Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental Demonstration of Ferroelectric Spiking Neurons for Unsupervised Clustering

We report the first experimental demonstration of ferroelectric field-effect transistor (FEFET) based spiking neurons. A unique feature of the ferroelectric (FE) neuron demonstrated herein is the availability of both excitatory and inhibitory input connections in the compact 1T-1FEFET structure, which is also reported for the first time for any neuron implementations. Such dual neuron functionality is a key requirement for bio-mimetic neural networks and represents a breakthrough for implementation of the third generation spiking neural networks (SNNs)—also reported herein for unsupervised learning and clustering on real world data for the first time. The key to our demonstration is the careful design of two important device level features: (1) abrupt hysteretic transitions of the FEFET with no stable states therein, and (2) the dynamic tunability of the FEFET hysteresis by bias conditions which allows for the inhibition functionality. Experimentally calibrated, multi-domain Preisach based FEFET models were used to accurately simulate the FE neurons and project their performance at scaled nodes. We also implement an SNN for unsupervised clustering and benchmark the network performance across analog CMOS and emerging technologies and observe (1) unification of excitatory and inhibitory neural connections, (2) STDP based learning, (3) lowest reported power (3.6nW) during classification, and (4) a classification accuracy of 93%.
Investigating Wind Generation Investment Indices in Multi-Stage Planning

This paper presents a Multi-stage stochastic bi-level model for the expansion planning of Wind resources in power systems at a multi-stage horizon. In this paper, the power system consists of a combination of fossil fuel technologies and Wind resources for investment. Demand is characterized by a certain number of demand blocks. The uncertainty of demand for each this block (for each time period of the curve) is determined by the scenario. Afterwards, the suggested model is converted to a mathematical programming with some equilibrium constraints. Following that, after linearization, a mixed integer linear program is obtained. This framework is examined on the IEEE RTS 24-bus network. The obtained simulation results confirm that this model can be appropriately used as a means to analyze the behavior of investments in wind and thermal units.
Analyzing and Enhancing the Security of Ultrasonic Sensors for Autonomous Vehicles

Autonomous vehicles rely on sensors to measure road condition and make driving decisions, and their safety relies heavily on the reliability of these sensors. Out of all obstacle detection sensors, ultrasonic sensors have the largest market share and are expected to be increasingly installed on automobiles. Such sensors discover obstacles by emitting ultrasounds and analyzing their reflections. By exploiting the built-in vulnerabilities of sensors, we designed random spoofing, adaptive spoofing, and jamming attacks on ultrasonic sensors, and we managed to trick a vehicle to stop when it should keep moving, and let it fail to stop when it should. We validate our attacks on stand-alone sensors and moving vehicles, including a Tesla Model S with the “Autopilot” system. The results show that the attacks cause blindness and malfunction of not only sensors but also autonomous vehicles, which can lead to collisions. To enhance the security of ultrasonic sensors and autonomous vehicles, we propose two defense strategies, single-sensor-based physical shift authentication that verifies signals on the physical level, and multiple sensor consistency check that employs multiple sensors to verify signals on the system level. Our experiments on real sensors and MATLAB simulation reveal the validity of both schemes.
Single RF-Chain Beam Training for MU-MIMO Energy Efficiency and Information-Centric IoT Millimeter Wave Communications

In a multi-user multiple-input multiple-output (MU-MIMO) communication system, multiple RF (radio frequency) chains are generally connected corresponding to the antenna elements. However, the system complexity, hardware cost, and energy consumption are challenging while using many RF chains. In addition, an information-centric Internet of Things (IoT) will be significant to guarantee fast service demand for radio access in the fifth generation (5G) mmWave wireless systems. In this paper, a new beam training method based on a single RF chain is proposed to overcome these challenges by using the downlink–uplink and downlink–downlink beam training techniques to establish the elite users’ subset group which can be trained in a single training time slot. Additionally, the users’ subset mechanism is implemented by selecting an arbitrary function to guarantee the convergence. Moreover, the proposed techniques are compared with the conventional full search method regarding searching time, cost, and complexity. Different user subset functions are modeled, evaluated, and compared with the existing ones with respect to effective channel gain and average system capacity. The simulation results show that the proposed method can achieve a significant system performance, and the single RF chain is serviceable for a large number of users when connected with a maximum of 64 antenna elements at the BS. The proposed method can also be implemented in a massive MU-MIMO system by using multiple RF chains in which each RF chain is connected to a group of 64 antenna elements. This work is applicable in 5G mmWave communications, which require robust infrastructure to meet the future IoT service demand in 5G hot scenarios.
Fuzzy Echo State Neural Network with Differential Evolution Framework for Time Series Forecasting

In this paper, differential evolution (DE) is used to find optimal weights for echo state neural network model and also to optimize the number of rules of the modeled fuzzy system that presents the input to the echo state neural network (ESNN) model. ESNN designed in this work possess a recurrent neuronal infra-structure called as reservoir. This work aims to develop a good reservoir for the ESNN model employing the coherent features and the ability of the differential evolution algorithm and fuzzy rule base system. DE aims to pre-train the fixed weight values of the network with its effective exploration and exploitation capability and fuzzy rule base system (FRBS) formulates a set of rules, which provides inferences for the inputs presented to the echo state network model. The performance of the developed optimized network is evaluated based on the error metrics and the computational time incurred for the training of the model. The test results of ESNN model using DE and FRBS are compared with that of ESNN without optimization and fuzzy rule to prove its validity and also with the related existing techniques. The perceived DE based fuzzy ESNN model is verified for its effectiveness with a set of time series forecasting benchmark problems. The empirical results prove the superiority and the effectiveness of the DE based fuzzy ESNN learning outcomes.
Age-Related Differences in Visual Search for Color Targets Manipulated Based on Cone-Contrast Model

Visual attention is the system by which we selectively process visual information based on prioritization of an area within the visual field, and has been explored in terms of a visual search task. Color perception in older is affected by reductions in intensity at the retina, and yellow-blue (YB) discrimination have been reported to be deficient in older observers. This paper aimed to investigate the effect of color on the visual attention of older observers using a visual search task with color condition based on response of cone. We designed color stimuli based on the DKL color space and conducted the experiments with three color conditions (red-green opponent (RG), yellow-blue opponent (YB), and luminance (WB)). The result showed that the color difference in the target and distractors influenced the response times for the older observers, especially in the YB opponent colors. This study provides preliminary evidence that when the stimuli are fixed by the YB axis, the saliency of target for older observers may appear lower than that for younger observers.
A Miniature Multi-sensor Shoe-Mounted Platform for Accurate Positioning

In this work a miniature low-cost and low-power platform for foot-mounted navigation and foot clearance estimation is presented. The developed system includes an inertial measurement unit (IMU) and three accurate distance sensors. The distance sensors enable reliable foot clearance monitoring that additionally increases the accuracy of horizontal localization. The device can be easily clipped onto the user's shoe for easy data collection by means of fast data logging, Bluetooth communication, USB connection as well as a computer interface. We present the device hardware and sensor fusion formulations along with the test results and estimation of the measurement quality with and without proximity sensors.
Approximate feedback linearization for nonlinear systems

The purpose of this paper is to explain a design method of an approximate feedback linearization. Our procedure shows first the problem formulation and then clarifies the approximate feedback linearization method. In this method, we settle a state transformation matrix in order to transform approximately the nonlinear system into a controllable canonical form. After that, we apply a nonlinear control input making the approximation term small, and use a standard nonlinear linearization method. That makes our system in the new coordinates becomes a stable linear one. Lastly, through the application to the ACROBOT, we show the effectiveness of this new method and compare it to a previous method.
Animation Generation of Injured Gait Based on Body Status Using Phase-Functioned Neural Network

We developed a system that generates character animations according to several factors, such as the state of the character and the conditions of the open world, using a phase-functioned neural network (PFNN). PFNN is a new neural network architecture that computes weights for every frame using a phase input. When used for character control, it enables the character to adapt its behaviors to various environments and user inputs, producing superior quality animations as compared to other methods. This study examines an approach that adapts character animations to injuries and accounts for the severity of the injuries. We present an injury representation and its implementation into an existing PFNN system. We tested single and concurrent injuries to various body parts with different severity levels while the character walked and ran. The character adapted realistically to various scenarios accurately representing the effects of its injuries, environment and user inputs, while maintaining a fast prediction time.
Sensors and Sensor Fusion in Autonomous Vehicles

In this paper, we are presenting a short overview of the sensors and sensor fusion in autonomous vehicles. We focused on the sensor fusion from the key sensors in autonomous vehicles: camera, radar, and lidar. The current state-of-the-art in this area will be presented, such as 3D object detection method for leveraging both image and 3D point cloud information, moving object detection and tracking system, and occupancy grid mapping used for navigation and localization in dynamic environments. It is shown that including more sensors into sensor fusion system benefits with better performance and the robustness of the solution. Moreover, usage of camera data in localization and mapping, that is traditionally solved by radar and lidar data, improves the perceived model of the environment. Sensor fusion has a crucial role in autonomous systems overall, therefore this is one of the fastest developing areas in the autonomous vehicles domain.
Power Management Techniques in Popular Operating Systems for IoT Devices

This paper investigates different operating systems recently adopted for various IoT applications from an energy perspective which is the main challenge in these applications. The whole IoT communication stack has been dedicated to meet this challenge but lesser attention has been paid towards the design of an energy efficient operating system. The reason is that the energy management is a hardware support feature which was not available in legacy hardware. The introduction of low power modes in recent hardware designs also necessitates the incorporation of energy management at the operating system level. We have classified popular IoT operating systems on the basis of three different energy conservation techniques i.e. idle CPU management, sleep modes and tick suppression. This work will be helpful in choosing the appropriate operating system for the specific IoT application. In the end, we have evaluated tick suppression as a potential energy conservation technique which is being deployed in some advanced operating systems for IoT devices.
Enhancing intelligence SOC with big data tools

Traditional security tools are based on predetermined signatures or rules that show rigidity in the face of the dynamics of the interconnections that occur in organizations. The inclusion of analytic in security systems extends the field of vision of security analysts for proactive threat detection. This allows SOC specialists to make decisions in real time and focus on the protection of critical assets of the organization. Our contribution in this work is to analyze the applicability of the use of Big Data as a complementary tool for the detection of security events in a real CSIRT environment, validating the architecture, configuration and visualization using the ELK stack as a Big Data platform.
Sustainability in the Network Development Plan: Systemic Sustainability and Stakeholder Engagement

The world of energy is rapidly changing and against this backdrop, Terna's goal is to enable the transition to a more efficient, secure and sustainable energy system. Recent developments have brought a series of new challenges, such as how to manage the de-carbonization process and the need to guarantee security of supply and the cost-effectiveness of the service for businesses and households. Sustainability has fundamental role in the Italian National Development Plan. Indeed, environmental and social indicators are included in the cost-benefit analysis (CBA) and are applied to development projects of transmission network. Dialogue with stakeholders forms the basis for all activities. in fact a proactive approach to the engagement with interested parties is necessary, such as national and local authorities, municipalities, the general public, trade unions and environmental organizations, because stakeholders' involvements key to delivering development targets.
Non-Contact and Real-Time Pulse-Based Radar with Sensitivity Improvement for Vital-Sign Monitoring

A real-time pulse-based sensing radar with an adjusted transmitting power for long-term monitoring application is presented to detect vital-sign information from subjects. The radar using the technique of pulse width modulation in the transmitter helps adjust sensing energy, which in turn improves the overall signal-to-noise ratio (SNR), thereby increasing sensitivity in particular at the non-ideal sensing environment. To increase radar adaptability and verify its concept, we focus on two cases of different probing pulse width and expect to receive the echoes for monitoring respiration signal. The measured results show that the obtained SNR can indeed improve by altering the propagation pulses.
Radiation Hardness Comparison of CMOS Image Sensor Technologies at High Total Ionizing Dose Levels

The impact of the manufacturing process on the radiation-induced degradation effects observed in CMOS image sensors (CISs) at the MGy total ionizing dose (TID) levels is investigated. Moreover, the vulnerability of the partially pinned PHDs at moderate-to-high TIDs is evaluated for the first time to our knowledge (PHD stands for “photodiode”). It is shown that the 3T-standard partially pinned PHD has the lowest dark current before irradiation, but its dark current increases to ~1 pA at 10 kGy(SiO2). Beyond 10 kGy(SiO2), the pixel functionality is lost. The comparison between several CIS technologies points out that the manufacturing process impacts the two main radiation-induced degradations: the threshold voltage shift of the readout chain MOSFETs and the dark current increase. For all the tested technologies, 1.8-V MOSFETs exhibit the lower threshold voltage shift, and the nMOSFETs are the most radiation tolerant. Among all the tested devices, 1.8-V sensors achieve the best dark current performance. Several radiation-hardening-by-design solutions are evaluated at the MGy level to improve further the understanding of CIS radiation hardening at extreme TID.
XScope: Memory Introspection Based Malicious Application Detection

The malicious applications may hide itself to evade the detection of security tools placed inside or outside of the system. To detect these hidden applications, existing approaches employ virtual machine introspection to intercept the process switches and identify the hidden process once it is being switched. However, this method imposes heavy overhead due to frequent process switches. Moreover, it fails to detect some malicious applications which hide in the system without execution for a long time. To solve these problems, we propose XScope, a memory introspection based hidden application detection approach. First, XScope categorizes the memory pages of virtual machine (VM) using memory introspection, and identifies the anonymous pages which are related to user applications. Second, XScope extracts the processes by analyzing the anonymous memory pages, to acquire a full map of processes at the virtual machine monitor (VMM) layer. Third, XScope compares the map acquired at the VMM layer against the map acquired in the VM for detecting the hidden applications. We implement a prototype system and conduct a set of experiments. The experimental results show that XScope is able to detect the hidden malicious application without significant overhead.
Miniaturized Substrate Integrated Components

This paper presents the most recent achievement of the microwave group of the University of Pavia in the design and experimental verification of miniaturized substrate integrated waveguide (SIW) components. The SIW technology is more and more considered as an enabling technology for the implementation of 5G and IoT circuits and systems. However, beside many advantages, in its original configuration the SIW technology may lead to large device. For this reason, this paper presents miniaturization solution based on ridge interconnect, folded structures, and half-/quarter-mode structures. Some examples are reported to demonstrate the effectiveness of the proposed solutions, and others will be presented at the conference.
High Efficiency Wideband Power Amplifier with Class-J Configuration

The design and implementation of a high efficiency Class-J mode RF power amplifier (PA) for wireless communications applications is described here. In this study, a broadband class-J power amplifier with GaN HEMT transistor is designed to achieve 40 dBm output power and 10 dB gain, high efficiency and linearity performance in the 2.5-3.5 GHz frequency band. Experimental results indicate good agreement with simulations verifying and demonstrating the class-J mode operation. The obtained power added efficiency (PAE) is above 68% and the output power is 39.84 dBm at an input drive of 30 dBm. High efficiency is achieved in the frequency range from 2.5 GHz to 3.5 GHz. Simulation and experimental results show that the class-J RF power amplifier can be realized more effectively in terms of a better compromise between power and efficiency trade-offs over a significant RF bandwidth than conventional class-AB/B operations. the ACPR value for the linearity measurement was found to be around −30 dBc.
Fingerprint Indexing based on Ridge Orientation and Frequency on GPU

Fingerprint is a unique characteristic owned by human and is often used as identity. Identifying an unknown fingerprint is done by doing fingerprint matching to a fingerprint database. However, fingerprint matching on a large database needs a lot of time. Therefore, fingerprint indexing is often used to speed up fingerprint identification. Fingerprint indexing consists of creation of index and searching. In this paper, we describe a GPU implementation of the searching phase of fingerprint indexing based on ridge orientation and frequency. The GPU implementation reaches a speedup to 250 times compared to CPU sequential implementation.
A Ka-Band LTCC Magneto-Electric Dipole Array for 5G Communications

A $\pmb{4\ \times\ 4}$ millimeter-wave magneto-electric (ME) dipole antenna array is demonstrated based on the low-temperature co-fired ceramic (LTCC) technique. The array has a compact configuration, a wide operating bandwidth of more than 50% that can cover the whole Ka-band, gain up to 15.9 dBi with a variation of less than 3 dB across the operating band, and symmetrical broadside radiation patterns. Due to the satisfying performance, the proposed design would be an attractive candidate for millimeter-wave wireless applications in the fifth-generation (5G) mobile communications.
5G RAN Slicing for Verticals: Enablers and Challenges

This article investigates the slicing concept in the 5G RAN with the related challenges and research problems. The objective is to identify the plausible options for implementing the slicing concept at the RAN level by the mobile network operator to respond to the needs of verticals. We start by identifying the different slice granularity options, that is, how to define slices by combining customer and service needs. We then present how the 5G NR features can be used for facilitating slice implementation and provide typical configurations for different slice types from technology and RAN architecture perspectives. The main challenges for RAN slicing are then discussed, with special attention to the resource allocation problem between slices sharing the same spectrum band. We also investigate the multi-tenant slicing implementation in terms of the openness of the network to third parties, which is regarded as a key issue that may encourage vertical players to use operators' networks rather than deploying their own infrastructure.
Feasibility Study of Dual-PPG Sensors for Blood Velocity and Pressure Estimation

In this study, we used a microcontroller to develop the blood pressure evaluating devices of dual-PPG and recording two groups of photoplethysmography simultaneously. Cardiac systolic and diastolic change the blood volume and also affect the amount of light reflection. Thus, light sensor will receive the pulse wave changes. After using FIR filtering noise from the signal, the device detects PPG peak and then two groups of PPG can calculate pulse transit time. Multiple regression analysis models for evaluating blood pressure was established by using various parameters such as height, weight, age, and BMI. With this model, we can investigate the feasibility of dual-PPG. The result shows the systolic blood pressure, average deviation and standard deviation is 6.74 ± 8.03mmHg, which closes to the AAMI standard, 5mmHg ± 8mmHg. It means estimating blood pressure using physiological parameters is feasible.