous implementation. The results show that by adopting the method of heterogeneous optimization design, compared with the CPU calculation method, both the power consumption and the speed have been greatly improved, meeting the requirements of the data processing speed of the unmanned vehicle.
Anti-Forensics: A Practitioner Perspective

With the increase in cybercrime, digital evidence is becoming an integral part of the judicial system. Digital evidence is to be found everywhere from computers, to mobile phones, ATMs and surveillance cameras, and it is hard to imagine a crime that does not contain any element of digital evidence. It is however not simple to extract such evidence and present it to court in such a way that there is no uncertainty that it was not changed in any way. Thus the responsibility placed on a Digital Forensics (DF) practitioner to present usable evidence to a court is increasing fast. In some respects, however, it is relatively easy to get rid of digital evidence or to hide it. Many tools exist for cybercrime criminals to prevent DF practitioners from getting their hands on information of probative value. Such tools and methods known as AntiForensics (AF). The purpose of this study is to identify the abilities of DF practitioners to identify the use of AF in their active investigations. The research model used, attempts to identify all the factors and constructs of AF that impacts on investigations. This model was then used to develop a survey instrument to gather empirical data from South African DFs. The research has shown that whilst South African DF practitioners perceive DF as having an impact on their investigations, they also perceive electronic evidence as forming only part of the evidence presented to court, and that even if most of the usable evidence of lost, some will generally remain. It was also found that while most DF practitioners in South Africa are well versed only in the more commonly known AF techniques. They do not rate their abilities on more complex techniques well. Finally, most DF practitioners appear not to actively attempt to identify AF techniques as part of their investigations. This combined with a lack of understanding of more complex AF techniques could leave South African DF practitioners exposed by missing important evidence due to lack of technical proficiency.
Scalable Learning with Privacy Over Graphs

Graphs have well-documented merits for modeling complex systems, including financial, biological, and social networks. Network nodes can also include attributes such as age or gender of users in a social network. However, the size of real-world networks can be massive, and nodal attributes can be unavailable. Moreover, new nodes may emerge over time, and their attributes must be inferred in real time. In this context, the present paper deals with scalable learning of nodal attributes by estimating a nodal function based on noisy observations at a subset of nodes. A multikernel-based approach is developed which is scalable to large-size networks. The novel method is capable of providing real-time evaluation of the function values on newly-joining nodes without resorting to a batch solver. In addition, the novel scheme only relies on an encrypted version of each node’s connectivity, which promotes privacy. Experiments on real datasets corroborate the effectiveness of the proposed methods.
Security Hardening of Implantable Cardioverter Defibrillators

Contemporary healthcare has witnessed a wide deployment of Implantable Cardioverter Defibrillators (ICDs), which have the capability to be controlled remotely, making them equally accessible from both home and hospitals. The therapeutic benefits of ICDs seem to outweigh potential security concerns, yet overlooking the presence of malicious attacks cannot be justified. This study investigates the scenario where an adversary falsifies a controller command and sends instructions to issue high electric shocks in succession. We propose a novel security hardening mechanism to protect data communications between ICD and controller from malicious data manipulations. Our proposed method verifies the correctness of an external command with respect to the history of heart rhythms. The proposed method is evaluated using real data. Multi-aspect analyses show the effectiveness of the proposed scheme.
Stealthy Trojan Detection Based on Feature Analysis of Circuit Structure

The design methods and the detection methods for Hardware Trojan develop rapidly. Existing trustiness verification methods are effective to obviously malicious HT but no effect on Stealthy Trojan. Stealthy Trojan is an advanced attack form and hard to be detected. In this paper, we analyze the characteristic of stealthy Trojan and propose a static detection method based on feature analysis. The results on ISCAS benchmarks show that the proposed method can detect the Stealthy Trojan node and is convenient to be implanted into other scalable verification framework.
Self-Powering High Frequency Modulated SiC Power MOSFET Isolated Gate Driver

A novel implementation of an isolated gate driver for power switches is proposed in this study. The driver is bespoke designed for SiC power mosfets. The proposal achieves the main driver characteristics—isolation capability, gate switching command, and power transfer to the secondary side by using a unique magnetic transformer. The resulting simple circuitry enables the integration of the driver into the power cell, achieving theoretical higher power density values of the final system. The principle of operation of the driver is described in detail. The original pulsewidth modulation signal is modulated with a pure ac square waveform under a two-level modulation scheme, that guarantees no saturation of the magnetic core. The signal is then magnetically coupled to the secondary side, where it is rectified and reconstructed to effectively drive the target device. In addition, the steps needed to provide a suitable design taking into account the parameters of the target power switch are also detailed. The resulting driver is characterized and a prototype of the driver is built and tested. The main results of the driver performance on a SiC mosfet-based prototype are presented. Based on the analysis of these results, this study experimentally demonstrates the feasibility of the proposal.
Mechanism of Buffer-Related Current Collapse in AlGaN/GaN HEMT

Current collapse, as one of the most important factors that affect the characteristics of AlGaN/GaN HEMTs has studied for long, but there is still no uniform conclusion on its mechanism. Transient pulse method and DC method are employed to analyze the dependence of current collapse on buffer layer trap based on numerical simulation. The transient pulse simulation shows that, during the negative part of RF cycle, a large number of channel electrons(2DEG) at the gate edge of the drain side are trapped by buffer layer traps, which is dependent on the high electric field intensity. When the RF waveform on its positive half cycle, the electric field intensity under the gate edge of the drain side is reduced and the buffer layer trap begin to emit the trapped electrons. However, due to the slower emission rate, the density of the 2DEG cannot be fully recovered before the next half of RF cycle. That’s the reason why the output current is scaled smaller in transient condition than it is in DC condition. The DC simulation suggests that hot electrons influence the charge transport by spilling over into the bulk GaN where they are captured by traps. It leads to negative differential conductivity in the output characteristics.
Reliability of etching angle estimation of MEMS resonators from electrical measurements

This paper focuses on the determination of etching imperfections based on electrical measurements in electrostatic MEMS. It aims at providing results and methodology to experimenters to help them determine under which conditions they should operate a MEMS electrostatic resonator to estimate etching defects such as the mean etching angle. We analyze the parameters of interest for such a study and show how to evaluate the driving level required for a reasonable estimation. The method is based on an analytical expression of the electrostatic force and squeeze-film damping taking into account the non-ideal verticality of the electrodes due to the etching process. We prove that the aspect ratio of the resonator is a critical parameter that strongly impacts the precision of the estimation. Keywords— Electrostatic MEMS, defects detection, etching, frequency response
Extensible piezoelectric energy harvesting circuit based on synchronous electric charge extraction with single inductor

Piezoelectric energy harvesting (PEH) is one of the effective methods to solve the limited power supply life of the wireless sensor network nodes. Most of the current research on piezoelectric energy harvesting is based on a single piezoelectric transducer (PZT). However, based on a single vibration source, multiple PZTs can be used to obtain more power. In response to this, an extensible piezoelectric synchronous electric charge extraction (E-SECE) circuit is proposed in this paper. The proposed circuit consists of several PEH cells, an inductor, a freewheeling diode and a storage capacitor, which can harvest energy from multiple PZTs with a higher inductor utilization. The detailed implementation of a self-powered double PZTs ESECE circuit is presented, and the theoretical analysis and simulation verify the superiority of the circuit. According to the simulation results, under the same excitation conditions, the output power of the E-SECE circuit with double PZTs can be up to 1.99 times that with single PZT, and the output power of single PZT E-SECE circuit can up to 3.08 times the maximum output power of standard energy harvesting (SEH) circuit.
Operating Point Dependent Variable Switching Point Predictive Current Control for PMSM Drives

This contribution presents a direct model predictive current control approach that achieves favorable performance during transients while minimizing the torque and current ripples at steady-state operation by increasing the granularity at which switching can be performed. To meet the control goals, an optimization problem is solved in real-time that decides whether only one discrete voltage space vector or a combination of two is selected. In the latter case, a variable switching point, i.e., a time instant within the control interval at which the converter switches change state, is computed. The proposed method is advantageous, e.g., for electric drives in machine tools, in which, depending on the operating point, fast dynamics and a low torque ripple are important. The approach is evaluated at the example of a two-level voltage source inverter driving a permanent magnet synchronous machine.
Magnetic-Based Indoor Localization Using Smartphone via a Fusion Algorithm

The detection of indoor location based on magnetic fields collected by embedded sensors in smartphones has been progressed rapidly. Most current approaches rely on the particle filter (PF) scheme which combines the pedestrian dead reckoning (PDR) technique with patterns of previously recorded magnetic field intensity. The key challenges include inherent blindness and particle degradation problems. Here, a fusion algorithm combining the extended Kalman filter (EKF) and the PF scheme is proposed to address these issues. EKF is first used to reduce the possible location regions by fusing the PDR and magnetic field intensity results. The particle generation, update, and resampling processes are conducted afterward, and the final position is calculated by using the weighted mean of particles. As such, the blindness and particle degradation problems are alleviated by using particles in the reduced location regions at each processing step. Experiments show a localization accuracy of 1-2m when the user walks smoothly, which is better than those of traditional PF schemes, especially in cases under heavy magnetic distortions by using a reduced number of particles.
Analysis of Input-Dependent Noise in Self-Timed Reset Dynamic Vision Sensor and its Impact on Data Quality

Threshold temporal contrast (TTC), as a key feature of event-based signal encoding scheme, always fluctuates due to the various noise sources in circuit implementation. This fluctuation (or noise) distorts the information that each event contains, leading to the data quality degeneration. To avoid the errors that may appear in the subsequent signal processing, the degeneration needs to be within the limit demanded by the algorithm. Therefore, modeling the relationship between noise and data quality is important for the event-based hardware application. This paper focuses on analyzing the input-dependent noise on TTC caused by dark current and charge injection, which are labeled as <inline-formula> <tex-math notation="LaTeX">$\delta \Theta _{\mathrm {dc}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\delta \Theta _{\mathrm {ci}}$ </tex-math></inline-formula> respectively, in self-timed reset dynamic vision sensors (STR-DVS), and evaluates the corresponding impacts on data quality. As for the theoretical derivation part, <inline-formula> <tex-math notation="LaTeX">$\delta \Theta _{\mathrm {dc}}$ </tex-math></inline-formula> is calculated using a logarithmic photocurrent-to-voltage model with dark current taken into account, and <inline-formula> <tex-math notation="LaTeX">$\delta \Theta _{\mathrm {ci}}$ </tex-math></inline-formula> is figured out with a three-phase on-to-off-transient switch model applied in differencing circuit. The data quality evaluation is implemented with event clusters generated from the nonideality-contained model, and the signal-to-distortion ratio (SDR) of the signal reconstructed by a perfect nonlinear recovery algorithm is taken as the metric. The analysis and simulation results are expected to offer a reference on assessing the applicability of STR-DVS for a specific application, or system parameter settings to make the data quality satisfy the requirement while using the sensor.
U-Net Plus: Deep Semantic Segmentation for Esophagus and Esophageal Cancer in Computed Tomography Images

The effective segmentation and 3-D rendering of the esophagus and esophageal cancer from the computed tomography (CT) images can assist doctors in diagnosing esophageal cancer. Irregular and vague boundary causes great difficulty in the segmentation of esophagus and esophageal cancer. In this paper, U-Net Plus is proposed to segment esophagus and esophageal cancer from a 2-D CT slice. In the new network architecture, two blocks are employed to enhance the feature extraction performance of complex abstract information, which can effectively resolve irregular and vague boundaries. A block is a skip connection operation that is similar to convolution. The architecture is trained through a dataset of 1924 slices from 10 CT scans and tested through 295 slices from 6 CT scans. The training and test datasets are expanded tenfold to simulate the segmentation of the 3-D CT image. Using the new framework, we report a 0.79 ± 0.20 dice value and 5.87 ± 9.91 Hausdorff distance. A semi-automatic scheme is then designed for the 3-D segmentation of the esophagus or esophageal cancer. The 3-D rendering of the esophagus or esophageal cancer is implemented to assist in the diagnosis of esophageal cancer.
A New Quality-Aware Quality-Control Data Compression Framework for Power Reduction in IoT and Smartphone PPG Monitoring Devices

In this article, we propose a new real-time quality-aware quality-control data compression framework for maximizing the battery life of Internet of Things (IoT) and Smartphone based health monitoring devices. The proposed framework is implemented on Arduino Due with a 32-bit Atmel SAM3X8E ARM Cortex-M3 processor and validated using four standard databases and real-time signals obtained by using our sensing hardware. The proposed framework achieves compression ratios between seven and 28 with energy saving between 83% and 92%. Results demonstrate that our framework provides a promising energy saving solution by discarding the noisy photoplethysmogram (PPG) signals and pulse-free signals before performing data compression and compressing the noise-free PPG signals efficiently with very small feature parameter error.
Fostering Co-Creation with Games: Transforming Formal into Non-Formal Learning and Vice Versa

This paper presents four game-based learning designs with focus on situated development of students' creativity and active participation in the learning process. Presented cases are based on role-play, interdisciplinary team work and co-creation, using games and gamification as additional motivation to engage students in project and problem based learning. The reported case studies are from formal learning settings at the university from bachelor and master level and nonformal learning setting with open access to everyone.
Importance of journalism and mass communication for nuclear emergency management

Volume 2 • Issue 8 • 1000e127 J Mass Commun Journalism ISSN: 2165-7912 JMCJ, an open access journal It has become clear from the lessons identified and, unfortunately, not well learnt from past nuclear and radiological events, communication is one of the most important challenges of emergency management [1-3]. The nuclear accident in Fukushima (Japan) in 2011 has once more emphasized the need to better understand how riskrelated messages are processed and how the public receives and accepts messages, related to protective actions in nuclear emergencies [4,5]. For instance, it is well known that one possible protective measure in case of a nuclear emergency is to take stable iodine tablets. What happened in Japan was that quite some people actually swallowed gargling agents containing povidone-iodine as a substitute for stable iodide tablets, an action which can actually be quite detrimental to someone’s health [4].
A Comparative Study of Three Classic Five-Level Inverters Connected to the Power Grid

Recently, the power quality issue becomes quite a serious problem when an electrical conversion energy system is connected to the grid. Thus, multilevel inverters are intensively employed due to their role in reducing Total Harmonic Distortion (THD) with a small output filter size. This work is focused on a comparative analysis between the most common five-level (5L) inverters: the Neutral Point Clamp (NPC), the Flying Capacitors (FC) and the Cascade H-Bridge (CHB) multilevel inverter. The comparison criteria are based on the conduction and the switching losses as well as the current rating and the voltage stress in all switching devices. The obtained comparison results are performed through a simulation study under Matlab/Simulink environment where each structure of the considered 5L inverters is connected to the same grid.
Energy Efficient Indivisible Workload Distribution in Geographically Distributed Data Centers

In this paper, we investigate the problem of energy cost minimization for geographically distributed data centers with the guaranteed quality of service (i.e., service delay) under time-varying system dynamics. In order to satisfy the user demands, these data centers (DCs) consume a large amount of energy. The increasing energy cost of the DCs is a contemporary problem for the online service providers. To reduce the energy cost of the DCs, recent research studies suggest the workload distribution techniques among geo-distributed data centers by exploiting the dynamic electricity prices and an increased use of the renewable energy. In this paper, we propose a green geographical load balancing (GreenGLB) online algorithm based on the greedy algorithm design technique for the interactive and indivisible workload distribution. An indivisible workload is a sequential task, which cannot be further divided and must be assigned to a single data center. The basic idea of our algorithm is to assign the incoming workload at each time considering the current offered prices of electricity, the renewable energy levels, and respecting the given set of constraints. The experimental results based on the real-world traces illustrate the effectiveness of GreenGLB over the existing workload distribution techniques and attain a significant reduction in the energy cost of the geo-distributed DCs.
Not Your Father’s Timing Anymore – Novel Approaches to Timing of Digital Circuits

In traditional digital circuits, flip-flops are used as memory components for state machines and synchronization components to implement pipelining. The clock frequency of a circuit is determined by the longest combinational path between flip-flops. To improve circuit performance, combinational logic blocks between flip-flops have been the focus of optimization, with techniques such as sizing and retiming. However, the ever-increasing challenges from factors such as variations and aging have made further increase of circuit performance extremely difficult. In this paper, timing of digital circuits is examined and a new concept to introduce wave-pipelining into sequential design is presented. Two application scenarios of this concept, VirtualSync and TimingCamouflage, are presented to demonstrate the potential of this concept in improving circuit performance and netlist security, respectively.