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Due to the potential for high energy harvesting capacity, subwavelength scale semiconductor nanostructured arrays are used to address the issue of single-junction thin-film solar cells' limited solar energy harvesting. Along with numerical simulations, an effective and efficient algorithm is crucial to maximizing the optical field modulation and energy trapping capacity of nanostructures. Based on the effective medium theory and the leaky mode resonance, an analytical feedback algorithm is suggested in this study to determine the precise the dimensions of vertically aligned InP stepped nanocylinders (SNCs) for maximum solar energy absorption. For both square and hexagonally arranged two-segment or three-segment InP SNC arrays, the ideal geometrical dimensions were quantitatively estimated for maximum energy harvesting. Densities of short-circuit current Jscs under the AM 1.5G spectrum's illumination as the measurement standard, they were computed for each SNC array. The maximal Jsc of 32.85 mA/cm2 was obtained with square three-segment InP SNC arrays. The optimized SNC arrays for the maximum light absorption are also validated and examined using thorough finite-difference time-domain computational simulations. The algorithm estimated maximum Jsc had tolerances of under 1.8% for all scenarios, which, when compared to simulations, shows that this analytical method offers a practical and efficient means to direct the design of high-performance InP SNC arrays solar cells.

Driven by growing applications that use computer screens as interfaces for daily remote interactions, almost all current video coding standards have included screen content coding (SCC) tools. Recently, an efficient SCC tool called intra string copy (ISC) has been adopted in the third-generation of audio video coding standard in China (AVS3). ISC has two coding unit (CU) level sub-modes: fully-matching-string and partially-matching-string based string prediction (FPSP) sub-mode and equal-value-string, unit-basis-vector-string, and unmatched-pixel-string based string prediction (EUSP) sub-mode. To further improve the coding efficiency of SCC, this paper proposes four enhancement techniques of ISC (EISC), including CU partition improvements, point vector (PV) relocation and reactivation, line-based overlapping string prediction, and an optimized coding method for string length in the EUSP sub-mode. Compared with the latest AVS3 reference software HPM with EISC disabled, using AVS3 SCC common test condition and YUV test sequences in text and graphics with motion and mixed content categories, the proposed technique achieves an average Y BD-rate reduction of 2.39% and 1.49% for all intra (AI) and low-delay B (LDB) configurations, respectively, with low additional encoding complexity and almost no additional decoding complexity. All proposed ISC enhancement techniques have been adopted in AVS3.

Encountering difficulties that emerge in programming learning are ubiquitous for novices. Those difficulties may reduce novices’ motivation for learning, even lead them to drop out. Automatically detecting the difficulties has been proved as an effective way to solve this problem. A potential method for the detection is to identify programmers’ behavioural patterns based on the sequences of programming events (e.g., editing, deleting, or debugging) when they experience programming difficulties. However, due to the diversity of these events, the plentiful difficulties generated from novices, and the naturally complicated behavioural patterns, accurately detecting the difficulties remains challenging. In this study, we propose a deep representation based on the programming events as the pre-treatment of a recurrent neural network with an attention mechanism to detect programming difficulty. This representation can be conducive to encode the complicated behavioural features (e.g., co-occurrence, sequences, and time intervals of the events) of the difficulty patterns for deep learning models. The results show that the accuracy of our method reaches 94%, which increases by 16% compared with the traditional machine learning model.

The Internet of Things (IoT) has become the infrastructure to widely support ubiquitous services. In this setting, a large number of smart object devices provide IoT services for people to carry on day-to-day activities. These services are easy to access, and thus, can be easily used by different users with different requirements through service allocation and composition. Nevertheless, with the increasing adoption and presence of IoT services, it becomes more difficult to find the appropriate IoT service compositions that can maximize the service utility of all the users’ requests. Moreover, the IoT devices are usually resource constrained and cannot effectively finish some complex task of service requests, thereby reducing the service quality. To address this problem, we develop a novel IoT service allocation mechanism in collaborative device-edge-cloud architecture. First, prevalence of frequent function interaction is mined to help construct IoT service allocation index. Second, service requests’ priority and their preferences towards branch nodes are further set and estimated to help maximize the service composition utility of multiple user requests. Third, edge device is exploited to help service execution and guarantee service allocation utility by offloading task from IoT devices. The results are demonstrated by conducting a set of extensive experiments, which show that the proposed method performs better than the alternative techniques in reducing the total energy consumption and promoting the service utility maximum.

We proposed a compact and efficient polarization rotator (PR) based on an asymmetric hybrid plasmonics waveguide (HPW) on the lithium-niobate-on-insulator (LNOI) platform. We focus on the design of rotation angle and balance between the two hybrid modes. The presented device exhibits a polarization extinction ratio as high as 59.58 dB and an insertion loss of 1.44 dB with the rotation length of only 13.7 $\mu$m. The fabrication tolerances and a suitable process are also discussed to meet the state-of-art technology.

Electricity consumption of pump group accounts for a significant proportion of cost in water distribution system (WDS). This paper discusses the optimal regulation method for parallel water-intake pump group consisting of fixed-frequency pumps and variable-frequency pumps. For this purpose, the regulation strategy for the water level of clean water tank is designed, and then the intake water flow of parallel pump group is calculated. For overall optimazing operation of parallel water-intake pump group, the mimimization of electricity consumption is performed within the constraints of limited pressure fluctuations of WDS and required flow of water supply. Dynamic programming algorithm and particle swarm optimization algorithm are employed to solve the optimization problem with constraints. Experimental studies have been conducted and the results show that 4.20% electricity-saving can be achieved by using the proposed optimal regulation method.

In this paper, a photonic based pre-coding for multiple input multiple output (MIMO) satellite communication is realized without digital signal processing (DSP) structure. It should be noted that when the microwave signal transmits through the wireless channel, the distortion is inevitable. Usually, the zero force (ZF) algorithm is considered to mitigate the distortion that introduced by the atmosphere and it is always realized through DSP. Here, we present an all-optical pre-coding structure that may perform the same function of DSP to realize ZF algorithm. The simulation result shows that, the eye diagram shows a clear “eye” and the bit error rate (BER) is about 2.13503E-7, the system capacity is about 0.4276 after using the proposed photonic pre-coding structure. In addition, the influence of the non-ideal parameters on the photonic pre-coding structure is also investigated. The performance of the system is sensitive to the variation of the desired parameters.

Accurate heat load prediction algorithm provides important support for the stable and efficient operation of smart district heating system (SDHS) and helps to realize energy saving and consumption reduction. However, previous researches on heat load prediction are mostly carried out on various regression analyses and modeling prediction, without considering the inherent time delay and spatial dependence between heat exchange stations during regulation. Therefore, a novel heat load prediction model based on the hybrid spatial-temporal attention long short-term memory (STALSTM) is proposed. The STALSTM model introduces the spatial dependence characteristics of heating pipe network into the heat load prediction model, and the influencing factors of heat consumption are considered comprehensively from the time and space dimensions. Then, the LSTM algorithm is used to memory the information of historical data sequence, and the attention mechanism is used to realize the adaptive estimation of the characteristic weight of each influencing factor, which improves the prediction accuracy. And in order to verify the effectiveness of the proposed model, a detailed experimental comparison is made between the STALSTM model and the state-of-the-art algorithms. The results show that the STALSTM model has the best prediction accuracy, and the correctness of introducing the spatial-temporal characteristics and the attention mechanism is also proved.

An unmanned aerial vehicle (UAV) aeromagnetic measurement system can precisely obtain large-scale data with high efficiency and low cost in regions with complex exploration conditions, which has aroused interest in the field of aerial survey. In this article, based on the unmanned helicopter platform, an aeronautical magnetic field measurement system is integrated to complete the integration of a rotor UAV, magnetometers, and data recorders. Equipped with a three-axis fluxgate magnetometer for real-time flight compensation, the compensation deviation is ±0.052 nT, which meets the aeromagnetic survey specifications. The UAV can fly autonomously with a maximum load of 45 kg at a speed of 60 km/h for 2 h, and the wind resistance level is level 7. The system has completed a field test of 111.9 km in the Ma'anshan crisis mine region of China and collected 145,495 data points. Then, the adaptive down-sampling method is used to compress the data (to 1.7% of the original data), and it is applied to the three-dimensional magnetic inversion, which successfully obtains the spatial distribution characteristics of the underground orebody, improving the inversion efficiency while ensuring the target area resolution. Six favorable magnetite prospecting sites are delineated in the survey area through inversion results, which demonstrates the potential of the developed UAV aeromagnetic system in the exploration over the areas with complex terrain.

When the inner insulation of oil-impregnated paper bushing breaks down in service, gases decomposed by oil and paper may cause an explosion. The explosion of bushing occurs from time to time because of the lack of an effective monitoring method. In order to provide an effective online monitoring method for the bushing to avoid explosion, this paper presents a method to monitor the PD defect in bushing by measuring the inner pressure of bushing. In this paper, the relationship of variation between characteristic gases content and inner pressure is studied, the effect of inner temperature on inner pressure is analyzed and the method for reducing this effect is also presented, and the monitoring method is proved to be useful by PD tests on a real bushing. The results show that the inner pressure increases obviously due to the generation of characteristic gases by internal discharge in oil-impregnated paper bushing, and the pressure increases with the deterioration of the discharge; although the inner pressure has a great influence on the inner pressure, the influence can be reduced or eliminated greatly by the method of common-mode rejection. According to the results, the internal discharge defect of the oil-impregnated paper bushing can be detected by monitoring the pressure, and the bushing explosion can be avoided.

Most of the current closed-loop deep brain stimulation (DBS) devices use a single biomarker in their feedback loop, which may limit their performance and applications. This paper presents the design, fabrication, and validation of a programmable multi-biomarker neural sensor which can be integrated into closed-loop DBS devices. The device is capable of sensing a combination of low-frequency (7–45 Hz), and high-frequency (200–1000 Hz) neural signals. The signals can be amplified with a digitally programmable gain within the range of 50–100 dB. The neural signals can be stored into a local memory for processing and validation. The sensing and storage functions are implemented via a combination of analog and digital circuits involving pre-amplifiers, filters, programmable post-amplifiers, microcontroller, digital potentiometer, and flash memory. The device is fabricated, and its performance is validated through: 1) bench tests using sinusoidal and pre-recorded neural signals; 2) in-vitro tests using pre-recorded neural signals in saline solution; and 3) in-vivo tests by recording neural signals from freely moving laboratory mice. The animals were implanted with a PlasticsOne electrode, and recording was conducted after recovery from the electrode implantation surgery. The experimental results are presented and discussed confirming the successful operation of the device. The size and weight of the device enable tetherless back-mountable use in pre-clinical trials.

Photonic generation and transmission of linearly chirped microwave waveform with the increased time-bandwidth product (TBWP) is proposed and demonstrated by phase-encoding and splitting parabolic waveform. In the approach, dual-polarization Mach-Zehnder modulator (DPol-MZM) along with polarization controller (PC) is employed to generate two orthogonally polarized wavelengths, and polarization modulator is driven by a parabolic signal. After splitting parabolic waveform and convolving with a binary pseudo random sequence, chirped microwave waveform with high bandwidth and large temporal duration is generated, corresponding to an improved TBWP. Dispersion-induced power fading can be effectively avoided in the approach, and chirped signal with constant initial phase can be achieved by adjusting the PC. The proposed approach is verified by simulations. Linearly chirped microwave waveform with the central frequency of 40 GHz, bandwidth of 12.5 GHz, and TBWP of 20160 is generated. RF spurious suppression ratios of the generated signal are investigated, and pulse compression performances are also analyzed. The system features filter-free, high tunability, compact structure, and large TBWP, which has potential application in the modern radar system.

Quantifying full left ventricular (LV) metrics including cavity area, myocardium area, cavity dimensions and wall thicknesses from cardiac magnetic resonance (MR) images, and then assessing regional and global cardiac function plays a crucial role in clinical practice. However, due to highly variable cardiac structures across different subjects, it is challenging to obtain an accurate estimation of LV metrics. In this paper, we propose a novel deep learning framework, called cascaded segmentation and regression network (CSRNet), to improve the quantification results. The CSRNet consists of two components: a segmentation component and a regression component. The segmentation component yields myocardial contours of the left ventricle from the input cardiac MR images, and then the regression component learns hierarchical representations from the segmented images and estimates the desired LV metrics. By introducing the myocardial contours, the regression component can pay more attention to the left ventricle, which contributes to more accurate quantification results, although the cardiac structures are variable. The extensive experiments on a dataset of 145 subjects demonstrate that our framework outperforms the state-of-the-art methods.

Software testing plays a crucial role in ensuring the quality of software systems. Mutation testing is designed to measure the adequacy of test suites by detecting artificially induced software faults. Despite their potential, the expensive cost and the scalability of mutation testing with large programs is a big obstacle in its practical use. The selective mutation has been widely investigated and considered to be an effective approach to reduce the cost of mutation testing. In the case of large programs where source code has hundreds of classes and more than 10 KLOC lines of code, the selective mutation can still generate thousands of mutants. Executing each mutant against the test suite is cost-intensive in terms of robustness, resource usage, and computational cost. In this paper, we introduce a new approach to extract features from mutant programs based on mutant killing conditions, i.e. reachability, necessity and sufficiency along with mutant significance and test suite metrics to extract features from mutant programs. A deep learning Keras model is proposed to predict killed and alive mutants from each program. First, the features are extracted using the Eclipse JDT library and program dependency analysis. Second, preprocessing techniques such as Principal Component Analysis and Synthetic Minority Oversampling are used to reduce the high dimensionality of data and to overcome the imbalanced class problem respectively. Lastly, the deep learning model is optimized using fine-tune parameters such as dropout and dense layers, activation function, error and loss rate respectively. The proposed work is analyzed on five opensource programs from GitHub repository consisting of thousands of classes and LOC. The experimental results are appreciable in terms of effectiveness and scalable mutation testing with a slight loss of accuracy.

Code reuse has been perceived to be an effective tool for user interface development that is known to be complex and messy. A fundamental problem in user interface code reuse is how to effectively retrieve working code from existing code repositories, which renders functional interfaces similar to a programmer's input sketch. Existing generic code retrieval techniques are normally inadequate for user interface code due to its associated visual information. In this paper, we put forward a novel approach that retrieves matching candidate code of an interface sketch based on their visual-representation trees. To better capture sub-region similarities, we propose to decompose a visual-representation tree into an ordered list of paths (or sequences). We then devise a novel algorithm to measure the similarity between two sequences by leveraging various visual information and another algorithm to calculate the similarity of two ordered lists by considering the relative positions of different widgets. To evaluate our solution, we carefully design an experiment to generate the similarity scores of 6,750 user interface pairs based on real users' judgement. Based on this ground truth, we show that our method is able to generate accurate similarity scores, outperforming several state-of-the-art competitors.

In this paper, a simple image rejection mixer based on the dispersion effect in the single mode fiber is proposed and experimentally demonstrated for the radio-over-fiber (RoF) system. The key idea is to control the DC bias voltages of the dual parallel Mach-Zehnder modulator (DPMZM) to compensate for the desired components and eliminate the image induced components simultaneously during the fiber transmission. The experimental results show, the image rejection ratio (IRR) of ~50 dB is realized, and the spurious free dynamic range (SFDR) is about 100.75 dB·Hz2/3. In addition, the results also demonstrate an image-free intermediate frequency (IF) range of 0.5 GHz-9.5 GHz. This image-free mixer can be widely employed in the radio-over-fiber (RoF) system, and greatly simplify the receiver structure in the base station.

A photonic approach to generate a frequency and bandwidth multiplying dual-chirp microwave waveform using a single dual-polarization quadrature phase shift keying (DP-QPSK) modulator is proposed and demonstrated. In the proposed scheme, an RF carrier is applied to one QPSK modulator and a baseband single-chirp waveform is applied to another. By setting the driving signals applied to the modulator and adjusting the dc bias phases in the modulator, high-order optical sidebands can be obtained with the optical carrier suppressed. After optical to electrical conversion, a frequency-doubling and bandwidth-quadrupling, or frequency-quadrupling and bandwidth-octupling dual-chirp microwave can be generated. The approach is verified by simulation. Dual-chirp microwave waveforms with 16-GHz central frequency, 2048-MHz bandwidth and 32-GHz central frequency, 4096-MHz bandwidth are generated through an 8-GHz RF carrier and 512-MHz bandwidth baseband signal. The generated dual-chirp microwave waveform can be used in a radar system to improve its range-Doppler resolution.

We propose a linearized analog photonic link (APL) using a single z-cut LiNbO3 dual-output Mach-Zehnder modulator (MZM) to remove the third-order intermodulation distortion (IMD3). A polarizer, a z-cut LiNbO3 dual-output MZM, and a polarization beam combiner are combined to realize orthogonal polarized complementary intensity modulation with diverse modulation depth. Two IMDs3 with identical amplitude and adverse phase are eliminated destructively via appropriately controlling the optical power between two orthogonal polarized paths. A theoretical analysis is carried out and demonstrated by a simulation. Simulation results show that the spurious-free dynamic range of the proposed linearized APL is 24.5 dB and 18.5 dB higher than that of the conventional optical double sideband modulation link in back-to-back transmission and after 20 km transmission in single-mode fiber, respectively. The proposed scheme features simple structure with good performance.

A novel analog photonic link with enhanced spurious-free dynamic range (SFDR) based on an integrated electro-optic dual-polarization modulator is presented and demonstrated. The dual-polarization modulator, which consists of a polarization beam splitter, a polarization beam combiner, and two z-cut LiNbO3 MZMs, can achieve complementary intensity modulation along the orthogonal polarization directions with different modulation indexes. By properly adjusting the power relationship of the orthogonal polarization directions, two third-order intermodulation distortions (IMD3) have equal intensity and opposite phase and cancel each other out. Combined with a low-biasing technique to reduce the noise power, the SFDR of the link can be further improved. A theoretical analysis is presented and validated by a simulation. The IMD3 can be suppressed by 30.6 dB, giving an improvement in SFDR of about 22 dB compared with a conventional optical double-sideband modulation link.