Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep CSI Compression and Coordinated Precoding for Multicell Downlink Systems

This work proposes a deep-learning (DL) based coordinated precoder design for multicell downlink systems with rate-limited exchange of channel state information (CSI) among base-stations (BSs). Two CSI compression techniques are proposed, one based on a binarized convolutional neural network (CNN) and one based on a learned vector-quantization (VQ) codebook. The former utilizes a CNN-based CSI feature extractor to directly compute the binary feature vector that is to be exchanged with other BSs. The latter utilizes a DL-based VQ codebook to encode the CSI feature vector that is obtained at the output of the feature extractor. In both cases, each BS takes the rate-limited CSI received from other BSs as input to a precoder network that produces the normalized precoding vectors and the transmit powers using a multitask learning architecture. By using solutions of the weighted minimum mean square error (WMMSE) algorithm as the output labels, end-to-end training of both the CSI compression and transmit precoder networks is performed jointly at all BSs. By doing so, the CSI compression networks will be able to extract the CSI features that are most effective for precoder computation at the BSs. Our simulation results show that the proposed schemes can achieve weighted sum rates close to that in the full CSI scenario, even when the number of exchanged bits is small, and outperform existing random VQ methods.
Detection of Fake and Fraudulent Faces via Neural Memory Networks

Advances in computer vision have brought us to the point where we have the ability to synthesise realistic fake content. Such approaches are seen as a source of disinformation and mistrust, and pose serious concerns to governments around the world. Convolutional Neural Networks (CNNs) demonstrate encouraging results when detecting fake images that arise from the specific type of manipulation they are trained on. However, this success has not transitioned to unseen manipulation types, resulting in a significant gap in the line-of-defense. We propose a Hierarchical Attention Memory Network (HAMN), motivated by the social cognition processes of the human brain, for the detection of fake faces. Through visual cues and by utilising knowledge stored in neural memories, we allow the network to reason about the perceived face and anticipate it’s future semantic embeddings. This renders a generalisable face tampering detection framework. Experimental results demonstrate the proposed approach achieves superior performance for fake and fraudulent face detection.
Comparative termite resistance of 12 Malaysian timber species in laboratory tests

Abstract The effect of wood density and extractive compounds on heartwood durability against subterranean termites was studied. Twelve species of Malaysian hardwoods were tested against Coptotermes curvignathus and C. gestroi using a standard laboratory method. The results indicated that amount of extractive content is related to Malaysian hardwood termite durability.
A New Learning Rate Tuning For Nonlinear System Emulation

An arbitrary choice of neural emulator's learning rate adversely affects the system performances. This paper introduces a new approach to update the learning rate for a Neural Emulator (NE) of the complex processes with unknown behavior. The originality of the proposed method consists in the adaptation of the learning rate by the minimization of a cost function generated by the emulation errors. For carrying out this study, two simulation examples are considered. It is found that performances of the proposed approaches are better than those obtained in previous works.
Brain Tumor Classification Using Deep Learning

Brain tumor is a very common and destructive malignant tumor disease that leads to a shorter life if it is not diagnosed early enough. Brain tumor classification is a very critical step after detection of the tumor to be able to attain an effective treatment plan. This research paper aims to increase the level and efficiency of MRI machines in classifying brain tumors and identifying their types, using AI Algorithm, CNN and Deep Learning. We have trained our brain tumor dataset using five pre-trained models: Xception, ResNet50, InceptionV3, VGG16, and MobileNet. The F1-scores measure of unseen images were 98.75%, 98.50%, 98.00%, 97.50%, and 97.25% respectively. These accuracies have a positive impact on early detection of tumors before the tumor causes physical side effects, such as paralysis and others disabilities.
Switched Inductor Double Switch High Gain DC-DC Converter for Renewable Applications

High voltage gain DC-DC converter is a prime requirement for renewable applications, in particular for PV. Though numerous DC-DC converter is available for increasing the voltage gain, the passive elements requirement is higher which reduces the compactness, consequently, increases the cost of the system. To address this issue, a high gain DC-DC converter is reported recently. However, the number of passive elements is quite high which increases the size. To reduce the number of passive elements and maintain the same number of semiconductor devices, in this paper, a new switched inductor arrangement is proposed which is named as switched inductor double switch DC-DC converter (SL-DS-DC). Moreover, the proposed converter has a higher gain as compared to the recently reported converter. The proposed converter is analyzed in steady state and a comparative analysis is presented to prove the suitability. Finally, the proposed converter is validated experimentally.
Joint Source Channel Anytime Coding

Joint source channel coding (JSCC) is an effective technique in the non-asymptotic and low latency regime, while suffers from high error floor for sequences with high source probabilities and short block-lengths (HSP-SB). Aiming to address this issue, a joint source channel anytime coding (JSCAC) based on the anytime spatially coupled repeat-accumulate (ASC-RA) codes is presented. In the proposed JSCAC scheme, the adopted exponential distributed coupling (EDC) and partial joint expanding window decoding (PJEWD) can efficiently recover the early transmitted HSP-SB messages that are not fully corrected. Meanwhile, the updating mechanisms in the proposed PJEWD mitigate the complexity of expanding window decoding and the error propagation between the source and channel decoders, attributing to a better error performance. The proposed JSCAC is suitable for HSP-SB source transmission, which is a competitive candidate for communications with high reliability and low delay demands, such as streaming source and control applications, etc.
YOLOMuskmelon: Quest for Fruit Detection Speed and Accuracy Using Deep Learning

Fruit detection plays a vital role in harvesting robot platforms. However, complicated environment attributes such as illumination variation, occlusion, have made fruit detection a challenging task. A robust YOLOMuskmelon model that is accurate and fast was proposed to solve detection difficulties. The YOLOMuskmelon model incorporated ReLU activated ResNet43 backbone with new 2,3,4,3,2 residual block arrangement, spatial pyramid pooling(SPP), complete Intersection over Union (CIoU) loss, feature pyramid network(FPN), and distance Intersection over Union−Non Maximum Suppression(DIoU−NMS) to improve detection performance. The obtained average precision (AP) results of YOLOMuskmelon at 89.6% is greater than YOLOv3 at 82.3%, YOLOResNet50 at 85.5%, but less than YOLOv4 at 91.6%. However, the detection speed of YOLOMuskmelon at 96.3 frame per second(fps) outperformed YOLOv3 at 56.6fps, YOLOv4 at 54.1fps and YOLOResNet50 at 71.2fps. Meanwhile, the YOLOMuskmelon which is 56.1% faster than YOLOv4 model showed a better generalization and real−time fruit harvesting robots prospect.
Association Between Pain in Knee Osteoarthritis and Mechanical Properties of Soft Tissue Around Knee Joint

Pain is the most significant symptom of knee osteoarthritis (OA), which seriously affects the quality of life of patients. However, the cause of pain, pain relief, and targeted treatment methods are still unclear. Based on the diversified characteristics of knee pain location, this study was aimed to explore the associations between knee pain location and the mechanical properties of soft tissues in patients with knee OA. Fifteen healthy participants and 55 patients with knee OA were recruited and divided into five groups: no pain, anterior superior pain, anterior inferior pain, medial pain, and lateral pain. The Young’s moduli of the muscles, tendons, and ligaments around the knee joint were measured by shear wave elastography (SWE), and the differences between the five groups of soft tissue material properties were compared in this study. The Young’s moduli of the quadriceps tendon (QT), patellar tendon (PT), medial collateral ligament (MCL), and lateral patellar retinaculum (LPR) were strongly associated with pain location and were significantly increased compared with the other four groups, respectively. In addition, the Young’s moduli of the remaining soft tissue were not associated with pain location, and did not change significantly compared with the other four groups. This study provides data reference for the mechanical properties of soft tissue of healthy people and patients with knee OA, and provides a theoretical basis for more targeted pain relief, treatment, and rehabilitation in knee OA.
News Influence Model Based on Neural Network

Network news is an important carrier of information spread in the digital society, and its spread mode and influence calculation are the main research directions. The existing models of news influence have some problems, such as incomplete indexes, artificial selection of parameters, and insufficient flexibility of models, which cannot describe the breadth and depth of news influence well. With the help of BP neural network's good nonlinear mapping ability and characteristic of flexible network structure, in this paper, a news influence model was established with four indexes: average number of news source readings, news type, number of user behavior stabilization day's comments and number of user behavior stabilization day's readings. This model fully considered the breadth and the depth of news spread, the quality of news source, the field of news topic and time factor, which has a good ability to describe news influence. At the same time, this model was tested on the real news data set (the last week of July 2019 on Sohu, China). Compared with the existing method, its accuracy rate has increased by 9.17%, which fully shows the rationality and effectiveness of this model.
Balancing Quality of Experience and Traffic Volume in Adaptive Bitrate Streaming

Adaptive bitrate (ABR) streaming services have spread with advances in the codec, video streaming, and network technologies. For smooth video playback in ABR streaming services, a video player runs an ABR algorithm, which dynamically adjusts the bitrate of video data on the basis of the statuses of the network and player. Existing ABR algorithms calculate a suitable bitrate to maximize the quality of experience (QoE). However, providing a high-QoE video increases network investment costs and content delivery network (CDN) usage fees. According to a survey conducted by the Streaming Video Alliance, mobile users prefer low-traffic videos to high-QoE videos. To reduce traffic volume, commercial video-streaming services enable users to set an upper limit of the bitrate. However, this cannot always achieve the required QoE because they cannot select a high bitrate even when the communication environment improves during viewing. In this paper, we propose BANQUET, a novel ABR algorithm that can reduce the traffic volume while maintaining QoE above the target QoE. The target QoE can be set by users or streaming providers considering user’s preferences or CDN budget. BANQUET selects a suitable bitrate by estimating QoE and traffic volume that will be experienced by all the bitrate patterns for the next several chunks on the basis of future throughput and a buffer transition calculation. The trace-based simulation showed that BANQUET reduces traffic volume 18.3%–51.2% on average in the mobile environment and 1.2%–38.3% in the broadband environment while maintaining QoE the same as or better than existing algorithms.
An ICN-based Approach for Service Caching in Edge/Fog Environments

Edge and Fog computing represent today a realistic alternative to traditional cloud data centers in order to support data-intensive and time-sensitive applications, such as those laying under the Internet of Things (IoT) umbrella. One of the main problems associated to this is the service placement problem, or how to efficiently manage the available computing and storage resources while deploying the plethora of application services, to be made available to clients, at the network edge/fog? Due to the similarities shared by this problem and the traditional data caching problem, multiple researches started looking at the adaptation of the Information-Centric Networking (ICN) paradigm to answer the aforementioned question, giving birth to what we depict as an ICN-Edge/Fog architecture. Leveraging such an architecture, we propose in this paper a novel service caching strategy, called 3Q, that is the first to argue on the caching of both, the service instances, consuming computing resources, as well as their associated source codes, consuming storage resources. Being characterized by its low-complexity and its low-overhead, 3Q proves to achieve near-optimal results in terms of cached services hits, latency and cloud usage.
Partially Observable Multi-Agent Deep Reinforcement Learning for Cognitive Resource Management

In this paper, the problem of dynamic resource management in a cognitive radio network (CRN) with multiple primary users (PUs), multiple secondary users (SUs), and multiple channels is investigated. An optimization problem is formulated as a multi-agent partially observable Markov decision process (POMDP) problem in a dynamic and not fully observable environment. We consider using deep reinforcement learning (DRL) to address this problem. Based on the channel occupancy of PUs, a multi-agent deep Q-network (DQN)-based dynamic joint spectrum access and mode selection (SAMS) scheme is proposed for the SUs in the partially observable environment. The current observation of each SU is mapped to a suitable action. Each secondary user (SU) takes its own decision without exchanging information with other SUs. It seeks to maximize the total sum rate. Simulation results verify the effectiveness of our proposed schemes.
Classification of Remote Sensing Images Using EfficientNet-B3 CNN Model With Attention

Scene classification is a highly useful task in Remote Sensing (RS) applications. Many efforts have been made to improve the accuracy of RS scene classification. Scene classification is a challenging problem, especially for large datasets with tens of thousands of images with a large number of classes and taken under different circumstances. One problem that is observed in scene classification is the fact that for a given scene, only one part of it indicates which class it belongs to, whereas the other parts are either irrelevant or they actually tend to belong to another class. To address this issue, this paper proposes a deep attention Convolutional Neural Network (CNN) for scene classification in remote sensing. CNN models use successive convolutional layers to learn feature maps from larger and larger regions (or receptive fields) of the scene. The attention mechanism computes a new feature map as a weighted average of these original feature maps. In particular, we propose a solution, named EfficientNet-B3-Attn-2, based on the pre-trained EfficientNet-B3 CNN enhanced with an attention mechanism. A dedicated branch is added to layer 262 of the network, to compute the required weights. These weights are learned automatically by training the whole CNN model end-to-end using the backpropagation algorithm. In this way, the network learns to emphasize important regions of the scene and suppress the regions that are irrelevant to the classification. We tested the proposed EfficientNet-B3-Attn-2 on six popular remote sensing datasets, namely UC Merced, KSA, OPTIMAL-31, RSSCN7, WHU-RS19, and AID datasets, showing its strong capabilities in classifying RS scenes.
Path Tracking and Handling Stability Control Strategy With Collision Avoidance for the Autonomous Vehicle Under Extreme Conditions

A novel control strategy is proposed for the autonomous vehicle equipped with the front and rear independent steering systems to enhance the capability of collision avoidance and improve path tracking and handling stability under critical maneuvers. The novel control system includes a path replanning controller, a path tracking controller, and a stability controller. The path replanning controller is designed to generate a collision-free trajectory by optimizing a cost function. The cost function can be updated quickly and iteratively, according to the dynamic fitting matrix and measurement signals. The path tracking controller and the stability controller with considering multi-constraints are designed to generate the steering angle of the front wheel and the rear wheel, respectively, which improve the ability of path tracking and handling stability of the autonomous vehicle under extreme conditions. The real-time effectiveness of the proposed control strategy is verified and evaluated in Hardware-in-the-loop (HIL) simulation environment. The function of collision avoidance of the control algorithm is verified in the scenario of the preset obstacle artificially on the globally planned path. The proposed control strategy, presents superior performance against two other control strategies based on model predictive control algorithm, and also exhibits satisfactory performance in terms of path tracking as well as handling stability of the autonomous vehicle.
Structural and spectroscopic characterization of dioxovanadium(V) complexes with asymmetric Schiff base ligands

A wide range of asymmetric Schiff base ligands containing primary amine functions can be prepared in one step and in high yield by the reaction of VO(OEt) 3 with salicylaldehyde and the appropriate diamine. The resulting VO 2 L complexes contain tridentate ligands with phenolate, imine, and amine coordination to the dioxovanadium(V) complex ion. An X-ray structure of one compound ([VO 2 (1,2-pnSAL), 5A, where 1,2-pnSAL=1(N-salicylideneamina)-2-aminopropane) demonstrates that it is a dimer in the solid state with a V 2 O 4 score
A Scalable Hardware Accelerator for Mobile DNA Sequencing

DNA sequencers are being miniaturized and increasingly targeted toward mobile applications. However, the intense bioinformatic computing needs of sequencers present a challenge for remote use with limited energy supply. This article presents a step toward realizing a low-power and high-speed bioinformatic engine, a hardware-accelerated basecaller, for mobile sequencing applications. The design is targeted for nanopore-based sequencers and is architected to easily scale to the complexity of this sensor. In addition to accelerating the CPU in real time with a custom field-programmable gate array (FPGA) through a high-speed serial link, the proposed framework envisages the challenging memory requirement of high-order nanopore sensors. The framework proposes a memory management scheme, which provisions the memory requirement problem in three dimensions: the basecalling speed, the circuit’s area, and power consumption. The implementation results demonstrate a $142\times $ basecalling speed improvement over a 12-core CPU-only reference, as well as significant speedup compared with other existing solutions. Also, an energy efficiency improvement of three orders of magnitude is measured.
EnerGAN++: A Generative Adversarial Gated Recurrent Network for Robust Energy Disaggregation

Energy disaggregation, namely the separation of the aggregated household energy consumption signal into its additive sub-components, bears resemblance to the signal (source) separation problem and poses several challenges, not only as an ill-posed problem, but also, due to unsteady appliance signatures, abnormal behaviour that is usually detected in appliances operation and the existence of noise in the aggregated signal. In this paper, we propose EnerGAN++, a model based on Generative Adversarial Networks (GAN) for robust energy disaggregation. We attempt to unify the autoencoder (AE) and GAN architectures into a single framework, in which the autoencoder achieves a non-linear power signal source separation. EnerGAN++ is trained adversarially using a novel discriminator, to enhance robustness to noise. The discriminator performs sequence classification, using a recurrent convolutional neural network to handle the temporal dynamics of an appliance energy consumption time series. In particular, the proposed architecture of the discriminator leverages the ability of Convolutional Neural Networks (CNN) in rapid processing and optimal feature extraction, among with the need to infer the data temporal character and time dependence. Experimental results indicate the proposed method’s superiority compared to the current state of the art.
Superpixel-Based Local Features for Image Matching

Image matching is the research basis of many computer vision problems, such as intelligent driving, object recognition and structure from motion. However, the traditional feature-based image-matching results are usually very sparse and unevenly distributed for wide baseline or weakly textured images. Implementing an efficient and robust image-matching technology is a challenging task. To solve these problems, we propose an efficient extractor and binary descriptor based on superpixels and a modified binary robust independent elementary features (BRIEF) descriptor called FSRB. FSRB can improve the computational efficiency, number of matches, feature distribution and robustness of feature-based image matching. In theory, FSRB is rotation-, scale-, affine-, distorted-, and intensity-invariant. A comprehensive performance evaluation of FSRB is performed. The experimental results show that our method can effectively obtain many matches for different types of images. Compared with state-of-the-art algorithms, our method performed very well in terms of the number of correct matches (which increased by 2–5 times), time consumption, matching accuracy, matching success rate and feature repetition rate. In addition, our method is applied to sparse 3D reconstruction of multiview images, and satisfactory results are obtained.
OPTIMA: An Approach for Online Management of Cache Approximation Levels in Approximate Processing Systems

In this article, we present an approach for adjusting the approximation levels of the cache memories in the memory hierarchy of an approximate processing system. The technique, which is called online management of cache approximation level (OPTIMA), adjusts the approximation levels of the caches under a predefined accuracy constraint. OPTIMA may also be employed for multicore processors, which comprise cores with private and shared caches running applications with different error constraints. To reduce the energy consumption, OPTIMA determines the proper approximation level of each cache memory using heuristic algorithms in two main steps. In the first step, the approximate levels are adjusted to maximize the power efficiency by dropping the application accuracy to a level that still meets a desirable minimum output quality. In the second step, output accuracy variations due to input pattern changes are compensated by fine tuning. We suggest two algorithms (with different adjustment speeds of approximate levels) for the first step and another algorithm for the second step. To assess the efficacy of OPTIMA, we integrate it in the gem5 simulator and simulate some multiprocessor configurations by running eight approximate benchmarks. The results show that the proposed approach provides up to 44% power consumption reduction in the memory hierarchy.