Miracle-dz/UniFAR-FTUs huggingface dataset:
Logic-In-Memory Architecture For Min/Max Search

Algorithms for data-analytics executed on classical Von Neumann architectures proved highly energy inefficient. In-Memory Computing have been indicated as a practical solution to improve speed and to reduce power consumption. This work introduces a new memory architecture that extends the use of bit-wise logic operations to a more complex iterative algorithm, i.e., search of minimum/maximum values across bulk data. Such new design preserves all the characteristics of architectures for minimum/maximum detection, achieving high performance and low power consumption. It serves both as a local memory, and as an acceleration engine. It can be implemented with MOS transistors or with emerging devices. A novel implementation of the minimum/maximum search algorithm has been tailored to fit the internal organization of the proposed memory architecture. A parametric analysis conducted on a 28nm FDSOI CMOS technology highlights remarkable results, with an energy consumption for single bit as low as 0.09fJ and a maximum operating frequency of 2GHz.
Ensemble Learning to EEG-Based Brain Computer Interfaces with Applications on P300-Spellers

Brain-Computer Interfaces (BCI) are systems in which the electrical activity of an animal brain becomes the main controller of an external electronic device capable of reading and processing electroencephalographic (EEG) signals. One early application of such systems is the attention-based spellers utilizing the P300 visually evoked potential in a framework known as the oddball paradigm. In this paper, we propose novel variants of machine learning model ensembles in addressing the task of P300 detection and attended target recognition in attention-based speller systems. Proposed ensembles adopted Bootstrap aggregation (Bagging) of calibrated Support Vector Machines (SVM) as well as data-driven learners. The latter is dominantly represented by Convolutional Neural Networks (CNN) with several variants, including what is referred to as Inception, Xception, and Interleaved Group Convolutions (IGC) modules. The proposed models are evaluated on a publicly available EEG dataset developed specifically for BCI applications and published in public contests, namely the 2nd dataset of the 3rd BCI competitions. The proposed models consistently outperform all previous works on the same dataset and show the highest 5- and 15-trial recognition rates of 76.5% and 98.5%, respectively, for both subjects in the dataset jointly. Additionally, we introduce in this work the first study on inter-subject evaluation under similar training protocols, reaching between 30%-40% recognition rates for either subject. We further investigate the effect of reducing the training data on the performance of the proposed models showing possibilities of reduced training time for a target recognition rate.
Effects of Trapped Charges in Gate Dielectric and High- ${k}$  Encapsulation on Performance of MoS2 Transistor

The effects of trapped charges in gate dielectric and high-<inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> encapsulation layer on the performance of MoS<sub>2</sub> transistor are investigated by using SiO<sub>2</sub> with different thicknesses as the gate dielectric and HfO<sub>2</sub> as the encapsulation layer of the MoS<sub>2</sub> surface. Results indicate that the positive trapped charges in SiO<sub>2</sub> can increase the electrons in MoS<sub>2</sub> for screening the scattering of charged impurity (CI) in SiO<sub>2</sub> and at the SiO<sub>2</sub>/MoS<sub>2</sub> interface to increase the carrier mobility. However, the CI scattering becomes stronger for thicker gate dielectric with more trapped charges and can dominate the electron screening effect to reduce the mobility. On the other hand, with the HfO<sub>2</sub> encapsulation, the OFF-currents of the devices greatly increase and their threshold voltages shift negatively due to more electrons induced by more positive charges trapped in HfO<sub>2</sub>. Moreover, the screening effect of these electrons on the CI scattering results in a mobility increase, which increases with the magnitude of the CI scattering. A 51% improvement in mobility is obtained for the sample suffering from the strongest CI scattering, fully demonstrating the effective screening role of high-<inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> dielectric on the CI scattering.
Driver Modeling and Implementation of a Fuel-Saving ADAS

Controlling vehicle velocity, by coaching the driver to eco-drive with an advanced driver assistance system (ADAS), is a promising method to decrease fuel consumption and greenhouse gas emissions for combustion engine-driven road vehicles. By using optimal control techniques, such a system may find velocity profiles in real-time that minimize fuel consumption. This is particularly useful to recommend the optimal time to initiate coasting, which is otherwise difficult to estimate by a driver. However, this ADAS should not choose velocities and accelerations that the driver will dislike, such as those that leave too much or too little space to the preceding vehicle, or those that take corners at high speed. To remedy this, we introduce an optimal control model of acceleration that mimics drivers' behavior and combine this with a model of fuel consumption to trade-off driver preferences and fuel savings. We give examples of the velocity profiles recommended in a typical driving scenario to demonstrate the potential fuel savings. Finally, we give details of a prototype system, which has recently been implemented in the driving simulator at the University of Southampton.
Towards Classifier Visualisation in 3D Source Space

In the context of brain-computer interfacing, it is important to investigate what regions of the brain a classifier focuses on. For one, this will clarify to what extent the classifier relies on brain activity, as opposed to undesirable non-cortical signals. More generally, the practice is informative as it allows conclusions to be drawn about the cortical regions—and thus, cortical functions—that contribute to the effect under investigation. In this study, we start to investigate different methods to visualise the regions of interest of classifiers based on windowed means and on common spatial patterns. Specifically, we take individually reconstructed source spaces and transform the classifier filter weights into relevance weights indicating the relative contribution of each source to the classifier. This is visualised across participants in an average brain. By decomposing the classifier weights into separate sources and localising these in the brain, this method provides a tool to evaluate classifiers and test hypotheses.
A Deep Fully Convolution Neural Network for Semantic Segmentation Based on Adaptive Feature Fusion

Fully convolutional neural network is a special deep neural networks based on convolutional neural networks and are often used for semantic segmentation. This paper proposes an improved fully convolutional neural network which fuses the feature maps of deeper layers and shallower layers to improve the performance of image segmentation. In the process of feature fusion, adaptive parameters are introduced to enable different layers to participate in feature fusion as different proportion. The deep layers of neural network mainly extract the abstract information of the object, and the shallow layers of neural network extracts the refined features of objects, such as edge information and precise shape. Adaptive parameters can speed up the training speed and improve the prediction accuracy. In the early stages of training, the feature maps of shallow layers have a larger fusion coefficient, which allows the neural network to learn the feature of object's location and shape quickly. As the training progresses, gradually weakening the fusion coefficient of shallow layers and increasing the fusion coefficient of deep layers which can enhance the network's ability of predicting the details of the objects. This paper uses Scene Parsing Challenge 2016 dataset presented by MIT for training. Experiments show that the method proposed in this paper speeds up the training and improves the pixel prediction accuracy.
Resource Size Control for Reliability Improvement in Cellular-Based V2V Communication

In vehicle-to-vehicle (V2V) communication, link reliability has been regarded as an important performance metric, especially for safety-critical broadcast services. In this paper, we analyze the link reliability of the centralized mode (Mode 3) for long-term evolution (LTE)-based V2V (LTE-V2V) from the PHY/MAC perspectives. Moreover, we derive the statistical distribution of the interference distance and interference to noise ratio (INR) for LTE-V2V. Based on this analytical framework, we propose a resource size control (RSC) method for improving link reliability. The proposed RSC adapts the resource size according to the macroscopic network parameters such as vehicle density, communication range, and message size. Numerical results show that the proposed method improves link reliability compared with the fixed resource size setting in a highway scenario. Moreover, it is observed that larger-sized resources are preferred when the vehicle density decreases, the message size increases, or the communication range decreases.
A Planar 24 GHz Switched-Beam Antenna Based on PIN Diodes for Remote Sensing Applications

This paper presents a planar switched-beam antenna for remote sensing applications at 24 GHz. The antenna consists of $\mathbf{9x8}$ radiating patches, arranged as nine linear arrays that are connected by phase shifters based on PIN diodes, which allow to quickly switch the main lobe between two symmetric states by using a small control current. The single array elements were designed and simulated in CST Microwave Studio and the array feed network including the phase shifters was analyzed using Keysight ADS. Measurements of all individual components and the final array antenna were taken to confirm the simulations and to prove the concept.
High-Performance Long-Wavelength InAs/GaSb Superlattice Detectors Grown by MOCVD

We demonstrate the high-performance long-wavelength InAs/GaSb superlattice (SL) infrared photodetectors based on an Al-free single heterojunction grown by metalorganic chemical vapor deposition. The devices feature a mid-wavelength InAs/GaSb SL p-n junction (PN) and a long-wavelength InAs/GaSb SL n-type absorber (n), so-called PNn design, to reduce the dark current. In addition, a shallow-etch technique was employed by exposing only the mid-wavelength materials during pixel isolation to suppress the surface leakage currents. At 77 K and a bias voltage of −0.1 V, the device exhibited a 50% cutoff wavelength at <inline-formula> <tex-math notation="LaTeX">$8.0~\mu \text{m}$ </tex-math></inline-formula>, a dark current density of <inline-formula> <tex-math notation="LaTeX">$2.4\times 10^{-5}$ </tex-math></inline-formula> A/cm<sup>2</sup>, and a peak responsivity of 2.1 A/W. Temperature-dependent dark current measurement indicated the diffusion-limited behavior down to 75 K. The specific detectivity was estimated to be <inline-formula> <tex-math notation="LaTeX">$7.3\times 10^{11}\,\,\text {cm} \cdot \text {Hz}^{1/2}/{\mathrm{ W}}$ </tex-math></inline-formula>, which is comparable with that of detectors grown by the molecular beam epitaxy at similar cutoff wavelengths.
Stability Analysis of the t-SNE Algorithm for Human Activity Pattern Data

Health technological systems learning from and reacting on how humans behave in sensor equipped environments are today being commercialized. These systems rely on the assumptions that training data and testing data share the same feature space, and residing from the same underlying distribution - which is commonly unrealistic in real-world applications. Instead, the use of transfer learning could be considered. In order to transfer knowledge between a source and a target domain these should be mapped to a common latent feature space. In this work, the dimensionality reduction algorithm t-SNE is used to map data to a similar feature space and is further investigated through a proposed novel analysis of output stability. The proposed analysis, Normalized Linear Procrustes Analysis (NLPA) extends the existing Procrustes and Local Procrustes algorithms for aligning manifolds. The methods are tested on data reflecting human behaviour patterns from data collected in a smart home environment. Results show high partial output stability for the t-SNE algorithm for the tested input data for which NLPA is able to detect clusters which are individually aligned and compared. The results highlight the importance of understanding output stability before incorporating dimensionality reduction algorithms into further computation, e.g. for transfer learning.
Adaptive Sliding Mode Control for Biped Robots with sEMG Signals

The new human-robot interaction technology based on biological signals is an advanced method which can reflect the wearer’s motion intentions. This paper presents a hybrid control method for biped lower limb exoskeleton robot with the surface electromyography (sEMG) signals. Compared with physical signals, sEMG has a better performance of identifying the intention of user’s movement for the interaction with robots. In this paper, the Fisher Discriminant Analysis (FDA) of motion recognition method is applied to discriminating three-class gait movements of biped robot using sEMG which are collected from human arms. Considering the system’s uncertainties and disturbances, the adaptive sliding mode control strategy is designed to approximate the nonlinear dynamic system of biped robot, which can ensure the robustness capacity and high control precision. The simulation results exhibit the affection of the proposed approach.
Cognitive Band Manipulations using Twistable Paper-based Antenna for IoT Applications

Currently, there is a massive demand for Internet of Things (IoT) devices that can interact and cooperate with each other over multiple wireless networks. Therefore, data transmission between IoT devices emerged as an essential process to ensure connectivity and achieve diverse and intelligent services. Typically, most of IoT-devices share the same frequency band at 2.4 GHz (ISM band), which is free and suitable for many devices. While, the continuous growing of IoT devices makes such frequency band suffers from increasing packet collisions and congestion due to accretion of uplink demand request packets. Therefore, the simple solution relies on using multiple antennas for each band. This solution proposes several challenges toward design implementation and the attached antenna's sizes. In this paper, we propose a quite new technique that can be used to tune frequency band over runtime through a single antenna with the same design. This is achieved through the recent technology of inkjet printing onto flexible substrate to expose such paper-based antenna to different mechanical deformation forces. This will enable manipulation of the antenna operating frequency and bandwidth to dynamically introduce/shift/remove the operating frequency bands over runtime. Simulation results show the ability of antenna to enable direct connectivity between IoT devices depending on twisting the antenna with different angles to establish new several frequency bands.
Compressed Sensing based Speech Compression using Dictionary Learning and IRLS algorithm

Compressed sensing (CS) has gained much interests in the speech processing community and especially in compression. The success of this appealing paradigm relies, heavily, on the sparsity of speech signals in a given dictionary. Thanks to machine learning, it is possible to go beyond the limits of analytical dictionaries by designing learned dictionaries, which are more able to fit the nature of data. In this paper, we propose speech compression scheme based on CS using K-singular value decomposition (K-SVD) algorithm to learn a dictionary for sparse representation and iteratively reweighted least square (IRLS) algorithm for signal reconstruction from randomly generated measurements. Different sizes of dictionaries are trained and compared with discrete cosine transform (DCT). Signal quality results, measured with perceptual evaluation speech quality (PESQ) revealed that learned dictionary using K-SVD improves the performance of speech compressed sensing comparing to DCT.
WaSP: Hierarchical Warping, Merging, and Sparse Prediction for Light Field Image Compression

We propose a versatile light field compression scheme that is organized on hierarchical levels, where all views belonging to a particular level are encoded using several views already encoded in the previous hierarchical levels. The new scheme builds on an earlier version of our codec, and provides a more generalized functionality with improved view merging. The operations needed when one view is encoded conditional on its reference views are: first warping its reference views to the location of the current view and partitioning the pixels according to their state of occlusion in various warped versions; then merging the warped references using one optimal LS merger for each class of occluded pixels; finally, adjustment of the overall merged image to the original view by using a sparse predictor. The new scheme is applied to both plenoptic camera images and high density camera array data, and is evaluated in accordance with the JPEG Pleno test conditions. We compare the performance of the proposed codec to that of the HEVC anchors defined in the JPEG Pleno test conditions. We also make comparisons to the performance achieved by our earlier scheme. The proposed codec is publicly available on GitHub and it was accepted as the Verification Model (VM) 1.0 software for JPEG Pleno Light Field coding standard.
Deep Reinforcement Learning for Fairness in Distributed Robotic Multi-type Resource Allocation

As autonomous robots are becoming a reality, we discover new challenges in coordination among these robots. We present a unique new problem that each robot makes decisions in achieving tasks that require multiple robots with different capabilities. Fair resource allocation is essential to ensure that all resource requesters acquire adequate robot resources and accomplish their tasks. We propose solutions to the fairness problem in multi-type resource allocation for multi-robot systems that have multiple resource requesters requiring heterogeneous robots with different capabilities to accomplish tasks. In particular, this work focuses on systems of single-tasking robots with multi-robot tasks (STR-MRT). In STR-MRT, the capability of a robot is the resource for accomplishing a specific task. In this problem, 1) each robot can perform only one task at a time, 2) tasks are divisible, and 3) accomplishing each task requires resources from one or more robots. We model the decentralized resource allocation in STR-MRT as a coordination game between the robots. Each robot strategically selects one resource requester. Then a consensus-based algorithm conducts formation of a robotic team for each task. We leverage the Dominant Resource Fairness (DRF) and Deep Q-learning Network (DQN) to support requester selection. The results suggest that the DQN outperforms the common Q-learning.
Channel Estimation Scheme in Oversampled Frequency Domain for FBMC-QAM Systems Based on Prototype Filter Set

This paper proposes a channel estimation scheme for the filter-bank multicarrier systems with quadrature amplitude modulation (FBMC-QAM). Under frequency selective channels, the adjacent data symbols cause interference to the pilot symbols in FBMC-QAM systems and degrade the performance of the channel estimation. To handle this problem, we analyze the interference of the pilot symbols in an oversampled frequency domain before the receiver filtering process. Exploiting the interference analysis, we propose a pilot symbol configuration and condition of filter coefficients to cancel the inter-carrier interference. Moreover, we apply a time domain data symbol precoding to eliminate inter-symbol interference. The simulation results show that the proposed scheme outperforms conventional channel estimation schemes.
A Novel 3.5 GHz Low-Loss Bandpass Filter Using I.H.P. SAW Resonators

Regarding higher frequency above 3 GHz, up until now BAW filters have been believed to be better solution than SAW filters due to their increase of electrodes resistance. This paper describes a new high performance SAW filter surpassing BAW, with 3.5 GHz center frequency employing I.H. P. SAW technology which is new type SAW structure with high Q factor. Experimental studies for one-port resonators in 3.6 GHz show very high Bode-Q max of 2500 and optimum aperture lengths of IDTs. Using the results, a ladder type filter for LTE Band 42 was designed and fabricated. Very low insertion loss of 1.7 dB max has been achieved in the passband of $\mathbf{3.5+/-0.1 \ GHz}$:. Steeper skirt characteristics and adequate attenuation have been also realized.
Optical Braille Recognition Platform for Sinhala

The Braille system is a tactile format of written communication for visually-impaired people worldwide. Early days Braille embossed on papers done manually and now use machines as well. Sighted people do not understand these codes. Sighted society motivated the development of Optical Braille Recognition for different languages such as English, Arabic, and Chinese. Mostly, in Sri Lankan context, this problem occurs in education systems such as universities, schools because the documents written by blind students such as exam papers, evaluation works has to send to a braille translator before marking. This research addresses the issue of written communication gap between Sinhala blind and sighted people. Braille dots composing characters are identified on single sided documents of average quality with maximum accuracy. Work present executable system including proposed methods in order to transcript to recognized braille characters. Introduce of new extraction methods of Braille characters from a Braille document and decoding them into Sinhala characters. Then the decoded Sinhala characters are normalized to Sinhala text which is in human-understandable form.
Investigation about Control of False Positive Rate for Automatic Sperm Detection in Assisted Reproductive Technology

This research aims to realize sperm selection support system for assisted reproductive technology. Sperm detection is one of the important component technology. High detection rate is required even the expense of false positive detections so as not to miss promising sperms as much as possible. False positive rate control is key technology for assure high detection rate and adaptive threshold adjustment methods in boosting is proposed. Our experiments evaluate and compare adaptive thresholding method and normal thresholding method as for detection rate and false positive rate of sperm detection.
Implementation and Evaluation of Information Set Monte Carlo Tree Search for Pokémon

Artificial intelligence has shown remarkable performance in perfect information games. However, it is still no match for human players when it comes to most imperfect information games. Information set Monte Carlo tree search (ISMCTS) has been developed to reduce the effects of strategy fusion caused by determinization of the imperfect information and demonstrated advantages over the conventional Monte Carlo tree search (MCTS) that uses determinization. Because ISMCTS has only been used for games with relatively simple structure, it is still unknown whether it works effectively for more complex games. In this study, we take Pokemon as an example of a complex imperfect information game and implement a simulator to evaluate the effectiveness of ISMCTS. Experimental results show that ISMCTS outperforms the conventional MCTS that uses determinization.