Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Energy Efficiency of Multi-Cell Massive MIMO With Beamforming Training

This paper is concerned with a multi-cell downlink (DL) massive multiple-input multiple-output (MIMO) system operating over spatially correlated Rician fading channels. We not only consider estimating channel state information (CSI) at the base station (BS), but also adopt beamforming training (BT) to obtain CSI at the users. With maximum-ratio transmission (MRT) or zero-forcing (ZF) employed at the BS to process the transmit signals, the paper derives closed-form expressions of the sum spectral efficiency for both cases: with and without BT. Based on the obtained closed-form expressions, we investigate the effect of the DL pilot length on the system performance for MRT and ZF precoding and with or without BT. Moreover, when the DL pilot length falls within different ranges, we find out whether using BT leads to better system performance. To address the energy efficiency (EE) maximization problem under the constraints of a given sum spectral efficiency and a maximum total DL transmit power, we transform the problem into a geometric program (GP), which can be solved more efficiently. In particular, we develop one iterative power allocation algorithm for the system with BT scheme. Simulation and numerical results demonstrate that the proposed power allocation algorithm can improve the system EE. Numerical results also show that when the BS uses MRT precoding, the sum spectral efficiency in the high signal-to-noise ratio (SNR) region is much improved with BT.
FYO: A Novel Multimodal Vein Database With Palmar, Dorsal and Wrist Biometrics

Multimodal biometric systems are preferred as a defense compared to unimodal systems. This study introduces an open access multimodal vein database named FYO with each letter dedicated to each author’s name. The database involves three biometric traits; palm vein, dorsal vein and wrist vein of the same individuals, to explore and enhance research in the area of using these traits to create a spoof-proof multimodal authentication system. The vein images of FYO are acquired using medical vein finder in a controlled environment. Comparisons are performed to show the differences with the existing well known databases and the state-of-the-art recognition algorithms. Hand-crafted feature extractors such as Binarized Statistical Image Features (BSIF), Gabor filter and Histogram of Oriented Gradients (HOG) are applied to show the viability of the vein datasets. Additionally, a deep learning based Convolutional Neural Networks (CNN) architecture is proposed with two models using decision-level fusion of palmar, dorsal and wrist biometric traits on vein images. Unimodal systems, multimodal systems and the proposed architecture are tested on several vein datasets including palmar, dorsal and wrist vein images. Experimental results based on accuracy and computation time on our FYO datasets showed competitive output with that of other databases such as Tongji Contactless Palm Vein database, VERA, PUT, Badawi and Bosphorus hand vein databases. Moreover, the proposed CNN architecture on three vein biometric traits show superior performance compared to hand-crafted methods.
Accident Detection & Rescue Information System for vehicles

The rapidly changing technology and substructure made our lives easier. The technology has arrival to ease the human life, also increasing the traffic hazards at the road accident take placed, many times which causes loss of life cause of the poor urgency facility. This research work will provide the optimum solution for the said problem. The information about detection of vehicle accident and send the location information of the accident place to vehicle owner rescue team and family members of the vehicle owner. Notifications were sent to users through web application, mobile application or SMS.
Intention Detection Based on Siamese Neural Network With Triplet Loss

Understanding the user’s intention is an essential task for the spoken language understanding (SLU) module in the dialogue system, which further illustrates vital information for managing and generating future action and response. In this paper, we propose a triplet training framework based on the multiclass classification approach to conduct the training for the intention detection task. Precisely, we utilize a Siamese neural network architecture with metric learning to construct a robust and discriminative utterance feature embedding model. We modified the RMCNN model and fine-tuned BERT model as Siamese encoders to train utterance triplets from different semantic aspects. The triplet loss can effectively distinguish the details of two input data by learning a mapping from sequence utterances to a compact Euclidean space. After generating the mapping, the intention detection task can be easily implemented using standard techniques with pre-trained embeddings as feature vectors. Besides, we use the fusion strategy to enhance utterance feature representation in the downstream of intention detection task. We conduct experiments on several benchmark datasets of intention detection task: Snips dataset, ATIS dataset, Facebook multilingual task-oriented datasets, Daily Dialogue dataset, and MRDA dataset. The results illustrate that the proposed method can effectively improve the recognition performance of these datasets and achieves new state-of-the-art results on single-turn task-oriented datasets (Snips dataset, Facebook dataset), and a multi-turn dataset (Daily Dialogue dataset).
Advanced Energy-Efficient Computation Offloading Using Deep Reinforcement Learning in MTC Edge Computing

Mobile edge computing (MEC) supports the internet of things (IoT) by leveraging computation offloading. It minimizes the delay and consequently reduces the energy consumption of the IoT devices. However, the consideration of static communication mode in most of the recent work, despite varying network dynamics and resource diversity, is the main limitation. An energy-efficient computation offloading method using deep reinforcement learning (DRL) is proposed. Both delay-tolerant and non-delay tolerant scenarios are considered using capillary machine type communication (MTC). Depending upon the type of service, an intelligent MTC edge server using DRL decides either process the incoming request at the MTC edge server or sends it to the cloud server. To control communication, we draft a markov decision problem (MDP). This minimizes the long-term power consumption of the system. The formulation of the optimization problem is considered under the constraint of computing power resources and delays. Simulation results delineate the significant performance gain of 12% in computation offloading through the proposed DRL approach. The effectiveness and superiority of the proposed model are compared with other baselines and are demonstrated numerically.
PACFR: Pixel Arithmetic Computation based Face Recognition

The face recognition approach plays an important role in electronic security systems. In this paper, we propose Pixel Arithmetic Computation based Face Recognition (PACFR). The color face images are changed into grey scale images and also the different sizes of face images to $ 90 \times 90$ size in pre-processing unit. The novel concept of Pixel Arithmetic Computation (PAC) is introduced for sharp representation of edges and extracts more stable features for local changes in images. The pre-processed image is segmented into overlapped $3\times 3$ matrices. The PAC technique I is used to extract sharp edge information using arithmetic operations on pixel values of $3\times 3$ matrix The middle pixels of borders of $3\times 3$ matrix are considered and multiplied by power of two in PAC technique II to enhance pixel values. The final features are obtained by concatenative PAC I and PAC II techniques. The Euclidian Distance (ED) is used here to evaluate final features of database and test images to calculate performance parameters. It is seen that the performance parameters of the proposed technique is improved compared to existing technique.
Robust Similarity-Based Concept Factorization for Data Representation

Non-negative matrix factorization (NMF) known as learnt parts-based representation has become a data analysis tool for clustering tasks. It provides an alternative learning paradigm to cope with non-negative data clustering. In this paradigm, concept factorization (CF) and symmetric non-negative factorization (SymNMF) are two typically important representative models. In general, they have distinct behaviors: in CF, each cluster is modeled as a linear combination of samples, and vice versa, i.e., sample reconstruction, while SymNMF built on pair-wise sample similarity measure, is to preserve similarity of samples in a low-dimensional subspace, namely similarity reconstruction. In this paper, we propose a similarity-based concept factorization (SCF) as a synthesis of the two behaviors. This design can be formulated as: the similarity of reconstructed samples by CF is close to that of original samples. To optimize it, we develop an optimization algorithm which leverages the alternating direction of multipliers (ADMM) method to solve each sub-problem of SCF. Besides, we take a further step to consider the robust issue of similarity reconstruction and explore a robust SCF model (RSCF), which penalizes the hardest pair-wise similarity reconstruction via $l_\infty $ . Thus, RSCF enjoys similarity preservation, robustness to similarity perturbation, and ability of reconstructing samples. Extensive experiments validate such properties and show that the proposed SCF and RSCF achieve large performance gains as compared to their counterparts.
High Performance 20-T based Hybrid Full Adder using 90nm CMOS Technology

In this paper, a high-performance full adder design is proposed using the hybrid logic style. There are three modules in the hybrid logic structure. The module I generates the XOR/XNOR output simultaneously whereas module II and module III are realized using the XOR/XNOR and CIN signal. The proposed design is simulated in the cadence software using the 90 nm CMOS technology at 1.2V. The proposed design gives the improvement 53% and 57 % in terms of delay and PDP, respectively when compared with its best counterpart. The proposed circuit also performs well with different supply voltage.
Recent Progress in the Development of Biosensors for Chemicals and Pesticides Detection

Chemicals and pesticides contamination in the food, drinking water, and environment ecosystem have become one of the most serious problems for human public health in the world due to their large amount used and wide application in the agriculture industry. Therefore, the detection and analysis of contamination in the food and drinking water by using techniques that are simple and suitable for fast screening are important. This review gives an overview of the last trends and recent advances biosensors for chemicals and pesticides detection based on electrochemical, optical and mechanical transducers strategies. Furthermore, the biosensors are classified according to their immobilized biorecognition elements including aptamer, antibodies, enzymes, and molecularly imprinted polymers. The implementation of nanomaterials such as graphene, carbon nanotubes, and metals nanoparticles are also emphasized and discussed in this review, these nanomaterials provides remarkable features to the biosensors such highly sensitive and accurate which allowing efficient pesticides detection. In addition to highlighting and summarizing various novel sensors, this review also provided some drawbacks, challenging, prospects as well as the current efforts to enhanced optical sensors.
An Investigation into the Usage-trends of Canada’s Research Computing Clouds

Cloud platforms are gaining more footprint in many Information Technology sectors, including research computing. Infrastructure as a service (IaaS) remains a popular cloud delivery model for research computing clouds, enabling researchers to create isolated computing, storage, and network environments on demand. Despite the maturity of the IaaS model, cloud providers face several challenges due to the lack of insight into usage-trends of their platforms, including sub-optimal VM placement and arbitrary selection of adopted technologies. These problems become more difficult in the research computing domain, where the resource requirements and nature of computational workloads of VMs are highly diverse.In this paper, we illustrate the value of usage-trends’ analysis in VM placement decisions and technology adoption choices by IaaS providers. We examine the usage-trends of four of Canada’s research computing clouds, presenting our analysis of usage metrics that were collected since their inception and up to early 2019. We focus on examining the usage metrics with potential in guiding VM placement and technology adoption choices, namely the lifetime of VMs, the frequency of VMs’ creation, and the VMs’ resource allocations.
Current Ripple Estimation for 3-Level ILCL Filtered Grid-Connected Inverters with the SVPWM or the DPWM Method

The improved LCL filter (ILCL) is widely applied on transformer-less 3-level grid-connected PV inverters to suppress the common current flowing into the grid. Since the neutral point of the DC bus is directly connected to the common point of the filter capacitor, the current ripple distribution with ILCL filter is different from that with the LCL filter. In this paper, we analysis the current ripple firstly for the 3-level ILCL- filtered inverter with the space vector pulse width modulation (SVPWM) or the discontinuous pulse width modulation (DPWM) in consideration of the filter inductance variation. Finally, the analysis result is verified by the experiment.
Ad Hoc Carpooling Prediction Based on Improved kNN

At present, taxi applications have been popularized in our lives. Carpooling has become a new trend in saving money and protecting environment. Two kinds of carpool applications in today’s market are mainly focused either on ad hoc carpooling service for express cars or reservation service for shuttles. Normally in a real-world carpool scenario, a prior estimated fare is the mere reference available for a rider to make further decision, which is actually insufficient in terms of user experiences, e.g. whether to keep waiting as in a period of taxicab shortage during rush hours or after major events. This paper presents a novel algorithm to ad hoc carpooling prediction based on improved kNN for practical use. First, based on the idea of density reachability, a set of reasonable determined neighbors can be effectively identified and making the subsequent voting process more accurate. Second, with awareness of data distribution both across and within categories, the accuracy of noise neighbor detection may less be influenced by the uneven distribution of data. It is proved that the algorithm does have positive effects on unbalanced data through experiments with an industrial carpool dataset.
A Trusted Agent Strategy in Decentralized Network Environments

A problem currently faced by autonomous systems, learning agents, multi-agent systems and other cognitive systems is the extreme susceptibility to bad information and malicious misdirection. In this paper, we propose a strategy to identify trusted agents in a multi-agent system, so that a system can eliminate the knowledge provided by malicious or unknown agents. The strategy is covered in three phases by each agent; firstly sharing information with other agents in the system, secondly calculating the conclusion based on the majority of information received from others and lastly identifying the trusted, malicious and unknown agents in the system. The theoretical analysis shows that the trusted agent strategy (TAS) is capable of identifying malicious or compromised agents if a system has less than 25% of malicious agents. This is further improved in the advanced-TAS which can handle up to 40% malicious agents. The experimental results show that trusted and malicious agents can be identified with high accuracy.
Smart Car Parking Management System

The demand for vehicles increases with the increase of the population. A densely replenished country like Bangladesh faces many challenges in managing this increased number of vehicles. Especially in city areas, where the road-side parking is not legitimate. Parking management can play an important role to diminish congestion on the roads. In our country, most of the parking areas are maintained by a manual parking system. In this paper, our aim is to design and develop a smart car parking system to solve the chaos, perplexity and long queues at the entry and exit of a parking space located inside public buildings including shopping malls and office spaces. In the smart car parking system, a radio frequency identification (RFID) card is used for every vehicle to store the information of the entrance. Time is automatically enumerated from the entry time to exit time, and thus fare will be shown to the client for his used parking space. In this paper, we have developed a system composed of both hardware and software components. Our system provides a user-friendly interface for the employees who maintain the system. The system also generates different reports including revenue and usage reports directly from the database using the software section. There will be an LED display in the parking that will show the number of available parking slots. Finally, we have measured the performance in terms of response time by deploying the system in the local server and remote Cloud server.
Management strategies for chicory (Cichorium intybus) and plantain (Plantago lanceolata): impact on dry matter yield, nutritive characteristics and plant density

Abstract. Chicory (Cichorium intybus L.) and narrow-leaved plantain (Plantago lanceolata L.) are able to grow a large amount of high-quality summer feed. Limited information is available on the effect of grazing management on plantain, and no comparison been undertaken of modern chicory and plantain cultivars used in dairy production systems. This study determined the effect of defoliation interval (as determined by the extended leaf height, ELH) and residual height on the yield, nutritive characteristics and plant density of chicory and plantain over 18 months. Chicory leaf yield was reduced in swards defoliated at 150 mm ELH compared with those defoliated at 250, 350 or 550 mm (14.3 v. 17.5 t DM ha–1), and chicory stem yield was least in swards defoliated at 150 or 250 mm. Plantain swards defoliated at 350 or 450 mm ELH yielded more leaf than those defoliated at 150 or 250 mm (20.4 v. 16.7 t DM ha–1); however, stem yield also increased with increasing defoliation interval. Over all seasons, as defoliation interval increased, generally, neutral detergent fibre content increased and crude protein, ash and digestibility declined. Residual height had less of an effect on yield and nutritive characteristics than did defoliation interval. To maximise chicory leaf growth while minimising growth of lower quality stem, the optimal ELH over 18 months was 250 mm, or if the chicory was used only as a 9-month ‘summer’ crop, 350 mm. Recommendations for plantain are not as simple because longer defoliation intervals increase both leaf and stem yield and reduce nutritive value. Defoliating plantain swards at 250 mm ELH appeared to provide a balance between yield and nutritive value; however, further work is required to determine the impact of applying these recommendations on a dairy farm system.
A Highly Independent Multiband Bandpass Filter Using a Multi-Coupled Line Stub-SIR With Folding Structure

The main problem in designing a multiband bandpass filter (BPF) is making each passband response highly independent, where each bandwidth of multiband BPF can be controlled and adjusted separately. To overcome this problem, this paper proposes a highly independent multiband BPF based on a multicoupled line stub-SIR with a folding structure. The proposed multiband BPF is constructed as a multicoupled line to generate a highly independent inter-passband. Moreover, the multiband performance is produced separately and independently by using three sets of resonators: resonator <italic>A1/A2</italic> (<inline-formula> <tex-math notation="LaTeX">$R_{A1}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$R_{A2}$ </tex-math></inline-formula>), resonator <italic>B1/B2</italic> (<inline-formula> <tex-math notation="LaTeX">$R_{B1}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$R_{B2}$ </tex-math></inline-formula>), and resonator <italic>C1/C2</italic> (<inline-formula> <tex-math notation="LaTeX">$R_{C1}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$R_{C2}$ </tex-math></inline-formula>). The three passband frequencies can be independently arranged and designed. To miniaturize the multiband BPF, a folding structure is also proposed. As a result, the multiband BPF has a compact size that is reduced by over 61.29 % compared to previous structures. The even-odd excitation model and the equivalent circuit model are used to analyze the multiband BPF structure. This BPF is designed for GPS applications at 1.57 GHz, WCDMA (3G) at 1.8 GHz, WLAN (WiFi) at 2.4 GHz, LTE (4G) at 2.6 GHz, and 5G communication at 3.5 GHz. To evaluate and validate the proposed structure of the multiband BPF, the circuits are fabricated and tested. The simulated and measured results of the multiband BPF show good agreement. In conclusion, the proposed multiband BPF structure has a highly independent inter passband response and a compact size.
Non-invasive Brain Tumor Detection using Magnetic Resonance Imaging based Fractal Texture Features and Shape Measures

This study presents a novel non-invasive quantitative feature set using magnetic resonance imaging (MRI) for diagnosis of brain tumor and their grade classification. Texture features using segmentation-based fractal texture analysis (SFTA), and selected shape measures were extracted from the segmented tumor volume to differentiate low-grade (LG) tumor and high-grade (LG) brain tumor. Classification of the tumor grade is performed with support vector machine (SVM) classifier and testing and training dataset are obtained using k-fold cross-validation method. The brain detection method proposed in this study gave an overall specificity and sensitivity of 86% and 88% respectively. Also, an accuracy of 87% was achieved while classifying LG and HG brain tumor.