Miracle-dz/UniFAR-FTUs huggingface dataset:
Unconventional Uses of Structural Compliance in Adaptive Hands

Adaptive robot hands are typically created by introducing structural compliance either in their joints (e.g., implementation of flexure joints) or in their finger-pads. In this paper, we present a series of alternative uses of structural compliance for the development of simple, adaptive, compliant and/or under-actuated robot grippers and hands that can efficiently and robustly execute a variety of grasping and dexterous, in-hand manipulation tasks. The proposed designs utilize only one actuator per finger to control multiple degrees of freedom and they retain the superior grasping capabilities of the adaptive grasping mechanisms even under significant object pose or other environmental uncertainties. More specifically, in this work, we introduce, discuss, and evaluate: a) the concept of compliance adjustable motions that can be predetermined by tuning the in-series compliance of the tendon routing system and by appropriately selecting the imposed tendon loads, b) a design paradigm of pre-shaped, compliant robot fingers that adapt / conform to the object geometry and, c) a hyper-adaptive finger-pad design that maximizes the area of the contact patches between the hand and the object, maximizing also grasp stability. The proposed hands use mechanical adaptability to facilitate and simplify the efficient execution of robust grasping and dexterous, in-hand manipulation tasks by design.
Robust Self-Calibration for Rectangle Shape UWB Anchor Locations

A self-calibration algorithm of UWB anchor positions is proposed. It is assumed that anchor position is a rectangle shape and a mobile node is placed inside the rectangle. With this assumption, an algorithm is proposed to give at most three possible anchor positions, where two measurement data (each contains four distance between a mobile node and four anchor nodes) are used. Using this algorithm, a robust self-calibration algorithm is proposed using the M-estimator sample and consensus method. Through simulations and experiments, the proposed algorithm is verified.
A 102dB-SFDR 16-bit Calibration-Free SAR ADC in 180-nm CMOS

A 16-bit successive approximation register analog-to-digital converter (ADC) is presented achieving superior spurious-free dynamic range (SFDR). This ADC uses VCM-based and binary-window digital-to-analog converter (DAC) switching schemes to improve the signal-to-noise and distortion ratio (SNDR). Moreover, a level-2 capacitor swapping scheme is proposed to improve the DAC linearity. A prototype ADC is fabricated in 180-nm CMOS and occupies an active area of 0.52 mm2. At 500 kS/s, it consumes a total power of 633 µW from a supply of 1.5 V. The measured differential and integral nonlinearity are −0.65/+0.9 and −2.7/+2.5 LSB, respectively. With 1 kHz input, the measured SNDR and SFDR are 77.9 dB and 102 dB, respectively. The effective number of bits is 12.7, equivalent to a Scherier figure-of-merit of 165 dB.
A Reconfigurable Neural Recording Front-End IC for Multimodal Operation

This paper presents a reconfigurable neural recording front-end integrated circuit (IC) for multimodal operation. The proposed IC performs the voltage sensing to acquire the electrical neural signal and detects the photodiode current to monitor the concentration of calcium ions in the neuronal cell population. The front-end IC is composed of the analog front-end, voltage-to-current converters, path switches, and current-to-digital converters for recording voltage and current signals. When monitoring the electrical neural activity, the IC is configured to operate in the voltage sensing mode. When reading the photodiode current, the IC operates in the current sensing mode. The proposed front-end IC is designed using a 180-nm CMOS process. The neural recording IC consumes the power of 5.4 µW/channel and 2 µW/channel in the voltage and current sensing modes, respectively. The active area occupied by the single-channel circuit is 0.23 mm2.
Nonlinear dynamics of MEMS resonators: numerical modelling and experiments

Numerical modelling of MicroElectroMechanical Systems (MEMS) resonators is attracting increasing interest from the sensors community especially when the nonlinear regime is activated by challenging applications of the device. Here, the dynamic response of a double-ended tuning fork MEMS resonator is studied both in the linear and nonlinear regime. A one Degree Of Freedom (1 dof) model able to predict the frequency response of the device is proposed. Geometric and electrostatic nonlinearities are simulated through a Finite Element Method (FEM) and a Boundary Element Method (BEM) code, respectively. The total damping of the resonator is computed by taking into account both the thermoelastic and the nonlinear fluid contributions. Experimental measurements performed on a resonator fabricated in polysilicon through a commercial surface micromachining process, validate the proposed model showing a very good agreement with theoretical predictions.
Improving Color Discrimination for Color Vision Deficiency (CVD) with Temporal-Domain Modulation

Color Vision Deficiency (CVD) is often characterized by the inability to distinguish color due to a defective or missing cone in the eye. Although it is possible to modify the observed color to make it easier for users to distinguish, this can lead to color confusion with unaffected colors. To address this problem we investigate how flicker can assist distinguishing colors for CVD patients. In preliminary study, we evaluated the efficiency of color and brightness modulation with 4 participants with normal vision. Our findings suggests that while brightness modulation was ineffective, color modulation can help users distinguish between different colors.
Analysis of the Factors Influencing Learners’ Performance Prediction With Learning Analytics

The advancement of learning analytics has enabled the development of predictive models to forecast learners’ behaviors and outcomes (e.g., performance). However, many of these models are only applicable to specific learning environments and it is usually difficult to know which factors influence prediction results, including the predictor variables as well as the type of prediction outcome. Knowing these factors would be relevant to generalize to other contexts, compare approaches, improve the predictive models and enhance the possible interventions. In this direction, this work aims to analyze how several factors can make an influence on the prediction of students’ performance. These factors include the effect of previous grades, forum variables, variables related to exercises, clickstream data, course duration, type of assignments, data collection procedure, question format in an exam, and the prediction outcome (considering intermediate assignment grades, including the final exam, and the final grade). Results show that variables related to exercises are the best predictors, unlike variables about forum, which are useless. Clickstream data can be acceptable predictors when exercises are not available, but they do not add prediction power if variables related to exercises are present. Predictive power was also better for concept-oriented assignments and best models usually contained only the last interactions. In addition, results showed that multiple-choice questions were easier to predict than coding questions, and the final exam grade (actual knowledge at a specific moment) was harder to predict than the final grade (average knowledge in the long term), based on different assignments during the course.
Track Cyclist Detection and Identification using Mask R-CNN and K-means Clustering

Automatically identifying a cyclist as they pass the starting line in a track cycling race is primarily restricted to physical detection systems assigned to individual competitors. These current solutions are expensive and require physical hardware to be attached to the cyclists during a race. This paper proposes a method to both detect when a track cyclist passes the lap line and identify the cyclist using a region convolutional neural network (R-CNN) and K-means clustering. This method aims to overcome several limitations of prior research by identifying multiple cyclists in a single frame, requiring only a single image to train per cyclist, and increasing identification accuracy by combining a neural network with K-means clustering. The proposed method uses the Mask R-CNN with COCO 2017 dataset and K-means clustering to train the detector with a single image of each cyclist. Cyclists are then able to be identified during a race. The method achieved an average identification accuracy of 95.1% with single image training. Multiple cyclists could be detected in an individual frame through the successful integration of a combined Mask R-CNN K-means detector. The primary limitation of the method is the reduction in identification accuracy for cyclists wearing similar coloured clothing. By using K-means clustering and RGB range thresholding, the cyclists may be mis-identified if the colours are too similar. Further research that would benefit this solution includes developing additional points-of-interest to identify cyclists and additional cameras with different angles for instances of total overlap between cyclists.
SiMPOSE - Configurable N-Way Program Merging Strategies for Superimposition-Based Analysis of Variant-Rich Software

Modern software often exists in many different, yet similar versions and/or variants, usually derived from a common code base (e.g., via clone-and-own). In the context of product-line engineering, family-based analysis has shown very promising potential for improving efficiency in applying quality-assurance techniques to variant-rich software, as compared to a variant-by-variant approach. Unfortunately, these strategies rely on a product-line representation superimposing all program variants in a syntactically well-formed, semantically sound and variant-preserving manner, which is manually hard to obtain in practice. We demonstrate the SiMPOSE methodology for automatically generating superimpositions of N given program versions and/or variants facilitating family-based analysis of variant-rich software. SiMPOSE is based on a novel N-way model-merging technique operating at the level of control-flow automata (CFA) representations of C programs. CFAs constitute a unified program abstraction utilized by many recent software-analysis tools. We illustrate different merging strategies supported by SiMPOSE, namely variant-by-variant, N-way merging, incremental 2-way merging, and partition-based N/2-way merging, and demonstrate how SiMPOSE can be used to systematically compare their impact on efficiency and effectiveness of family-based unit-test generation. The SiMPOSE tool, the demonstration of its usage as well as related artifacts and documentation can be found at http://pi.informatik. uni-siegen.de/projects/variance/simpose.
A New Insight into Local Coin-Based Randomized Consensus

This paper presents a binary randomized consensus algorithm for n-process asynchronous message-passing systems in which (1) up to t < n/2 processes may crash, and (2) an asynchrony adversary can read the content of the messages and re-order their deliveries according to their values. In addition to its simplicity, the main property of this algorithm lies in the fact it allows the processes to decide in a constant number of rounds when t = O(sqrt(n)). To attain this goal the algorithm combines Ben-Or's randomized consensus algorithm (PODC 1983) and the condition-based approach to solve consensus (JACM 2003). (Among other points, if the most frequent proposed value appears more than t times than the other value, decision requires three communication steps.) An improvement of the proposed algorithm is given for the case where t < n/4. We conjecture that if t > O(sqrt(n)) it is no longer possible to implement a randomized consensus algorithm ensuring a constant number of communication steps despite unfair channels.
77-GHz Active Quasi-Circulator based Monostatic Radar with LO Feed-Through Functionality

A modified QC based transceiver system is presented with focus on the enhancement of system efficiency. In common TX/RX separation units for W-band automotive radars, half of the transmit power is terminated due to the structure. We present a solution, which reuses this terminated power as LO signal for the down-conversion. Thus, a separate LO path for the down-conversion mixer can be omitted, which shrinks the foot print of the system and improves the overall system efficiency. In the previous design, half of the transmit power is terminated in a W-band impedance tuner, which acts as TX/RX leakage canceler. This impedance tuner is modified on circuit level to migrate the LO feed-through functionality. Based on on-wafer measurements, it can be shown, that the proposed modification doesn’t degrade the performance of the QC according TX/RX isolation and noise figure as well as the overall radar system performance.
Multiple Attention Network for Facial Expression Recognition

One key challenge in facial expression recognition (FER) is the extraction of discriminative features from critical facial regions. Because of their promising ability to learn discriminative features, visual attention mechanisms are increasingly used to address pattern recognition problems. This paper presents a novel multiple attention network that simulates humans’ coarse-to-fine visual attention to improve expression recognition performance. In the proposed network, a region-aware sub-net (RASnet) learns binary masks for locating expression-related critical regions with coarse-to-fine granularity levels and an expression recognition sub-net (ERSnet) with a multiple attention (MA) block learns comprehensive discriminative features. Embedded in the convolutional layers, the MA block fuses diversified attention using the learned masks from the RASnet. The MA block contains a hybrid attention branch with a series of sub-branches, where each sub-branch provides region-specific attention. To explore the complementary benefits of diversified attention, the MA block also has a weight learning branch that adaptively learns the contributions of the different critical regions. Experiments have been carried out on two publicly available databases, RAF and CK+, and the reported accuracies are 85.69% and 96.28%, respectively. The results indicate that our method achieves competitive or better performance than state-of-the-art methods.
Broadcast Approach for the Information Bottleneck Channel

This work considers a layered coding approach for efficient transmission of data over a wireless block fading channel without transmitter channel state information (CSI), which is connected to a limited capacity reliable link, known as the bottleneck channel. Two main approaches are considered, the first is an oblivious approach, where the sampled noisy observations are compressed and transmitted over the bottleneck channel without having any knowledge of the original information codebook. The second approach is a non-oblivious decode-forward (DF) relay where the sampled noisy data is decoded, and whatever is successfully decoded is reliably transmitted over the bottleneck channel. The bottleneck channel from relay to destination has a fixed capacity $C$ . We examine also the case where the channel capacity can dynamically change due to variable loads on the backhaul link. The broadcast approach is analyzed for cases that only the relay knows the available capacity for next block, and for the case that neither source nor relay know the capacity per block, only its capacity distribution. Fortunately, it is possible to analytically describe in closed form expressions, the optimal continuous layering power distribution which maximizes the average achievable rate. Numerical results demonstrate the achievable broadcast rates.
Simultaneous Phase and Frequency Stepping in Time-of-Flight Range Imaging

Time-of-flight (ToF) range imaging is an established full-field depth acquisition technology. ToF is prone to several forms of error, notably multi-path interference (MPI). Frequency stepping ToF mitigates these errors, but efficient computational methods suffer from bandwidth limitations. We introduce a new acquisition method based on simultaneous phase and frequency stepping, which is capable of running in real time and reduces error compared to phase stepping and frequency stepping alone.
Enhancing Clinical Name Entity Recognition Based on Hybrid Deep Learning Scheme

This paper describes a novel machine learning approach based on deeper and wider deep learning model, for better feature learning and latent feature discovery for the clinical name entity recognition task. The performance evaluation of the proposed framework with a benchmark clinical NLP dataset, the clinical CLEF eHealth challenge 2016 dataset, has led to promising performance, when assessed in terms of F-measure, Recall and Precision. The Hybrid CNN model with hyperparameter optimization led to an F-score 89 % for the CLEF eHealth 2016 Challenge task involving synthetic nursing handover dataset.
Local Binary Pattern based Multimodal Biometric Recognition using Ear and FKP with Feature Level Fusion

Biometrics is a mode of popularity and identification affirmation that uses physical attributes of a particular person that are impossible or as a minimum hard to mask. Finger-Knuckle-Print and ear print are examined to be maximum reliable physiological bio metric methods. These days a Finger Knuckle Print authentication based totally bio metric system has been efficaciously applied in various sectors. These unique Finger Knuckle Print and ear print authentication offers in reveals its user image that matches with its very own template that is stored inside the database for verification. An ear print is a replica of the parts outside the ear which have impressed a particular surface [3]. Many variants of local binary patterns are widely used for feature extraction process due to their satisfactory performance, for recognition purpose. An effective method of extracting feature patterns for Finger Knuckle Print (FKP) and ear has been proposed in this project. After extracting features of ear and FKP separately, they are fused by feature level fusion process which is done registration phase. The fused results of FKP and ear have been compared with the stored fused values in authentication phase. If the level of matching is achieved, that particular person gets authenticated to access the things else the person will be impostor [4]. This system gives more accuracy in less time. Challenges: The accuracy of authentication is improved in less duration. The chances of intruding is reduced.
Development of Gripping Assistive Device for Training System

Gripping is an everyday task which goes unnoticeable. Since it is an essential daily movement, without this motion a lot of activities involving this movement could not be done. However due to maturing process and also sometimes mishaps, the grasping movement once in a while become disrupted which then is cause difficulties to whom is influenced. How these impacts, relies upon an individual and also in different ways regardless of age and how the body normally winds up more fragile, less adaptable and less impervious to sickness and damage. Patients with hand gripping issues normally require long term care. This also contributes towards their ability to recover much longer as their regular activity reduces and dependency on other increases. By produce therapy as an, early rehabilitation within the golden period (less than three months prior to incident) is required to improve the gripping motion and regain back the strength of the affected joints as well as muscles. Rehabilitation also helps to improve the patient's ability to return the patient to the level of premorbid function. The current situation is that the rehabilitation process takes longer as there are few physiotherapists available in hospitals. It is anticipated that a mechatronics approach using robotics based devices have been seen as a promising candidate to assist existing forms of the rehabilitation process. The idea is to develop a basic function rehabilitation robot is to support the physiotherapists. The device is aimed to help with the gripping process, where the system is based on a master-slave mechanism which needs a healthy hand to control the weak hand. The system uses a leap motion sensor as the input and the output is an exoskeleton. A gyroscope was used to indicate the finger position and placed on top of the exoskeleton.
Optimization of Hybrid Renewable Energy in Sarawak Remote Rural Area Using HOMER Software

In past few years, there has been a huge concern about decentralized electricity supply in Sarawak, Malaysia. Renewable energy resources can be seen as an alternative energy to capture the remote rural electrification’s problems. Installing and operating a renewable energy generation is not difficult but it requires cautious step as to make sure maximum use of energy can improve the electrification. Therefore, the aim of this paper is to critically analyze five types of optimized hybrid renewable energy systems in term of financial and power production using HOMER software. This paper approaches four renewable energy resources, namely solar, hydro, wind and biomass in three remote rural areas located in Limbang, Kapit and Sri Aman regions. Different combination of solar PV system, micro-hydro generator, wind turbine and biomass generator together with battery storage and converter were studied. The optimization between these different sizes of system was built based on energy demand satisfaction, system cost and carbon gas emission. Comparison between operational of behaviors hybrid renewable energy systems with stand-alone diesel generator only were also carried out to elucidate the effects on investment cost and environment. The results revealed that hybrid hydro with battery system is seen to be the cheapest system and has the best technical performance in all locations. Solar PV and biomass generator system were combined with hydro generator as a backup power. It is also found that hybrid renewable energy is more cost-effective and reduce of carbon emission compared to standalone diesel generator.