BestHub
Sign in
Tagged articles
21 articles
Page 1 of 1
Black & White Path
Black & White Path
May 23, 2026 · Information Security

GopherTrunk: A Pure‑Go Cluster Radio Scanner Supporting All Major Protocols (P25, DMR, TETRA, NXDN)

GopherTrunk is an open‑source, pure‑Go cluster radio scanner that decodes control channels for ten major digital trunking protocols—including P25, DMR, TETRA, NXDN—and amateur modes, offering zero‑dependency binaries, cross‑platform support, multiple UI options, and advanced DSP pipelines for physical‑penetration testing and radio security research.

DSPGoOpen Source
0 likes · 15 min read
GopherTrunk: A Pure‑Go Cluster Radio Scanner Supporting All Major Protocols (P25, DMR, TETRA, NXDN)
Didi Tech
Didi Tech
Apr 16, 2024 · Artificial Intelligence

Optimizing DSP Deep Model Latency by Externalizing Feature Processing with EzFeaFly

By externalizing feature processing with the EzFeaFly tool and feeding a dense index/value tensor directly to the GPU, the DSP platform decouples feature transformation from model inference, cutting instance usage by ~40%, reducing inference latency 70‑80%, and achieving over 60% end‑to‑end latency improvement while lowering costs.

DSPGPU AccelerationPython
0 likes · 11 min read
Optimizing DSP Deep Model Latency by Externalizing Feature Processing with EzFeaFly
Liangxu Linux
Liangxu Linux
Oct 7, 2023 · Fundamentals

Motor Control vs Linux Driver Development: Which Career Path Fits You?

This article compares motor‑control engineering and Linux driver development, outlining the core tasks, required knowledge, and typical technologies for each field, and helps readers decide which specialization aligns better with their skills and interests.

C ProgrammingDSPLinux driver
0 likes · 5 min read
Motor Control vs Linux Driver Development: Which Career Path Fits You?
DataFunSummit
DataFunSummit
Mar 19, 2022 · Artificial Intelligence

Intelligent Advertising in Real Estate: Challenges, Practices, and Insights from Beike

This article presents Beike's experience in intelligent real‑estate advertising, detailing the business background, key technical challenges such as conversion‑rate estimation, delayed‑feedback modeling, GEO targeting, and budget allocation, and sharing practical solutions that improved conversion rates by over 10%.

AIDSPMachine Learning
0 likes · 11 min read
Intelligent Advertising in Real Estate: Challenges, Practices, and Insights from Beike
Youzan Coder
Youzan Coder
Jul 6, 2021 · Big Data

Overview of Youzan DSP Advertising Platform and Its Testing Challenges

The article outlines Youzan’s Demand‑Side Platform, detailing its ad channels, placement and bidding strategies, data tracking and order attribution mechanisms, and multi‑channel feedback, while highlighting testing difficulties such as Kafka‑driven tracking, link parameter loss, UUID inconsistencies, and the need for robust third‑party API monitoring.

AdvertisingBidding StrategyDSP
0 likes · 8 min read
Overview of Youzan DSP Advertising Platform and Its Testing Challenges
DataFunSummit
DataFunSummit
Jan 7, 2021 · Backend Development

Architecture and Practices of 58.com Alliance Advertising Platform

This article presents a comprehensive overview of the 58.com alliance advertising platform, detailing its business model, core modules such as SSP, DSP, media and creative platforms, system architecture, performance optimizations, big‑data processing, and future directions for programmatic creative generation.

Ad TechAdvertisingDSP
0 likes · 13 min read
Architecture and Practices of 58.com Alliance Advertising Platform
OPPO Kernel Craftsman
OPPO Kernel Craftsman
Sep 30, 2020 · Mobile Development

SIMD Acceleration Techniques on Qualcomm Hexagon DSP for Mobile Devices

The article explains how SIMD acceleration on Qualcomm’s Hexagon DSP, using its HVX vector engine and specialized instructions, can off‑load compute‑intensive tasks such as image, video, and AI processing from the CPU, delivering up to 8× speed‑up, lower power consumption, reduced thermal throttling, and longer battery life on mobile devices.

DSPHexagonMobile
0 likes · 9 min read
SIMD Acceleration Techniques on Qualcomm Hexagon DSP for Mobile Devices
Tencent Music Tech Team
Tencent Music Tech Team
Apr 30, 2020 · Mobile Development

Edge Deep Learning Inference on Mobile Devices: Challenges, Hardware Diversity, and Optimization Strategies

Edge deep learning inference on mobile devices faces hardware and software fragmentation, diverse CPUs, GPUs, DSPs, and NPUs, and limited programmability; optimization techniques such as model selection, quantization, and architecture‑specific tuning enable real‑time performance, with most inference on CPUs, GPUs offering 5–10× speedups, and co‑processor support varying across Android and iOS.

DSPGPU programmingNPU
0 likes · 17 min read
Edge Deep Learning Inference on Mobile Devices: Challenges, Hardware Diversity, and Optimization Strategies
Youku Technology
Youku Technology
Apr 3, 2020 · Industry Insights

How Youku Scaled Millions of DAU with DSP‑Driven RTB Advertising Algorithms

This article explains how Youku combined user‑growth objectives with real‑time bidding (RTB) advertising, designing and optimizing DSP algorithms to maximize high‑value daily active users under cost constraints, covering business background, RTB mechanics, ranking models, pricing strategies, engineering pipelines, and future directions.

AdvertisingDSPIndustry Insights
0 likes · 13 min read
How Youku Scaled Millions of DAU with DSP‑Driven RTB Advertising Algorithms
Architects' Tech Alliance
Architects' Tech Alliance
Jan 16, 2020 · Fundamentals

FPGA, ASIC, and DSP: Competition, Cooperation, and Future Prospects

The article examines the evolving roles of FPGA, ASIC, and DSP in semiconductor technology, highlighting how advances in FPGA have reduced cost and power, enabling it to challenge ASIC and DSP markets, while discussing their distinct strengths, development flows, and the likely coexistence of these three technologies in the future.

ASICDSPFPGA
0 likes · 11 min read
FPGA, ASIC, and DSP: Competition, Cooperation, and Future Prospects
Youku Technology
Youku Technology
Dec 6, 2019 · Backend Development

Technical Architecture and Strategies of Youku's Demand‑Side Platform (DSP)

Youku’s Demand‑Side Platform combines integrated RTB channel access, sophisticated delivery strategies, personalized recommendation algorithms, and real‑time monitoring to acquire and re‑engage segmented users, optimizing budgets, creative performance, and conversion funnels while continuously iterating on traffic value estimation and bidding models.

DSPbackenddata monitoring
0 likes · 13 min read
Technical Architecture and Strategies of Youku's Demand‑Side Platform (DSP)
DataFunTalk
DataFunTalk
Dec 6, 2019 · Backend Development

Technical Architecture and Practices of Youku's Demand‑Side Platform (DSP)

This article explains how Youku built and iterated its Demand‑Side Platform, covering business goals, system architecture, channel integration, bidding and frequency strategies, material selection, smooth budget delivery, retargeting, algorithmic recall, data monitoring, and conversion‑funnel optimization to drive user growth.

Backend ArchitectureDSPdata monitoring
0 likes · 12 min read
Technical Architecture and Practices of Youku's Demand‑Side Platform (DSP)
Qunar Tech Salon
Qunar Tech Salon
Sep 5, 2019 · Artificial Intelligence

Implementing Bilinear Interpolation on FPGA for Neural Network Acceleration

The article explains the principles of bilinear interpolation, why it is needed for smooth image scaling in neural‑network layers such as Interp and Resize, and details FPGA‑specific optimizations—including lookup‑table based coefficient pre‑computation, two‑line BRAM caching, and index‑driven data swapping—to reduce DSP usage and improve throughput.

BRAMBilinear InterpolationDSP
0 likes · 14 min read
Implementing Bilinear Interpolation on FPGA for Neural Network Acceleration
HomeTech
HomeTech
Jan 18, 2019 · Big Data

Data Mill: A Real‑Time Spark Streaming Framework for DSP Business Support

Data Mill is a Spark‑Streaming‑based real‑time computation framework that abstracts tasks as DataFrames, enables SQL‑driven development, and supports DSP business requirements by reducing latency to 15‑30 minutes while providing a scalable architecture, caching strategy, and automated fault handling.

CacheDSPReal‑Time Computing
0 likes · 10 min read
Data Mill: A Real‑Time Spark Streaming Framework for DSP Business Support
360 Quality & Efficiency
360 Quality & Efficiency
Oct 12, 2018 · Backend Development

Automated Testing and Monitoring Solution for DSP Advertising Business

The article outlines a comprehensive automated testing framework for a DSP advertising platform, covering income, interface, and log layers, and detailing the use of protobuf, Pytest, Logstash, ElasticSearch, Jenkins, and Allure to achieve efficient, real‑time quality assurance and continuous integration.

DSPElasticsearchJenkins
0 likes · 8 min read
Automated Testing and Monitoring Solution for DSP Advertising Business
Meituan Technology Team
Meituan Technology Team
May 5, 2017 · Artificial Intelligence

Meituan DSP Strategy: Real‑time Bidding, Recall, CTR and Value Prediction

Meituan’s demand‑side platform combines real‑time bidding with a two‑service architecture—RecServer for multi‑scenario ad recall and PredictorServer for CTR and conversion‑value prediction—leveraging behavior, location, collaborative‑filtering and matrix‑factorization features, logistic‑regression and GBDT models, and continuous A/B and metric monitoring to optimize ROI.

AdvertisingDSPMachine Learning
0 likes · 20 min read
Meituan DSP Strategy: Real‑time Bidding, Recall, CTR and Value Prediction
Meituan Technology Team
Meituan Technology Team
Mar 18, 2016 · Artificial Intelligence

Why FM and FFM Still Dominate Large‑Scale Sparse CTR Prediction

This article explains the principles of Factorization Machines (FM) and Field‑aware Factorization Machines (FFM), their implementation details, and how Meituan‑Dianping applied FFM in a DSP platform to achieve superior CTR and CVR estimation for sparse, high‑dimensional advertising data.

AdvertisingCTR predictionDSP
0 likes · 4 min read
Why FM and FFM Still Dominate Large‑Scale Sparse CTR Prediction
Architects' Tech Alliance
Architects' Tech Alliance
Sep 1, 2015 · Industry Insights

How Distributed Segment Processing Boosts Backup Efficiency in Data Domain Systems

Distributed Segment Processing (DSP) offloads segmenting, hashing, and compression to the backup host, reducing bandwidth and CPU load on Data Domain appliances, while the appliance handles fingerprint filtering and reference tracking, offering bandwidth savings but increasing host CPU usage, so it should be enabled only when host resources permit.

DSPData DomainPerformance
0 likes · 4 min read
How Distributed Segment Processing Boosts Backup Efficiency in Data Domain Systems
High Availability Architecture
High Availability Architecture
Aug 5, 2015 · Big Data

DSP Advertising System Architecture and Key Technologies

This article presents a comprehensive overview of DSP advertising system architecture, covering real‑time bidding infrastructure, audience targeting, data processing pipelines using Hadoop, Spark and Storm, click‑through‑rate prediction models, and anti‑fraud mechanisms, offering practical insights for engineers building high‑performance ad platforms.

Ad TechDSPanti-fraud
0 likes · 24 min read
DSP Advertising System Architecture and Key Technologies