Miracle-dz/UniFAR-FTUs huggingface dataset:
Depth-based Visual Predictive Control of Tendon-Driven Continuum Robots

Image-based visual servoing (IBVS) scheme has become popular due to its advantages such as robustness to camera calibration errors and enabling direct control over the image features. However, in IBVS approaches several issues such as image singularities, and camera retreat problems must be addressed. Also, they lack a mechanism for enforcing constraints into control formulation. This paper presents a novel depth-based visual predictive control (DVPC) method to overcome several shortcomings of the previous IBVS methods. In particular, the proposed approach enables constraints enforcement and addresses one of the critical issues of the IBVS method, namely camera retreat problem. Furthermore, the proposed approach is applied for the control of continuum robots (CR). Simulation results are presented to verify the efficiency and functionality of the proposed approach.
On the testing of special memories in GPGPUs

Nowadays, data-intensive processing applications, such as multimedia, high-performance computing and safety-critical ones (e.g., in automotive) employ General Purpose Graphics Processing Units (GPGPUs) due to their parallel processing capabilities and high performance. In these devices, multiple levels of memories are employed in GPGPUs to hide latency and increase the performance during the operation of a kernel. Moreover, modern GPGPU architectures implement cutting-edge semiconductor technologies, reducing their size and power consumption. However, some studies proved that these technologies are prone to faults during the operative life of a device, so compromising reliability. In this work, we developed functional test techniques based on parallel Software-Based Self-Test routines to test memory structures in the memory hierarchy of a GPGPU (FlexGripPlus) implementing the G80 architecture of Nvidia.
Meta-ensemble based classifier approach for attack detection in multi-tenant distributed systems

Distributed systems in a network have always been vulnerable to cyber attacks due to exposure of vulnerabilities in shared memories, configurations, and network access. Being co-holders in the same server provides attackers with an opportunity to track the activities of other users and accordingly make an attack when the security of the latter is in peril. Traditionally, the effort has been to build full proof network architectures free from any threats and attacks. However, owing to its open-ended nature, it is impossible to come up with a completely secure architecture. A common factor that can always be observed irrespective of the type of attacks is the specific values and configuration of the system through which the attack is instantiated. The rise in the use of machine learning algorithms and data collection facilities provides us with an opportunity to use network parameter values for modeling any anomaly and subsequent possibility of attacks. In this paper, we propose an ensemble-based meta classifier for predicting the probability of attack instantiation based on certain system parameter values. We also contribute to the research in this area by creating a dataset for analysis purpose. Efforts are taken by us to tackle the class imbalance problem often found in this domain where the instances of attack being carried out are rare. The results obtained by us on our data demonstrate that machine learning coupled with ensembling can be used as a good aid for network attacks analysis. While increased measures are being taken to improve security in the digital space, machine learning algorithms can be used to analyze any aberrant behavior left unchecked.
Hierarchical Gated Deep Memory Network With Position-Aware for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis aims at identifying the sentiment polarity of specific aspect in the sentence. Previous work has realized the importance of the information interaction between aspect term and context. However, most existing information interaction methods are coarse-grained, which results in a certain loss of information. In addition, most methods ignore the role of position information in identifying the sentiment polarity of the aspect. To better address the two problems, we propose a novel approach, called hierarchical gated deep memory network with position-aware. Our approach has two characteristics: 1) it has fine-grained information interaction attention mechanism which models the word-level interaction between aspect and context. The sentence-to-aspect attention is used to capture the most indicative sentiment words in context. And the aspect-to-sentence attention is used to capture the most important word in the aspect term. 2) The position information is embedded as a feature in the sentence representation. Finally, we conduct sentiment classification comparative experiment on laptop and restaurant datasets. The experimental results show that our model achieves state-of-the-art performance on aspect-based sentiment analysis.
Embroidered Inductive Strain Sensor for Wearable Applications

This paper presents a textile-based, embroidered inductive strain sensor that can be fabricated with standard embroidery processes and aesthetically integrated with apparel and garments fabrics. The sensor consists of two embroidered coupling coils (called coupling planar coils, or CPC) connected in series, and stacked on top of each other. The mutual inductance of this coupling coils pair is determined by the relative positions and displacement between them. This structure can be used as a restorable and repeatable strain sensor for human movements and activities, such as the measurement of respiratory rate. This structure can also form a passive sensor by using the coils as antennas to connect with RFID/NFC chips, such that the sensing information can be extracted wirelessly as phase and amplitude variations by RFID/NFC readers. Experiments demonstrate that a wearable belt embedded with this proposed sensor can be worn on human body to monitor breathing activities reliably under normal wearing conditions. The sensor structure can be fabricated by commercial embroidery machines using off-the-shelf conductive yarns and all the materials are commercially available in large quantity; therefore, it also has good potentials to be commercialized as a low-cost health monitoring system in mass production.
3D Printed Soft Pneumatic Bending Sensing Chambers for Bilateral and Remote Control of Soft Robotic Systems

This work reports on soft pneumatic bending sensing chambers that are directly 3D printed without requiring any support material and postprocessing using a low-cost and open-source fused deposition modeling (FDM) 3D printer and a commercially available soft thermoplastic polyurethane (TPU). These bending sensing chambers have multiple advantages including very fast response to any change in their internal volume, linearity, negligible hysteresis, repeatability, reliability, stability over time, long lifetime and very low power consumption. The performance of these soft sensing chambers is accurately predicted and optimized using finite element modeling (FEM) and a hyperelastic material model for the TPU used for 3D printing. The soft sensing chambers are tailored to several soft robotic applications such as bending sensors for bilateral control of soft robotic fingers and structures and soft wearable gloves for remote control of soft monolithic robotic fingers and adaptive grippers.
Feedforward for Lateral Trajectory Tracking of Automated Vehicles

This paper presents two approaches for the determination of a feedforward for lateral trajectory tracking control. Most trajectory planning approaches for automated vehicles are based on a mass point description. Thus, the reference trajectory only provides information about the position in x- and y-coordinates over time. Therefore, this paper proposes a method to estimate the side-slip angle and to calculate the yaw rate from the course rate. With this additional enriching information about the reference trajectory, a feedforward control based on the inversion of the linear single-track model is introduced. On the other hand, a flat output is used to invert the nonlinear single-track model and thus determine a feedforward control.
Energy-Efficient Ultra-Dense Network using Deep Reinforcement Learning

With the explosive growth in mobile data traffic, pursuing energy efficiency has become one of key challenges for the next generation communication systems. In recent years, an approach to reduce the energy consumption of base stations (BSs) by selectively turning off the BSs, referred to as the sleep mode technique, has been suggested. However, due to the macro-cell oriented network operation and also computational overhead, this approach has not been so successful in the past. In this paper, we propose an approach to determine the BS active/sleep mode of ultra-dense network (UDN) using deep reinforcement learning (DRL). A key ingredient of the proposed scheme is to use action elimination network to reduce the wide action space (active/sleep mode selection). Numerical results show that the proposed scheme can significantly reduce the energy consumption of UDN while ensuring the QoS requirement of the network.
A Comparative Study on security breach in Fog computing and its impact

Budding technologies like IoT requires minimum latency for performing real-time applications. The IoT devices collect a huge amount of big data and stores in the cloud environment, because of its on-demand services and scalability. But processing the needed information of the IoT devices from the cloud computing environment is found to be time-sensitive one. To eradicate this issue fog computing environment was created which acts an intermediate between the IoT devices and cloud computing environment. The fog computing performs intermediate computation and storage which is needed by IoT devices and it eliminates the drawbacks of latency and bandwidth limitation faced by directly using cloud computing for storage and accessing. The fog computing even though more advantageous it is more exposed to security issues by its architecture. This paper concentrates more on the security issues met by fog computing and the present methods used by the researchers to secure fog with their pros and cons.
A Privacy-Centered System Model for Smart Connected Homes

Smart connected homes are integrated with heterogeneous Internet-connected devices interacting with the physical environment and human users. While they have become an established research area, there is no common understanding of what composes such a pervasive environment making it challenging to perform a scientific analysis of the domain. This is especially evident when it comes to discourse about privacy threats. Recognizing this, we aim to describe a generic smart connected home, including the data it deals with in a novel privacy-centered system model. Such is done using concepts borrowed from the theory of Contextual Integrity. Furthermore, we represent privacy threats formally using the proposed model. To illustrate the usage of the model, we apply it to the design of an ambient-assisted living use-case and demonstrate how it can be used for identifying and analyzing the privacy threats directed to smart connected homes.
A Perturbation based Algorithm for Inference of Gene Regulatory Networks for Multiple Myeloma

The expression of genes within the body of an organism is responsible for manufacturing normal proteins and other molecular compounds required for normal operation of the cell and whole body. Often the genes influence each other and the activity of one gene leads to activation or deactivation of one or more other genes. Such groups of interacting genes is known as a gene regulatory network. Many computational researchers have used machine learning algorithms on genomic datasets for analysis and discovery of the gene regulatory networks. This knowledge is being used to discover new drug targets for genetic diseases like cancer. This paper has proposed an algorithm using partial correlation with data perturbation for inference of gene regulatory networks that are involved in incidence of blood malignancy called as multiple myeloma. The proposed method has been compared with existing methods and the results show superior performance of the proposed method.
Concurrent Dual Band Six-port based Receivers: Topologies and Calibration Technique

In this paper, two concurrent dual band architectures for six-port based receiver (SPR) are compared in terms of complexity, performance and hardware implementations. Experimental set-up of each architecture and the recovering of the two envelop signals using augmented Hammerstein model based calibration approach have been performed to illustrate the difference between the two topologies. In order to evaluate the performance of each architecture a comparative study in term of Error Vector Magnitude (EVM), spectrums and constellations has been performed.
Progressive Latent Fingerprint Enhancement Using Two-Stage Spectrum Boosting with Matched Filter and Sparse Autoencoder

We propose a progressive enhancement algorithm to improve friction ridges in latent fingerprint images that are usually of poor quality or partially missing. The proposed method is designed for performing in the frequency domain to propagate a friction ridge pattern from good to poor-quality areas. In the first stage, the algorithm starts with a block of high ridge signal strength to initially boost ridge spectra by a matched filter. The boosted block is then padded back to the input image, so that neighboring blocks can absorb the stronger ridge signal. We carry on this process iteratively at neighbors to propagate the friction ridge pattern until the entire image is done. However, at the low ridge signal strength, the matched filter cannot enhance the ridge signal well enough. In the second stage, we use a sparse autoencoder-based spectrum generator to approximate the matched filter for the spectrum boosting process. The proposed method was benchmarked with two existing latent fingerprint enhancement methods. The experimental result shows that the proposed method provided the promising accuracy on publicly available IIT-D MOLF latent fingerprint database.
Evaluation of Paenibacillus spp. isolates for the biological control of black rot in Brassica oleracea var. capitata (cabbage)

ABSTRACT The ability of isolates of Paenibacillus spp. to protect Brassica oleracea var. capitata (cabbage) against the black rot pathogen, Xanthomonas campestris pv. campestris (Xcc),was evaluated. Twenty-four isolates of Paenibacillus spp. , isolated from New Zealand-grown brassica hosts or soil, were evaluated for in vitro antagonism towards six Xcc isolates. Seven Paenibacillus spp. isolates with different levels of in vitro suppressive activity against Xcc were screened in pot experiments for their capacity to reduce black rot symptoms on cabbage. Two Paenibacillus isolates (P10 and P16) exhibited biocontrol activity against Xcc, and four isolates (P1, P6, P9, and P24) reduced cabbage seed germination and seedling emergence. The dependence of bioactivity on inoculum rate was investigated with three Paenibacillus isolates (P6, P10, and P16) at three different concentrations (5 × 108, 5 × 109, and 5 × 1010 CFU ml−1). Negative effects on seedling emergence were detected with isolate P6 at concentrations ≥5 × 109 CFU ml−1. All three isolates applied at the three concentrations reduced black rot symptoms on the cotyledons and true leaves. There was poor or no relationship between the inhibitory effect of Paenibacillus spp. isolates on the growth of Xcc in vitro, and their biocontrol activity in vivo. Paenibacillus isolate P16 was identified as a potential biological control of black rot in cabbage.
Q-PointNet: Intelligent Stacked-Objects Grasping Using a RGBD Sensor and a Dexterous Hand

We report on the development of a deep-learnt grasping algorithm, Q-PointNet, which is capable of determining an adequate strategy for grasping a partially exposed object in a stacked pile. The grasping strategy includes the gripper’s posture and the finger mode, whether two fingers or three fingers. Because our predicted outputs are quaternion and mode, we also explain fully how to utilize the hybrid loss function to reach our goal within the limited training dataset. Moreover, according to the pose prediction, we developed an algorithm to estimate object width, and it is used to adjust the width from finger to finger of the gripper. In the end, the grasp experiments and their operation flows are presented, and the results show that our approach can grasp specific objects with a high accuracy in a stacked scenario.
High-Performance Microring Resonator Ge-on-Si Photodetectors by Optimizing Absorption Layer Length

We studied the relationship between the absorption layer length and the performance of Ge-on-Si microring resonator photodetectors. The principle of optimizing the absorption layer length based on the light field distribution was proposed. In the Ge-on-Si photodetectors, the transmission light field is alternately distributed among the germanium absorption layer and the silicon waveguide layer, and gradually absorbed by the germanium layer. For the Ge-on-Si microring resonator photodetectors, the length of the germanium absorption layer should be set to achieve the maximum light field distribution in the silicon layer at the end of the photodetector, then the remaining optical power can be coupled back to the silicon waveguide and transmit in the microring for absorption again. We demonstrated by simulation that, the device with optimized length of 11 μm has larger bandwidth, smaller dark current, and higher responsivity than the device with 14 μm absorption layer by simulation @1550nm.
Step-Down Switched-Inductor Hybrid DC-DC Converter for Small Power Wind Energy Conversion Systems With Hybrid Storage

This work is focused on a step-down switched-inductor hybrid dc-dc converter (SIHDC) integrated in a small power wind energy conversion system (WECS). The converter has two roles, to maintain the wind turbine at the maximum power point by controlling the electric generator loading, and to charge a high power density supercapacitor, which is part of a hybrid storage unit that also contains rechargeable batteries. The paper points out the steady-state analysis of the converter in continuous and discontinuous current modes, specific for this application. The stability investigation is presented in detail, including the influence of the electrical generator. The parameterized small-signal transfer function of the control variable derived in this paper can be used to quickly obtain the information needed to evaluate the stability of any similar WECS. A current controller was designed to achieve a stable operation in continuous current mode, and afterwards the stability was checked for discontinuous current mode. A 5kW step-down SIHDC prototype was built and extensively tested, both in laboratory, by using a wind turbine emulator, and in a small power (5kW) WECS industrial platform. Experimental results obtained under real operating conditions confirm the theoretical analysis and laboratory tests, showing that SIHDC converter is a valid solution for low power wind energy conversion systems.
Prediction of Forthcoming Anger of Customer in Call Center Dialogs

Call center is a department that is most relevant to audio data usage. One of its major tasks is to monitor customers’ anguish because it has a negative impact on the organization. One challenging task is to develop a model that can predict whether a customer is getting angry in the next turn of conversation. Such model can assist agents in taking appropriate action(s) to prevent the incidents. In this study, we investigate an approach to build an anger prediction model from customers’ voice in call center dialogs. To create the model requires 5 processes: (1) Customer’s turn extraction (2) Emotion annotation (3) Voice feature selection (4) Data pre-processing for long short-term memory networks, and (5) Anger prediction modeling. Five long short-term memory networks were built with the time series data sets of 1, 2, 3, 4, and 5 consecutive turns. The experimental results showed that the long short-term memory network built with the 3-consecutive turn data has promising performance in aspect of Average Precision and False Negative Rate when compared to the random and good guess benchmarks.
Discrimination Amongst Various Degrees of Left Ventricular Ejection Fraction in CAD Patients Using Circadian Heart Rate Variability Features

This study explored how 24-hour Heart Rate Variability (HRV) features differentiate amongst Coronary Artery Disease (CAD) patients with “at risk”, “borderline”, and “normal” Left Ventricular Ejection Fraction (LVEF). Hourly segmentation of heart rate signals was completed by Cosinor Analysis fitting. Time, frequency, and non-linear HRV features were estimated for each hour and averaged across all CAD patients for each group. Statistical analysis to identify differences between the groups was based on one-way ANOVA test, followed by a multiple comparison analysis (Tukey test). The results showed a statistically significant difference between the three groups when using as discriminative features the normalized low frequency (0.04 to 0.15 Hz) HRV (LF-HRV) power and the sample entropy (SE) occurring only between 2:00-3:00, 18:00-19:00, and 19:00-20:00. In addition, the averaged normal-to-normal values show variation during the night time (from 23:00 to 5:00) between the three groups. These results pave the way for further investigation of the interaction of the sympathetic and parasympathetic nervous systems (as reflected in LF - HRV) and the cardiovascular autonomic regulation (as reflected in SE) in LVEF.
Shape Memory Effect of Benchmark Compliant Mechanisms Designed With Topology optimization

Shape Memory Alloys (SMA) are a class of Smart Materials that are ideal for creating compact actuators. The complex nature of their behavior results in the use of these materials in simple geometries (wires, sheets) limiting their performances. A design method to develop complex compliant mechanisms made of SMA is proposed in this paper. Using topology optimization for conventional materials, a set of benchmark problems are designed. Finite element modelling is then able to validate the shape memory effect of the designs (with strain retention up to 80%), confirming the notion that this methodology can, in fact, be used to design the next generation of SMA actuators.
Redundant Haptic Interfaces for Enhanced Force Feedback Capability Despite Joint Torque Limits

In this paper, an actuator saturation compensation method (ASCM) is proposed to enhances the force feedback capability of a redundant haptic interface (RHI) by leveraging its kinematic redundancy. This method acts in the null-space of the Jacobian matrix of the RHI and distributes the overloaded actuator’s torque among the available unsaturated actuators at the joints. This method empowers design engineers to utilize smaller actuators that have lower rotor inertia and friction in the design of new haptic interfaces. This is advantageous because having low apparent inertia and friction is a requisite for truthfully recreating the feeling of moving in free space. By employing ASCM, the required torque for rendering a stiff environment will be optimally distributed among smallcapacity actuators that otherwise become saturated. Moreover, manipulability enhancement of the RHI along the direction of the task is proposed as a tertiary objective – the primary objective is force reflection and the secondary objective is actuator saturation compensation. The tertiary objective acts if the primary and secondary objectives are feasible, and the haptic interface still has remaining redundancy. Simulation examples are provided throughout the paper to demonstrate the concept. Also, experimental results with a four degree-offreedom (Dof) planar haptic interface are reported that verify the practicality of the proposed method.