Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-time Autonomous Trading in the Electricity-and-Heat Distribution Market Based on Blockchain

Prosumers have flexible distributed energy resources, such as renewables and storages. With high penetration of prosumers in the electricity-and-heat distribution market, we propose an autonomous trading mechanism based on the blockchain technology. The information written down in the block is the summation of transaction fees during a period. The central operator gets the summation and completes value transfer between prosumers, without knowing the detailed price and quantities of each prosumer, which greatly protects the prosumers' privacy. To fulfill the clearing process, a real-time economic dispatch problem in the electricity-and-heat distribution market is formulated and a modified primal-dual gradient method is presented to split the global economic dispatch problem into small local problems within each participant. Simulation results verify the effectiveness of the proposed distributed algorithm and demonstrate the functions of the blockchain.
Energy-Quality Scalable Memory-Frugal Feature Extraction for Always-On Deep Sub-mW Distributed Vision

In this work, an energy-quality (EQ) scalable and memory-frugal architecture for video feature extraction is introduced to reduce circuit complexity, power and silicon area. Leveraging on the inherent resiliency of vision against noise and inaccuracies, the proposed approach introduces properly selected EQ tuning knobs to reduce the energy of feature extraction at graceful quality degradation. As opposed to prior art, the proposed architecture enables the adjustment of such knobs, and adapts its cycle-level timing to reduce the amount of computation per frame at lower quality targets. As further benefit, the approach adds opportunities for energy reduction via aggressive voltage scaling. The proposed architecture mitigates the traditionally dominant area/energy of the on-chip memory by reducing the number of pixels stored on chip, introducing memory access reuse and on-the-fly computation. At the same time, EQ tuning preserves the ability to conventionally operate at maximum quality, when required by the task or the visual context. A 0.55 mm2 testchip in 40nm exhibits power down to $82~\mu \text{W}$ at 5fps frame rate (i.e., 33X lower than prior art), while assuring successful object detection at VGA resolution. To the best of the authors’ knowledge, this is the first feature extractor with sub-mW operation and sub-mm2 area, making the proposed approach well suited for tightly power-constrained and low-cost distributed vision systems (e.g., video sensor nodes).
Key-Frame based Event Recognition in Unconstrained Videos using Temporal Features

Unconstrained videos, captured by inexperienced amateurs, suffer from wide-ranging quality aspects such as poor illumination, considerable camera motion, cluttered background, occlusion and substantial viewpoint variations. Event recognition in these videos is a challenging task in the field of computer vision, serving as potential solution to several practical applications. In this paper, we propose a novel approach for recognizing events in the unconstrained videos that relies on the temporal features of the representative frames (key-frames) in each video. This graph-based key-frame extraction technique exploits the inter-frame temporal variation for the entire video and reduces temporal redundancy amongst consecutive frames. The problem is thereby formulated as a constrained optimization problem, where the temporal distinctness as well as the distance of demarcation amongst the chosen frames is maximized simultaneously. For classification, we propose a model that fuses a deep residual network with the Recurrent Neural Network (RNN) model of Long Short-Term Memory (LSTM) to capture long-term temporal motion patterns. Experimental results reveal better outcome than most of the state-of-the-art methods based on test data from Columbia Consumer Video (CCV), Kodak’s consumer video and UCF-101 benchmark datasets. The videos having high content diversity, pose as classification challenges to the research.
Robust Loop Closure Selection for Multi-robot Mapping Under Perceptual Aliasing

Robust loop closure detection is crucial in simultaneous localization and mapping (SLAM) as failure to correctly choose the loop closures can severely distort the map. This applies not only to single-robot cases but also for multi-robot systems. When initial relative poses between robots are unknown, inter-robot loop closures must be found in order to merge their local maps and produce a global map. This becomes an even more challenging issue in the presence of perceptual aliasing that arises from repetitive features and patterns in highly structured settings such as indoor environments. In this paper, we propose a robust inter-robot loop closure selection method that finds the inlier loop closure set that maximizes the consistencies between measurements. We show that this can be formulated and solved as a maximum weight clique problem in graph theory. To demonstrate the performance of the algorithm, simulation is performed using synthetic data and the result is compared to a recent method dealing with the same problem.
A Detection Method for Liver Cancer Region Based on Faster R-CNN

In recent years, liver cancer has become the fourth-largest number of deaths in the world. Surgery is a typical treatment for liver cancer. Therefore, advance information about the number and size of cancer is important for surgery. Multi-phase CT images are well known diagnostic method. By extracting the region of the liver and the region of cancer from the obtained CT image, the shape can be finally restored in 3D. In this paper, as a preliminary step to construct an image analysis method for efficiently extracting cancerous regions in multi-phase CT, we propose a method of obtaining a rectangular region as a rough cancerous region of interest. As a method, after preprocessing the input image, using Faster R-CNN, the region of interest including the cancer region is extracted as a rectangle. As a result of applying this method to 11 cases of arterial phase of multi-phase CT, the detection performance was different depending on the network model adopted for backbone part.
Experiences Designing a Multi-Tier Architecture for a Decentralized Blockchain Application in the Energy Domain

In recent years the emergence of the Ethereum Blockchain has introduced a new alternative perspective on how web applications can be build. More precisely, the Ethereum Blockchain allows the development of applications, where programming code can be executed in a decentralized manner with no restrictions imposed by a central authority. However, as it is the case with many emerging technologies, there is a fair amount of trade-offs that have to be considered when this technology is used as a platform for implementing decentralized applications. In this work we present two architectural designs for building decentralized applications (DApps) based on the Ethereum Blockchain technology. Within this context, we discuss the inherent strengths and weaknesses of each of the architectural designs as well as the set of challenges that we faced during the development process.
Delivery of High Definition Video Content over Bandwidth Constrained Links in Heterogeneous Wireless Networks

In order to deal with the fluctuations in available bandwidth that mobile users experience, modern video streaming employs adaptive bitrate (ABR) algorithms. MPEGDASH enabled clients adapt the bitrate of requested video segments to match as closely as possible the available bandwidth of the wireless link in use. If link conditions are poor or the link is congested, the ABR algorithm will select the lowest available bitrate supported by the video stream. This approach works well for standard video content since there is no restriction on using low bitrates.However, for users wishing to view High Definition (HD) content, there is a lower bound to the bitrate below which they cannot go if the content is to be considered HD. ABR algorithms, forced to maintain HD bitrates over wireless links with constrained bandwidth, experience significant numbers of stalling events. High numbers of stalling events, particularly towards the end of a video, have a serious negative impact on end-user QoE.This paper proposes Adaptive Interface Selection (AIS) to address this issue. AIS leverages the multi-homed nature of modern mobile devices and the segmented nature of video content prepared for DASH-enabled clients to overcome the bandwidth constraints that hobble ABR algorithms. AIS uses multiple wireless interfaces in parallel to download content to maintain the playout buffer level to prevent stalling events. By minimizing the number of stalling events and streaming at HD compatible bitrates end-user QoE is maintained. Downloading as little content as possible over mobile networks helps protect the end-user’s mobile data-cap.
Predicting Supervise Machine Learning Performances for Sentiment Analysis Using Contextual-Based Approaches

Sentiment Analysis (SA) is focused on mining opinion (identification and classification) from unstructured text data such as product reviews or microblogs. It is widely used for brand reviews, political campaigns, marketing analysis or gaining feedback from customers. One of the prominent approaches for SA is using supervised machine learning (SML), an algorithm that uses datasets with defined class labels based on mathematical learning from a training dataset. While the results are promising especially with in-domain sentiments, there is no guarantee the model provides the same performance against real time data due to the diversity of new data. In addition, previous studies suggest the result of SML decrease when applied to cross-domain datasets because new features are appeared in different domains. So far, studies in SA emphasise the improvement of the sentiment result whereas there is little discussion focusing on how to detect the degradation of performance for the proposed model. Therefore, we provide a method known as Contextual Analysis (CA), a mechanism that constructs a relationship between words and sources that is constructed in a tree structure identified as Hierarchical Knowledge Tree (HKT). Then, Tree Similarity Index (TSI) and Tree Differences Index (TDI), a formula generate from tree structure are proposed to find similarity as well as changes between train and actual dataset. The regression analysis of datasets reveals that there is a highly significant positive relationship between TSI and SML accuracies. As a result, the prediction model created indicated estimation error within 2.75 to 3.94 and 2.30 for 3.51 for average absolute differences. Moreover, this method also can cluster sentiment words into positive and negative without having any linguistics resources used and at the same time capturing changes of sentiment words when a new dataset is applied.
Deep Learning-based Beverage Recognition for Unmanned Vending Machines: An Empirical Study

In recent years, deep learning techniques have been commonly used in the fields of image processing and computer vision. With the popularity of deep learning models, researchers have developed many effective object detection methods. Unmanned retail applications start to utilizing object detection algorithms for changing traditional retail modes. Until now, there is no public datasets for object detection in unmanned retail application environments. Moreover, state-of-the-art deep learning-based object detection models have not yet been examined in this application scenario. In this paper, we compiled a large-scale dataset which contains over 30,000 images captured in a refrigerator equipped with different cameras. 10 kinds of beverages were utilized for targeted objects. An empirical study on this dataset is performed by using several recent developed deep learning models. Results demonstrate the effectiveness of using deep learning techniques real-life unmanned retail environments.
Spatial-Cue Based Audio Channel Extension Using Convolutional Neural Networks

In this paper, we introduce an audio channel extension tool using a spatial audio cue predicted using convolutional neural networks. The channel extension tool is applied into a common stereo signal to produce a high-dimensional audio signal, such as a 5.1 layout. To extend the channels from a stereo signal, we predict the spatial cues from the stereo signal, and synthesize the multichannel signals by allocating the spectral components according to the direction of the predicted spatial cues. Our subjective evaluation shows that a synthesized multichannel signal guarantees a high quality when compared with the original stereo signal.
D-band Transceiver Utilizing 70-nm GaAs-mHEMT Technology for FDD System

This paper presents a D-band transceiver utilizing a 70-nm GaAs metamorphic high electron mobility transistor (mHEMT) technology for a frequency division duplex (FDD) system. The transceiver includes a duplexer, transmitter and receiver modules, an LO and IF circuit board, and a real-time modem. Each module comprises a silica-based package on which D-band converter and E-band multiplier monolithic microwave integrated circuits (MMICs) are mounted using a flip-chip bonding technique. Real-time communication tests are performed at 142- and 157-GHz duplex frequencies. A 10-Gbps transmission with a high spectral efficiency are achieved using a 128 quadrature amplitude modulation (QAM) signal.
Research Issues in Energy Harvesting Internet of Things

Recent advancements in renewable energy systems, power electronics, wireless communication and Internet of Things (IoT) are combined into to a new concept known as energy harvesting IoT (EH-IoT). The new IEEE P2413 standard [1] allows the use of energy harvesting feature into the existing IoT infrastructure. The silicon solar photovoltaic (PV) cells produce energy of few milli-watts $( \sim15$ mW/cm2), which can drive sensors, low-power microcontrollers and RF transmitters for a complete self-powered wireless embedded system. In this paper, we present open research issues, possible solutions, recent technologies & present state of the art of EH-IoT. We divide the research issues into four broad areas of research in EH-IoT, i.e. sensing, computations, communications, and energy harvesting. The biggest challenge in EH-IoT is designing of suitable energy harvester circuits, dynamic power management and development of energy-efficient communication protocols. This paper is useful for researchers, and design engineers for identifying the various state of the art parameters, problems and design solutions in the field of EH-IoT.
Study of modular APF parallel combination system aiming at power quality improvement in distribution network

In order to deal with the increasingly serious power quality problem of distribution network, this paper focuses on the APF compensation scheme and a variety of parallel combination schemes are studied for the limited capacity of APF applications. Then a parallel combination scheme of improved modular APF with independent controller that can be flexibly configured with compensation coefficient is proposed. The hierarchical control strategy is designed. The first layer is the coordination control strategy between modules based on bus communication and current-sharing control. The second layer is the fine control strategy inside module based on the outer voltage loop and inner current loop, in which the inner current loop adopts the compound control structure consisting of repetitive control and PI control. Finally, the validity of proposed system and its control strategy is verified by simulation and experiment.
Knowledge Diffusion of User Innovation Community Based on BBV Weighted Network

As knowledge constitutes an important innovative force, it is significant to understand the performance of knowledge diffusion in user innovation community. In this paper, we use the model of knowledge diffusion based on BBV weighted network, which introduces the effects of different network structures, knowledge absorption capacity and the degree of interaction between community users and managers on knowledge diffusion. The results show that the BBV weighted network is easier to accelerate knowledge diffusion, and the community knowledge variance is smaller. Knowledge absorptive capacity of community users is conducive to knowledge growth, but knowledge variance increases accordingly. If the interaction between community users and managers is maintained at a moderate level, it will help to reduce the knowledge variance. In fact, if these factors are neglected, it is difficult to predict the process and results of knowledge diffusion correctly.
New GaInAs/InAs/InP Composite Channels for mm-Wave Low-Noise InP HEMTs

InP-based HEMTs with composite GaInAs/InAs channels are recognized for their high frequency performance thanks to their superior carrier transport properties. On the other hand, the addition of an InP subchannel to a GaInAs channel can effectively suppress channel impact ionization, lower the output conductance, and improve noise properties. Although the literature provides a number of reports about both separate approaches, we merge both strategies in the present study and report the first GaInAs/InAs/InP composite channel HEMTs. The influence of the InAs inset location is studied through DC, RF and noise characterization. Devices with the InAs inset located further from the top AlInAs barrier exhibit an improved minimum noise figure NFMIN due to a reduced gate leakage current. The NFMIN improvement is accompanied by a slight degradation in both the output characteristics and cutoff frequencies resulting from the increased average gate-to-2DEG separation. Our results indicate that the combination of an InAs inset and an InP subchannel opens promising HEMT design avenues for broadband low-noise applications.
Things as a Service: Service model for IoT

Leveraging the benefits of service computing technologies for Internet of Things (IoT) can help in rapid system development, composition and deployment. But due to the massive scale, computational and communication constraints, existing software service models cannot be directly applied for IoT based systems. Service discovery and composition mechanism need to be decentralized unlike majority of other service models. Moreover, IoT services’ interfaces require to be light weight and able to expose the device profile for seamless discovery onto the IoT based system infrastructure. In addition to this, the "things" data should be associated with its present context. To address these issues, this paper proposes a formal model for IoT services. The service model includes the physical property of "things" and exposes it to the user. It also associates the context with the "things" output, which in turn helps in extracting relevant information from the "things" data. To evaluate our IoT service model, a weather monitoring system and its associated services are implemented using node.js [31]. The service data is mapped to SSN ontology for generating context-rich RDF data. This way, the proposed IoT service model can expose the device profile to the user and incorporate relevant context information with the things data.
Make Page Coloring more Efficient on Slice-Based Three-Level Cache

On modern multi-core machines, page coloring has been used to alleviate the competition at Last Level Cache (LLC). However, the latest development of CPU architecture has brought new issues to page coloring. Firstly, in the case of three-level cache, previous works about page coloring did not discuss the impact on L2 cache of color allocation and the competition for L2 cache is not considered concurrently under hyper-threading. In addition, as the last level cache structure is changed from shared to slice-based and undocumented hash function is applied, page coloring is more complex and slice information is also not fully utilized. This paper presents solutions to these issues. Firstly, by making small changes to the traditional page coloring, the problem that page coloring may waste L2 cache is alleviated. At the same time, we rethink the vertical allocation of L2 cache and LLC in page coloring under hyper-threading, and discuss the impact of color allocation on programs, especially those with different sensitivity to L2 cache and LLC. Finally, we make full use of slice information and propose Partial Conflict Color (PCC). At the same time, we also propose a fast method to obtain PCC. Experiments show that using PCC can improve system performance when the number of colors is insufficient.
Classification of Motor Imagery EEG Signals with multi-input Convolutional Neural Network by augmenting STFT

Motor imagery EEG classification is a crucial task in the Brain Computer Interface (BCI) system. In this paper, we propose a Motor Imagery EEG signal classification framework based on Convolutional Neural Network (CNN) to enhance the classification accuracy. For the classification of 2 class motor imagery signals, firstly we apply Short Time Fourier Transform (STFT) on EEG time series signals to transform signals into 2D images. Next, we train our proposed multi-input convolutional neural network with feature concatenation to achieve robust classification from the images. Batch normalization is added to regularize the network. Data augmentation is used to increase samples and as a secondary regularizer. A three input CNN was proposed to feed the three channel EEG signals. In our work, the dataset of EEG signal collected from BCI Competition IV dataset 2b and dataset III of BCI Competition II were used. Experimental results show that average classification accuracy achieved was 89.19% on dataset 2b, whereas our model achieved the best performance of 97.7% accuracy for subject 7 on dataset III. We also extended our approach and explored a transfer learning based scheme with pre-trained ResNet -50 model which showed promising result. Overall, our approach showed competitive performance when compared with other methods.
Non-Destructive Tree Volume Estimation using Terrestrial Lidar Data in Teak Dominated Central Indian Forests

Tree structure parameters like diameter at breast height (DBH), height and crown descriptors are essential to evaluate tree volume and biomass in the forest ecosystem. Destructive measurement is the most accurate method to estimate tree height and volume. However, in the recent past, advancements in LiDAR technology have enabled us to look at trees with precise 3D description. The main aim of this study is to extract different tree parameters (viz., DBH, height and tree volume) from 3D terrestrial LiDAR (TLS) data and in turn evaluate its accuracy in terms of manual destructive measurements. About five trees are scanned in multi-scan mode and destructive sampling measurements are carried out. Fitting methods such as Random Sample Consensus (RANSAC) based on circle and Quantitative Structure Model (QSM) based on the cylinder is used to extract the DBH and volume respectively from 3D point clouds. The results show that TLS based variables such as DBH (R2=0.995), height (R2=0.998) and volume (R2=0.958) are in good accordance with manual measurements. On average, tree volume bias for five trees is about 5.13 % from the manual measurements. The results of the study indicate that the TLS based measurement is one of the handy methods to obtain tree parameters without destructing the tree, with equal precision.