Miracle-dz/UniFAR-FTUs huggingface dataset:
Response of Mouse Visual Cortical Neurons to Electric Stimulation of the Retina

Retinal prostheses strive to restore vision to the blind by electrically stimulating the neurons that survive the disease process. Clinical effectiveness has been limited however, and much ongoing effort is devoted toward the development of improved stimulation strategies, especially ones that better replicate physiological patterns of neural signaling. Here, to better understand the potential effectiveness of different stimulation strategies, we explore the responses of neurons in the primary visual cortex to electric stimulation of the retina. A 16-channel implantable microprobe was used to record single unit activities in vivo from each layer of the mouse visual cortex. Layers were identified by electrode depth as well as spontaneous rate. Cell types were classified as excitatory or inhibitory based on their spike waveform and as ON, OFF, or ON-OFF based on the polarity of their light response. After classification, electric stimulation was delivered via a wire electrode placed on the surface of cornea (extraocularly) and responses were recorded from the cortex contralateral to the stimulated eye. Responses to electric stimulation were highly similar across cell types and layers. Responses (spike counts) increased as a function of the amplitude of stimulation, and although there was some variance across cells, the sensitivity to amplitude was largely similar across all cell types. Suppression of responses was observed for pulse rates ≥3 pulses per second (PPS) but did not originate in the retina as RGC responses remained stable to rates up to 5 PPS. Low-frequency sinusoids delivered to the retina replicated the out-of-phase responses that occur naturally in ON vs. OFF RGCs. Intriguingly, out-of-phase signaling persisted in V1 neurons, suggesting key aspects of neural signaling are preserved during transmission along visual pathways. Our results describe an approach to evaluate responses of cortical neurons to electric stimulation of the retina. By examining the responses of single cells, we were able to show that some retinal stimulation strategies can indeed better match the neural signaling patterns used by the healthy visual system. Because cortical signaling is better correlated to psychophysical percepts, the ability to evaluate which strategies produce physiological-like cortical responses may help to facilitate better clinical outcomes.
An Integrated Bipolar GNR-RTD with Robustness to Edge Defects Based on h-BN

Resonant tunneling diodes (RTDs), due to their large dimension in conventional semiconductors, require a serious reduction in it while maintaining their electronic properties. Although the use of 2D materials has been able to reduce the size of the devices, they still suffer from both extreme sensitivity to defects and low current density. In this paper, a new integrated RTD with about 3nm length that has similar features to those found in the conventional semiconductors is proposed. The use of compound 2-D materials with high unit cell similarity to get both advantages of them along with bipolar doping is one of the ideas that has led to the improved structure. Peak-to-Valley current ratio studies, as well as the effects of geometric parameters on the electronic properties, indicate that this diode can alternate the silicon resonant tunneling diodes. Finally, as the most important consideration, the robustness to edge defects has been proven.
Model Predictive based Sliding Mode Control for Actuator Fault Tolerant Control

This paper, proposes a Model Predictive based Sliding Mode Control (MPSMC) scheme for Actuator Fault Tolerant Control (AFTC). A state observer will be used to construct the innovation signal. The proposed method, uses partial knowledge of the Actuator Fault (AF) to restore the effectiveness of the faulty actuator. The paper provides the stability analysis in the presence of AF for the proposed method. To demonstrate the effectiveness of the proposed method, the benchmark Inverted Pendulum will be used as a performance criterion. The performance of the proposed methods will be compared with the conventional Sliding Mode Controller (SMC).
Real-Time Monocular Visual SLAM by Combining Points and Lines

This paper presents a real-time monocular SLAM algorithm which combines points and line segments. We extend traditional point-based SLAM system with line features which are usually abundant in man-made scenes. The system is more robust and accurate than traditional point-based and direct-based monocular SLAM algorithms. In order to improve the timeliness of multi-feature based SLAM, we propose a novel feature level parallel processing framework and a fast line matching algorithm. For improving the reconstruction accuracy of 3D line segments which is usually affected by unreliable line endpoints, a sample point-based 3D reconstruction algorithm for line segments is proposed. Our system is implemented based on a popular monocular SLAM known as ORB-SLAM and tested on the TUM RGB-D benchmark. The experiment results demonstrate that the proposed system performs better than current state-of-the-art visual SLAM with respect to accuracy.
Low-Cost Interior Permannent Magnet Machine with a Blend of Magnet Types

This paper is a contribution to answering the following question: Is it possible to design a permanent-magnet machine with the performance expected from rare-earth magnets, but at a lower cost? Performance being understood as torque, size, efficiency, demagnetization and temperature rise together. The question is addressed with a systematic exploration of different interior permanent-magnet machine topologies mixing rare-earth and ferrite permanent magnets. The study starts from a production baseline, the Prius 2010 traction motor, with interior magnets placed in a single V pattern. It investigates various rotor designs, most specifically, single V and double V patterns as well as spoke configurations. The stator cross-section design and winding selection are fixed, providing a solid comparison basis from the point of view of machine cooling. For each rotor design, torque potential and machine material cost are assessed, the latter expressed as torque per dollar. A promising configuration was found, based on a spoke pattern, for which further modeling was performed to assess efficiency as well as resistance to demagnetization. It reduces the rare earth magnet volume by over 60%.
Quantitative Proteomic and Transcriptomic Analyses of Metabolic Regulation of Adult Reproductive Diapause in Drosophila suzukii (Diptera: Drosophilidae) Females

Diapause is a form of dormancy used by many insects to survive adverse environmental conditions, which can occur in specific developmental stages in different species. Drosophila suzukii is a serious economic pest and we determined the conditions for adult reproductive diapause by the females in our previous studies. In this study, we combined RNA-Seq transcriptomic and quantitative proteomic analyses to identify adult reproductive diapause-related genes and proteins. According to the transcriptomic analysis, among 242 annotated differentially expressed genes in non-diapause and diapause females, 129 and 113 genes were up- and down-regulated, respectively. In addition, among the 2,375 proteins quantified, 39 and 23 proteins were up- and down-regulated, respectively. The gene expression patterns in diapause- and non-diapause were confirmed by qRT-PCR or western blot analysis. The overall analysis of robustly regulated genes at the protein and mRNA levels found four genes that overlapped in the up-regulated group and six genes in the down-regulated group, and thus these proteins/genes may regulate adult reproductive diapause. These differentially expressed proteins/genes act in the citrate cycle, insulin signaling pathway, PI3K-Akt signaling pathway, and amino acid biosynthesis pathways. These results provide the basis for further studies of the molecular regulation of reproductive diapause in this species.
Cognitive and neural processes underlying memory for time and context

AND TITLE EMBARGOES An embargo on the full text copy of your thesis in the electronic and printed formats will be granted automatically in the first instance. This embargo includes the abstract and title except that the title will be used in the graduation booklet. If you have selected an embargo option indicate below if you wish to allow the thesis abstract and/or title to be published. If you do not complete the section below the title and abstract will remain embargoed along with the text of the thesis. a) I agree to the title and abstract being published YES b) I require an embargo on abstract NO c) I require an embargo on title NO Date 02/05/2017 signature of candidate signature of supervisor signature of supervisor
Detecting Lung Cancer Lesions in CT Images using 3D Convolutional Neural Networks

Early diagnosis of lung cancer is very important in improving patients life expectancies. Due to the high number of Computed Tomography (CT) images, fast and accurate diagnosis is difficult for radiologists. Therefore, there is an increasing demand for Computer-Aid Diagnosis (CAD) lung cancer. The core of all lung cancer detection systems is the distinction between cancer and non-cancerous tissues. This operation is performed in the false positive reduction phase, which is one of the most critical part of the lung cancer detection systems. The primary objective of this paper is to present a new method based on 3D Convolutional Neural Networks (CNN) that can reduce the false positives rate while providing a high sensitivity in detecting lung cancer lesions. We obtained 91.23% accuracy for 3.99 false positive per scan using a new method for fusion. The reason for accuracy improvement while reducing the false positive rate is by taking advantage of knowledge obtained from the classifiers in using a new fusion method.
Surface charging on HVDC spacers considering time-varying effect of temperature and electric fields

The dynamic behavior of surface charging on spacers in DC-GILs can be influenced by multi-factors including the non-uniform distributed electric field as well as the time-varying temperature gradient. In this paper, the time-varying effect of surface charging phenomenon on spacers is studied and a time-varying mathematical model is established, based on the influence of temperature and electric field on the ion mobility at the gas phase and the bulk conductivity in the solid phase. The results verify that the bulk conductivity can be greatly influenced by temperature, which leads to an increase in the surface charge density on the spacer. This allows the surface charge accumulation to stabilize more quickly. However, the ion mobility from the gas phase is less affected by temperature. When the non-uniform distributed electric field changes from 1.3 to 6.4 kV/mm, ion mobility is less influenced and the surface charge density on the spacer varies slightly. In this case, the effects of the non-uniformly distributed electric field in surface charge density variation is much smaller and can be ignored.
Sclera-Net: Accurate Sclera Segmentation in Various Sensor Images Based on Residual Encoder and Decoder Network

Sclera segmentation is revealed to be of noteworthy importance for ocular biometrics. The paramount step for biometric recognition methods is the segmentation of the area of interest, i.e., the sclera in our case. The sclera segmentation process plays a pivotal part in retaining the accuracy of the sclera-based recognition schemes by restraining the errors. However, accurate sclera segmentation in the images from various sensors in a real environment is quite challenging due to the saturated and/or defocused vessel patterns and the vessel structure, which has complex nonlinear deformations due to the multilayered sclera. With the development of deep learning algorithms, studies that are based on the sclera segmentation using convolutional neural networks (CNNs) have achieved promising results for sclera recognition. However, previous CNN-based methods are based on the repeated subsampling stages of convolution strides, or spatial pooling leads to losing much of the finer image structure that significantly decreases overall performance in tasks, such as semantic segmentation. In this paper, we present Sclera-Net, a residual encoder and decoder network that exploits identity and non-identity mapping residual skip connections to take benefit of the high-frequency information from the prior layers of both encoder and decoder networks to determine the accurate sclera region as well as other ocular regions. In this way, the finer image structure that was being lost due to repeated subsampling during convolution and pooling can be reutilized using residual skip connections to enhance overall performance. Furthermore, the proposed Sclera-Net does not enhance the performance on the cost of increasing depth, complexity, or the number of parameters. We performed comprehensive experiments and obtained optimum performance not only on sclera datasets but also on the iris datasets. In particular, we achieved an equal error rate and mean F1-score of 0.0093 and 96.2421, respectively, on the challenging SBVPI database, which is the best-reported result to date.
Novel Genetic Algorithm with Efficient Routing Paradigm for Multi-hop WSNs

Wireless Sensor Networks (WSNs) have emerged from the last three decades along with many applications including tracking and monitoring. Due to limited batteries of WSNs, the computation and processing power is limited depending on the efficiency of the routing paradigm in use. This paper presents a Genetic Algorithm (GA) to optimize and determine the routes among the nodes to/from the sink, minimizing the energy expenditures. In intensive experiments with 40 nodes set in an adhoc manner of a WSN field, GA performed comparatively better than threshold sensitive energy efficient sensor network (TEEN) protocol under various deployments. The TEEN protocol is observed to lose its 25 nodes on the 2500 nodes and all of the nodes dead up to 4000 rounds, whereas the proposed algorithm has saved many of the nodes on the same number of rounds. The approach can be extended to various scenarios with the increased size of the nodes, sinks and the network
Acoustic Scene Classification with Mismatched Recording Devices Using Mixture of Experts Layer

Recently, a mismatch in acoustic conditions such as a temporal recording gap as well as different recording devices for the development and the evaluation data has been considered in Acoustic Scene Classification (ASC). This brings ASC closer to real-world conditions. In this paper, we address ASC with mismatching recording devices. This has been introduced as task 1B of the DCASE 2018 challenge. We proposed a flexible and robust model that uses a mixture of experts (MoE) layer replacing the fully connected dense layer such that each expert can adapt to the specific domains of the data. Furthermore, we observe different Convolutional Neural Network (CNN) models as well as the number of the experts of the MoE dense layer using log mel features. In addition, we perform mixup data augmentation to enhance the robustness of our models. In experiments, the classification performance is 66.1% using 15 experts in the MoE dense layer with approximately 2M parameters. This outperforms the best model of task 1B of the DCASE 2018 challenge by 2.5% (absolute). This model uses an ensemble selection of 12 individual models with ~12M parameters.
Performance metrics for a modern BOPP capacitor film

In this paper, a set of performance metrics for modern biaxially oriented polypropylene (BOPP) capacitor films is established. The fundamental and applied properties of BOPP films required for application in state-of-the-art DC metallized film capacitors are reviewed, highlighting aspects related to high temperature operation, base PP properties and film processing. Commercial BOPP films — both base films and metallized films based on classic isotactic PP — are studied comprehensively, encompassing structural–morphological characterization and short- to medium-term dielectric characterization. Dielectric spectroscopy results demonstrate the negligible dielectric losses of BOPP, being in the range of 10–4 or less in the expected operation temperature regime. Thermally stimulated depolarization current (TSDC) measurements indicated a modest density of shallow traps (~0.75 eV) and a high density of deep traps (~1.08 eV) in the 5 μm and 10 μm film variants showing differences presumably arising from film processing. Such an electronic structure was found to be connected with ultra-low conductivity (in the range of 10–17–10–16 S/m), high breakdown strength (~700 V/μm) and negligible space charge accumulation up to temperatures of ~70 °C. It is shown that at current design stresses (~200 V/μm at ~60 °C) BOPP is operated close to its fundamental thermal and electrical limitations. Voltage endurance tests at higher fields revealed the onset of high-field degradation and drastically reduced insulation life, and thermal activation of deep traps in the high temperature region (~100 °C) was found to result in reduced dielectric performance.
Spectrum Trading for Device-to-Device Communication in Cellular Networks using Incomplete Information Bandwidth-Auction Game

Device-to-device (D2D) communication that allows proximity users to communicate directly has been recently proposed to improve spectral efficiency of cellular networks. In this paper, we assume a cellular network consisting of multiple cellular user equipments (CUEs), which are the primary users, and a cognitive D2D pair, which is the secondary user. The D2D pair needs a bandwidth for data transmission that can be obtained via spectrum trading. We introduce a bandwidth-auction game for the spectrum trading problem. The base station (BS) and CUEs are able to sell their spectrum or share it with the D2D pair, which allows the D2D pair to operate in orthogonal sharing, cellular, or non-orthogonal sharing (NOS) modes. Operation of the D2D pair in the NOS mode causes interference to the CUEs, which is possible under low interference condition. In the auction, the D2D pair can buy its required spectrum from three different service providers (SPs) corresponding to each mode that operate on different frequency spectrums. The D2D pair bids a price-bandwidth demand curve and the SPs offer a price-demand supply curve. Since each player is not aware of the strategy of other players in practical scenarios, the game is assumed to be an incomplete information repeated one. A best response based learning method is proposed for the decision making procedure of all players, the D2D pair and SPs. It is shown that the proposed method converges to the Nash equilibrium (NE) point of the game more rapidly than the state-of-the-art methods when the game is played repeatedly. The sensitivity of the proposed method to the learning rate variable is also less than the state-of-the-art methods and hence can be considered as a robust one.
Performance of Halbach Cycloidal Magnetic Gears with Respect to Torque Density and Gear Ratio

This paper investigates the volumetric torque density performance of a radial and an axial cycloidal magnetic gear when using a Halbach magnet rotor typology. The analysis is conducted in order to determine the geometric design rules that yield the highest volumetric torque density. The performance is studied with respect to pole-pair number, orbit length as well as the permanent magnet thickness and pole-pitch. Several parameter design guidelines are presented and shown to be valid regardless of gear ratio. Using 3-D finite element analysis a calculated torque density of up to 463 N.m/L and 475 N.m/L is shown to be achievable for the radial and axial cycloidal magnetic gear typology. The problems with unbalanced radial forces within both the radial and axial cycloidal magnetic gear is highlighted.
A Seven-Port Receiver for MIMO 2x2 Communication Systems

In this paper, a single front-end seven-port receiver is proposed and analyzed for MIMO systems based on Alamouti coding. This architecture uses a single front-end which reduces the complexity in the RF section. The performances of our proposed seven-port receiver is assesed in term of two input QPSK modulated signals and bit error rates. The obtained results demonstrate that the proposed seven-port receiver can be a candidate for using in MIMO communication systems.
Urban Nature Experiences Reduce Stress in the Context of Daily Life Based on Salivary Biomarkers

Stress reduction through contact with nature is well established, but far less is known about the contribution of contact parameters – duration, frequency, and nature quality. This study describes the relationship between duration of a nature experience (NE), and changes in two physiological biomarkers of stress – salivary cortisol and alpha-amylase. It is the first study to employ long-term, repeated-measure assessment and the first evaluation wherein study participants are free to choose the time of day, duration, and the place of a NE in response to personal preference and changing daily schedules. During an 8-week study period, 36 urban dwellers were asked to have a NE, defined as spending time in an outdoor place that brings a sense of contact with nature, at least three times a week for a duration of 10 min or more. Their goal was compliance within the context of unpredictable opportunity for taking a nature pill. Participants provided saliva samples before and after a NE at four points over the study period. Before-NE samples established the diurnal trajectory of each stress indicator and these were in line with published outcomes of more closely controlled experiments. For salivary cortisol, an NE produced a 21.3%/hour drop beyond that of the hormone’s 11.7% diurnal drop. The efficiency of a nature pill per time expended was greatest between 20 and 30 min, after which benefits continued to accrue, but at a reduced rate. For salivary alpha-amylase, there was a 28.1%/h drop after adjusting for its diurnal rise of 3.5%/h, but only for participants that were least active sitting or sitting with some walking. Activity type did not influence cortisol response. The methods for this adaptive management study of nature-based restoration break new ground in addressing some complexities of measuring an effective nature dose in the context of normal daily life, while bypassing the limitations of a clinical pharmacology dose–response study. The results provide a validated starting point for healthcare practitioners prescribing a nature pill to those in their care. This line of inquiry is timely in light of expanding urbanization and rising healthcare costs.
On the Correlation of CNN Performance and Hardware Metrics for Visual Inference on a Low-Cost CPU-based Platform

While providing the same functionality, the various Deep Learning software frameworks available these days do not provide similar performance when running the same network model on a particular hardware platform. On the contrary, we show that the different coding techniques and underlying acceleration libraries have a great impact on the instantaneous throughput and CPU utilization when carrying out the same inference with Caffe, OpenCV, TensorFlow and Caffe2 on an ARM Cortex-A53 multi-core processor. Direct modelling of this dissimilar performance is not practical, mainly because of the complexity and rapid evolution of the toolchains. Alternatively, we examine how the hardware resources are distinctly exploited by the frameworks. We demonstrate that there is a strong correlation between inference performance – including power consumption – and critical parameters associated with memory usage and instruction flow control. This identified correlation is a preliminary step for the development of a simple empirical model. The objective is to facilitate selection and further performance tuning among the ever-growing zoo of deep neural networks and frameworks, as well as the exploration of new network architectures.