Miracle-dz/UniFAR-FTUs huggingface dataset:
ABACa: Access Based Allocation on Set Wise Multi-Retention in STT-RAM Last Level Cache

Exhibition of potential advantages of high density, non-volatility, and low static power consumption makes STTRAM a credible successor to SRAM in caches. However, higher write energy and latency of the STT-RAM limit its potential towards commercial usage. Relaxation of STT-RAM’s retention time can be a viable solution to alleviate these obstacles by reducing both write time and energy. However, significant reduction in retention time might lead to premature expiry of the blocks requiring frequent refreshes or write-backs, which can incorporate unnecessary stalls along with the increased miss-rate.This paper proposes ABACa, an approach that logically bifurcates a cache set-wise for two different retention times where cache blocks are segregated upon their arrival and placed in the corresponding set, accordingly. In particular, if a block’s arrival is triggered by a read miss, the block is placed into a set with a higher retention time, called as read-set. On the other hand, the block is placed into a write-set having a lower retention time, if the block’s arrival is caused by a write miss. Our empirical analysis shows that, ABACa achieves a significant improvement of 40.75% in miss-rate and 61.35% EDP (Energy Delay Product) gain compared to baseline multi-retention STT-RAM-based and SRAM-based last level caches, respectively.
Ensemble Deep Learning Assisted VNF Deployment Strategy for Next-Generation IoT Services

Due to the massive Internet of Things (IoT) connectivity and substantial growth of communication traffic, Virtual Network Function (VNF) orchestration scheme is anticipated to function promptly, dynamically, and intelligently for next-generation networks. Hence, we urge the necessity to move beyond the traditional paradigm and employ VNFs on the network edge located cloudlet. Overall, multi-access edge computing can intensify the performance of delay-sensitive IoT applications compared to the core cloud based VNF deployments. In this paper, we intend to investigate how to simultaneously leverage the ensembling of multiple deep learning models for proper calibration to provide real-time VNF placement solutions. We also address the challenges associated with state-of-the-art approaches to deal with dynamic network traffic and topology patterns. Our envisioned methods, based on Convolutional Neural Networks and Artificial Neural Networks named as E-ConvNets and E-ANN respectively, suggest two proactive VNF deployment strategies. These ensembled VNF deployment strategies demonstrate encouraging performance (optimality gap nearly 7%) in terms of minimizing relocation and communication costs, and high scalability intelligence factor (around 0.93) through simulation results compared to standalone deep learning models. Furthermore, the presented results indicate the potentialities of applying deep learning-based strategies into similar research enigmas for future telecommunication network researches.
Truncated and Suspended Microstrip Patch Antennas Over an EBG Ground Plane

An extensive investigation has been performed with the objective of reducing the weight and profile of an EBG-backed microstrip antenna. Resulting from this study, two EBG-backed microstrip patch antennas, which are named "suspended" and "truncated," are introduced in this paper. Simulations are conducted to show that more than 70% reduction in weight and profile are achieved, while keeping the impressive antenna performance due to the presence of the EBG structure.
A Magnetically-Steerable Stenting Catheter for Minimally Invasive Cardiovascular Interventions

Magnetically-guided catheters present a promising treatment option to reduce the incidence of post-operative trauma in minimally invasive procedures. In this study, we present a novel catheter design that combines the characteristics of standard angiography and angioplasty catheters in combination with magnetic guidance. The catheter body consists of a 5F size angiography catheter that has been adapted to hold an intra-aortic balloon (8F diameter). It uses two magnets, one for tip guidance and another for easy steering into blood vessel branches. Magnetic guidance of the catheter is demonstrated using an array of six mobile electromagnetic coils with a custom-built graphical user interface. A 3R pseudo-rigid-body model is used within a closed-loop controller to calculate the coil currents and positions to obtain the required magnetic field in order to control the orientation of the catheter tip. Magnetic guidance of the catheter is demonstrated in a phantom model of the aorta. The angiography and angioplasty capabilities of the catheter are illustrated through separate experiments.
Wearable Haptics for Object Compliance Discrimination Through Passive Touch

The ability to estimate compliance is an important human skill related to the tactile sense. When the perception of compliance is mediated by haptic devices, the interaction with objects usually takes place through active exploration from the subject. In this paper, we present a wearable haptic device for transmitting information about object compliance through passive touch, meaning that stimuli are generated by an external agent rather than by user motion. More precisely, the dynamics describing the object indentation is reproduced on the user’s forearm in the form of decoupled cues representing applied force and surface displacement. The development of such a device has a twofold purpose. Firstly, it allows the study of the human capacity for processing decoupled information to deduce object compliance. Secondly, it makes possible to convey object compliance to someone who is not performing the object indentation. This is especially important in contexts like telemedicine and human-robot collaboration.Users’ perception of stiffness, force, and displacement were estimated. Then, a two-phase experiment was carried out to compare the proposed approach with the state of the art. Results of the experimental campaign revealed the effectiveness of the proposed approach. Users were able to discriminate and order objects with different compliance with a success rate 9% greater than the one obtained exploiting state of the art strategies reproducing only forces.
Perceived Hardness of Virtual Surface: A Function of Stiffness, Damping, and Contact Transient

Haptic rendering of virtual objects requires a model for the object’s hardness. The classical spring-damper model is effective in resisting the user’s penetration into the virtual surface, while a short vibratory transient signal superimposed at contact increases the user-perceived hardness. In this paper, we are concerned with the combined case: the perceived hardness of a virtual surface rendered by the spring-damper model with a contact transient, in the tool-mediated perception. We report three perceptual experiments conducted to clarify the effects of five rendering parameters—stiffness and damping (spring-damper model); and amplitude, frequency, and decay rate (contact transient). All the parameters except decay rate are shown to have statistically significant effects on the perceived hardness. We also present a psychophysical magnitude function of perceived hardness for the four significant parameters of stiffness, damping, amplitude, and frequency. This mathematical model helps virtual environment designs build the virtual surfaces of desired perceived hardness.
Vehicle Route Optimization for Solid Waste Management: A Case Study of Maltepe, Istanbul

Solid waste management, which includes collection, transportation, disposal and recycling, is a critical environmental issue increasing in severity due to industrialization, urban population growth and consumption. In this study, we present a vehicle routing approach based on the ant colony optimization algorithm. We work on the real-world data of Maltepe Municipality, Istanbul, Turkey. The experimental results show that we estimate the shortest route path with 13% efficiency compared to the existing route. We believe this would play an important role in solid waste management, help reduce costs as well as provide fast and efficient waste collection for local municipalities.
Distributed Compressed Sensing of Hyperspectral Images According to Spectral Library Matching

The ever-increasing resolution puts tremendous pressure to the onboard hyperspectral imaging system. Compressed sensing technology is one of the important ways to solve this problem. Distributed compressed sensing was proposed to exploit both intra- and inter-correlation structures of hyperspectral images. However, the implementation method of distributed compressed sampling has not been reported, and the joint sparsity reconstruction algorithm cannot achieve excellent image reconstruction performance. In this paper, a distributed compressed sampling strategy inspired by distributed compressed video sensing and optical implementation model are proposed to collect compressed hyperspectral data. In the image reconstruction process, we discard the direct application of the joint sparsity constraint on the data itself. Instead, we explore the estimation method of abundance and endmember with the help of the existing spectral library. Then, the images are recovered by applying the linear mixing model of hyperspectral. The comparison experiments of various schemes show that the proposed compressed sensing scheme has an obvious advantage in reconstruction performance under the low sampling rate. The proposed compressed sensing scheme has great potential in a high-compression ratio onboard hyperspectral imaging system.
Simulation of Geometrical Parameters of Screen Printed Electrode (SPE) for Electrochemical-Based Sensor

Screen printing is a known method to produce disposable and low-cost sensors. Depending on the application such as food analysis, environmental health monitoring, disease detection and toxin detection, screen-printed electrodes can be fabricated in a variety of sizes and shapes. Modification of the electrode's material and geometrical dimension may be done to produce effective screen-printed three-electrodes system. Thus, the effects of varying the working electrode (WE) area in radius of 0.9 mm to 2 mm, gap spacing between electrodes ranging from 0.5 mm to 1.6 mm, and the width of the counter electrode in range of 0.7 to 1.3 mm on sensor's performance was investigated in this study through COMSOL simulation. It was found that the modification of the working electrode radius and the gap between the electrodes has the most significant effect on sensor's performance, while modifying the width of the counter electrode (CE) shows no significant effect. Sensors with 0.9 mm radius or 2.54 mm2 WE area and 0.5 mm gap spacing has shown the optimum performance with 0.026 A/m2 current density which is contributed by 0.044 pF capacitance value. As a conclusion, regardless of the width of counter electrodes, a smaller gap between electrodes and a smaller working area would lead to optimal performance of a screen-printed three-electrode sensor system.
Modulation of Curvature of Soft Ball by Facing Motion

Two fingers coming into contact with each other at the same speed sometimes produces the sensation of contact with a flat rigid surface. This illusory sensation is considered to be caused by skin deformation and kinesthesia similar to those that would be produced by contact with a hard surface. We speculated that it would be possible to produce the same tactile sensation as that of contact with a flat rigid plate by placing a soft sphere with the same curvature and hardness as a finger against a finger and moving it at a speed equal to that of the finger. We conducted an experiment to verify the modulation effect of perceived curvature by comparison with various curvature samples. In addition, we developed a prototype of an encountered-type haptic display using this illusion and verified the applicability of the illusion.
Technosols in the World Reference Base for Soil Resources – history and definitions

ABSTRACT The World Reference Base for Soil Resources (WRB) is an international soil classification system for naming soils and creating legends for soil maps. The currently valid version is the update 2015 of its third edition. WRB has two levels: first and second. The first level comprises 32 Reference Soil Groups (RSGs), identified using a Key. At the second level, the soil names are constructed by adding a set of qualifiers to the name of the RSG. In the WRB, diagnostic horizons, properties and materials are defined. Diagnostic materials are materials that significantly influence soil-forming processes. Diagnostic properties and horizons have a combination of attributes that mostly reflect results of soil-forming processes. The RSG Technosols was introduced in the second edition of the WRB in 2006. In the current version of the WRB, two diagnostic materials are defined for Technosols: artefacts and technic hard material. Artefacts are substances that are created or substantially modified by humans or brought to the surface from a depth, where they were not influenced by surface processes. The technic hard material is a (relatively) continuous consolidated material resulting from an industrial process. The Technosols are at the third place in the Key after Histosols and Anthrosols. A soil is a Technosol if it has technic hard material within 5 cm or a geomembrane or a significant amount of artefacts within 100 cm. If a soil has no technic hard material and no geomembrane but a layer with artefacts that has undergone enough soil formation to develop a diagnostic horizon typical for advanced pedogenesis, the soil is excluded from the Technosols. There are specific qualifiers to further characterise the Technosols. They are also important to characterise soils other than Technosols that have artefacts or technic hard material. Human-transported natural soil material does not qualify as Technosol.
Natural Tactile Interaction with Virtual Clay

Despite many past efforts to develop haptic experiences of virtual clay modeling, natural interaction with virtual clay remains a hard challenge. In this work, we propose a computational solution for the interactive simulation of clay-like materials with unprecedented realism, coupled with free-air tactile rendering that provides a natural tangible experience. Our solution includes a novel particle-based model of viscoplasticity for efficient interactive simulation, and an optimization-based ultrasound rendering algorithm that takes as input the interaction forces between a virtual hand model and the clay-like material. We demonstrate the effectiveness of our method through expressive creative experiences.
Analyzing the Design Space of Spatial Tensor Accelerators on FPGAs

Tensor algebra applications are widely used in various domains, and these applications can deliver high performance and low power when accelerated on FPGA-based spatial hardware accelerators. The architecture of spatial accelerator has a large design space including PE unit, workload mapping, PE interconnection, and memory subsystem. An efficient design space exploration (DSE) framework is required to find the optimal architecture for an tensor algorithm with limited FPGA resources. Previous works propose DSE frameworks for hardware design and hardware-software mapping. But the design space is limited and prior works lack of accurate performance models. In this paper, we propose a systematic approach to analyze the design space when mapping tensor applications onto the spatial accelerators on FPGAs. We also propose performance models to evaluate the performance of different architecture design choices. Our presented design space is more comprehensive than prior works and we support a wider range of tensor applications. Our performance models consider the FPGA-specific hardware specifications including DSP usage, memory capacity, bandwidth and frequency.
An Adaptive Lockstep Architecture for Mixed-Criticality Systems

software with different criticality levels. Currently, research mostly focuses on software aspects for those systems and disregards hardware aspects as well as fault tolerance. In this paper, we propose and investigate a novel processor architecture for adaptive fault tolerance. This is achieved by an adaptive lockstep architecture, where non-critical software is executed in a performance mode and critical software in a lockstep processor configuration. The program code runs on both processor cores when software is executed in performance mode. While in lockstep mode both processor pipelines appear as one to the software. Switching between both modes happens seamlessly and neither software interrupt nor processor reset is required. Our concept of an adaptive lockstep architecture is demonstrated by extending the Cobham Gaisler’s LEON3 processor. The evaluation shows a reasonable hardware overhead as well as a minimal runtime overhead due to the additional pipeline stage.
A preliminary study on Reading the Mind in the Eyes of the Robot *

As social robots are increasingly deployed, the interaction between human and robot is becoming more important. Affective theory of mind (ToM) is one of the core components of emotional communication between human, enabling recognize and understand other’s emotional state as an independent agent separate from oneself. Current study developed robot version of widely adopted test of affective ToM, Reading the Mind in the Eyes Test (RMET) using a social robot, Vector. In the preliminary study, we compared the participants performance in both robot version, ingroup (East Asian) and outgroup (Caucasian) human version of RMET, the individual variance of affective ToM toward human expression is reproduced to the expressions of the Vector robot. Moreover, only the performance toward the expressions of same ethnicity (RMET-K) predicts the performance on the robot expression (RMET-R). The result suggests that not only the quality of robot’s expression but the variance of social cognition level of the human should be considered in social robot development.
Participatory Design of a Robotic Mental Well-being Coach

Recent research is emerging in the field of Social Robotics where robots have the potential to serve as tools to improve human well-being. However, research exploring the expectations and perceptions of prospective users of such robots, and the professionals who currently deliver these interventions, is limited. In this paper, we present qualitative analysis of discussions with prospective users and experienced coaches regarding the design of robot well-being coaches. We invited participants interested in well-being practices to take-part in a Participatory Design (PD) study, consisting of individual interviews and a focus group discussion (NP = 8). Discussions focused on ideating how a robot could function as a mental well-being coach, based on their experiences with well-being practices. Data triangulation was employed by interviewing three professional coaches as additional sources of information. This resulted in a rich set of data, which we transcribed and analysed using Thematic Analysis (TA). The developed themes regarding robot features, form, behaviours, robot-led well-being practices, and the advantages and disadvantages these could provide, were compiled and are discussed in detail. We present this data together with tabulated quotes from the participants and coaches, to pave the way towards designing robot coaches that can provide supportive interventions to improve the mental health and well-being of their users.
Beats-to-Beats Estimation of Blood Pressure During Supine Cycling Exercise Using a Probabilistic Nonparametric Method

Blood pressure (BP) is an important clinical vital sign that varies from beat-to-beat. Nevertheless, these variations cannot be captured by the conventional cuff-based BP monitors. This study proposes and evaluates novel cuffless frameworks to continuously estimate the 10-beat averaged systolic BP (SBP) and diastolic BP (DBP) during dynamic exercise by fusing information from multiple biosensors using five machine learning algorithms. Over 100 thousand beats of data were collected from 62 subjects (aged 59 ± 10 years), each underwent a maximal exercise stress test. The average length of recording for each subject was 35 minutes. The BP ranges were 75-280 mmHg for SBP and 36-157 mmHg for DBP respectively. Multiple physiological parameters were measured continuously and used as inputs to five machine learning algorithms for estimating the 10-beat SBP and DBP averages before, during and after the cycling exercise. The mean absolute error (MAE) of Gaussian process regression (GPR) model was 4.8 mmHg and 3.4 mmHg for SBP and DBP, respectively. The MAE of multiple linear regression (MLR), regression tree (RT), ensemble of trees (ETs), and support vector machine (SVM) models varied from 6.1 mmHg to 17.6 mmHg and from 4.0 mmHg to 9.7 mmHg for SBP and DBP, respectively. The GPR model outperformed the other four models and showed promising results in estimating the 10-beat averages of both SBP and DBP without a cuff in a general elderly population under dynamic conditions.
Predicted information gain and convolutional neural network for prediction of gait periods using a wearable sensors network

This work presents a method for recognition of walking activities and prediction of gait periods using wearable sensors. First, a Convolutional Neural Network (CNN) is used to recognise the walking activity and gait period. Second, the output of the CNN is used by a Predicted Information Gain (PIG) method to predict the next most probable gait period while walking. The output of these two processes are combined to adapt the recognition accuracy of the system. This adaptive combination allows us to achieve an optimal recognition accuracy over time. The validation of this work is performed with an array of wearable sensors for the recognition of level-ground walking, ramp ascent and ramp descent, and prediction of gait periods. The results show that the proposed system can achieve accuracies of 100% and 99.9% for recognition of walking activity and gait period, respectively. These results show the benefit of having a system capable of predicting or anticipating the next information or event over time. Overall, this approach offers a method for accurate activity recognition, which is a key process for the development of wearable robots capable of safely assist humans in activities of daily living.
Generating Kinesthetic Feedback Using Self Contact and Velocity Scaling

Despite the considerable technological progress of haptic technology in recent years, to date there are still no wearable systems capable of providing both kinesthetic and cutaneous feedback that are universally recognized and adopted outside the research contexts. This is particularly evident when dealing with virtual reality, where the lack of truthful tactile feedback has often been addressed by exploiting Pseudo-Haptics methods. Being designed to indirectly stimulate the somatosensory system, these methods are not meant to be integrated with haptic devices. With the idea of providing a meeting point between the haptic and pseudo-haptic fields, this work proposes Self Contact: exploiting the pseudo-haptic principles to lead the user in generating a real kinesthetic feedback through the contact between his/her fingers. Self Contact can be implemented alone or in combination with haptic thimbles, allowing to complete the set of tactile stimuli that is necessary for a realistic interaction with virtual objects in pick and place operations. A step-wise validation demonstrated that the proposed approach is suitable for recovering the kines-thetic feedback into virtual reality, towards the development of increasingly immersive environments.