BitFlow Enters into Definitive Agreement to be acquired by Advantech

WOBURN, MA, OCTOBER 2, 2023 — BitFlow, Inc., an innovator in frame grabber technology for the machine vision industry, today announced that it has entered into a definitive agreement to be acquired by Advantech in an all-cash transaction representing a 100% equity stake in the company. The transaction was unanimously approved by BitFlow’s Board of Directors and is expected to close during the fourth quarter of 2023.

Advantech is a global leader in embedded, industrial, Internet of Things (IoT), and automation solution platforms headquartered in Taipei City, Taipei, Taiwan, with more than 80 office locations worldwide. Advantech has been a pioneer in integrating Artificial Intelligence (AI) into machine vision systems that transform traditional inspection processes into self-learning Smart Factory applications to improve profitability, drive innovation, and optimize operational efficiency. AI-powered imaging systems go beyond high-quality automated inspection to opportunities for generating information to determine root cause failures, independently measure key performance measures, predict maintenance requirements that reduce costly downtime, and increase the visibility of supply chains, among numerous other value-adding services.

Avner Butnaru, CEO of BitFlow, commented on the acquisition. “The choice to cooperate with Advantech was based on several key factors, including Advantech’s well-known global brand presence in machine vision applications in North America, as well as its global industrial hardware supply capabilities and complete after-sale support and services. Advantech’s manufacturing capability is also crucial to BitFlow. In addition, because of similar corporate cultures, the impact on customers brought about by the integration of Advantech and BitFlow will be greatly reduced. BitFlow believes that by combining its advanced imaging technology and Advantech’s R&D, sales and manufacturing capabilities, BitFlow products will play a much greater role in the AI vision market.”

Upon completion of the transaction, research and development teams for BitFlow and Advantech’s North American business development team will work together to launch innovative new 2D and 3D network devices for the industrial imaging market and fast-emerging AI vision sectors. For instance, BitFlow CoaXPress over Fiber (CoF) frame grabbers will make it possible to link Advantech AI cameras and compute devices using low-cost Fiber cables and connectors for transmission speeds that may soon approach 100 Gbps — two times the current CXP standard — to meet the high bandwidth requirements for AI processing.

As one of the founding technologies in factory automation dating back to the 1970s, machine vision is today at the forefront of the Industrial Internet of Things (IIoT) and AI. Machine vision’s capabilities are being drastically expanded by increasingly powerful computing, embedded and IIoT devices at the network edge, and a growing universe of deep learning AI models. This can permit an imaging system to detect incredibly minute defects, such as microscopic anomalies in the bond wires on a circuit board, resulting in enhanced product quality, a significant reduction in waste, and increased production throughput for companies both large and small.

Magic Pao, Associate Vice President of Industrial Cloud & Video Group said, “The application of advanced computer vision has been highly integrated with AI solutions. Over the last three years, in particular, strong growth in industrial AI has become much more evident. However, in the past, Advantech mainly focused on applications for traditional machine vision equipment, providing industrial-grade cameras and frame grabber cards to meet the basic needs of production inspection. But, as the industry moves towards high-end machine vision applications, such as for advanced semiconductor manufacturing and medical imaging, Advantech will need to supplement high-end image acquisition products to fulfill the demands for high-precision advanced vision inspection.”

BitFlow Inks New European Distribution Deal 

WOBURN, MA, AUGUST 25, 2022 — BitFlow, Inc. today announced expansion in its international distributor network by signing MaVis Imaging GmbH to represent its portfolio of frame grabbers in Germany, France, Italy, Spain and Portugal through MaVis’ integrated supply chain and customer-centric sales force. 

Headquartered in Taufkirchen, Germany, MaVis Imaging is staffed by a highly regarded team of sales and engineering professionals who will support BitFlow customers to ensure the best image acquisition solution for their application. The collaboration begins immediately with a first order placed this week for BitFlow CoaXPress and CameraLink frame grabbers. 

“We continue to seek outstanding distributor partners who will add value to our frame grabbers by providing outstanding customer service and technical knowledge,” said Donal Waide, Director of Sales for BitFlow, Inc. “MaVis has proven itself to be a successful distributor for many of the industry’s biggest brands, and we are thrilled to have them aboard to represent BitFlow. Our newly formed partnership enables us to expand our presence in targeted markets throughout Europe that are poised for growth opportunities in 2022 and beyond.”

MaVis Imaging GmbH is a company of Framos GmbH, focusing on providing machine vision components and cutting-edge solutions for nearly four decades. From cameras and lighting, to lenses and software, it represents a powerful portfolio of well-known brands such as Effilux, Kowa, Zeiss, Huaray, Sony, and of course, Framos. 

“BitFlow frame grabbers are an ideal compliment to our growing portfolio, especially in light of industry trends towards faster, higher resolution sensors that require the high-speed CoaXPress interface,” said Lorenzo Cassano, CEO of MaVis Imaging. “BitFlow has been setting the global standard for CoaXPress frame grabbers since CXP’s inception with a track record of innovation and success. In addition, BitFlow CameraLink frame grabbers will play a vital role in serving our customers who are seeking a cost-effective acquisition solution for more traditional imaging needs.”

European customers can contact MaVis Imaging at +39 039 888 0585, or visit www.mavis-imaging.com

BitFlow Frame Grabbers Enable Researchers to Leverage CoaXPress into Experimental 3D Profilometry Imaging Technique

Profilometry is an imaging technique used to extract topographical data from a surface in order to obtain surface morphology, step heights and surface roughness. Dynamic 3D surface imaging by phase-shifting fringe projection profilometry (PSFPP) has been widely implemented in diverse applications, including industrial manufacturing, archaeological inspection, entertainment, and biomedicine. PSFPP works by first projecting sets of phase-shifting sinusoidal fringe patterns onto 3D objects and then analyzing deformed structure images reflected from the objects to retrieve 3D surface information.

Existing PSFPP techniques have fallen short in simultaneously providing the robustness in solving spatially isolated 3D objects, the tolerance of large variation in surface reflectance, and the flexibility of tunable working distances with meter-square-level fields of view at video rate. To overcome these limitations, researchers at the INRS Énergie Matériaux Télécommunications Research Centre in Quebec, Canada developed a technique they termed Multi-Scale Band-Limited Illumination Profilometry or MS-BLIP. Supported by the synergy of dual-level intensity projection, multi-frequency fringe projection, and an iterative method for distortion compensation, MS-BLIP can accurately discern spatially separated 3D objects with highly varying reflectance.

The MS-BLIP system begins with a pulsed laser used as the light source. After expansion and collimation, the beam is directed to a 0.45” DMD (Digital Micromirror Device) at an incident angle of ∼24° to its surface normal. Binary fringe masks, generated by an error diffusion algorithm from their corresponding grayscale patterns, are loaded onto the DMD and displayed at up to 1 kHz. A band-limited 4f imaging system that consists of two lenses and one pinhole converts these binary patterns to grayscale fringes at the intermediate image plane. The smallest period in the used sinusoidal fringe patterns is 388.8 µm, which demands a 150-µm-diameter pinhole to pass the spatial frequency components of these patterns while filtering all noise induced by the digital half-toning. A dove prism rotates the generated fringe patterns to match the aspect ratio of the targeted scene. Then, a camera lens (AF-P DX NIKKOR 10-20mm f/4.5-5.6G VR, Nikon) projects these fringe patterns onto 3D objects. The deformed structure images are captured by an Optronis CP70-1HS-M-1900 CoaXPress camera with an Azure lens. Synchronized by the DMD’s trigger signal, the acquired images are transferred to a computer via a cable to a BitFlow Cyton-CXP CoaXPress frame grabber built on a half-size x8 PCI Gen 3.0 express board compliant with the CXP 1.1 standard.

CoaXPress (CXP) is an asymmetric high-speed point-to-point serial communication standard for the transmission of video and still images, scalable over single or multiple coaxial cables. It has a high speed downlink of up to 12.5 Gbps per cable for video, images and data, plus a lower speed uplink up to 42 Mbps for communications and control. Power is also available over the cable (“Power-over-Coax”) and cable lengths of greater than 100m may be achieved.

“Applications for CoaXPress are evolving with new use cases being found in precise medical research and 3D inspection where Camera Link or GigE Vision previously were the go-to standard,” said Donal Waide, Director of Sales for BitFlow, Inc. “Speed combined with stability, plus a growing choice of compatible cameras, have sparked a great deal of interest for CoaXPress in laboratory settings.”

To demonstrate MS-BLIP’s potential in industrial inspection, researchers imaged the rotational movement of a bamboo vase with extending branches rotating at 0.6 rad/s. MS-BLIP was operated at a working distance of 2 meters (m), with an FOV of 1.5 m × 1.0 m, and at a 3D imaging speed of 20.8 frames-per-second (fps). Under these working conditions, the depth resolution was quantified to be 3.7 mm, and the lateral resolution was measured to be 1.7 mm. Close-up views of the vase presented detailed structural information on its surface with depth-encoded color changes of the branches reflecting the rotation movement of the object.

Along with testing with the rotational movements of a craft vase, MS-BLIP also proved successful in the dynamic 3D visualization of translational movements of an engineered box, and full human body movements at a measurement volume 3X greater than existing BLIP systems. Future work will be carried out to improve MS-BLIP’s imaging speed by adopting multiple cameras, a faster DMD, and a more powerful light source. Besides technical improvement, the researchers will continue to explore new applications including automated industrial inspection human-computer interaction.

High-speed dual-view band-limited illumination profilometry using temporally interlaced acquisition C Jiang, P Kilcullen, Y Lai, T Ozaki, J Liang Photonics Research 8 (11), 1808-1817, May 2022

Researchers Develop Optical Tomography System with BitFlow Frame Grabber to Better Diagnosis Eye Diseases

WOBURN, MA, MARCH 11, 2022 — High resolution 3D imaging of biological tissue is used extensively in the diagnosis of eye diseases, typically by applying a technique known as Optical Coherence Tomography (OCT). OCT testing has become a standard of care for the assessment and treatment of most retinal conditions. It is comparable to ultrasound, except that OCT employs light rather than sound and thereby achieves clearer, sharper resolution.
In a typical OCT system, an optical signal from a broadband source is divided into sample-arm and reference-arm signals using a beam splitter. Both signals are combined and an interference signal is detected by a detector assembly. Some systems employ a wavelength-tuning optical source and are termed “swept source” OCT (SS-OCT). Meanwhile, a system where a stationary broadband signal is dispersed spatially and detected using a spectrometer is referred as a Fourier Domain OCT (FD-OCT).
Both SS-OCT and FD-OCT techniques suffer from changes in the polarization of the optical signal when the signal is transmitted through materials possessing anisotropic properties, meaning they have a different value when measured from different directions. This results in artifacts that compromise the quality of the image, and therefore, the ability of doctors to diagnose a disease.
Reducing Polarization Artifacts Funded by Max-Planck-Gesellschaft and Massachusetts General Hospital, a team of researchers have developed a polarization insensitive detection unit (PIDU) for a spectrometer-based FD-OCT system that greatly minimized polarization associated artifacts in OCT images. The spectrometer unit employed diffraction grating (set at 1200 lines per mm), an 80mm lens, and a Sensors Unlimited InGaAs line-scan camera with a resolution of 2048 pixels.
Data from the spectrometer was collected at a line-scan speed of 100kHz utilizing a BitFlow Axion-CL Camera Link frame grabber. The Axion-CL supports a single Base CL camera, Power over Camera Link (PoCL), and can acquire up to 24 bits at 85MHz. The frame grabber benefits from a PCIe Gen 2 interface and a DMA optimized for fully loaded computers. Data collected by the Axion-CL was processed on LabVIEW software.
To demonstrate the proof of principle in biological tissue the researchers imaged chicken breast because of its high birefringence. Tests were conducted on the OCT system with and without the PIDU. During the imaging, the tissue was held in hand and maneuvered constantly to mimic real clinical conditions. Images were acquired and recorded for 10 seconds.
For the OCT system without PIDU, it was observed that the bright and dark bands of the sample were constantly fluctuating which can be attributed to the polarization dependent phase changes in the sample light. The OCT system with PIDU, however, showed that the image artifacts were not noticeable, making for images that are more accurate for a doctor to observe. Researchers found that in close examination, it was not only the light from the tissue that changes in intensity but also the light from the inner wall of the capsule which is not in tissue contact. This supports the idea that polarization artifacts come not solely from a tissue sample, but can also arise from the system itself.
The researchers believe their new design will be particularly useful in clinical settings where the sample arm is constantly under motion during probe introduction or when it is subjected to peristaltic motion. Further studies are planned on other biological tissues.
David Odeke Otuya, Gargi Sharma, Guillermo J. Tearney, and Kanwarpal Singh, “All fiber polarization insensitive detection for spectrometer based optical coherence tomography using optical switch,” OSA Continuum 2, 3465-3469 (2019)
Schematic of the FD-OCT system employing polarization insensitive detection scheme is shown. SMF: Single mode fiber, Cr: circulator, BS: beam splitter, PC: polarization controller, Co: collimator, NDF: neutral density filter, M: mirror, MPU: motor power unit, EC: electrical connection, MW: motor wire, PBS: polarizing beam splitter, OS: optical switch, G: grating, L: lens, LSC: line scan camera (Image courtesy of Otuya, Sharma, Tearney, and Singh)
(left) Image of chicken breast tissue acquired with OCT system without PIDU and (right) image of the same tissue acquired with OCT system with PIDU (Image courtesy of Otuya, Sharma, Tearney, and Singh)

BitFlow BFPython API provides Python Wrapper to Enable Rapid Prototyping

WOBURN, MA, FEBRUARY 23, 2022 — BitFlow, a global leader in frame grabbers for machine vision, life sciences and industrial imaging, has introduced BFPython, an application programming interface that allows engineers with Python expertise to acquire images from BitFlow’s broad range of frame grabbers. Available immediately, these Python bindings wrap the BitFlow SDK’s configuration, acquisition, buffer management and camera control APIs. The download also includes several Python examples that illustrate how bindings can be used.

A free, open source programming language, Python is simple to learn and use, making it one of the most popular languages for developing imaging applications, whether in Linux, Windows or embedded platforms. In machine vision, where prototyping is mission critical to understanding how a proposed imaging solution is progressing, BitFlow BFPython accelerates the building process and reduces final development costs for those experienced with Python code. To further assist in development, BFPython includes several sub-modules that provide convenient interfaces to access features such as CoaXPress camera control (via GenICam), Camera Link camera control (via the CL Serial API), among others. 

Supporting the full line of BitFlow frame grabbers, the BitFlow SDK enables developers to bring high-speed image acquisition into machine vision applications, from cost-efficient simple inspection to ultra high-speed, high-resolution systems. The SDK includes a large number of example applications with full source code for aiding in the understanding the available functions, along with a number of utilities for developing and debugging. The free SDK version is for use with third-party applications such as LabVIEW, VisionPro and HALCON. The paid version is required for users developing their own applications, and offers such high-level advantages as header files, libraries and extensive example programs with detailed source code. 

BitFlow Frame Grabber Helps Researchers Generate 3D Structural Images of Biological Tissues

WOBURN, MA, JANUARY 24, 2022 — Biology researchers at Indiana University1 have developed an integrated system combining high-resolution optical coherence microscopy (HR-OCM) with dual-channel scanning confocal fluorescence microscopy (DC-SCFM) to enable 3D visual evaluation of cell activities involved in pupil developmental and disease conditions. Still in its experimental stages, this dual-modality 3D system simultaneously co-registers reflectance and fluorescence signals, giving it the ability to accurately track structural and functional changes in live specimens over time. Indiana University researchers hope to use their system to enable new investigations of biological processes in small animal models.
BitFlow Axion Camera Link frame grabber is a critical component of the hybrid system. It acquires the output signal from a spectrometer equipped with a Teledyne e2v high-speed line-scan camera operating at the rate of 250 kHz. A lateral resolution of 2-μm and axial resolution of 2.4-μm is captured in tissue over a field-of-view of 1.1 mm ×1.1 mm. The analog scanning signals, as well as the trigger signals for the BitFlow frame grabber, are generated synchronously through a four-channel analog output data acquisition card. Simultaneous recording of HR-OCM and DC-SCFM data was performed using custom software developed in LabVIEW 2017.
As data generated by faster, higher-resolution Camera Link cameras continues to grow exponentially, the Axion’s PCIe Gen 2 interface, with its StreamSync™ DMA optimized for modern computers, is needed to optimize their full performance. Features such as easier switching between different tap formats, a powerful acquisition engine, and a more flexible I/O and timing generator are all readily available in a dedicated low cost CL Base orientated frame grabber.
During development, researchers applied different strategies to enable the simultaneous recording of information, as well as to overcome the focal plane mismatch between both imaging modalities. The system’s performances were evaluated in imaging fluorescence microspheres embedded in multi-layer tape and silicone phantom. 
The combined system is synergistic in generating structural and functional information of samples; the DC-SCFM allows for the discrimination between different fluorophores, while the HR-OCM enables the 3D localization of the features inside tissue samples and enabled the depth localization.

1 “Development of high-speed, integrated high-resolution optical coherence microscopy and dual-channel fluorescence microscopy for the simultaneous co-registration of reflectance and fluorescence signals” Reddikumar Maddipatla, PatriceTankam School of Optometry, Indiana University, Bloomington, IN 47405, USA

System diagram

How BitFlow Overcame Supply Chain Constraints to Maintain Frame Grabber Availability

Dec. 15, 2021 – Supply chain disruptions, chip shortages and the rising cost of raw materials continue to bedevil the machine vision industry. System integrators are confronted with an experience once rare: no stock available at distributors, and no idea when cameras, frame grabbers, computers and other necessary accessories will come in.
 
Bucking this trend is BitFlow, Inc., a global manufacturer of CoaXPress and Camera Link frame grabbers headquartered outside Boston. At the onset of the pandemic, the company’s management began to take steps to mitigate the impact of shortages on production. BitFlow’s close relationships with its trusted suppliers and partners of over 25 years enabled it to build in-house stock when shortages started becoming apparent. For example, Altera FPGAs (Field Programmable Gate Array) are key components of its cards. An FPGA is an integrated circuit with modifiable logic and memory blocks configurable in the field that help BitFlow customers reduce cost and improve design cycle times. To ensure a steady supply of FPGAs, BitFlow met with Altera to establish forecasting and lead times, ultimately resulting in purchasing additional stock.
 
Donal Waide, director of sales for BitFlow, notes that the company is experiencing stronger than expected demand yet has continued to reliably deliver its industry-leading products. “In the short term, lead times may, in some cases, be longer than we would like. For the most part, however, we have boards on hand and are able to go into new projects, whereas many of our competitors are struggling to meet their production goals.”
 
Although there are no silver bullets, Waide points to several steps BitFlow took to stay ahead of supply chain disruptions:

  • ​Source alternative suppliers, and review your existing suppliers for common components.
  • Increase collaboration and visibility with suppliers to determine their technology roadmaps so you can evolve product designs accordingly. Suppliers may drop the mature, less-profitable components used in your legacy products in favor of newer technologies with higher margins.
  • When reviewing suppliers, consider their global footprint and their ability to mitigate disruptions in one region with manufacturing capabilities in another.
  • Remember that during shortages, suppliers determine who to support, not the other way around, underscoring the importance of strong relationships.
  • If possible, move away from single-sourced parts. 

Ironically, strained global supply chains have led to heightened demand for machine vision systems as manufacturers struggle to maximize yield from scarce components. Manufacturers are turning to machine vision to track goods throughout the production process with quality control measures that minimize waste and ensure compliance with customer and regulatory standards. In addition, with both parts and labor in short supply, plant maintenance is another area where machine vision is adding value, in particular when combined with AI. Cameras can be used to identify vibration issues before a failure occurs, for instance, or to detect wear on conveyor belts or leakage in remote pipes. Monitoring can be performed without service personnel shutting down a production line, and opening cabinets or enclosures to check mechanical parts for wear. Keeping lines running yields an immediate production increase.

Laser Scanning System uses BitFlow Frame Grabber to Improve Driver Visibility on Foggy Roads

WOBURN, MA, NOVEMBER 15, 2021 — Fog is produced by the suspension of very fine moisture droplets in the air. When light hits these droplets, it scatters and results in a loss of contrast and a dense white background. As these droplets get smaller, fog gets thicker and makes roadways more blanketed, reducing visibility, limiting contrast, and distorting the perception of speed. Reports from the Federal Highway Administration cite an annual average of 31,385 fog-related car accidents resulting in more than 500 deaths.

To help drivers achieve improved visibility through fog, researchers1 from Purdue University and the University of Science and Technology of China developed an experimental off-axis spatiotemporally gated multimode laser scanning system. Extensive testing has shown the system yields high-quality images at seven scattering path lengths, which far exceeds the capability of conventional imaging solutions, such as LIDAR that typically lacks the spatial resolution and contrast of optical measurement.

During testing, image capture was performed using a Photonfocus 2-megapixel CMOS camera with full-well capacity recording at 128 x 118 pixel resolution to simulate pupil plane detection. The camera was configured for external exposure control mode so that the external trigger signal controlled both the exposure start and duration. Using a region of interest containing the 128 × 118 pixels, researchers achieved 4 kHz frame rate with 50% duty cycle. These images were continuously transferred to a computer memory through a BitFlow Neon CLB Base/PoCL Camera Link frame grabber. Featuring PoCL, this board can acquire from all Base CL cameras up to 24 bits at 85 MHz and has enough industrial I/O to handle even the most complicated synchronization tasks.

Image quality was evaluated by placing a flat wood deer shape figurine inside a rectangle glass tank filled with water and subject to different levels of scattering. Milk gradually was added into the water tank while scattering path length was measured. Researchers utilized a 592 nm diode laser source of 7 mm coherence length and employed hologram recording to achieve temporal gating.

To adapt the system for practical implementation on motor vehicles, researchers plan to abandon laser interferometry and directly employ a nanosecond pulsed light source and electronic gating on the detected signal as in LIDAR imaging. Also, they will locate an illumination module and detection module on each side of a vehicle using two separated synchronized beam scanners that will scan a common focus.

No Fish Story: BitFlow Frame Grabber Optimizes Hyperspectral Imaging System Assessing Salmon Health

WOBURN, MA, SEPTEMBER 21, 2021 — Smoltification is a complex series of physiological changes that allow young Atlantic salmon to adapt from living in fresh water to living in seawater. In salmon farming, this transition from “parr” to “smolt” is controlled using lights or functional feed to ensure a continuous and predictable supply of fish to grocery stores, restaurants and other seafood markets.


Scientists at SINTEF, one of Europe’s largest independent research institutes located in Trondheim, Norway, recently developed a hyperspectral imaging (HSI) system1 to study the vital aspects in detecting smoltification, relying in part upon a BitFlow Camera Link frame grabber to grab high-speed video frames for analysis at more than 100 frames-per-second.

The ability to verify smoltification is critical since incomplete seawater adaptation may result in poor animal welfare and increased mortality. Animal welfare is of increasing importance in salmon farming, as the industry is under pressure to improve production and farming operations due to ethical concerns. Conventional smoltification assessments measure chloride content in blood samples after exposing fish to saline water, or by detecting the presence of ion-transporting enzymes through analysis of tissue samples from gills. These methods are time-consuming so only a few salmon are typically tested from populations of several hundreds of thousands of fish.


To evaluate the robustness of its HSI approach, SINTEF placed emphasis on collecting diverse data with variations in fish color, patterning, size, and shape using three different salmon farming sites. Data were collected weekly in synchronization with the sites’ respective production and testing schedules. A Shuttle SH110G computer with Intel i7 processor had the BitFlow frame grabber installed to grab frames from a Specim® FX10 hyperspectral camera (Figure 1) equipped with a 23 mm/f.2.4 (OLE23) lens. Exposure settings were regularly adjusted depending on local conditions and the state of the fish. And because smolt transition involves salmon becoming more reflective, shutter speed was adjusted to keep the exposure within the sensor’s dynamic range. To make all data sets comparable, despite differences in ambient lighting conditions and exposure settings, all were normalized for comparison using white and dark reference images.


The raw data obtained from HSI were multidimensional images of individual fish, including their background. Each layer of this multidimensional image represented a single gray-scale image corresponding to the intensity of the reflectance measurement at a specific wavelength. When stacked, all the layers and reflectance measurements represented a 3D cube (Figure 2). A step-wise procedure was used to process and analyze the data so the low-dimensional spectral characteristics could be observed, and classification of parr or smolt made possible. Wavelengths were optimized by factoring in water temperature, dissolved oxygen, water opacity, and color, as well as lighting and feeding regimes.


Upon conclusion of its study, SINTEF demonstrated a HSI system where only three wavelengths are needed to identify smoltification status of Atlantic salmon, and that this system could serve either as a supplementary or free-standing verification tool in fish production. In doing so, the researchers also laid a pathway to manufacturing low-cost HSI instruments for use in production tanks or integrated in existing sorting and vaccination systems for faster, wider and more cost-effective population sampling of Atlantic salmon.