In the modern digital landscape, the rapid evolution of malware poses a serious threat to individuals, enterprises, and government infrastructures. Traditional signature-based detection techniques are inherently reactive and increasingly ineffective against sophisticated and zero-day attacks, leading to critical security vulnerabilities. To address these limitations, this work presents the design and implementation of an AIpowered malware detection system that enables proactive and intelligent threat identification. The proposed approach transforms executable binaries into grayscale images and employs a Convolutional Neural Network (CNN) to automatically learn discriminative structural patterns associated with malicious behavior, thereby eliminating reliance on predefined signatures. Two CNN architectures were developed and evaluated, and the optimized model achieved an accuracy of 91.72 %, a precision of 90.28 %, a recall of 95.00 %, and an F 1 -score of 92.58 %, demonstrating robust detection performance. The system is integrated into a web-based framework featuring a user-friendly interface and a scalable FastAPI backend capable of real-time analysis. Experimental results confirm that the proposed solution provides fast, accurate, and effective malware detection, making it suitable for deployment in modern cybersecurity environments.

This work presents the design of a compact dual band microstrip patch antenna and a learning based approach to streamline its performance evaluation. The proposed antenna employs circular slots on the radiating patch to achieve dual band operation at 3.5 GHz and 5.66 GHz. To minimize reliance on computationally intensive full wave electromagnetic simulations, a convolutional neural network model is developed to predict the S11 parameter using antenna dimensions and operating frequency as inputs. The CNN model demonstrates strong agreement between predicted and simulated results, achieving a high coefficient of determination ($\mathbf{R}^{2}=0.982$) with low prediction error (RMSE = 0.19, MAE = 0.15). The integrated antenna design and CNN-based prediction framework provides a practical and efficient methodology for rapid antenna analysis and optimization, with potential for extension to more complex antenna structures in future research.

Short-range reliable and secure communication is a major priority in the tactical, military and disaster response settings where the traditional communication infrastructure is either off-line or prone to interception. Current VHF/UHF radios and software-defined radios are popular but large-sized devices and require lots of power, making them not suitable to be used as lightweight wearable devices with seamless hand-free use. In this paper, the design and theoretical framework of a miniature, LoRa based encrypted intercommunication device that can be used in secure field communication over a range of 1-1.5km and under line-of-sight conditions is provided. The suggested system consists of a voice-activated acquisition block, digital audio compression, an embedded microcontroller processor, and AES-128 encryption followed by a low-power transmission via the LoRa protocol. Through the ability of chirp spread spectrum modulation to utilize the long-range and low-energy properties, the system is guaranteed reliable communications coupled with low power consumption and low electromagnetic footprint. The theoretical analysis of the proposed communication range is justified using a link-budget that justifies the practicability of the communication range in the real propagation conditions. This architecture focuses on infrastructural agnosticism, peer-to-peer security as well as wearable ergonomics. The given scheme shows the possibilities of LoRa technology in the scope of other traditional IoT telemetry, and it can be further extended to include secure tactical voice communication platforms.

The increasing demand for compact, wideband and efficient antennas in 5G sub-6 GHz networks has driven research toward designs offering stable gain and high radiation performance within limited dimensions. This paper presents a gain stabilized wideband patch antenna developed through a systematic six-stage design approach incorporating circular parasitic stubs, L-slots, a meandered feed line and a defected ground structure. These elements interactively work for the provision of multi-frequency impedance matching and the introduction of resonance modes takes place with further extension of the effective electrical length of the circular parasitic stubs, whereas the L-slots are used for the perturbation of the surface currents. The meandered feed line further elongates the current path, lowering the fundamental resonance frequency by 49% without physical size increase. The defected ground structure disrupts ground plane currents, creating capacitive loading that fine-tunes impedance matching and suppresses higher order modes, collectively enabling wideband operation with minimal gain variation. The optimized design exhibits dual resonances at 3.6 and 6.1 GHz with return losses of - 44 and - 27 dB respectively, covering a wide operational band of 3.2-6.6 GHz. The antenna maintains a consistent realized gain of 2.6-2.7 dBi (± 0.8 dB), radiation efficiency above 90%, group delay variation below 0.5 ns and a low quality factor ([Formula: see text]). The novelty lies in the systematic integration of these complementary techniques to simultaneously achieve wideband operation, exceptional gain stability and low group delay, making it suitable for compact 5G wireless devices.

Microwave band pass filters are one of the vital components of wireless communication systems. Microwave communication systems, expanding radio frequency (RF) filters operating in the microwave frequency range are needed for applications including wireless and satellite communications as well as military applications. Most communication system contains an RF front end which performs signal processing with RF filters. Planar or printed circuit board (PCB) based filters are popular and relatively practical to design. Band pass filters are important components of wireless communication networks. Signals that are sent and received must be filtered at a certain centre frequency and bandwidth. This paper describes the design of low-cost C-band parallel coupled microstrip band pass filter (BPF) by using microstrip layout at center frequency 5.8 GHz for permittivity 4.4 value with a substrate thickness 1.6 mm for order n = 3.

Real-time object detection in adverse weather and low-light conditions is crucial for applications such as autonomous driving and intelligent surveillance. This paper presents MDAT-YOLO, a novel object detection framework designed to balance accuracy and efficiency in challenging environments. The model integrates multi-dimensional attention mechanisms and transformer-based enhancements to strengthen feature extraction and adaptability. It introduces two core modules: DWConv_O, an optimized depthwise separable convolution layer, and ODConv++, an omni-dimensional dynamic convolution module that enhances spatial, channel, and kernel-level interactions for improved feature selectivity and dynamic response. A lightweight C3 Transformer (C3TR) block further reduces computational overhead while maintaining strong representational capacity. MDAT-YOLO is evaluated on four benchmark datasets, including RTTS, VOC-Foggy, ExDark, and a custom foggy VOC-PASCAL subset, achieving accuracy improvements of 70.50%, 65.14%, 77.40%, and 49.00%, respectively. The model sustains real-time speeds up to 145 FPS, demonstrating robustness and practicality for real-world deployment under diverse environmental conditions.

This paper presents a compact ultra-super-wideband Tapered Polygonal Shaped Monopole Antenna (TPSMA) and its systematic extension to a high-isolation two-element MIMO configuration for Ku-/K-/Ka-band satellite communication, fixed wireless access (FWA), and advanced 5G mmWave applications, including the 28-GHz band corresponding to 3GPP NR bands n256 and n257. The single-element antenna is progressively evolved from a conventional elliptical monopole through radiator reshaping and ground-plane optimization, achieving wide impedance bandwidth without employing slots, vias, multilayer substrates, or external matching networks. The optimized TPSMA occupies a compact footprint of 11 × 12 × 0.787 mm3 and exhibits stable radiation characteristics with high efficiency over the operating band. The antenna is further extended to a two-element MIMO configuration using planar and orthogonal orientations with individual and shared ground-plane structures, maintaining a compact overall size of 23 × 11 × 0.787 mm3. High isolation is achieved through optimized inter-element spacing, intra-element gap control, and orthogonal element placement, eliminating the need for additional decoupling structures. A comprehensive comparison between planar and orthogonal arrangements, as well as individual and common ground configurations, is presented. The proposed MIMO antenna achieves an ultra-super-wide impedance bandwidth of 12.15–36.10 GHz (|S11|< − 10 dB) with a minimum reflection coefficient of − 36.89 dB, isolation exceeding 22.55 dB, stable radiation patterns, and excellent diversity performance. The key novelty of this work lies in realizing ultra-wideband, compact, and high-isolation MIMO performance exclusively through radiator geometry and ground-plane engineering, making the proposed antenna a strong candidate for next-generation wideband wireless and satellite communication systems.

This paper presents the design and optimization of a compact wideband microstrip patch antenna, created through an evolutionary approach for future wireless communication systems. The antenna design goes through six stages, starting from a standard rectangular patch and adding circular stubs, semicircular and L shaped slots, meandered line and a defected ground structure. Each change is made to improve resonant behavior, surface current distribution and impedance bandwidth. The optimized antenna operates at 3.58 GHz and 5.3 GHz. It has a wide impedance bandwidth of 2.6 GHz (FBW ≈ 57.8%), high radiation efficiency of 78% and a low Q-factor of 1.73, indicating a strong wideband behavior (estimated using fractional bandwidth). The proposed antenna exhibits minimal gain variation across the operating band, with values between 2.9 dBi and 3.0 dBi, while maintaining consistent radiation and impedance characteristics. Electric field and surface current analyses show that controlled phase distribution and a small group delay variation (0.3 ns to 0.5 ns), which supports near-linear phase characteristics. The proposed antenna combines wide bandwidth, low group delay, minimal gain variation, high efficiency, low Q-factor and compact size, making it suitable for 5G sub-6 GHz, internet of things and wearable devices.

A small-sized four-port MIMO antenna for sub-6 GHz fifth generation (5G) wireless communication and Internet of Things (IoT) systems is presented in this paper. The antenna consists of a cost-effective FR-4 substrate ($$\varepsilon _r=4.4$$, $$h=1.6$$ mm), and the size of the antenna is only $$0.68\lambda _0 \times 0.68\lambda _0$$$$\hbox {mm}^2$$. Orthogonal placement of four modified monopole antennas together with a defected ground structure (DGS) is utilized to overcome the problem of mutual coupling and achieving a compact size along with stable impedance matching. The designed MIMO antenna works in the frequency range of 3.44–3.72 GHz (7.82%) with a minimum reflection coefficient of $$-23.4$$ dB and more than 22.7 dB isolation between antennas. Moreover, the designed antenna yields a maximum realized gain of 3.6 dBi and a maximum radiation efficiency of 85%. The proposed MIMO antenna also shows good diversity performance, and its ECC, DG, CCL, TARC, and MEG values are found to be 0.0006, 10 dB, less than 0.04 bits/s/Hz, $$-16$$ dB, and $$-3.08$$ to $$-3.11$$ dB, respectively.

In this paper, we propose an energy efficient hybrid beamforming architecture for millimeter-wave (mmWave) massive MIMO communication systems operating at 28 GHz, utilizing a 16 × 16 antenna array. The design focuses on minimize the number of RF chains requirements through a dynamic subarray configuration, where the antenna elements are grouped into subarrays that vary according to the channel conditions. To further improve system performance, the successive interference cancellation method utilized in the hybrid beamforming process, effectively mitigating multi-user interference, and improving signal decoding accuracy. The proposed approach balances between system complexity and performance by minimizing the RF chain count while maintaining robust beamforming capabilities. Simulation results demonstrate that our method achieves substantial energy savings and enhanced spectral efficiency compared to traditional hybrid beamforming techniques, making it a feasible approach for futuristic wireless communication system.

Electromagnetic simulation of microstrip antennas for wide-band operation often requires extensive computational resources, which limits rapid design optimization for modern wireless communication systems. In this work, a compact wide-band crescent-slot microstrip antenna with a partial ground plane is designed to operate at 3.5 GHz and 7.0 GHz. The antenna achieves excellent impedance matching with return-loss values of -28 dB and -26 dB at the respective resonant frequencies while maintaining stable radiation characteristics across both bands. To minimize the computational load due to repeated full-wave simulations, a machine learning approach based on the Bayesian Optimized Blended Ensemble (BOBE) algorithm is proposed to predict the reflection coefficient (S-11) parameter. The proposed algorithm combines different regression models namely Random Forest, XGBoost, CatBoost and Extra Trees, and uses Bayesian optimization to blend their predictions. In the proposed approach, the non-linear relationships between the antenna geometry and the EM characteristics are well captured, with an accuracy of R-2 = 0.991, RMSE= 0.252, MAE= 0.24, and MAPE= 2.05%. In comparison to other conventional simulation-based methods that take up to several hours of computational time, the proposed model achieves the task of predicting S-11 responses within milliseconds while maintaining high accuracy. The integration of the optimized design of the antenna with the proposed data-driven ensemble learning framework provides an efficient methodology to accelerate the design of antennas for 5G and next-generation wireless communication systems.

This paper proposes a standard applications of Radio Frequency Identification (RFID) system to manage vehicles at electronic toll gate system without compromising the vehicle speed. The existing electronic toll gate system has low latency as each car needs to wait in a queue for passing the gate. Installation of reader antenna at each lane makes the system expensive. We propose a two-ray ground model for using minimal reader antenna to define required antenna height and downtilt angle to cover the total gate area. We estimate that reader antenna at a height of 15 m and 45.4° downtilit angle are required for optimal signal reception from the car at a distance of 35–38 m. Modified Pulse Protocol algorithm is suggested for mitigating reader collision and increase throughput and utilization of the system by 14.71% and 15.2% respectively. The probability distribution function of the contention window is modified to triangular function. The M-ary Trimming Q-tree protocol is suggested to resolve the tag collision by trimming the idle nodes from the query tree, which reduces identification time. The total time slot decreases with higher elements, and the total throughput also increases to 98%. The “no-lane” strategy established a fast, efficient and cost effective vehicle toll collection system.

The advent of transparent antenna technology has opened new horizons in the design of wireless communication systems, where visual discretion and technical performance are harmoniously blended. This paper presents a novel transparent antenna that employs a glass substrate, a departure from the conventional use of opaque materials. The innovative use of Indium Tin Oxide-coated Polyethylene Terephthalate (ITO-PET) in tandem with the glass substrate has culminated in an antenna that not only integrates seamlessly into its environment but also exhibits exceptional high-frequency characteristics. Our design diverges from existing models by incorporating a unique structural configuration that optimizes frequency response and maximizes performance metrics. Operating within the Ultra High Frequency (UHF) and S-band spectrum, our antenna demonstrates a significant enhancement in bandwidth and signal gain, surpassing the limitations of previous designs. The meticulous design process, coupled with a comprehensive performance evaluation, underscores the antenna's operational efficacy and its potential to revolutionize transparent antenna applications in modern wireless networks. The results of this study not only confirm the practicality of using glass as a conductive medium but also showcase the antenna's superior performance, thereby establishing a new benchmark in transparent antenna technology. The implications of this research extend beyond the immediate scope of antenna design, suggesting a future where transparent electronic components are both functionally robust and aesthetically unobtrusive, paving the way for their integration into a wide array of consumer and industrial applications.

This paper presents the design, analysis, and validation of an optically transparent, circularly polarized fractal wideband antenna for 5G sub-6 GHz applications. The antenna employs Indium Tin Oxide-coated Polyethylene Terephthalate (ITO-PET) on a glass substrate, offering both optical clarity and structural support. A five-stage iterative design process is adopted, beginning with a conventional rectangular patch and progressively incorporating a meander line feed and fractal geometries. The meander line feed introduces additional current paths and effective electrical length, thereby enabling impedance tuning and enhancing bandwidth characteristics. The antenna operates from 2.99 to 6.82 GHz with circular polarization in the 3.34-3.44 GHz and 4.07-4.56 GHz bands. It achieves gains of 0.6dBi at 3.5 GHz and 1.3dBi at 5.2 GHz. Radiation efficiency remains above 40%, peaking at 68%. The design balances transparency and RF performance despite ITO-PET's low conductivity. With slightly less than 87% optical transparency, it suits compact, integrated wireless systems.

Autonomous navigation in constrained environments such as university campuses necessitates real-time, reliable and cost-effective solutions. Cloud based systems often face challenges related to latency, dependence on network connectivity and scalability. To address these issues, this study introduces a multimodal intelligent transportation framework implemented on an edge computing platform, specifically the Raspberry Pi 5. The system incorporates object detection with YOLOv5, lane detection utilizing U-Net, emergency siren recognition through spectrogram based ML audio classification and LiDAR based distance measurement. These integrated modalities offer visual, auditory and spatial awareness, thereby enhancing safety and situational awareness across various conditions. Benchmarking results on the Raspberry Pi 5 indicate average inference times of 166 ms for YOLOv5, 182 ms for U-Net and 157 ms for audio classification, demonstrating the feasibility of real-time deployment tailored for campus autonomous mobility applications.

The power requirement in IoT is essential to fulfill the energy demand of the power-hungry sensors at end nodes. The use of fixed batteries restricts sustainability and makes the system costly. This work presents a battery-less solar energy harvesting system (EHS). Designing a state-of-the-art EHS needs a lot of exercise. Proper modeling of each unit makes the system robust and can be tuned at every stage to get an optimum result. The proposed EHS comprises a clock generator, DC-DC converters, analog-to-digital converters (ADCs), a maximum power point tracking (MPPT) unit, and a digital controller. The DC-DC converter and ADCs are designed in Verilog-A. The MPPT module digital controller is designed using Verilog HDL. The digital controller decides the mode of operation of the EHS based on power availability. Verilog-AMS allows us to do the mixed-mode simulation very early, so errors can only be eliminated in the initial stages at the circuit level. The proposed EHS is simulated in the Cadence Virtuoso AMS Designer Simulator (using the Incisive Run tool). The input solar voltage is 1 V to 1.5 V, and the output is 3 V to 3.5 V. The EHS provides supply voltages of 3.3 V, 1.8 V, and 1 V to the end node devices in IoT. The EHS is further designed with the parameters obtained from modeling in Cadence using virtuoso (for analog circuits) and genus (for digital circuits) and finally combined in Innovous (mixed-mode tool) for tape-out.

Traditional robotic grippers designed for collaborative robots have often been constrained to specific part geometries or low-temperature operations, creating a need for more adaptable solutions. A high-temperature gripper has been developed with fingers made from alloy steel 4140 and an aluminium oxide ceramic insulator to support automation during postprocessing in metal additive manufacturing (AM). Mechanical and thermal characterization tests have been performed to validate the insulator's performance under extreme conditions. Thermal simulations have indicated a temperature difference of 767.58 degrees C across the insulator when subjected to a 1000 degrees C steel plate, confirming its role as an effective thermal barrier. The gripper has been designed to withstand temperatures up to 1000 degrees C and integrated with thermocouples for continuous temperature monitoring during manipulation. This advancement has enabled safe handling of heated components, reduced risks to human operators, and supported greater automation in high-temperature environments, thereby improving safety and productivity in demanding industrial settings.

In the forefront of wireless communication advancements, the development of specialized transparent antennas tailored for specific frequency bands has become a focal point. This paper introduces a cutting-edge transparent antenna designed exclusively for the S-band frequencies, leveraging the unique properties of a glass substrate. By employing Indium Tin Oxide-coated Polyethylene Terephthalate (ITO-PET), we have engineered an antenna that not only melds invisibly with its surroundings but also delivers outstanding performance within the targeted bandwidths of 3.2 GHz, 3.5 GHz, and 3.7 GHz. Distinct from broader-spectrum models, our antenna's design is fine-tuned to enhance S-band frequency responses, achieving a remarkable balance between bandwidth optimization and signal gain. The antenna's structure, characterized by its slotted circular configuration and a refined ground plane, is the result of rigorous research and innovation. This design specificity enables the antenna to excel in S-band applications, offering a substantial improvement over traditional designs in both bandwidth and gain within this narrower frequency range. The empirical results of this investigation not only validate the efficacy of glass as a conductive medium for S-band frequencies but also highlight the antenna's exceptional performance, setting a precedent in the field of specialized transparent antennas. The implications of this research are significant, indicating a trajectory towards the integration of functionally specialized and visually inconspicuous electronic components into a myriad of applications, from consumer electronics to industrial systems, thereby shaping the future landscape of wireless technology.

This paper presents a real-time traffic monitoring system that integrates computer vision, edge AI, and IoT technologies for accurate traffic density estimation and live analytics via a mobile interface. The system leverages YOLOv8, trained on a high-resolution custom dataset tailored for smart campus and urban environments, enabling precise multi-class vehicle detection and object counting. A Raspberry Pi 5 serves as the edge computing unit, executing YOLOv8 inference with low computational overhead. It generates real-time analytics, including per-frame object counts, vehicle classification, and traffic state categorization. Processed data is transmitted via Wi-Fi using IoT protocols and visualized on the Blynk cloud platform. A custom Android app, developed with MIT App Inventor, displays real-time traffic statistics and historical trends through a responsive, user-friendly interface. This scalable, cost-effective solution addresses key challenges in smart transportation and is well-suited for deployment in educational campuses and urban settings requiring efficient traffic monitoring and control.

The Internet of Things (IoT) has revolutionized global device interconnectivity, fostering seamless data exchange through expansive sensing networks. Similarly, swarm robotics emulates the collective behaviors observed in nature, such as bird flocks and ant colonies, aiming to adapt these principles for practical use. This paper explores the dynamics of robotic swarms operating under a centralized network model using master-slave communication. Two implementation approaches are examined: direct, small-scale interactions utilizing device MAC addresses and large-scale connectivity facilitated by an IoT-based cloud server. Our experiments employ the NodeMCU ESP8266 platform, which utilizes the IEEE 802.11 Wi-Fi standard for data handling and transfer. For cloud-based communication, we utilize the ThingSpeak server to manage data flow. This study evaluates the strengths and limitations of these approaches in fostering swarm behavior, aiming to derive strategies for enhancing the performance and coordination of swarm robotic systems.