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Vascular orientation recognition (VOR) holds widespread applications in biomedical domains such as vascular image processing. Although many methods based on deep learning (DL) running on CPUs have been proposed and achieved appreciable results, they encounter a speed bottleneck. Therefore, an FPGA-based hardware design scheme is proposed in this paper to achieve high-speed VOR using small circuits. The design employs a streaming processing method for image thresholding and replaces static lookup tables (LUT) with the CORDIC algorithm to reduce resource consumption. Additionally, further compression of the angle mapping range is employed to achieve superior hardware resource optimization. Compared to purely software solutions, experimental results obtained with the simulated and actual vascular images demonstrate a 12.9x computational acceleration employing the proposed scheme. Moreover, the proposed scheme, in contrast to hardware implementations based on Lookup Table and Function Approximation, reduces LUT resource consumption by over 60%. Compared with two previous CORDIC algorithm studies, the proposed algorithm achieves a 14.6% and 20.5% reduction in LUT resource usage, thus showing enormous potential in real-time medical devices for tracking and computation of main vascular trajectory.

Oceanic eddies are ubiquitous phenomena carrying large amounts of energy, thus of great importance for marine ecology and sea–air exchange. As an all-weather, high-resolution sensor, synthetic aperture radar (SAR) could provide valuable observations for oceanic eddies. However, there are few well-established methods for eddy detection on SAR images except for manual seeking, which is laborious and time-consuming. In combination with deep learning, this study is among the earliest in the literature that attempts end-to-end eddy detection on SAR images. Due to obscure pictures and indistinct eddy boundaries, ordinary deep learning models are not adaptable to the objective. Therefore, an customized model, stacked attention network (SANet), is designed to recognize the unique eddy pattern presented on radar images automatically. SANet is a two-unit stacking architecture, with each an hourglass structure for bottom–up, top–down reference and the overall stacking network for iterative extractions of eddy textures contained in shallow layers of each unit. Besides, SANet has included the inner-hourglass attention gates and the outer-hourglass GCblock for the extracted features to be more concentrated on the interested areas. Using SANet, we have identified 87.75% of eddies in the constructed dataset collected from ESA-2 and ENVISAT SAR products. The result is much better than the no-stacking counterpart U-net, as well as state-of-the-art deep learning models DeepLabV3+ and SegFomer, thus verifying the superiority of the proposed method. The generalization ability of the algorithm has also been tested. The code and the constructed SAR dataset have been made public for broader use.

At present, the raw echo data captured by spaceborne synthetic aperture radar is usually downlinked to the ground stations for imaging and analysis. If the SAR data can be processed on-board, the processing results could be transmitted to users directly through broadcast distribution, which can greatly reduce the delay time for emergency applications. However, on-board processing implementation faces many challenges due to the space radiation environment and limited resources of the satellite. In this article, a field programmable gate array (FPGA)-based implementation of ship detection for on-board processing is proposed, which has high efficiency in logic and memory resources. First, the hardware-oriented clustering approach is proposed. Image processing operations with similar regularity of data access can be mapped in one processing engine, which can reduce the employment of logic resources. Second, the concept of the data-buffering cycle (DBC) is proposed. The DBC provides a method to analyze the intermediate data and optimize the memory reuse. Finally, based on the above optimization methods, the FPGA-based implementation for a ship detection algorithm is presented. Compared with the traditional methods, the experimental results show the efficacy of our proposed method with lower consumption in logic and memory resources.

The scheme of combining relay and optical code division multiple access (OCDMA) is one of the effective methods to improve the reliability and security of free space optical (FSO) communication system. We experimentally investigate a 10 Gb/s decode-and-forward (DF) relaying FSO-CDMA system and a relaying amplify-and-forward (AF) FSO-CDMA system, respectively. Bit error rates (BERs) of DF and AF relaying FSO-CDMA are measured and compared under different turbulence conditions. The experimental results show that the BER of DF relaying FSO-CDMA is one to two orders of magnitude lower than that of AF relaying FSO-CDMA when the receiving power is −5.6 dBm. However, the performance gap is decreasing with the increase of receiving power, especially when there is an interfering user. At the receiving power −2.8 dBm, two-user AF and DF relaying FSO-CDMA systems have almost the same BER. Under both weak and moderate turbulence conditions, at the receiving power −2.8 dBm, the measured BER of the eavesdropper exceeds 0.1, which is larger than the BER threshold of soft-decision forward error correction code. Hence, physical layer security of FSO relaying system can be achieved by optical coding.

Identifying the sources of spreading dynamics on networks has drawn extensive attention in recent years, where a variety of epidemic models have been adopted to simulate the propagation dynamics among network nodes, such as the Susceptible-Infectious (SI) model. The objective is to identify the most possible source of infection based on the observed state transition events (e.g., from susceptible to infectious) on a small set of monitored nodes. Most existing studies assumed that once a monitored node is infected, it will be immediately observed. However, in reality, it is likely that for many infectious diseases, a newly infected person becomes infectious without showing any disease symptoms. In this case, the state transition cannot be observed until symptoms arise after a time period (i.e., the incubation period). Accordingly, in this paper, we focus on investigating the source identification problem of asymptomatic spread on complex networks by monitoring only a small number of network nodes. Specifically, we adopt a continuous-time SI model with a contagious incubation period to simulate the asymptomatic spread on networks, where the length of the incubation period is assumed to be an independent and identically distributed exponential distribution. In doing so, we formulate the source identification problem as a likelihood maximization problem and solve it using the Monte Carlo approximation and importance sampling. Finally, we validate the performance of our method on both synthetic and real-world networks with different experimental settings. The results show that our method can achieve higher identification accuracy than several benchmark methods.

The need to accurately measure the mechanical properties of freestanding thin films such as carbon nanotube reinforced nanocomposite thin films and various buckypaper is increasingly urgent. This paper presents the design and control of a novel compliant tensile testing module based on buckled fixed-guided beams aimed at accurately measuring the mechanical properties of freestanding thin films. The piezoelectric stack actuator that can provide high resolution for the output displacement is chosen to actuate the tensile testing module. The advantages of the module are its compactness, no friction, no wear, low cost and good repeatability. The performance is further evaluated by the established analytical model using the elliptic integral approach and the nonlinear finite-element analysis (FEA). A metal prototype of the tensile testing design has been fabricated and the performance of the fabricated prototype is investigated through open-loop displacement response experiments. Experimental results demonstrate the effectiveness of the proposed ideas for the mechanism. The design holds great potential applications in measuring mechanical properties of freestanding thin-film materials such as carbon nanotube reinforced nanocomposite thin films and various buckypaper as well as other freestanding thin-film materials.

Heterogeneous Information Networks (HINs) are ubiquitous in our daily life, as they can describe complex interactions among various types of objects. Heterogeneous information network embedding aims to project the network elements of HINs into low-dimensional node representation vectors, which can facilitate effective analyze of HINs. To learn appropriate HIN embeddings, most existing methods usually adopt meta-paths to capture the heterogeneous semantic information, which require domain knowledge and trial-and-error to find suitable meta-paths. Besides, most existing methods fail to preserve high-order local structural information, and pay little attention to the implicit semantics in HINs. In this paper, we propose a local structural aware heterogeneous information network embedding model named LSA-HNE. Specifically, we first design a relational self-attention graph neural network model to aggregate heterogeneous information and automatically extract semantic similarity without using meta-paths. In addition, we employ a biased random walk based sampling method to extract the local structural information and preserve the implicit semantics in HINs. The experiments conducted on four real-world datasets show that our proposed model is effective compared with the state-of-the-art methods.

This paper investigates the problem of sliding mode controller design based on output feedback for discrete-time Markov jump systems. Considering the typical asynchronous phenomenon for jump systems, here we adopt the Hidden Markov Model (HMM) to quantify the asynchronous degree. Based on this, a static output feedback sliding surface is constructed. Sufficient conditions in terms of bilinear matrix inequalities are proposed ensuring the sliding motion dynamic to be stochastically stable and its $\mathcal {H}_\infty $ performance for the considered discrete time Markov jump systems. Moreover, the reachability of the sliding mode surface is ensured by a predesigned control law. The whole design scheme is presented by using an iterative algorithm. Finally, simulation results are presented to illustrate the effectiveness of the design methodology.

In this paper, 10 Gb/s free space optical (FSO) wiretap channel using optical code division multiple access (OCDMA) time-diversity reception is designed and demonstrated for the first time. Reliability and security are investigated under weak and strong turbulence conditions. Two-dimensional optical encoder and decoder are constructed by wavelength selective switches and optical delay lines, and one-dimensional optical encoder and decoder are constructed by couplers and tunable optical delay lines. Experimental results show that reliability and security can be enhanced simultaneously using OCDMA time-diversity reception. At the received power of 2.58 dBm in strong turbulence, compared to non-diversity system, bit error rate (BER) of legitimate user in OCDMA time-diversity system is reduced from 3.9 × 10-7 to 6.73 × 10-8. Furthermore, the reliability improvement is more obvious when the correlation between the two signals of OCDMA time-diversity is smaller. The secrecy capacity can be increased from 0.546 bit/symbol to 0.665 bit/symbol in strong turbulence when received power is 2.28 dBm. On the other hand, the secrecy capacity of strong turbulence is about 15% higher than that of weak turbulence.

Proximate spacecraft formation flying has many applications in high accurate earth observation and astronomy. In comparison to conventional or electronic thrust, Coulomb thrust has obvious advantages in close-formation control, such as fast throttling, nearly propellantless features and no thruster plume impingement. This paper presents the concept of charged-spacecraft formation and deployment, also investigates the Coulomb-force control for desired spacecraft formations. It is assumed that the charged chief spacecraft has several controllable charged spheres distributed around it and deputies are charged spheres. The deputies are deployed from the chief spacecraft to the desired formation orbits under the active charge control. In order to deploy these deputies subject to the constraints on the limited controllable charges, the transition trajectories are planned by using pseudo-spectral discretization method. Then a charge feedback controller is designed to track the transition trajectories and the desired formation. Numerical simulation results show that one or more deputies can be deployed by controlling the limited charge of the chief spacecraft.

In this paper, a novel distributed algorithm derived from the event-triggered strategy is proposed for achieving resilient consensus of multi-agent networks (MANs) under deception attacks. These malicious deception attacks are intended to interfere with the communication channel causing periods in time at which the sending information among nodes is modified. In particular, we develop an event-triggered update rule which can mitigate the influence of the attackers and at the same time reduce the computing and communication consumption. Each node chooses the instances to update its state information by checking whether its neighbor set meets a given cardinality-dependent function or not. With specified prerequisite on the coupling weights and the sampling period, the consensus achievement of the MANs is independent of the deception attacks, but strictly depends on the robustness of the interconnection topology. Simulation examples are finally given to illustrate the efficacy of the theoretical results.

This paper concerns the quantized H∞ feedback control problem for a class of semi-Markov jump systems with limited mode information. In networked control loop, the network-induced phenomenon limits the accuracy of the signal transmission. The mode of the jump system is thus hardly synchronized with the mode of the designed controller. To well address this asynchronous scenario, we introduce a hidden semi-Markov model to quantitatively describe its degree by setting a conditional probability, which further is assumed to be not completely known considering the sensor or measurement approach limitations. The control signal is to be quantized by a logarithmic quantizer before being transmitted to the actuator. Under the framework of Lyapunov theorem, sufficient conditions are presented by linear matrix inequality techniques to eliminated the quantization effects and ensure the stability of the closed loop system with a prescribed H∞ performance. The parameterization scheme of the quantized feedback controller is finally given. A numerical example is presented in the end to illustrate the validity of the proposed results.

The existing atmospheric mass density models (AMDM) would produce considerable errors in orbital prediction for Low Earth Orbit (LEO) satellites. In order to reduce these errors and correct the AMDM, this paper presents methods based on data mining with historical data of two-line element (TLE). Starting from a typical LEO satellite, TIANHUI, two orbital dynamical models are firstly proposed as the simulation environment to generate training data. The historical TLE data are regarded as actual space environment and used to generate application data. Secondly, three data mining methods, Random Forest (RF), Artificial Neural Network (ANN) and Support Vector Machine (SVM), are combined with the training data to investigate their feasibility in recovering the known deviation of AMDM under simulation environment. Training results show that RF displays the best performance and achieves the accuracy of 99.99%, while the other two methods only achieve 86.83% and 71.90% respectively. Thirdly, under the actual space environment, this paper uses new training and application data to research the ability of the three methods in recovering the unknown deviation of the AMDM and improve the accuracy of orbital prediction. Numerical results are evidential to the accuracy of the proposed methods based on data mining. It is concluded that the capabilities of the data mining for correction for the atmospheric model are very promising, with great potential to advance practical applications on on-orbit propagation.

Due to the effect of atmospheric turbulence, the transmission reliability and physical-layer security of free space optical (FSO) communication system are facing many challenges. In this paper, for the first time, we design and investigate a 10 Gb/s reconfigurable FSO wiretap channel based on spatial diversity and optical code division multiple access (OCDMA). Reconfigurable two-dimensional optical encoder and decoder are constructed by wavelength selective switch and optical delay lines, and reconfigurable one-dimensional optical encoder and decoder are constructed by couplers and tunable optical delay lines. Under three different turbulence conditions, bit error rates (BER) of legitimate user and eavesdropper are measured. It can be seen that under turbulence effects, BER of legitimate user in spatial- diversity is lower than that of non-diversity system. By the rapid code reconfiguration of legitimate users, the eavesdropper can only use unmatched decoder. Thus, the eavesdropping methods of code interception and brute-force searching can be avoided. Experimental results show that this scheme can ensure the reliable transmission of legitimate users, and the eavesdropper can't crack the information when the extraction ratio is 1%. Hence, reliability and physical-layer security can be enhanced simultaneously.

An event-based consensus filtering control scheme for multi-agents with multiple mixing delays is proposed in the paper. Firstly, a piece-wise sampling model with transmission delay defined from sensors to controllers is built, and the effect of time-varying delay on sampling is analyzed. Secondly, a self-triggered scheme is proposed to take into consideration of reducing redundant data and complexity. Thirdly, to fully utilize the available information, by employing an improved generalized free-weighting matrix inequality, a novel Lyapunov-Krasovskii functional approach is proposed to achieve global asymptotically synchronization. At last, an example of multiple unmanned aerial vehicles is offered to show the effectiveness of proposed method.

Grey wolf optimizer (GWO) is a relatively new algorithm in the field of swarm intelligence for solving numerical optimization as well as real-world optimization problems. However, the paramount challenge in GWO is that it is prone to stagnation in local optima. The main goal of this paper is to improve the searchability of GWO when a new learning strategy is introduced in the algorithm. This new operator, called refraction learning, is essentially an opposite-learning strategy that is inspired by the principle of light refraction in physics. This proposed operator is applied to the current global optima of the swarm in the GWO algorithm and is beneficial to help the population for jumping out of the local optima. A novel variant of GWO called RL-GWO based on refraction learning is proposed. A theoretical proof of convergence is provided. We investigate the performance of RL-GWO using two sets of benchmark test functions, i.e., 23 widely used benchmark test functions, and 30 test functions from the IEEE CEC 2014. A non-parametric Wilcoxon's test is performed to observe the impact of improving the global optima in the algorithm. It is concluded that RL-GWO is an efficient, effective, and reliable algorithm for solving function optimization problems.

As an innovative networking paradigm in the Internet of Things, information-centric networking (ICN) has improved security than the traditional IP networks. However, it still faces a large number of security threats, especially those threats from internal attacks. The trust management technology is an effective approach for defending against internal attacks. In this paper, our contributions lie in our investigating the requirements of cybersecurity in ICN and analyzing the typical attack behaviors and defense schemes. Then, we propose a fast and efficient trust management scheme (FETMS) for defending against the on-off attack, which is an intelligent internal attack. The simulation results show that FETMS can efficiently detect and remove the malicious node in a short period of time; meanwhile, it can achieve lower latency and add its mobility.

With the proliferation of cloud platforms, users are inclined to outsource their encrypted data to cloud platforms. However, the existing encryption schemes only provide users with limited query types over encrypted data. Meanwhile, if users encrypt their data by deterministic encryption, their encrypted data will be easily subject to the frequency attack. Besides, users' data privacy is likely to be disclosed to the cloud server when their encrypted data is updated on cloud platforms. To address these problems, in this paper, we propose an effective encrypted query scheme over outsourcing data on cloud platforms. In our scheme, users' data is encrypted based on all possible queries to meet users' diverse query demands. Furthermore, a double AES encryption method is adopted to cope with the frequency attack existing in deterministic encryption. To protect users' privacy when their data is updated, a neighbor rows exchange method is designed in our scheme. The theoretical analysis and comparative experiments demonstrate the effectiveness of our scheme.

Data clustering analysis is proposed to detect the orbital maneuvers of satellites at different scales. In this study, the unsupervised classification methods of K-means, hierarchical, and fuzzy C-means clustering are used to handle the two-line element (TLE) historical data. The K-means-based contour map method is applied to the characteristic variable selection and cluster number determination. The TLE data of large-, medium-, and small-scale orbital maneuvers are clustered by the aforementioned three methods. Through a series of numerical experiments, it is found that for different scales of orbital maneuvers, the clustering methods have different performances and that they can essentially fulfill the functional requirements of orbital detection. By data mining, the orbital maneuvers of the remote sensing satellites “YAOGAN-9”, “TIANHUI-1”, and “Envisat” can be easily detected, which will provide useful information for further orbital supervision and prediction.

Adaptive steganography has become unprecedentedly prevalent compared with non-adaptive ones due to its remarkable performance when resisting modern steganalysis. It prefers hiding bits into pixels from texture regions as such modification is considerably difficult to detect. Current steganalysis capable of locating steganographic payload has only been investigated in the non-adaptive domain, while the works of locating hidden bits modified by adaptive steganographic algorithms have not been studied yet. In this paper, we propose a novel algorithm to locate flipped pixels modified by adaptive steganography in the spatial domain. By re-embedding randomly generated messages upon one single image, we observe that adaptive steganographic methods are prone to modify pixels in the same region, namely texture region. Such property straightforward inspires us to re-embed a random message at the same relative payload into the stego image to obtain the modification map. Then, we extend the modification map with a given margin to locate the modified pixels. The extensive experiments have verified the effectiveness of our designed algorithm in locating flipped pixels modified by the adaptive steganography in the spatial domain.

This paper addresses the time synchronization problem of asynchronous wireless sensor networks (WSNs) under false data injection attacks. First, we extend the fundamental metric of r-robustness with the notion of trusted links and then show that the structural robustness of networks can be significantly improved without adding additional links after setting a small subset of links to be trusted. Then, for a more practical case where the intercommunication topologies between each sensor node are time-varying, we develop a resilient consensus-based distributed algorithm where the sampling period is allowed to be chosen by each sensor independently and it is shown that the time synchronization problem for WSNs can be solved via the proposed controller. Numerical simulations are conducted to illustrate the effectiveness of our proposed method.

Modern adaptive image steganography with minimizing a distortion function has high performance of undetectability. However, when an image with hidden information is attacked by JPEG compression, its robustness cannot be guaranteed, that remarkably limits its extension from the lab to the real world. In this paper, a novel image steganographic algorithm is proposed that is robust to JPEG compression. First, by using the sign of DCT coefficients, that remains unchangeable before and after JPEG compression, we select the candidate coefficients for resisting JPEG compression. Second, the designed distortion function assigns cost for each candidate DCT coefficient. Finally, relying on both error correction code and Syndrome-Trellis Codes, an encoded message is embedded into the cover image with minimum embedding distortion. Compared with prior arts, extensive experimental results highlight both undetectability and robustness of our proposed algorithm.

A multi-user cross-layer security network based on optical code division multiple access (OCDMA) and algorithmic cryptography is proposed, and its security performance is analyzed quantitatively for the first time. In this scheme, eavesdroppers have to not only intercept the optical code of OCDMA in physical-layer, but also break the algorithmic cryptography in data-layer. Thus, the information security will be enhanced effectively. Synchronous transmission system and asynchronous transmission system are analyzed respectively. It can be seen that the cross-layer security of asynchronous transmission is far greater than that of synchronous transmission when the number of users is large enough. OptiSystem simulation is used to investigate the image transmission in cross-layer security system. The transmission distance of single mode fiber is 100 km, and transmission rates are 10 Gbit/s and 1 Gbit/s respectively. It can be seen that legitimate user can recover original image correctly with matched decoder and correct encryption algorithm. However, eavesdroppers cannot recover the image correctly with matched decoder/wrong encryption algorithm or unmatched decoder/correct encryption algorithm. Therefore, our scheme can greatly enhance the security of image transmission system.

Grey wolf optimizer (GWO) algorithm is a swarm intelligence optimization technique that is recently developed to mimic the hunting behavior and leadership hierarchy of grey wolves in nature. It has been successfully applied to many real world applications. In the GWO algorithm, “C”is an important parameter which favoring exploration. At present, the researchers are few study the parameter “C”in GWO algorithm. In addition, during the evolution process, the other individuals in the population move towards to the α, β, and δ wolves which are to accelerate convergence. However, GWO is easy to trap in the local optima. This paper presents a modified parameter “C”strategy to balance between exploration and exploitation of GWO. Simultaneously, a new random opposition-based learning strategy is proposed to help the population jump out of the local optima. The experiments on 23 widely used benchmark test functions with various features, 30 benchmark problems from IEEE CEC 2014 Special Session, and three engineering design optimization problems. The results reveal that the proposed algorithm shows better or at least competitive performance against other compared algorithms on not only global optimization but also engineering design optimization problems.

With the prevalence of adopting data-driven convolution neural network (CNN)-based algorithms into the community of digital image forensics, some novel supervised classifiers have indeed increasingly sprung up with nearly perfect detection rate, compared with the conventional supervised mechanism. The goal of this paper is to investigate a robust multi-classifier for dealing with one of the image forensic problems, referred to as source camera identification. The main contributions of this paper are threefold: (1) by mainly analyzing the image features characterizing different source camera models, we design an improved architecture of CNN for adaptively and automatically extracting characteristics, instead of hand-crafted extraction; (2) the proposed efficient CNN-based multi-classifier is capable of simultaneously classifying the tested images acquired by a large scale of different camera models, instead of utilizing a binary classifier; and (3) numerical experiments show that our proposed multi-classifier can effectively classify different camera models while achieving an average accuracy of nearly 100% relying on majority voting, which indeed outperforms some prior arts; meanwhile, its robustness has been verified by considering that the images are attacked by post-processing such as JPEG compression and noise adding.

Long range (LoRa) technology is a wireless communication technology, which may provide low-power, low-rate, and LoRa communication. It may widely be used into many applications, such as smart metering and factory monitoring. However, some features of LoRa technology also bring new security weaknesses. Therefore, we propose an improved session key management to secure the meter-reading system based on LoRa technology. In our proposed scheme, we add a trusted key distribution server for the related devices to generate their session keys and manage all the keys, where the symmetric session keys are used to protect the communication data between the servers and the terminal devices. In addition, in order to maintain the long-term security of the proposed meter reading system, our proposed lightweight key management scheme can update the session keys automatically and remotely. Finally, we make analysis and experiments to evaluate our proposed key management scheme for the meter-reading system, the related results show our proposed scheme is secure and effective.

In this paper, we have demonstrated an efficient perovskite light-emitting device (PeLED) by improving perovskite crystallization. The improved perovskite crystallization has been achieved by pretreating the underneath poly (3, 4-ethylenedioxythiophene): poly(styrenesulfonate) (PEDOT: PSS) layer with N, N-dimethylformamide (DMF) solvent before single-step spin-coating the perovskite, which results in a prolonged wet environment during the perovskite thermal annealing process to promote the grain growth. The improved perovskite crystallization induces a perovskite thin film with higher surface coverage, fewer defects, and larger grain size with less scattering of grain boundaries. As a result, the PeLEDs exhibit maximum external quantum efficiency of 0.728% and maximum luminance of 2088 cd/m2, which represent a significant improvement over the control devices. Moreover, perovskite films with improved crystalline quality have proved its beneficial effect on the device stability.

Surface morphology of perovskite film is critical for highly efficient perovskite-based solar cells. The solution processed perovskite films tend to have voids and pin-holes between the crystals, which has been cited as very detrimental to device performance. The voids and pin-holes not only cause electrical shorting but deleteriously impact charge dissociation/transport/recombination because of the defects existed on the surface and grain boundaries of the perovskite films as well. Here, we demonstrate passivation of the perovskite surface and grain boundaries by small molecules through infiltration of subphthalocyanines (SubPc) into the perovskite film. Reduced surface defects have been obtained, which was verified by XRD and time-resolved dynamics measurements. As a result, the power conversion efficiency for the solution-processed planar heterojunction solar cells has been enhanced from 9.96% for the contrastive perovskite solar cells to 13.6% for the SubPc passivated perovskite solar cells.

The indium–tin-oxide-free organic solar cells (OSCs) have been fabricated using a semitransparent Au film as anode. A comparative study between the OSCs with different metal cathodes shows that formation of an ideal ohmic contact at low-workfunction Ca and the organic interface reduces series resistance and increases the carrier collection. Moreover, a periodic corrugation was introduced into the device structure, which leads to an enhanced light absorption in the OSCs by elongating the optical path of the incident light inside the absorber material. The highest power conversion efficiency of an optimal OSC with a Au anode and a corrugated Ca/Ag cathode is 1.4%, which is significantly improved in contrast to 0.61% for the OSC with a planar Al cathode.