Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Segmentation of Metastasis Lesions in SPECT Bone Scan Images

SPECT bone imaging is among the main nuclear medicine functional imaging modalities, which has the potential of early diagnosis of various serious diseases such as cancers. However, the low resolution and contrast of SPECT bone images bring huge challenges to performance on segmenting hotspots or lesions in SPECT bone scan images. In this work, three different algorithms including K-means clustering method, region growth method and C- V model are introduced to segment lesions in SPECT bone scan images by fine-tuning parameters, focusing on the bone metastatic lesions area in SPECT imaging. Specifically, each of the original 256×1024 whole body images is cropped into a 256×256 thoracic region including ribs and spine, followed by the data normalization. Then, different parameters are experimentally determined for the above three algorithms, leading to three different segmentation algorithms. Last, experimental evaluation conducted on a group of real-world samples of SPECT bone scan images reveals that our methods are workable for segmenting lesions with SPECT imaging, obtaining a value of 0.7307, 0.7768 and 0.8076 for Tanimoto similarity coefficient metric by the three methods, respectively. Particularly, the C- V model based segmentation method is able to provide more assistive information for oncologists on diagnose of tumors and other related diseases.
Towards Parallel Multi-density Clustering for Urban Hotspots Detection

Detecting city hotspots in urban environments is a valuable organization methodology for framing detailed knowledge of a metropolitan area, providing high-level summaries for spatial urban datasets. Such knowledge is a valuable support for planner, scientist and policy-maker's decisions. Classic density-based clustering algorithms show to be suitable to discover hotspots characterized by homogeneous density, but their application on multi-density data can produce inaccurate results. For such a reason, since metropolitan cities are heavily characterized by variable densities, multi-density clustering approaches show higher effectiveness to discover city hotspots. Moreover, the growing volumes of data collected in urban environments require high-performance computing solutions, to guarantee efficient, scalable and elastic task executions. This paper describes the design and implementation of a parallel multi-density clustering algorithm, aimed at analyzing high volume of urban data in an efficient way. The experimental evaluation shows that the proposed parallel clustering approach takes out encouraging advantages in terms of execution time and speedup.
Permittivity Estimation of Dielectric Substrate Materials via Enhanced SIW Sensor

An enhanced substrate integrated waveguide (SIW) cavity sensor is proposed and designed in this work operating under sub −6 GHz 5G band suitable for the accurate permittivity estimation of low-loss dielectric materials. The proposed SIW sensor is designed for the fundamental TE<inline-formula> <tex-math notation="LaTeX">$_{\mathbf {101}}$ </tex-math></inline-formula> mode at the resonance frequency of 3 GHz. The designed sensor is excited by employing a newly optimized external coupling topology incorporating a transition offset, contrary to the conventional microstrip feed. Due to which, a fully planar SIW sensor is developed for the first time without incorporating any active component, exhibiting a substantially high unloaded quality factor of nearly 515 basically required for the accurate testing of low loss tangents. Besides, the designed sensor is yielding a high sensitivity of 20 MHz (equivalent to 0.67% in terms of normalized sensitivity) desirable for segregating nearly resembling dielectric constants. Several standard dielectric samples are tested for the performance validation revealing that the proposed sensor is efficiently differentiating between Plexiglass (<inline-formula> <tex-math notation="LaTeX">$\boldsymbol {\varepsilon }_{r} =2.6$ </tex-math></inline-formula>) and PVC (<inline-formula> <tex-math notation="LaTeX">$\boldsymbol {\varepsilon }_{r} =2.65$ </tex-math></inline-formula>). Simultaneously, the sensor is accurately determining even the low loss tangent of Teflon (tan<inline-formula> <tex-math notation="LaTeX">$\delta = 3.2\times 10 ^{-4}$ </tex-math></inline-formula>). The measurement results are in close agreement with the references available in the literature. A thorough comparison with the state of the art literature corroborates the advantages offered by the proposed sensor and hence, its usability. Apart from characterizing the commercial grade dielectrics, this resonator is also potentially appropriate for designing high performance RF filters, oscillators and other devices.
A Study on Human Emotion Detection

This paper centers around an arrangement of perceiving a human’s feeling from an identified human’s face. The dissected data is passed on by the locales of the eye and the mouth into a combined new picture in different outward appearances relating to six widespread essential facial feelings. The yield data got could be taken care of as a contribution to a machine fit for collaborating with social abilities, with regards to building socially wise frameworks. The approach utilizes an order method of data into another melded picture which is made out of two squares coordinated by the territory of the eyes and mouth, delicate regions to changes human’s demeanor and that are especially pertinent for the disentangling of enthusiastic articulations. At long last we utilize the blended picture as a contribution to a feed-forward neural organization prepared by back-engendering. Such investigation of blended pictures causes it conceivable, to acquire important data through the mix of appropriate information in a similar picture and diminish the preparation set time while saving grouping rate. It is appeared by test results that the proposed calculation can distinguish feeling with great exactness.
A Hybrid Multi-Objective Teaching-Learning Based Optimization for Scheduling Problem of Hybrid Flow Shop With Unrelated Parallel Machine

Efficient scheduling benefits productivity promotion, energy savings and the customer’s satisfaction. In recent years, with a growing concern about the energy saving and environmental impact, energy oriented scheduling is going to be a hot issue for sustainable manufacturing. In this study, we investigate an energy-oriented scheduling problem deriving from the hybrid flow shop with unrelated parallel machine. First, we formulate the scheduling problem with a mixed integer linear programming (MILP) model, which considers two objectives including minimizing the completion time and energy consumption. Second, a hybrid multi-objective teaching-learning based optimization (HMOTLBO) algorithm based on decomposition is proposed. In the proposed HMOTLBO, a new solution presentation and five decoding rules are designed for mining the optimal solution. To reduce the standby energy consumption and turning on/off energy consumption, a greedy shifting algorithm is developed without changing the completion time of a scheduling. To improve the converge speed of the algorithm, a weight matching strategy is designed to avoid randomly matching weight vectors with students. To enhance the exploration and exploitation capacities of the algorithm, A teaching operator based on crossover and a self-learning operator based on a variable neighborhood search(VNS) are proposed. Finally, fourth different experiments are performed on 15 cases, the comparison result verified the effectiveness and the superiority of the proposed algorithm.
Text Preprocessing Method on Twitter Sentiment Analysis using Machine Learning

In real world, twitter sentimental analysis (TSA) acting a major role in observing the public opinion about customer side. TSA is complex compared to general sentiment analysis due to pre-processing of text on Twitter. The maximum limit on the number of characters allowed on Twitter is 280. In this article we discuss the influence of the text pre-processing technique on the classification efficiency of emotions in two kinds of classification problems and summarize the classification efficiency of the four pre-processing methods. This paper contributes to the consumer satisfaction classification sentiment analysis and is useful in evaluating the details in the context of the amount of tweets where views are somewhat unstructured and are either positive or negative, or somewhere in between. We first pre-processed the dataset, then extracted the adjective from the dataset with some meaning called the feature vector, then selected the feature vector list and subsequently applied machine learning based classification algorithms namely: Naive Bayes, Random Forest and SVM along with WordNet based Semantic Orientation which extracts synonyms and similarity for the features of content. Experiments display that the accuracy (Acc) and average F1-measure (F1-M) of the classification classifier on Twitter are enhanced by using methods of pre-processing the extension of acronyms and swapping negation, but barely deleting numbers or stop words
Learning-based modelling of physical interaction for assistive robots

Deploying companion robots for assisting humans requires safe and robust interaction with the environment, both in terms of mobility and object manipulation. To extend a robot’s workspace, we are here concerned with multiﬂoor operation and staircase traversal, as a building block for the development of object fetching services. In this article, we advocate a life-long learning treatment of this problem within a reinforcement learning (RL) framework. In view of sparse earlier work for the scenario of interest, we hereby identify relevant methodological aspects and report our preliminary developments.
An Ant Colony Algorithm for HRES Size and Configuration Optimisation

An Ant colony algorithm (ACO) is proposed for hybrid renewable energy system size and configuration optimisation using continuous search space approach. In the proposed algorithm the pheromone distribution across search space is determined by gaussian distribution, and the probabilistic path selection is performed based on pheromone deposit value via roulette wheel principle. The ACO algorithm is implemented in the software tool MOHRES and three case studies are conducted to optimise the size and configuration of standalone hybrid renewable energy system derived from the full configuration wind-PV-battery-diesel-FC Electrolyser system. To evaluate the performance of the proposed ACO, the optimum solutions are compared with the solutions obtained by the genetic algorithm (GA) optimisation algorithm implemented in MOHRES.
Physical Layer Security for Multi-User MIMO Visible Light Communication Systems With Generalized Space Shift Keying

We consider the physical layer security (PLS) of multi-user (MU) multiple-input-multiple-output visible light communication (VLC) systems with an eavesdropper (Eve) and propose a novel spatial constellation design technique based on generalized space shift keying (MU-GSSK-SCD). The received signals of the legitimate users are optimized jointly, such that their bit error ratios (BERs) are minimized and Eve’s BER is significantly degraded. The emission power of randomly selected light-emitting diodes is adjusted, by exploiting users’ channel state information at the transmitter. Our strategy ensures that legitimate users receive confidential messages fully in an undistorted fashion, while any meaningful leakage to Eve is strongly prohibited, without any artificial noise addition. Every user can decode only its information, hence inter-user security is also guaranteed. The PLS improvements are presented in terms of both BERs and achievable secrecy rates in practical VLC scenarios. For various user configurations, it is shown that MU-GSSK-SCD increases the BER at Eve to the 0.5 level, while providing minimized BERs to the legitimate users. The achievable secrecy rate region is derived for MU-GSSK-SCD and it is shown that full secrecy can be achieved at 0 dB signal-to-noise ratio (SNR) level with a user separation as small as 90 cm.
Emotional Analysis and Recognition Based on EEG Brain Network

Emotions are closely linked to the life of human beings. Studying the difference of brain mechanism and brain function of human emotional expression helps to improve the human-computer interaction ability and realize the automatic diagnosis of related mental diseases. However, the emotion recognition rate is generally low. In this study, phase lock is used to construct EEG functional brain network based on static connection and dynamic connection under the condition of audiovisual stimuli of different core emotions. The network features are extracted for emotion recognition. The results demonstrate that the use of non-singular local attributes can effectively improve the rate of emotional recognition compared to the traditional use of global attributes of the network. At the same time, the dynamic brain network is introduced for the deficiency of the brain network analysis method based on a static connection. Constructing a dynamic connection-based EEG brain network whose network characteristics can further improve the emotional recognition rate. This conclusion provides some experimental basis and methods for relating emotion recognition based on brain network.
Multi-scale 3D convolutional neural network lung nodule detection method

In order to solve the problem of low detection sensitivity and high false positives of traditional lung nodule detection models, a multi-scale three dimensional (3D) convolutional neural network (CNN) based lung nodule detection method was proposed. First, in order to improve the operating speed and network flexibility, a single-stage mode was adopted, and there was no false positive reduction stage. Secondly, based on the above model, we built a new network structure, innovatively used the 3D UNet++-like architecture as the backbone of the region proposal network (RPN), and adopted the flexible nesting mode of residual blocks. Finally, the three input sizes were input into the 3D neural network, and their classification results were merged. Experiments showed that our model had an average sensitivity of 87.3% in false positive screening based on the LUNA16 datasets, which was an increase of 7.8% compared with the UNet++ network. The total sensitivity was as high as 96.2%. It can be seen that our model can significantly improve detection sensitivity and reduce false positives, which can provide a theoretical reference for clinical applications.
Enhanced Phase Estimation for Long-Haul Multi-Carrier Systems Using a Dual-Reference Subcarrier Approach

Multi-subcarrier (MSC) modulation is recently standing out as a notable new feature in the latest generations of high-speed coherent optical transceivers, providing distinctive advantages over legacy single-carrier systems. However, the transition from single-carrier to MSC modulation still requires significant optimization of digital signal processing subsystems, in order to maximize the inherent potential of subcarrier multiplexing. In this article, the penalty associated with the usage of carrier phase estimation (CPE) methods inherited from single-carrier systems is quantitatively studied and the need for enhanced MSC-tailored solutions is evidenced. A novel pilot-based joint-subcarrier CPE method based on a dual-reference subcarrier (DRS) approach is described and numerically assessed. The proposed DRS-CPE exploits the frequency-dependent walk-off effect imposed by chromatic dispersion (CD) to enable the separation of phase noise processes incurred by the transmitter and local oscillator lasers, thereby allowing to effectively apply joint-subcarrier CPE independently of the symbol-rate per subcarrier. Our numerical results demonstrate that the proposed DRS-CPE enables to operate with near-optimum performance at symbol-rates per subcarrier as low as 1 Gbaud over ultra-long-haul distances.
Facial Landmarks and Expression Label Guided Photorealistic Facial Expression Synthesis

Facial expression manipulation plays an increasingly important role in the field of computer graphics and has been widely used in generating facial animations. However, it is still a very challenging task as it needs full understanding of the input face and very depending on the facial appearance. In this paper, we present an end-to-end generative adversarial network for facial expression synthesis. Given the facial landmarks and the expression label of a target image, our method automatically generates a corresponding expression facial image with the identity information and facial details well preserved. Both qualitative and quantitative experiments are conducted on the CK+ and Oulu-CASIA datasets. Experimental results show that our method has the compelling perceptual results even there exist large differences in facial shapes for unseen subjects.
Practically Deploying Heavyweight Adaptive Bitrate Algorithms With Teacher-Student Learning

Major commercial client-side video players employ adaptive bitrate (ABR) algorithms to improve the user quality of experience (QoE). With the evolvement of ABR algorithms, increasingly complex methods such as neural networks have been adopted to pursue better performance. However, these complex methods are too heavyweight to be directly deployed in client devices with limited resources, such as mobile phones. Existing solutions suffer from a trade-off between algorithm performance and deployment overhead. To make the deployment of sophisticated ABR algorithms practical, we propose PiTree, a general, high-performance, and scalable framework that can faithfully convert sophisticated ABR algorithms into decision trees with teacher-student learning. In this way, network operators can train complex models offline and deploy converted lightweight decision trees online. We also present theoretical analysis on the conversion and provide two upper bounds of the prediction error during the conversion and the generalization loss after conversion. Evaluation on three representative ABR algorithms with both trace-driven emulation and real-world experiments demonstrates that PiTree could convert ABR algorithms into decision trees with < 3% average performance degradation. Moreover, compared to original deployment solutions, PiTree could save considerable operating expenses for content providers.
Malware Subspecies Detection Method by Suffix Arrays and Machine Learning

Malware such as metamorphic virus changes its codes and it cannot be detected by pattern matching. Such malware can be detected by surface analysis, dynamic analysis or static analysis. We focused on surface analysis since neither virtual environments nor high level engineering is required. A representative method in surface analysis is n-gram with machine learning. On the other hand, important features are sometimes cut off by n-gram since n is not variable in some existing methods. Hence, scores of malware detection methods are not perfect. Moreover, creating n-gram features takes long time for comparing files. Furthermore, in some n-gram methods, invisible malware can be created when the methods are known to attackers. Therefore, we proposed a new malware subspecies detection method by suffix arrays and machine learning. We evaluated the method with four real malware subspecies families and succeeded to classify them with almost 100% accuracy.
Design and Analysis of Electric bike Hub-Motor using Motor-CAD

Inner rotor and outer rotor brushless dc motors are in vogue nowadays as they are currently being used in designing electric bikes. BLDC motors have a wide range of advantages (like higher efficiency smaller size). The E-Magnetic analysis and Lab model analysis are an important part of designing an outer rotor BLDC Hub motor because the motor is attached to the wheels of the bike. It is significantly different from a petrol engine motorbike as in a petrol engine motorbike, the engine and the wheel are separate. So, any heating of the engine would not heat the wheel but for the case of an electric bike, since the hub motor and the bike wheel are not separated from each other, heating of the hub motor would produce a similar type of heating in the wheels. If the temperatures and the total magnetic flux density of the hub motor go beyond normal values, then there would be a chance that the electric motorbike would not start at all. In this work, both the E Magnetic analysis and the Lab model analysis are done on a 36 slot, 24 poles outer rotor BLDC motor which was also designed in Ansys Motor-CAD. All the E Magnetic plots and Lab plots obtained from the analysis are clearly shown in this paper. The different views of the model in terms of axial, radial, and schematic are also shown clearly. This research work provides a clear-cut idea of how correctly analyze a motor E-magnetically which is the first very important step in further analysis of the thermal model in Ansys Motor-CAD.
A Common Ground Transformer-less High Gain DC-DC Buck-Boost Converter

enhancing the voltage ratio in a converter is a leading aim to improve its performance. Regarding this goal, this paper proposes a new topology of buck-boost converter. The proposed converter offers a much higher voltage gain ratio in comparison to its relevant high gain buck-boost structures. The range of duty cycle has widened in this converter without adopting any transformers or coupled inductors in its configuration. The number of components is quite a few. Therefore, this converter is quite easy to be controlled. Furthermore, the common electrical ground between the input and output ports of the presented converter has preserved. Hence, this structure is an appropriate choice to be utilized in several industrial applications. Calculations of voltage and current have made for this converter. The amount of each component has designed. The proper performance of this topology has evaluated and validated by PSIM Simulink software.
Time Domain Discrete Fourier Domain Mode Locked Laser With k-Space Uniform Comb Lines

Frequency comb swept lasers or wavelength stepped swept lasers with comb filters have been proposed in optical domain subsampling or circular-ranging optical coherence tomography (OCT) to achieve high axial scan rate and extended imaging range with a given acquisition bandwidth. However, the comb filters lack flexibility in varying the free spectral range (FSR). In this article, we propose and demonstrate a novel discrete Fourier domain mode locked (FDML) laser by using intracavity optical intensity modulation. Swept signals with comb lines uniformly distributed in the frequency domain is generated with tailor-made pulse patterns according to the sweep trace of the swept filter. Discrete k-space uniform swept signals with different FSRs and pulse durations are generated in the same laser. The FDML laser discretized by temporal modulation is more stable than that with a comb filter. The discrete FDML lasers will be potential sources of OCT and the fast-developing swept source LiDAR.