Miracle-dz/UniFAR-FTUs huggingface dataset:
Auditory Scene-Attention Model For Speech Enhancement

In this work, we propose a new speech enhancement model referred to as auditory scene-attention model (ASAM), that can adapt dynamically to changes in the auditory scene components, such as speaker gender, input SNR levels, and background noise properties. To this end, a representative set of so-called Universal Scene Models (USM), each associated to a different auditory scene component, are first created, where each model attempts to predict a corresponding ideal ratio mask (IRM). The dynamic adaptation to changes in the auditory scene is then carried by computing the outputs of the USMs and forming a weighted combination of the most relevant scene models. This adaptation process is implemented via a frame-based attention mechanism, allowing to realize a soft selection of USM models, and taking advantage from both scene-dependent and scene-independent models. The evaluation of the proposed ASAM model, under different noise conditions and input SNR levels, shows substantial improvements in terms of standard speech Quality and intelligibility measures.
Security Framework for Cloud Control Systems Against False Data Injection Attacks

This paper analyzes the security problem of cloud control systems (CCSs), where a malicious false data injection (FDI) attacker may deteriorate the system performance by tampering with the measurements being transmitted. The CCS defender allocates defense budgets among nodes to ensure the safe operation of CCSs. The strategic interactions between the FDI attacker and the CCS defender are modeled as a Stackelberg game, and the optimal strategies for both sides are analyzed in the sense of Nash equilibrium. Numerical examples illustrate the main results of this paper.
Human Gait Analysis and Activity Recognition: A Review

Gait refers the walking pattern of human being. It is the manifestation of change in the joint angles of lower extremity. Locomotion enables the person to perform the daily life activities, to maneuver over the discontinue surfaces. In this paper, we are presenting the systematic survey of bipedal legged locomotion and activity recognition. Human walking plays crucial role in our daily life activities. It provides the much needed mobility to perform daily life activities efficiently. Recognition of human activity is an intriguing issue that can be solved in a variety of ways. The majority of researchers recommended effective deep learning techniques such as CNN, inception CNN, LSTM, Bi-LSTM, and hybrid approaches. Human walk pattern reflects the health condition of any person. Various clinical examinations are performed through human walk to check their clinical success, post treatment recovery and rehabilitation. It also helps early diagnosis of various walking impairment at early stage. The main objective of this research is to review the different state of art gait analysis & activity recognition approaches for human health issues using various wearable sensors and other techniques. The discussion held in his paper would help the coming researchers in the field of human gait analysis and activity recognition.
High-Performance FPGA Implementation of Fully Connected Networks of SAM Neurons

Neuromorphic computers have been presented as alternatives to traditional von Neumann systems. Neuromorphic systems mimic neural structures of the human brain to make the energy-efficient and high-performance computations. This paper proposes high-speed with no DSP resources FPGA implementation of the SAM neuron model and its fully connected networks with random synaptic weights. The synthesis reports of the implemented SAM neuron with 50, 100, 500, 1000, 2000, 4000, 6000, and 8000 random inputs have been presented. Also, the results of the synthesized fully connected populations comprising 50, 100, 500, 1000, and 1500 SAM neurons have been reported. Accordingly, the FPGA synthesis results of the proposed spiking neuron and networks are noteworthy compared to the state of the arts in terms of performance and DSP resources.
Autonomous Intent Detection for Intent-Driven Satellite Network

Satellite networks are promising paradigms for the sixth-generation (6G) global communications. However, the current satellite network is facing novel technical challenges, such as poor dynamic adjustment capability, diverse user types, and mismatches between service demands and network resources. Therefore, we propose a more general intent detection method under the intent-driven satellite network framework to achieve more intelligent satellite network management. We analyze various characteristics such as user types, user quality of service requirements, and the air interface of different types of satellites. Then we construct an intent classification model and intent extraction model based on transfer learning to provide an intent detection method for on-demand service. Our experimental results demonstrate that the proposed intent detection method exhibits a high degree of flexibility in handling user inputs and achieves high detection accuracy.
Low-Voltage Clocked Comparator With Flexible Oxide TFT Technology *

This paper presents a novel clocked comparator circuit using amorphous indium gallium zinc oxide (a-IGZO) thin-film transistor technology. The circuit is fabricated on a 30 $\mu \mathrm{m}$ flexible polymide substrate. Experimental characterization took place under normal ambient conditions. The comparator circuit employs inverters using pseudo CMOS topology to obtain better swing. It employs a clocked architecture, where the pre-amplification and regeneration phases are controlled by this clock. From measurements, the circuit is showing a VinCM of 1V - 2.5 V, power consumption of 80 $\mu \mathrm{W}$ average input static offset of 119mV and a swing of 70% at a clock frequency of 5 MHz and an input signal frequency of 5 kHz with a supply voltage $(\mathrm{V}_{\text{DD}})$ of 4 V. This circuit is showing a 62.5% improvement in speed compared to the state-of-the-art work at a relatively low $\mathrm{V}_{\text{DD}}$ using single-gate a-IGZO TFT technology. This circuit finds potential applications in smart sensing systems on flexible substrates.
From labels to tracks: it's complicated

The recently developed Labeled Random Finite Set (RFS) filter seems to make the problem of forming tracks across time trivial: Connect the track points with the same label, and we get a track. This paper shows different ways of forming tracks: through connecting filtered or smoothed track points, through tracing the pedigree of the last best hypothesis, and through solving the batched problem with the entire data set. It shows that the problem is nontrivial, and there are unanswered questions.
Comparative Review and Assessment of Energy Generation Potential from Municipal Solid Waste Generated in Zimbabwe

This study reviewed and assessed the energy recovery potential from the biochemical treatment (landfill gas to energy and anaerobic digestion) of biodegradable waste and thermochemical treatment (incineration and gasification) of waste for 2016 to 2035 and 2010 to 2050 temporal scales in Zimbabwe. The estimation of energy recovery potential from waste will likely motivate the development and deployment of integrated waste management systems that will address the prevailing double challenges of energy poverty and waste-associated environmental and human health impacts. Mathematical models coupled with an extensive literature review were used to estimate the anaerobic digestion, incineration and gasification-derived energy whilst the 2019 Refinement to the 2006 Intergovernmental Panel on Climate Change (IPCC) Guidelines for estimating emissions of methane from solid waste disposal sites (SWDS) was used for landfill derived energy. National mean annual energy generation potential of 129.2 GWh from anaerobic digestion, 415.8 GWh from incineration, and 168.5 GWh from gasification have been estimated for the 2016 to 2035 spatial scale whilst those for 2010 to 2050 temporal scale have been estimated at 131.8, 424.2 and 171.9 GWh for anaerobic digestion, incineration, and gasification, respectively. Considering the landfill gas to energy national annual average electrical power potentials of 43.1 and 66.8 GWh have been respectively estimated for the 2010 - 2050 and 2016 - 2035 spatial scales. The estimated energy potentials from anaerobic digestion, landfill gas to energy, incineration, and gasification contribute 1.5%, 2.5%, 5%, and 2% respectively to the national electricity consumption. The review and analysis showed that an untapped energy potential from waste generated in Zimbabwe's urban centers exists, which requires urgent strategies for its exploration.
TeSec: Accurate Server-side Attack Investigation for Web Applications

The user interface (UI) of web applications is usually the entry point of web attacks against enterprises and organizations. Finding the UI elements utilized by the intruders is of great importance both for attack interception and web application fixing. Current attack investigation methods targeting web UI either provide rough analysis results or have poor performance in high concurrency scenarios, which leads to heavy manual analysis work. In this paper, we propose TeSec, an accurate attack investigation method for web UI applications. TeSec makes use of two kinds of correlations. The first one, built from annotated audit log partitioned by PID/TID and delimiter-logs, captures the correspondence between audit log entries and web requests. The second one, modeled by an Aho-Corasick automaton built during system testing period, captures the correspondence between requests and the UI elements/events. Leveraging these two correlations, TeSec can accurately and automatically locate the UI elements/events (i.e., the root cause of the alarm) from an alarm, even in high concurrency scenarios. Furthermore, TeSec only needs to be deployed in the server and does not need to collect logs from the client-side browsers. We evaluate TeSec on 12 web applications. The experimental results show that the matching accuracy between UI events/elements and the alarm is above 99.6%. And security analysts only need to check no more than 2 UI elements on average for each individual forensics analysis. The maximum overhead of average response time and audit log space overhead are low (4.3% and 4.6% respectively).
Poster: BioFactCheck: Exploring the Feasibility of Explainable Automated Inconsistency Detection in Biomedical and Health Literature

There are inconsistencies in conclusions drawn from the studies that address the same research question in the biomedical literature. This paper presents preliminary work on the approaches taken to build an inconsistency detection and explanation model starting with the development of a gold-standard contradiction sentences corpus. First, we utilize SemRep, a third-party tool that can automatically segment any biomedical sentence into the form of a subject, predicate, and object. A pair of sentences with the same subject/object but different predicates is identified as contradictory sentences. These sentences are then manually curated by domain experts to filter out noise. In the future, we plan to generate a large manually curated gold-standard contradiction sentence dataset and use that for developing an automated tool for detecting and extracting contradictions in biomedical and health text.
Gait Design and Numerical Simulation for a Quadruped Robotic System

One of the important areas of research in legged robotics is the locomotion. In this paper a brief overview on representative gaits used by legged rovers is presented. A review on various kinematic and dynamic design analyses along with the control strategies used for quadruped system is carried out. The main focus of this paper is to perform a kinematic analysis for dynamic and static walking gaits for a quadruped robot. Based on the result from this analysis typical trajectories and gaits are developed and simulated in MATLAB. Since a dynamic gait is considered here, the system is classified under underactuated systems and the stability of the system is discussed as such. An overview of the work done toward building an experimental setup to carry out the experimental study is outlined.
PRTMTM: A Priori Regularization Method for Tooth-Marked Tongue Classification

Tooth-marks on tongues usually indicate the weakness of the spleen and stomach in traditional Chinese medicine (TCM). Therefore, tooth-marked tongue classification is important to health diagnosis in TCM clinic. Existing classification methods usually do not use the prior knowledge such as the location and width of tooth-marks, resulting in easily misclassification of unremarkable tongues. In this paper, we propose a prior regularization tooth-marked tongue method (PRTMTM), which makes full use of the prior knowledge of the position and width of tooth-marks. With PRTMTM, the original tongue image is first segmented to obtain the tongue edge position. Then, the prior mask map of tooth-marks is obtained by corroding the tongue from edge to interior according to the tooth-mark width. Finally, with a proposed regularization method, the tooth-marked tongues are classified accurately in the training process together with the prior mask map of tooth-marks. To verify our method comprehensively, we build twenty-five sub-training sets with different number of images and label distributions. Compared with state-of-the-art methods, the accuracy of our method is improved by 4.78% on average and 10.61% on maximum, the AUC by 0.04 on average and 0.07 on maximum, and the generated heatmap can highlight tooth-marked regions.
Evaluating the efficiency of photovoltaic cells in complex picture fuzzy soft environment

Worldwide, people try to change their lifestyle and technologies depending on the environmental factors. Photovoltaic is direct conversion of sunlight into electricity. Certain photoelectric materials absorb photons from light and sets free electrons. In this paper complex picture fuzzy soft Hybrid aggregation operator and a score function are defined. The ranking of the alternatives are according to the score values. Here, five types of photovoltaic cells which are Crystalline silicon, Gallium arsenide, Amorphous silicon, Monocrystalline silicon and Orgomantallics are chosen. These photovoltaic cells are tested for their lifestyle, efficiency, energy conversion and band gap properties. The result specified under hybrid aggregation operation and score values on complex picture fuzzy soft set determine that Gallium arsenide photovoltaic cell is the best.
An Open Source Compatible Framework to Fully Autonomous Digital LDO Generation

This work presents an open-source methodology to automate the design and layout of a low dropout (LDO) regulator from high-level performance specifications. LDO designs with this methodology have been demonstrated in commercial 65nm, 12nm, 130nm processes and the open-source 130nm Skywater PDK. The tool currently supports LDO designs with 50mV/100mV dropout for an input voltage range of 0.6V-1.3V (130nm and 65nm), 0.6V-0.9V (12nm), 1.8V-3.3V (Skywater 130nm) and a maximum load current ranging from 0.5mA-25mA (130nm and 65nm), 1mA-20mA (12nm), 0.5mA-50mA (Skywater 130nm). Cell-based design approach is adopted using an auxiliary cell library to enable mixed-signal design synthesis. A port to a new technology only requires a one-time manual layout for auxiliary library generation. The design automation includes a technology-agnostic modeling step and generates the LDO layout automatically. A bi-directional shift register based DLDO with a 1-bit comparator and a stochastic flash ADC (achieving 15x faster settling time) for error detection has been generated using the tool and validated using silicon measurements from 65nm process. LDO designs with different switch types and load configurations have been fabricated in open-source Skywater 130nm.
Spoofing Real-world Face Authentication Systems through Optical Synthesis

Facial information has been used for authentication purposes. Recent face authentication systems leverage multi-modal cameras to defeat spoofing attacks. Multimodal cameras are able to simultaneously observe the targeting people from multiple physical aspects, such as the visible, infrared, and depth domains. Known spoofing attacks are not effective in evading the detection since they cannot simulate multiple modalities at the same time.This paper presents a new class of spoofing attacks on multimodal face authentication systems. Its main idea is to forge each and every modality and then combine them together to present to the camera. The attack is realized with a special display device called Hua-pi display. It costs less than $500 and incorporates dedicated scene generators to optically reproduce multimodal scenes of an authorized user, and then synthesizes the scenes together at the camera’s view point through optical combiners to fool face authentication systems. We evaluate the risks of this attack by systematically testing it against the latest commercial face authentication products from major vendors in the field. The results not only demonstrate a successful bypass rate of 80% but also characterize the impacting factors and their feasible regions, revealing a new and realistic threat in the field.
A multi-core memristor chip for Stochastic Binary STDP

This paper describes the design of a monolithic CMOS-memristive neuromorphic chip performing vector matrix multiplication between spike coded input vectors and the synaptic weights stored in the memristive array. A computing core including a $64\times 64$ memristive array connecting 64 input and 64 output neurons has been fabricated. A Spiking Neural Network with a memristive synaptic layer exhibiting Stochastic Binary Spike-Time-Dependent-Plasticity has been experimentally demonstrated with the fabricated core. The CMOS-memristive neuromorphic processor is designed following a compact pseudo-CMOL design style that results in a modular and scalable computing core with a synaptic density of 22Ksynapses/mm2. A single core has been fabricated in CEA-LETI 130nm CMOS-RRAM technology and its operation has been experimentally characterized. A multicore architecture with reconfigurable connectivity, where cores can be interconnected to either share pre-synaptic neurons and expand post-synaptic neurons, or vice versa, share post-synaptic neurons and expand pre-synaptic neurons, is proposed and presented here.
Power Control Approach in HetNets based-Qlearning Technique

Femtocells have progressively been employed to meet the needs of the forthcoming heterogeneous mobile systems (HetNets). However, the co-channel congestion between cells have soared to the extent that they hamper the effectiveness of thick HetNets. In this respective, Reinforcement learning (RL) can be a reliable tool to manage the assets in femtocell systems. In this paper, the HetNet is patterned as a multi-agent system in which the base station is deemed as an agent. In this context, A Qlearning Algorithm is proposed to efficiently cope with HetNets resources and to recognize the problem of large states space brought about by the aggregation of all agents states. In comparison with the previous works carried out in this context, this one is an attempt to boost Power control as well as the system-global capacity.
An Intelligent Controller for an Assisted Electric Wheelchair based on Interval Type-2 Fuzzy Logic

This paper proposes an intelligent controller design for an assisted electric wheelchair using interval type-2 fuzzy logic controller. The controller design has been structured using cascaded fuzzy inference system with feedback loop to avoid obstacles. In addition, to control the dynamics of the wheelchair, a speed fuzzy inference system has been added to the proposed controller. The controller is designed so that the user input is firstly obtained from the wheelchair joystick. The surrounding obstacles, thereafter, are detected using the input of three sensors attached to the wheelchair. At each time step, and until reaching the target point, the performed controller output generates both the motion angle and the wheelchair speed commands. The performance of the controller is tested by simulating the wheelchair travelling in different navigation scenarios. The simulation results confirm the effectiveness of the presented controller.
On-ground and in-orbit characterisation plan for the PLATO CCD normal cameras

PLAnetary Transits and Ocillations (PLATO) is the third European Space Agency (ESA) medium class mission in ESA's cosmic vision programme due for launch in 2026. PLATO will carry out high precision un-interrupted photometric monitoring in the visible band of large samples of bright solar-type stars. The primary mission goal is to detect and characterise terrestrial exoplanets and their systems with emphasis on planets orbiting in the habitable zone, this will be achieved using light curves to detect planetary transits. PLATO uses a novel multi- instrument concept consisting of 26 small wide field cameras The 26 cameras are made up of a telescope optical unit, four Teledyne e2v CCD270s mounted on a focal plane array and connected to a set of Front End Electronics (FEE) which provide CCD control and readout. There are 2 fast cameras with high read-out cadence (2.5 s) for magnitude ∼ 4–8 stars, being developed by the German Aerospace Centre and 24 normal (N) cameras with a cadence of 25 s to monitor stars with a magnitude greater than 8. The N-FEEs are being developed at University College London's Mullard Space Science Laboratory (MSSL) and will be characterised along with the associated CCDs. The CCDs and N-FEEs will undergo rigorous on-ground characterisation and the performance of the CCDs will continue to be monitored in-orbit. This paper discusses the initial development of the experimental arrangement, test procedures and current status of the N-FEE. The parameters explored will include gain, quantum efficiency, pixel response non-uniformity, dark current and Charge Transfer Inefficiency (CTI). The current in-orbit characterisation plan is also discussed which will enable the performance of the CCDs and their associated N-FEE to be monitored during the mission, this will include measurements of CTI giving an indication of the impact of radiation damage in the CCDs.
Millimeter-Waves Non-linearities Study and Throughput Measurements in Indoor Corridor and Stairwell Environments for B5G Radio Communication

This paper presented a study of nonlinearities and throughput of an experimental millimeter wave system in an Indoor Corridor and Stairwell environments. We use the Error Vector Magnitude (EVM) of system to determine the link range using an Orthogonal Frequency Division Multiplexing (OFDM) signal with 64QAM modulation. A new method to optimize the system EVM has been proposed for Testbed with multiple system calibration sources. The system reaches the EVM limits set by 3GPP at 8, 6 and 3 meters respectively for the 24, 28 and 32 GHz frequencies. Finally, the throughput is measured over a distance of 10 meters. The results obtained in a LOS (Line of Sight) scenario show a maximum throughput of 2.95 Gbps. The throughput is reduced by 73% and 77% over 10 meters in Corridor and Stairwell respectively.