This study includes the fabrication of in-situ TiC reinforced AlCrFeNi2Ti0.5 compositionally complex composites through graphene nano platelets (GNPs) incorporation (in different proportions) via vacuum arc melting route. A distinct microstructure evolution was driven by GNP addition. Beyond the B2/L21(AlNi2Ti) and BCC (Fe-Cr) matrix phases of unreinforced AlCrFeNi2Ti0.5, the composites featured in-situ formed TiC, significant grain refinement, and increased network of low-angle grain boundaries with peak effects observed at 0.2wt.% GNPs. The optimal 0.2wt.% composite revealed a hardness of 625.8 Hv, yield strength of 1637.0MPa, and ultimate compressive strength of 2882.3MPa, i.e., the enhancement of 13.7%, 20.6%, and 11.0%, respectively, compared to the unreinforced alloy, despite an 11.4% decrease in ductility. The properties enhancement is attributed to the synergistic effect of in-situ TiC phase formation (specifically its optimal size and uniform distribution), Hall-Petch strengthening, dislocation strengthening, improved load transfer capacity and Orowan strengthening. However, the formation of TiC phase also leads to brittle-dominant mixed-mode failure. Higher content of GNP (>0.2wt.%) decreased the mechanical properties due to weak interfacial bonding and agglomeration of particles.

This study reports the fabrication and characterization of PVDF/Ni nanowire (NW) composite films for thermoelectric (TE) applications. The composite films were prepared with varying Ni NW concentrations (30-90 wt. %) by dispersing them in PVDF using dimethylformamide (DMF) as a solvent, followed by a drying process to form uniform films. X-ray diffraction (XRD) and fourier-transform infrared spectroscopy (FTIR) analyses confirmed the presence of alpha and beta crystalline phases in the PVDF matrix, while scanning electron microscopy (SEM) showed uniform dispersion of Ni NWs, forming conductive pathways. X-ray photoelectron spectroscopy (XPS) analysis provided insights into the chemical states and interactions within the composites. Electrical characterization revealed that increasing Ni NW content enhanced electrical conductivity, while maintaining consistent n-type Seebeck behavior. The composite with 90 wt. % Ni exhibited the highest power factor of 15.1 mu W/mK2 at room temperature and reached an optimized value of 120 mu W/mK2 at 400 K outperforming similar PVDF-based materials reported in the literature. Furthermore, a flexible thermoelectric generator (TEG) was developed using p-type PEDOT and n-type PVDF/Ni NW composites, achieving a maximum power output (P max) of 230 nW at a temperature difference (Delta T) of 25 K. These findings demonstrate the potential of PVDF/Ni NW composites for use in flexible, wearable thermoelectric energy-harvesting devices.

This study successfully explores the performance enhancement of a zinc oxide (ZnO)/ poly(3-hexylthiophene2,5-diyl) (P3HT) hybrid photodetector with copper sulphide (CuS) incorporation into the P3HT matrix. Lowcost solution processing techniques, such as hydrothermal and co-precipitation, were employed in this study to form ZnO nanorods and CuS nanoparticles, respectively. An inverted structured architecture was employed to fabricate two distinct device types, with and without CuS in ZnO/P3HT heterojunctions, on flexible indium tin oxide-coated polyethyleneterephthalate (ITO-PET) substrates. The samples were characterized structurally and optically using XRPD, FTIR, UV-VIS, and PL studies. XRPD spectra investigate the hexagonal structured growth of ZnO and confirm the deposition of P3HT and P3HT-CuS films on ZnO. FTIR spectra confirm the molecular bonding in the P3HT-CuS matrix, and increased UV-VIS absorption is observed for the ZnO/P3HT-CuS sample. The current-voltage (I-V) characteristics and photoresponse parameters were also examined, and CuS nanoparticle incorporation increased photocurrent density from 31.1 mu A/cm2 to 43.7 mu A/cm2, indicating better charge transfer between ZnO and P3HT-CuS compared to ZnO and P3HT, as supported by PL spectra. The current density vs time (J-T) curves indicate the fast response time of less than 30 ms. The improved device performance was also calculated in terms of responsivity and detectivity and found to be 0.437 mA/W and 2.87 x 109 Jones. The flexibility study and ambient time stability of the samples has also been analyzed by measuring the J-T characteristics of the devices. The current findings thus strengthen the understanding of plasmonic nanoparticles integration into organic polymers, facilitating the investigation of enhanced light trapping and plasmon-induced interfacial charge transfer mechanisms for improving the optoelectronic properties of the devices.

In the present work, we have reported the nickel oxide (NiO)-graphene oxide (GO) composite thin films on flexible indium tin oxide-coated poly-ethyleneterephthalate (ITO PET) substrates by a simple solution processing approach (spin coating method). The dispersion of GO nanostructures (synthesized by the modified Hummers' method) was introduced in the NiO (synthesized by the hydrothermal method) dispersion solution in different volume ratios of 1:0, 1:0.2, 1:0.5, and 1:1; and the corresponding thin films were named as NG 0, NG 2, NG 5, and NG 10, respectively. The zeta potential study reveals the moderate stability of the prepared dispersions, and the hydrodynamic diameter increases with GO inclusion in NiO dispersion. The variation of GO concentrations on the structural, morphological, optical, and electrical properties of thin films was investigated. Powder X-ray diffraction (PXRD) results reveal the crystalline structure of thin films. The morphology of the films was investigated by field emission scanning electron microscopy (FESEM), which shows the more ordered and porous hexagonal network obtained for composite films. The UV-VIS-NIR study reveals the optical properties of thin films. The optical absorption increases in the visible region with an increase in GO concentrations in composite films, and a decrease in band gap from 3.88 ​eV to 3.50 ​eV was observed for NG 2 to NG 10 thin films. The presence of Ni–O stretching and CC stretching, as well as carbon bonding with oxygen functionalities, were also confirmed by Fourier Transform Infrared (FTIR) spectroscopy. The current-voltage characteristics were measured, and the corresponding resistance of the thin films was obtained in the range of MΩ. The experimental result demonstrates the decrease in resistivity and increase in current for both forward and reverse bias ranges with the incorporation of GO in NiO thin films. The obtained results highlight the possibility of using these composite thin films for achieving good performance and suitability for flexible optoelectronic applications.

This review paper analyzes the modifications induced by effects of swift heavy ion (SHI) irradiation on gallium nitride (GaN), emphasizing its structural, optical, and electrical modifications. Recognized for its exceptional semiconducting properties, GaN has become a focal material in radiation environments. SHI irradiation offers a distinct technique for tuning GaN’s properties through controlled defect formation. The review elaborates on the interactions between swift heavy ions and GaN, highlighting key energy loss mechanisms—electronic and nuclear energy losses—that govern the nature and extent of damage. It incorporates theoretical models such as Coulomb explosion, thermal spike, and molecular dynamics simulations to interpret the physical processes underlying defect generation and evolution. The study primarily addresses the role of varying projectile ion masses (A = 7–238), categorized as light (A < 20), medium (20 ≤ A < 50), heavy (50 ≤ A < 150), and super-heavy (A ≥ 150) ions. Irradiation conditions span fluences from 2.5 × 10⁷ to 1 × 1014 ions/cm2 and energies between 3 and 2300 MeV. The induced changes in GaN are characterized using techniques such as X-ray diffraction (XRD), Raman, photoluminescence (PL), transmission electron microscopy (TEM), and Hall effect measurements. In addition to mass effects, the influence of ion energy, energy loss parameters such as electronic energy loss (Se) and nuclear energy loss (Sn), fluence, flux, and post-irradiation annealing is critically reviewed for their cumulative impact on GaN’s behavior. Although challenges remain in fully controlling these irradiation effects, the review outlines potential future research directions, including the extension of SHI studies to other wide bandgap materials such as ZnO, Ga₂O₃, and SiC. In conclusion, the findings underscore the relevance of SHI irradiation as a potent tool for engineering GaN-based materials and devices for emerging applications in next-generation semiconductor technologies.

In the era of vision Transformers, the recent success of VanillaNet shows the hugepotential of simple and concise convolutional neural networks (ConvNets). Wheresuch models mainly focus on runtime, it is also crucial to simultaneously focuson other aspects, e.g., FLOPs, parameters, etc, to strengthen their utility further.To this end, we introduce a refreshing ConvNet macro design called ColumnarStage Network (CoSNet). CoSNet has a systematically developed simple andconcise structure, smaller depth, low parameter count, low FLOPs, and attention-less operations, well suited for resource-constrained deployment. The key noveltyof CoSNet is deploying parallel convolutions with fewer kernels fed by inputreplication, using columnar stacking of these convolutions, and minimizing the useof 1×1 convolution layers. Our comprehensive evaluations show that CoSNet rivalsmany renowned ConvNets and Transformer designs under resource-constrainedscenarios. Pretrained models shall be open-sourced.

Herein, we have proposed a simple, quick, cost-effective, and enhanced chemiluminescence (eCL) imaging method capitalized on the luminol-O2/PDTC@AuNPs CL probe based on a smartphone as a portable hand-held CL detector for the sensitive and selective detection of choline (Chol) in standard and human serum samples. The luminol-O2 CL reagent is integrated with the chain-like Propane-1,3-dithiol (PDT) crosslinked gold (Au) nano-particles (PDTC@AuNPs) and acts as a catalyst to obtain a visible and efficient CL probe (luminol-O2/ PDTC@AuNPs). PDTC@AuNPs is prepared by chemical reduction of Au-salt followed by simple mixing of AuNPs and PDT and is successfully characterized by various characterization techniques. The PDTC@AuNPs facilitate the production of reactive species (& sdot;O2, 1O2, & sdot;OH) and impart good catalytic behavior in the luminol-O2 CL reaction, thereby greatly enhancing the CL signal compared to AuNPs. A mechanism for these observations is thoroughly investigated. Further, based on the enhancement property of luminol-O2/PDTC@AuNPs towards Chol's catalytic product (H2O2), an eCL method has been proposed for detecting Chol. Under optimal conditions, the as-proposed eCL imaging probe exhibits a linear increase in CL imaging response in a broad range from 0.001 mM to 1.2 mM standard choline concentrations, with the LOD 0.001 mM. Finally, we observed a recovery of 94.0 % to 108.0 % in human serum samples of healthy individuals and pregnant women.

Channel squeezing is a crucial operation in convolutional neural networks (ConvNets). It is carried out via 1 × 1 convolution layers and dominates a large portion of computations and parameters of a given network. ResNet-50, for instance, consists of 16 such layers, forming 33% of total layers and 25% (1.05B/4.12B) of total FLOPs. In light of their predominance, we present a new multi-purpose module for dynamic channel sampling, namely Pick-or-Mix (PiX). PiX divides a set of channels into subsets and then picks from them, where the picking decision is dynamically made per each pixel based on the input activations. We show that PiX allows ConvNets to learn better data representation than vanilla channel squeezing in far fewer computations. We plug PiX into prominent ConvNet architectures and verify its multi-purpose utilities. After replacing 1 × 1 channel squeezing layers in the ResNet family with PiX, the networks become 25% faster without losing accuracy. We also show that PiX can achieve state-of-the-art performance on network downscaling and dynamic channel pruning.

Designing ConvNet and exploring its design space is a highly challenging research area. In this paper, inspired by the structural organization of cortical modules in the biological visual cortex, we present a pragmatically designed ConvNet architecture, called CoMNet which is simplified yet powerful. The bio-inspired design of CoM- Net offers efficiency in multiple dimensions such as network depth, parameters, FLOPs, latency, branching, and memory budget at once while having a simple design space, in contrast to the existing designs which are limited only to fewer dimensions. We also develop a Multi-Dimensional Efficiency (MDE) evaluation protocol to compare models across dimensions. Our comprehensive evaluations show that in the MDE setting, CoMNet outperforms many representative ConvNet designs such as ResNet, ResNeXt, RegNet, RepVGG, and ParNet (Figure 1).

In this paper, we present a comprehensive UAV system design to perform the highly complex task of off-centered aerial grasping. This task has several interdisciplinary research challenges which need to be addressed at once. The main design challenges are GPS-denied functionality, solely onboard computing, and avoiding off-the-shelf costly positioning systems. While in terms of algorithms, visual perception, localization, control, and grasping are the leading research problems. Hence in this paper, we make interdisciplinary contributions: ( i ) A detailed description of the fundamental challenges in indoor aerial grasping, ( ii ) a novel lightweight gripper design, ( iii ) a complete aerial platform design and in-lab fabrication, and ( iv ) localization, perception, control, grasping systems, and an end-to-end flight autonomy state-machine. Finally, we demonstrate the resulting aerial grasping system Drone-Bee achieving a high grasping rate for a highly challenging agricultural task of apple-like fruit harvesting, indoors in a vertical farming setting (Fig. 1). To our knowledge, such a system has not been previously discussed in the literature, and with its capabilities, this system pushes aerial manipulation towards $4^{th}$ generation.

GaN is a material of strategic importance due to its versatility in high temperature, radiation prone and chemical environments. Present study explores evolution of defects in GaN thin films in swift heavy ion radiation regime. Thin films of GaN grown by metal organic chemical vapour deposition techniques were irradiated with 200 MeV Ag ions at different fluences starting from 5 × 10 10 to 5 × 10 12 ions/cm 2 . X-ray diffraction analysis revealed accumulation of lattice damage with increasing fluence. Photoluminescence studies showed evolution of different optical active defects in thin films. Raman spectroscopy along with other techniques was utilized to understand the origin and mechanism of irradiation induced defects.

Detection Transformers (DETR) are renowned object detection pipelines, however computationally efficient multiscale detection using DETR is still challenging. In this paper, we propose a Cross-Resolution Encoding-Decoding (CRED) mechanism that allows DETR to achieve the accuracy of high-resolution detection while having the speed of low-resolution detection. CRED is based on two modules; Cross Resolution Attention Module (CRAM) and One Step Multiscale Attention (OSMA). CRAM is designed to transfer the knowledge of low-resolution encoder output to a high-resolution feature. While OSMA is designed to fuse multiscale features in a single step and produce a feature map of a desired resolution enriched with multiscale information. When used in prominent DETR methods, CRED delivers accuracy similar to the high-resolution DETR counterpart in roughly 50 with CRED (calling CRED-DETR), becomes 76 its high-resolution counterpart with 202 G FLOPs on MS-COCO benchmark. We plan to release pretrained CRED-DETRs for use by the community. Code: https://github.com/ashishkumar822/CRED-DETR

Road extraction from aerial imagery is not a trivial task. It plays a pivotal role in urban planning, navigation, disaster assessment and various other fields. It poses challenges due to complex scenarios and factors, including occlusion. Hence conventional methods prove to be inefficient for the purpose. Image segmentation and deep learning models are extensively employed in recent times to extract objects from images. In this paper, the performance of Unet architecture-based model has been improved by Resnet50, VGG16, DenseNet169, Xception and Efficientnet-b4. Further, to investigate the performance of Unet model, three other models FPN, PSPNet and PAN were implemented and evaluated on Massachusetts road dataset. The work presents the comparative analyses of the performance of models.

In this work, we propose an end-to-end Thrust Microstepping and Decoupled Control (TMDC) of quadrotors. TMDC focuses on precise off-centered aerial grasping of payloads dynamically, which are attached rigidly to the UAV body via a gripper contrary to the swinging payload. The dynamic payload grasping quickly changes UAV's mass, inertia etc, causing instability while performing a grasping operation in-air. We identify that to handle unknown payload grasping, the role of thrust controller is crucial. Hence, we focus on thrust control without involving system parameters such as mass etc. TMDC is based on our novel Thrust Microstepping via Acceleration Feedback ( TMAF ) thrust controller and Decoupled Motion Control ( DMC ). TMAF precisely estimates the desired thrust even at smaller loop rates while DMC decouples the horizontal and vertical motion to counteract disturbances in the case of dynamic payloads. We prove the controller's efficacy via exhaustive experiments in practically interesting and adverse real-world cases, such as fully onboard state estimation without any positioning sensor, narrow and indoor flying workspaces with intense wind turbulence, heavy payloads, non-uniform loop rates, etc. Our TMDC outperforms recent direct acceleration feedback thrust controller (DA) and geometric tracking control (GT) in flying stably for aerial grasping and achieves RMSE below 0.04 m in contrast to 0.15 m of DA and 0.16 m of GT.

GaN epitaxial layers were studied after being exposed to 100 MeV oxygen ions at different fluences, including 1 x 1011/cm2, 5 x 1011/cm2, 1 x 1012/cm2, 1 x 1013/cm2, 5 x 1013/cm2. In order to establish a relationship between the structural and optical characteristics of GaN epilayers, Raman and Photoluminescence (PL) spectra were examined. O ion irradiation samples show an apparent change in the position and form of the A1 (LO) mode Raman peak and full width at half maxima (FWHM)-the increased crystallinity in the GaN samples after O irradiation is supported by Raman analysis. The sorts of defects were further identified by analysis of the PL spectra. Ion beams may significantly contribute to the creation and transformation of defects in GaN, according to the quantification of different defects.

We present an accurate and GPU-accelerated Stereo Visual SLAM design called Jetson-SLAM. It exhibits frame processing rates above 60FPS on NVIDIA's low-powered 10W Jetson-NX embedded computer and above 200FPS on desktop grade 200W GPUs, even in stereo configuration and in the multiscale setting. Our contributions are threefold: (i) a Bounded Rectification technique to prevent tagging many non-corner points as a corner in FAST detection, improving SLAM accuracy. (ii) A novel Pyramidal Culling and Aggregation (PyCA) technique that yields robust features while suppressing redundant ones at high speeds by harnessing a GPU device. PyCA uses our new Multi-Location Per Thread culling strategy (MLPT) and Thread Efficient Warp-Allocation (TEWA) scheme for GPU to enable Jetson-SLAM achieving high accuracy and speed on embedded devices. (iii) Jetson-SLAM library achieves resource efficiency by having a data-sharing mechanism. Our experiments on three challenging datasets: KITTI, EuRoC, and KAIST-VIO, and two highly accurate SLAM backends: Full-BA and ICE-BA show that Jetson-SLAM is the fastest available accurate and GPU-accelerated SLAM system (Fig. 1).

Autonomous aerial harvesting is a highly complex problem because it requires numerous interdisciplinary algorithms to be executed on mini low-powered computing devices. Object detection is one such algorithm that is compute-hungry. In this context, we make the following contributions: (i) Fast Fruit Detector (FFD), a resource-efficient, single-stage, and postprocessing-free object detector based on our novel latent object representation (LOR) module, query assignment, and prediction strategy. FFD achieves 100FPS@FP32 precision on the latest 10W NVIDIA Jetson-NX embedded device while co-existing with other time-critical sub-systems such as control, grasping, SLAM, a major achievement of this work. (ii) a method to generate vast amounts of training data without exhaustive manual labelling of fruit images since they consist of a large number of instances, which increases the labelling cost and time. (iii) an open-source fruit detection dataset having plenty of very small-sized instances that are difficult to detect. Our exhaustive evaluations on our and MinneApple dataset show that FFD, being only a single-scale detector, is more accurate than many representative detectors, e.g. FFD is better than single-scale Faster-RCNN by 10.7AP, multi-scale Faster-RCNN by 2.3AP, and better than latest single-scale YOLO-v8 by 8AP and multi-scale YOLO-v8 by 0.3 while being considerably faster.

Trap characterization on GaN Schottky barrier diodes (SBDs) has been carried out using deep-level transient spectroscopy (DLTS). Selective probing by varying the ratio of the rate window values ( r ) incites different trap signatures at similar temperature regimes. Electron traps are found to be within the values: 0.05–1.2 eV from the conduction band edge whereas the hole traps 1.37–2.66 eV from the valence band edge on the SBDs. In the lower temperature regime, the deeper electron traps contribute to the capacitance transients with increasing r values, whereas at the higher temperatures >300 K, a slow variation of the trap levels (both electrons and holes) is observed when r is varied. These traps are found to be mainly contributed to dislocations, interfaces, and vacancies within the structure.

The structural, electronic, thermal and lattice thermal transport properties of the three hypothetical quaternary Heusler alloys FeRuTiX (X=Si, Ge, Sn) were investigated with the aid of first-principles calculations. All compounds were found to be semiconducting with a small indirect band gap. Flat bands near the conduction band edge and degenerate multi-bands near the valance band edge suggest that these systems should exhibit both large Seebeck coefficients and good electrical conductivity. The analysis of the calculated vibrational spectra showed that the three compounds are thermodynamically stable. The computed lattice thermal conductivity indicates that among the three compounds that of FeRuTiSn is rather low at high temperature. Indeed, a low lattice thermal conductivity (similar to 3.5 Wm(-1) K-1 at 1000 K) together with a small electronic band gap (0.51 eV) with an appropriate electronic structure (disperse and flat bands) render FeRuTiSn a promising candidate as a high-temperature thermoelectric material.

In this work, we show how to learn a visual walking policy that only uses a monocular RGB camera and proprioception. Since simulating RGB is hard, we necessarily have to learn vision in the real world. We start with a blind walking policy trained in simulation. This policy can traverse some terrains in the real world but often struggles since it lacks knowledge of the upcoming geometry. This can be resolved with the use of vision. We train a visual module in the real world to predict the upcoming terrain with our proposed algorithm Cross-Modal Supervision (CMS). CMS uses time-shifted proprioception to supervise vision and allows the policy to continually improve with more real-world experience. We evaluate our vision-based walking policy over a diverse set of terrains including stairs (up to 19cm high), slippery slopes (inclination of 35°), curbs and tall steps (up to 20cm), and complex discrete terrains. We achieve this performance with less than 30 minutes of real-world data. Finally, we show that our policy can adapt to shifts in the visual field with a limited amount of real-world experience.