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Autonomous driving research is an emerging area in the machine learning domain. Most existing methods perform single-task learning, while multi-task learning (MTL) is more efficient due to the leverage of shared information between different tasks. However, MTL is challenging because different tasks may have different significance and varying ranges. In this work, we propose an end-to-end deep learning architecture for statistically correlated MTL using a single input image. Statistical correlation of the tasks is handled by including shared layers in the architecture. Later network separates into different branches to handle the difference in the behavior of each task. Training a multi-task model with varying ranges may converge the objective function only with larger values. To this end, we explore different normalization schemes and empirically observe that the inverse validation-loss weighted scheme has best performed. In addition to estimating steering angle, braking, and acceleration, we also estimate the number of lanes on the left and the right side of the vehicle. To the best of our knowledge, we are the first to propose an end-to-end deep learning architecture to estimate this type of lane information. The proposed approach is evaluated on four publicly available datasets including Comma.ai, Udacity, Berkeley Deep Drive, and Sully Chen. We also propose a synthetic dataset GTA-V for autonomous driving research. Our experiments demonstrate the superior performance of the proposed approach compared to the current state-of-the-art methods. The GTA-V dataset and the lane annotations on the four existing datasets will be made publicly available via https://cvlab.lums.edu.pk/scmtl/.

Precision agriculture is now essential in today’s world, especially for countries with limited water resources, fertile land, and enormous population. Smart irrigation systems can help countries efficiently utilize fresh water and use the excess water for barren lands. Smart water management platform (SWAMP) is an IoT-based smart irrigation project designed for efficient freshwater utilization in agriculture. The primary aim of SWAMP is to auto manage water reserves, distribution, and consumption of various levels, avoid over-irrigation and under-irrigation problems, and auto manage time to maximize production. This research proposed an energy-efficient water management platform (EEWMP), an improved version of SWAMP. EEWMP is an IoT-based smart irrigation system that uses field-deployed sensors, sinks, fusion centres, and open-source clouds. Both models’ performance is evaluated in energy consumption, network stability period, packet sent to destination, and packet delivery ratio. The experimental results show that EEWMP consumes 30% less energy and increases network stability twice than SWAMP. EEWMP can be used in different irrigation models such as drip irrigation, sprinkler irrigation, surface irrigation, and lateral move irrigation with subtle alterations. Moreover, it can also be used in small farms of third-world countries with their existing communication infrastructures such as 2G or 3G.

Recent advancements in learning from graph-structured data have shown promising results on the graph classification task. However, due to their high time complexities, making them scalable on large graphs, with millions of nodes and edges, remains a challenge. In this paper, we propose NetKI, an algorithm to extract sparse representation from a given graph with n nodes and m edges in O ( m ∊ - 2 log 4 n ) time. Our approach follows the notion of Kirchhoff index that encodes the structure of the graph by estimating effective resistance - relying on this approach yields nearly linear time graph representation method that allows scalability on sufficiently large graphs. Through extensive experiments, we show that NetKI provides improved results in terms of running time on large networks and the classification accuracy is within range 2% from the state-of-the-art results.

In this article, we demonstrate a conflicting relationship between two crucial properties— controllability and robustness —in linear dynamical networks of diffusively coupled agents. In particular, for any given number of nodes $N$ and diameter $D$ , we identify networks that are maximally robust using the notion of Kirchhoff's index and then analyze their strong structural controllability. For this, we compute the minimum number of leaders, which are the nodes directly receiving external control inputs, needed to make such networks controllable under all feasible coupling weights between agents. Then, for any $N$ and $D$ , we obtain a sharp upper bound on the minimum number of leaders needed to design strong structurally controllable networks with $N$ nodes and $D$ diameter. We also discuss that the bound is best possible for arbitrary $N$ and $D$ . Moreover, we construct a family of graphs for any $N$ and $D$ such that the graphs have maximal edge sets (maximal robustness) while being strong structurally controllable with the number of leaders in the proposed sharp bound. We then analyze the robustness of this graph family. The results suggest that optimizing robustness increases the number of leaders needed for strong structural controllability. Our analysis is based on graph-theoretic methods and can be applied to exploit network structure to co-optimize robustness and controllability in networks.

Retinal blood vessels, the diagnostic bio-marker of ophthalmologic and diabetic retinopathy, utilise thick and thin vessels for diagnostic and monitoring purposes. The existing deep learning methods attempt to segment the retinal vessels using a unified loss function. However, a difference in spatial features of thick and thin vessels and a biased distribution creates an imbalanced thickness, rendering the unified loss function to be useful only for thick vessels. To address this challenge, a patch-based generative adversarial network-based technique is proposed which iteratively learns both thick and thin vessels in fundoscopic images. It introduces an additional loss function that allows the generator network to learn thin and thick vessels, while the discriminator network assists in segmenting out both vessels as a combined objective function. Compared with state-of-the-art techniques, the proposed model demonstrates the enhanced accuracy, sensitivity, specificity, and area under the receiver operating characteristic curves on STARE, DRIVE, and CHASEDB1 datasets.

In this research, scalable framework for Smart Logistics based Cyber-Physical System (SLCPS) is emulated for stable coverage and connectivity of Internet of Things (IoT) devices. This work is modern manifestation of three laws of computing. Moore’s and Koomey’s laws recommend performance gain and energy efficiency whereas Metcalfe’s law imply network scalability. Combination of these laws suggests the research proposition that development of scalable and performance efficient IoT networks is inevitable. Although IoT has improved specific logistics modules considerably, but incorporation of IoT in complete supply chain of food and random placement of IoT devices due to which unstable coverage and connectivity occurred are major challenges in logistics. The proposed SLCPS framework is designed firstly, to develop apt IoT protocol stack for logistics. Secondly, for bonded connectivity and coverage, mathematical models are proposed instead of random placement and coverage map is based on binary coverage model. Thirdly, for scalability supply chain of food for smart logistics process is designed in terms of container, storehouse and warehouse comprising of varying number of IoT devices. The architecture of SLCPS framework has three modules i.e. internal IoT network, border router and external network, emulated in Cooja simulator. The contikimac protocol is used for efficient traffic flow and power consumption. Single hop, multiple hops and random IoT devices placement scenarios are used for results comparison and validation. The performance evaluation results, i.e. throughput, network convergence time, packet delivery ratio, average latency, power consumption and timeline investigation validated utilization of proposed framework in terms of enhanced network performance. Significance of proposed SLCPS framework results in cost minimization, reducing communication and computation overhead, resilience to IoT device failures and an interference free network connectivity and coverage. Coverage and connectivity are measure of quality of service in IoT network. Therefore, this research provided bonded coverage and connectivity in smart logistics using mathematical models. In addition, a baseline framework is provided for extended research in CPS and IoT applications.

Highly defective molybdenum disulfide (MoS2) nanosheets of 1T phase are synthesized on a three-dimensional reduced graphene oxide (3D RGO) network through a one-pot hydrothermal method. By switching the reaction time, the resulting MoS2's phases (1T or 2H) and defect-density are successfully controlled. Reducing the reaction time from 20 to 6 h increases the defect-density and induces a phase transition from hybrid 2H/1T to 1T phase. High defect-density coupled with high 1T-phase portion in MoS2 nanosheets/RGO heterostructures enhances the electrochemical performance for supercapacitor application by inducing additional active sites, which provide fast charge transfer rate and a large number of ion diffusion channels. Among all samples, the defect rich 1T-phase MoS2 nanosheets/RGO heterostructure synthesized in 6 h (M/RGO-6) gives an outstanding specific capacitance of 442.0 F g−1 at a current density of 1 A g−1. The M/RGO-6 also exhibits excellent cycling stability with capacitance retention of 90.3% over 1000 cycles at 5 A g−1. These make M/RGO-6 attractive to the electrode of high-performance supercapacitors.

As the Industrial Internet of Things (IIoT) becomes more ubiquitous in critical application domains, such as smart water-distribution and transportation systems, providing security and resilience a...

Adversaries may cause significant damage to smart infrastructure using malicious attacks. To detect and mitigate these attacks before they can cause physical damage, operators can deploy anomaly detection systems (ADS), which can alarm operators to suspicious activities. However, detection thresholds of ADS need to be configured properly, as an oversensitive detector raises a prohibitively large number of false alarms, while an undersensitive detector may miss actual attacks. This is an especially challenging problem in dynamical environments, where the impact of attacks may significantly vary over time. Using a game-theoretic approach, we formulate the problem of computing optimal detection thresholds which minimize both the number of false alarms and the probability of missing actual attacks as a two-player Stackelberg security game. We provide an efficient dynamic programming-based algorithm for solving the game, thereby finding optimal detection thresholds. We analyze the performance of the proposed algorithm and show that its running time scales polynomially as the length of the time horizon of interest increases. In addition, we study the problem of finding optimal thresholds in the presence of both random faults and attacks. Finally, we evaluate our result using a case study of contamination attacks in water networks, and show that our optimal thresholds significantly outperform fixed thresholds that do not consider that the environment is dynamical.

The study explores carbon quantum dots (C-dots) as potential candidates for enhancing the signal sensitivity of an electrochemical sensor devised for biologically important molecule, such as uric acid (UA). The C-dots were evaluated for their electrochemical characteristics in combination with Fe3O4 nanoparticles (Fe3O4 NPs), which were applied as the primary electro-catalytic promoter. The hybrid nanocomposite (C-dots/Fe3O4 HCs) formation was achieved by facilitating the adsorption of C-dots over Fe3O4 NPs using amine-carbonyl interactions. Unlike, one pot method, the proposed strategy enables aggregation-free coverage of Fe3O4 NPs with highly conductive layer of C-dots that can act as conduction centres to support ultra-fast electron transfer kinetics to satisfy the need of high signal sensitivity. The hybrid composite demonstrated remarkable signal improvement when tested against the electrochemical oxidation of UA. The heighten current response and lower over-potential values enabled development of a DC-amperometric (DC-AMP) sensor for UA with a linear working range of 0.01 to 0.145 μM and signal sensitivity measurable up to 6.0 × 10−9 M. The said improvement was manifested as a synergetic outcome of active redox couple (Fe (III/II)), larger surface area of Fe3O4 NPs engulfed with a layer of highly conductive C-dots acting as efficient charge sensitisers.

Excessive usage of phenolic compounds such as catechol (Cat-), cresol (Cre-) and p-nitrophenol (PNP) in food processing and pharmaceutical industry may lead to severe health issues including nerve cells disruption and even infertility. Thus, designing sensitive nanoscale electrochemical sensors to boost single screen signalling of these phenolic compounds from environmental samples is highly demanded. However, control monitoring and quantification of these phenolic compounds from real samples such as commercially available pesticides and insecticides is quite challenging because of their low concentration along with excess of co-existing interfering species. Herein, for the simultaneous electrochemical monitoring of these phenolic pollutants, we fabricate C-encapsulated three-dimensional Cu nano-pyramids (C@3DCu-PY) through a simple, scalable freeze-drying and carbonization process. The resulting hybrid contains 3D arrangement of controlled Cu nano-pyramids with atomic arrangement along all four lateral faces with sharp apex which result in exposition of greater fraction of Cu {111} catalytic active sites, large number of sp2 carbon atoms, and maximum surface defects along with dipolar/quadrupolar radiative plasmons, thus offering highly sensitive and selective behaviour toward simultaneous monitoring of Cat-, cresol and PNP. The designed C@3DCu-PY electrode also shows fast diffusion and kinetics of ions compared to C@almond (AL)-like Cu nanoparticles (NPs) sample, thus indicating shape dependent efficacy of the designed electrode. Because of high sensitivity, the developed novel C@3DCu-PY electrode was successfully applied to monitor PNP from commercially available insecticide. The results demonstrated the potential of the designed electrode to be used in preventing serious health issue associated with phenolic pollutants.

BiFe1-xNixCoxO3 multiferroics (x = 0.00, 0.05, 0.1, 0.15 and 0.2) were fabricated by micro-emulsion process. Differential scanning calorimeter (DSC) plot exhibited broad exothermic peaks at 264.33 °C and 320.8 °C. X-ray diffraction (XRD) patterns indicated the rhombohedral phase with the R3c phase group. The crystallite size ranges from 48 to 11 nm. Bulk and X-ray densities were optimized by increasing cobalt and nickel concentration. Magnetic parameters such as retentivity (Mr), saturation magnetization (Ms) and coercivity (Hc) were studied for different compositions of BiFe1-xNixCoxO3. BiFe0.6Ni0.4Co0.4O3 behaves as ferromagnetic material having maximum saturation magnetization (10.6 emu/g) and maximum Mr (5.2 emu/g). Enhanced magnetic properties were attributed to the difference in magnetic moment of doping element and Fe3+ ions. Dielectric parameters had great influence of doping (Ni Co) in high frequency region. It was observed that by increasing doping level dielectric parameters were decreased appreciably.

Due to its fuel-efficient and environmentally friendly nature, the electrocatalytic nitrogen reduction reaction (NRR) has drawn significant attention. However, most of the catalysts reported for the NRR are noble metals or oxides, which are expensive and poorly conductive. Therefore, the development of effective electrocatalysts for nitrogen (N2) fixation under ambient conditions has become a promising field. Herein, we report fluorine-free Ti3C2Tx (T = O, OH) nanosheets with a small size (∼50–100 nm lateral size) as catalysts for the NRR. The as-prepared materials achieved a high NH3 yield (36.9 μg h−1 mgcat−1) and faradaic efficiency (9.1%) at −0.3 V vs. reversible hydrogen electrode (RHE) in 0.1 M HCl. The size effect and fluorine-free properties make the performance around two times higher than that in the case of a fluorine-based treatment. This paper inspires us to pursue the extreme size (e.g. quantum dots) of MXenes which will enhance its NRR property greatly.

Surface modification for templated synthesis is crucial to achieving three-dimensional (3D) architectured materials for catalysis, photonics, energy storage, etc. However, the existing facile and versatile modification methods (e.g. with dopamine and catechol) generate modification layers that are unstable in harsh environments. These methods are thus unsuitable for electrical applications. Here we report that Ce4+ can act as an effective surface modification reagent for a broad range of substrates (chitinous butterfly wings, carbon paper, nickel foam, and polyethylene terephthalate planks) with various structural features owing to its strong oxidizing ability and Lewis acid nature. The modification yields discrete CeO2 seed layers on substrate surfaces in ca. 0.25-2 h, important for the subsequent conformal growth of CeO2 nanoparticles, Ni(OH)2 nanowires, FeOOH nanosheets, and WO3 nanosheets into 3D architectured materials. The conformally synthesized FeOOH on nickel foam (NF) yields an overpotential of 241 mV at 10 mA cm-1 for an oxygen evolution reaction. This value is comparable to a typical catalyst Ni(Fe)OOH-NF for which the Ni/Fe ratio must be well-optimized. This facile and versatile strategy might have broad applications in the conformal fabrication and application of 3D architectured materials, especially when applied in electrical applications of architectured materials (e.g. Li-ion battery).

In this chapter, we will evaluate the synthesis and purification of carbon nanotubes. Carbon nanotubes are cylindrical molecules that consists of graphene (rolled up of a single-layer carbon atom). A wide variety of synthesis techniques such as arc discharge synthesis, laser ablation of graphite/laser vaporization synthesis method, chemical vapor deposition (CVD), high pressure carbon monoxide synthesis and flame synthesis techniques, have been implemented to grow single and multi-walled carbon nanotubes for technological applications. All of the above methods exploit transition metals, like iron, cobalt, and nickel, as a catalyst. There are number of methods (filtering, chromatography and centrifugation) used to purify the carbon nanotubes, but the degree of purity remained questionable in these methods. In order to enhance the purification extent, alternate techniques such as Gas phase purification, Liquid phase purification and Purification by Intercalation are introduced. Here we will discuss the advantages and disadvantages of these purification routes. It will help researchers in selecting appropriate and effective method for synthesis and purification of carbon nanotubes.

A miniaturized on-chip substrate integrated waveguide (SIW) bandpass filter (BPF) and diplexer are designed in a 65nm CMOS process. The miniaturization of 42% is achieved by carving slots in the top layer of the SIW cavity. The designed two pole bandpass filter has a simulated 3 dB bandwidth of 11.1% around 70 GHz, while the diplexer has 3-dB bandwidths of 15.3% GHz and 14.86% around 71 and 90.9 GHz, respectively. Moreover, the isolation between two output ports of the diplexer is greater than -27.3 dB.

We study the strong structural controllability (SSC) of diffusively coupled networks, where the external control inputs are injected to only some nodes, namely the leaders. For such systems, one measure of controllability is the dimension of strong structurally controllable subspace, which is equal to the smallest possible rank of controllability matrix under admissible (positive) coupling weights. In this paper, we compare two tight lower bounds on the dimension of strong structurally controllable subspace: one based on the distances of followers to leaders, and the other based on the graph coloring process known as zero forcing. We show that the distance-based lower bound is usually better than the zero-forcing-based bound when the leaders do not constitute a zero-forcing set. On the other hand, we also show that any set of leaders that can be shown to achieve complete SSC via the distance-based bound is necessarily a zero-forcing set. These results indicate that while the zero-forcing based approach may be preferable when the focus is only on verifying complete SSC, the distance-based approach is usually more informative when partial SSC is also of interest. Furthermore, we also present a novel bound based on the combination of these two approaches, which is always at least as good as, and in some cases strictly greater than, the maximum of the two bounds. We support our analysis with numerical results for various graphs and leader sets., Accepted to the 59th IEEE Conference on Decision and Control

In this paper, we study the relationship between resilience and accuracy in the resilient distributed multi-dimensional consensus problem. We consider a network of agents, each of which has a state in $\mathbb{R}^d$. Some agents in the network are adversarial and can change their states arbitrarily. The normal (non-adversarial) agents interact locally and update their states to achieve consensus at some point in the convex hull $\calC$ of their initial states. This objective is achievable if the number of adversaries in the neighborhood of normal agents is less than a specific value, which is a function of the local connectivity and the state dimension $d$. However, to be resilient against adversaries, especially in the case of large $d$, the desired local connectivity is large. We discuss that resilience against adversarial agents can be improved if normal agents are allowed to converge in a bounded region $\calB\supseteq\calC$, which means normal agents converge at some point close to but not necessarily inside $\calC$ in the worst case. The accuracy of resilient consensus can be measured by the Hausdorff distance between $\calB$ and $\calC$. As a result, resilience can be improved at the cost of accuracy. We propose a resilient bounded consensus algorithm that exploits the trade-off between resilience and accuracy by projecting $d$-dimensional states into lower dimensions and then solving instances of resilient consensus in lower dimensions. We analyze the algorithm, present various resilience and accuracy bounds, and also numerically evaluate our results., Comment: IEEE Conference on Decision and Control (CDC) 2020

The detection of lungs nodule cancer by Computer Aided Diagnosis (CAD) system, provide a great support to the medical experts and can lower the death rate. In this research, CAD system is established for early detection of lungs nodule cancer. First, the contrast of an input image is enhanced by applying Contrast Limited Adaptive Histogram Equalization (CLAHE). The Otsu thresholding is applied for segmentation of lungs tumor followed by morphological filters to remove background and other geometrical objects. The resultant image is de-noised by applying Discrete Wavelet Transform (DWT). Gray Level Co-occurrence Matrix (GLCM) is used for features extraction such as correlation, energy, etc. Principle Component Analysis (PCA) is employed for feature selection. Finally, Support Vector Machine (SVM) is used to classify the image into benign (non-cancerous) or malignant (cancerous). The performance parameters such as accuracy, sensitivity, specificity, Peak Signal to Noise Ratio (PSNR), Root Mean Square Errors (RMSE) and Area under the Curve are used for the evaluation of the proposed method. The proposed system is verified on the LIDC dataset. The visual and parametric results of the proposed method are computed and compared with other existing methods.

A 65&ndash, 67 GHz phase-locked loop (PLL) with a novel low power phase-frequency detector (PFD) in 65 nm LP CMOS is presented. The PLL consists of a V-band voltage-controlled oscillator (VCO), a divide-by-two injection-locked frequency divider (ILFD), and a current-mode logic (CML) divider chain. A charge pump (CP) and a 2nd-order loop filter are used with PFD for VCO tuning. The PFD is implemented with 16 transistors with dead-zone-free capability. The measured locking range of the PLL is from 65.15 to 67.4 GHz, with &minus, 11.5 dBm measured output power at 66.05 GHz while consuming 88 mW. The measured phase noise at 1 MHz offset is &minus, 84.43 dBc/Hz. The chip area of the PLL is 0.84 mm2 including probing pads. The proposed PLL can be utilized as a frequency synthesizer for carrier signal generation in IEEE 802.11ad standard high data rate transceiver circuits.

The study was conducted to evaluate the effects of dietary yeast β-glucan (YG) on performance and immune functions in breeder hens in a non-challenged setting. A total of 512 43-week-old Hy-Line Brown breeder hens were assigned into four treatments, and fed a basal diet with YG at 0, 50, 100 and 200 mg /kg for 8 weeks, respectively. Results showed that supplementation of YG did not affect production performance, but linearly increased hatchability (P 0.05). Compared with the control, hens fed with 200 mg/kg YG had improved eggshell color and reduced mortality. Moreover, feeding 200 mg/kg YG significantly (P 0.05) enhanced lymphocyte proliferation response to LPS, increased the percentage of peripheral blood CD3

In this article, we study resilient distributed diffusion for multi-task estimation in the presence of adversaries where networked agents must estimate distinct but correlated states of interest by processing streaming data. We show that in general diffusion strategies are not resilient to malicious agents that do not adhere to the diffusion-based information processing rules. In particular, by exploiting the adaptive weights used for diffusing information, we develop time-dependent attack models that drive normal agents to converge to states selected by the attacker. We show that an attacker that has complete knowledge of the system can always drive its targeted agents to its desired estimates. Moreover, an attacker that does not have complete knowledge of the system including streaming data of targeted agents or the parameters they use in diffusion algorithms, can still be successful in deploying an attack by approximating the needed information. The attack models can be used for both stationary and non-stationary state estimation. In addition, we present and analyze a resilient distributed diffusion algorithm that is resilient to any data falsification attack in which the number of compromised agents in the local neighborhood of a normal agent is bounded. The proposed algorithm guarantees that all normal agents converge to their true target states if appropriate parameters are selected. We also analyze trade-off between the resilience of distributed diffusion and its performance in terms of steady-state mean-square-deviation (MSD) from the correct estimates. Finally, we evaluate the proposed attack models and resilient distributed diffusion algorithm using stationary and non-stationary multi-target localization.

Embedding networks into a fixed dimensional feature space, while preserving its essential structural properties is a fundamental task in graph analytics. These feature vectors (graph descriptors) are used to measure the pairwise similarity between graphs. This enables applying data mining algorithms (e.g classification, clustering, or anomaly detection) on graph-structured data which have numerous applications in multiple domains. State-of-the-art algorithms for computing descriptors require the entire graph to be in memory, entailing a huge memory footprint, and thus do not scale well to increasing sizes of real-world networks. In this work, we propose streaming algorithms to efficiently approximate descriptors by estimating counts of sub-graphs of order \(k\le 4\), and thereby devise extensions of two existing graph comparison paradigms: the Graphlet Kernel and NetSimile. Our algorithms require a single scan over the edge stream, have space complexity that is a fraction of the input size, and approximate embeddings via a simple sampling scheme. Our design exploits the trade-off between available memory and estimation accuracy to provide a method that works well for limited memory requirements. We perform extensive experiments on real-world networks and demonstrate that our algorithms scale well to massive graphs.

To observe and control a networked system, especially in failure-prone circumstances, it is imperative that the underlying network structure be robust against node or link failures. A common approach for increasing network robustness is redundancy: deploying additional nodes and establishing new links between nodes, which could be prohibitively expensive. This paper addresses the problem of improving structural robustness of networks without adding extra links. The main idea is to ensure that a small subset of nodes, referred to as the trusted nodes, remains intact and functions correctly at all times. We extend two fundamental metrics of structural robustness with the notion of trusted nodes, network connectivity, and $r$ -robustness, and then show that by controlling the number and location of trusted nodes, any desired connectivity and robustness can be achieved without adding extra links. We study the complexity of finding trusted nodes and construction of robust networks with trusted nodes. Finally, we present a resilient consensus algorithm with trusted nodes and show that, unlike existing algorithms, resilient consensus is possible in sparse networks containing few trusted nodes.

In this work, the Al3+-Sm3+ substituted M-type Ba–Sr hexaferrites, having a composition of Ba0.4Sr0.6Al0.4-xSmxFe11.60O19 (BSAMFO; Pure, 0.0 ≤ x ≤ 0.4) were synthesized using the sol-gel technique. To achieve the hexagonal phase, the optimum annealing temperature was measured by the TGA analysis. X-ray diffraction (XRD) was employed to identify the phase of magnetic powders (MPs). The XRD pattern reveals that the MPs with Al3+ and Sm3+ contents (for x ≤ 0.4) represent α-Fe2O3 as a second phase and the MPs (with 0.1 ≤ x ≤ 0.4) show the presence of a small amount of FeSmO3 phases. Fourier transform infrared spectroscopy (FTIR) and XRD examination clearly expose the different band modes in structure, formation and the compositions of the hexagonal structure, whereas scanning electron microscopy (SEM) was employed to examine the morphologies of the MPs. The hexagonal platelet-like shape with size 0.54–1.23 µm of MPs is observed by SEM images. The presence of ferrite elements is confirmed by energy dispersive spectrometer (EDS) analysis. The magnetic characteristics of MPs were analyzed by a physical property measurement system-vibrating sample magnetometer (PPMS-VSM). Results reveal that Al3+-Sm3+ substitutions modify the saturation magnetization (Ms), a remanant magnetization (Mr) and coercivity (Hc). Additionally, the Hc value decreased while Ms and Mr exhibited the monotonic behavior as the function of x.

This study explores the potential of a newly-developed indium tin oxide (ITO) based electrode for the development of an electro-catalytic inhibition sensor system for organophosphorus pesticides. The sensor relies on the redox signal inhibition of pralidoxime chloride (PAM) immobilised over the pimelic acid functionalised CuO nanostructures grown in-situ over an ITO substrate. The in-situ growth enabled on-pot modification and functionalisation of ITO electrodes with the formation of uniform nanostructures possessing high surface area and excellent interface contact. The versatility of the proposed electrode was evident from its excellent electrochemical characteristics evaluated in comparison to bare and slurry-driven glassy carbon electrodes (GCEs). The high structural uniformity and greater surface coverage achieved by in-situ growth provided a uniform surface environment for electrode-analyte interaction, leading to good inhibition signal sensitivity and repeatability. The developed sensor was successful in detecting chlorpyrifos, fenthion and methyl parathion within the concentration range of 0.01–0.16 μM with signal sensitivity reaching down to 1.6 × 10−9, 2.5 × 10−9 and 6.7 × 10−9 M respectively. Moreover, the proposed sensor demonstrated excellent applicability when tested for chlorpyrifos from vegetable extracts using a standard addition method.

MXenes, 2D compounds generated from layered bulk materials, have attracted significant attention in energy-related fields. However, most syntheses involve HF, which is highly corrosive and harmful to lithium-ion battery and supercapacitor performance. Here an alkali-assisted hydrothermal method is used to prepare a MXene Ti3 C2 Tx (T=OH, O). This route is inspired from a Bayer process used in bauxite refining. The process is free of fluorine and yields multilayer Ti3 C2 Tx with ca. 92 wt % in purity (using 27.5 m NaOH, 270 °C). Without the F terminations, the resulting Ti3 C2 Tx film electrode (ca. 52 μm in thickness, ca. 1.63 g cm-3 in density) is 314 F g-1 via gravimetric capacitance at 2 mV s-1 in 1 m H2 SO4 . This surpasses (by ca. 214 %) that of the multilayer Ti3 C2 Tx prepared via HF treatments. This fluorine-free method also provides an alkali-etching strategy for exploring new MXenes for which the interlayer amphoteric/acidic atoms from the pristine MAX phase must be removed.

A cognitive radio network has a high promise to improve spectrum utilization, through exploitation of spectrum holes from the primary network. However, in a distributed environment, employing contention based random access, collisions and unattempted slots result in waste of the discovered holes. This waste is even costlier for a CRN as resources are consumed in finding the holes. In this paper, we have proposed a self-organizing distributed CR-MAC protocol so that the contention is not required for medium access, thereby, minimizing the waste of opportunity due to collisions or for being left idle in random access, consequently, improving the spectral efficiency. This is achieved by organizing and queuing the active secondary users through a timer value and directing them in an orderly fashion to use the discovered holes. The proposed scheme successfully assigns a channel to more than 99% of the secondary users in less than 5% of the idle slots from the primary network, compared to that required in contention based random access schemes.

The negative replica of biomorphic TiO2 with nano-holes structure has been effectively fabricated directly from nano-nipple arrays structure of cicada wings by using a simple, low-cost and highly effective sol-gel ultrasonic method. The nano-holes array structure was well maintained after calcination in air at 500 °C. The Ag nanoparticles (10 nm–25 nm) were homogeneously decorated on the surface and to the side wall of nano-holes structure. It was observed that the biomorphic Ag-TiO2 showed remarkable photocatalytic activity by degradation of methyl blue (MB) under UV-vis light irradiation. The biomorphic Ag-TiO2 with nano-holes structure showed superior photocatalytic activity compared to the biomorphic TiO2 and commercial Degussa P25. This high-performance photocatalytic activity of the biomorphic Ag-TiO2 may be attributed to the nano-holes structure, localized surface plasmon resonance (LSPR) property of the Ag nanoparticles, and enhanced electron-hole separation. Moreover, the biomorphic Ag-TiO2 showed more absorption capability in the visible wavelength range. This work provides a new insight to design such a structure which may lead to a range of novel applications.

Sensor networks monitoring spatially distributed physical systems often comprise battery-powered sensor devices. To extend lifetime, battery power may be conserved using sleep scheduling : activating and deactivating some of the sensors from time to time. Scheduling sensors with the goal of maximizing average coverage, which is the average fraction of time for which each monitoring target is covered by some active sensor, have been studied extensively. However, many applications also require time-critical monitoring in the sense that one has to minimize the average delay until an unpredictable change or event at a monitoring target is detected. In this letter, we study the problem of sleep scheduling sensors to minimize the average delay in detecting such time-critical events in the context of monitoring physical systems that can be modeled using graphs, such as water-distribution networks. We provide a game-theoretic solution that computes schedules with near optimal average delays. We illustrate that schedules that optimize average coverage may result in large average detection delays, whereas schedules minimizing average detection delays using our proposed scheme also result in near optimal average coverage.

We investigate how the graph topology influences the robustness to noise in undirected linear consensus networks. We consider the expected steady state population variance of states as the measure of vulnerability to noise. We quantify the structural robustness of a network by using the smallest value this measure can attain under edge weights from the unit interval. Our main result shows that the average distance between nodes and the average node degree define tight upper and lower bounds on the structural robustness. Using these bounds, we characterize the networks with different types of robustness scaling. We also present a fundamental trade-off between the structural robustness and the sparsity of networks. We then show that random regular graphs typically have near-optimal structural robustness among the graphs with same size and average degree. Some simulation results are also provided.

We investigate how the graph topology influences the robustness to noise in undirected linear consensus networks. We measure the structural robustness by using the smallest possible value of steady state population variance of states under the noisy consensus dynamics with edge weights from the unit interval. We derive tight upper and lower bounds on the structural robustness of networks based on the average distance between nodes and the average node degree. Using the proposed bounds, we characterize the networks with different types of robustness scaling under increasing size. Furthermore, we present a fundamental trade-off between the structural robustness and the average degree of networks. While this trade-off implies that a desired level of structural robustness can only be achieved by graphs with a sufficiently large average degree, we also show that there exist dense graphs with poor structural robustness. We then show that, random k-regular graphs (the degree of each node is k) with n nodes typically have near-optimal structural robustness among all the graphs with size n and average degree k for sufficiently large n and k. We also show that when k increases properly with n, random k-regular graphs maintain a structural robustness within a constant factor of the complete graph's while also having the minimum average degree required for such robustness., Accepted for publication in IEEE Transactions on Automatic Control

A networked system can be made resilient against adversaries and attacks if the underlying network graph is structurally robust. For instance, to achieve distributed consensus in the presence of adversaries, the underlying network graph needs to satisfy certain robustness conditions. A typical approach to making networks structurally robust is to strategically add extra links between nodes, which might be prohibitively expensive. In this paper, we propose an alternative way of improving network's robustness, that is by considering heterogeneity of nodes. Nodes in a network can be of different types and can have multiple variants. As a result, different nodes can have disjoint sets of vulnerabilities, which means that an attacker can only compromise a particular type of nodes by exploiting a particular vulnerability. We show that, by such a diversification of nodes, attacker's ability to change the underlying network structure is significantly reduced. Consequently, even a sparse network with heterogeneous nodes can exhibit the properties of a structurally robust network. Using these ideas, we propose a distributed control policy that utilizes heterogeneity in the network to achieve resilient consensus in adversarial environment. We extend the notion of $(r,s)$-robustness to incorporate the diversity of nodes and provide necessary and sufficient conditions to guarantee resilient distributed consensus in heterogeneous networks. Finally we study the properties and construction of robust graphs with heterogeneous nodes., 8th IFAC Workshop on Distributed Estimation and Control in Networked Systems (NecSys) 2019

In this paper, we demonstrate a conflicting relationship between two crucial properties---controllability and robustness---in linear dynamical networks of diffusively coupled agents. In particular, for any given number of nodes $N$ and diameter $D$, we identify networks that are maximally robust using the notion of Kirchhoff index and then analyze their strong structural controllability. For this, we compute the minimum number of leaders, which are the nodes directly receiving external control inputs, needed to make such networks controllable under all feasible coupling weights between agents. Then, for any $N$ and $D$, we obtain a sharp upper bound on the minimum number of leaders needed to design strong structurally controllable networks with $N$ nodes and diameter $D$. We also discuss that the bound is best possible for arbitrary $N$ and $D$. Moreover, we construct a family of graphs for any $N$ and $D$ such that the graphs have maximal edge sets (maximal robustness) while being strong structurally controllable with the number of leaders in the proposed sharp bound. We then analyze the robustness of this graph family. The results suggest that optimizing robustness increases the number of leaders needed for strong structural controllability. Our analysis is based on graph-theoretic methods and can be applied to exploit network structure to co-optimize robustness and controllability in networks., IEEE Transactions on Control of Network Systems

In a networked system, any change in the underlying network structure, such as node and link removals due to an attack, could severely affect the overall system behavior. Typically, by adding more links and connections between nodes, networks can be made structurally robust. However, this approach is not always feasible, especially in sparse networks. In this paper, we aim to improve the structural robustness in networks using the notions of diversity and trustiness. Diversity means that nodes in a network are of different types and have many variants. Trustiness means that a small subset of nodes are immune to failures and attacks. We show that by combining diversity and trustiness within the network, we can significantly limit the attacker's ability to change the underlying network structure by strategically removing nodes. Using pairwise connectivity as a measure, we show that by appropriately distributing trusted nodes and assigning types to nodes, network robustness can be significantly improved. We analyze the complexity of diversifying and computing a set of trusted nodes, and then present heuristics to compute attacks consisting of node removals. We also present heuristics to defend networks against such attacks by distributing node types and trusted nodes. Finally, we evaluate our results on various networks to demonstrate the usefulness of our approach.

Multi-task learning aims to enhance the performance of a model by inductive transfer of information among tasks. However, joint optimization of multiple tasks is challenging due to unbalanced data ranges and variations in task difficulties which can cause the model to converge only for a single task which has large values. To address these problems, we propose a novel weighting scheme based on validation loss. The proposed weighted scheme is evaluated on three datasets, including publicly available Comma.ai and Udacity benchmark dataset and GTA-V dataset. Our experiments demonstrate the superior performance of the proposed approach compared to the current state-of-the-art methods.

Retinal blood vessels are considered to be the reliable diagnostic biomarkers of ophthalmologic and diabetic retinopathy. Monitoring and diagnosis totally depends on expert analysis of both thin and thick retinal vessels which has recently been carried out by various artificial intelligent techniques. Existing deep learning methods attempt to segment retinal vessels using a unified loss function optimized for both thin and thick vessels with equal importance. Due to variable thickness, biased distribution, and difference in spatial features of thin and thick vessels, unified loss function are more influential towards identification of thick vessels resulting in weak segmentation. To address this problem, a conditional patch-based generative adversarial network is proposed which utilizes a generator network and a patch-based discriminator network conditioned on the sample data with an additional loss function to learn both thin and thick vessels. Experiments are conducted on publicly available STARE and DRIVE datasets which show that the proposed model outperforms the state-of-the-art methods.

Objective Some researchers have studied about early prediction and diagnosis of major adverse cardiovascular events (MACE), but their accuracies were not high. Therefore, this paper proposes a soft voting ensemble classifier (SVE) using machine learning (ML) algorithms. Methods We used the Korea Acute Myocardial Infarction Registry dataset and selected 11,189 subjects among 13,104 with the 2-year follow-up. It was subdivided into two groups (ST-segment elevation myocardial infarction (STEMI), non ST-segment elevation myocardial infarction NSTEMI), and then subdivided into training (70%) and test dataset (30%). Third, we selected the ranges of hyper-parameters to find the best prediction model from random forest (RF), extra tree (ET), gradient boosting machine (GBM), and SVE. We generated each ML-based model with the best hyper-parameters, evaluated by 5-fold stratified cross-validation, and then verified by test dataset. Lastly, we compared the performance in the area under the ROC curve (AUC), accuracy, precision, recall, and F-score. Results The accuracies for RF, ET, GBM, and SVE were (88.85%, 88.94%, 87.84%, 90.93%) for complete dataset, (84.81%, 85.00%, 83.70%, 89.07%) STEMI, (88.81%, 88.05%, 91.23%, 91.38%) NSTEMI. The AUC values in RF were (98.96%, 98.15%, 98.81%), ET (99.54%, 99.02%, 99.00%), GBM (98.92%, 99.33%, 99.41%), and SVE (99.61%, 99.49%, 99.42%) for complete dataset, STEMI, and NSTEMI, respectively. Consequently, the accuracy and AUC in SVE outperformed other ML models. Conclusions The performance of our SVE was significantly higher than other machine learning models (RF, ET, GBM) and its major prognostic factors were different. This paper will lead to the development of early risk prediction and diagnosis tool of MACE in ACS patients.

In the present study, Sr2CuxNi2-xCrxFe28-xO46 where (x = 0.0, 0.1, 0.2, 0.3, 0.4, 0.5) X-type ferrites were fabricated by micro-emulsion procedure. X-ray diffraction technique assured the phase of fabricated ferrites to be that of pure X-type ferrites. The reduction in lattice constants (a & c) seemed with the incorporation of Cr3+-Cu2+ metals. The crystalline size of the synthesized materials was obtained in 19–23 nm range, using the Scherer's formula. The iron-oxygen bands of X-type hexagonal ferrites at tetrahedral and octahedral sites were confirmed from FTIR spectra. The FTIR spectra also showed the strong influence of Cr3+-Cu2+ substitution, which confirmed that the dopants have a meaningful role in these ferrites. The dielectric parameters were explored in a large frequency range (i.e. from 1 MHz to 3 GHz) and these parameters showed significant variations with Cr3+-Cu2+ doping contents. Moderate values of dielectric constant, low tangent loss, high Q values, and significant response to the high frequency of these X-type ferrites ensured their applicability for the high (GHz) frequency applications. Vibrating sample magnetometry technique (VSM) was applied to unfold the magnetic parameters. The Coercivity (Hc), saturation magnetization (Ms) as well as remanence magnetization (Mr) revealed considerable variations with doping Cr3+-Cu2+cations. The enhancement in magnetic parameter i.e. coercivity Hc with the doping of Cr3+-Cu2+ reflects the uses of these materials in longitudinal recording media.

A typical thin and fully flexible hybrid electrode was developed by integrating the encapsulation of silver nanowires (AgNWs) network between a monolayer graphene and polymer film as a sandwich structure. Compared with the reported flexible electrodes based on PET or PEN substrate, this unique electrode exhibits the superior optoelectronic characteristics (sheet resistance of 8.06 Ω/□ at 88.3% light transmittance). Meanwhile, the specific up-to-bottom fabrication process could achieve the superflat surface (RMS = 2.58 nm), superthin thickness (∼8 μm thickness), high mechanical robustness, and lightweight. In addition, the strong corrosion resistance and stability for the hybrid electrode were proved. With these advantages, we employ this electrode to fabricate the simple flexible organic light-emitting device (OLED) and perovskite solar cell device (PSC), which exhibit the considerable performance (best PCE of OLED = 2.11 cd/A2; best PCE of PSC = 10.419%). All the characteristics of the unique hybrid elec...

Three-dimensional (3D) aerogels based on in-situ growth of tetragonal molybdenum disulfide (1T-MoS2) on hydrothermally acid-treated porous carbon (HAPC) derived from sugarcane bagasse and reduced graphene oxide (rGO) composites were hydrothermally synthesized. The resultant composites, HAPC/MoS2/rGO (1:2:0.5) and HAPC/MoS2/rGO (1:1:0.5) were evaluated as an anode for lithium-ion batteries (LIBs) and as a supercapacitor, respectively. This HAPC/MoS2/rGO (1:2:0.5) composite delivers a high reversible discharge capacity of 952 mAh g−1 after 200 cycles at a current density of 0.2 A g−1 and outstanding high-rate capability as an anode for LIBs. While another composite HAPC/MoS2/rGO (1:1:0.5) reveals the best performance as a supercapacitor by delivering a specific capacitance of 385 F g−1 at 1 A g−1. Such an excellent and stable performance of the composites in energy storage can be ascribed as the synergistic effect of three-dimensional aerogel consisting of 1T-MoS2 nanosheets, graphene sheets, and porous carbon. The 1T-MoS2 layered structured nanosheets are tightly lying on the surface of HAPC, making their strong contact with each other and therefore reducing the diffusion path for both electrons and lithium ions. While the electrical conductivity of the composite system is enhanced by the graphene sheets.

Oxygen octahedra tilt (OOT) transition is the most common type of distortion in inorganic ABO3 compounds with a perovskite crystal structure. The importance of OOT transitions is underlined by accompanying changes in the B-O and A-O bonding environments, which consequently affects the electronic states and hence influences electrical, magnetic, and superconducting properties of many perovskite compounds. In recent years, controlled manipulation of the OOT order in perovskite thin film ferroelectrics has been attempted through heteroepitaxial strain engineering. The current study demonstrates an alternative approach whereby OOT ordering in a 200 nm thick polycrystalline thin film of (Na1/2Bi1/2)TiO3 (NBT) Pb-free ferroelectric is induced by applying electric-field along the 111 octahedral tilt axis, which is furthermore enabled by a strong (111) crystallographic texture normal to the film surface. In situ x-ray diffraction reveals that electric-field-induced OOT ordering proceeds through nucleation and rapid growth of domains with ordered a−a−a− tilting, followed by an increase in the tilt angle within the ordered domains.

We address the problem of distributed state estimation of a linear dynamical process in an attack-prone environment. A network of sensors, some of which can be compromised by adversaries, aim to estimate the state of the process. In this context, we investigate the impact of making a small subset of the nodes immune to attacks, or “trusted”. Given a set of trusted nodes, we identify separate necessary and sufficient conditions for resilient distributed state estimation. We use such conditions to illustrate how even a small trusted set can achieve a desired degree of robustness (where the robustness metric is specific to the problem under consideration) that could otherwise only be achieved via additional measurement and communication-link augmentation. We then establish that, unfortunately, the problem of selecting trusted nodes is NP-hard. Finally, we develop an attack-resilient, provably-correct distributed state estimation algorithm that appropriately leverages the presence of the trusted nodes.

Use of Motor Imagery in EEG signals is gaining importance to develop Brain Computer Interface (BCI) applications in various fields ranging from bio-medical to entertainment. Filter Bank Common Spatial Pattern (FBCSP) algorithm is a promising feature extraction technique to deal with subject-specific behavior in Motor Imagery classification. Using FBCSP on EEG we have developed an accurate but less computationally expensive approach by making use of Time Domain Parameters (TDP) and Band Power (BP) features to form a combined feature set. The novelty of our approach is also the use of optimal time segmentation to overcome non-stationary state behavior of Event-Related Desynchronization (ERD) and Event-Related Synchronization (ERS) over time. We analyzed the impact of parameter variations on classification accuracy and achieved 0.59 mean kappa value for Dataset 2a BCI competition IV, the highest reported for FBCSP approaches, along with the lowest inter-subject variation.

Use of Motor Imagery in EEG signals is gaining importance to develop Brain Computer Interface (BCI) applications in various fields ranging from bio-medical to entertainment and its classification at lower complexity in EEG signals is gaining importance. Filter Bank Common Spatial Pattern (FBCSP) has been a promising feature extraction approach to deal with subject-specific behavior in Motor Imagery classification. Though its computational complexity has been on the lower side, the approach has not yet matched the classification accuracy of non-FBCSP approaches. By introducing a combined feature set of Band Power (BP) features and Time Domain (TDP) features in FBCSP approach we have succeeded in proposing an approach of high classification accuracy while out competing the computational complexity of the best reported non-FBCSP approaches. We have also analyzed the impact of parameter variations on classification accuracy and achieved 0.59 mean kappa value for Dataset 2a BCI competition IV on four class problem. This is the highest reported for FBCSP approaches along with the lowest inter-subject variation.

In recent years, state-of-the-art traffic-control devices have evolved from standalone hardware to networked smart devices. Smart traffic control enables operators to decrease traffic congestion and environmental impact by acquiring real-time traffic data and changing traffic signals from fixed to adaptive schedules. However, these capabilities have inadvertently exposed traffic control to a wide range of cyber-attacks, which adversaries can easily mount through wireless networks or even through the Internet. Indeed, recent studies have found that a large number of traffic signals that are deployed in practice suffer from exploitable vulnerabilities, which adversaries may use to take control of the devices. Thanks to the hardware-based failsafes that most devices employ, adversaries cannot cause traffic accidents directly by setting compromised signals to dangerous configurations. Nonetheless, an adversary could cause disastrous traffic congestion by changing the schedule of compromised traffic signals, thereby effectively crippling the transportation network. To provide theoretical foundations for the protection of transportation networks from these attacks, we introduce a game-theoretic model of launching, detecting, and mitigating attacks that tamper with traffic-signal schedules. We show that finding optimal strategies is a computationally challenging problem, and we propose efficient heuristic algorithms for finding near optimal strategies. We also introduce a Gaussian-process based anomaly detector, which can alert operators to ongoing attacks. Finally, we evaluate our algorithms and the proposed detector using numerical experiments based on the SUMO traffic simulator.

In Brain-Computer Interface (BCI) Research,Electroencephalography (EEG) has obtained great attention for biomedical applications. In BCI system, feature representation and classification are important tasks as the accuracy of classification highly depends on these stages. In this paper, we propose a model in which Common Spatial Pattern (CSP) is used to discriminate inter-class data using co-variance maximization and Fast Fourier Transform Energy Map (FFTEM) is used for feature selection and mapping of 1D data into 2D data (energy maps). Convolutional Neural Network is used for classification of multi-class Motor Imagery (MI) signals. Further, this paper investigates near-optimal parameter selection for feature mapping, frequency bands selection, and temporal segmentation. It is shown that our proposed method outperformed the reported methods by achieving 0.61 mean kappa value.

A necessary condition for global performance guarantees in most of the motion coordination algorithms is connectivity of the underlying network topology. In proximity networks, connectivity maintenance becomes critical because the neighborhood set of each agent is dynamic and depends on the locations of all the other agents in the network. We present an efficient framework for distributed motion coordination in proximity networks. The proposed framework relies on identifying agents in a so called weakly connected dominating (WCD) set of the underlying network graph. Maintaining only the edges incident to the agents in WCD, which we call as critical edges, preserves the connectivity of the overall network. The proposed framework is presented in the context of rendezvous problem, which is selected because of its canonical importance in distributed systems with mobile agents. We propose a controller that drives all the agents to a common point by preserving the critical edges only. The proposed scheme is robust to failure of edges that are not critical and nodes that do not belong to WCD. Moreover, it performs well in terms of energy consumption and computational complexity.

Routing hole problem is one of the most important issues in the underwater wireless sensor networks (UWSNs). It aims to analyze the routing hole boundary to prevent the formation of routing hole such that network lifetime and all other parameters are also increased. In this paper, we have proposed the two techniques to transmit the data in the presence of routing hole. In our protocol, the nodes transmit the data on the basis of an energy gradation (EG) and depth adjustment (DA) of the void nodes. However, these techniques are used for avoidance of routing hole. The efficient load balanced routing (ELBAR) using DA performed better than other two schemes. Simulation results depict that our schemes perform the better role in terms of energy efficiency, lifetime, deviation of energy consumption, path length and euclidean stretch.

The treatment of organic pollution via photocatalysis has been investigated for a few decades. However, earth-abundant, cheap, stable, and efficient substrates are still to be developed. Here, we prepare an efficient visible-light-driven photocatalyst via the deposition of Ag nanoparticles (