Digital Applications in Networking and Informatics

Radar Signal Processing: A Comprehensive Review

2026-03-22T08:45:39+00:00

Signal processing is at the heart of modern radar system functionality, enabling the accurate extraction of target information in environments filled with clutter and noise. This article presents a comprehensive review of the evolution and current state of radar signal processing techniques, ranging from fundamental principles to cutting-edge innovations. The discussion begins by dissecting the basic framework of data processing, including waveform design, pulse compression, and processing of the radar data cube. We evaluate classical detection algorithms such as Constant False Alarm Rate (CFAR) and super-resolution parameter estimation techniques. Furthermore, this article examines advancements in array processing, particularly in Multiple-Input Multiple-Output (MIMO) systems and Space-Time Adaptive Processing (STAP), which are crucial for mitigating complex interference. A key part of this review highlights the integration of Machine Learning and Deep Learning into cognitive radar schemes, as well as the emergence of Compressive Sensing technology for data efficiency. We also explore new paradigms such as radar-communication convergence (RadCom) within the 6G ecosystem and the potential of quantum radar. In conclusion, this article identifies open challenges, including interference management in automotive radar and the need for real-time computation for targets with low Radar Cross Section (RCS), to provide strategic guidance for future research.

Integrated Quality Assurance Information System for Higher Education: A Web-Based Platform for SPMI, HRIS, SIAKAD Integration, and Accreditation Readiness

2026-06-21T05:35:23+00:00

Quality assurance in higher education depends on continuous evidence management, yet SPMI, SIAKAD, HRIS, AMI, and accreditation data are often fragmented across separate repositories. This study designs and evaluates a web-based quality assurance information system that links the PPEPP cycle with BAN-PT/LAM accreditation preparation. Following Design Science Research Methodology, the artifact integrates SPMI workflows, SIAKAD/HRIS data mapping, LKPS/LED evidence management, readiness dashboards, role-based access control, audit trails, and accreditation export packages. Evaluation in a staging environment used black-box testing, RBAC testing, API smoke testing, integration-flow testing, and UAT with 10 stakeholders. Operational readiness increased from a 23% baseline to 82%, LED preparation time decreased from 21 days to 6.7 days (68%), 42 of 45 API endpoints passed (93.3%), and the SUS score reached 81.2. These results indicate improved traceability and operational readiness, but they should not be interpreted as a direct prediction of accreditation rank.

Android-Based Drainage Information System with Daily Precipitation Forecasts for Community Inundation Monitoring in Malang City

2026-06-12T07:05:47+00:00

Rapid urbanization in Malang City has reduced natural infiltration surfaces and intensified localized inundation, while drainage condition data has remained confined to agency archives and inaccessible to the public it most affects. This study reports the design, implementation, and evolution of Sistem Informasi Banyu Malang (SIBAMA), an Android application backed by a Laravel REST service and a MySQL spatial datastore, developed to disseminate drainage-network profiles and inundation information to citizens of Malang. The system integrates the Google Maps API to render survey-derived drainage polylines and inundation pins ingested from KML/KMZ and GeoJSON sources, and provides multi-temporal access across annual survey layers together with live CCTV at flood-prone points. The current release (v.6, 2025) extends the system from a static archive to a forecast-coupled tool: a daily synchronization with the Open-Meteo API retrieves the 24-hour precipitation sum (R24) and maximum precipitation probability, and the current-day R24 drives a derived runoff status that recolors each channel segment as normal or inundation-prone and animates the cross-section overflow illustration. The release also integrates the municipal SAMBAT complaint channel, extends drainage coverage to all five districts, and is implemented in Kotlin. Functional verification used black-box testing with equivalence partitioning, complemented by a user acceptance testing instrument. The work contributes a replicable, low-infrastructure architecture that couples a multi-temporal survey record with public weather-forecast data for participatory municipal inundation monitoring.

The Development of Gate-All-Around FET (GAA FET) Technology as a Successor to FinFET: Architecture, Fabrication Challenges, and Future Opportunities

2026-06-17T04:27:59+00:00

As conventional FinFET architecture approaches its physical scaling limits below the 3nm node due to severe short-channel effects, subthreshold leakage, and degraded gate control, Gate-All-Around Field-Effect Transistor (GAA FET) technology has emerged as the definitive successor for next-generation semiconductor fabrication. This review paper provides a comprehensive analysis of GAA FET technology, focusing on its structural evolution from planar and FinFET architectures to 360-degree electrostatic control systems, including nanowire, nanosheet (MBCFET), and forksheet variants. Through a systematic literature review and comparative performance analysis, this study examines the operational advantages of GAA FETs, such as significant drive current (I_on) enhancement, superior power efficiency, and design flexibility via continuous sheet width scaling. Furthermore, the critical manufacturing hurdles hindering high-yield industrial integration are thoroughly dissected, including precise Si/SiGe superlattice epitaxy, highly selective isotropic etching, inner spacer formation, and self-heating effects (SHE). Finally, this paper outlines the future outlook of transistor scaling, highlighting the transition toward Complementary FET (CFET) architectures. Ultimately, this review underscores that GAA FET is no longer a mere laboratory concept but the fundamental cornerstone enabling the continuation of Moore's Law and the advancement of High-Performance Computing (HPC) in the artificial intelligence era.