Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Deep Learning to Identify Potential Roof Spaces for Solar Panels

Solar photovoltaic (PV) installation businesses frequently encounter problems with lead generation. A commonly used approach to identify credible customers involves cold-calling contacts from a purchased database containing very limited information or information that is inaccurate, out of date, and doesn't identify whether the building already has solar PV installed. This process of contacting potential customers, therefore, is often time-consuming, cost-ineffective, and inefficient, which results in increased costs for customers to account for these limitations. The objective of the current research project is to propose a method of automating this industry problem by applying Deep Neural Networks (DNNs). A Semantic Segmentation Network (SegNet) will be utilized, with a database of satellite images and corresponding pixel label images. The SegN et will seek to identify buildings from satellite imagery, and to in turn identify whether buildings have pre-existing solar installations, using a cascaded Convolutional Neural Network (CNN). Transfer learning on the CNN will fine-tune the network to classify roofs of buildings into two categories of having solar PV installed and not having solar PV installed. The CNN will be trained and tested on separate augmented databases to improve the classification accuracy with the output of the system recording a database of buildings without solar PV installed. By automating what was previously a time-consuming manual process, the savings incurred can be passed onto customers. Results of the current project demonstrate successful segmentation of buildings and identification of pre-existing solar PV installations. Implications of results are discussed.
An Energy-efficient Reconfigurable Hybrid DNN Architecture for Speech Recognition with Approximate Computing

This paper proposes an hybrid deep neural network (DNN) for speech recognition and an energy-efficient reconfigurable architecture with approximate computing for accelerating the DNN. The hybrid DNN consists of two network models: a binary weight network (BWN) for twenty key words recognition; a recurrent neural network (RNN) for processing acoustic model of high precision common words recognition. To accelerate the hybrid DNN and reduce the energy cost, we propose a digital-analog mixed reconfigurable architecture with approximate computing units, including: a BWN accelerator with analog multi-chain delay-addition units for bit-wise approximate computing, and a RNN accelerator with approximate multiplication units for different calculation accuracy requirements. Implementation and simulation with TSMC 28nm HPC+ process technology, the energy efficiency of proposed architecture can achieves 163.8TOPS/W for twenty key words recognition and 3.3TOPS/W for common words recognition. Comparing with State-of-the-Art architectures, this work achieves over 1.7X better in energy efficiency with approximate computing.
A Comparative Analysis of Word Embedding Representations in Authorship Attribution of Bengali Literature

Word Embeddings can be used by deep layers of neural networks to extract features from them to learn stylo-metric patterns of authors based on context and co-occurrence of the words in the field of Authorship Attribution. In this paper, we investigate the effects of different types of word embeddings in Authorship Attribution of Bengali Literature, specifically the skip-gram and continuous-bag-of-words(CBOW) models generated by Word2Vec and fastText along with the word vectors generated by Glove. We experiment with dense neural network models, such as the convolutional and recurrent neural networks and analyse how different word embedding models effect the performance of the classifiers and discuss their properties in this classification task of Authorship Attribution of Bengali Literature. The experiments are performed on a data set we prepared, consisting of 2400 on-line blog articles from 6 authors of recent times.
A Comparative Analysis of DAG-Based Blockchain Architectures

Blockchain is a shared distributed ledger that promises tamper-proof secure transactions over the highly available and resilient network involving multiple participants. Directed Acyclic Graph (DAG) has revolutionized the blockchain technology. Owing to its optimized validation mechanism, high scalability, efficient provenance, support for IoT and multiparty involvement, DAG is rapidly over-shadowing traditional blockchain architecture. In this paper, we present a comparative analysis of most popular DAG based blockchain architectures including Nxt, IOTA, Orumesh, DagCoin,Byteball, Nano and XDAG. The comparison is based on the functional data structures for maintaining the ledger, consensus algorithms, transaction validation, ledger size, scalability and popularity. Extracting the best features various DAG based blockchains, we move on to outline the best of all worlds DAG-based blockchain architecture.
Linearized Augmented Lagrangian Method For Sparse Solution Of Underdetermined Linear Equations

In this paper, we consider to find the sparest solution of the large underdetermined system of linear equations $y =Ax$ with nonnegative constraint $x \geq 0$. Such problems are frequently encountered in signal and image processing, in handling of multispectral data, considering nonnegative factorization for recognition. Based on the classical augmented Lagrangian method, we propose an efficient linearized augmented Lagrangian method for this problem. The convergence theorem of the proposed method is given. Experimental results are given to illustrate feasibility and effectiveness of our method.
A Proposed Algorithm and Architecture for Automated Meeting Scheduling and Document Management

Meeting is an important part of our professional life to share and discuss issues based on multiple confidential documents. There are different types of approaches, methods and techniques that are used in organizations for meeting management to track these meetings and store all the meeting files accordingly. In this research work, a new idea of meeting scheduling system is proposed. The objective of this work is to propose two simple algorithms that will detect the conflict of time among the attendees of the meeting and automatically schedule a meeting by using the necessary information. The algorithm is tested in web-based technology. The system is designed to be a secure system to preserve all the documents related to the meeting. In the proposed system, firstly, a meeting list is maintained in a sorted way which helps to schedule meetings without conflict of time. In case of conflict of time, the system proposes available time automatically. Secondly, the option to upload files of the meetings is provided with the security of the files through encoding and decoding. The system is tested with satisfaction and implemented in the university for the use.
Modeling and Evaluation of Pre-Copy Live VM Migration Using Probabilistic Model Checking

The proliferation of cloud technologies hosting complex applications has taken availability, scalability and affordabil-ity of IT services to the next level. Live VM migration is one of the core tools that automates the process of workload transfer without interfering with the applications running in dynamic cloud environments. In this paper we propose and implement a probabilistic model checking framework to study live VM migration process. In particular, we modeled well-known precopy live migration approach and evaluated the influence of various system parameters on the performance of live migration. The proposed implementation can be used as a stepping stone for modeling more complex live migration scenarios, and it can be a very beneficial tool for cloud service providers in predetermining various critical performance parameters of their cloud data centers in relation to the optimized utilization of the valuable IT resources.
Future Networks 2030: Architecture & Requirements

New technologies are approaching with big footsteps. In the decades ahead it is expected significant changes in technologies regarding to new types of devices, system and functions that they perform. We are expecting new services with various requirements, and new applications such as holographic telepresence, instant data transmission, remote surgery, minimized IoT terminals, autonomous transportation system, etc. Wide adaptation of new applications depends on how good they are supported by the network infrastructure. That is why the estimation of next generation network architecture is required. By the year 2030 the applications of new technologies are expected to generate an enormous amount of traffic. The architecture of the future networks must consider all the gaps of already existing networks in order to ensure the highest robustness, extremely low latency, Ubiquitous coverage as new applications and services require.
Mask R-CNN and Optical Flow Based Method for Detection and Marking of Handball Actions

To build a successful supervised learning model for action recognition a large amount of training data needs to be labeled first. Labeling is normally done manually and it is a tedious and time-consuming task, especially in the case of video footage, when each individual athlete performing a given action should be labeled. To minimize the manual labor, we propose a Mask R-CNN and Optical flow based method to determine the active players who perform a given action among all players presented on the scene. The Mask R-CNN is a deep learning object recognition method used for player detection and optical flow measures player activity. Combining both methods ensures tracking and labeling of active players in handball video sequences. The method was successfully tested on a dataset of handball practice videos recorded in the wild.
Approximate On-chip Memory Optimization Method For Deep Residual Networks

Approximate memory is a significant improvement in a variety of application domains, especially which are both computationally intensive and memory intensive just as image classification. In this paper, we proposed an approximate on-chip memory optimization method with bit-width scaling and random bit flipping suited the deep residual neural network, which can be used to take the image classification. We evaluate the power and area benefits of our method. It demonstrated about 66.51% improvement in memory power and 56.93% improvement in memory areas with 0.91% quality loss for ResNet-50 across ImageNet. We also use this method for ResNet-101 and ResNet152, and achieve similar results.
Performance Analysis of Compressive Sensing Based Physical Layer Authentication for AMI

Advanced metering infrastructure (AMI) systems play a crucial role in smart grid, where smart meters have two-way communications to remotely send meter readings up on request. While wireless technologies are highly desirable for flexible, adaptable, and cost-effective smart metering deployments, there are a number of issues to be addressed in terms of security and privacy. In this paper, we study a compressive sensing (CS) based physical layer authentication scheme over a multicarrier system and analyze its security performance against impersonation attack. In particular, we derive conditional distributions for the hypothesis testing used in authentication. From them, we are able to guarantee security performances in terms of the probabilities of false alarm and authentication error.
Deep Transfer Learning for Power Substation Recognition with Google Earth

Power substation is a vital component of power transmission systems. However, their location information, longitude and latitude, maybe incorrect due to human errors. For example, an employee of a power grid company may carelessly input wrong location information to the system database. In practice, these errors residue in database and are difficult to be corrected or even unaware of because human naturally are insensitive to these long numbers. Correct location information is critical and it will ensure destroyed power substations to be quickly pinpointed and repaired after earthquake. In this paper, we develop a deep transfer learning approach to automatically verify power substation location information based upon Google Earth (GE)images. We first extract an image patch for a substation from GE using the recorded location information in database. We then apply the deep transfer learning model to verify whether the image patch contains a power substation. We validated the proposed method on more than 1000 substations in Yunnan Electric Power Grid. Our experimental results showed that the Xception deep transfer model achieved the best accuracy of 98.5% among four compared machine learning methods with an image size of 299⨯299 pixels. The developed model is a promising tool to verify the location information of power substations in database.
Performance Analysis of MICS-Based RF Wireless Power Transfer System for Implantable Medical Devices

In medical applications, implant devices are used to measure and remotely transmit the human biological signals to off-body devices. To date, providing the implantable medical devices (IMDs) with a constant and perpetual energy source remains an ongoing challenge. Accordingly, a far-field radio-frequency powering, represented by an access point (AP), in conjunction with energy-harvesting capability is deployed in this paper for continuous powering of the IMDs. In this respect, theoretical analysis is used to establish safe powering conditions in order to comply with the safety limits established by the Federal Communications Commission. The feasibility of the wireless power transfer to the IMDs is investigated by deriving the analytical closed-form expressions for outage probability and average harvested energy, both of which are validated with Monte Carlo simulations. The findings of this paper suggest not to exceed a distance of 0.5 m between the AP and the body surface, as the system performance has experienced high outage probability beyond this value, while the minimum allowable distance is 17 cm at a powering frequency of 403 MHz. It is also presented that the AP should be equipped with a minimum transmit power of 0.4 W in order to maintain an outage probability for the energy harvesting to be less than 10−1.
Activations Quantization for Compact Neural Networks

As optimizing neural networks to improve processing speed has become a necessary process in industrial deployment, emerging challenges due to limited storage and computation when adopting neural networks on embedded system need to be addressed. There are some previous studies focusing on training neural networks with low precision weights and activations, like Binarized neural network and DoReFa-Net. Meanwhile, an alternative approach is deployed to reduce number of operations and model size, like MobileNet and SqueezeNet. In this work, by adopting SQ(Scale Quantization) method to quantify activations of each neuron, we have gained a more satisfying result especially in compact network models. For SqueezeDet on KITTI dataset, SQ achieves 22.86% more accuracy on means correct detections, while means location and classification error decrease by 41.84% and 68.57%, compared with DoReFa-Net with 2-bits activations. Additionally, we also propose the SQT(Scale Quantization with Threshold) method to further improve the quantization flexibility. It is worth mentioned that our approach is primarily designed for activations quantization, but it can also be complemented with other orthogonal methods to further achieving efficient neural network processing at the edge.
Fusion of Infrared and Visible Images Based on Multi-Scale Edge-Preserving Decomposition and Sparse Representation

In this paper, a novel image fusion method based on multi-scale edge-preserving decomposition and sparse representation is proposed. Firstly, the guided filter is used to decompose the source images into a base layer, and a series of edge and textural detail layers. Then, the base layers are fused based on the sparse representation based method. While, the edge and texture detail layers are merged by the weighted averaging fusion method, the fused weights are constructed by the corresponding edge layer. Experimental results show that the proposed method can highlight the infrared objects while preserving as much textural detail information as possible, which is superior to the existing image fusion methods.
Enabling Application Independent Redundancy by Using Software Defined Networking

In critical infrastructures such as Smart Grids, reliability is crucial. Traditionally, this is achieved via redundant devices and switch-over functionality. Existing solutions often handle redundancy on the application layer, which can be complex to install and maintain. This paper presents a novel and generic approach to provide redundancy for Smart Grids via software defined networking (SDN). Existing approaches of utilizing SDN in Smart Grids mainly focus on replacing technologies like Multiprotocol Label Switching (MPLS) and Virtual Local Area Networks (VLANs) to reduce configuration effort and increase network reliability. The proposed concept however aims to simplify control infrastructure redundancy setup and to increase flexibility. This is achieved by moving the redundancy logic from the application to the network, creating an application-agnostic solution to manage redundancy transparently across communication layers. Especially in scenarios with interruption-tolerant application-layer protocols and policies, benefits like reduced costs and additional configuration options are expected.
Angle Tracking Adaptive Backstepping Control for a Mechanism of Pneumatic Muscle Actuators via an AESO

This paper presents an adaptive backstepping controller for a mechanism of pneumatic muscle actuators via an adaptive extended state observer. A dynamic model of the mechanism is established with two unknown parameters estimated by using adaptive laws. An adaptive extended state observer is established to estimate total disturbances and states of the mechanism. Moreover, adaptive extended state observer gains are obtained by adaptive laws and parallel D-eigenvalues, whose time-varying multiplier $n$th-order derivatives are derived by tracking differentiators. Finally, a nonlinear adaptive backstepping controller is designed and the effectiveness of the proposed method is expressed by experimental results.
Flexible Low-Voltage IGZO Thin-Film Transistors With Polymer Electret Gate Dielectrics on Paper Substrates

Polymer electret shows a good electrostatic polarization effect, resulting in very high-density charge accumulation in semiconducting channel layer at low gate voltage. In this letter, graphene oxide enhanced poly(vinyl alcohol) film was used as the gate dielectric for flexible low-voltage indium-gallium-zinc-oxide thin-film transistors (TFTs) on paper substrates. High performance with a high current ON/OFF ratio of <inline-formula> <tex-math notation="LaTeX">$\sim 1.8\times 10^{\textsf {7}}$ </tex-math></inline-formula>, a large field-effect mobility of ~42 cm<sup>2</sup> V<sup>−1</sup> s<sup>−1</sup>, and a low subthreshold swing of 106 mV/decade was obtained. In addition, such flexible TFTs exhibit a good stability under bending condition due to the reinforcement effect of graphene oxide. Such flexible TFTs on paper substrates have potential application in next-generation low-cost flexible paper electronics.
An Accurate and Calibration-free Approach for RSS-based WiFi Localization

We propose an accurate and calibration-free WiFi localization approach using received signal strength (RSS) to mitigate the impact of RSS variations caused by changing environment and heterogeneous hardware. In the online phase, we first divide a positioning area into G grid points and index each grid by a label. All the indexes of grid points form a label set. We collect labeled RSS fingerprints to construct an offline database at all grid points. Then, we first construct a supporting set (SS), which is a subset of the label set, selected by the similarity between the online RSS sample and offline database. So, SS is a latent space that likely includes the true label (location) of the user. We then derive an expectation maximization (EM) algorithm by incorporating the fingerprint quality into the estimation of the true label. Furthermore, we propose an optimal size selection (OSS) algorithm using bayesian information criterion (BIC) to adaptively determine the size of SS. Experimental results verify that the proposed approach performs significantly better than other existing methods.
Chromosome Classification with Convolutional Neural Network Based Deep Learning

Karyotyping plays a crucial role in genetic disorder diagnosis. Currently Karyotyping requires considerable manual efforts, domain expertise and experience, and is very time consuming. Automating the karyotyping process has been an important and popular task. This study focuses on classification of chromosomes into 23 types, a step towards fully automatic karyotyping. This study proposes a convolutional neural network (CNN) based deep learning network to automatically classify chromosomes. The proposed method was trained and tested on a dataset containing 10304 chromosome images, and was further tested on a dataset containing 4830 chromosomes. The proposed method achieved an accuracy of 92.5%, outperforming three other methods appeared in the literature. To investigate how applicable the proposed method is to the doctors, a metric named proportion of well classified karyotype was also designed. An result of 91.3% was achieved on this metric, indicating that the proposed classification method could be used to aid doctors in genetic disorder diagnosis.
A Dual Circular-Polarized Extremely Thin Monopulse Feeder at W-Band for Prime Focus Reflector Antenna

A thin monopulse feeder is very important in a radar system with strict profile limitation. Besides, to implement polarization-agile radar, a monopulse feeder with two orthogonal polarizations is required to synthesize an arbitrary polarization. To achieve these requirements, a dual circular-polarized extremely thin monopulse feeder at W-band is investigated and realized for a prime focus reflector antenna, which can be used in a tri-mode seeker. To begin with, two kinds of the 2 × 2 substrate integrated waveguide (SIW) slot antenna array topologies are discussed based on the standard of circular polarization efficiency. After that, the better one integrated with a compact and single-layer SIW comparator is used to feed the reflector antenna. The total thickness of the developed feeder is only 0.56 mm. For a prime focus 140 mm diameter reflector antenna with the blockage of a diameter of 40 mm, the measured gain of the entire antenna system is 35.6 dBi at 94 GHz. The DIFF-beam null depth is −20.1 dB in xoz plane and −20.5 dB in yoz plane. The gain ratios of the SUM- and DIFF-pattern are 3.5 dB in xoz plane and 4 dB in yoz plane. The results show that this type of antenna has great prospects in a monopulse tracking system with polarization agile function.
Generative Adversarial Networks with Dense Connection for Optical Coherence Tomography Images Denoising

Optical coherence tomography (OCT) is widely used in the diagnosis of ophthalmic diseases. However, OCT is affected by ubiquitous speckle noise which make it difficult to analysis the retinal structures. To efficiently remove the noise as well as preserve clinical detail information contained in the images, we suggest to train a denoise generative adversarial network (DNGAN) jointly with a densely connected convolutional network to estimate clean OCT images from noisy OCT images. A generator convolutional neural network (CNN) with several dense connections, is trained to transform noisy OCT image into clean OCT image. At the same time, an adversarial CNN is trained to improve the denoising performance of the generator. The experimental results demonstrate the superior performance of our network on OCT images.